diff --git a/spaces/101-5/gpt4free/g4f/.v1/testing/you_test.py b/spaces/101-5/gpt4free/g4f/.v1/testing/you_test.py
deleted file mode 100644
index 1e9f620507a3bb4ff5e546cf693cfe3764ac437f..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/testing/you_test.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from gpt4free import you
-
-# simple request with links and details
-response = you.Completion.create(prompt="hello world", detailed=True, include_links=True)
-
-print(response)
-
-# {
-#     "response": "...",
-#     "links": [...],
-#     "extra": {...},
-#         "slots": {...}
-#     }
-# }
-
-# chatbot
-
-chat = []
-
-while True:
-    prompt = input("You: ")
-
-    response = you.Completion.create(prompt=prompt, chat=chat)
-
-    print("Bot:", response.text)
-
-    chat.append({"question": prompt, "answer": response.text})
diff --git a/spaces/101-5/gpt4free/g4f/Provider/Providers/Liaobots.py b/spaces/101-5/gpt4free/g4f/Provider/Providers/Liaobots.py
deleted file mode 100644
index 76b13c31924b443423c8376f1b0082e2f900a0b7..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/Provider/Providers/Liaobots.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os, uuid, requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://liaobots.com'
-model = ['gpt-3.5-turbo', 'gpt-4']
-supports_stream = True
-needs_auth = True
-
-models = {
-    'gpt-4': {
-        "id":"gpt-4",
-        "name":"GPT-4",
-        "maxLength":24000,
-        "tokenLimit":8000
-    },
-    'gpt-3.5-turbo': {
-        "id":"gpt-3.5-turbo",
-        "name":"GPT-3.5",
-        "maxLength":12000,
-        "tokenLimit":4000
-    },
-}
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    print(kwargs)
-
-    headers = {
-        'authority': 'liaobots.com',
-        'content-type': 'application/json',
-        'origin': 'https://liaobots.com',
-        'referer': 'https://liaobots.com/',
-        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
-        'x-auth-code': kwargs.get('auth')
-    }
-
-    json_data = {
-        'conversationId': str(uuid.uuid4()),
-        'model': models[model],
-        'messages': messages,
-        'key': '',
-        'prompt': "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown.",
-    }
-
-    response = requests.post('https://liaobots.com/api/chat', 
-                             headers=headers, json=json_data, stream=True)
-
-    for token in response.iter_content(chunk_size=2046):
-        yield (token.decode('utf-8'))
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/17TheWord/RealESRGAN/scripts/generate_meta_info_pairdata.py b/spaces/17TheWord/RealESRGAN/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 76dce7e41c803a8055f3627cccb98deb51419b09..0000000000000000000000000000000000000000
--- a/spaces/17TheWord/RealESRGAN/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
-    txt_file = open(args.meta_info, 'w')
-    # sca images
-    img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
-    img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
-
-    assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
-                                                    f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
-
-    for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
-        # get the relative paths
-        img_name_gt = os.path.relpath(img_path_gt, args.root[0])
-        img_name_lq = os.path.relpath(img_path_lq, args.root[1])
-        print(f'{img_name_gt}, {img_name_lq}')
-        txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
-
-
-if __name__ == '__main__':
-    """This script is used to generate meta info (txt file) for paired images.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--input',
-        nargs='+',
-        default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
-        help='Input folder, should be [gt_folder, lq_folder]')
-    parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
-    parser.add_argument(
-        '--meta_info',
-        type=str,
-        default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
-        help='txt path for meta info')
-    args = parser.parse_args()
-
-    assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
-    assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
-    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
-    for i in range(2):
-        if args.input[i].endswith('/'):
-            args.input[i] = args.input[i][:-1]
-        if args.root[i] is None:
-            args.root[i] = os.path.dirname(args.input[i])
-
-    main(args)
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Arc2earth Free Download.md b/spaces/1gistliPinn/ChatGPT4/Examples/Arc2earth Free Download.md
deleted file mode 100644
index 5656358104c99f93c5ef50b8b758d0f1f850a0b3..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Arc2earth Free Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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-<br />
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Cultural History Of India By Om Prakash Pdf 22.md b/spaces/1gistliPinn/ChatGPT4/Examples/Cultural History Of India By Om Prakash Pdf 22.md
deleted file mode 100644
index 9a474b9bf5af06828bea5f15c724e65cf48496e6..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Cultural History Of India By Om Prakash Pdf 22.md	
+++ /dev/null
@@ -1,70 +0,0 @@
-<br />
-<h1>Cultural History Of India By Om Prakash Pdf 22: A Book Review</h1>
-
-<p>Cultural History Of India By Om Prakash Pdf 22 is a book that explores the various aspects of development of Indian culture from ancient times to the present day. It is written by Om Prakash, a renowned historian and professor of history at Delhi University. The book is divided into three parts, each dealing with a different theme: religion, art, and social institutions.</p>
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-<p>The book is based on extensive research and analysis of primary and secondary sources , such as literary texts , inscriptions , coins , sculptures , paintings , monuments , etc . It also draws on the works of other eminent scholars and experts in the field of Indian history and culture . The book is written in a clear and lucid style , with ample illustrations and examples to support the arguments and facts . The book also provides a bibliography and an index for further reference .</p>
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-<p>The book is based on extensive research and analysis of primary and secondary sources, such as literary texts, inscriptions, coins, sculptures, paintings, monuments, etc. It also draws on the works of other eminent scholars and experts in the field of Indian history and culture. The book is written in a clear and lucid style, with ample illustrations and examples to support the arguments and facts. The book also provides a bibliography and an index for further reference.</p>
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\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Challenge Your Friends and Rivals with 8 Ball Pool APK.md b/spaces/1phancelerku/anime-remove-background/Challenge Your Friends and Rivals with 8 Ball Pool APK.md
deleted file mode 100644
index a86969901bdd62299b15d815a0716edde8353f49..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Challenge Your Friends and Rivals with 8 Ball Pool APK.md	
+++ /dev/null
@@ -1,131 +0,0 @@
-<br />
-<h1>8 Ball Pool Apkpure: A Guide to Download and Play the World's Best Pool Game</h1>
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- <p>As you play 8 Ball Pool Apkpure, you can also earn various rewards and complete various challenges. You can earn coins and cash by winning matches, tournaments, and mini-games. You can also earn trophies by ranking up in the leaderboard. You can also earn pool passes by completing daily missions and seasonal events. You can also earn free gifts by logging in daily, watching videos, inviting friends, etc.</p>
- <p>You can also take on different challenges in 8 Ball Pool Apkpure to test your skills and win more rewards. You can play in the <em>Spin and Win</em> mini-game to win coins, cash, cues, balls, and other prizes. You can play in the <em>Hi-Lo</em> mini-game to guess the outcome of a coin toss and win coins. You can play in the <em>Golden Shot</em> mini-game to hit the golden ball and win coins, cash, cues, balls, and other prizes. You can also join the <em>Clubs</em> feature to create or join a club with other players and compete for club points and rewards.</p>
- <h2>What are the rules of 8 Ball Pool Apkpure?</h2>
-<h3>The basic rules of 8 Ball Pool, such as legal break, object balls, pocketing the 8 ball, and fouls</h3>
-<p>The basic rules of 8 Ball Pool are simple and easy to learn. Here are some of them:</p>
-<ul>
-<li>The game is played with 15 object balls (numbered 1 to 15) and a cue ball (white).</li>
-<li>The object balls are divided into two groups: solids (numbered 1 to 7) and stripes (numbered 9 to 15). The 8 ball (black) is the most important ball in the game.</li>
-<li>The game starts with a break shot, where the player hits the cue ball into the rack of object balls. The break shot must be legal, which means that at least four object balls must hit a cushion or a ball must be pocketed.</li>
-<li>After the break shot, the player who pockets a ball or has a legal break shot gets to choose which group of balls they want to play: solids or stripes. The player must then try to pocket all their group of balls before their opponent.</li>
-<li>The player who pockets all their group of balls first gets to shoot for the 8 ball. The player must call the pocket where they intend to pocket the 8 ball before shooting. The player who pockets the 8 ball legally wins the game.</li>
-<li>If a player commits a foul during the game, their turn ends and their opponent gets ball in hand, which means they can place the cue ball anywhere on the table for their next shot. Some common fouls are: hitting the wrong group of balls first; not hitting any ball; hitting the cue ball off the table; pocketing the cue ball; pocketing the 8 ball before clearing their group of balls; pocketing the 8 ball in the wrong pocket; or pocketing the 8 ball when it is not their turn.</li>
-</ul>
- <h3>The different variations of 8 Ball Pool rules, such as WPA, APA, VNEA, and BCAPL</h3>
-<p>While the basic rules of 8 Ball Pool are generally the same, there are some variations of the rules that are used by different organizations and tournaments. Here are some of them:</p>
-<ul>
-<li><strong>WPA</strong>: The World Pool-Billiard Association is the international governing body of pool. The WPA rules are the official rules of 8 Ball Pool for international competitions. Some of the WPA rules are: the break shot must be taken from behind the head string; if no ball is pocketed on the break shot, the incoming player can choose to play from where the cue ball lies or ask for a re-rack; if a player pockets a ball on the break shot, they can either accept that group of balls or continue to shoot until they miss or foul; if a player pockets both a solid and a stripe on the break shot, they can choose which group of balls they want to play; if a player pockets the 8 ball on the break shot, they can either win the game or ask for a re-rack.</li>
-<li><strong>APA</strong>: The American Poolplayers Association is the largest amateur pool league in the world. The APA rules are the most common rules of 8 Ball Pool for recreational and league play in the United States. Some of the APA rules are: the break shot can be taken from anywhere behind the head string; if no ball is pocketed on the break shot, the table is open for both players; if a player pockets a ball on the break shot, they must shoot at that group of balls until they miss or foul; if a player pockets both a solid and a stripe on the break shot, they must shoot at either group of balls until they miss or foul; if a player pockets the 8 ball on the break shot, they win the game.</li>
-<li><strong>VNEA</strong>: The Valley National Eight-Ball Association is one of the largest pool organizations in North America. The VNEA rules are similar to the APA rules, but with some differences. Some of the VNEA rules are: if no ball is pocketed on the break shot, the table is open for both players; if a player pockets a ball on the break shot, they must shoot at that group of balls until they miss or foul; if a player pockets both a solid and a stripe on the break shot, they must shoot at either group of balls until they miss or foul; if a player pockets the 8 ball on the break shot, they win the game; however, if a player scratches (pockets the cue ball) on the break shot, they lose the game.</li>
-<li><strong>BCAPL</strong>: The Billiard Congress of America Pool League is another large pool organization in North America. The BCAPL rules are similar to the WPA rules, but with some differences. Some of the BCAPL rules are: the break shot must be taken from behind the head string; if no ball is pocketed on the break shot, the incoming player can choose to play from where the cue ball lies or push out to a new position; if a player pockets a ball on the break shot, they can either accept that group of balls or continue to shoot until they miss or foul; if a player pockets both a solid and a stripe on the break shot, they can choose which group of balls they want to play; if a player pockets the 8 ball on the break shot, they can either win the game or ask for a re-rack.</li>
-</ul>
- <h3>The tips and tricks to improve your skills and win more matches in 8 Ball Pool Apkpure</h3>
-<p>8 Ball Pool Apkpure is a game that requires both skill and strategy to win. Here are some tips and tricks to help you improve your game and beat your opponents:</p>
-<ul>
-<li>Practice your aim and power. You can use the aiming lines to help you align your shots, but you also need to adjust your power according to the distance and angle of the shot. You can practice your aim and power in the <em>Practice</em> mode or by using the <em>Guideline in All Rooms</em> option in the settings.</li>
-<li>Use spin wisely. You can use spin to change the direction and speed of the cue ball after it hits an object ball. You can use spin to avoid scratches, position the cue ball for your next shot, or make tricky shots. You can apply spin by using the spin wheel on the bottom right corner of the screen.</li>
-<li>Plan ahead. You should always think ahead and plan your shots before you shoot. You should consider which balls are easy or hard to pocket, which pockets are open or blocked, and which shots will leave you with a good or bad position for your next shot. You should also try to clear any clusters or obstacles as soon as possible.</li>
-<li>Play smart. You should always play according to your skill level and your opponent's skill level. You should not take unnecessary risks or try to show off. You should also know when to play defensively or offensively, depending on the situation. You should also use the chat and emoji features to communicate with your opponent and show respect or sportsmanship.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>8 Ball Pool Apkpure is a fun and exciting game that lets you play pool with players from all over the world. You can download and install it on your Windows PC with Gameloop emulator, and enjoy its features, rules, and challenges. You can also improve your skills and win more matches with some tips and tricks. So what are you waiting for? Download 8 Ball Pool Apkpure today and join the millions of pool lovers who play this game every day!</p>
- <h2>FAQs</h2>
-<h3>Q1: Is 8 Ball Pool Apkpure safe and legal?</h3>
-<p>A1: Yes, 8 Ball Pool Apkpure is safe and legal to download and play. Apkpure is a reputable website that verifies all its apps and games for safety and quality. Gameloop is also a trusted emulator that does not contain any malware or viruses. However, you should always download 8 Ball Pool Apkpure from its official website or Gameloop app store, and not from any third-party sources.</p>
- <h3>Q2: How can I play 8 Ball Pool Apkpure with my friends?</h3>
-<p>A2: You can play 8 Ball Pool Apkpure with your friends by using the <em>Play with Friends</em> feature in the game. You can invite your friends by using their unique ID, Facebook account, or Miniclip account. You can also join or create a club with your friends and chat with them in the club chat room.</p>
- <h3>Q3: How can I earn more coins and cash in 8 Ball Pool Apkpure?</h3>
-<p>A3: You can earn more coins and cash in 8 Ball Pool Apkpure by winning matches, tournaments, and mini-games. You can also earn coins and cash by completing daily missions, seasonal events, pool passes, and achievements. You can also earn free coins and cash by logging in daily, watching videos, inviting friends, etc. You can also buy coins and cash with real money if you want to.</p>
- <h3>Q4: How can I upgrade my cue and pool table in 8 Ball Pool Apkpure?</h3>
-<p>A4: You can upgrade your cue and pool table in 8 Ball Pool Apkpure by using coins or cash. You can buy new cues and tables from the shop, or unlock them from surprise boxes, golden shots, scratch and win, etc. You can also upgrade your cues by using cash or cue pieces. You can improve the attributes and effects of your cues by leveling them up. You can also change the design and color of your cues and tables by using coins or cash.</p>
- <h3>Q5: How can I contact the support team of 8 Ball Pool Apkpure?</h3>
-<p>A5: You can contact the support team of 8 Ball Pool Apkpure by using the <em>Help and Support</em> feature in the game. You can access it by clicking on the <em>Settings</em> icon on the top right corner of the screen, and then clicking on the <em>Help and Support</em> button. You can then browse through the frequently asked questions, or submit a ticket to the support team. You can also contact the support team by sending an email to <a href="mailto:support@miniclip.com">support@miniclip.com</a>.</p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Darah Tak Terbatas and Unlimited Money in Hungry Shark World Get Mod Apk Here.md b/spaces/1phancelerku/anime-remove-background/Darah Tak Terbatas and Unlimited Money in Hungry Shark World Get Mod Apk Here.md
deleted file mode 100644
index a7208e24b81ad40fcffa492ab8be32763f76acea..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Darah Tak Terbatas and Unlimited Money in Hungry Shark World Get Mod Apk Here.md	
+++ /dev/null
@@ -1,115 +0,0 @@
-<br />
-<h1>Download Hungry Shark World Mod Apk Unlimited Blood and Enjoy the Ultimate Shark Experience</h1>
-<p>Do you love sharks? Do you love eating everything in your path? Do you love action-packed games with stunning graphics and sound effects? If you answered yes to any of these questions, then you will love Hungry Shark World, a thrilling game where you control a hungry shark and eat everything that gets in your way. But wait, there's more! You can also download Hungry Shark World Mod Apk Unlimited Blood, a modified version of the game that gives you unlimited blood, coins, gems, and all sharks unlocked. Sounds awesome, right? Read on to find out more about this amazing game and how to download it.</p>
- <h2>What is Hungry Shark World?</h2>
-<h3>A thrilling action game where you control a hungry shark</h3>
-<p>Hungry Shark World is an addictive action game where you control a shark and eat everything in your path. The objective is to survive as long as you can and devour as much prey as possible to score tons of points before you eventually die. True to its name, your hungry shark is constantly losing HP due to its insatiable hunger and must continue to eat in order to stay alive. However, the sea is riddled with plenty of hazards and hostile fish that don't just let themselves get eaten, so you have to play smart in order to score high.</p>
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- <h3>Features of Hungry Shark World</h3>
-<h4>Over 40 different sharks to unlock and upgrade</h4>
-<p>One of the best features of Hungry Shark World is that it offers a wide variety of sharks to choose from. You can start with a small porbeagle shark and work your way up to bigger and badder sharks like the great white, hammerhead, megalodon, or even a prehistoric mosasaurus. Each shark has its own stats, abilities, appearance, and personality. You can also upgrade your sharks by spending coins and gems on their bite, speed, boost, or health.</p>
- <h4>Four stunning locations to explore and devour</h4>
-<p>Hungry Shark World features four different locations that you can travel to as you progress in the game. Each location has its own theme, scenery, <p>challenges, and secrets. You can explore the Pacific Islands, the Arabian Sea, the South China Sea, and the Arctic Ocean. Each location has its own unique creatures, landmarks, and events that you can discover and enjoy. You can also switch between locations at any time by using the map.</p>
- <h4>Hundreds of enemies and prey to eat and collect</h4>
-<p>As a hungry shark, you have a lot of options when it comes to your diet. You can eat fish, crabs, turtles, squid, octopus, dolphins, whales, seals, penguins, birds, humans, and more. Each prey has its own value and effect on your shark. Some prey will give you more points, some will heal you more, some will boost your speed or power, and some will even unlock new items or achievements. However, not all prey are easy to catch or harmless. Some prey will fight back, some will poison you, some will explode, and some will even damage your shark. You have to be careful and choose wisely what you eat.</p>
- <h4>Daily chests, missions, and achievements to earn rewards</h4>
-<p>Hungry Shark World also offers plenty of ways to earn rewards and bonuses in the game. You can find daily chests that contain coins, gems, or items. You can complete missions that challenge you to perform certain tasks or feats. You can also unlock achievements that reward you for reaching milestones or doing something extraordinary. These rewards will help you buy and upgrade sharks and accessories, as well as unlock new features and content in the game.</p>
- <h2>Why Download Hungry Shark World Mod Apk Unlimited Blood?</h2>
-<h3>Benefits of Hungry Shark World Mod Apk Unlimited Blood</h3>
-<h4>Unlimited blood to survive longer and score higher</h4>
-<p>The main benefit of downloading Hungry Shark World Mod Apk Unlimited Blood is that you get unlimited blood for your shark. This means that you don't have to worry about losing HP due to hunger or damage. You can survive longer and eat more without dying. This will allow you to score higher and reach new levels of fun and excitement in the game.</p>
- <h4>Unlimited coins and gems to buy and upgrade sharks and accessories</h4>
-<p>Another benefit of downloading Hungry Shark World Mod Apk Unlimited Blood is that you get unlimited coins and gems for your shark. This means that you don't have to grind or spend real money to buy and upgrade sharks and accessories. You can buy any shark you want from the start and upgrade it to the max without any limitations. You can also buy any accessory you want from the shop and equip it to your shark for extra benefits. This will make your shark more powerful and stylish in the game.</p>
- <h4>All sharks unlocked and available from the start</h4>
-<p>A third benefit of downloading Hungry Shark World Mod Apk Unlimited Blood is that you get all sharks unlocked and available from the start. This means that you don't have to play for hours or complete certain requirements to unlock new sharks in the game. You can choose any shark you want from the start and switch between them at any time by using the map. This will give you more variety and freedom in the game.</p>
- <h4>No ads or in-app purchases to interrupt your gameplay</h4>
-<p>A fourth benefit of downloading Hungry Shark World Mod Apk Unlimited Blood is that you get no ads or in-app purchases to interrupt your gameplay. This means that you don't have to watch annoying ads or pay real money to enjoy the game fully. You can play without any distractions or interruptions in the game.</p>
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- <h3>How to Download Hungry Shark World Mod Apk Unlimited Blood</h3>
-<h4>Step 1: Click on the link below to download the mod apk file</h4>
-<p>The first step to download Hungry Shark World Mod Apk Unlimited Blood is to click on the link below to download the mod apk file. The link will take you to a secure site where you can download the file safely and easily.</p>
- <h4>Step 2: Allow unknown sources on your device settings</h4>
-<p>The second step to download Hungry Shark World Mod Apk Unlimited Blood is to allow unknown sources on your device settings. This will enable you to install apps from sources other than the Google Play Store. To do this, go to your device settings > security > unknown sources > enable.</p>
- <h4>Step 3: Install the mod apk file and launch the game</h4>
-<p>The third step to download Hungry Shark World Mod Apk Unlimited Blood is to install the mod apk file and launch the game. To do this, go to your file manager > downloads > hungry-shark-world-mod-apk-unlimited-blood.apk > install > open.</p>
- <h4>Step 4: Enjoy the ultimate shark experience with unlimited blood and resources</h4>
-<p>The fourth and final step to download Hungry Shark World Mod Apk Unlimited Blood is to enjoy the ultimate shark experience with unlimited blood and resources. You can now play the game with no limitations or interruptions and have fun as a hungry shark. You can eat everything in your path, unlock and upgrade all sharks and accessories, explore all locations, and score higher than ever before.</p>
- <h2>Tips and Tricks for Playing Hungry Shark World</h2>
-<h3>Use your boost wisely to catch prey and avoid enemies</h3>
-<p>One of the tips and tricks for playing Hungry Shark World is to use your boost wisely to catch prey and avoid enemies. Your boost is a powerful tool that can help you speed up, jump out of the water, or perform special attacks. However, your boost also consumes your stamina, which regenerates slowly over time. Therefore, you should use your boost sparingly and strategically, depending on the situation. For example, you can use your boost to catch fast or fleeing prey, to escape from dangerous enemies or obstacles, or to reach hidden areas or items.</p>
- <h3>Buy the map to find hidden items and locations</h3>
-<p>Another tip and trick for playing Hungry Shark World is to buy the map to find hidden items and locations. The map is an accessory that you can buy from the shop for 500 coins. The map will show you the layout of the location you are in, as well as the locations of chests, missions, pets, enemies, and more. The map will also show you the boundaries of the location and the portals to other locations. The map is very useful for finding secrets and completing objectives in the game.</p>
- <h3>Recruit pets to help you in your journey</h3>
-<p>A third tip and trick for playing Hungry Shark World is to recruit pets to help you in your journey. Pets are small creatures that you can find and collect in the game. Each pet has its own ability and effect that can benefit your shark. For example, some pets can heal you, some can attack enemies, some can collect coins or gems, some can boost your stats, and some can even unlock new features or content. You can equip up to three pets at a time and switch between them at any time by using the map.</p>
- <h3>Avoid dangerous creatures like jellyfish, pufferfish, lionfish, giant squids, etc.</h3>
-<p>A fourth tip and trick for playing Hungry Shark World is to avoid dangerous creatures like jellyfish, pufferfish, lionfish, giant squids, etc. These creatures are not only hard to eat but also harmful to your shark. They can poison you, stun you, damage you, or even kill you instantly. You should steer clear of these creatures unless you have a pet or an accessory that can protect you from them.</p>
- <h3>Eat humans on the surface and complete missions for extra points</h3>
-<p>A fifth tip and trick for playing Hungry Shark World is to eat humans on the surface and complete missions for extra points. Humans are one of the most valuable prey in the game as they give you a lot of points and sometimes coins or gems. You can find humans on beaches, boats, jet skis, helicopters, balloons, etc. You can also jump out of the water and grab them in mid-air. However, be careful as some humans will fight back with weapons or call for help from other humans or military forces. You should also complete missions that involve eating humans as they will give you bonus points and rewards.</p>
- <h2>Conclusion</h2>
-<h3>Hungry Shark World is an addictive and fun game that lets you experience life as a shark</h3>
-<p>In conclusion, Hungry Shark World is an addictive and fun game that lets you experience life as a shark. You can control a hungry shark and eat everything in your path while avoiding dangers and obstacles. You can also unlock and upgrade over 40 different sharks and explore four stunning locations in the game.</p>
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-<p>If you want to enjoy the game without any limitations or interruptions, you should download Hungry Shark World Mod Apk Unlimited Blood. This mod apk will give you unlimited blood, coins, gems, and all sharks unlocked in the game. You can play the game with no worries about dying or running out of resources. You can also buy and upgrade any shark or accessory you want from the start.</p>
- <h3>Follow the tips and tricks above to maximize your score and become the king of the ocean</h3>
-<p>If you want to maximize your score and become the king of the ocean, you should follow the tips and tricks above. These tips and tricks will help you play smarter and better in the game. You will be able to catch more prey and avoid enemies, find hidden items and locations, recruit pets and accessories, eat humans and complete missions, and use your boost wisely in the game.</p>
- <h2>FAQs</h2>
-<h3>Q: What is the difference between Hungry Shark World and Hungry Shark Evolution?</h3>
-<p>A: Hungry Shark World is the sequel to Hungry Shark Evolution, a popular game that was released in 2012. Hungry Shark World has improved graphics, sound effects, gameplay, and features compared to Hungry Shark Evolution. Hungry Shark World also has more sharks, locations, enemies, prey, items, and content than Hungry Shark Evolution.</p>
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-<p>A: Yes, Hungry Shark World Mod Apk Unlimited Blood is safe to download and install. The mod apk file is scanned and tested for viruses and malware before being uploaded to the site. The mod apk file also does not require any root or jailbreak to work on your device.</p>
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diff --git a/spaces/232labs/VToonify/README.md b/spaces/232labs/VToonify/README.md
deleted file mode 100644
index 40ded2ce4dc80cc8764c8fee50476d44bee383b6..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: VToonify
-emoji: 👨‍🎨
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: other
-duplicated_from: PKUWilliamYang/VToonify
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/2gauravc/search_summary_chatgpt/app.py b/spaces/2gauravc/search_summary_chatgpt/app.py
deleted file mode 100644
index 289c7a0e64ceeae6759b866b17b4ae4ba11e36c7..0000000000000000000000000000000000000000
--- a/spaces/2gauravc/search_summary_chatgpt/app.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import streamlit as st
-import openai 
-import sys, getopt
-from datetime import datetime
-from streamlit.components.v1 import html
-import boto3
-
-from main import chatgpt_prompt, get_chatgpt_resp, generate_kyc_output, gsearch, save_to_s3
-
-# Function to perform the search
-# This is a placeholder function, replace it with your actual search implementation
-def perform_search(pname, keywords, num_results):
-    # record current timestamp
-    start_time = datetime.now()
-    
-    # Google search for the person name and get the first 20 query links 
-    query = pname + " " + keywords
-    search_links = gsearch(query, num_results)
-
-    # Construct the prompt 
-    prompt_text = chatgpt_prompt(pname, search_links)
-    #get ChatGPT response 
-    resp = get_chatgpt_resp(prompt_text)
-    # Create PDF with links and summary 
-    rep_txt= generate_kyc_output(query, search_links, resp, start_time)
-    return (rep_txt)
-
-main_tab, help_tab, rel_tab = st.tabs(["Run the Bot", "FAQ", "Release Plan"])
-
-with main_tab: 
-    # Streamlit app
-    st.title("Adverse News Detection Assistant")
-
-    # Input fields
-    names_txt = st.text_input("Enter party name (or multiple names separated by ,)")
-    plc_text = "laundering OR terrorist OR fraud OR corrupt OR criminal OR investigation OR prosecute OR evasion OR bribe OR sanction"
-    keywords = st.text_input("Enter other search words:", value=plc_text)
-
-    st.sidebar.markdown("## Controls")
-    st.sidebar.markdown("Choose your **search** *parameters*")
-    num_results = st.sidebar.slider("Choose the number of search results:", 5, 30, 20, 5)
-    st.sidebar.markdown("## Model")
-    st.sidebar.markdown("GPT v3.5")
-    st.sidebar.markdown("## App")
-    st.sidebar.markdown("v0.4")
-
-    col1, col2 = st.columns(2)
-    with col1:
-        adv_nw = st.radio(
-        "Did you find adverse news when you performed this search manually",
-        ('Yes', 'No', 'Dont Know'), index=2)
-    with col2:
-        #st.markdown("Touch time (manual) in mins")
-        man_tt = st.number_input('Touch time (manual) in mins', value=0, step=1)
-        #st.markdown("Touch time (with bot) in mins")
-        bot_tt = st.number_input('Touch time (with bot) in mins', value=0, step=1)
-
-    # Search button
-    if st.button("Search"):
-        names = names_txt.split(",")
-        #print(len(names))
-        metrics_ent = (adv_nw != "Dont Know") and (man_tt > 0) and (bot_tt > 0) 
-        # Perform the search and display the results
-        if names and metrics_ent:
-            search_results = ""
-            for name in names:
-                #print("trying for name {} \n".format(name))
-                search_results += perform_search(name, keywords, num_results)
-
-            html(f"<pre>{search_results}</pre>", height=200, scrolling=True)
-            st.download_button('Download Report',search_results)
-            try:
-                date_time = datetime.now()
-                save_to_s3(search_results,date_time )
-                print ("Completed processing for {} names: {} at {} \n".format(len(names), names_txt, str(date_time)))
-            except: 
-                print ("Completed processing with S3 write error for {} names: {} at {} \n".format(len(names),names_txt, str(date_time)))
-        else:
-            st.error("Please enter party name, adverse news selection (Yes or No) and Touch Time before searching.")
-
-with help_tab:
-    st.title("FAQ")
-    
-    st.markdown("Q. How do I get a count of number of adverse news?")
-    st.markdown("A. This functionality isnt implemented yet. A workaround is to manually count the number of links with adverse news")
-
-    st.markdown("Q. How do I summarise all the adverse news?")
-    st.markdown("A. This functionality isnt implemented yet. A workaround is to aggregate the summary of all adverse news items manually, and get a sumary from ChatGPT (chat.openai.com")
-
-    st.markdown("Q. Can I search in other lauguages?")
-    st.markdown("A. This functionality isnt implemented yet. We are planning to test this feature out with Chinese first")
-
-    st.markdown("Q. Can I search without the other search words?")
-    st.markdown("A. Just enter a blank space in the text space and search")
-
-with rel_tab: 
-    st.markdown(f""" 
-            | NO. | Issue / Enhancement                                                                                                                       | Rel | Status    |
-            |-----|--------------------------------------------------------------------------------------------------------------------------------------------|-----|-----------| 
-            | 1   | Capture productivity and adverse news metrics from the user                                                                                | 0.4 | Completed | 
-            | 2   | Save productivity and adverse news metrics in a DB                                                                                         | 0.4 | TBD       |  
-            | 3   | Convert bot output to structured JSON  - Count of adverse news  - Summary of all adverse news  - Identification of links with adverse news | 0.6 | TBD       | 
-            | 4   | Offer alternate solution path with web text scraping and                                                                                   | 0.6 | TBD       | 
-            | 5   | Create a page on metrics report                                                                                                            | 0.5 | TBD       |""")
diff --git a/spaces/AIML-TUDA/semantic-diffusion/README.md b/spaces/AIML-TUDA/semantic-diffusion/README.md
deleted file mode 100644
index 9a565ba9bbdf1a267b51f89519bc48c7ccd6b8a0..0000000000000000000000000000000000000000
--- a/spaces/AIML-TUDA/semantic-diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Semantic Diffusion
-emoji: ⚡
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AP123/dreamgaussian/readme.md b/spaces/AP123/dreamgaussian/readme.md
deleted file mode 100644
index dbeab5bf5bc995b6343769ff2389a138da6e5356..0000000000000000000000000000000000000000
--- a/spaces/AP123/dreamgaussian/readme.md
+++ /dev/null
@@ -1,120 +0,0 @@
-# DreamGaussian
-
-This repository contains the official implementation for [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation]().
-
-### [Project Page](https://dreamgaussian.github.io) | [Arxiv]()
-
-
-https://github.com/dreamgaussian/dreamgaussian/assets/25863658/db860801-7b9c-4b30-9eb9-87330175f5c8
-
-
-## Install
-```bash
-pip install -r requirements.txt
-
-# a modified gaussain splatting (+ depth, alpha rendering)
-git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization
-pip install ./diff-gaussian-rasterization
-
-# simple-knn
-pip install ./simple-knn
-
-# nvdiffrast
-pip install git+https://github.com/NVlabs/nvdiffrast/
-
-# kiuikit
-pip install git+https://github.com/ashawkey/kiuikit
-```
-
-Tested on:
-* Ubuntu 22 with torch 1.12 & CUDA 11.6 on a V100.
-* Windows 10 with torch 2.1 & CUDA 12.1 on a 3070.
-
-## Usage
-
-Image-to-3D:
-```bash
-### preprocess
-# background removal and recenter, save rgba at 256x256
-python process.py data/name.jpg
-
-# save at a larger resolution
-python process.py data/name.jpg --size 512
-
-# process all jpg images under a dir
-python process.py data
-
-### training gaussian stage
-# train 500 iters (~1min) and export ckpt & coarse_mesh to logs
-python main.py --config configs/image.yaml input=data/name_rgba.png save_path=name
-
-# gui mode (supports visualizing training)
-python main.py --config configs/image.yaml input=data/name_rgba.png save_path=name gui=True
-
-# load and visualize a saved ckpt
-python main.py --config configs/image.yaml load=logs/name_model.ply gui=True
-
-# use an estimated elevation angle if image is not front-view (e.g., common looking-down image can use -30)
-python main.py --config configs/image.yaml input=data/name_rgba.png save_path=name elevation=-30
-
-### training mesh stage
-# auto load coarse_mesh.obj and refine 50 iters (~1min), export fine_mesh to logs
-python main2.py --config configs/image.yaml input=data/name_rgba.png save_path=name
-
-# specify coarse mesh path explicity
-python main2.py --config configs/image.yaml input=data/name_rgba.png save_path=name mesh=logs/name_mesh.obj
-
-# gui mode
-python main2.py --config configs/image.yaml input=data/name_rgba.png save_path=name gui=True
-
-### visualization
-# gui for visualizing mesh
-python -m kiui.render logs/name.obj
-
-# save 360 degree video of mesh (can run without gui)
-python -m kiui.render logs/name.obj --save_video name.mp4 --wogui
-
-# save 8 view images of mesh (can run without gui)
-python -m kiui.render logs/name.obj --save images/name/ --wogui
-
-### evaluation of CLIP-similarity
-python -m kiui.cli.clip_sim data/name_rgba.png logs/name.obj
-```
-Please check `./configs/image.yaml` for more options.
-
-Text-to-3D:
-```bash
-### training gaussian stage
-python main.py --config configs/text.yaml prompt="a photo of an icecream" save_path=icecream
-
-### training mesh stage
-python main2.py --config configs/text.yaml prompt="a photo of an icecream" save_path=icecream
-```
-Please check `./configs/text.yaml` for more options.
-
-Helper scripts:
-```bash
-# run all image samples (*_rgba.png) in ./data
-python scripts/runall.py --dir ./data --gpu 0
-
-# run all text samples (hardcoded in runall_sd.py)
-python scripts/runall_sd.py --gpu 0
-
-# export all ./logs/*.obj to mp4 in ./videos
-python scripts/convert_obj_to_video.py --dir ./logs
-```
-
-## Acknowledgement
-
-This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
-
-* [gaussian-splatting](https://github.com/graphdeco-inria/gaussian-splatting) and [diff-gaussian-rasterization](https://github.com/graphdeco-inria/diff-gaussian-rasterization)
-* [threestudio](https://github.com/threestudio-project/threestudio)
-* [nvdiffrast](https://github.com/NVlabs/nvdiffrast)
-* [dearpygui](https://github.com/hoffstadt/DearPyGui)
-
-## Citation
-
-```
-
-```
diff --git a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Xiaor.py b/spaces/ASJMO/freegpt/g4f/Provider/Providers/Xiaor.py
deleted file mode 100644
index 5757f9971157116cbbfabbe5420e3b7e88fed4e7..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Xiaor.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://xiaor.eu.org'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k',
-         'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-    }
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-    response = requests.post(url + '/p1/v1/chat/completions',
-                             json=data, stream=True)
-
-    if stream:
-        for chunk in response.iter_content(chunk_size=None):
-            chunk = chunk.decode('utf-8')
-            if chunk.strip():
-                message = json.loads(chunk)['choices'][0]['message']['content']
-                yield message
-    else:
-        message = response.json()['choices'][0]['message']['content']
-        yield message
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Phind.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Phind.py
deleted file mode 100644
index 0db4e3c2662e6ec3b4a4231b9c55bf0744085da6..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Phind.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from __future__ import annotations
-
-import random
-from datetime import datetime
-
-from ..typing import AsyncGenerator
-from ..requests import StreamSession
-from .base_provider import AsyncGeneratorProvider, format_prompt
-
-
-class Phind(AsyncGeneratorProvider):
-    url = "https://www.phind.com"
-    working = True
-    supports_gpt_4 = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator:
-        chars = 'abcdefghijklmnopqrstuvwxyz0123456789'
-        user_id = ''.join(random.choice(chars) for _ in range(24))
-        data = {
-            "question": format_prompt(messages),
-            "webResults": [],
-            "options": {
-                "date": datetime.now().strftime("%d.%m.%Y"),
-                "language": "en",
-                "detailed": True,
-                "anonUserId": user_id,
-                "answerModel": "GPT-4",
-                "creativeMode": False,
-                "customLinks": []
-            },
-            "context":""
-        }
-        headers = {
-            "Authority": cls.url,
-            "Accept": "application/json, text/plain, */*",
-            "Origin": cls.url,
-            "Referer": f"{cls.url}/"
-        }
-        async with StreamSession(headers=headers, timeout=(5, 180), proxies={"https": proxy}, impersonate="chrome107") as session:
-            async with session.post(f"{cls.url}/api/infer/answer", json=data) as response:
-                response.raise_for_status()
-                new_lines = 0
-                async for line in response.iter_lines():
-                    if not line:
-                        continue
-                    if line.startswith(b"data: "):
-                        line = line[6:]
-                    if line.startswith(b"<PHIND_METADATA>"):
-                        continue
-                    if line:
-                        if new_lines:
-                            yield "".join(["\n" for _ in range(int(new_lines / 2))])
-                            new_lines = 0
-                        yield line.decode()
-                    else:
-                        new_lines += 1
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("proxy", "str"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/base/Base.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/base/Base.d.ts
deleted file mode 100644
index 816e8c28c78951670544fe253a5432672031d948..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/base/Base.d.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-// import * as Phaser from 'phaser';
-import BaseShape from '../../../plugins/gameobjects/shape/shapes/BaseShapes';
-
-export default Base;
-
-declare namespace Base {
-
-    interface IConfig {
-        x?: number, y?: number,
-        width?: number, height?: number,
-        color?: number,
-
-        duration?: number,
-        start?: boolean,
-
-        ease?: string,
-    }
-
-}
-
-declare class Base extends BaseShape {
-    constructor(
-        scene: Phaser.Scene,
-        config?: Base.IConfig
-    )
-
-    start(duration?: number): this;
-    pause(): this;
-    resume(): this;
-    stop(): this;
-    readonly isRunning: boolean;
-
-    setValue(t: number): this;
-    value: number;
-
-    setColor(color: number): this;
-    color: number;
-
-    setDuration(duration: number): this;
-    duration: this;
-
-    setEase(ease: string): this;
-    ease: string;
-
-    readonly centerX: number;
-    readonly centerY: number;
-    readonly radius: number;
-}
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Box.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Box.d.ts
deleted file mode 100644
index 878fd1b453568adc62244c84d3992011c97dd574..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Box.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import Base from '../base/Base';
-export default class Box extends Base { }
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/anchor/Anchor.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/anchor/Anchor.d.ts
deleted file mode 100644
index cffd99e192cb929c71d7fc3dfb54a69774b2f2ab..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/anchor/Anchor.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import Anchor from '../../../plugins/behaviors/anchor/Anchor';
-export default Anchor;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/EaseDataMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/EaseDataMethods.js
deleted file mode 100644
index b71567abae8a4e4a0a455b43a59265ee55f9e618..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/EaseDataMethods.js
+++ /dev/null
@@ -1,44 +0,0 @@
-import { EaseData } from '../../../plugins/easedata.js';
-import { WaitEvent } from '../utils/WaitEvent.js';
-
-var OnInitEaseData = function (gameObject, easeData) {
-    // Route 'complete' of easeData to gameObject
-    easeData.on('complete', function (key) {
-        gameObject.emit(`easedata.${key}.complete`, gameObject);
-        gameObject.emit('easedata.complete', key, gameObject);
-    })
-}
-
-export default {
-    easeDataTo(key, value, duration, ease) {
-        if (!this._easeData) {
-            this._easeData = new EaseData(this);
-            OnInitEaseData(this, this._easeData);
-        }
-        this._easeData.easeTo(key, value, duration, ease);
-        return this;
-    },
-
-    easeDataToPromise(key, value, duration, ease) {
-        this.easeDataTo(key, value, duration, ease);
-        return WaitEvent(this._easeData, `complete-${key}`);
-    },
-
-    stopEaseData(key, toEnd) {
-        if (!this._easeData) {
-            return this;
-        }
-
-        this._easeData.stopEase(key, toEnd);
-        return this;
-    },
-
-    stopAllEaseData(toEnd) {
-        if (!this._easeData) {
-            return this;
-        }
-
-        this._easeData.stopAll(toEnd);
-        return this;
-    }
-}
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/Layout.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/Layout.js
deleted file mode 100644
index 67bf7d7c76c58b604f6e2495f59b4fa27d41510c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/Layout.js
+++ /dev/null
@@ -1,19 +0,0 @@
-var Layout = function () {
-    // Save scale
-    var scaleXSave = this.scaleX;
-    var scaleYSave = this.scaleY;
-    var scale1 = (scaleXSave === 1) && (scaleYSave === 1);
-    if (!scale1) {
-        this.setScale(1);
-    }
-
-    // Run layout with scale = 1
-    this.runLayout();
-
-    // Restore scale
-    if (!scale1) {
-        this.setScale(scaleXSave, scaleYSave);
-    }
-    return this;
-}
-export default Layout;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch/NinePatch.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch/NinePatch.js
deleted file mode 100644
index 664a6cad4e76d2dc9f64d4d312a72f964128e24c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch/NinePatch.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import NinePatch from '../../../plugins/ninepatch.js'
-export default NinePatch;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch2/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch2/Factory.js
deleted file mode 100644
index f03930a91233bbff5678efd4ed7ed17c4417a884..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch2/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import NinePatch from './NinePatch.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('ninePatch2', function (x, y, width, height, key, columns, rows, config) {
-    var gameObject = new NinePatch(this.scene, x, y, width, height, key, columns, rows, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.NinePatch2', NinePatch);
-
-export default NinePatch;
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/dataset.py b/spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/dataset.py
deleted file mode 100644
index 605aa877f7031a5cd2b98c0f831410aa80fddefa..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/dataset.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import bisect
-import warnings
-
-from torch._utils import _accumulate
-from torch import randperm
-
-
-class Dataset(object):
-    """An abstract class representing a Dataset.
-
-    All other datasets should subclass it. All subclasses should override
-    ``__len__``, that provides the size of the dataset, and ``__getitem__``,
-    supporting integer indexing in range from 0 to len(self) exclusive.
-    """
-
-    def __getitem__(self, index):
-        raise NotImplementedError
-
-    def __len__(self):
-        raise NotImplementedError
-
-    def __add__(self, other):
-        return ConcatDataset([self, other])
-
-
-class TensorDataset(Dataset):
-    """Dataset wrapping data and target tensors.
-
-    Each sample will be retrieved by indexing both tensors along the first
-    dimension.
-
-    Arguments:
-        data_tensor (Tensor): contains sample data.
-        target_tensor (Tensor): contains sample targets (labels).
-    """
-
-    def __init__(self, data_tensor, target_tensor):
-        assert data_tensor.size(0) == target_tensor.size(0)
-        self.data_tensor = data_tensor
-        self.target_tensor = target_tensor
-
-    def __getitem__(self, index):
-        return self.data_tensor[index], self.target_tensor[index]
-
-    def __len__(self):
-        return self.data_tensor.size(0)
-
-
-class ConcatDataset(Dataset):
-    """
-    Dataset to concatenate multiple datasets.
-    Purpose: useful to assemble different existing datasets, possibly
-    large-scale datasets as the concatenation operation is done in an
-    on-the-fly manner.
-
-    Arguments:
-        datasets (iterable): List of datasets to be concatenated
-    """
-
-    @staticmethod
-    def cumsum(sequence):
-        r, s = [], 0
-        for e in sequence:
-            l = len(e)
-            r.append(l + s)
-            s += l
-        return r
-
-    def __init__(self, datasets):
-        super(ConcatDataset, self).__init__()
-        assert len(datasets) > 0, 'datasets should not be an empty iterable'
-        self.datasets = list(datasets)
-        self.cumulative_sizes = self.cumsum(self.datasets)
-
-    def __len__(self):
-        return self.cumulative_sizes[-1]
-
-    def __getitem__(self, idx):
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
-        if dataset_idx == 0:
-            sample_idx = idx
-        else:
-            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
-        return self.datasets[dataset_idx][sample_idx]
-
-    @property
-    def cummulative_sizes(self):
-        warnings.warn("cummulative_sizes attribute is renamed to "
-                      "cumulative_sizes", DeprecationWarning, stacklevel=2)
-        return self.cumulative_sizes
-
-
-class Subset(Dataset):
-    def __init__(self, dataset, indices):
-        self.dataset = dataset
-        self.indices = indices
-
-    def __getitem__(self, idx):
-        return self.dataset[self.indices[idx]]
-
-    def __len__(self):
-        return len(self.indices)
-
-
-def random_split(dataset, lengths):
-    """
-    Randomly split a dataset into non-overlapping new datasets of given lengths
-    ds
-
-    Arguments:
-        dataset (Dataset): Dataset to be split
-        lengths (iterable): lengths of splits to be produced
-    """
-    if sum(lengths) != len(dataset):
-        raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
-
-    indices = randperm(sum(lengths))
-    return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)]
diff --git a/spaces/Alican/pixera/README.md b/spaces/Alican/pixera/README.md
deleted file mode 100644
index 19804d6664f64ba557e5991ebd7908a71e647b7d..0000000000000000000000000000000000000000
--- a/spaces/Alican/pixera/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Pixera
-emoji: 💻
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/discriminator.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/discriminator.py
deleted file mode 100644
index 16bf3722c7f2e35cdc9bd177a33ed0975e67200d..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/discriminator.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from torch import nn
-
-
-class LatentCodesDiscriminator(nn.Module):
-    def __init__(self, style_dim, n_mlp):
-        super().__init__()
-
-        self.style_dim = style_dim
-
-        layers = []
-        for i in range(n_mlp-1):
-            layers.append(
-                nn.Linear(style_dim, style_dim)
-            )
-            layers.append(nn.LeakyReLU(0.2))
-        layers.append(nn.Linear(512, 1))
-        self.mlp = nn.Sequential(*layers)
-
-    def forward(self, w):
-        return self.mlp(w)
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 989928ab7f98da86e291451040ff85669a9fbddb..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './ccnet_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
deleted file mode 100644
index b4a9d4e1b9123b3c965cd430237ce9fcc7018a11..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 69bef7238345cf6aabb126012af992602f910287..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=128,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/evaluate.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/evaluate.py
deleted file mode 100644
index 8044e203151157a6473fa11c98414a27d45a32af..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/evaluate.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import datetime
-from pathlib import Path
-
-import pandas as pd
-import torch
-from datasets import load_dataset
-from tqdm import tqdm
-
-from modules import shared
-from modules.models import load_model, unload_model
-from modules.models_settings import get_model_metadata, update_model_parameters
-from modules.text_generation import encode
-
-
-def load_past_evaluations():
-    if Path('logs/evaluations.csv').exists():
-        df = pd.read_csv(Path('logs/evaluations.csv'), dtype=str)
-        df['Perplexity'] = pd.to_numeric(df['Perplexity'])
-        return df
-    else:
-        return pd.DataFrame(columns=['Model', 'LoRAs', 'Dataset', 'Perplexity', 'stride', 'max_length', 'Date', 'Comment'])
-
-
-past_evaluations = load_past_evaluations()
-
-
-def save_past_evaluations(df):
-    global past_evaluations
-    past_evaluations = df
-    filepath = Path('logs/evaluations.csv')
-    filepath.parent.mkdir(parents=True, exist_ok=True)
-    df.to_csv(filepath, index=False)
-
-
-def calculate_perplexity(models, input_dataset, stride, _max_length):
-    '''
-    Based on:
-    https://huggingface.co/docs/transformers/perplexity#calculating-ppl-with-fixedlength-models
-    '''
-
-    global past_evaluations
-    cumulative_log = ''
-    cumulative_log += "Loading the input dataset...\n\n"
-    yield cumulative_log
-
-    # Copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/triton/utils/datautils.py
-    if input_dataset == 'wikitext':
-        data = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
-        text = "\n\n".join(data['text'])
-    elif input_dataset == 'ptb':
-        data = load_dataset('ptb_text_only', 'penn_treebank', split='validation')
-        text = "\n\n".join(data['sentence'])
-    elif input_dataset == 'ptb_new':
-        data = load_dataset('ptb_text_only', 'penn_treebank', split='test')
-        text = " ".join(data['sentence'])
-    else:
-        with open(Path(f'training/datasets/{input_dataset}.txt'), 'r', encoding='utf-8') as f:
-            text = f.read()
-
-    for model in models:
-        if is_in_past_evaluations(model, input_dataset, stride, _max_length):
-            cumulative_log += f"{model} has already been tested. Ignoring.\n\n"
-            yield cumulative_log
-            continue
-
-        if model != 'current model':
-            try:
-                yield cumulative_log + f"Loading {model}...\n\n"
-                model_settings = get_model_metadata(model)
-                shared.settings.update({k: v for k, v in model_settings.items() if k in shared.settings})  # hijacking the interface defaults
-                update_model_parameters(model_settings)  # hijacking the command-line arguments
-                shared.model_name = model
-                unload_model()
-                shared.model, shared.tokenizer = load_model(shared.model_name)
-            except:
-                cumulative_log += f"Failed to load {model}. Moving on.\n\n"
-                yield cumulative_log
-                continue
-
-        cumulative_log += f"Processing {shared.model_name}...\n\n"
-        yield cumulative_log + "Tokenizing the input dataset...\n\n"
-        encodings = encode(text, add_special_tokens=False)
-        seq_len = encodings.shape[1]
-        if _max_length:
-            max_length = _max_length
-        elif hasattr(shared.model.config, 'max_position_embeddings'):
-            max_length = shared.model.config.max_position_embeddings
-        else:
-            max_length = 2048
-
-        nlls = []
-        prev_end_loc = 0
-        for begin_loc in tqdm(range(0, seq_len, stride)):
-            yield cumulative_log + f"Evaluating... {100*begin_loc/seq_len:.2f}%"
-            end_loc = min(begin_loc + max_length, seq_len)
-            trg_len = end_loc - prev_end_loc  # may be different from stride on last loop
-            input_ids = encodings[:, begin_loc:end_loc]
-            target_ids = input_ids.clone()
-            target_ids[:, :-trg_len] = -100
-
-            with torch.no_grad():
-                outputs = shared.model(input_ids=input_ids, labels=target_ids)
-
-                # loss is calculated using CrossEntropyLoss which averages over valid labels
-                # N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels
-                # to the left by 1.
-                neg_log_likelihood = outputs.loss
-
-            nlls.append(neg_log_likelihood)
-
-            prev_end_loc = end_loc
-            if end_loc == seq_len:
-                break
-
-        ppl = torch.exp(torch.stack(nlls).mean())
-        add_entry_to_past_evaluations(float(ppl), shared.model_name, input_dataset, stride, _max_length)
-        save_past_evaluations(past_evaluations)
-        cumulative_log += f"The perplexity for {shared.model_name} is: {float(ppl)}\n\n"
-        yield cumulative_log
-
-
-def add_entry_to_past_evaluations(perplexity, model, dataset, stride, max_length):
-    global past_evaluations
-    entry = {
-        'Model': model,
-        'LoRAs': ', '.join(shared.lora_names) or '-',
-        'Dataset': dataset,
-        'Perplexity': perplexity,
-        'stride': str(stride),
-        'max_length': str(max_length),
-        'Date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
-        'Comment': ''
-    }
-    past_evaluations = pd.concat([past_evaluations, pd.DataFrame([entry])], ignore_index=True)
-
-
-def is_in_past_evaluations(model, dataset, stride, max_length):
-    entries = past_evaluations[(past_evaluations['Model'] == model) &
-                               (past_evaluations['Dataset'] == dataset) &
-                               (past_evaluations['max_length'] == str(max_length)) &
-                               (past_evaluations['stride'] == str(stride))]
-
-    if entries.shape[0] > 0:
-        return True
-    else:
-        return False
-
-
-def generate_markdown_table():
-    sorted_df = past_evaluations.sort_values(by=['Dataset', 'stride', 'Perplexity', 'Date'])
-    return sorted_df
diff --git a/spaces/AnnonSubmission/xai-cl/data_transforms.py b/spaces/AnnonSubmission/xai-cl/data_transforms.py
deleted file mode 100644
index da17ef24463c190e5e5a49708a48c28ace8c04e0..0000000000000000000000000000000000000000
--- a/spaces/AnnonSubmission/xai-cl/data_transforms.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import torch
-import torchvision
-import torchvision.transforms as transforms
-import torch.nn as nn
-from PIL import Image, ImageOps, ImageFilter
-import random
-
-def add_normalization_to_transform(unnormalized_transforms):
-    """Adds ImageNet normalization to all transforms"""
-    normalized_transform = {}
-    for key, value in unnormalized_transforms.items():
-        normalized_transform[key] = transforms.Compose([value, 
-                                                        transforms.Normalize(mean=[0.485, 0.456, 0.406], 
-                                                                             std=[0.229, 0.224, 0.225])]) 
-    return normalized_transform
-
-def modify_transforms(normal_transforms, no_shift_transforms, ig_transforms):
-    normal_transforms = add_normalization_to_transform(normal_transforms)
-    no_shift_transforms = add_normalization_to_transform(no_shift_transforms)
-    ig_transforms = add_normalization_to_transform(ig_transforms)
-    return normal_transforms, no_shift_transforms, ig_transforms
-    
-class Solarization(object):
-    def __init__(self, p):
-        self.p = p
-
-    def __call__(self, img):
-        if random.random() < self.p:
-            return ImageOps.solarize(img)
-        else:
-            return img
-        
-# no imagent normalization for simclrv2
-pure_transform = transforms.Compose([transforms.Resize(256), 
-                                     transforms.CenterCrop(224), 
-                                     transforms.ToTensor()])   
-
-aug_transform = transforms.Compose([transforms.RandomResizedCrop(224), 
-                                    transforms.RandomHorizontalFlip(p=0.5),
-                                    transforms.RandomApply([transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)], p=0.8),
-                                    transforms.RandomGrayscale(p=0.2),
-                                    transforms.RandomApply([transforms.GaussianBlur(kernel_size=(21,21), sigma=(0.1,2.0))], p=0.5),
-                                    transforms.ToTensor()])  
-
-ig_pure_transform = transforms.Compose([transforms.Resize(256), 
-                                        transforms.CenterCrop(224), 
-                                        transforms.ToTensor()])   
-
-ig_transform_colorjitter = transforms.Compose([transforms.Resize(256), 
-                                               transforms.CenterCrop(224),
-                                               transforms.RandomApply([transforms.ColorJitter(0.8, 0.8, 0.8, 0.4)], p=1),
-                                               transforms.ToTensor()])  
-
-ig_transform_blur = transforms.Compose([transforms.Resize(256), 
-                                        transforms.CenterCrop(224),
-                                        transforms.RandomApply([transforms.GaussianBlur(kernel_size=(11,11), sigma=(5,5))], p=1),
-                                        transforms.ToTensor()])  
-
-ig_transform_solarize = transforms.Compose([transforms.Resize(256), 
-                                            transforms.CenterCrop(224),
-                                            Solarization(p=1.0),
-                                            transforms.ToTensor()]) 
-
-ig_transform_grayscale = transforms.Compose([transforms.Resize(256), 
-                                             transforms.CenterCrop(224),
-                                             transforms.RandomGrayscale(p=1),
-                                             transforms.ToTensor()])  
-
-
-ig_transform_combine = transforms.Compose([transforms.Resize(256), 
-                                           transforms.CenterCrop(224),
-                                           transforms.RandomApply([transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)], p=0.8),
-                                           transforms.RandomGrayscale(p=0.2),
-                                           transforms.RandomApply([transforms.GaussianBlur(kernel_size=(21,21), sigma=(0.1, 2.0))], p=0.5),
-                                           transforms.ToTensor()])  
-
-pure_transform_no_shift = transforms.Compose([transforms.Resize((224, 224)), 
-                                              transforms.ToTensor()])   
-
-aug_transform_no_shift = transforms.Compose([transforms.Resize((224, 224)),
-                                             transforms.RandomApply([transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)], p=0.8),
-                                             transforms.RandomGrayscale(p=0.2),
-                                             transforms.ToTensor()])  
-
-normal_transforms = {'pure': pure_transform, 
-                     'aug': aug_transform}
-
-no_shift_transforms = {'pure': pure_transform_no_shift, 
-                       'aug': aug_transform_no_shift}
-
-ig_transforms = {'pure': ig_pure_transform, 
-                 'color_jitter': ig_transform_colorjitter, 
-                 'blur': ig_transform_blur, 
-                 'grayscale': ig_transform_grayscale, 
-                 'solarize': ig_transform_solarize,
-                 'combine': ig_transform_combine}
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Counter Strike 1.6 Original.md b/spaces/Benson/text-generation/Examples/Descargar Counter Strike 1.6 Original.md
deleted file mode 100644
index 50c7e6ce8ec72a2abef7eacdfea66799cf142045..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Counter Strike 1.6 Original.md	
+++ /dev/null
@@ -1,149 +0,0 @@
-
-<h1>Cómo descargar y jugar Counter-Strike 1.6 Original</h1>
- <p></p>
-<h2>descargar counter strike 1.6 original</h2><br /><p><b><b>Download Zip</b> &rArr;&rArr;&rArr; <a href="https://bltlly.com/2v6K4r">https://bltlly.com/2v6K4r</a></b></p><br /><br />
- <h2>Introducción</h2>
- <p>Counter-Strike 1.6 original, también conocido como Half-Life: Counter-Strike o CS 1.6, es un juego de disparos táctico en primera persona que fue lanzado en 2000 por Valve. Inicialmente fue desarrollado y lanzado como una modificación de Half-Life por Minh "Gooseman" Le y Jess Cliffe en 1999, antes de ser contratados por Valve y la propiedad intelectual del juego fue adquirida. </p>
- <p>El juego se desarrolla en varios lugares alrededor del mundo, donde los jugadores asumen el papel de fuerzas antiterroristas o militantes terroristas. Durante cada ronda de juego, los dos equipos tienen la tarea de derrotarse entre sí mediante el logro de los objetivos del mapa o la eliminación de todos los combatientes enemigos. Cada jugador puede personalizar su arsenal de armas y accesorios al comienzo de cada partido, con la moneda que se gana después del final de cada ronda. </p>
- <p>Counter-Strike 1.6 original es uno de los juegos FPS multijugador más populares e influyentes de todos los tiempos, con millones de jugadores y fans en todo el mundo. Ha generado varias secuelas, remakes, ports, spin-offs, mods y torneos a lo largo de los años. También es uno de los títulos de esports más grandes, con equipos profesionales y jugadores compitiendo por la fama y la fortuna en varias ligas y eventos. </p>
-<p></p>
- <p>Algunas de las principales características y modos de juego de Counter-Strike 1.6 original son:</p>
- <ul>
-<li><b>Diseño original:</b> El juego tiene un aspecto clásico y auténtico, con armas originales, modelos, sonidos, mapas y menús. </li>
-<li><b>Bots originales:</b> El juego incluye bots incorporados (zbots) que se pueden controlar presionando el botón H. Se pueden usar para practicar sin conexión o llenar espacios vacíos en servidores en línea. </li>
-
-<li><b>Rescate de rehenes:</b> Este es otro modo de juego popular en Counter-Strike 1.6 original. Los antiterroristas deben rescatar a un grupo de rehenes retenidos por los terroristas en su base y escoltarlos a una zona segura en el mapa. Los terroristas ganan si impiden que los rehenes sean rescatados o eliminan a todos los antiterroristas. </li>
-<li><b>Asesinato:</b> Este es un modo de juego raro en Counter-Strike 1.6 original. Uno de los antiterroristas es elegido para actuar como un VIP y debe ser escoltado por sus compañeros de equipo a un lugar designado en el mapa. Los terroristas ganan si matan la cuenta si no tienes una ya. Puedes registrarte gratis en el sitio web de Steam o descargar el cliente de Steam y registrarte desde allí. </li>
-<li>Inicia sesión en tu cuenta de Steam y busca el original de Counter-Strike 1.6 en la tienda de Steam. También puedes usar este enlace para acceder directamente a la página del juego: [Counter-Strike on Steam]. </li>
-<li>Haga clic en el "Añadir al carrito" botón y proceder a la caja. El juego cuesta $9.99 USD a partir de junio de 2023, pero puede encontrar descuentos o paquetes durante las ventas o promociones. </li>
-<li>Después de completar tu pago, el juego se agregará a tu biblioteca de Steam. A continuación, puede descargarlo e instalarlo haciendo clic en el botón "Instalar" en la página del juego o en su biblioteca. </li>
-<li>Una vez que el juego está instalado, puede iniciarlo haciendo clic en el botón "Jugar" en la página del juego o en su biblioteca. También puede crear un acceso directo de escritorio para facilitar el acceso. </li>
-</ol>
- <h3>Cómo descargar el juego de otras fuentes</h3>
- <p>Si no quieres comprar el juego en Steam, también puedes descargarlo de otras fuentes que ofrecen versiones gratuitas o no oficiales de Counter-Strike 1.6 original. Sin embargo, debe tener cuidado y precaución al hacerlo, ya que algunas de estas fuentes pueden contener virus, malware u otros archivos dañinos que podrían dañar su computadora o comprometer su seguridad. Estos son algunos consejos a seguir:</p>
- <ul>
-
-<li>Escanee el archivo descargado con un programa antivirus o anti-malware antes de abrirlo o instalarlo. Elimine cualquier archivo que se detecte como infectado o sospechoso. </li>
-<li>Asegúrese de que el archivo descargado es compatible con su sistema operativo y cumple con los requisitos mínimos del sistema para el juego. Es posible que necesite instalar software o controladores adicionales para ejecutar el juego correctamente. </li>
-<li>Siga las instrucciones proporcionadas por el sitio web o el archivo para instalar y ejecutar el juego. Algunos archivos pueden requerir que los extraigas usando un programa como WinRAR o 7-Zip antes de instalarlos. </li>
-<li>Ten en cuenta los riesgos y limitaciones de descargar el juego desde otras fuentes. Es posible que no puedas jugar en línea con otros jugadores que tengan la versión oficial del juego, o que puedas encontrar errores, errores o fallos que afecten tu experiencia de juego. </li>
-</ul>
- <h3>Cómo comprobar los requisitos del sistema y la compatibilidad</h3>
- <p>Antes de descargar y jugar Counter-Strike 1.6 original, usted debe asegurarse de que su computadora cumple con los requisitos mínimos del sistema para el juego y es compatible con él. Estos son algunos de los requisitos del sistema y problemas de compatibilidad que debe comprobar:</p>
- <tabla>
-<tr><th>Requisito del sistema</th><th>Mínimo</th><th>Recomendado</th></tr>
-<tr><td>Sistema operativo</td><td>Windows XP/Vista/7/8/10</td><td><td>Windows XP/Vista/7/8/10</td></tr>
-<tr><td>CPU</td><td>Pentium III 500 MHz o equivalente</td><td>Pentium 4 1 GHz o equivalente</td></tr>
-<tr><td>RAM</td><td>96 MB</td><td>256 MB</td></tr>
-<tr><td>Tarjeta gráfica</td><td>Tarjeta de video de 16 MB con soporte para DirectX 8</td><td>Tarjeta de video de 32 MB con soporte para DirectX 9</td></tr>
-<tr><td>Espacio en disco duro</td><td>500 MB</td><td>1 GB</td></tr>
-<tr><td>Conexión a Internet</td><td>Banda ancha (para jugar en línea)</td><td>Banda ancha (para jugar en línea)</td></tr>
-<tr><td>Tarjeta de sonido</td><td>Tarjeta de sonido compatible con DirectX</td><td>Tarjeta de sonido compatible con DirectX</td></tr>
-
-<tr><td>DVD-ROM Drive (para copia física)</td><td>N/A</td><td><N/A</td></tr>
-<tr><th colspan="3">Problemas de compatibilidad</th></tr>
-<tr><td colspan="3">Algunos usuarios pueden experimentar problemas al ejecutar Counter-Strike 1.6 original en versiones más recientes de Windows, como Windows 10. Algunos de estos problemas incluyen bajo FPS, pantalla negra, retraso del ratón, problemas de sonido, etc. Para solucionar estos problemas, es posible que tenga que ajustar algunos ajustes en las propiedades del juego, como el modo de compatibilidad, modo administrador, resolución, etc. Puede encontrar más información y soluciones en varios foros y sitios web dedicados a Counter Strike 1.6 original. </td></tr>
-</tabla>
- <h2>Cómo jugar Counter-Strike 1.6 original</h2>
- <p>Ahora que has descargado e instalado Counter-Strike 1.6 original, estás listo para jugar y divertirte. Estos son algunos de los pasos y consejos básicos para ayudarte a empezar:</p>
- <h3>Cómo unirse a un servidor y seleccionar un equipo</h3>
- <p>Para jugar Counter-Strike 1.6 original en línea, es necesario unirse a un servidor que alberga el juego. Puede unirse a un servidor existente o crear su propio servidor. Estos son los pasos para unirse a un servidor existente:</p>
- <ol>
-<li> Inicie el juego y haga clic en el botón "Buscar servidores" en el menú principal. </li>
-<li>Aparecerá una lista de servidores disponibles, mostrando su nombre, mapa, reproductores, ping, etc. Puede filtrar la lista usando las pestañas en la parte superior o el cuadro de búsqueda en la parte inferior. </li>
-<li>Seleccione un servidor al que desea unirse y haga clic en el botón "Conectar" en la parte inferior derecha. También puede hacer doble clic en el nombre del servidor o hacer clic derecho y elegir "Conectar" en el menú. </li>
-<li>El juego cargará el mapa y te conectará al servidor. Es posible que deba esperar unos segundos o minutos dependiendo de la velocidad de Internet y la configuración del servidor. </li>
-
-<li>A continuación, verá una pantalla que muestra los miembros de su equipo y sus resultados. También puede chatear con sus compañeros de equipo o todos los jugadores utilizando las teclas Y o U respectivamente. Pulse OK para continuar. </li>
-</ol>
- <p>Para crear tu propio servidor, debes seguir estos pasos:</p>
- <ol>
-<li> Inicie el juego y haga clic en el botón "Crear servidor" en el menú principal. </li>
-<li>Aparecerá una ventana que le permite configurar la configuración de su servidor, como nombre, contraseña, mapa, modo de juego, jugadores máximos, etc. También puede habilitar o desactivar bots, trucos, fuego amigo, etc.</li>
-<li>Después de configurar la configuración de su servidor, haga clic en el botón "Inicio" en la parte inferior derecha. El juego cargará el mapa y creará su servidor. </li>
-<li>A continuación, verá una pantalla que le pide que elija un equipo: Terroristas o Antiterroristas. También puede elegir ser un espectador y ver jugar a otros jugadores. Haga clic en el equipo al que desea unirse y pulse OK.</li>
-<li>A continuación, verá una pantalla que muestra los miembros de su equipo y sus resultados. También puede chatear con sus compañeros de equipo o todos los jugadores utilizando las teclas Y o U respectivamente. Pulse OK para continuar. </li>
-</ol>
- <h3>Cómo comprar armas y equipos</h3>
- <p>Al comienzo de cada ronda, usted tiene una cantidad limitada de tiempo y dinero para comprar armas y equipos para usted y sus compañeros de equipo. Puede comprar artículos desde el menú de compra presionando la tecla B o usando la rueda del ratón. Estos son algunos de los artículos que puede comprar:</p>
- <ul>
-<li><b>Pistolas:</b> Estas son armas secundarias que son baratas y fáciles de usar, pero tienen poco daño y precisión. Algunos ejemplos son Glock, USP, Desert Eagle, etc.</li>
-<li><b>Rifles:</b> Estas son armas primarias que son caras y poderosas, pero tienen un alto retroceso y peso. Algunos ejemplos son AK-47, M4A1, AWP, etc.</li>
-<li><b>Escopetas:</b> Estas son armas primarias que son baratas y eficaces a corta distancia, pero tienen baja precisión y capacidad de munición. Algunos ejemplos son XM1014, M3 Super 90, etc.</li>
-
-<li><b>Ametralladoras:</b> Estas son armas primarias que son muy caras y de fuego pesado, pero tienen un alto retroceso y ruido. Algunos ejemplos son M249 Para, Negev, etc.</li>
-<li><b>Granadas:</b> Estos son elementos desechables que pueden causar daño o efectos a enemigos o aliados. Algunos ejemplos son la granada HE (explosiva), flashbang (cegadora), granada de humo (oscurecimiento), etc.</li>
-<li><b>Armadura:</b> Este es un artículo que puede protegerte de balas y granadas. Puedes comprar kevlar (armadura corporal) o kevlar + casco (armadura para la cabeza). </li>
-<li><b>Gafas de visión nocturna:</b> Este es un elemento que puede ayudarte a ver en áreas oscuras. Puedes activarlo o desactivarlo presionando la tecla N. </li>
-<li><b>Desactivar kit:</b> Este es un elemento que puede ayudarle a desactivar la bomba más rápido. Solo puede comprarla si es un antiterrorista. </li>
-</ul>
- <p>También puedes comprar artículos para tus compañeros de equipo usando el menú de compra o tirándolos al suelo. Puede soltar elementos pulsando la tecla G o utilizando la rueda del ratón. También puedes solicitar elementos a tus compañeros de equipo usando los comandos de radio o el chat. </p>
- <h3>Cómo comunicarse con compañeros de equipo y usar comandos de radio</h3>
- <p>La comunicación es muy importante en el original de Counter-Strike 1.6, ya que puede ayudarte a coordinar con tus compañeros de equipo y compartir información sobre la ubicación, el estado y las acciones del enemigo. Puede comunicarse con sus compañeros de equipo mediante el chat de voz, el chat de texto o los comandos de radio. </p>
- <p>Para usar el chat de voz, necesitas tener un micrófono y activarlo en las opciones del juego. A continuación, puede presionar y mantener pulsada la tecla K para hablar con sus compañeros de equipo. También puede ajustar el volumen y silenciar a otros jugadores en las opciones del juego. </p>
- <p>Para usar el chat de texto, debe presionar la tecla Y para chatear con todos los jugadores o la tecla U para chatear solo con sus compañeros de equipo. A continuación, puede escribir su mensaje y pulse Enter para enviarlo. También puedes usar algunos comandos y atajos en el chat, como /me, /quit, /timeleft, etc.</p>
-
- <h3>Cómo completar objetivos y ganar rondas</h3>
- <p>El objetivo principal de Counter-Strike 1.6 original es completar los objetivos de tu equipo y ganar rondas contra el equipo enemigo. Los objetivos varían según el modo de juego y el mapa, pero por lo general implican plantar o desactivar una bomba, rescatar o proteger a los rehenes, asesinar o proteger a un VIP, o escapar o evitar una fuga. </p>
- <p>Para ganar una ronda, necesitas completar el objetivo de tu equipo o eliminar a todos los jugadores enemigos antes de que se acabe el tiempo. El límite de tiempo para cada ronda suele ser de 2 minutos y 30 segundos, pero puede variar dependiendo de la configuración del servidor. Si ningún equipo completa su objetivo o elimina a todos los jugadores enemigos antes de que acabe el tiempo, la ronda terminará en un empate. </p>
- <p>Para ganar un partido, necesitas ganar más rondas que el equipo enemigo. El número de rondas para cada partido suele ser de 30, pero puede variar dependiendo de la configuración del servidor. Si ambos equipos ganan un número igual de rondas después de 30 rondas, el partido terminará en un empate. </p>
- <h2>Consejos y trucos para Counter-Strike 1.6 original</h2>
- <p>Counter-Strike 1.6 original es un juego que requiere habilidad, estrategia, trabajo en equipo y práctica para dominar. Estos son algunos consejos y trucos que pueden ayudarte a mejorar tu jugabilidad y rendimiento:</p>
- <h3>Cómo mejorar tu puntería y control de retroceso</h3>
- <p>Apuntar y controlar el retroceso son dos de las habilidades más importantes en Counter-Strike 1.6 original, ya que determinan con qué precisión y eficacia puedes disparar a tus enemigos. Estos son algunos consejos para mejorar tu puntería y control de retroceso:</p>
- <ul>
-<li><b>Práctica:</b> La mejor manera de mejorar tu puntería y control de retroceso es practicar de forma regular y consistente. Puedes practicar offline con bots o online con otros jugadores en diferentes mapas y modos. También puede utilizar mapas de entrenamiento o servidores dedicados que ofrecen varios ejercicios y desafíos para apuntar y controlar el retroceso. </li>
-
-<li><b>Punto de mira:</b> El punto de mira es el símbolo que muestra dónde irán tus balas cuando dispares. Usted debe elegir un punto de mira que sea cómodo y visible para usted, pero no demasiado grande o distracción. Puedes personalizar tu punto de mira en las opciones del juego o mediante comandos de consola. </li>
-<li><b>Posicionamiento:</b> La posición de tu punto de mira en la pantalla afecta la rapidez y precisión con la que puedes apuntar a tus enemigos. Siempre debes mantener tu punto de mira a la altura de la cabeza y cerca de las esquinas o bordes de paredes, puertas, ventanas, etc. De esta manera, puedes reducir la distancia y el tiempo que necesitas para mover tu punto de mira para disparar a tus enemigos. </li>
-<li><b>Movimiento:</b> El movimiento de tu personaje afecta la precisión y estabilidad de tus disparos. Siempre debes dejar de moverte antes de disparar, ya que moverte mientras disparas hará que tus balas se extiendan y se desvíen de tu punto de mira. Puedes usar la tecla shift para caminar lenta y silenciosamente, o la tecla ctrl para agacharte y bajar tu perfil. </li>
-<li><b>Estallido:</b> La técnica de estallido consiste en disparar algunas balas a la vez, en lugar de rociar o tocar. De esta manera, puede controlar mejor el retroceso y la propagación de sus disparos, y conservar su munición y precisión. Debes disparar de 2 a 4 balas por disparo, dependiendo del arma y la distancia. También debe hacer una pausa breve entre cada ráfaga para dejar que el retroceso se restablezca. </li>
-<li><b>Pulverización:</b> La técnica de pulverización consiste en disparar muchas balas a la vez, sin detenerse o detenerse. De esta manera, puedes infligir más daño y suprimir a tus enemigos más rápido, pero a costa de precisión y munición. Solo debes rociar cuando estás muy cerca de tus enemigos, o cuando no tienes otra opción. También debe aprender los patrones de pulverización de diferentes armas y compensarlos moviendo el ratón en la dirección opuesta. </li>
-
-</ul>
- <h3>Cómo usar los auriculares y ajustar el volumen</h3>
- <p>El sonido es otro aspecto importante del original de Counter-Strike 1.6, ya que puede ayudarte a escuchar y localizar a tus enemigos y aliados, así como otros sonidos como pasos, disparos, granadas, etc. Estos son algunos consejos para usar auriculares y ajustar tu volumen:</p>
- <ul>
-<li><b>Auriculares:</b> Siempre debe usar auriculares en lugar de altavoces al reproducir Counter-Strike 1.6 original, ya que los auriculares pueden proporcionar una mejor calidad de sonido y direccionalidad que los altavoces. Los auriculares también pueden bloquear los ruidos externos y las distracciones que pueden interferir con su juego. Usted debe elegir los auriculares que son cómodos y caben bien en sus oídos, y que tienen buen balance de graves y agudos. </li>
-<li><b>Volumen:</b> Debes ajustar tu volumen a un nivel lo suficientemente alto para que puedas escuchar todos los sonidos del juego con claridad, pero no demasiado fuerte para que te duela los oídos o cause daño auditivo. También debes evitar usar cualquier potenciador de sonido o ecualizador que pueda distorsionar o alterar los sonidos originales del juego. </li>
-<li><b>Configuración:</b> Deberías revisar y ajustar tu configuración de sonido en las opciones del juego o usando comandos de consola. Debes habilitar el sonido 3D o HRTF (función de transferencia relacionada con la cabeza) si está disponible, ya que pueden mejorar la conciencia espacial y el realismo de los sonidos en el juego. También debes desactivar cualquier música o sonido ambiental que pueda distraerte o molestarte durante el juego. </li>
-</ul>
- <h3>Cómo permanecer quieto cuando se dispara y moverse constantemente cuando no</h3>
- <p>El movimiento es otra habilidad importante en Counter-Strike 1.6 original, ya que afecta a lo rápido y ágil que estás en el campo de batalla. Aquí hay algunos consejos para quedarse quieto al disparar y moverse constantemente cuando no:</p>
- <ul>
-
-<li><b>Muévete constantemente cuando no dispares:</b> Cuando no estés disparando, siempre debes seguir moviéndote alrededor del mapa, ya que quedarte quieto te hará un objetivo fácil para tus enemigos. Puede usar las teclas W, A, S, D para avanzar, izquierda, hacia atrás, derecha respectivamente. También puede utilizar las teclas Q y E para inclinarse hacia la izquierda o hacia la derecha, respectivamente. También puede utilizar el ratón para mirar a su alrededor y apuntar. Debes moverte de forma impredecible y aleatoria, y evitar correr en líneas rectas o permanecer en áreas abiertas. </li>
-<li><b>Strafe:</b> Strafing es una técnica que consiste en moverse de lado mientras se mira hacia adelante. De esta manera, puedes esquivar el fuego enemigo y mantener tu puntería al mismo tiempo. Puede pulsar las teclas A o D mientras mueve el ratón en la dirección opuesta. También puede usar las teclas Q y E para inclinarse hacia la izquierda o hacia la derecha mientras se desvía. </li>
-<li><b>Bunny hop:</b> Bunny hopping es una técnica que consiste en saltar repetidamente mientras se avanza. De esta manera, puede aumentar su velocidad y movilidad, y hacerse más difícil de golpear. Puede saltar saltando presionando las teclas W y barra espaciadora alternativamente mientras mueve el ratón ligeramente hacia la izquierda o la derecha. </li>
-</ul>
- <h3>Cómo disparar a través de las paredes y los puntos comunes de pre-fuego</h3>
- <p>Disparar a través de paredes y puntos comunes previos al disparo son dos técnicas avanzadas que pueden darte una ventaja sobre tus enemigos en Counter-Strike 1.6 original. Aquí hay algunos consejos para disparar a través de las paredes y los puntos comunes pre-fuego:</p>
- <ul>
-<li><b>Dispara a través de las paredes:</b> Algunas paredes y objetos en Counter-Strike 1.6 original son penetrables, lo que significa que puedes disparar a través de ellos y golpear a tus enemigos detrás de ellos. Puedes usar esto para sorprender o dañar a tus enemigos sin exponerte. Puedes saber si una pared u objeto es penetrable disparándole y viendo si deja un agujero de bala o una marca. También puede utilizar el comando de consola r_decals 0 para eliminar todas las calcomanías del mapa, lo que facilita ver las paredes y objetos penetrables. </li>
-
-</ul>
- <h3>Cómo mantener la calma y tomar el tiro</h3>
- <p>Mantener la calma y tomar el disparo son dos habilidades esenciales en Counter-Strike 1.6 original, ya que afectan a lo bien que se realiza bajo presión y lo seguro que está en sus habilidades. Aquí hay algunos consejos para mantener la calma y tomar la foto:</p>
- <ul>
-<li><b>Respirar:</b> La respiración es una forma simple pero efectiva de calmarse y relajar la mente y el cuerpo. Debe respirar profunda y lentamente, inhalando por la nariz y exhalando por la boca. También debes enfocarte en tu respiración e ignorar cualquier distracción o pensamientos negativos. </li>
-<li><b>Piensa:</b> Pensar es una forma crucial pero a menudo pasada por alto para mejorar la toma de decisiones y las habilidades para resolver problemas. Debes pensar de forma lógica y estratégica, analizando la situación y sopesando los pros y los contras de cada opción. También debes pensar positiva y optimistamente, creyendo en ti mismo y en tus compañeros de equipo. </li>
-<li><b>Act:</b> Actuar es la forma final pero más importante de ejecutar tu plan y lograr tu objetivo. Debes actuar con rapidez y confianza, confiando en tus instintos y habilidades. También debe actuar con calma y paciencia, esperando el momento adecuado y la oportunidad de tomar la foto. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>En conclusión, Counter-Strike 1.6 original es un juego que ofrece mucha diversión, desafío y emoción para los fans de FPS de todas las edades y niveles. Es un juego que requiere habilidad, estrategia, trabajo en equipo y práctica para dominar, pero también te recompensa con satisfacción, disfrute y mejora. Es un juego que tiene una rica historia, una comunidad leal y un futuro brillante. </p>
-
- <p>Espero que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta, comentario o retroalimentación, no dude en compartirlos conmigo. Me encantaría saber de ti y ayudarte. ¡Gracias por leer y jugar feliz! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre Counter-Strike 1.6 original:</p>
- <h3>P: ¿Cuál es la diferencia entre Counter-Strike 1.6 original y Counter-Strike: Fuente? </h3>
- <p>A: Counter-Strike: Source es un remake de Counter-Strike 1.6 original que fue lanzado en 2004 por Valve. Utiliza el motor Source, que ofrece gráficos, física, sonido y jugabilidad mejorados. Sin embargo, algunos jugadores prefieren Counter-Strike 1.6 original por su diseño clásico y auténtico, jugabilidad y sensación. </p>
- <h3>P: ¿Cuál es la diferencia entre Counter-Strike 1.6 original y Counter-Strike: Ofensiva Global? </h3>
- <p>A: Counter-Strike: Global Offensive es la última entrega de la serie Counter-Strike que fue lanzada en 2012 por Valve. Cuenta con gráficos actualizados, modos, mapas, armas y personajes, así como nuevas características como pieles, rangos, matchmaking, etc. Sin embargo, algunos jugadores prefieren Counter-Strike 1.6 original por su simplicidad y nostalgia. </p>
- <h3>Q: ¿Cómo puedo jugar Counter-Strike 1.6 original en un ordenador Mac o Linux? </h3>
- <p>A: Desafortunadamente, Counter-Strike 1.6 original no está soportado oficialmente en computadoras Mac o Linux. Sin embargo, es posible que pueda jugarlo utilizando un programa como Wine o CrossOver que le permite ejecutar aplicaciones de Windows en computadoras Mac o Linux. Puede encontrar más información e instrucciones sobre cómo hacer esto en varios sitios web y foros en línea. </p>
- <h3>Q: ¿Cómo puedo jugar Counter-Strike 1.6 original en un dispositivo móvil? </h3>
-
- <h3>Q: ¿Cómo puedo jugar Counter-Strike 1.6 original con mods o mapas personalizados? </h3>
- <p>A: Hay muchos mods y mapas personalizados que han sido creados por fans y desarrolladores para Counter-Strike 1.6 original a lo largo de los años. Estos mods y mapas pueden añadir nuevas características, modos, armas, personajes, etc. al juego, o cambiar su apariencia, jugabilidad o dificultad. Puede encontrar y descargar estos mods y mapas de varios sitios web y servidores en línea. También puedes crear tus propios mods y mapas usando herramientas como Hammer Editor o AMX Mod X.</p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/adapter.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/adapter.py
deleted file mode 100644
index 94c75e1a05b47922945c5233e90e9f936b108b66..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/adapter.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-import types
-import functools
-import zlib
-
-from pip._vendor.requests.adapters import HTTPAdapter
-
-from .controller import CacheController, PERMANENT_REDIRECT_STATUSES
-from .cache import DictCache
-from .filewrapper import CallbackFileWrapper
-
-
-class CacheControlAdapter(HTTPAdapter):
-    invalidating_methods = {"PUT", "PATCH", "DELETE"}
-
-    def __init__(
-        self,
-        cache=None,
-        cache_etags=True,
-        controller_class=None,
-        serializer=None,
-        heuristic=None,
-        cacheable_methods=None,
-        *args,
-        **kw
-    ):
-        super(CacheControlAdapter, self).__init__(*args, **kw)
-        self.cache = DictCache() if cache is None else cache
-        self.heuristic = heuristic
-        self.cacheable_methods = cacheable_methods or ("GET",)
-
-        controller_factory = controller_class or CacheController
-        self.controller = controller_factory(
-            self.cache, cache_etags=cache_etags, serializer=serializer
-        )
-
-    def send(self, request, cacheable_methods=None, **kw):
-        """
-        Send a request. Use the request information to see if it
-        exists in the cache and cache the response if we need to and can.
-        """
-        cacheable = cacheable_methods or self.cacheable_methods
-        if request.method in cacheable:
-            try:
-                cached_response = self.controller.cached_request(request)
-            except zlib.error:
-                cached_response = None
-            if cached_response:
-                return self.build_response(request, cached_response, from_cache=True)
-
-            # check for etags and add headers if appropriate
-            request.headers.update(self.controller.conditional_headers(request))
-
-        resp = super(CacheControlAdapter, self).send(request, **kw)
-
-        return resp
-
-    def build_response(
-        self, request, response, from_cache=False, cacheable_methods=None
-    ):
-        """
-        Build a response by making a request or using the cache.
-
-        This will end up calling send and returning a potentially
-        cached response
-        """
-        cacheable = cacheable_methods or self.cacheable_methods
-        if not from_cache and request.method in cacheable:
-            # Check for any heuristics that might update headers
-            # before trying to cache.
-            if self.heuristic:
-                response = self.heuristic.apply(response)
-
-            # apply any expiration heuristics
-            if response.status == 304:
-                # We must have sent an ETag request. This could mean
-                # that we've been expired already or that we simply
-                # have an etag. In either case, we want to try and
-                # update the cache if that is the case.
-                cached_response = self.controller.update_cached_response(
-                    request, response
-                )
-
-                if cached_response is not response:
-                    from_cache = True
-
-                # We are done with the server response, read a
-                # possible response body (compliant servers will
-                # not return one, but we cannot be 100% sure) and
-                # release the connection back to the pool.
-                response.read(decode_content=False)
-                response.release_conn()
-
-                response = cached_response
-
-            # We always cache the 301 responses
-            elif int(response.status) in PERMANENT_REDIRECT_STATUSES:
-                self.controller.cache_response(request, response)
-            else:
-                # Wrap the response file with a wrapper that will cache the
-                #   response when the stream has been consumed.
-                response._fp = CallbackFileWrapper(
-                    response._fp,
-                    functools.partial(
-                        self.controller.cache_response, request, response
-                    ),
-                )
-                if response.chunked:
-                    super_update_chunk_length = response._update_chunk_length
-
-                    def _update_chunk_length(self):
-                        super_update_chunk_length()
-                        if self.chunk_left == 0:
-                            self._fp._close()
-
-                    response._update_chunk_length = types.MethodType(
-                        _update_chunk_length, response
-                    )
-
-        resp = super(CacheControlAdapter, self).build_response(request, response)
-
-        # See if we should invalidate the cache.
-        if request.method in self.invalidating_methods and resp.ok:
-            cache_url = self.controller.cache_url(request.url)
-            self.cache.delete(cache_url)
-
-        # Give the request a from_cache attr to let people use it
-        resp.from_cache = from_cache
-
-        return resp
-
-    def close(self):
-        self.cache.close()
-        super(CacheControlAdapter, self).close()
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/codingstatemachine.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/codingstatemachine.py
deleted file mode 100644
index 8ed4a8773b8404c2705aa8728e5fd692362ba168..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/codingstatemachine.py
+++ /dev/null
@@ -1,90 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-import logging
-
-from .codingstatemachinedict import CodingStateMachineDict
-from .enums import MachineState
-
-
-class CodingStateMachine:
-    """
-    A state machine to verify a byte sequence for a particular encoding. For
-    each byte the detector receives, it will feed that byte to every active
-    state machine available, one byte at a time. The state machine changes its
-    state based on its previous state and the byte it receives. There are 3
-    states in a state machine that are of interest to an auto-detector:
-
-    START state: This is the state to start with, or a legal byte sequence
-                 (i.e. a valid code point) for character has been identified.
-
-    ME state:  This indicates that the state machine identified a byte sequence
-               that is specific to the charset it is designed for and that
-               there is no other possible encoding which can contain this byte
-               sequence. This will to lead to an immediate positive answer for
-               the detector.
-
-    ERROR state: This indicates the state machine identified an illegal byte
-                 sequence for that encoding. This will lead to an immediate
-                 negative answer for this encoding. Detector will exclude this
-                 encoding from consideration from here on.
-    """
-
-    def __init__(self, sm: CodingStateMachineDict) -> None:
-        self._model = sm
-        self._curr_byte_pos = 0
-        self._curr_char_len = 0
-        self._curr_state = MachineState.START
-        self.active = True
-        self.logger = logging.getLogger(__name__)
-        self.reset()
-
-    def reset(self) -> None:
-        self._curr_state = MachineState.START
-
-    def next_state(self, c: int) -> int:
-        # for each byte we get its class
-        # if it is first byte, we also get byte length
-        byte_class = self._model["class_table"][c]
-        if self._curr_state == MachineState.START:
-            self._curr_byte_pos = 0
-            self._curr_char_len = self._model["char_len_table"][byte_class]
-        # from byte's class and state_table, we get its next state
-        curr_state = self._curr_state * self._model["class_factor"] + byte_class
-        self._curr_state = self._model["state_table"][curr_state]
-        self._curr_byte_pos += 1
-        return self._curr_state
-
-    def get_current_charlen(self) -> int:
-        return self._curr_char_len
-
-    def get_coding_state_machine(self) -> str:
-        return self._model["name"]
-
-    @property
-    def language(self) -> str:
-        return self._model["language"]
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/repr.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/repr.py
deleted file mode 100644
index f284bcafa6ab2e1c9ae51be54107836e68cfb0d3..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/repr.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import inspect
-from functools import partial
-from typing import (
-    Any,
-    Callable,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    overload,
-)
-
-T = TypeVar("T")
-
-
-Result = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]]
-RichReprResult = Result
-
-
-class ReprError(Exception):
-    """An error occurred when attempting to build a repr."""
-
-
-@overload
-def auto(cls: Optional[Type[T]]) -> Type[T]:
-    ...
-
-
-@overload
-def auto(*, angular: bool = False) -> Callable[[Type[T]], Type[T]]:
-    ...
-
-
-def auto(
-    cls: Optional[Type[T]] = None, *, angular: Optional[bool] = None
-) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
-    """Class decorator to create __repr__ from __rich_repr__"""
-
-    def do_replace(cls: Type[T], angular: Optional[bool] = None) -> Type[T]:
-        def auto_repr(self: T) -> str:
-            """Create repr string from __rich_repr__"""
-            repr_str: List[str] = []
-            append = repr_str.append
-
-            angular: bool = getattr(self.__rich_repr__, "angular", False)  # type: ignore[attr-defined]
-            for arg in self.__rich_repr__():  # type: ignore[attr-defined]
-                if isinstance(arg, tuple):
-                    if len(arg) == 1:
-                        append(repr(arg[0]))
-                    else:
-                        key, value, *default = arg
-                        if key is None:
-                            append(repr(value))
-                        else:
-                            if default and default[0] == value:
-                                continue
-                            append(f"{key}={value!r}")
-                else:
-                    append(repr(arg))
-            if angular:
-                return f"<{self.__class__.__name__} {' '.join(repr_str)}>"
-            else:
-                return f"{self.__class__.__name__}({', '.join(repr_str)})"
-
-        def auto_rich_repr(self: Type[T]) -> Result:
-            """Auto generate __rich_rep__ from signature of __init__"""
-            try:
-                signature = inspect.signature(self.__init__)
-                for name, param in signature.parameters.items():
-                    if param.kind == param.POSITIONAL_ONLY:
-                        yield getattr(self, name)
-                    elif param.kind in (
-                        param.POSITIONAL_OR_KEYWORD,
-                        param.KEYWORD_ONLY,
-                    ):
-                        if param.default == param.empty:
-                            yield getattr(self, param.name)
-                        else:
-                            yield param.name, getattr(self, param.name), param.default
-            except Exception as error:
-                raise ReprError(
-                    f"Failed to auto generate __rich_repr__; {error}"
-                ) from None
-
-        if not hasattr(cls, "__rich_repr__"):
-            auto_rich_repr.__doc__ = "Build a rich repr"
-            cls.__rich_repr__ = auto_rich_repr  # type: ignore[attr-defined]
-
-        auto_repr.__doc__ = "Return repr(self)"
-        cls.__repr__ = auto_repr  # type: ignore[assignment]
-        if angular is not None:
-            cls.__rich_repr__.angular = angular  # type: ignore[attr-defined]
-        return cls
-
-    if cls is None:
-        return partial(do_replace, angular=angular)
-    else:
-        return do_replace(cls, angular=angular)
-
-
-@overload
-def rich_repr(cls: Optional[Type[T]]) -> Type[T]:
-    ...
-
-
-@overload
-def rich_repr(*, angular: bool = False) -> Callable[[Type[T]], Type[T]]:
-    ...
-
-
-def rich_repr(
-    cls: Optional[Type[T]] = None, *, angular: bool = False
-) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
-    if cls is None:
-        return auto(angular=angular)
-    else:
-        return auto(cls)
-
-
-if __name__ == "__main__":
-
-    @auto
-    class Foo:
-        def __rich_repr__(self) -> Result:
-            yield "foo"
-            yield "bar", {"shopping": ["eggs", "ham", "pineapple"]}
-            yield "buy", "hand sanitizer"
-
-    foo = Foo()
-    from pip._vendor.rich.console import Console
-
-    console = Console()
-
-    console.rule("Standard repr")
-    console.print(foo)
-
-    console.print(foo, width=60)
-    console.print(foo, width=30)
-
-    console.rule("Angular repr")
-    Foo.__rich_repr__.angular = True  # type: ignore[attr-defined]
-
-    console.print(foo)
-
-    console.print(foo, width=60)
-    console.print(foo, width=30)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/__init__.py
deleted file mode 100644
index 5acd7687d642f06de84b38f5842c41ae14d5f24a..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from distutils.command.bdist import bdist
-import sys
-
-if 'egg' not in bdist.format_commands:
-    try:
-        bdist.format_commands['egg'] = ('bdist_egg', "Python .egg file")
-    except TypeError:
-        # For backward compatibility with older distutils (stdlib)
-        bdist.format_command['egg'] = ('bdist_egg', "Python .egg file")
-        bdist.format_commands.append('egg')
-
-del bdist, sys
diff --git a/spaces/C6AI/HDRL/Dockerfile b/spaces/C6AI/HDRL/Dockerfile
deleted file mode 100644
index 68d00607e5bc2aaed0942c5eab512753fcbff559..0000000000000000000000000000000000000000
--- a/spaces/C6AI/HDRL/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM ghcr.io/livebook-dev/livebook:latest-cuda11.8
-
-ENV LIVEBOOK_APP_SERVICE_NAME "🐳 Hugging Face - $SPACE_TITLE"
-ENV LIVEBOOK_APP_SERVICE_URL "https://huggingface.co/spaces/$SPACE_AUTHOR_NAME/$SPACE_REPO_NAME"
-ENV LIVEBOOK_UPDATE_INSTRUCTIONS_URL "https://livebook.dev"
-ENV LIVEBOOK_WITHIN_IFRAME "true"
-ENV LIVEBOOK_DATA_PATH "/data"
-ENV LIVEBOOK_PORT 7860
-
-EXPOSE 7860
-
-USER root
-RUN mkdir -p /data
-RUN chmod 777 /data
diff --git a/spaces/CVPR/LIVE/thrust/thrust/memory/detail/host_system_resource.h b/spaces/CVPR/LIVE/thrust/thrust/memory/detail/host_system_resource.h
deleted file mode 100644
index ded1c4d0bfac5efed867743b5e1a1ad70e736cb3..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/memory/detail/host_system_resource.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- *  Copyright 2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// #include the host system's memory_resource header
-#define __THRUST_HOST_SYSTEM_MEMORY_HEADER <__THRUST_HOST_SYSTEM_ROOT/memory_resource.h>
-#include __THRUST_HOST_SYSTEM_MEMORY_HEADER
-#undef __THRUST_HOST_SYSTEM_MEMORY_HEADER
-
-namespace thrust
-{
-
-typedef thrust::system::__THRUST_HOST_SYSTEM_NAMESPACE::memory_resource
-    host_memory_resource;
-
-} // end thrust
-
diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py
deleted file mode 100644
index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000
--- a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py
+++ /dev/null
@@ -1,221 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Backbone modules.
-"""
-
-from typing import Dict, List
-
-import torch
-import torch.nn.functional as F
-import torchvision
-from torch import nn
-from torchvision.models._utils import IntermediateLayerGetter
-
-from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
-
-from .position_encoding import build_position_encoding
-from .swin_transformer import build_swin_transformer
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
-    without which any other models than torchvision.models.resnet[18,34,50,101]
-    produce nans.
-    """
-
-    def __init__(self, n):
-        super(FrozenBatchNorm2d, self).__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        num_batches_tracked_key = prefix + "num_batches_tracked"
-        if num_batches_tracked_key in state_dict:
-            del state_dict[num_batches_tracked_key]
-
-        super(FrozenBatchNorm2d, self)._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def forward(self, x):
-        # move reshapes to the beginning
-        # to make it fuser-friendly
-        w = self.weight.reshape(1, -1, 1, 1)
-        b = self.bias.reshape(1, -1, 1, 1)
-        rv = self.running_var.reshape(1, -1, 1, 1)
-        rm = self.running_mean.reshape(1, -1, 1, 1)
-        eps = 1e-5
-        scale = w * (rv + eps).rsqrt()
-        bias = b - rm * scale
-        return x * scale + bias
-
-
-class BackboneBase(nn.Module):
-    def __init__(
-        self,
-        backbone: nn.Module,
-        train_backbone: bool,
-        num_channels: int,
-        return_interm_indices: list,
-    ):
-        super().__init__()
-        for name, parameter in backbone.named_parameters():
-            if (
-                not train_backbone
-                or "layer2" not in name
-                and "layer3" not in name
-                and "layer4" not in name
-            ):
-                parameter.requires_grad_(False)
-
-        return_layers = {}
-        for idx, layer_index in enumerate(return_interm_indices):
-            return_layers.update(
-                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
-            )
-
-        # if len:
-        #     if use_stage1_feature:
-        #         return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
-        #     else:
-        #         return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
-        # else:
-        #     return_layers = {'layer4': "0"}
-        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
-        self.num_channels = num_channels
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self.body(tensor_list.tensors)
-        out: Dict[str, NestedTensor] = {}
-        for name, x in xs.items():
-            m = tensor_list.mask
-            assert m is not None
-            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
-            out[name] = NestedTensor(x, mask)
-        # import ipdb; ipdb.set_trace()
-        return out
-
-
-class Backbone(BackboneBase):
-    """ResNet backbone with frozen BatchNorm."""
-
-    def __init__(
-        self,
-        name: str,
-        train_backbone: bool,
-        dilation: bool,
-        return_interm_indices: list,
-        batch_norm=FrozenBatchNorm2d,
-    ):
-        if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
-            backbone = getattr(torchvision.models, name)(
-                replace_stride_with_dilation=[False, False, dilation],
-                pretrained=is_main_process(),
-                norm_layer=batch_norm,
-            )
-        else:
-            raise NotImplementedError("Why you can get here with name {}".format(name))
-        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
-        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
-        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-        num_channels_all = [256, 512, 1024, 2048]
-        num_channels = num_channels_all[4 - len(return_interm_indices) :]
-        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
-
-
-class Joiner(nn.Sequential):
-    def __init__(self, backbone, position_embedding):
-        super().__init__(backbone, position_embedding)
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self[0](tensor_list)
-        out: List[NestedTensor] = []
-        pos = []
-        for name, x in xs.items():
-            out.append(x)
-            # position encoding
-            pos.append(self[1](x).to(x.tensors.dtype))
-
-        return out, pos
-
-
-def build_backbone(args):
-    """
-    Useful args:
-        - backbone: backbone name
-        - lr_backbone:
-        - dilation
-        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
-        - backbone_freeze_keywords:
-        - use_checkpoint: for swin only for now
-
-    """
-    position_embedding = build_position_encoding(args)
-    train_backbone = True
-    if not train_backbone:
-        raise ValueError("Please set lr_backbone > 0")
-    return_interm_indices = args.return_interm_indices
-    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-    args.backbone_freeze_keywords
-    use_checkpoint = getattr(args, "use_checkpoint", False)
-
-    if args.backbone in ["resnet50", "resnet101"]:
-        backbone = Backbone(
-            args.backbone,
-            train_backbone,
-            args.dilation,
-            return_interm_indices,
-            batch_norm=FrozenBatchNorm2d,
-        )
-        bb_num_channels = backbone.num_channels
-    elif args.backbone in [
-        "swin_T_224_1k",
-        "swin_B_224_22k",
-        "swin_B_384_22k",
-        "swin_L_224_22k",
-        "swin_L_384_22k",
-    ]:
-        pretrain_img_size = int(args.backbone.split("_")[-2])
-        backbone = build_swin_transformer(
-            args.backbone,
-            pretrain_img_size=pretrain_img_size,
-            out_indices=tuple(return_interm_indices),
-            dilation=False,
-            use_checkpoint=use_checkpoint,
-        )
-
-        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
-    else:
-        raise NotImplementedError("Unknown backbone {}".format(args.backbone))
-
-    assert len(bb_num_channels) == len(
-        return_interm_indices
-    ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
-
-    model = Joiner(backbone, position_embedding)
-    model.num_channels = bb_num_channels
-    assert isinstance(
-        bb_num_channels, List
-    ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
-    # import ipdb; ipdb.set_trace()
-    return model
diff --git a/spaces/DCandE/rvc-models/infer_pack/models.py b/spaces/DCandE/rvc-models/infer_pack/models.py
deleted file mode 100644
index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000
--- a/spaces/DCandE/rvc-models/infer_pack/models.py
+++ /dev/null
@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            x, y_lengths, self.segment_size
-        )
-
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice
-
-    def infer(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/DHEIVER/AnimeGANv2/app.py b/spaces/DHEIVER/AnimeGANv2/app.py
deleted file mode 100644
index c4a0d3562d40be0fae8e5441b76d812419a1eae0..0000000000000000000000000000000000000000
--- a/spaces/DHEIVER/AnimeGANv2/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from PIL import Image
-import torch
-import gradio as gr
-
-model2 = torch.hub.load(
-    "AK391/animegan2-pytorch:main",
-    "generator",
-    pretrained=True,
-    device="cpu",
-    progress=False
-)
-
-model1 = torch.hub.load(
-    "AK391/animegan2-pytorch:main",
-    "generator",
-    pretrained="face_paint_512_v1",
-    device="cpu"
-)
-
-face2paint = torch.hub.load(
-    'AK391/animegan2-pytorch:main',
-    'face2paint',
-    size=512,
-    device="cpu",
-    side_by_side=False
-)
-
-def inference(img, ver):
-    if ver == 'versão 2 (🔺 robustez, 🔻 estilização)':
-        out = face2paint(model2, img)
-    else:
-        out = face2paint(model1, img)
-    return out
-
-title = "AnimeGANv2"
-description = "Demonstração do AnimeGanv2 para retratos de rostos. Para usá-lo, simplesmente faça o upload da sua imagem."
-article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Repositório do Github Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='insígnia de visitantes'></center></p>"
-
-gr.Interface(
-    fn=inference,
-    inputs=[
-        gr.inputs.Image(type="pil"),
-        gr.inputs.Radio(
-            ['versão 1 (🔺 estilização, 🔻 robustez)', 'versão 2 (🔺 robustez, 🔻 estilização)'],
-            type="value",
-            default='versão 2 (🔺 robustez, 🔻 estilização)',
-            label='versão'
-        )
-    ],
-    outputs=gr.outputs.Image(type="pil"),
-    title=title,
-    description=description,
-    article=article,
-    allow_flagging=False,
-    allow_screenshot=False
-).launch()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/web_middlewares.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/web_middlewares.py
deleted file mode 100644
index fabcc449a2107211fd99cd59f576a2d855d0e042..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/web_middlewares.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import re
-from typing import TYPE_CHECKING, Awaitable, Callable, Tuple, Type, TypeVar
-
-from .typedefs import Handler
-from .web_exceptions import HTTPPermanentRedirect, _HTTPMove
-from .web_request import Request
-from .web_response import StreamResponse
-from .web_urldispatcher import SystemRoute
-
-__all__ = (
-    "middleware",
-    "normalize_path_middleware",
-)
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .web_app import Application
-
-_Func = TypeVar("_Func")
-
-
-async def _check_request_resolves(request: Request, path: str) -> Tuple[bool, Request]:
-    alt_request = request.clone(rel_url=path)
-
-    match_info = await request.app.router.resolve(alt_request)
-    alt_request._match_info = match_info
-
-    if match_info.http_exception is None:
-        return True, alt_request
-
-    return False, request
-
-
-def middleware(f: _Func) -> _Func:
-    f.__middleware_version__ = 1  # type: ignore[attr-defined]
-    return f
-
-
-_Middleware = Callable[[Request, Handler], Awaitable[StreamResponse]]
-
-
-def normalize_path_middleware(
-    *,
-    append_slash: bool = True,
-    remove_slash: bool = False,
-    merge_slashes: bool = True,
-    redirect_class: Type[_HTTPMove] = HTTPPermanentRedirect,
-) -> _Middleware:
-    """Factory for producing a middleware that normalizes the path of a request.
-
-    Normalizing means:
-        - Add or remove a trailing slash to the path.
-        - Double slashes are replaced by one.
-
-    The middleware returns as soon as it finds a path that resolves
-    correctly. The order if both merge and append/remove are enabled is
-        1) merge slashes
-        2) append/remove slash
-        3) both merge slashes and append/remove slash.
-    If the path resolves with at least one of those conditions, it will
-    redirect to the new path.
-
-    Only one of `append_slash` and `remove_slash` can be enabled. If both
-    are `True` the factory will raise an assertion error
-
-    If `append_slash` is `True` the middleware will append a slash when
-    needed. If a resource is defined with trailing slash and the request
-    comes without it, it will append it automatically.
-
-    If `remove_slash` is `True`, `append_slash` must be `False`. When enabled
-    the middleware will remove trailing slashes and redirect if the resource
-    is defined
-
-    If merge_slashes is True, merge multiple consecutive slashes in the
-    path into one.
-    """
-    correct_configuration = not (append_slash and remove_slash)
-    assert correct_configuration, "Cannot both remove and append slash"
-
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        if isinstance(request.match_info.route, SystemRoute):
-            paths_to_check = []
-            if "?" in request.raw_path:
-                path, query = request.raw_path.split("?", 1)
-                query = "?" + query
-            else:
-                query = ""
-                path = request.raw_path
-
-            if merge_slashes:
-                paths_to_check.append(re.sub("//+", "/", path))
-            if append_slash and not request.path.endswith("/"):
-                paths_to_check.append(path + "/")
-            if remove_slash and request.path.endswith("/"):
-                paths_to_check.append(path[:-1])
-            if merge_slashes and append_slash:
-                paths_to_check.append(re.sub("//+", "/", path + "/"))
-            if merge_slashes and remove_slash:
-                merged_slashes = re.sub("//+", "/", path)
-                paths_to_check.append(merged_slashes[:-1])
-
-            for path in paths_to_check:
-                path = re.sub("^//+", "/", path)  # SECURITY: GHSA-v6wp-4m6f-gcjg
-                resolves, request = await _check_request_resolves(request, path)
-                if resolves:
-                    raise redirect_class(request.raw_path + query)
-
-        return await handler(request)
-
-    return impl
-
-
-def _fix_request_current_app(app: "Application") -> _Middleware:
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        with request.match_info.set_current_app(app):
-            return await handler(request)
-
-    return impl
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/chunk.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/chunk.py
deleted file mode 100644
index 076cbc4370b4471c2074cade279250a3ebec9041..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/chunk.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import math
-
-
-def calc_chunk_sizes(
-    chunk_size: int | tuple[int, int] | None,
-    chunk_count: int | tuple[int, int] | None,
-    total_chunk_count: int | None,
-    ny: int,
-    nx: int,
-) -> tuple[int, int]:
-    """Calculate chunk sizes.
-
-    Args:
-        chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same
-            size in both directions if only one is specified.
-        chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the
-            same count in both irections if only one is specified.
-        total_chunk_count (int, optional): Total number of chunks.
-        ny (int): Number of grid points in y-direction.
-        nx (int): Number of grid points in x-direction.
-
-    Return:
-        tuple(int, int): Chunk sizes (y_chunk_size, x_chunk_size).
-
-    Note:
-        A maximum of one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` may be
-        specified.
-    """
-    if sum([chunk_size is not None, chunk_count is not None, total_chunk_count is not None]) > 1:
-        raise ValueError("Only one of chunk_size, chunk_count and total_chunk_count should be set")
-
-    if total_chunk_count is not None:
-        max_chunk_count = (nx-1)*(ny-1)
-        total_chunk_count = min(max(total_chunk_count, 1), max_chunk_count)
-        if total_chunk_count == 1:
-            chunk_size = 0
-        elif total_chunk_count == max_chunk_count:
-            chunk_size = (1, 1)
-        else:
-            factors = two_factors(total_chunk_count)
-            if ny > nx:
-                chunk_count = factors
-            else:
-                chunk_count = (factors[1], factors[0])
-
-    if chunk_count is not None:
-        if isinstance(chunk_count, tuple):
-            y_chunk_count, x_chunk_count = chunk_count
-        else:
-            y_chunk_count = x_chunk_count = chunk_count
-        x_chunk_count = min(max(x_chunk_count, 1), nx-1)
-        y_chunk_count = min(max(y_chunk_count, 1), ny-1)
-        chunk_size = (math.ceil((ny-1) / y_chunk_count), math.ceil((nx-1) / x_chunk_count))
-
-    if chunk_size is None:
-        y_chunk_size = x_chunk_size = 0
-    elif isinstance(chunk_size, tuple):
-        y_chunk_size, x_chunk_size = chunk_size
-    else:
-        y_chunk_size = x_chunk_size = chunk_size
-
-    if x_chunk_size < 0 or y_chunk_size < 0:
-        raise ValueError("chunk_size cannot be negative")
-
-    return y_chunk_size, x_chunk_size
-
-
-def two_factors(n: int) -> tuple[int, int]:
-    """Split an integer into two integer factors.
-
-    The two factors will be as close as possible to the sqrt of n, and are returned in decreasing
-    order.  Worst case returns (n, 1).
-
-    Args:
-        n (int): The integer to factorize.
-
-    Return:
-        tuple(int, int): The two factors of n, in decreasing order.
-    """
-    i = math.ceil(math.sqrt(n))
-    while n % i != 0:
-        i -= 1
-    j = n // i
-    if i > j:
-        return i, j
-    else:
-        return j, i
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/encodings/codecs.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/encodings/codecs.py
deleted file mode 100644
index 3ac0268d6a11a1be99bb2cf7fde5979da2853d4a..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/encodings/codecs.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""Extend the Python codecs module with a few encodings that are used in OpenType (name table)
-but missing from Python.  See https://github.com/fonttools/fonttools/issues/236 for details."""
-
-import codecs
-import encodings
-
-
-class ExtendCodec(codecs.Codec):
-    def __init__(self, name, base_encoding, mapping):
-        self.name = name
-        self.base_encoding = base_encoding
-        self.mapping = mapping
-        self.reverse = {v: k for k, v in mapping.items()}
-        self.max_len = max(len(v) for v in mapping.values())
-        self.info = codecs.CodecInfo(
-            name=self.name, encode=self.encode, decode=self.decode
-        )
-        codecs.register_error(name, self.error)
-
-    def _map(self, mapper, output_type, exc_type, input, errors):
-        base_error_handler = codecs.lookup_error(errors)
-        length = len(input)
-        out = output_type()
-        while input:
-            # first try to use self.error as the error handler
-            try:
-                part = mapper(input, self.base_encoding, errors=self.name)
-                out += part
-                break  # All converted
-            except exc_type as e:
-                # else convert the correct part, handle error as requested and continue
-                out += mapper(input[: e.start], self.base_encoding, self.name)
-                replacement, pos = base_error_handler(e)
-                out += replacement
-                input = input[pos:]
-        return out, length
-
-    def encode(self, input, errors="strict"):
-        return self._map(codecs.encode, bytes, UnicodeEncodeError, input, errors)
-
-    def decode(self, input, errors="strict"):
-        return self._map(codecs.decode, str, UnicodeDecodeError, input, errors)
-
-    def error(self, e):
-        if isinstance(e, UnicodeDecodeError):
-            for end in range(e.start + 1, e.end + 1):
-                s = e.object[e.start : end]
-                if s in self.mapping:
-                    return self.mapping[s], end
-        elif isinstance(e, UnicodeEncodeError):
-            for end in range(e.start + 1, e.start + self.max_len + 1):
-                s = e.object[e.start : end]
-                if s in self.reverse:
-                    return self.reverse[s], end
-        e.encoding = self.name
-        raise e
-
-
-_extended_encodings = {
-    "x_mac_japanese_ttx": (
-        "shift_jis",
-        {
-            b"\xFC": chr(0x007C),
-            b"\x7E": chr(0x007E),
-            b"\x80": chr(0x005C),
-            b"\xA0": chr(0x00A0),
-            b"\xFD": chr(0x00A9),
-            b"\xFE": chr(0x2122),
-            b"\xFF": chr(0x2026),
-        },
-    ),
-    "x_mac_trad_chinese_ttx": (
-        "big5",
-        {
-            b"\x80": chr(0x005C),
-            b"\xA0": chr(0x00A0),
-            b"\xFD": chr(0x00A9),
-            b"\xFE": chr(0x2122),
-            b"\xFF": chr(0x2026),
-        },
-    ),
-    "x_mac_korean_ttx": (
-        "euc_kr",
-        {
-            b"\x80": chr(0x00A0),
-            b"\x81": chr(0x20A9),
-            b"\x82": chr(0x2014),
-            b"\x83": chr(0x00A9),
-            b"\xFE": chr(0x2122),
-            b"\xFF": chr(0x2026),
-        },
-    ),
-    "x_mac_simp_chinese_ttx": (
-        "gb2312",
-        {
-            b"\x80": chr(0x00FC),
-            b"\xA0": chr(0x00A0),
-            b"\xFD": chr(0x00A9),
-            b"\xFE": chr(0x2122),
-            b"\xFF": chr(0x2026),
-        },
-    ),
-}
-
-_cache = {}
-
-
-def search_function(name):
-    name = encodings.normalize_encoding(name)  # Rather undocumented...
-    if name in _extended_encodings:
-        if name not in _cache:
-            base_encoding, mapping = _extended_encodings[name]
-            assert name[-4:] == "_ttx"
-            # Python 2 didn't have any of the encodings that we are implementing
-            # in this file.  Python 3 added aliases for the East Asian ones, mapping
-            # them "temporarily" to the same base encoding as us, with a comment
-            # suggesting that full implementation will appear some time later.
-            # As such, try the Python version of the x_mac_... first, if that is found,
-            # use *that* as our base encoding.  This would make our encoding upgrade
-            # to the full encoding when and if Python finally implements that.
-            # http://bugs.python.org/issue24041
-            base_encodings = [name[:-4], base_encoding]
-            for base_encoding in base_encodings:
-                try:
-                    codecs.lookup(base_encoding)
-                except LookupError:
-                    continue
-                _cache[name] = ExtendCodec(name, base_encoding, mapping)
-                break
-        return _cache[name].info
-
-    return None
-
-
-codecs.register(search_function)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Form-3812b7f1.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Form-3812b7f1.css
deleted file mode 100644
index 772d43d65ae1a3157ab24e69b7ecb88a3649b4fe..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Form-3812b7f1.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-sfqy0y{display:flex;flex-direction:inherit;flex-wrap:wrap;gap:var(--form-gap-width);box-shadow:var(--block-shadow);border:var(--block-border-width) solid var(--border-color-primary);border-radius:var(--block-radius);background:var(--border-color-primary);overflow-y:hidden}div.svelte-sfqy0y .block{box-shadow:none!important;border-width:0px!important;border-radius:0!important}.hidden.svelte-sfqy0y{display:none}
diff --git a/spaces/DUOMO-Lab/TransGPT/README.md b/spaces/DUOMO-Lab/TransGPT/README.md
deleted file mode 100644
index 433341850c56ac0ba53cf55b55b9f98b056eafe7..0000000000000000000000000000000000000000
--- a/spaces/DUOMO-Lab/TransGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TransGPT
-emoji: 🔥
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Datasculptor/DescriptionGPT/detic/custom_solver.py b/spaces/Datasculptor/DescriptionGPT/detic/custom_solver.py
deleted file mode 100644
index 0284ae14ed2e93b2664ef52ad938061f78363516..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/DescriptionGPT/detic/custom_solver.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from enum import Enum
-import itertools
-from typing import Any, Callable, Dict, Iterable, List, Set, Type, Union
-import torch
-
-from detectron2.config import CfgNode
-
-from detectron2.solver.build import maybe_add_gradient_clipping
-
-def match_name_keywords(n, name_keywords):
-    out = False
-    for b in name_keywords:
-        if b in n:
-            out = True
-            break
-    return out
-
-def build_custom_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
-    """
-    Build an optimizer from config.
-    """
-    params: List[Dict[str, Any]] = []
-    memo: Set[torch.nn.parameter.Parameter] = set()
-    custom_multiplier_name = cfg.SOLVER.CUSTOM_MULTIPLIER_NAME
-    optimizer_type = cfg.SOLVER.OPTIMIZER
-    for key, value in model.named_parameters(recurse=True):
-        if not value.requires_grad:
-            continue
-        # Avoid duplicating parameters
-        if value in memo:
-            continue
-        memo.add(value)
-        lr = cfg.SOLVER.BASE_LR
-        weight_decay = cfg.SOLVER.WEIGHT_DECAY
-        if "backbone" in key:
-            lr = lr * cfg.SOLVER.BACKBONE_MULTIPLIER
-        if match_name_keywords(key, custom_multiplier_name):
-            lr = lr * cfg.SOLVER.CUSTOM_MULTIPLIER
-            print('Costum LR', key, lr)
-        param = {"params": [value], "lr": lr}
-        if optimizer_type != 'ADAMW':
-            param['weight_decay'] = weight_decay
-        params += [param]
-
-    def maybe_add_full_model_gradient_clipping(optim):  # optim: the optimizer class
-        # detectron2 doesn't have full model gradient clipping now
-        clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
-        enable = (
-            cfg.SOLVER.CLIP_GRADIENTS.ENABLED
-            and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
-            and clip_norm_val > 0.0
-        )
-
-        class FullModelGradientClippingOptimizer(optim):
-            def step(self, closure=None):
-                all_params = itertools.chain(*[x["params"] for x in self.param_groups])
-                torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
-                super().step(closure=closure)
-
-        return FullModelGradientClippingOptimizer if enable else optim
-
-    
-    if optimizer_type == 'SGD':
-        optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
-            params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM, 
-            nesterov=cfg.SOLVER.NESTEROV
-        )
-    elif optimizer_type == 'ADAMW':
-        optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
-            params, cfg.SOLVER.BASE_LR, 
-            weight_decay=cfg.SOLVER.WEIGHT_DECAY
-        )
-    else:
-        raise NotImplementedError(f"no optimizer type {optimizer_type}")
-    if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
-        optimizer = maybe_add_gradient_clipping(cfg, optimizer)
-    return optimizer
\ No newline at end of file
diff --git a/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/app_training.py b/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/app_training.py
deleted file mode 100644
index 09660a26b4d99f8ff8457a454fdddcc57d7f3756..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/app_training.py
+++ /dev/null
@@ -1,144 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-
-import gradio as gr
-
-from constants import UploadTarget
-from inference import InferencePipeline
-from trainer import Trainer
-
-
-def create_training_demo(trainer: Trainer,
-                         pipe: InferencePipeline | None = None) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                with gr.Box():
-                    gr.Markdown('Training Data')
-                    instance_images = gr.Files(label='Instance images')
-                    instance_prompt = gr.Textbox(label='Instance prompt',
-                                                 max_lines=1)
-                    gr.Markdown('''
-                        - Upload images of the style you are planning on training on.
-                        - For an instance prompt, use a unique, made up word to avoid collisions.
-                        ''')
-                with gr.Box():
-                    gr.Markdown('Output Model')
-                    output_model_name = gr.Text(label='Name of your model',
-                                                max_lines=1)
-                    delete_existing_model = gr.Checkbox(
-                        label='Delete existing model of the same name',
-                        value=False)
-                    validation_prompt = gr.Text(label='Validation Prompt')
-                with gr.Box():
-                    gr.Markdown('Upload Settings')
-                    with gr.Row():
-                        upload_to_hub = gr.Checkbox(
-                            label='Upload model to Hub', value=True)
-                        use_private_repo = gr.Checkbox(label='Private',
-                                                       value=True)
-                        delete_existing_repo = gr.Checkbox(
-                            label='Delete existing repo of the same name',
-                            value=False)
-                    upload_to = gr.Radio(
-                        label='Upload to',
-                        choices=[_.value for _ in UploadTarget],
-                        value=UploadTarget.LORA_LIBRARY.value)
-                    gr.Markdown('''
-                    - By default, trained models will be uploaded to [LoRA Library](https://huggingface.co/lora-library) (see [this example model](https://huggingface.co/lora-library/lora-dreambooth-sample-dog)).
-                    - You can also choose "Personal Profile", in which case, the model will be uploaded to https://huggingface.co/{your_username}/{model_name}.
-                    ''')
-
-            with gr.Box():
-                gr.Markdown('Training Parameters')
-                with gr.Row():
-                    base_model = gr.Text(
-                        label='Base Model',
-                        value='stabilityai/stable-diffusion-2-1-base',
-                        max_lines=1)
-                    resolution = gr.Dropdown(choices=['512', '768'],
-                                             value='512',
-                                             label='Resolution')
-                num_training_steps = gr.Number(
-                    label='Number of Training Steps', value=1000, precision=0)
-                learning_rate = gr.Number(label='Learning Rate', value=0.0001)
-                gradient_accumulation = gr.Number(
-                    label='Number of Gradient Accumulation',
-                    value=1,
-                    precision=0)
-                seed = gr.Slider(label='Seed',
-                                 minimum=0,
-                                 maximum=100000,
-                                 step=1,
-                                 value=0)
-                fp16 = gr.Checkbox(label='FP16', value=True)
-                use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True)
-                checkpointing_steps = gr.Number(label='Checkpointing Steps',
-                                                value=100,
-                                                precision=0)
-                use_wandb = gr.Checkbox(label='Use W&B',
-                                        value=False,
-                                        interactive=bool(
-                                            os.getenv('WANDB_API_KEY')))
-                validation_epochs = gr.Number(label='Validation Epochs',
-                                              value=100,
-                                              precision=0)
-                gr.Markdown('''
-                    - The base model must be a model that is compatible with [diffusers](https://github.com/huggingface/diffusers) library.
-                    - It takes a few minutes to download the base model first.
-                    - It will take about 8 minutes to train for 1000 steps with a T4 GPU.
-                    - You may want to try a small number of steps first, like 1, to see if everything works fine in your environment.
-                    - You can check the training status by pressing the "Open logs" button if you are running this on your Space.
-                    - You need to set the environment variable `WANDB_API_KEY` if you'd like to use [W&B](https://wandb.ai/site). See [W&B documentation](https://docs.wandb.ai/guides/track/advanced/environment-variables).
-                    - **Note:** Due to [this issue](https://github.com/huggingface/accelerate/issues/944), currently, training will not terminate properly if you use W&B.
-                    ''')
-
-        remove_gpu_after_training = gr.Checkbox(
-            label='Remove GPU after training',
-            value=False,
-            interactive=bool(os.getenv('SPACE_ID')),
-            visible=False)
-        run_button = gr.Button('Start Training')
-
-        with gr.Box():
-            gr.Markdown('Output message')
-            output_message = gr.Markdown()
-
-        if pipe is not None:
-            run_button.click(fn=pipe.clear)
-        run_button.click(fn=trainer.run,
-                         inputs=[
-                             instance_images,
-                             instance_prompt,
-                             output_model_name,
-                             delete_existing_model,
-                             validation_prompt,
-                             base_model,
-                             resolution,
-                             num_training_steps,
-                             learning_rate,
-                             gradient_accumulation,
-                             seed,
-                             fp16,
-                             use_8bit_adam,
-                             checkpointing_steps,
-                             use_wandb,
-                             validation_epochs,
-                             upload_to_hub,
-                             use_private_repo,
-                             delete_existing_repo,
-                             upload_to,
-                             remove_gpu_after_training,
-                         ],
-                         outputs=output_message)
-    return demo
-
-
-if __name__ == '__main__':
-    hf_token = os.getenv('HF_TOKEN')
-    trainer = Trainer(hf_token)
-    demo = create_training_demo(trainer)
-    demo.queue(max_size=1).launch(share=False)
diff --git a/spaces/Datasculptor/MusicGen/audiocraft/modules/streaming.py b/spaces/Datasculptor/MusicGen/audiocraft/modules/streaming.py
deleted file mode 100644
index fdbdf5e90fc0c6560873d66bf273460b38e5ed7e..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/MusicGen/audiocraft/modules/streaming.py
+++ /dev/null
@@ -1,135 +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.
-
-"""
-Streaming module API that should be implemented by all Streaming components,
-"""
-
-from contextlib import contextmanager
-import typing as tp
-from torch import nn
-import torch
-
-
-State = tp.Dict[str, torch.Tensor]
-
-
-class StreamingModule(nn.Module):
-    """Common API for streaming components.
-
-    Each streaming component has a streaming state, which is just a dict[str, Tensor].
-    By convention, the first dim of each tensor must be the batch size.
-    Don't use dots in the key names, as this would clash with submodules
-    (like in state_dict).
-
-    If `self._is_streaming` is True, the component should use and remember
-    the proper state inside `self._streaming_state`.
-
-    To set a streaming component in streaming state, use
-
-        with module.streaming():
-            ...
-
-    This will automatically reset the streaming state when exiting the context manager.
-    This also automatically propagates to all streaming children module.
-
-    Some module might also implement the `StreamingModule.flush` method, although
-    this one is trickier, as all parents module must be StreamingModule and implement
-    it as well for it to work properly. See `StreamingSequential` after.
-    """
-    def __init__(self) -> None:
-        super().__init__()
-        self._streaming_state: State = {}
-        self._is_streaming = False
-
-    def _apply_named_streaming(self, fn: tp.Any):
-        for name, module in self.named_modules():
-            if isinstance(module, StreamingModule):
-                fn(name, module)
-
-    def _set_streaming(self, streaming: bool):
-        def _set_streaming(name, module):
-            module._is_streaming = streaming
-        self._apply_named_streaming(_set_streaming)
-
-    @contextmanager
-    def streaming(self):
-        """Context manager to enter streaming mode. Reset streaming state on exit.
-        """
-        self._set_streaming(True)
-        try:
-            yield
-        finally:
-            self._set_streaming(False)
-            self.reset_streaming()
-
-    def reset_streaming(self):
-        """Reset the streaming state.
-        """
-        def _reset(name: str, module: StreamingModule):
-            module._streaming_state.clear()
-
-        self._apply_named_streaming(_reset)
-
-    def get_streaming_state(self) -> State:
-        """Return the streaming state, including that of sub-modules.
-        """
-        state: State = {}
-
-        def _add(name: str, module: StreamingModule):
-            if name:
-                name += "."
-            for key, value in module._streaming_state.items():
-                state[name + key] = value
-
-        self._apply_named_streaming(_add)
-        return state
-
-    def set_streaming_state(self, state: State):
-        """Set the streaming state, including that of sub-modules.
-        """
-        state = dict(state)
-
-        def _set(name: str, module: StreamingModule):
-            if name:
-                name += "."
-            module._streaming_state.clear()
-            for key, value in list(state.items()):
-                # complexity is not ideal here, but probably fine.
-                if key.startswith(name):
-                    local_key = key[len(name):]
-                    if '.' not in local_key:
-                        module._streaming_state[local_key] = value
-                        del state[key]
-
-        self._apply_named_streaming(_set)
-        assert len(state) == 0, list(state.keys())
-
-    def flush(self, x: tp.Optional[torch.Tensor] = None):
-        """Flush any remaining outputs that were waiting for completion.
-        Typically, for convolutions, this will add the final padding
-        and process the last buffer.
-
-        This should take an optional argument `x`, which will be provided
-        if a module before this one in the streaming pipeline has already
-        spitted out a flushed out buffer.
-        """
-        if x is None:
-            return None
-        else:
-            return self(x)
-
-
-class StreamingSequential(StreamingModule, nn.Sequential):
-    """A streaming compatible alternative of `nn.Sequential`.
-    """
-    def flush(self, x: tp.Optional[torch.Tensor] = None):
-        for module in self:
-            if isinstance(module, StreamingModule):
-                x = module.flush(x)
-            elif x is not None:
-                x = module(x)
-        return x
diff --git a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/augment.py b/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/augment.py
deleted file mode 100644
index ad4059c233efd87b20eea71c86ac7cbad93b6e77..0000000000000000000000000000000000000000
--- a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/augment.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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.
-# author: adefossez
-
-import random
-import torch as th
-from torch import nn
-from torch.nn import functional as F
-
-from . import dsp
-
-
-class Remix(nn.Module):
-    """Remix.
-    Mixes different noises with clean speech within a given batch
-    """
-
-    def forward(self, sources):
-        noise, clean = sources
-        bs, *other = noise.shape
-        device = noise.device
-        perm = th.argsort(th.rand(bs, device=device), dim=0)
-        return th.stack([noise[perm], clean])
-
-
-class RevEcho(nn.Module):
-    """
-    Hacky Reverb but runs on GPU without slowing down training.
-    This reverb adds a succession of attenuated echos of the input
-    signal to itself. Intuitively, the delay of the first echo will happen
-    after roughly 2x the radius of the room and is controlled by `first_delay`.
-    Then RevEcho keeps adding echos with the same delay and further attenuation
-    until the amplitude ratio between the last and first echo is 1e-3.
-    The attenuation factor and the number of echos to adds is controlled
-    by RT60 (measured in seconds). RT60 is the average time to get to -60dB
-    (remember volume is measured over the squared amplitude so this matches
-    the 1e-3 ratio).
-
-    At each call to RevEcho, `first_delay`, `initial` and `RT60` are
-    sampled from their range. Then, to prevent this reverb from being too regular,
-    the delay time is resampled uniformly within `first_delay +- 10%`,
-    as controlled by the `jitter` parameter. Finally, for a denser reverb,
-    multiple trains of echos are added with different jitter noises.
-
-    Args:
-        - initial: amplitude of the first echo as a fraction
-            of the input signal. For each sample, actually sampled from
-            `[0, initial]`. Larger values means louder reverb. Physically,
-            this would depend on the absorption of the room walls.
-        - rt60: range of values to sample the RT60 in seconds, i.e.
-            after RT60 seconds, the echo amplitude is 1e-3 of the first echo.
-            The default values follow the recommendations of
-            https://arxiv.org/ftp/arxiv/papers/2001/2001.08662.pdf, Section 2.4.
-            Physically this would also be related to the absorption of the
-            room walls and there is likely a relation between `RT60` and
-            `initial`, which we ignore here.
-        - first_delay: range of values to sample the first echo delay in seconds.
-            The default values are equivalent to sampling a room of 3 to 10 meters.
-        - repeat: how many train of echos with differents jitters to add.
-            Higher values means a denser reverb.
-        - jitter: jitter used to make each repetition of the reverb echo train
-            slightly different. For instance a jitter of 0.1 means
-            the delay between two echos will be in the range `first_delay +- 10%`,
-            with the jittering noise being resampled after each single echo.
-        - keep_clean: fraction of the reverb of the clean speech to add back
-            to the ground truth. 0 = dereverberation, 1 = no dereverberation.
-        - sample_rate: sample rate of the input signals.
-    """
-
-    def __init__(self, proba=0.5, initial=0.3, rt60=(0.3, 1.3), first_delay=(0.01, 0.03),
-                 repeat=3, jitter=0.1, keep_clean=0.1, sample_rate=16000):
-        super().__init__()
-        self.proba = proba
-        self.initial = initial
-        self.rt60 = rt60
-        self.first_delay = first_delay
-        self.repeat = repeat
-        self.jitter = jitter
-        self.keep_clean = keep_clean
-        self.sample_rate = sample_rate
-
-    def _reverb(self, source, initial, first_delay, rt60):
-        """
-        Return the reverb for a single source.
-        """
-        length = source.shape[-1]
-        reverb = th.zeros_like(source)
-        for _ in range(self.repeat):
-            frac = 1  # what fraction of the first echo amplitude is still here
-            echo = initial * source
-            while frac > 1e-3:
-                # First jitter noise for the delay
-                jitter = 1 + self.jitter * random.uniform(-1, 1)
-                delay = min(
-                    1 + int(jitter * first_delay * self.sample_rate),
-                    length)
-                # Delay the echo in time by padding with zero on the left
-                echo = F.pad(echo[:, :, :-delay], (delay, 0))
-                reverb += echo
-
-                # Second jitter noise for the attenuation
-                jitter = 1 + self.jitter * random.uniform(-1, 1)
-                # we want, with `d` the attenuation, d**(rt60 / first_ms) = 1e-3
-                # i.e. log10(d) = -3 * first_ms / rt60, so that
-                attenuation = 10**(-3 * jitter * first_delay / rt60)
-                echo *= attenuation
-                frac *= attenuation
-        return reverb
-
-    def forward(self, wav):
-        if random.random() >= self.proba:
-            return wav
-        noise, clean = wav
-        # Sample characteristics for the reverb
-        initial = random.random() * self.initial
-        first_delay = random.uniform(*self.first_delay)
-        rt60 = random.uniform(*self.rt60)
-
-        reverb_noise = self._reverb(noise, initial, first_delay, rt60)
-        # Reverb for the noise is always added back to the noise
-        noise += reverb_noise
-        reverb_clean = self._reverb(clean, initial, first_delay, rt60)
-        # Split clean reverb among the clean speech and noise
-        clean += self.keep_clean * reverb_clean
-        noise += (1 - self.keep_clean) * reverb_clean
-
-        return th.stack([noise, clean])
-
-
-class BandMask(nn.Module):
-    """BandMask.
-    Maskes bands of frequencies. Similar to Park, Daniel S., et al.
-    "Specaugment: A simple data augmentation method for automatic speech recognition."
-    (https://arxiv.org/pdf/1904.08779.pdf) but over the waveform.
-    """
-
-    def __init__(self, maxwidth=0.2, bands=120, sample_rate=16_000):
-        """__init__.
-
-        :param maxwidth: the maximum width to remove
-        :param bands: number of bands
-        :param sample_rate: signal sample rate
-        """
-        super().__init__()
-        self.maxwidth = maxwidth
-        self.bands = bands
-        self.sample_rate = sample_rate
-
-    def forward(self, wav):
-        bands = self.bands
-        bandwidth = int(abs(self.maxwidth) * bands)
-        mels = dsp.mel_frequencies(bands, 40, self.sample_rate/2) / self.sample_rate
-        low = random.randrange(bands)
-        high = random.randrange(low, min(bands, low + bandwidth))
-        filters = dsp.LowPassFilters([mels[low], mels[high]]).to(wav.device)
-        low, midlow = filters(wav)
-        # band pass filtering
-        out = wav - midlow + low
-        return out
-
-
-class Shift(nn.Module):
-    """Shift."""
-
-    def __init__(self, shift=8192, same=False):
-        """__init__.
-
-        :param shift: randomly shifts the signals up to a given factor
-        :param same: shifts both clean and noisy files by the same factor
-        """
-        super().__init__()
-        self.shift = shift
-        self.same = same
-
-    def forward(self, wav):
-        sources, batch, channels, length = wav.shape
-        length = length - self.shift
-        if self.shift > 0:
-            if not self.training:
-                wav = wav[..., :length]
-            else:
-                offsets = th.randint(
-                    self.shift,
-                    [1 if self.same else sources, batch, 1, 1], device=wav.device)
-                offsets = offsets.expand(sources, -1, channels, -1)
-                indexes = th.arange(length, device=wav.device)
-                wav = wav.gather(3, indexes + offsets)
-        return wav
diff --git a/spaces/Detomo/ai-avatar-frontend/src/setupTests.js b/spaces/Detomo/ai-avatar-frontend/src/setupTests.js
deleted file mode 100644
index 8f2609b7b3e0e3897ab3bcaad13caf6876e48699..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-avatar-frontend/src/setupTests.js
+++ /dev/null
@@ -1,5 +0,0 @@
-// jest-dom adds custom jest matchers for asserting on DOM nodes.
-// allows you to do things like:
-// expect(element).toHaveTextContent(/react/i)
-// learn more: https://github.com/testing-library/jest-dom
-import '@testing-library/jest-dom';
diff --git a/spaces/DragGan/DragGan/viz/capture_widget.py b/spaces/DragGan/DragGan/viz/capture_widget.py
deleted file mode 100644
index 72bf3cfd2d361e85f9a24e1d0c5f3abc9bba0b39..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/viz/capture_widget.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import re
-import numpy as np
-import imgui
-import PIL.Image
-from gui_utils import imgui_utils
-from . import renderer
-import torch
-import torchvision
-
-#----------------------------------------------------------------------------
-
-class CaptureWidget:
-    def __init__(self, viz):
-        self.viz            = viz
-        self.path           = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '_screenshots'))
-        self.dump_image     = False
-        self.dump_gui       = False
-        self.defer_frames   = 0
-        self.disabled_time  = 0
-
-    def dump_png(self, image):
-        viz = self.viz
-        try:
-            _height, _width, channels = image.shape
-            print(viz.result)
-            assert image.dtype == np.uint8
-            os.makedirs(self.path, exist_ok=True)
-            file_id = 0
-            for entry in os.scandir(self.path):
-                if entry.is_file():
-                    match = re.fullmatch(r'(\d+).*', entry.name)
-                    if match:
-                        file_id = max(file_id, int(match.group(1)) + 1)
-            if channels == 1:
-                pil_image = PIL.Image.fromarray(image[:, :, 0], 'L')
-            else:
-                pil_image = PIL.Image.fromarray(image[:, :, :3], 'RGB')
-            pil_image.save(os.path.join(self.path, f'{file_id:05d}.png'))
-            np.save(os.path.join(self.path, f'{file_id:05d}.npy'), viz.result.w)
-        except:
-            viz.result.error = renderer.CapturedException()
-
-    @imgui_utils.scoped_by_object_id
-    def __call__(self, show=True):
-        viz = self.viz
-        if show:
-            with imgui_utils.grayed_out(self.disabled_time != 0):
-                imgui.text('Capture')
-                imgui.same_line(viz.label_w)
-
-                _changed, self.path = imgui_utils.input_text('##path', self.path, 1024,
-                    flags=(imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE),
-                    width=(-1),
-                    help_text='PATH')
-                if imgui.is_item_hovered() and not imgui.is_item_active() and self.path != '':
-                    imgui.set_tooltip(self.path)
-                imgui.text(' ')
-                imgui.same_line(viz.label_w)
-                if imgui_utils.button('Save image', width=viz.button_w, enabled=(self.disabled_time == 0 and 'image' in viz.result)):
-                    self.dump_image = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-                imgui.same_line()
-                if imgui_utils.button('Save GUI', width=viz.button_w, enabled=(self.disabled_time == 0)):
-                    self.dump_gui = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-
-        self.disabled_time = max(self.disabled_time - viz.frame_delta, 0)
-        if self.defer_frames > 0:
-            self.defer_frames -= 1
-        elif self.dump_image:
-            if 'image' in viz.result:
-                self.dump_png(viz.result.image)
-            self.dump_image = False
-        elif self.dump_gui:
-            viz.capture_next_frame()
-            self.dump_gui = False
-        captured_frame = viz.pop_captured_frame()
-        if captured_frame is not None:
-            self.dump_png(captured_frame)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/ECCV2022/storydalle/dalle/models/stage2/layers.py b/spaces/ECCV2022/storydalle/dalle/models/stage2/layers.py
deleted file mode 100644
index e576ab08ef2c3706d03e99a6b2902744806023d7..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/storydalle/dalle/models/stage2/layers.py
+++ /dev/null
@@ -1,221 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Minimal DALL-E
-# Copyright (c) 2021 KakaoBrain. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------
-# Modified from minGPT (https://github.com/karpathy/minGPT)
-# Copyright (c) 2020 Andrej Karpathy. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-
-class GELU(nn.Module):
-    def __init__(self, use_approx=False):
-        super().__init__()
-        self.use_approx = use_approx
-
-    def forward(self, x):
-        if self.use_approx:
-            return x * torch.sigmoid(1.702 * x)
-        else:
-            return F.gelu(x)
-
-
-class MultiHeadSelfAttention(nn.Module):
-
-    def __init__(self,
-                 ctx_len: int,
-                 embed_dim: int,
-                 n_heads: int,
-                 resid_pdrop: float,
-                 attn_pdrop: float,
-                 attn_bias: bool,
-                 use_mask: bool = True):
-        super().__init__()
-        assert embed_dim % n_heads == 0
-
-        # key, query, value projections for all heads
-        self.key = nn.Linear(embed_dim, embed_dim, bias=attn_bias)
-        self.query = nn.Linear(embed_dim, embed_dim, bias=attn_bias)
-        self.value = nn.Linear(embed_dim, embed_dim, bias=attn_bias)
-
-        # regularization
-        self.attn_drop = nn.Dropout(attn_pdrop)
-        self.resid_drop = nn.Dropout(resid_pdrop)
-
-        # output projection
-        self.proj = nn.Linear(embed_dim, embed_dim, attn_bias)
-
-        self.n_heads = n_heads
-        self.ctx_len = ctx_len
-        self.use_mask = use_mask
-        if self.use_mask:
-            self.register_buffer("mask", torch.ones(ctx_len, ctx_len), persistent=False)
-            self.mask = torch.tril(self.mask).view(1, ctx_len, ctx_len)
-
-    def forward(self, x, use_cache=False, layer_past=None):
-        B, T, C = x.shape
-        x = x.transpose(0, 1).contiguous()  # (B, T, C) -> (T, B, C)
-
-        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
-        k = self.key(x).view(T, B*self.n_heads, C//self.n_heads).transpose(0, 1)  # (B*nh, T, hs)
-        q = self.query(x).view(T, B*self.n_heads, C//self.n_heads).transpose(0, 1)  # (B*nh, T, hs)
-        v = self.value(x).view(T, B*self.n_heads, C//self.n_heads).transpose(0, 1)  # (B*nh, T, hs)
-
-        if use_cache:
-            present = torch.stack([k, v])
-
-        if layer_past is not None:
-            # print(layer_past.shape, k.shape, v.shape, q.shape)
-            # print("LayerPast shape", layer_past.shape)
-            past_key, past_value = layer_past
-
-            if len(past_key.shape) == 4:
-                _, _, seq_len, dim = past_key.shape
-                k = torch.cat([past_key.reshape(-1, seq_len, dim), k], dim=-2)
-                v = torch.cat([past_value.reshape(-1, seq_len, dim), v], dim=-2)
-            elif len(past_key.shape) == 3:
-                past_key, past_value = layer_past
-                k = torch.cat([past_key, k], dim=-2)
-                v = torch.cat([past_value, v], dim=-2)
-            else:
-                raise ValueError
-
-        if use_cache and layer_past is not None:
-            # Tensor shape below: (B * nh, 1, hs) X (B * nh, hs, K) -> (B * nh, 1, K)
-            att = torch.bmm(q, (k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))))
-            att = F.softmax(att, dim=-1)
-            att = self.attn_drop(att)
-            y = torch.bmm(att, v)  # (B*nh, 1, K) X (B*nh, K, hs) -> (B*nh, 1, hs)
-        else:
-            # Tensor shape below: (B * nh, T, hs) X (B * nh, hs, T) -> (B * nh, T, T)
-            att = torch.bmm(q, (k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))))
-            if self.use_mask:
-                # TODO : Flip when not prompt tunign
-                # mask = self.mask if T == self.ctx_len else self.mask[:, :T, :T]
-                if T == self.ctx_len:
-                    mask = self.mask
-                else:
-                    mask = torch.tril(torch.ones(T, T)).view(1, T, T).to(att.device)
-                att = att.masked_fill(mask == 0, float('-inf'))
-            att = F.softmax(att, dim=-1)
-            att = self.attn_drop(att)
-            y = torch.bmm(att, v)  # (B*nh, T, T) X (B*nh, T, hs) -> (B*nh, T, hs)
-        y = y.transpose(0, 1).contiguous().view(T, B, C)  # re-assemble all head outputs side by side
-
-        # output projection
-        y = self.resid_drop(self.proj(y))
-        if use_cache:
-            return y.transpose(0, 1).contiguous(), present  # (T, B, C) -> (B, T, C)
-        else:
-            return y.transpose(0, 1).contiguous()  # (T, B, C) -> (B, T, C)
-
-    def forward_with_context(self, x, context, mask=None):
-        B, T, C = x.shape
-        x = x.transpose(0, 1).contiguous()  # (B, T, C) -> (T, B, C)
-
-        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
-        q = self.query(x).view(T, B*self.n_heads, C//self.n_heads).transpose(0, 1)  # (B*nh, T, hs)
-
-        B, T_c, C = context.shape
-        k = self.key(context).view(T_c, B * self.n_heads, C // self.n_heads).transpose(0, 1)  # (B*nh, T, hs)
-        v = self.value(context).view(T_c, B*self.n_heads, C//self.n_heads).transpose(0, 1)  # (B*nh, T, hs)
-
-        # Tensor shape below: (B * nh, T, hs) X (B * nh, hs, Tc) -> (B * nh, T, Tc)
-        att = torch.bmm(q, (k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))))
-        att = F.softmax(att, dim=-1)
-        att = self.attn_drop(att)
-        y = torch.bmm(att, v)  # (B*nh, T, T) X (B*nh, T, hs) -> (B*nh, T, hs)
-        y = y.transpose(0, 1).contiguous().view(T, B, C)  # re-assemble all head outputs side by side
-
-        # output projection
-        y = self.resid_drop(self.proj(y)).transpose(0, 1).contiguous()
-        if mask is not None:
-            y = y.masked_fill(mask == 0, float('0.0'))
-        return y  # (T, B, C) -> (B, T, C)
-
-
-class Block(nn.Module):
-
-    def __init__(self,
-                 ctx_len: int,
-                 embed_dim: int,
-                 n_heads: int,
-                 mlp_bias: bool,
-                 attn_bias: bool,
-                 resid_pdrop: bool,
-                 attn_pdrop: bool,
-                 gelu_use_approx: bool):
-        super().__init__()
-        self.ln1 = nn.LayerNorm(embed_dim)
-        self.ln2 = nn.LayerNorm(embed_dim)
-
-        self.attn = MultiHeadSelfAttention(ctx_len=ctx_len,
-                                           embed_dim=embed_dim,
-                                           n_heads=n_heads,
-                                           attn_pdrop=attn_pdrop,
-                                           resid_pdrop=resid_pdrop,
-                                           attn_bias=attn_bias,
-                                           use_mask=True)
-        self.mlp = nn.Sequential(
-            nn.Linear(embed_dim, 4 * embed_dim, bias=mlp_bias),
-            GELU(gelu_use_approx),
-            nn.Linear(4 * embed_dim, embed_dim, bias=mlp_bias),
-            nn.Dropout(resid_pdrop),
-        )
-
-    def forward(self, x, layer_past=None):
-        x = x + self.attn(self.ln1(x), layer_past=layer_past)
-        x = x + self.mlp(self.ln2(x))
-        return x
-
-    def sample(self, x, layer_past=None):
-        attn, present = self.attn(self.ln1(x), use_cache=True, layer_past=layer_past)
-        x = x + attn
-        x = x + self.mlp(self.ln2(x))
-        return x, present
-
-    def sample_with_context(self, x, context, context_mask, cross_attn_layer, layer_past=None):
-        attn, present = self.attn(self.ln1(x), use_cache=True, layer_past=layer_past)
-        x = x + attn
-
-        c_attn = cross_attn_layer(x.to(device=context.device),
-                                  context,
-                                  context_mask.to(device=context.device))
-
-        x = x + c_attn.to(device=x.device)
-
-        x = x + self.mlp(self.ln2(x))
-        return x, present
-
-
-class CrossAttentionLayer(nn.Module):
-
-    def __init__(self,
-                 ctx_len: int,
-                 embed_dim: int,
-                 n_heads: int,
-                 attn_bias: bool,
-                 resid_pdrop: bool,
-                 attn_pdrop: bool):
-        super().__init__()
-
-        self.ln1 = nn.LayerNorm(embed_dim)
-        self.ln2 = nn.LayerNorm(embed_dim)
-        self.attn = MultiHeadSelfAttention(ctx_len=ctx_len,
-                                           embed_dim=embed_dim,
-                                           n_heads=n_heads,
-                                           attn_pdrop=attn_pdrop,
-                                           resid_pdrop=resid_pdrop,
-                                           attn_bias=attn_bias,
-                                           use_mask=False)
-
-    def forward(self, x, context, context_mask=None):
-        attn = self.attn.forward_with_context(self.ln1(x), self.ln2(context), context_mask)
-        # x = x + attn
-        # return x
-        return attn
\ No newline at end of file
diff --git a/spaces/Eddycrack864/Applio-Inference/colab_for_mdx.py b/spaces/Eddycrack864/Applio-Inference/colab_for_mdx.py
deleted file mode 100644
index 274846d0b5395865a05fce0da86b96d26ac06999..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/colab_for_mdx.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import json
-import os
-import gc
-import psutil
-import requests
-import subprocess
-import time
-import logging
-import sys
-import shutil
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-first_cell_executed = False
-file_folder = "Colab-for-MDX_B"
-def first_cell_ran():
-    global first_cell_executed
-    if first_cell_executed:
-        #print("The 'first_cell_ran' function has already been executed.")
-        return
-
-    
-
-    first_cell_executed = True
-    os.makedirs("tmp_models", exist_ok=True)
-
-
-
-    class hide_opt:  # hide outputs
-        def __enter__(self):
-            self._original_stdout = sys.stdout
-            sys.stdout = open(os.devnull, "w")
-
-        def __exit__(self, exc_type, exc_val, exc_tb):
-            sys.stdout.close()
-            sys.stdout = self._original_stdout
-
-    def get_size(bytes, suffix="B"):  # read ram
-        global svmem
-        factor = 1024
-        for unit in ["", "K", "M", "G", "T", "P"]:
-            if bytes < factor:
-                return f"{bytes:.2f}{unit}{suffix}"
-            bytes /= factor
-        svmem = psutil.virtual_memory()
-
-
-    def use_uvr_without_saving():
-        print("Notice: files won't be saved to personal drive.")
-        print(f"Downloading {file_folder}...", end=" ")
-        with hide_opt():
-            #os.chdir(mounting_path)
-            items_to_move = ["demucs", "diffq","julius","model","separated","tracks","mdx.py","MDX-Net_Colab.ipynb"]
-            subprocess.run(["git", "clone", "https://github.com/NaJeongMo/Colab-for-MDX_B.git"])
-            for item_name in items_to_move:
-                item_path = os.path.join(file_folder, item_name)
-                if os.path.exists(item_path):
-                    if os.path.isfile(item_path):
-                        shutil.move(item_path, now_dir)
-                    elif os.path.isdir(item_path):
-                        shutil.move(item_path, now_dir)
-            try:
-                shutil.rmtree(file_folder)
-            except PermissionError:
-                print(f"No se pudo eliminar la carpeta {file_folder}. Puede estar relacionada con Git.")
-
-    
-    use_uvr_without_saving()
-    print("done!")
-    if not os.path.exists("tracks"):
-        os.mkdir("tracks")
-first_cell_ran()
\ No newline at end of file
diff --git a/spaces/Ekimetrics/climate-question-answering/climateqa/vectorstore.py b/spaces/Ekimetrics/climate-question-answering/climateqa/vectorstore.py
deleted file mode 100644
index 2f68ed4e507e6ea688e9cbf206ce2b4a2f41ec65..0000000000000000000000000000000000000000
--- a/spaces/Ekimetrics/climate-question-answering/climateqa/vectorstore.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Pinecone
-# More info at https://docs.pinecone.io/docs/langchain
-# And https://python.langchain.com/docs/integrations/vectorstores/pinecone
-import os
-import pinecone
-from langchain.vectorstores import Pinecone
-
-# LOAD ENVIRONMENT VARIABLES
-try:
-    from dotenv import load_dotenv
-    load_dotenv()
-except:
-    pass
-
-
-def get_pinecone_vectorstore(embeddings,text_key = "content"):
-
-    # initialize pinecone
-    pinecone.init(
-        api_key=os.getenv("PINECONE_API_KEY"),  # find at app.pinecone.io
-        environment=os.getenv("PINECONE_API_ENVIRONMENT"),  # next to api key in console
-    )
-
-    index_name = os.getenv("PINECONE_API_INDEX")
-    vectorstore = Pinecone.from_existing_index(index_name, embeddings,text_key = text_key)
-    return vectorstore
-
-
-# def get_pinecone_retriever(vectorstore,k = 10,namespace = "vectors",sources = ["IPBES","IPCC"]):
-
-#     assert isinstance(sources,list)
-
-#     # Check if all elements in the list are either IPCC or IPBES
-#     filter = {
-#         "source": { "$in":sources},
-#     }
-
-#     retriever = vectorstore.as_retriever(search_kwargs={
-#         "k": k,
-#         "namespace":"vectors",
-#         "filter":filter
-#     })
-
-#     return retriever
\ No newline at end of file
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py b/spaces/EronSamez/RVC_HFmeu/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py
deleted file mode 100644
index 73a5b836177b706c306e27875f8391c1aed4b948..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_33966KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 16)
-        self.stg1_high_band_net = BaseASPPNet(2, 16)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(8, 16)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(16, 32)
-
-        self.out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_datasets/ST_MJ_alphanumeric_train.py b/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_datasets/ST_MJ_alphanumeric_train.py
deleted file mode 100644
index 5fc1abac0a48b9deef3ac41353dc24d3748d2426..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_datasets/ST_MJ_alphanumeric_train.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: SynthText, Syn90k
-# Both annotations are filtered so that
-# only alphanumeric terms are left
-
-train_root = 'data/mixture'
-
-train_img_prefix1 = f'{train_root}/Syn90k/mnt/ramdisk/max/90kDICT32px'
-train_ann_file1 = f'{train_root}/Syn90k/label.lmdb'
-
-train1 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix1,
-    ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_img_prefix2 = f'{train_root}/SynthText/' + \
-    'synthtext/SynthText_patch_horizontal'
-train_ann_file2 = f'{train_root}/SynthText/alphanumeric_label.lmdb'
-
-train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
-train2['ann_file'] = train_ann_file2
-
-train_list = [train1, train2]
diff --git a/spaces/FantasticGNU/AnomalyGPT/model/ImageBind/model_card.md b/spaces/FantasticGNU/AnomalyGPT/model/ImageBind/model_card.md
deleted file mode 100644
index c7bb26500b6590b64ffa6350f37be80dc88612d8..0000000000000000000000000000000000000000
--- a/spaces/FantasticGNU/AnomalyGPT/model/ImageBind/model_card.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# Model Card for ImageBind
-
-Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images.
-Input any of the six modalities and get the same sized embedding that can be used for cross-modal and multimodal tasks.
-
-# Model Details
-
-## Model Description
-
-<!-- Provide a longer summary of what this model is/does. -->
-Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images
-
-- **Developed by:** Meta AI
-- **Model type:** Multimodal model
-- **Language(s) (NLP):** en
-- **License:** CC BY-NC-SA 4.0
-- **Resources for more information:**
-    - [GitHub Repo](https://github.com/facebookresearch/ImageBind)
-
-
-# Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-This model is intended only for research purposes. It provides a joint embedding space for different modalities -- image/video, text, audio, depth, IMU and thermal images.
-We hope that these joint embeddings can be used for a variety of different cross-modal research, e.g., cross-modal retrieval and combining embeddings from different modalities.
-
-## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
-This model is *NOT* intended to be used in any real world application -- commercial or otherwise.
-It may produce harmful associations with different inputs.
-The model needs to be investigated and likely re-trained on specific data for any such application.
-The model is expected to work better on web-based visual data since it was trained on such data.
-The text encoder is likely to work only on English language text because of the underlying training datasets.
-
-# Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-Open-domain joint embedding models are prone to producing specific biases, e.g., study from [CLIP](https://github.com/openai/CLIP/blob/main/model-card.md#bias-and-fairness).
-Since our model uses such models as initialization, it will exhibit such biases too.
-Moreover, for learning joint embeddings for other modalities such as audio, thermal, depth, and IMU we leverage datasets that are relatively small. These joint embeddings are thus limited to the concepts present in the datasets. For example, the thermal datasets we used are limited to outdoor street scenes, while the depth datasets are limited to indoor scenes.
-
-
-
-# Training Details
-
-## Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-ImageBind uses image-paired data for training -- (image, X) where X is one of text, audio, depth, IMU or thermal data.
-In particular, we initialize and freeze the image and text encoders using an OpenCLIP ViT-H encoder.
-We train audio embeddings using Audioset, depth embeddings using the SUN RGB-D dataset, IMU using the Ego4D dataset and thermal embeddings using the LLVIP dataset.
-We provide the exact training data details in the paper.
-
-
-## Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-Please refer to the research paper and github repo for exact details on this.
-
-# Evaluation
-
-## Testing Data, Factors & Metrics
-
-We evaluate the model on a variety of different classification benchmarks for each modality.
-The evaluation details are presented in the paper.
-The models performance is measured using standard classification metrics such as accuracy and mAP.
-
-# Citation
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-```
-@inproceedings{girdhar2023imagebind,
-  title={ImageBind: One Embedding Space To Bind Them All},
-  author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
-and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
-  booktitle={CVPR},
-  year={2023}
-}
-```
-
-
-# Model Card Contact
-
-Please reach out to the authors at: rgirdhar@meta.com imisra@meta.com alaaelnouby@gmail.com
-
-# How to Get Started with the Model
-
-Our github repo provides a simple example to extract embeddings from images, audio etc.
diff --git a/spaces/FlippFuzz/whisper-webui/src/modelCache.py b/spaces/FlippFuzz/whisper-webui/src/modelCache.py
deleted file mode 100644
index 680a4b386fc37e17ed2353e72d04a646ece2c4a6..0000000000000000000000000000000000000000
--- a/spaces/FlippFuzz/whisper-webui/src/modelCache.py
+++ /dev/null
@@ -1,17 +0,0 @@
-class ModelCache:
-    def __init__(self):
-        self._cache = dict()
-
-    def get(self, model_key: str, model_factory):
-        result = self._cache.get(model_key)
-
-        if result is None:
-            result = model_factory()
-            self._cache[model_key] = result
-        return result
-
-    def clear(self):
-        self._cache.clear()
-
-# A global cache of models. This is mainly used by the daemon processes to avoid loading the same model multiple times.
-GLOBAL_MODEL_CACHE = ModelCache()
\ No newline at end of file
diff --git a/spaces/GAIR/Factool/version.py b/spaces/GAIR/Factool/version.py
deleted file mode 100644
index 3dc1f76bc69e3f559bee6253b24fc93acee9e1f9..0000000000000000000000000000000000000000
--- a/spaces/GAIR/Factool/version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.1.0"
diff --git a/spaces/GXSA/bingo/src/components/chat-scroll-anchor.tsx b/spaces/GXSA/bingo/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}
diff --git a/spaces/GeekTony/Gradio-Ontology/app (1).py b/spaces/GeekTony/Gradio-Ontology/app (1).py
deleted file mode 100644
index 706bd9475c92d36d9016e1f271dbb4ea003c39ea..0000000000000000000000000000000000000000
--- a/spaces/GeekTony/Gradio-Ontology/app (1).py	
+++ /dev/null
@@ -1,269 +0,0 @@
-import gradio as gr
-import pandas as pd
-import json
-from collections import defaultdict
-
-# Create tokenizer for biomed model
-from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
-tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")    # https://huggingface.co/d4data/biomedical-ner-all?text=asthma
-model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
-pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-
-# Matplotlib for entity graph
-import matplotlib.pyplot as plt
-plt.switch_backend("Agg")
-
-# Load examples from JSON
-import os 
-
-# Load terminology datasets:
-basedir = os.path.dirname(__file__)
-#dataLOINC = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
-#dataPanels = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-#dataSNOMED = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-#dataOMS = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-#dataICD10 = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv') 
-
-dataLOINC = pd.read_csv(f'LoincTableCore.csv')
-dataPanels = pd.read_csv(f'PanelsAndForms-ACW1208Labeled.csv')     
-dataSNOMED = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-dataOMS = pd.read_csv(f'SnomedOMS.csv')   
-dataICD10 = pd.read_csv(f'ICD10Diagnosis.csv')   
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-EXAMPLES = {}
-#with open(dir_path + "\\" + "examples.json", "r") as f:
-with open("examples.json", "r") as f:
-    example_json = json.load(f)
-    EXAMPLES = {x["text"]: x["label"] for x in example_json}
-
-def MatchLOINC(name):
-    #basedir = os.path.dirname(__file__)
-    pd.set_option("display.max_rows", None)
-    #data = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')    
-    data = dataLOINC
-    swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchLOINCPanelsandForms(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-    data = dataPanels
-    # Assessment Name:
-    #swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
-    # Assessment Question:
-    swith=data.loc[data['LoincName'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchSNOMED(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-    data = dataSNOMED
-    swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchOMS(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-    data = dataOMS
-    swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchICD10(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv')   
-    data = dataICD10
-    swith=data.loc[data['Description'].str.contains(name, case=False, na=False)]
-    return swith
-
-def SaveResult(text, outputfileName):
-    #try:
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    print("Saving: " + text + " to " + savePath)
-    from os.path import exists
-    file_exists = exists(savePath)
-    if file_exists:
-        with open(outputfileName, "a") as f: #append
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    else:
-        with open(outputfileName, "w") as f: #write
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    #except ValueError as err:
-    #    raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return
-
-def loadFile(filename):
-    try:
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-
-        print("Loading: " + loadPath)
-
-        from os.path import exists
-        file_exists = exists(loadPath)
-
-        if file_exists:
-            with open(loadPath, "r") as f: #read
-                contents = f.read()
-                print(contents)
-                return contents
-
-    except ValueError as err:
-        raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return ""
-
-def get_today_filename():
-    from datetime import datetime
-    date = datetime.now().strftime("%Y_%m_%d-%I.%M.%S.%p")
-    #print(f"filename_{date}")  'filename_2023_01_12-03-29-22_AM'
-    return f"MedNER_{date}.csv"
-
-def get_base(filename): 
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-        #print("Loading: " + loadPath)
-        return loadPath
-
-def group_by_entity(raw):
-    outputFile = get_base(get_today_filename())
-    out = defaultdict(int)
-
-    for ent in raw:
-        out[ent["entity_group"]] += 1
-        myEntityGroup = ent["entity_group"]
-        print("Found entity group type: " + myEntityGroup)
-
-#        if (myEntityGroup in ['Sign_symptom', 'Detailed_description', 'History', 'Activity', 'Medication', 'DISEASE_DISORDER' ]):
-        if (myEntityGroup not in ['Match All']):
-            eterm = ent["word"].replace('#','')
-            minlength = 3
-            if len(eterm) > minlength:
-                print("Found eterm: " + eterm)
-                eterm.replace("#","")
-                g1=MatchLOINC(eterm)
-                g2=MatchLOINCPanelsandForms(eterm)
-                g3=MatchSNOMED(eterm)
-                g4=MatchOMS(eterm)
-                g5=MatchICD10(eterm)
-                sAll = ""
-
-                print("Saving to output file " + outputFile)
-                # Create harmonisation output format of input to output code, name, Text
-
-                try: # 18 fields, output to labeled CSV dataset for results teaching on scored regret changes to action plan with data inputs
-                    col = "          1                            2            3         4            5                     6                    7                       8                   9              10                   11                         12       13               14                      15                  16                            17                    18                       19"
-                    
-                    #LOINC
-                    g11 = g1['LOINC_NUM'].to_string().replace(","," ").replace("\n"," ")
-                    g12 = g1['COMPONENT'].to_string().replace(","," ").replace("\n"," ")
-                    s1 = ("LOINC," + myEntityGroup + "," + eterm + ",questions of ," + g12 + "," + g11 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g11 != 'Series([]  )': SaveResult(s1, outputFile)
-
-                    #LOINC Panels
-                    g21 = g2['Loinc'].to_string().replace(","," ").replace("\n"," ")
-                    g22 = g2['LoincName'].to_string().replace(","," ").replace("\n"," ")
-                    g23 = g2['ParentLoinc'].to_string().replace(","," ").replace("\n"," ")
-                    g24 = g2['ParentName'].to_string().replace(","," ").replace("\n"," ")
-                    # s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + ", and Parent codes of ," + g23 + ", with Parent names of ," + g24 + ", Label,Value  ")
-                    s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + "," + g24 + ", and Parent codes of ," + g23 + "," + ", Label,Value  ")
-                    if g21 != 'Series([]  )': SaveResult(s2, outputFile)
-
-                    #SNOMED
-                    g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    g32 = g3['term'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    s3 = ("SNOMED Concept," + myEntityGroup + "," + eterm + ",terms of ," + g32 + "," + g31 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g31 != 'Series([]  )': SaveResult(s3, outputFile)
-
-                    #OMS
-                    g41 = g4['Omaha Code'].to_string().replace(","," ").replace("\n"," ")
-                    g42 = g4['SNOMED CT concept ID'].to_string().replace(","," ").replace("\n"," ")
-                    g43 = g4['SNOMED CT'].to_string().replace(","," ").replace("\n"," ")
-                    g44 = g4['PR'].to_string().replace(","," ").replace("\n"," ")
-                    g45 = g4['S&S'].to_string().replace(","," ").replace("\n"," ")
-                    s4 = ("OMS," + myEntityGroup + "," + eterm + ",concepts of ," + g44 + "," + g45 + ", and SNOMED codes of ," + g43 + ", and OMS problem of ," + g42 + ", and OMS Sign Symptom of ," + g41)
-                    if g41 != 'Series([]  )': SaveResult(s4, outputFile)
-
-                    #ICD10
-                    g51 = g5['Code'].to_string().replace(","," ").replace("\n"," ")
-                    g52 = g5['Description'].to_string().replace(","," ").replace("\n"," ")
-                    s5 = ("ICD10," + myEntityGroup + "," + eterm + ",descriptions of ," + g52 + "," + g51 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g51 != 'Series([]  )': SaveResult(s5, outputFile)
-
-                except ValueError as err:
-                    raise ValueError("Error in group by entity \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return outputFile
-
-
-def plot_to_figure(grouped):
-    fig = plt.figure()
-    plt.bar(x=list(grouped.keys()), height=list(grouped.values()))
-    plt.margins(0.2)
-    plt.subplots_adjust(bottom=0.4)
-    plt.xticks(rotation=90)
-    return fig
-
-
-def ner(text):
-    raw = pipe(text)
-    ner_content = {
-        "text": text,
-        "entities": [
-            {
-                "entity": x["entity_group"],
-                "word": x["word"],
-                "score": x["score"],
-                "start": x["start"],
-                "end": x["end"],
-            }
-            for x in raw
-        ],
-    }
-    
-    outputFile = group_by_entity(raw)
-    label = EXAMPLES.get(text, "Unknown")
-    outputDataframe = pd.read_csv(outputFile)
-    return (ner_content, outputDataframe, outputFile)
-
-demo = gr.Blocks()
-with demo:
-    gr.Markdown(
-    """
-    # 🩺⚕️NLP Clinical Ontology Biomedical NER 
-    """
-    )
-    input = gr.Textbox(label="Note text", value="")
-
-    with gr.Tab("Biomedical Entity Recognition"):
-        output=[
-            gr.HighlightedText(label="NER", combine_adjacent=True),
-            #gr.JSON(label="Entity Counts"),
-            #gr.Label(label="Rating"),
-            #gr.Plot(label="Bar"),
-            gr.Dataframe(label="Dataframe"),
-            gr.File(label="File"),
-        ]
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-        
-    with gr.Tab("Clinical Terminology Resolution"):
-        with gr.Row(variant="compact"):
-            btnLOINC = gr.Button("LOINC")
-            btnPanels = gr.Button("Panels")
-            btnSNOMED = gr.Button("SNOMED")
-            btnOMS = gr.Button("OMS")
-            btnICD10 = gr.Button("ICD10")
-
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-#layout="vertical"
-demo.launch(debug=True)
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/agents/transporter_image_goal.py b/spaces/Gen-Sim/Gen-Sim/cliport/agents/transporter_image_goal.py
deleted file mode 100644
index ea0cd9d993c9546abc9f51f635ac05a464c6bf48..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/agents/transporter_image_goal.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import numpy as np
-
-from cliport.utils import utils
-from cliport.agents.transporter import OriginalTransporterAgent
-from cliport.models.core.attention import Attention
-from cliport.models.core.attention_image_goal import AttentionImageGoal
-from cliport.models.core.transport_image_goal import TransportImageGoal
-
-
-class ImageGoalTransporterAgent(OriginalTransporterAgent):
-    def __init__(self, name, cfg, train_ds, test_ds):
-        super().__init__(name, cfg, train_ds, test_ds)
-
-    def _build_model(self):
-        stream_fcn = 'plain_resnet'
-        self.attention = AttentionImageGoal(
-            stream_fcn=(stream_fcn, None),
-            in_shape=self.in_shape,
-            n_rotations=1,
-            preprocess=utils.preprocess,
-            cfg=self.cfg,
-            device=self.device_type,
-        )
-        self.transport = TransportImageGoal(
-            stream_fcn=(stream_fcn, None),
-            in_shape=self.in_shape,
-            n_rotations=self.n_rotations,
-            crop_size=self.crop_size,
-            preprocess=utils.preprocess,
-            cfg=self.cfg,
-            device=self.device_type,
-        )
-
-    def attn_forward(self, inp, softmax=True):
-        inp_img = inp['inp_img']
-        goal_img = inp['goal_img']
-
-        out = self.attention.forward(inp_img, goal_img, softmax=softmax)
-        return out
-
-    def attn_training_step(self, frame, goal, backprop=True, compute_err=False):
-        inp_img = frame['img']
-        goal_img = goal['img']
-        p0, p0_theta = frame['p0'], frame['p0_theta']
-
-        inp = {'inp_img': inp_img, 'goal_img': goal_img}
-        out = self.attn_forward(inp, softmax=False)
-        return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta)
-
-    def trans_forward(self, inp, softmax=True):
-        inp_img = inp['inp_img']
-        goal_img = inp['goal_img']
-        p0 = inp['p0']
-
-        out = self.transport.forward(inp_img, goal_img, p0, softmax=softmax)
-        return out
-
-    def transport_training_step(self, frame, goal, backprop=True, compute_err=False):
-        inp_img = frame['img']
-        goal_img = goal['img']
-        p0 = frame['p0']
-        p1, p1_theta = frame['p1'], frame['p1_theta']
-
-        inp = {'inp_img': inp_img, 'goal_img': goal_img, 'p0': p0}
-        out = self.trans_forward(inp, softmax=False)
-        err, loss = self.transport_criterion(backprop, compute_err, inp, out, p0, p1, p1_theta)
-        return loss, err
-
-    def training_step(self, batch, batch_idx):
-        self.attention.train()
-        self.transport.train()
-        frame, goal = batch
-
-        # Get training losses.
-        step = self.total_steps + 1
-        loss0, err0 = self.attn_training_step(frame, goal)
-        if isinstance(self.transport, Attention):
-            loss1, err1 = self.attn_training_step(frame, goal)
-        else:
-            loss1, err1 = self.transport_training_step(frame, goal)
-        total_loss = loss0 + loss1
-        self.log('tr/attn/loss', loss0)
-        self.log('tr/trans/loss', loss1)
-        self.log('tr/loss', total_loss)
-        self.total_steps = step
-
-        self.trainer.train_loop.running_loss.append(total_loss)
-
-        self.check_save_iteration()
-
-        return dict(
-            loss=total_loss,
-        )
-
-    def validation_step(self, batch, batch_idx):
-        self.attention.eval()
-        self.transport.eval()
-
-        loss0, loss1 = 0, 0
-        for i in range(self.val_repeats):
-            frame, goal = batch
-            l0, err0 = self.attn_training_step(frame, goal, backprop=False, compute_err=True)
-            loss0 += l0
-            if isinstance(self.transport, Attention):
-                l1, err1 = self.attn_training_step(frame, goal, backprop=False, compute_err=True)
-                loss1 += l1
-            else:
-                l1, err1 = self.transport_training_step(frame, goal, backprop=False, compute_err=True)
-                loss1 += l1
-        loss0 /= self.val_repeats
-        loss1 /= self.val_repeats
-        val_total_loss = loss0 + loss1
-
-        self.trainer.evaluation_loop.trainer.train_loop.running_loss.append(val_total_loss)
-
-        return dict(
-            val_loss=val_total_loss,
-            val_loss0=loss0,
-            val_loss1=loss1,
-            val_attn_dist_err=err0['dist'],
-            val_attn_theta_err=err0['theta'],
-            val_trans_dist_err=err1['dist'],
-            val_trans_theta_err=err1['theta'],
-        )
-
-    def act(self, obs, info=None, goal=None):  # pylint: disable=unused-argument
-        """Run inference and return best action given visual observations."""
-        # Get heightmap from RGB-D images.
-        img = self.test_ds.get_image(obs)
-        goal_img = self.test_ds.get_image(goal[0])
-
-        # Attention model forward pass.
-        pick_conf = self.attention.forward(img, goal_img)
-        pick_conf = pick_conf.detach().cpu().numpy()
-        argmax = np.argmax(pick_conf)
-        argmax = np.unravel_index(argmax, shape=pick_conf.shape)
-        p0_pix = argmax[:2]
-        p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2])
-
-        # Transport model forward pass.
-        place_conf = self.transport.forward(img, goal_img, p0_pix)
-        place_conf = place_conf.permute(1, 2, 0)
-        place_conf = place_conf.detach().cpu().numpy()
-        argmax = np.argmax(place_conf)
-        argmax = np.unravel_index(argmax, shape=place_conf.shape)
-        p1_pix = argmax[:2]
-        p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2])
-
-        # Pixels to end effector poses.
-        hmap = img[:, :, 3]
-        p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size)
-        p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size)
-        p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta))
-        p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta))
-
-        return {
-            'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)),
-            'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)),
-            'pick': p0_pix,
-            'place': p1_pix,
-        }
diff --git a/spaces/GeorgeOrville/bingo/src/components/welcome-screen.tsx b/spaces/GeorgeOrville/bingo/src/components/welcome-screen.tsx
deleted file mode 100644
index f7449fcbb6c621875e235db98f2790bf7894fb0a..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/components/welcome-screen.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-import { useBing } from '@/lib/hooks/use-bing'
-
-const exampleMessages = [
-  {
-    heading: '🧐 提出复杂问题',
-    message: `我可以为我挑剔的只吃橙色食物的孩子做什么饭?`
-  },
-  {
-    heading: '🙌 获取更好的答案',
-    message: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?'
-  },
-  {
-    heading: '🎨 获得创意灵感',
-    message: `以海盗的口吻写一首关于外太空鳄鱼的俳句`
-  }
-]
-
-export function WelcomeScreen({ setInput }: Pick<ReturnType<typeof useBing>, 'setInput'>) {
-  return (
-    <div className="welcome-container flex">
-      {exampleMessages.map(example => (
-        <button key={example.heading} className="welcome-item w-4/5 sm:w-[240px]" type="button" onClick={() => setInput(example.message)}>
-          <div className="item-title">{example.heading}</div>
-          <div className="item-content">
-            <div className="item-body">
-              <div className="item-header"></div>
-              <div>&ldquo;{example.message}&rdquo;</div>
-            </div>
-          </div>
-        </button>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/config.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/config.py
deleted file mode 100644
index 1c21312f3de971bfa008254c6035cebc09f05e4c..0000000000000000000000000000000000000000
--- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/config.py
+++ /dev/null
@@ -1,45 +0,0 @@
-librispeech_datasets = {
-    "train": {
-        "clean": ["LibriSpeech/train-clean-100", "LibriSpeech/train-clean-360"],
-        "other": ["LibriSpeech/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriSpeech/test-clean"],
-        "other": ["LibriSpeech/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriSpeech/dev-clean"],
-        "other": ["LibriSpeech/dev-other"]
-    },
-}
-libritts_datasets = {
-    "train": {
-        "clean": ["LibriTTS/train-clean-100", "LibriTTS/train-clean-360"],
-        "other": ["LibriTTS/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriTTS/test-clean"],
-        "other": ["LibriTTS/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriTTS/dev-clean"],
-        "other": ["LibriTTS/dev-other"]
-    },
-}
-voxceleb_datasets = {
-    "voxceleb1" : {
-        "train": ["VoxCeleb1/wav"],
-        "test": ["VoxCeleb1/test_wav"]
-    },
-    "voxceleb2" : {
-        "train": ["VoxCeleb2/dev/aac"],
-        "test": ["VoxCeleb2/test_wav"]
-    }
-}
-
-other_datasets = [
-    "LJSpeech-1.1",
-    "VCTK-Corpus/wav48",
-]
-
-anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"]
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 92accfc703fc398d2845d7dc2f1d5336f24738e8..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet18',
-    backbone=dict(type='ResNet', depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 5efb61339cdbdde585f7814e9650be2e2df654ac..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './gcnet_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/exp/upernet_global_base/test_config_w32.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/exp/upernet_global_base/test_config_w32.py
deleted file mode 100644
index 56291ed20571a5d5ebcdf797c49372856a04276a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/exp/upernet_global_base/test_config_w32.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/upernet_uniformer.py', 
-    '../../configs/_base_/datasets/ade20k.py',
-    '../../configs/_base_/default_runtime.py', 
-    '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[5, 8, 20, 7],
-        head_dim=64,
-        drop_path_rate=0.4,
-        windows=True,
-        hybrid=False,
-        window_size=32,
-    ),
-    decode_head=dict(
-        in_channels=[64, 128, 320, 512],
-        num_classes=150
-    ),
-    auxiliary_head=dict(
-        in_channels=320,
-        num_classes=150
-    ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-
-lr_config = dict(_delete_=True, policy='poly',
-                 warmup='linear',
-                 warmup_iters=1500,
-                 warmup_ratio=1e-6,
-                 power=1.0, min_lr=0.0, by_epoch=False)
-                 
-data=dict(samples_per_gpu=2)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/utils/__init__.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/utils/__init__.py
deleted file mode 100644
index 3d3bdd349b9f2ae499a2fcb2ac1d2e3c77befebe..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/utils/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from .drop import DropPath
-from .inverted_residual import InvertedResidual, InvertedResidualV3
-from .make_divisible import make_divisible
-from .res_layer import ResLayer
-from .se_layer import SELayer
-from .self_attention_block import SelfAttentionBlock
-from .up_conv_block import UpConvBlock
-from .weight_init import trunc_normal_
-
-__all__ = [
-    'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual',
-    'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'DropPath', 'trunc_normal_'
-]
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/tests/data/test_audio_dataset.py b/spaces/GrandaddyShmax/AudioCraft_Plus/tests/data/test_audio_dataset.py
deleted file mode 100644
index b591ea6137f48d0d97fcd1243c5f5d258670a474..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/tests/data/test_audio_dataset.py
+++ /dev/null
@@ -1,352 +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.
-
-from functools import partial
-from itertools import product
-import json
-import math
-import os
-import random
-import typing as tp
-
-import pytest
-import torch
-from torch.utils.data import DataLoader
-
-from audiocraft.data.audio_dataset import (
-    AudioDataset,
-    AudioMeta,
-    _get_audio_meta,
-    load_audio_meta,
-    save_audio_meta
-)
-from audiocraft.data.zip import PathInZip
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestAudioMeta(TempDirMixin):
-
-    def test_get_audio_meta(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path('sample.wav')
-            save_wav(path, wav, sample_rate)
-            m = _get_audio_meta(path, minimal=True)
-            assert m.path == path, 'path does not match'
-            assert m.sample_rate == sample_rate, 'sample rate does not match'
-            assert m.duration == duration, 'duration does not match'
-            assert m.amplitude is None
-            assert m.info_path is None
-
-    def test_save_audio_meta(self):
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_audio_meta = []
-        for idx, meta in enumerate([audio_meta, empty_audio_meta]):
-            path = self.get_temp_path(f'data_{idx}_save.jsonl')
-            save_audio_meta(path, meta)
-            with open(path, 'r') as f:
-                lines = f.readlines()
-                read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines]
-                assert len(read_meta) == len(meta)
-                for m, read_m in zip(meta, read_meta):
-                    assert m == read_m
-
-    def test_load_audio_meta(self):
-        try:
-            import dora
-        except ImportError:
-            dora = None  # type: ignore
-
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_meta = []
-        for idx, meta in enumerate([audio_meta, empty_meta]):
-            path = self.get_temp_path(f'data_{idx}_load.jsonl')
-            with open(path, 'w') as f:
-                for m in meta:
-                    json_str = json.dumps(m.to_dict()) + '\n'
-                    f.write(json_str)
-            read_meta = load_audio_meta(path)
-            assert len(read_meta) == len(meta)
-            for m, read_m in zip(meta, read_meta):
-                if dora:
-                    m.path = dora.git_save.to_absolute_path(m.path)
-                assert m == read_m, f'original={m}, read={read_m}'
-
-
-class TestAudioDataset(TempDirMixin):
-
-    def _create_audio_files(self,
-                            root_name: str,
-                            num_examples: int,
-                            durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                            sample_rate: int = 16_000,
-                            channels: int = 1):
-        root_dir = self.get_temp_dir(root_name)
-        for i in range(num_examples):
-            if isinstance(durations, float):
-                duration = durations
-            elif isinstance(durations, tuple) and len(durations) == 1:
-                duration = durations[0]
-            elif isinstance(durations, tuple) and len(durations) == 2:
-                duration = random.uniform(durations[0], durations[1])
-            else:
-                assert False
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(channels, n_frames)
-            path = os.path.join(root_dir, f'example_{i}.wav')
-            save_wav(path, wav, sample_rate)
-        return root_dir
-
-    def _create_audio_dataset(self,
-                              root_name: str,
-                              total_num_examples: int,
-                              durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                              sample_rate: int = 16_000,
-                              channels: int = 1,
-                              segment_duration: tp.Optional[float] = None,
-                              num_examples: int = 10,
-                              shuffle: bool = True,
-                              return_info: bool = False):
-        root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels)
-        dataset = AudioDataset.from_path(root_dir,
-                                         minimal_meta=True,
-                                         segment_duration=segment_duration,
-                                         num_samples=num_examples,
-                                         sample_rate=sample_rate,
-                                         channels=channels,
-                                         shuffle=shuffle,
-                                         return_info=return_info)
-        return dataset
-
-    def test_dataset_full(self):
-        total_examples = 10
-        min_duration, max_duration = 1., 4.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration),
-            sample_rate=sample_rate, channels=channels, segment_duration=None)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] <= int(max_duration * sample_rate)
-            assert sample.shape[1] >= int(min_duration * sample_rate)
-
-    def test_dataset_segment(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-
-    def test_dataset_equal_audio_and_segment_durations(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-        # the random seek_time adds variability on audio read
-        sample_1 = dataset[0]
-        sample_2 = dataset[1]
-        assert not torch.allclose(sample_1, sample_2)
-
-    def test_dataset_samples(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-
-        create_dataset = partial(
-            self._create_audio_dataset,
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples,
-        )
-
-        dataset = create_dataset(shuffle=True)
-        # when shuffle = True, we have different inputs for the same index across epoch
-        sample_1 = dataset[0]
-        sample_2 = dataset[0]
-        assert not torch.allclose(sample_1, sample_2)
-
-        dataset_noshuffle = create_dataset(shuffle=False)
-        # when shuffle = False, we have same inputs for the same index across epoch
-        sample_1 = dataset_noshuffle[0]
-        sample_2 = dataset_noshuffle[0]
-        assert torch.allclose(sample_1, sample_2)
-
-    def test_dataset_return_info(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.total_frames == int(segment_duration * sample_rate)
-            assert segment_info.n_frames <= int(segment_duration * sample_rate)
-            assert segment_info.seek_time >= 0
-
-    def test_dataset_return_info_no_segment_duration(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = None
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == segment_info.total_frames
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.n_frames <= segment_info.total_frames
-
-    def test_dataset_collate_fn(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            assert batch.shape[0] == batch_size
-
-    @pytest.mark.parametrize("segment_duration", [1.0, None])
-    def test_dataset_with_meta_collate_fn(self, segment_duration):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            collate_fn=dataset.collater,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            wav, infos = batch
-            assert wav.shape[0] == batch_size
-            assert len(infos) == batch_size
-
-    @pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [
-        [1, True, True, 0.5, 0.5, 0.0],
-        [1, False, True, 0.25, 0.5, 0.25],
-        [1, True, False, 0.666, 0.333, 0.0],
-        [1, False, False, 0.333, 0.333, 0.333],
-        [None, False, False, 0.333, 0.333, 0.333]])
-    def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist):
-        random.seed(1234)
-        rng = torch.Generator()
-        rng.manual_seed(1234)
-
-        def _get_histogram(dataset, repetitions=20_000):
-            counts = {file_meta.path: 0. for file_meta in meta}
-            for _ in range(repetitions):
-                file_meta = dataset.sample_file(0, rng)
-                counts[file_meta.path] += 1
-            return {name: count / repetitions for name, count in counts.items()}
-
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(
-            meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight,
-            sample_on_duration=sample_on_duration)
-        hist = _get_histogram(dataset)
-        assert math.isclose(hist['a'], a_hist, abs_tol=0.01)
-        assert math.isclose(hist['b'], b_hist, abs_tol=0.01)
-        assert math.isclose(hist['c'], c_hist, abs_tol=0.01)
-
-    def test_meta_duration_filter_all(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        try:
-            AudioDataset(meta, segment_duration=11, min_segment_ratio=1)
-            assert False
-        except AssertionError:
-            assert True
-
-    def test_meta_duration_filter_long(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7)
-        assert len(dataset) == 2
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/test_fn.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/test_fn.py
deleted file mode 100644
index 039a73bf99b03ebcc827a79c4e3e1dc5736306ce..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/test_fn.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from typing import List, Optional
-from torch.utils.data import DataLoader
-from torch import Tensor
-import random
-import torch, os
-
-from .utils import plot_batch_of_pairs
-from .configs.base_config import base_cfg
-from .dataset_fn import TrainDataset
-
-def test_rgbd_dataset(cfg: base_cfg) -> None:
-    train_dataset = TrainDataset(
-        cfg.train_dataset_working_dir_path, cfg.image_size,
-        inputs=cfg.inputs, outputs=cfg.outputs,
-    )
-    train_dataloader = DataLoader(
-        train_dataset, batch_size=cfg.batch_size, 
-        shuffle=True, num_workers=cfg.num_workers
-    )
-
-    for i_batch, (images, depths, gts, indices) in enumerate(train_dataloader):
-        print(f'{i_batch}, {images.shape}, {depths.shape},  {indices.shape}')
-        plot_batch_of_pairs(images, depths, gts)
-        break
-
-def test_data_augmentation(
-    cfg: base_cfg,
-    data_augmentation_version: int,
-    index: Optional[int] = None,
-) -> None:
-    cfg.data_augmentation_version = data_augmentation_version
-    dataset = TrainDataset(cfg)
-
-    images: List[Tensor] = []
-    depths: List[Tensor] = []
-    gts: List[Tensor] = []
-    
-    if index is None:
-        index = random.randrange(len(dataset))
-    print(index)
-
-    # No transformation
-    image, depth, gt, i = dataset.__getitem__(index, False)
-    print(torch.max(depth), torch.min(depth))
-    images.append(torch.unsqueeze(image, 0))
-    depths.append(torch.unsqueeze(depth, 0))
-    gts.append(torch.unsqueeze(gt, 0))
-
-    # Random 3 times transformation
-    for _ in range(5):
-        image, depth, gt, i = dataset[index]
-        print(torch.max(depth), torch.min(depth))
-        images.append(torch.unsqueeze(image, 0))
-        depths.append(torch.unsqueeze(depth, 0))
-        gts.append(torch.unsqueeze(gt, 0))
-
-    os.makedirs(cfg.latex_dir_path, exist_ok=True)
-    plot_batch_of_pairs(
-        torch.cat(images),
-        torch.cat(depths),
-        torch.cat(gts),
-        os.path.join(
-            cfg.latex_dir_path, 
-            f'data_augmentation_v{cfg.data_augmentation_version}.png'
-        )
-    )
-
-# test_data_augmentation(cfg, index=10)
diff --git a/spaces/Haokko/AronaTTS/transforms.py b/spaces/Haokko/AronaTTS/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Haokko/AronaTTS/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/language_pair_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/language_pair_dataset.py
deleted file mode 100644
index ff3e14bf14770638524ef6067b558e455dbe5f2b..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/language_pair_dataset.py
+++ /dev/null
@@ -1,471 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-
-import numpy as np
-import torch
-from fairseq.data import FairseqDataset, data_utils
-
-
-logger = logging.getLogger(__name__)
-
-
-def collate(
-    samples,
-    pad_idx,
-    eos_idx,
-    left_pad_source=True,
-    left_pad_target=False,
-    input_feeding=True,
-    pad_to_length=None,
-    pad_to_multiple=1,
-):
-    if len(samples) == 0:
-        return {}
-
-    def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
-        return data_utils.collate_tokens(
-            [s[key] for s in samples],
-            pad_idx,
-            eos_idx,
-            left_pad,
-            move_eos_to_beginning,
-            pad_to_length=pad_to_length,
-            pad_to_multiple=pad_to_multiple,
-        )
-
-    def check_alignment(alignment, src_len, tgt_len):
-        if alignment is None or len(alignment) == 0:
-            return False
-        if (
-            alignment[:, 0].max().item() >= src_len - 1
-            or alignment[:, 1].max().item() >= tgt_len - 1
-        ):
-            logger.warning("alignment size mismatch found, skipping alignment!")
-            return False
-        return True
-
-    def compute_alignment_weights(alignments):
-        """
-        Given a tensor of shape [:, 2] containing the source-target indices
-        corresponding to the alignments, a weight vector containing the
-        inverse frequency of each target index is computed.
-        For e.g. if alignments = [[5, 7], [2, 3], [1, 3], [4, 2]], then
-        a tensor containing [1., 0.5, 0.5, 1] should be returned (since target
-        index 3 is repeated twice)
-        """
-        align_tgt = alignments[:, 1]
-        _, align_tgt_i, align_tgt_c = torch.unique(
-            align_tgt, return_inverse=True, return_counts=True
-        )
-        align_weights = align_tgt_c[align_tgt_i[np.arange(len(align_tgt))]]
-        return 1.0 / align_weights.float()
-
-    id = torch.LongTensor([s["id"] for s in samples])
-    src_tokens = merge(
-        "source",
-        left_pad=left_pad_source,
-        pad_to_length=pad_to_length["source"] if pad_to_length is not None else None,
-    )
-    # sort by descending source length
-    src_lengths = torch.LongTensor(
-        [s["source"].ne(pad_idx).long().sum() for s in samples]
-    )
-    src_lengths, sort_order = src_lengths.sort(descending=True)
-    id = id.index_select(0, sort_order)
-    src_tokens = src_tokens.index_select(0, sort_order)
-
-    prev_output_tokens = None
-    target = None
-    if samples[0].get("target", None) is not None:
-        target = merge(
-            "target",
-            left_pad=left_pad_target,
-            pad_to_length=pad_to_length["target"]
-            if pad_to_length is not None
-            else None,
-        )
-        target = target.index_select(0, sort_order)
-        tgt_lengths = torch.LongTensor(
-            [s["target"].ne(pad_idx).long().sum() for s in samples]
-        ).index_select(0, sort_order)
-        ntokens = tgt_lengths.sum().item()
-
-        if samples[0].get("prev_output_tokens", None) is not None:
-            prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target)
-        elif input_feeding:
-            # we create a shifted version of targets for feeding the
-            # previous output token(s) into the next decoder step
-            prev_output_tokens = merge(
-                "target",
-                left_pad=left_pad_target,
-                move_eos_to_beginning=True,
-                pad_to_length=pad_to_length["target"]
-                if pad_to_length is not None
-                else None,
-            )
-    else:
-        ntokens = src_lengths.sum().item()
-
-    batch = {
-        "id": id,
-        "nsentences": len(samples),
-        "ntokens": ntokens,
-        "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths,},
-        "target": target,
-    }
-    if prev_output_tokens is not None:
-        batch["net_input"]["prev_output_tokens"] = prev_output_tokens.index_select(
-            0, sort_order
-        )
-
-    if samples[0].get("alignment", None) is not None:
-        bsz, tgt_sz = batch["target"].shape
-        src_sz = batch["net_input"]["src_tokens"].shape[1]
-
-        offsets = torch.zeros((len(sort_order), 2), dtype=torch.long)
-        offsets[:, 1] += torch.arange(len(sort_order), dtype=torch.long) * tgt_sz
-        if left_pad_source:
-            offsets[:, 0] += src_sz - src_lengths
-        if left_pad_target:
-            offsets[:, 1] += tgt_sz - tgt_lengths
-
-        alignments = [
-            alignment + offset
-            for align_idx, offset, src_len, tgt_len in zip(
-                sort_order, offsets, src_lengths, tgt_lengths
-            )
-            for alignment in [samples[align_idx]["alignment"].view(-1, 2)]
-            if check_alignment(alignment, src_len, tgt_len)
-        ]
-
-        if len(alignments) > 0:
-            alignments = torch.cat(alignments, dim=0)
-            align_weights = compute_alignment_weights(alignments)
-
-            batch["alignments"] = alignments
-            batch["align_weights"] = align_weights
-
-    if samples[0].get("constraints", None) is not None:
-        # Collate the packed constraints across the samples, padding to
-        # the length of the longest sample.
-        lens = [sample.get("constraints").size(0) for sample in samples]
-        max_len = max(lens)
-        constraints = torch.zeros((len(samples), max(lens))).long()
-        for i, sample in enumerate(samples):
-            constraints[i, 0 : lens[i]] = samples[i].get("constraints")
-        batch["constraints"] = constraints.index_select(0, sort_order)
-
-    return batch
-
-
-class LanguagePairDataset(FairseqDataset):
-    """
-    A pair of torch.utils.data.Datasets.
-
-    Args:
-        src (torch.utils.data.Dataset): source dataset to wrap
-        src_sizes (List[int]): source sentence lengths
-        src_dict (~fairseq.data.Dictionary): source vocabulary
-        tgt (torch.utils.data.Dataset, optional): target dataset to wrap
-        tgt_sizes (List[int], optional): target sentence lengths
-        tgt_dict (~fairseq.data.Dictionary, optional): target vocabulary
-        left_pad_source (bool, optional): pad source tensors on the left side
-            (default: True).
-        left_pad_target (bool, optional): pad target tensors on the left side
-            (default: False).
-        shuffle (bool, optional): shuffle dataset elements before batching
-            (default: True).
-        input_feeding (bool, optional): create a shifted version of the targets
-            to be passed into the model for teacher forcing (default: True).
-        remove_eos_from_source (bool, optional): if set, removes eos from end
-            of source if it's present (default: False).
-        append_eos_to_target (bool, optional): if set, appends eos to end of
-            target if it's absent (default: False).
-        align_dataset (torch.utils.data.Dataset, optional): dataset
-            containing alignments.
-        constraints (Tensor, optional): 2d tensor with a concatenated, zero-
-            delimited list of constraints for each sentence.
-        append_bos (bool, optional): if set, appends bos to the beginning of
-            source/target sentence.
-        num_buckets (int, optional): if set to a value greater than 0, then
-            batches will be bucketed into the given number of batch shapes.
-        src_lang_id (int, optional): source language ID, if set, the collated batch
-            will contain a field 'src_lang_id' in 'net_input' which indicates the
-            source language of the samples.
-        tgt_lang_id (int, optional): target language ID, if set, the collated batch
-            will contain a field 'tgt_lang_id' which indicates the target language
-             of the samples.
-    """
-
-    def __init__(
-        self,
-        src,
-        src_sizes,
-        src_dict,
-        tgt=None,
-        tgt_sizes=None,
-        tgt_dict=None,
-        left_pad_source=True,
-        left_pad_target=False,
-        shuffle=True,
-        input_feeding=True,
-        remove_eos_from_source=False,
-        append_eos_to_target=False,
-        align_dataset=None,
-        constraints=None,
-        append_bos=False,
-        eos=None,
-        num_buckets=0,
-        src_lang_id=None,
-        tgt_lang_id=None,
-        pad_to_multiple=1,
-    ):
-        if tgt_dict is not None:
-            assert src_dict.pad() == tgt_dict.pad()
-            assert src_dict.eos() == tgt_dict.eos()
-            assert src_dict.unk() == tgt_dict.unk()
-        if tgt is not None:
-            assert len(src) == len(
-                tgt
-            ), "Source and target must contain the same number of examples"
-        self.src = src
-        self.tgt = tgt
-        self.src_sizes = np.array(src_sizes)
-        self.tgt_sizes = np.array(tgt_sizes) if tgt_sizes is not None else None
-        self.sizes = (
-            np.vstack((self.src_sizes, self.tgt_sizes)).T
-            if self.tgt_sizes is not None
-            else self.src_sizes
-        )
-        self.src_dict = src_dict
-        self.tgt_dict = tgt_dict
-        self.left_pad_source = left_pad_source
-        self.left_pad_target = left_pad_target
-        self.shuffle = shuffle
-        self.input_feeding = input_feeding
-        self.remove_eos_from_source = remove_eos_from_source
-        self.append_eos_to_target = append_eos_to_target
-        self.align_dataset = align_dataset
-        if self.align_dataset is not None:
-            assert (
-                self.tgt_sizes is not None
-            ), "Both source and target needed when alignments are provided"
-        self.constraints = constraints
-        self.append_bos = append_bos
-        self.eos = eos if eos is not None else src_dict.eos()
-        self.src_lang_id = src_lang_id
-        self.tgt_lang_id = tgt_lang_id
-        if num_buckets > 0:
-            from fairseq.data import BucketPadLengthDataset
-
-            self.src = BucketPadLengthDataset(
-                self.src,
-                sizes=self.src_sizes,
-                num_buckets=num_buckets,
-                pad_idx=self.src_dict.pad(),
-                left_pad=self.left_pad_source,
-            )
-            self.src_sizes = self.src.sizes
-            logger.info("bucketing source lengths: {}".format(list(self.src.buckets)))
-            if self.tgt is not None:
-                self.tgt = BucketPadLengthDataset(
-                    self.tgt,
-                    sizes=self.tgt_sizes,
-                    num_buckets=num_buckets,
-                    pad_idx=self.tgt_dict.pad(),
-                    left_pad=self.left_pad_target,
-                )
-                self.tgt_sizes = self.tgt.sizes
-                logger.info(
-                    "bucketing target lengths: {}".format(list(self.tgt.buckets))
-                )
-
-            # determine bucket sizes using self.num_tokens, which will return
-            # the padded lengths (thanks to BucketPadLengthDataset)
-            num_tokens = np.vectorize(self.num_tokens, otypes=[np.compat.long])
-            self.bucketed_num_tokens = num_tokens(np.arange(len(self.src)))
-            self.buckets = [
-                (None, num_tokens) for num_tokens in np.unique(self.bucketed_num_tokens)
-            ]
-        else:
-            self.buckets = None
-        self.pad_to_multiple = pad_to_multiple
-
-    def get_batch_shapes(self):
-        return self.buckets
-
-    def __getitem__(self, index):
-        tgt_item = self.tgt[index] if self.tgt is not None else None
-        src_item = self.src[index]
-        # Append EOS to end of tgt sentence if it does not have an EOS and remove
-        # EOS from end of src sentence if it exists. This is useful when we use
-        # use existing datasets for opposite directions i.e., when we want to
-        # use tgt_dataset as src_dataset and vice versa
-        if self.append_eos_to_target:
-            eos = self.tgt_dict.eos() if self.tgt_dict else self.src_dict.eos()
-            if self.tgt and self.tgt[index][-1] != eos:
-                tgt_item = torch.cat([self.tgt[index], torch.LongTensor([eos])])
-
-        if self.append_bos:
-            bos = self.tgt_dict.bos() if self.tgt_dict else self.src_dict.bos()
-            if self.tgt and self.tgt[index][0] != bos:
-                tgt_item = torch.cat([torch.LongTensor([bos]), self.tgt[index]])
-
-            bos = self.src_dict.bos()
-            if self.src[index][0] != bos:
-                src_item = torch.cat([torch.LongTensor([bos]), self.src[index]])
-
-        if self.remove_eos_from_source:
-            eos = self.src_dict.eos()
-            if self.src[index][-1] == eos:
-                src_item = self.src[index][:-1]
-
-        example = {
-            "id": index,
-            "source": src_item,
-            "target": tgt_item,
-        }
-        if self.align_dataset is not None:
-            example["alignment"] = self.align_dataset[index]
-        if self.constraints is not None:
-            example["constraints"] = self.constraints[index]
-        return example
-
-    def __len__(self):
-        return len(self.src)
-
-    def collater(self, samples, pad_to_length=None):
-        """Merge a list of samples to form a mini-batch.
-
-        Args:
-            samples (List[dict]): samples to collate
-            pad_to_length (dict, optional): a dictionary of
-                {'source': source_pad_to_length, 'target': target_pad_to_length}
-                to indicate the max length to pad to in source and target respectively.
-
-        Returns:
-            dict: a mini-batch with the following keys:
-
-                - `id` (LongTensor): example IDs in the original input order
-                - `ntokens` (int): total number of tokens in the batch
-                - `net_input` (dict): the input to the Model, containing keys:
-
-                  - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
-                    the source sentence of shape `(bsz, src_len)`. Padding will
-                    appear on the left if *left_pad_source* is ``True``.
-                  - `src_lengths` (LongTensor): 1D Tensor of the unpadded
-                    lengths of each source sentence of shape `(bsz)`
-                  - `prev_output_tokens` (LongTensor): a padded 2D Tensor of
-                    tokens in the target sentence, shifted right by one
-                    position for teacher forcing, of shape `(bsz, tgt_len)`.
-                    This key will not be present if *input_feeding* is
-                    ``False``.  Padding will appear on the left if
-                    *left_pad_target* is ``True``.
-                  - `src_lang_id` (LongTensor): a long Tensor which contains source
-                    language IDs of each sample in the batch
-
-                - `target` (LongTensor): a padded 2D Tensor of tokens in the
-                  target sentence of shape `(bsz, tgt_len)`. Padding will appear
-                  on the left if *left_pad_target* is ``True``.
-                - `tgt_lang_id` (LongTensor): a long Tensor which contains target language
-                   IDs of each sample in the batch
-        """
-        res = collate(
-            samples,
-            pad_idx=self.src_dict.pad(),
-            eos_idx=self.eos,
-            left_pad_source=self.left_pad_source,
-            left_pad_target=self.left_pad_target,
-            input_feeding=self.input_feeding,
-            pad_to_length=pad_to_length,
-            pad_to_multiple=self.pad_to_multiple,
-        )
-        if self.src_lang_id is not None or self.tgt_lang_id is not None:
-            src_tokens = res["net_input"]["src_tokens"]
-            bsz = src_tokens.size(0)
-            if self.src_lang_id is not None:
-                res["net_input"]["src_lang_id"] = (
-                    torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens)
-                )
-            if self.tgt_lang_id is not None:
-                res["tgt_lang_id"] = (
-                    torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens)
-                )
-        return res
-
-    def num_tokens(self, index):
-        """Return the number of tokens in a sample. This value is used to
-        enforce ``--max-tokens`` during batching."""
-        return max(
-            self.src_sizes[index],
-            self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
-        )
-
-    def num_tokens_vec(self, indices):
-        """Return the number of tokens for a set of positions defined by indices.
-        This value is used to enforce ``--max-tokens`` during batching."""
-        sizes = self.src_sizes[indices]
-        if self.tgt_sizes is not None:
-            sizes = np.maximum(sizes, self.tgt_sizes[indices])
-        return sizes
-
-    def size(self, index):
-        """Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with ``--max-positions``."""
-        return (
-            self.src_sizes[index],
-            self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
-        )
-
-    def ordered_indices(self):
-        """Return an ordered list of indices. Batches will be constructed based
-        on this order."""
-        if self.shuffle:
-            indices = np.random.permutation(len(self)).astype(np.int64)
-        else:
-            indices = np.arange(len(self), dtype=np.int64)
-        if self.buckets is None:
-            # sort by target length, then source length
-            if self.tgt_sizes is not None:
-                indices = indices[np.argsort(self.tgt_sizes[indices], kind="mergesort")]
-            return indices[np.argsort(self.src_sizes[indices], kind="mergesort")]
-        else:
-            # sort by bucketed_num_tokens, which is:
-            #   max(padded_src_len, padded_tgt_len)
-            return indices[
-                np.argsort(self.bucketed_num_tokens[indices], kind="mergesort")
-            ]
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.src, "supports_prefetch", False) and (
-            getattr(self.tgt, "supports_prefetch", False) or self.tgt is None
-        )
-
-    def prefetch(self, indices):
-        self.src.prefetch(indices)
-        if self.tgt is not None:
-            self.tgt.prefetch(indices)
-        if self.align_dataset is not None:
-            self.align_dataset.prefetch(indices)
-
-    def filter_indices_by_size(self, indices, max_sizes):
-        """Filter a list of sample indices. Remove those that are longer
-            than specified in max_sizes.
-
-        Args:
-            indices (np.array): original array of sample indices
-            max_sizes (int or list[int] or tuple[int]): max sample size,
-                can be defined separately for src and tgt (then list or tuple)
-
-        Returns:
-            np.array: filtered sample array
-            list: list of removed indices
-        """
-        return data_utils.filter_paired_dataset_indices_by_size(
-            self.src_sizes, self.tgt_sizes, indices, max_sizes,
-        )
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/strip_token_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/strip_token_dataset.py
deleted file mode 100644
index cae39ba4d2f8106398eccd7eb0cf5c2194ec0db5..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/strip_token_dataset.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from . import BaseWrapperDataset
-
-
-class StripTokenDataset(BaseWrapperDataset):
-    def __init__(self, dataset, id_to_strip):
-        super().__init__(dataset)
-        self.id_to_strip = id_to_strip
-
-    def __getitem__(self, index):
-        item = self.dataset[index]
-        while len(item) > 0 and item[-1] == self.id_to_strip:
-            item = item[:-1]
-        while len(item) > 0 and item[0] == self.id_to_strip:
-            item = item[1:]
-        return item
diff --git a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/util.py b/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/util.py
deleted file mode 100644
index 06053e5defb87977f9ab07e69bf4da12201de9b7..0000000000000000000000000000000000000000
--- a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/util.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os, hashlib
-import requests
-from tqdm import tqdm
-
-URL_MAP = {
-    "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"
-}
-
-CKPT_MAP = {
-    "vgg_lpips": "vgg.pth"
-}
-
-MD5_MAP = {
-    "vgg_lpips": "d507d7349b931f0638a25a48a722f98a"
-}
-
-
-def download(url, local_path, chunk_size=1024):
-    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
-    with requests.get(url, stream=True) as r:
-        total_size = int(r.headers.get("content-length", 0))
-        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
-            with open(local_path, "wb") as f:
-                for data in r.iter_content(chunk_size=chunk_size):
-                    if data:
-                        f.write(data)
-                        pbar.update(chunk_size)
-
-
-def md5_hash(path):
-    with open(path, "rb") as f:
-        content = f.read()
-    return hashlib.md5(content).hexdigest()
-
-
-def get_ckpt_path(name, root, check=False):
-    assert name in URL_MAP
-    path = os.path.join(root, CKPT_MAP[name])
-    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
-        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
-        download(URL_MAP[name], path)
-        md5 = md5_hash(path)
-        assert md5 == MD5_MAP[name], md5
-    return path
-
-
-class KeyNotFoundError(Exception):
-    def __init__(self, cause, keys=None, visited=None):
-        self.cause = cause
-        self.keys = keys
-        self.visited = visited
-        messages = list()
-        if keys is not None:
-            messages.append("Key not found: {}".format(keys))
-        if visited is not None:
-            messages.append("Visited: {}".format(visited))
-        messages.append("Cause:\n{}".format(cause))
-        message = "\n".join(messages)
-        super().__init__(message)
-
-
-def retrieve(
-    list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False
-):
-    """Given a nested list or dict return the desired value at key expanding
-    callable nodes if necessary and :attr:`expand` is ``True``. The expansion
-    is done in-place.
-
-    Parameters
-    ----------
-        list_or_dict : list or dict
-            Possibly nested list or dictionary.
-        key : str
-            key/to/value, path like string describing all keys necessary to
-            consider to get to the desired value. List indices can also be
-            passed here.
-        splitval : str
-            String that defines the delimiter between keys of the
-            different depth levels in `key`.
-        default : obj
-            Value returned if :attr:`key` is not found.
-        expand : bool
-            Whether to expand callable nodes on the path or not.
-
-    Returns
-    -------
-        The desired value or if :attr:`default` is not ``None`` and the
-        :attr:`key` is not found returns ``default``.
-
-    Raises
-    ------
-        Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is
-        ``None``.
-    """
-
-    keys = key.split(splitval)
-
-    success = True
-    try:
-        visited = []
-        parent = None
-        last_key = None
-        for key in keys:
-            if callable(list_or_dict):
-                if not expand:
-                    raise KeyNotFoundError(
-                        ValueError(
-                            "Trying to get past callable node with expand=False."
-                        ),
-                        keys=keys,
-                        visited=visited,
-                    )
-                list_or_dict = list_or_dict()
-                parent[last_key] = list_or_dict
-
-            last_key = key
-            parent = list_or_dict
-
-            try:
-                if isinstance(list_or_dict, dict):
-                    list_or_dict = list_or_dict[key]
-                else:
-                    list_or_dict = list_or_dict[int(key)]
-            except (KeyError, IndexError, ValueError) as e:
-                raise KeyNotFoundError(e, keys=keys, visited=visited)
-
-            visited += [key]
-        # final expansion of retrieved value
-        if expand and callable(list_or_dict):
-            list_or_dict = list_or_dict()
-            parent[last_key] = list_or_dict
-    except KeyNotFoundError as e:
-        if default is None:
-            raise e
-        else:
-            list_or_dict = default
-            success = False
-
-    if not pass_success:
-        return list_or_dict
-    else:
-        return list_or_dict, success
-
-
-if __name__ == "__main__":
-    config = {"keya": "a",
-              "keyb": "b",
-              "keyc":
-                  {"cc1": 1,
-                   "cc2": 2,
-                   }
-              }
-    from omegaconf import OmegaConf
-    config = OmegaConf.create(config)
-    print(config)
-    retrieve(config, "keya")
-
diff --git a/spaces/IVentureISB/Gen-AI/scrape_create_context.py b/spaces/IVentureISB/Gen-AI/scrape_create_context.py
deleted file mode 100644
index cf43c36d554ce9b1203ed01dfc56054f278a795c..0000000000000000000000000000000000000000
--- a/spaces/IVentureISB/Gen-AI/scrape_create_context.py
+++ /dev/null
@@ -1,331 +0,0 @@
- # -*- coding: utf-8 -*-
-"""ISB chatbot.ipynb
-
-
-
-Original file is located at
-    https://colab.research.google.com/drive/1GYmsZSR4MWuvORNpSWFWrXz79lQKb6oc
-"""
-
-"""# Scrape"""
-
-# Regex to match a URL
-# HTTP_URL_PATTERN = r'^http[s]{0,1}://.+$'
-
-# Define root domain to crawl
-domain = "i-venture.org"
-sitemap_url = "https://i-venture.org/sitemap.xml"
-full_url = "https://i-venture.org/"
-
-import os
-
-RESULTS_DIR = "scraped_files/"
-os.makedirs(RESULTS_DIR, exist_ok=True)
-
-import requests
-import re
-import urllib.request
-from bs4 import BeautifulSoup
-from collections import deque
-from html.parser import HTMLParser
-from urllib.parse import urlparse
-import os
-import pandas as pd
-import numpy as np
-
-def get_sitemap(url=sitemap_url):
-    try:
-        with urllib.request.urlopen(url) as response:
-            xml = BeautifulSoup(response,
-                                'lxml-xml',
-                                from_encoding=response.info().get_param('charset'))
-
-        urls = xml.find_all("url")
-        locs = []
-
-        for url in urls:
-
-            if xml.find("loc"):
-                loc = url.findNext("loc").text
-                locs.append(loc)
-
-        return locs
-    except Exception as e:
-        print(e)
-        return []
-
-
-def crawl(url):
-    # Parse the URL and get the domain
-    # local_domain = urlparse(url).netloc
-
-    queue = deque(get_sitemap())
-
-    os.makedirs(RESULTS_DIR + "text/", exist_ok=True)
-    os.makedirs(RESULTS_DIR + "processed", exist_ok=True)
-
-    # While the queue is not empty, continue crawling
-    while queue:
-        # Get the next URL from the queue
-        url = queue.pop()
-        print(url) # for debugging and to see the progress
-
-        # Save text from the url to a <url>.txt file
-        with open(f'{RESULTS_DIR}text/'+ url.strip("/").replace("/", "_") + ".txt", "w", encoding="UTF-8") as f:
-
-            soup = BeautifulSoup(requests.get(url).text, "html.parser")
-            text = soup.get_text()
-
-            # If the crawler gets to a page that requires JavaScript, it will stop the crawl
-            if ("You need to enable JavaScript to run this app." in text):
-                print("Unable to parse page " + url + " due to JavaScript being required")
-
-            f.write(text)
-
-        # # Get the hyperlinks from the URL and add them to the queue
-        # for link in get_domain_hyperlinks(local_domain, url):
-        #     if link not in seen:
-        #         queue.append(link)
-        #         seen.add(link)
-
-def remove_newlines(serie):
-    serie = serie.str.replace('\n', ' ')
-    serie = serie.str.replace('\\n', ' ')
-    serie = serie.str.replace('  ', ' ')
-    serie = serie.str.replace('  ', ' ')
-    return serie
-
-
-def get_df():
-    # Create a list to store the text files
-    texts=[]
-
-    for file in os.listdir(RESULTS_DIR + "text/"):
-        with open(RESULTS_DIR + "text/" + "/" + file, "r", encoding="UTF-8") as f:
-            text = f.read()
-
-            # Omit the first 11 lines and the last 4 lines, then replace -, _, and #update with spaces.
-            texts.append((file.replace('#update',''), text))
-
-    # Create a dataframe from the list of texts
-    df = pd.DataFrame(texts, columns = ['fname', 'text'])
-
-    # Set the text column to be the raw text with the newlines removed
-    df['text'] = df.fname + ". " + remove_newlines(df.text)
-    return df
-
-SCRAPING_DONE = False
-if not SCRAPING_DONE:
-    crawl(full_url)
-    df = get_df()
-    df.to_csv(RESULTS_DIR + 'processed/scraped.csv')
-    df.head()
-    !zip -r iventure_scrape.zip scraped_files
-else:
-    !unzip iventure_scrape.zip
-
-"""# Create Embeddings
-
-## Clean
-"""
-
-
-import tiktoken
-from openai.embeddings_utils import distances_from_embeddings, cosine_similarity
-
-# Load the cl100k_base tokenizer which is designed to work with the ada-002 model
-tokenizer = tiktoken.get_encoding("cl100k_base")
-
-df = pd.read_csv(RESULTS_DIR + 'processed/scraped.csv', index_col=0)
-df.columns = ['title', 'text']
-
-# Tokenize the text and save the number of tokens to a new column
-df['n_tokens'] = df.text.apply(lambda x: len(tokenizer.encode(x)))
-
-# Visualize the distribution of the number of tokens per row using a histogram
-df.n_tokens.hist()
-
-max_tokens = 500
-
-# Function to split the text into chunks of a maximum number of tokens
-def split_into_many(text, max_tokens = max_tokens):
-
-    # Split the text into sentences
-    sentences = text.split('. ')
-
-    # Get the number of tokens for each sentence
-    n_tokens = [len(tokenizer.encode(" " + sentence)) for sentence in sentences]
-
-    chunks = []
-    tokens_so_far = 0
-    chunk = []
-
-    # Loop through the sentences and tokens joined together in a tuple
-    for sentence, token in zip(sentences, n_tokens):
-
-        # If the number of tokens so far plus the number of tokens in the current sentence is greater
-        # than the max number of tokens, then add the chunk to the list of chunks and reset
-        # the chunk and tokens so far
-        if tokens_so_far + token > max_tokens:
-            chunks.append(". ".join(chunk) + ".")
-            chunk = []
-            tokens_so_far = 0
-
-        # If the number of tokens in the current sentence is greater than the max number of
-        # tokens, go to the next sentence
-        if token > max_tokens:
-            continue
-
-        # Otherwise, add the sentence to the chunk and add the number of tokens to the total
-        chunk.append(sentence)
-        tokens_so_far += token + 1
-
-    # Add the last chunk to the list of chunks
-    if chunk:
-        chunks.append(". ".join(chunk) + ".")
-
-    return chunks
-
-def shorten(df):
-    shortened = []
-
-    # Loop through the dataframe
-    for row in df.iterrows():
-
-        # If the text is None, go to the next row
-        if row[1]['text'] is None:
-            continue
-
-        # If the number of tokens is greater than the max number of tokens, split the text into chunks
-        if row[1]['n_tokens'] > max_tokens:
-            shortened += split_into_many(row[1]['text'])
-
-        # Otherwise, add the text to the list of shortened texts
-        else:
-            shortened.append( row[1]['text'] )
-
-    new_df = pd.DataFrame(shortened, columns = ['text'])
-    new_df['n_tokens'] = new_df.text.apply(lambda x: len(tokenizer.encode(x)))
-    return new_df
-
-df = shorten(df)
-df.n_tokens.hist()
-
-"""## Create embeds"""
-
-
-
-import openai
-from dotenv import load_dotenv
-load_dotenv()
-
-SECRET_IN_ENV = False
-
-import os
-SECRET_TOKEN = os.getenv("SECRET_TOKEN")
-
-
-def load_api_key():
-    with open("secret.txt", "r") as f:
-        return f.read()
-
-if SECRET_IN_ENV:
-    SECRET_TOKEN = os.getenv("SECRET_TOKEN")
-else:
-    SECRET_TOKEN = load_api_key()
-
-openai.api_key = SECRET_TOKEN
-
-# Note that you may run into rate limit issues depending on how many files you try to embed
-# Please check rate limit guide to learn more on how to handle this: https://platform.openai.com/docs/guides/rate-limits
-
-df['embeddings'] = df.text.apply(lambda x: openai.Embedding.create(input=x, engine='text-embedding-ada-002')['data'][0]['embedding'])
-df.to_csv('processed/embeddings.csv')
-df.head()
-
-"""# QnA"""
-
-from ast import literal_eval
-
-df = pd.read_csv('processed/embeddings.csv', index_col=0)
-df['embeddings'] = df['embeddings'].apply(literal_eval).apply(np.array)
-
-
-def create_context(
-    question, df, max_len=1800, size="ada"
-):
-    """
-    Create a context for a question by finding the most similar context from the dataframe
-    """
-
-    # Get the embeddings for the question
-    q_embeddings = openai.Embedding.create(input=question, engine='text-embedding-ada-002')['data'][0]['embedding']
-
-    # Get the distances from the embeddings
-    df['distances'] = distances_from_embeddings(q_embeddings, df['embeddings'].values, distance_metric='cosine')
-
-
-    returns = []
-    cur_len = 0
-
-    # Sort by distance and add the text to the context until the context is too long
-    for i, row in df.sort_values('distances', ascending=True).iterrows():
-
-        # Add the length of the text to the current length
-        cur_len += row['n_tokens'] + 4
-
-        # If the context is too long, break
-        if cur_len > max_len:
-            break
-
-        # Else add it to the text that is being returned
-        returns.append(row["text"])
-
-    # Return the context
-    return "\n\n###\n\n".join(returns)
-
-def answer_question(
-    df,
-    model="text-davinci-003",
-    question="Am I allowed to publish model outputs to Twitter, without a human review?",
-    max_len=1800,
-    size="ada",
-    debug=False,
-    max_tokens=150,
-    stop_sequence=None
-):
-    """
-    Answer a question based on the most similar context from the dataframe texts
-    """
-    context = create_context(
-        question,
-        df,
-        max_len=max_len,
-        size=size,
-    )
-    # If debug, print the raw model response
-    if debug:
-        print("Context:\n" + context)
-        print("\n\n")
-
-    try:
-        # Create a completions using the questin and context
-        response = openai.Completion.create(
-            prompt=f"Answer the question based on the context below, and if the question can't be answered based on the context, say \"I don't know\"\n\nContext: {context}\n\n---\n\nQuestion: {question}\nAnswer:",
-            temperature=0,
-            max_tokens=max_tokens,
-            top_p=1,
-            frequency_penalty=0,
-            presence_penalty=0,
-            stop=stop_sequence,
-            model=model,
-        )
-        return response["choices"][0]["text"].strip()
-    except Exception as e:
-        print(e)
-        return ""
-
-print(answer_question(df, question="What day is it?", debug=False))
-
-print(answer_question(df, question="What is our newest embeddings model?"))
-
diff --git a/spaces/Illumotion/Koboldcpp/convert-gptneox-hf-to-gguf.py b/spaces/Illumotion/Koboldcpp/convert-gptneox-hf-to-gguf.py
deleted file mode 100644
index 782410e44f2d1d92d832c699d749f830e0334434..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/convert-gptneox-hf-to-gguf.py
+++ /dev/null
@@ -1,251 +0,0 @@
-#!/usr/bin/env python3
-# HF gptneox--> gguf conversion
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import struct
-import sys
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import torch
-from transformers import AutoTokenizer  # type: ignore[import]
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
-import gguf
-
-# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
-
-
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a significant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    return dict(zip(bs, (chr(n) for n in cs)))
-
-
-def count_model_parts(dir_model: Path) -> int:
-    num_parts = 0
-    for filename in os.listdir(dir_model):
-        if filename.startswith("pytorch_model-"):
-            num_parts += 1
-
-    if num_parts > 0:
-        print("gguf: found " + str(num_parts) + " model parts")
-    return num_parts
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file")
-    parser.add_argument(
-        "--vocab-only", action="store_true",
-        help="extract only the vocab",
-    )
-    parser.add_argument(
-        "--outfile", type=Path,
-        help="path to write to; default: based on input",
-    )
-    parser.add_argument(
-        "model", type=Path,
-        help="directory containing model file, or model file itself (*.bin)",
-    )
-    parser.add_argument(
-        "ftype", type=int, choices=[0, 1], default=1, nargs='?',
-        help="output format - use 0 for float32, 1 for float16",
-    )
-    return parser.parse_args()
-
-args = parse_args()
-
-dir_model = args.model
-ftype = args.ftype
-if not dir_model.is_dir():
-    print(f'Error: {args.model} is not a directory', file = sys.stderr)
-    sys.exit(1)
-
-# possible tensor data types
-#   ftype == 0 -> float32
-#   ftype == 1 -> float16
-
-# map from ftype to string
-ftype_str = ["f32", "f16"]
-
-if args.outfile is not None:
-    fname_out = args.outfile
-else:
-    # output in the same directory as the model by default
-    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
-
-print("gguf: loading model "+dir_model.name)
-
-with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-    hparams = json.load(f)
-
-if hparams["architectures"][0] != "GPTNeoXForCausalLM":
-    print("Model architecture not supported: " + hparams["architectures"][0])
-
-    sys.exit()
-
-# get number of model parts
-num_parts = count_model_parts(dir_model)
-
-ARCH=gguf.MODEL_ARCH.GPTNEOX
-gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
-
-print("gguf: get model metadata")
-
-block_count = hparams["num_hidden_layers"]
-
-gguf_writer.add_name(dir_model.name)
-gguf_writer.add_context_length(hparams["max_position_embeddings"])
-gguf_writer.add_embedding_length(hparams["hidden_size"])
-gguf_writer.add_block_count(block_count)
-gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-gguf_writer.add_rope_dimension_count(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])))
-gguf_writer.add_head_count(hparams["num_attention_heads"])
-gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
-gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
-
-# TOKENIZATION
-
-print("gguf: get tokenizer metadata")
-
-tokens: list[bytearray] = []
-
-tokenizer_json_file = dir_model / 'tokenizer.json'
-if not tokenizer_json_file.is_file():
-    print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
-    sys.exit(1)
-
-# gpt2 tokenizer
-gguf_writer.add_tokenizer_model("gpt2")
-
-with open(tokenizer_json_file, "r", encoding="utf-8") as f:
-    tokenizer_json = json.load(f)
-
-print("gguf: get gpt2 tokenizer vocab")
-
-vocab_size = len(tokenizer_json["model"]["vocab"])
-
-# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
-tokenizer = AutoTokenizer.from_pretrained(dir_model)
-
-reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
-byte_encoder = bytes_to_unicode()
-byte_decoder = {v: k for k, v in byte_encoder.items()}
-
-for i in range(vocab_size):
-    if i in reverse_vocab:
-        try:
-            text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
-        except KeyError:
-            text = bytearray()
-            for c in reverse_vocab[i]:
-                if ord(c) < 256:  # single byte character
-                    text.append(byte_decoder[ord(c)])
-                else:  # multibyte special token character
-                    text.extend(c.encode('utf-8'))
-    else:
-        print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
-        pad_token = f"[PAD{i}]".encode("utf8")
-        text = bytearray(pad_token)
-
-    tokens.append(text)
-
-gguf_writer.add_token_list(tokens)
-
-special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
-special_vocab.add_to_gguf(gguf_writer)
-
-# TENSORS
-
-tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
-
-# tensor info
-print("gguf: get tensor metadata")
-
-if num_parts == 0:
-    part_names = iter(("pytorch_model.bin",))
-else:
-    part_names = (
-        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
-    )
-
-for part_name in part_names:
-    if args.vocab_only:
-        break
-    print("gguf: loading model part '" + part_name + "'")
-    model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
-
-    for name in model_part.keys():
-        data = model_part[name]
-
-        # we don't need these
-        if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
-            continue
-
-        old_dtype = data.dtype
-
-        # convert any unsupported data types to float32
-        if data.dtype != torch.float16 and data.dtype != torch.float32:
-            data = data.to(torch.float32)
-
-        data = data.squeeze().numpy()
-
-        # map tensor names
-        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
-        if new_name is None:
-            print("Can not map tensor '" + name + "'")
-            sys.exit()
-
-        n_dims = len(data.shape)
-        data_dtype = data.dtype
-
-        # if f32 desired, convert any float16 to float32
-        if ftype == 0 and data_dtype == np.float16:
-            data = data.astype(np.float32)
-
-        # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-        if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-            data = data.astype(np.float32)
-
-        # if f16 desired, convert any float32 2-dim weight tensors to float16
-        if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-            data = data.astype(np.float16)
-
-        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
-
-        gguf_writer.add_tensor(new_name, data)
-
-
-print("gguf: write header")
-gguf_writer.write_header_to_file()
-print("gguf: write metadata")
-gguf_writer.write_kv_data_to_file()
-if not args.vocab_only:
-    print("gguf: write tensors")
-    gguf_writer.write_tensors_to_file()
-
-gguf_writer.close()
-
-print(f"gguf: model successfully exported to '{fname_out}'")
-print("")
diff --git a/spaces/Jaehan/Text-Summarization-1/README.md b/spaces/Jaehan/Text-Summarization-1/README.md
deleted file mode 100644
index b9d05b4ac248fcb83ca0f5064d9bd670151c113b..0000000000000000000000000000000000000000
--- a/spaces/Jaehan/Text-Summarization-1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Summary 1
-emoji: 📉
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kevin676/Real-Time-Voice-Cloning/synthesizer/utils/plot.py b/spaces/Kevin676/Real-Time-Voice-Cloning/synthesizer/utils/plot.py
deleted file mode 100644
index f47d2713d4daa6cf387b37970fd879548abc8d88..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Real-Time-Voice-Cloning/synthesizer/utils/plot.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def split_title_line(title_text, max_words=5):
-	"""
-	A function that splits any string based on specific character
-	(returning it with the string), with maximum number of words on it
-	"""
-	seq = title_text.split()
-	return "\n".join([" ".join(seq[i:i + max_words]) for i in range(0, len(seq), max_words)])
-
-def plot_alignment(alignment, path, title=None, split_title=False, max_len=None):
-	if max_len is not None:
-		alignment = alignment[:, :max_len]
-
-	fig = plt.figure(figsize=(8, 6))
-	ax = fig.add_subplot(111)
-
-	im = ax.imshow(
-		alignment,
-		aspect="auto",
-		origin="lower",
-		interpolation="none")
-	fig.colorbar(im, ax=ax)
-	xlabel = "Decoder timestep"
-
-	if split_title:
-		title = split_title_line(title)
-
-	plt.xlabel(xlabel)
-	plt.title(title)
-	plt.ylabel("Encoder timestep")
-	plt.tight_layout()
-	plt.savefig(path, format="png")
-	plt.close()
-
-
-def plot_spectrogram(pred_spectrogram, path, title=None, split_title=False, target_spectrogram=None, max_len=None, auto_aspect=False):
-	if max_len is not None:
-		target_spectrogram = target_spectrogram[:max_len]
-		pred_spectrogram = pred_spectrogram[:max_len]
-
-	if split_title:
-		title = split_title_line(title)
-
-	fig = plt.figure(figsize=(10, 8))
-	# Set common labels
-	fig.text(0.5, 0.18, title, horizontalalignment="center", fontsize=16)
-
-	#target spectrogram subplot
-	if target_spectrogram is not None:
-		ax1 = fig.add_subplot(311)
-		ax2 = fig.add_subplot(312)
-
-		if auto_aspect:
-			im = ax1.imshow(np.rot90(target_spectrogram), aspect="auto", interpolation="none")
-		else:
-			im = ax1.imshow(np.rot90(target_spectrogram), interpolation="none")
-		ax1.set_title("Target Mel-Spectrogram")
-		fig.colorbar(mappable=im, shrink=0.65, orientation="horizontal", ax=ax1)
-		ax2.set_title("Predicted Mel-Spectrogram")
-	else:
-		ax2 = fig.add_subplot(211)
-
-	if auto_aspect:
-		im = ax2.imshow(np.rot90(pred_spectrogram), aspect="auto", interpolation="none")
-	else:
-		im = ax2.imshow(np.rot90(pred_spectrogram), interpolation="none")
-	fig.colorbar(mappable=im, shrink=0.65, orientation="horizontal", ax=ax2)
-
-	plt.tight_layout()
-	plt.savefig(path, format="png")
-	plt.close()
diff --git a/spaces/Kororinpa/Amadeus_Project/train.py b/spaces/Kororinpa/Amadeus_Project/train.py
deleted file mode 100644
index ef1cf02759017bbf972aef36369231e1d6ea85b6..0000000000000000000000000000000000000000
--- a/spaces/Kororinpa/Amadeus_Project/train.py
+++ /dev/null
@@ -1,295 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-
-import librosa
-import logging
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-import commons
-import utils
-from data_utils import (
-  TextAudioLoader,
-  TextAudioCollate,
-  DistributedBucketSampler
-)
-from models import (
-  SynthesizerTrn,
-  MultiPeriodDiscriminator,
-)
-from losses import (
-  generator_loss,
-  discriminator_loss,
-  feature_loss,
-  kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-
-torch.backends.cudnn.benchmark = True
-global_step = 0
-
-
-def main():
-  """Assume Single Node Multi GPUs Training Only"""
-  assert torch.cuda.is_available(), "CPU training is not allowed."
-
-  n_gpus = torch.cuda.device_count()
-  os.environ['MASTER_ADDR'] = 'localhost'
-  os.environ['MASTER_PORT'] = '80000'
-
-  hps = utils.get_hparams()
-  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-  global global_step
-  if rank == 0:
-    logger = utils.get_logger(hps.model_dir)
-    logger.info(hps)
-    utils.check_git_hash(hps.model_dir)
-    writer = SummaryWriter(log_dir=hps.model_dir)
-    writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-  dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
-  torch.manual_seed(hps.train.seed)
-  torch.cuda.set_device(rank)
-
-  train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
-  train_sampler = DistributedBucketSampler(
-      train_dataset,
-      hps.train.batch_size,
-      [32,300,400,500,600,700,800,900,1000],
-      num_replicas=n_gpus,
-      rank=rank,
-      shuffle=True)
-  collate_fn = TextAudioCollate()
-  train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
-      collate_fn=collate_fn, batch_sampler=train_sampler)
-  if rank == 0:
-    eval_dataset = TextAudioLoader(hps.data.validation_files, hps.data)
-    eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
-        batch_size=hps.train.batch_size, pin_memory=True,
-        drop_last=False, collate_fn=collate_fn)
-
-  net_g = SynthesizerTrn(
-      len(symbols),
-      hps.data.filter_length // 2 + 1,
-      hps.train.segment_size // hps.data.hop_length,
-      **hps.model).cuda(rank)
-  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-  optim_g = torch.optim.AdamW(
-      net_g.parameters(), 
-      hps.train.learning_rate, 
-      betas=hps.train.betas, 
-      eps=hps.train.eps)
-  optim_d = torch.optim.AdamW(
-      net_d.parameters(),
-      hps.train.learning_rate, 
-      betas=hps.train.betas, 
-      eps=hps.train.eps)
-  net_g = DDP(net_g, device_ids=[rank],find_unused_parameters = True)
-  net_d = DDP(net_d, device_ids=[rank],find_unused_parameters = True)
-
-  try:
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
-    global_step = (epoch_str - 1) * len(train_loader)
-  except:
-    epoch_str = 1
-    global_step = 0
-
-  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-
-  scaler = GradScaler(enabled=hps.train.fp16_run)
-
-  for epoch in range(epoch_str, hps.train.epochs + 1):
-    if rank==0:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-    else:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
-    scheduler_g.step()
-    scheduler_d.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-  net_g, net_d = nets
-  optim_g, optim_d = optims
-  scheduler_g, scheduler_d = schedulers
-  train_loader, eval_loader = loaders
-  if writers is not None:
-    writer, writer_eval = writers
-
-  train_loader.batch_sampler.set_epoch(epoch)
-  global global_step
-
-  net_g.train()
-  net_d.train()
-  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(train_loader):
-    x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-    spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-    y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-
-    with autocast(enabled=hps.train.fp16_run):
-      y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
-      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths)
-
-      mel = spec_to_mel_torch(
-          spec, 
-          hps.data.filter_length, 
-          hps.data.n_mel_channels, 
-          hps.data.sampling_rate,
-          hps.data.mel_fmin, 
-          hps.data.mel_fmax)
-      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-      y_hat_mel = mel_spectrogram_torch(
-          y_hat.squeeze(1), 
-          hps.data.filter_length, 
-          hps.data.n_mel_channels, 
-          hps.data.sampling_rate, 
-          hps.data.hop_length, 
-          hps.data.win_length, 
-          hps.data.mel_fmin, 
-          hps.data.mel_fmax
-      )
-
-      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice 
-
-      # Discriminator
-      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-      with autocast(enabled=False):
-        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-        loss_disc_all = loss_disc
-    optim_d.zero_grad()
-    scaler.scale(loss_disc_all).backward()
-    scaler.unscale_(optim_d)
-    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-    scaler.step(optim_d)
-
-    with autocast(enabled=hps.train.fp16_run):
-      # Generator
-      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-      with autocast(enabled=False):
-        loss_dur = torch.sum(l_length.float())
-        loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-        loss_fm = feature_loss(fmap_r, fmap_g)
-        loss_gen, losses_gen = generator_loss(y_d_hat_g)
-        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-    optim_g.zero_grad()
-    scaler.scale(loss_gen_all).backward()
-    scaler.unscale_(optim_g)
-    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-    scaler.step(optim_g)
-    scaler.update()
-
-    if rank==0:
-      if global_step % hps.train.log_interval == 0:
-        lr = optim_g.param_groups[0]['lr']
-        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-        logger.info('Train Epoch: {} [{:.0f}%]'.format(
-          epoch,
-          100. * batch_idx / len(train_loader)))
-        logger.info([x.item() for x in losses] + [global_step, lr])
-        
-        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-
-        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-        image_dict = { 
-            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), 
-            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-            "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
-        }
-        utils.summarize(
-          writer=writer,
-          global_step=global_step, 
-          images=image_dict,
-          scalars=scalar_dict)
-
-      if global_step % hps.train.eval_interval == 0:
-        evaluate(hps, net_g, eval_loader, writer_eval)
-        utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-        utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-    global_step += 1
-  
-  if rank == 0:
-    logger.info('====> Epoch: {}'.format(epoch))
-
- 
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    with torch.no_grad():
-      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(eval_loader):
-        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
-        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
-        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
-
-        # remove else
-        x = x[:1]
-        x_lengths = x_lengths[:1]
-        spec = spec[:1]
-        spec_lengths = spec_lengths[:1]
-        y = y[:1]
-        y_lengths = y_lengths[:1]
-        break
-      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, max_len=1000)
-      y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
-
-      mel = spec_to_mel_torch(
-        spec, 
-        hps.data.filter_length, 
-        hps.data.n_mel_channels, 
-        hps.data.sampling_rate,
-        hps.data.mel_fmin, 
-        hps.data.mel_fmax)
-      y_hat_mel = mel_spectrogram_torch(
-        y_hat.squeeze(1).float(),
-        hps.data.filter_length,
-        hps.data.n_mel_channels,
-        hps.data.sampling_rate,
-        hps.data.hop_length,
-        hps.data.win_length,
-        hps.data.mel_fmin,
-        hps.data.mel_fmax
-      )
-    image_dict = {
-      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-    }
-    audio_dict = {
-      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
-    }
-    if global_step == 0:
-      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-      audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
-
-    utils.summarize(
-      writer=writer_eval,
-      global_step=global_step, 
-      images=image_dict,
-      audios=audio_dict,
-      audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-                           
-if __name__ == "__main__":
-  main()
diff --git a/spaces/Lewislou/Lewislou-cell-seg-sribd/app.py b/spaces/Lewislou/Lewislou-cell-seg-sribd/app.py
deleted file mode 100644
index a608d6602c5f1021332766c371e3240671281e85..0000000000000000000000000000000000000000
--- a/spaces/Lewislou/Lewislou-cell-seg-sribd/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import gradio as gr
-from skimage import io, segmentation, morphology, measure, exposure
-from sribd_cellseg_models import MultiStreamCellSegModel,ModelConfig
-import numpy as np
-import tifffile as tif
-import requests
-import torch
-from PIL import Image
-from overlay import visualize_instances_map
-import cv2
-
-
-def normalize_channel(img, lower=1, upper=99):
-    non_zero_vals = img[np.nonzero(img)]
-    percentiles = np.percentile(non_zero_vals, [lower, upper])
-    if percentiles[1] - percentiles[0] > 0.001:
-        img_norm = exposure.rescale_intensity(img, in_range=(percentiles[0], percentiles[1]), out_range='uint8')
-    else:
-        img_norm = img
-    return img_norm.astype(np.uint8)
-
-def predict(img_name, model=None, device=None, reduce_labels=True):
-    if img_name.endswith('.tif') or img_name.endswith('.tiff'):
-        img_data = tif.imread(img_name)
-    else:
-        img_data = io.imread(img_name)
-	# normalize image data
-    if len(img_data.shape) == 2:
-        img_data = np.repeat(np.expand_dims(img_data, axis=-1), 3, axis=-1)
-    elif len(img_data.shape) == 3 and img_data.shape[-1] > 3:
-        img_data = img_data[:,:, :3]
-    else:
-        pass
-    pre_img_data = np.zeros(img_data.shape, dtype=np.uint8)
-    for i in range(3):
-        img_channel_i = img_data[:,:,i]
-        if len(img_channel_i[np.nonzero(img_channel_i)])>0:
-            pre_img_data[:,:,i] = normalize_channel(img_channel_i, lower=1, upper=99)
-
-    my_model = MultiStreamCellSegModel.from_pretrained("Lewislou/cellseg_sribd")
-    checkpoints = torch.load('model.pt',map_location=torch.device('cpu'))
-    my_model.__init__(ModelConfig())
-    my_model.load_checkpoints(checkpoints)
-    with torch.no_grad():
-        output = my_model(pre_img_data)
-        print(output.shape)
-    overlay = visualize_instances_map(pre_img_data,output)
-    print(pre_img_data.shape,overlay.shape)
-#cv2.imwrite('prediction.png', cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))
-    return pre_img_data,overlay
-gr.Interface(
-    predict,
-    inputs=[gr.components.Image(label="Upload Input Image", type="filepath"),
-            gr.components.Textbox(label='Model Name', value='sribd_med', max_lines=1)],
-    outputs=[gr.Image(label="Processed Image"),
-             gr.Image(label="Pred Image"),
-             ],
-    title="Cell Segmentation Results",
-).launch()
\ No newline at end of file
diff --git a/spaces/LinkSoul/Chinese-Llama-2-7b/USE_POLICY.md b/spaces/LinkSoul/Chinese-Llama-2-7b/USE_POLICY.md
deleted file mode 100644
index abbcc199b2d1e4feb5d7e40c0bd67e1b0ce29e97..0000000000000000000000000000000000000000
--- a/spaces/LinkSoul/Chinese-Llama-2-7b/USE_POLICY.md
+++ /dev/null
@@ -1,50 +0,0 @@
-# Llama 2 Acceptable Use Policy
-
-Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
-
-## Prohibited Uses
-We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
-
-1. Violate the law or others’ rights, including to:
-    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
-        1. Violence or terrorism 
-        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
-        3. Human trafficking, exploitation, and sexual violence
-        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
-        5. Sexual solicitation
-        6. Any other criminal activity
-    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
-    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
-    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
-    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
-    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
-    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 
-
-
-
-2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
-    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
-    2. Guns and illegal weapons (including weapon development)
-    3. Illegal drugs and regulated/controlled substances
-    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
-    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
-    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
-
-
-
-3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
-    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
-    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
-    3. Generating, promoting, or further distributing spam
-    4. Impersonating another individual without consent, authorization, or legal right
-    5. Representing that the use of Llama 2 or outputs are human-generated
-    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
-4. Fail to appropriately disclose to end users any known dangers of your AI system 
-
-Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
-
-* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
-* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
-* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
-* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
-
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_pipelines/seg_pipeline.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_pipelines/seg_pipeline.py
deleted file mode 100644
index 378474dfb5341ec93e73bb61047c43ba72d5e127..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_pipelines/seg_pipeline.py
+++ /dev/null
@@ -1,66 +0,0 @@
-img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-gt_label_convertor = dict(
-    type='SegConvertor', dict_type='DICT36', with_unknown=True, lower=True)
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='RandomPaddingOCR',
-        max_ratio=[0.15, 0.2, 0.15, 0.2],
-        box_type='char_quads'),
-    dict(type='OpencvToPil'),
-    dict(
-        type='RandomRotateImageBox',
-        min_angle=-17,
-        max_angle=17,
-        box_type='char_quads'),
-    dict(type='PilToOpencv'),
-    dict(
-        type='ResizeOCR',
-        height=64,
-        min_width=64,
-        max_width=512,
-        keep_aspect_ratio=True),
-    dict(
-        type='OCRSegTargets',
-        label_convertor=gt_label_convertor,
-        box_type='char_quads'),
-    dict(type='RandomRotateTextDet', rotate_ratio=0.5, max_angle=15),
-    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
-    dict(type='ToTensorOCR'),
-    dict(type='FancyPCA'),
-    dict(type='NormalizeOCR', **img_norm_cfg),
-    dict(
-        type='CustomFormatBundle',
-        keys=['gt_kernels'],
-        visualize=dict(flag=False, boundary_key=None),
-        call_super=False),
-    dict(
-        type='Collect',
-        keys=['img', 'gt_kernels'],
-        meta_keys=['filename', 'ori_shape', 'resize_shape'])
-]
-
-test_img_norm_cfg = dict(
-    mean=[x * 255 for x in img_norm_cfg['mean']],
-    std=[x * 255 for x in img_norm_cfg['std']])
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='ResizeOCR',
-        height=64,
-        min_width=64,
-        max_width=None,
-        keep_aspect_ratio=True),
-    dict(type='Normalize', **test_img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'filename', 'resize_shape', 'img_norm_cfg', 'ori_filename',
-            'img_shape', 'ori_shape'
-        ])
-]
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_sgd_100k_iters.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_sgd_100k_iters.py
deleted file mode 100644
index df2a3300f057145757b5164ec062b58e9d2f96c6..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_sgd_100k_iters.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-7, by_epoch=False)
-# running settings
-runner = dict(type='IterBasedRunner', max_iters=100000)
-checkpoint_config = dict(interval=10000)
diff --git a/spaces/MSLAB/PaperGPT/src/edit.py b/spaces/MSLAB/PaperGPT/src/edit.py
deleted file mode 100644
index 07477ceebc8e0d0f7c1a2143b8e9fbd97d447b48..0000000000000000000000000000000000000000
--- a/spaces/MSLAB/PaperGPT/src/edit.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import logging
-import tiktoken
-import gradio as gr
-from langchain.text_splitter import CharacterTextSplitter
-from utils import fetch_chat
-from typing import List
-
-
-class Editor():
-    
-    def __init__(self, model: str = "gpt-3.5-turbo"):
-        self.encoder = tiktoken.encoding_for_model(model)
-        self.model = model
-        with open("./sample/sample_abstract.tex", "r") as f:
-            self.sample_content = f.read()
-            
-    def split_chunk(self, text, chunk_size: int = 2000) -> List[str]:
-        text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
-            chunk_size=100, chunk_overlap=0
-        )
-        text_list = text_splitter.split_text(text)
-        return text_list
-            
-    def generate(self, text: str, openai_key: str):
-        
-        logging.info("start editing")
-        
-        try:
-            prompt = f"""
-            I am a computer science student.
-            I am writing my research paper.
-            You are my editor.
-            Your goal is to improve my paper quality at your best.
-            Please edit the following paragraph and return the modified paragraph.
-            If the paragraph is written in latex, return the modified paragraph in latex.
-
-            ```
-            {text}
-            ```
-            """
-            return fetch_chat(prompt, openai_key, model=self.model)
-        except Exception as e:
-            raise gr.Error(str(e))
\ No newline at end of file
diff --git a/spaces/MacYang/Diamond-Sutra/ingest.py b/spaces/MacYang/Diamond-Sutra/ingest.py
deleted file mode 100644
index 4dc66b602e0559a0dabee70c76fe1e47e01cdd3e..0000000000000000000000000000000000000000
--- a/spaces/MacYang/Diamond-Sutra/ingest.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""ingeste the jinggang book and save it to a vector store"""
-from typing import List
-import logging
-import pickle
-import os.path
-import re
-from langchain.document_loaders import PDFMinerLoader
-from langchain.docstore.document import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores.faiss import FAISS
-
-BOOK_PATH = "jinggang.pdf"
-VECTOR_STORE_PATH = "jinggang_embeddings.pkl"
-
-logging.basicConfig(level=logging.INFO)
-
-def _load_book(book_path: str) -> List[Document]:
-    loader = PDFMinerLoader(book_path)
-    docs = loader.load()
-    logging.info("document loaded")
-    return docs
-
-def _split_docs(docs: List[Document]) -> List[Document]:
-    separators=["。\n\n", "」\n\n", "\n\n", "\n", ""]
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10,
-                                                   separators=separators)
-    docs = text_splitter.split_documents(docs)
-    _rm_redundant_newline(docs)
-    logging.info("document splitted to chunks")
-    return docs
-
-def _rm_redundant_newline(docs: List[Document]):
-    #del "\n\n", "\n" "\x0c"(page breaker), but keep "。\n\n"
-    pattern = r"(?<!。)\n\n|(?<!\n)\n(?!\n)|\x0c"
-    for doc in docs:
-        doc.page_content = re.sub(pattern, "", doc.page_content)
-
-def _embed_doc(docs: List[Document]):
-    embeddings = OpenAIEmbeddings()
-    vectorstore = FAISS.from_documents(docs, embeddings)
-    logging.info("embedding created")
-    with open(VECTOR_STORE_PATH, "wb") as f:
-        pickle.dump(vectorstore, f)
-    logging.info("embedding saved to vector store at %s", VECTOR_STORE_PATH)
-
-def ingest_book(book_path: str, vector_store_path: str):
-    """ingest the book and save it to a vector store"""
-    if os.path.exists(vector_store_path):
-        logging.info("vector store found, no need to reingest, just load it")
-        return
-    _embed_doc(_split_docs(_load_book(book_path)))
-
-ingest_book(BOOK_PATH, VECTOR_STORE_PATH)
diff --git a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/preprocess.py b/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/preprocess.py
deleted file mode 100644
index aaedbf076c30114b3ac6c27dfb42fd54ac81a71c..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/preprocess.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import argparse
-import text
-from utils import load_filepaths_and_text
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--out_extension", default="cleaned")
-  parser.add_argument("--text_index", default=1, type=int)
-  parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
-  parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
-
-  args = parser.parse_args()
-    
-
-  for filelist in args.filelists:
-    print("START:", filelist)
-    filepaths_and_text = load_filepaths_and_text(filelist)
-    for i in range(len(filepaths_and_text)):
-      original_text = filepaths_and_text[i][args.text_index]
-      cleaned_text = text._clean_text(original_text, args.text_cleaners)
-      filepaths_and_text[i][args.text_index] = cleaned_text
-
-    new_filelist = filelist + "." + args.out_extension
-    with open(new_filelist, "w", encoding="utf-8") as f:
-      f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/interactive_utils.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/interactive_utils.py
deleted file mode 100644
index 9961f63aab4f59323454f34d76241a743190198f..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/interactive_utils.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# Modifed from https://github.com/seoungwugoh/ivs-demo
-
-import numpy as np
-
-import torch
-import torch.nn.functional as F
-from util.palette import davis_palette
-from dataset.range_transform import im_normalization
-
-def image_to_torch(frame: np.ndarray, device='cuda'):
-    # frame: H*W*3 numpy array
-    frame = frame.transpose(2, 0, 1)
-    frame = torch.from_numpy(frame).float().to(device)/255
-    frame_norm = im_normalization(frame)
-    return frame_norm, frame
-
-def torch_prob_to_numpy_mask(prob):
-    mask = torch.argmax(prob, dim=0)
-    mask = mask.cpu().numpy().astype(np.uint8)
-    return mask
-
-def index_numpy_to_one_hot_torch(mask, num_classes):
-    mask = torch.from_numpy(mask).long()
-    return F.one_hot(mask, num_classes=num_classes).permute(2, 0, 1).float()
-
-"""
-Some constants fro visualization
-"""
-color_map_np = np.frombuffer(davis_palette, dtype=np.uint8).reshape(-1, 3).copy()
-# scales for better visualization
-color_map_np = (color_map_np.astype(np.float32)*1.5).clip(0, 255).astype(np.uint8)
-color_map = color_map_np.tolist()
-if torch.cuda.is_available():
-    color_map_torch = torch.from_numpy(color_map_np).cuda() / 255
-
-grayscale_weights = np.array([[0.3,0.59,0.11]]).astype(np.float32)
-if torch.cuda.is_available():
-    grayscale_weights_torch = torch.from_numpy(grayscale_weights).cuda().unsqueeze(0)
-
-def get_visualization(mode, image, mask, layer, target_object):
-    if mode == 'fade':
-        return overlay_davis(image, mask, fade=True)
-    elif mode == 'davis':
-        return overlay_davis(image, mask)
-    elif mode == 'light':
-        return overlay_davis(image, mask, 0.9)
-    elif mode == 'popup':
-        return overlay_popup(image, mask, target_object)
-    elif mode == 'layered':
-        if layer is None:
-            print('Layer file not given. Defaulting to DAVIS.')
-            return overlay_davis(image, mask)
-        else:
-            return overlay_layer(image, mask, layer, target_object)
-    else:
-        raise NotImplementedError
-
-def get_visualization_torch(mode, image, prob, layer, target_object):
-    if mode == 'fade':
-        return overlay_davis_torch(image, prob, fade=True)
-    elif mode == 'davis':
-        return overlay_davis_torch(image, prob)
-    elif mode == 'light':
-        return overlay_davis_torch(image, prob, 0.9)
-    elif mode == 'popup':
-        return overlay_popup_torch(image, prob, target_object)
-    elif mode == 'layered':
-        if layer is None:
-            print('Layer file not given. Defaulting to DAVIS.')
-            return overlay_davis_torch(image, prob)
-        else:
-            return overlay_layer_torch(image, prob, layer, target_object)
-    else:
-        raise NotImplementedError
-
-def overlay_davis(image, mask, alpha=0.5, fade=False):
-    """ Overlay segmentation on top of RGB image. from davis official"""
-    im_overlay = image.copy()
-
-    colored_mask = color_map_np[mask]
-    foreground = image*alpha + (1-alpha)*colored_mask
-    binary_mask = (mask > 0)
-    # Compose image
-    im_overlay[binary_mask] = foreground[binary_mask]
-    if fade:
-        im_overlay[~binary_mask] = im_overlay[~binary_mask] * 0.6
-    return im_overlay.astype(image.dtype)
-
-def overlay_popup(image, mask, target_object):
-    # Keep foreground colored. Convert background to grayscale.
-    im_overlay = image.copy()
-
-    binary_mask = ~(np.isin(mask, target_object))
-    colored_region = (im_overlay[binary_mask]*grayscale_weights).sum(-1, keepdims=-1)
-    im_overlay[binary_mask] = colored_region
-    return im_overlay.astype(image.dtype)
-
-def overlay_layer(image, mask, layer, target_object):
-    # insert a layer between foreground and background
-    # The CPU version is less accurate because we are using the hard mask
-    # The GPU version has softer edges as it uses soft probabilities
-    obj_mask = (np.isin(mask, target_object)).astype(np.float32)
-    layer_alpha = layer[:, :, 3].astype(np.float32) / 255
-    layer_rgb = layer[:, :, :3]
-    background_alpha = np.maximum(obj_mask, layer_alpha)[:,:,np.newaxis]
-    obj_mask = obj_mask[:,:,np.newaxis]
-    im_overlay = (image*(1-background_alpha) + layer_rgb*(1-obj_mask) + image*obj_mask).clip(0, 255)
-    return im_overlay.astype(image.dtype)
-
-def overlay_davis_torch(image, mask, alpha=0.5, fade=False):
-    """ Overlay segmentation on top of RGB image. from davis official"""
-    # Changes the image in-place to avoid copying
-    image = image.permute(1, 2, 0)
-    im_overlay = image
-    mask = torch.argmax(mask, dim=0)
-
-    colored_mask = color_map_torch[mask]
-    foreground = image*alpha + (1-alpha)*colored_mask
-    binary_mask = (mask > 0)
-    # Compose image
-    im_overlay[binary_mask] = foreground[binary_mask]
-    if fade:
-        im_overlay[~binary_mask] = im_overlay[~binary_mask] * 0.6
-
-    im_overlay = (im_overlay*255).cpu().numpy()
-    im_overlay = im_overlay.astype(np.uint8)
-
-    return im_overlay
-
-def overlay_popup_torch(image, mask, target_object):
-    # Keep foreground colored. Convert background to grayscale.
-    image = image.permute(1, 2, 0)
-    
-    if len(target_object) == 0:
-        obj_mask = torch.zeros_like(mask[0]).unsqueeze(2)
-    else:
-        # I should not need to convert this to numpy.
-        # uUsing list works most of the time but consistently fails
-        # if I include first object -> exclude it -> include it again.
-        # I check everywhere and it makes absolutely no sense.
-        # I am blaming this on PyTorch and calling it a day
-        obj_mask = mask[np.array(target_object,dtype=np.int32)].sum(0).unsqueeze(2)
-    gray_image = (image*grayscale_weights_torch).sum(-1, keepdim=True)
-    im_overlay = obj_mask*image + (1-obj_mask)*gray_image
-
-    im_overlay = (im_overlay*255).cpu().numpy()
-    im_overlay = im_overlay.astype(np.uint8)
-
-    return im_overlay
-
-def overlay_layer_torch(image, mask, layer, target_object):
-    # insert a layer between foreground and background
-    # The CPU version is less accurate because we are using the hard mask
-    # The GPU version has softer edges as it uses soft probabilities
-    image = image.permute(1, 2, 0)
-
-    if len(target_object) == 0:
-        obj_mask = torch.zeros_like(mask[0])
-    else:
-        # I should not need to convert this to numpy.
-        # uUsing list works most of the time but consistently fails
-        # if I include first object -> exclude it -> include it again.
-        # I check everywhere and it makes absolutely no sense.
-        # I am blaming this on PyTorch and calling it a day
-        obj_mask = mask[np.array(target_object,dtype=np.int32)].sum(0)
-    layer_alpha = layer[:, :, 3]
-    layer_rgb = layer[:, :, :3]
-    background_alpha = torch.maximum(obj_mask, layer_alpha).unsqueeze(2)
-    obj_mask = obj_mask.unsqueeze(2)
-    im_overlay = (image*(1-background_alpha) + layer_rgb*(1-obj_mask) + image*obj_mask).clip(0, 1)
-
-    im_overlay = (im_overlay*255).cpu().numpy()
-    im_overlay = im_overlay.astype(np.uint8)
-
-    return im_overlay
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/checkpoint.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/checkpoint.py
deleted file mode 100644
index b29ca320679164432f446adad893e33fb2b4b29e..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/checkpoint.py
+++ /dev/null
@@ -1,707 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import io
-import os
-import os.path as osp
-import pkgutil
-import re
-import time
-import warnings
-from collections import OrderedDict
-from importlib import import_module
-from tempfile import TemporaryDirectory
-
-import torch
-import torchvision
-from torch.optim import Optimizer
-from torch.utils import model_zoo
-
-import annotator.uniformer.mmcv as mmcv
-from ..fileio import FileClient
-from ..fileio import load as load_file
-from ..parallel import is_module_wrapper
-from ..utils import mkdir_or_exist
-from .dist_utils import get_dist_info
-
-ENV_MMCV_HOME = 'MMCV_HOME'
-ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
-DEFAULT_CACHE_DIR = '~/.cache'
-
-
-def _get_mmcv_home():
-    mmcv_home = os.path.expanduser(
-        os.getenv(
-            ENV_MMCV_HOME,
-            os.path.join(
-                os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
-
-    mkdir_or_exist(mmcv_home)
-    return mmcv_home
-
-
-def load_state_dict(module, state_dict, strict=False, logger=None):
-    """Load state_dict to a module.
-
-    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
-    Default value for ``strict`` is set to ``False`` and the message for
-    param mismatch will be shown even if strict is False.
-
-    Args:
-        module (Module): Module that receives the state_dict.
-        state_dict (OrderedDict): Weights.
-        strict (bool): whether to strictly enforce that the keys
-            in :attr:`state_dict` match the keys returned by this module's
-            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
-        logger (:obj:`logging.Logger`, optional): Logger to log the error
-            message. If not specified, print function will be used.
-    """
-    unexpected_keys = []
-    all_missing_keys = []
-    err_msg = []
-
-    metadata = getattr(state_dict, '_metadata', None)
-    state_dict = state_dict.copy()
-    if metadata is not None:
-        state_dict._metadata = metadata
-
-    # use _load_from_state_dict to enable checkpoint version control
-    def load(module, prefix=''):
-        # recursively check parallel module in case that the model has a
-        # complicated structure, e.g., nn.Module(nn.Module(DDP))
-        if is_module_wrapper(module):
-            module = module.module
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
-        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
-                                     all_missing_keys, unexpected_keys,
-                                     err_msg)
-        for name, child in module._modules.items():
-            if child is not None:
-                load(child, prefix + name + '.')
-
-    load(module)
-    load = None  # break load->load reference cycle
-
-    # ignore "num_batches_tracked" of BN layers
-    missing_keys = [
-        key for key in all_missing_keys if 'num_batches_tracked' not in key
-    ]
-
-    if unexpected_keys:
-        err_msg.append('unexpected key in source '
-                       f'state_dict: {", ".join(unexpected_keys)}\n')
-    if missing_keys:
-        err_msg.append(
-            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
-
-    rank, _ = get_dist_info()
-    if len(err_msg) > 0 and rank == 0:
-        err_msg.insert(
-            0, 'The model and loaded state dict do not match exactly\n')
-        err_msg = '\n'.join(err_msg)
-        if strict:
-            raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
-        else:
-            print(err_msg)
-
-
-def get_torchvision_models():
-    model_urls = dict()
-    for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
-        if ispkg:
-            continue
-        _zoo = import_module(f'torchvision.models.{name}')
-        if hasattr(_zoo, 'model_urls'):
-            _urls = getattr(_zoo, 'model_urls')
-            model_urls.update(_urls)
-    return model_urls
-
-
-def get_external_models():
-    mmcv_home = _get_mmcv_home()
-    default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
-    default_urls = load_file(default_json_path)
-    assert isinstance(default_urls, dict)
-    external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
-    if osp.exists(external_json_path):
-        external_urls = load_file(external_json_path)
-        assert isinstance(external_urls, dict)
-        default_urls.update(external_urls)
-
-    return default_urls
-
-
-def get_mmcls_models():
-    mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
-    mmcls_urls = load_file(mmcls_json_path)
-
-    return mmcls_urls
-
-
-def get_deprecated_model_names():
-    deprecate_json_path = osp.join(mmcv.__path__[0],
-                                   'model_zoo/deprecated.json')
-    deprecate_urls = load_file(deprecate_json_path)
-    assert isinstance(deprecate_urls, dict)
-
-    return deprecate_urls
-
-
-def _process_mmcls_checkpoint(checkpoint):
-    state_dict = checkpoint['state_dict']
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k.startswith('backbone.'):
-            new_state_dict[k[9:]] = v
-    new_checkpoint = dict(state_dict=new_state_dict)
-
-    return new_checkpoint
-
-
-class CheckpointLoader:
-    """A general checkpoint loader to manage all schemes."""
-
-    _schemes = {}
-
-    @classmethod
-    def _register_scheme(cls, prefixes, loader, force=False):
-        if isinstance(prefixes, str):
-            prefixes = [prefixes]
-        else:
-            assert isinstance(prefixes, (list, tuple))
-        for prefix in prefixes:
-            if (prefix not in cls._schemes) or force:
-                cls._schemes[prefix] = loader
-            else:
-                raise KeyError(
-                    f'{prefix} is already registered as a loader backend, '
-                    'add "force=True" if you want to override it')
-        # sort, longer prefixes take priority
-        cls._schemes = OrderedDict(
-            sorted(cls._schemes.items(), key=lambda t: t[0], reverse=True))
-
-    @classmethod
-    def register_scheme(cls, prefixes, loader=None, force=False):
-        """Register a loader to CheckpointLoader.
-
-        This method can be used as a normal class method or a decorator.
-
-        Args:
-            prefixes (str or list[str] or tuple[str]):
-            The prefix of the registered loader.
-            loader (function, optional): The loader function to be registered.
-                When this method is used as a decorator, loader is None.
-                Defaults to None.
-            force (bool, optional): Whether to override the loader
-                if the prefix has already been registered. Defaults to False.
-        """
-
-        if loader is not None:
-            cls._register_scheme(prefixes, loader, force=force)
-            return
-
-        def _register(loader_cls):
-            cls._register_scheme(prefixes, loader_cls, force=force)
-            return loader_cls
-
-        return _register
-
-    @classmethod
-    def _get_checkpoint_loader(cls, path):
-        """Finds a loader that supports the given path. Falls back to the local
-        loader if no other loader is found.
-
-        Args:
-            path (str): checkpoint path
-
-        Returns:
-            loader (function): checkpoint loader
-        """
-
-        for p in cls._schemes:
-            if path.startswith(p):
-                return cls._schemes[p]
-
-    @classmethod
-    def load_checkpoint(cls, filename, map_location=None, logger=None):
-        """load checkpoint through URL scheme path.
-
-        Args:
-            filename (str): checkpoint file name with given prefix
-            map_location (str, optional): Same as :func:`torch.load`.
-                Default: None
-            logger (:mod:`logging.Logger`, optional): The logger for message.
-                Default: None
-
-        Returns:
-            dict or OrderedDict: The loaded checkpoint.
-        """
-
-        checkpoint_loader = cls._get_checkpoint_loader(filename)
-        class_name = checkpoint_loader.__name__
-        mmcv.print_log(
-            f'load checkpoint from {class_name[10:]} path: {filename}', logger)
-        return checkpoint_loader(filename, map_location)
-
-
-@CheckpointLoader.register_scheme(prefixes='')
-def load_from_local(filename, map_location):
-    """load checkpoint by local file path.
-
-    Args:
-        filename (str): local checkpoint file path
-        map_location (str, optional): Same as :func:`torch.load`.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    if not osp.isfile(filename):
-        raise IOError(f'{filename} is not a checkpoint file')
-    checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes=('http://', 'https://'))
-def load_from_http(filename, map_location=None, model_dir=None):
-    """load checkpoint through HTTP or HTTPS scheme path. In distributed
-    setting, this function only download checkpoint at local rank 0.
-
-    Args:
-        filename (str): checkpoint file path with modelzoo or
-            torchvision prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-        model_dir (string, optional): directory in which to save the object,
-            Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        checkpoint = model_zoo.load_url(
-            filename, model_dir=model_dir, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            checkpoint = model_zoo.load_url(
-                filename, model_dir=model_dir, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes='pavi://')
-def load_from_pavi(filename, map_location=None):
-    """load checkpoint through the file path prefixed with pavi. In distributed
-    setting, this function download ckpt at all ranks to different temporary
-    directories.
-
-    Args:
-        filename (str): checkpoint file path with pavi prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-          Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    assert filename.startswith('pavi://'), \
-        f'Expected filename startswith `pavi://`, but get {filename}'
-    model_path = filename[7:]
-
-    try:
-        from pavi import modelcloud
-    except ImportError:
-        raise ImportError(
-            'Please install pavi to load checkpoint from modelcloud.')
-
-    model = modelcloud.get(model_path)
-    with TemporaryDirectory() as tmp_dir:
-        downloaded_file = osp.join(tmp_dir, model.name)
-        model.download(downloaded_file)
-        checkpoint = torch.load(downloaded_file, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes='s3://')
-def load_from_ceph(filename, map_location=None, backend='petrel'):
-    """load checkpoint through the file path prefixed with s3.  In distributed
-    setting, this function download ckpt at all ranks to different temporary
-    directories.
-
-    Args:
-        filename (str): checkpoint file path with s3 prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-        backend (str, optional): The storage backend type. Options are 'ceph',
-            'petrel'. Default: 'petrel'.
-
-    .. warning::
-        :class:`mmcv.fileio.file_client.CephBackend` will be deprecated,
-        please use :class:`mmcv.fileio.file_client.PetrelBackend` instead.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    allowed_backends = ['ceph', 'petrel']
-    if backend not in allowed_backends:
-        raise ValueError(f'Load from Backend {backend} is not supported.')
-
-    if backend == 'ceph':
-        warnings.warn(
-            'CephBackend will be deprecated, please use PetrelBackend instead')
-
-    # CephClient and PetrelBackend have the same prefix 's3://' and the latter
-    # will be chosen as default. If PetrelBackend can not be instantiated
-    # successfully, the CephClient will be chosen.
-    try:
-        file_client = FileClient(backend=backend)
-    except ImportError:
-        allowed_backends.remove(backend)
-        file_client = FileClient(backend=allowed_backends[0])
-
-    with io.BytesIO(file_client.get(filename)) as buffer:
-        checkpoint = torch.load(buffer, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes=('modelzoo://', 'torchvision://'))
-def load_from_torchvision(filename, map_location=None):
-    """load checkpoint through the file path prefixed with modelzoo or
-    torchvision.
-
-    Args:
-        filename (str): checkpoint file path with modelzoo or
-            torchvision prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    model_urls = get_torchvision_models()
-    if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
-                      'use "torchvision://" instead')
-        model_name = filename[11:]
-    else:
-        model_name = filename[14:]
-    return load_from_http(model_urls[model_name], map_location=map_location)
-
-
-@CheckpointLoader.register_scheme(prefixes=('open-mmlab://', 'openmmlab://'))
-def load_from_openmmlab(filename, map_location=None):
-    """load checkpoint through the file path prefixed with open-mmlab or
-    openmmlab.
-
-    Args:
-        filename (str): checkpoint file path with open-mmlab or
-        openmmlab prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-          Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    model_urls = get_external_models()
-    prefix_str = 'open-mmlab://'
-    if filename.startswith(prefix_str):
-        model_name = filename[13:]
-    else:
-        model_name = filename[12:]
-        prefix_str = 'openmmlab://'
-
-    deprecated_urls = get_deprecated_model_names()
-    if model_name in deprecated_urls:
-        warnings.warn(f'{prefix_str}{model_name} is deprecated in favor '
-                      f'of {prefix_str}{deprecated_urls[model_name]}')
-        model_name = deprecated_urls[model_name]
-    model_url = model_urls[model_name]
-    # check if is url
-    if model_url.startswith(('http://', 'https://')):
-        checkpoint = load_from_http(model_url, map_location=map_location)
-    else:
-        filename = osp.join(_get_mmcv_home(), model_url)
-        if not osp.isfile(filename):
-            raise IOError(f'{filename} is not a checkpoint file')
-        checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes='mmcls://')
-def load_from_mmcls(filename, map_location=None):
-    """load checkpoint through the file path prefixed with mmcls.
-
-    Args:
-        filename (str): checkpoint file path with mmcls prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    model_urls = get_mmcls_models()
-    model_name = filename[8:]
-    checkpoint = load_from_http(
-        model_urls[model_name], map_location=map_location)
-    checkpoint = _process_mmcls_checkpoint(checkpoint)
-    return checkpoint
-
-
-def _load_checkpoint(filename, map_location=None, logger=None):
-    """Load checkpoint from somewhere (modelzoo, file, url).
-
-    Args:
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str, optional): Same as :func:`torch.load`.
-           Default: None.
-        logger (:mod:`logging.Logger`, optional): The logger for error message.
-           Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint. It can be either an
-           OrderedDict storing model weights or a dict containing other
-           information, which depends on the checkpoint.
-    """
-    return CheckpointLoader.load_checkpoint(filename, map_location, logger)
-
-
-def _load_checkpoint_with_prefix(prefix, filename, map_location=None):
-    """Load partial pretrained model with specific prefix.
-
-    Args:
-        prefix (str): The prefix of sub-module.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str | None): Same as :func:`torch.load`. Default: None.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    checkpoint = _load_checkpoint(filename, map_location=map_location)
-
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    else:
-        state_dict = checkpoint
-    if not prefix.endswith('.'):
-        prefix += '.'
-    prefix_len = len(prefix)
-
-    state_dict = {
-        k[prefix_len:]: v
-        for k, v in state_dict.items() if k.startswith(prefix)
-    }
-
-    assert state_dict, f'{prefix} is not in the pretrained model'
-    return state_dict
-
-
-def load_checkpoint(model,
-                    filename,
-                    map_location=None,
-                    strict=False,
-                    logger=None,
-                    revise_keys=[(r'^module\.', '')]):
-    """Load checkpoint from a file or URI.
-
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-        revise_keys (list): A list of customized keywords to modify the
-            state_dict in checkpoint. Each item is a (pattern, replacement)
-            pair of the regular expression operations. Default: strip
-            the prefix 'module.' by [(r'^module\\.', '')].
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = _load_checkpoint(filename, map_location, logger)
-    # OrderedDict is a subclass of dict
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    # get state_dict from checkpoint
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    else:
-        state_dict = checkpoint
-
-    # strip prefix of state_dict
-    metadata = getattr(state_dict, '_metadata', OrderedDict())
-    for p, r in revise_keys:
-        state_dict = OrderedDict(
-            {re.sub(p, r, k): v
-             for k, v in state_dict.items()})
-    # Keep metadata in state_dict
-    state_dict._metadata = metadata
-
-    # load state_dict
-    load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-
-
-def weights_to_cpu(state_dict):
-    """Copy a model state_dict to cpu.
-
-    Args:
-        state_dict (OrderedDict): Model weights on GPU.
-
-    Returns:
-        OrderedDict: Model weights on GPU.
-    """
-    state_dict_cpu = OrderedDict()
-    for key, val in state_dict.items():
-        state_dict_cpu[key] = val.cpu()
-    # Keep metadata in state_dict
-    state_dict_cpu._metadata = getattr(state_dict, '_metadata', OrderedDict())
-    return state_dict_cpu
-
-
-def _save_to_state_dict(module, destination, prefix, keep_vars):
-    """Saves module state to `destination` dictionary.
-
-    This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (dict): A dict where state will be stored.
-        prefix (str): The prefix for parameters and buffers used in this
-            module.
-    """
-    for name, param in module._parameters.items():
-        if param is not None:
-            destination[prefix + name] = param if keep_vars else param.detach()
-    for name, buf in module._buffers.items():
-        # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d
-        if buf is not None:
-            destination[prefix + name] = buf if keep_vars else buf.detach()
-
-
-def get_state_dict(module, destination=None, prefix='', keep_vars=False):
-    """Returns a dictionary containing a whole state of the module.
-
-    Both parameters and persistent buffers (e.g. running averages) are
-    included. Keys are corresponding parameter and buffer names.
-
-    This method is modified from :meth:`torch.nn.Module.state_dict` to
-    recursively check parallel module in case that the model has a complicated
-    structure, e.g., nn.Module(nn.Module(DDP)).
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (OrderedDict): Returned dict for the state of the
-            module.
-        prefix (str): Prefix of the key.
-        keep_vars (bool): Whether to keep the variable property of the
-            parameters. Default: False.
-
-    Returns:
-        dict: A dictionary containing a whole state of the module.
-    """
-    # recursively check parallel module in case that the model has a
-    # complicated structure, e.g., nn.Module(nn.Module(DDP))
-    if is_module_wrapper(module):
-        module = module.module
-
-    # below is the same as torch.nn.Module.state_dict()
-    if destination is None:
-        destination = OrderedDict()
-        destination._metadata = OrderedDict()
-    destination._metadata[prefix[:-1]] = local_metadata = dict(
-        version=module._version)
-    _save_to_state_dict(module, destination, prefix, keep_vars)
-    for name, child in module._modules.items():
-        if child is not None:
-            get_state_dict(
-                child, destination, prefix + name + '.', keep_vars=keep_vars)
-    for hook in module._state_dict_hooks.values():
-        hook_result = hook(module, destination, prefix, local_metadata)
-        if hook_result is not None:
-            destination = hook_result
-    return destination
-
-
-def save_checkpoint(model,
-                    filename,
-                    optimizer=None,
-                    meta=None,
-                    file_client_args=None):
-    """Save checkpoint to file.
-
-    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
-    ``optimizer``. By default ``meta`` will contain version and time info.
-
-    Args:
-        model (Module): Module whose params are to be saved.
-        filename (str): Checkpoint filename.
-        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
-        meta (dict, optional): Metadata to be saved in checkpoint.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-            `New in version 1.3.16.`
-    """
-    if meta is None:
-        meta = {}
-    elif not isinstance(meta, dict):
-        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
-    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
-
-    if is_module_wrapper(model):
-        model = model.module
-
-    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
-        # save class name to the meta
-        meta.update(CLASSES=model.CLASSES)
-
-    checkpoint = {
-        'meta': meta,
-        'state_dict': weights_to_cpu(get_state_dict(model))
-    }
-    # save optimizer state dict in the checkpoint
-    if isinstance(optimizer, Optimizer):
-        checkpoint['optimizer'] = optimizer.state_dict()
-    elif isinstance(optimizer, dict):
-        checkpoint['optimizer'] = {}
-        for name, optim in optimizer.items():
-            checkpoint['optimizer'][name] = optim.state_dict()
-
-    if filename.startswith('pavi://'):
-        if file_client_args is not None:
-            raise ValueError(
-                'file_client_args should be "None" if filename starts with'
-                f'"pavi://", but got {file_client_args}')
-        try:
-            from pavi import modelcloud
-            from pavi import exception
-        except ImportError:
-            raise ImportError(
-                'Please install pavi to load checkpoint from modelcloud.')
-        model_path = filename[7:]
-        root = modelcloud.Folder()
-        model_dir, model_name = osp.split(model_path)
-        try:
-            model = modelcloud.get(model_dir)
-        except exception.NodeNotFoundError:
-            model = root.create_training_model(model_dir)
-        with TemporaryDirectory() as tmp_dir:
-            checkpoint_file = osp.join(tmp_dir, model_name)
-            with open(checkpoint_file, 'wb') as f:
-                torch.save(checkpoint, f)
-                f.flush()
-            model.create_file(checkpoint_file, name=model_name)
-    else:
-        file_client = FileClient.infer_client(file_client_args, filename)
-        with io.BytesIO() as f:
-            torch.save(checkpoint, f)
-            file_client.put(f.getvalue(), filename)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/base.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/base.py
deleted file mode 100644
index f845256729458ced821762a1b8ef881e17ff9955..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/base.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numbers
-from abc import ABCMeta, abstractmethod
-
-import numpy as np
-import torch
-
-from ..hook import Hook
-
-
-class LoggerHook(Hook):
-    """Base class for logger hooks.
-
-    Args:
-        interval (int): Logging interval (every k iterations).
-        ignore_last (bool): Ignore the log of last iterations in each epoch
-            if less than `interval`.
-        reset_flag (bool): Whether to clear the output buffer after logging.
-        by_epoch (bool): Whether EpochBasedRunner is used.
-    """
-
-    __metaclass__ = ABCMeta
-
-    def __init__(self,
-                 interval=10,
-                 ignore_last=True,
-                 reset_flag=False,
-                 by_epoch=True):
-        self.interval = interval
-        self.ignore_last = ignore_last
-        self.reset_flag = reset_flag
-        self.by_epoch = by_epoch
-
-    @abstractmethod
-    def log(self, runner):
-        pass
-
-    @staticmethod
-    def is_scalar(val, include_np=True, include_torch=True):
-        """Tell the input variable is a scalar or not.
-
-        Args:
-            val: Input variable.
-            include_np (bool): Whether include 0-d np.ndarray as a scalar.
-            include_torch (bool): Whether include 0-d torch.Tensor as a scalar.
-
-        Returns:
-            bool: True or False.
-        """
-        if isinstance(val, numbers.Number):
-            return True
-        elif include_np and isinstance(val, np.ndarray) and val.ndim == 0:
-            return True
-        elif include_torch and isinstance(val, torch.Tensor) and len(val) == 1:
-            return True
-        else:
-            return False
-
-    def get_mode(self, runner):
-        if runner.mode == 'train':
-            if 'time' in runner.log_buffer.output:
-                mode = 'train'
-            else:
-                mode = 'val'
-        elif runner.mode == 'val':
-            mode = 'val'
-        else:
-            raise ValueError(f"runner mode should be 'train' or 'val', "
-                             f'but got {runner.mode}')
-        return mode
-
-    def get_epoch(self, runner):
-        if runner.mode == 'train':
-            epoch = runner.epoch + 1
-        elif runner.mode == 'val':
-            # normal val mode
-            # runner.epoch += 1 has been done before val workflow
-            epoch = runner.epoch
-        else:
-            raise ValueError(f"runner mode should be 'train' or 'val', "
-                             f'but got {runner.mode}')
-        return epoch
-
-    def get_iter(self, runner, inner_iter=False):
-        """Get the current training iteration step."""
-        if self.by_epoch and inner_iter:
-            current_iter = runner.inner_iter + 1
-        else:
-            current_iter = runner.iter + 1
-        return current_iter
-
-    def get_lr_tags(self, runner):
-        tags = {}
-        lrs = runner.current_lr()
-        if isinstance(lrs, dict):
-            for name, value in lrs.items():
-                tags[f'learning_rate/{name}'] = value[0]
-        else:
-            tags['learning_rate'] = lrs[0]
-        return tags
-
-    def get_momentum_tags(self, runner):
-        tags = {}
-        momentums = runner.current_momentum()
-        if isinstance(momentums, dict):
-            for name, value in momentums.items():
-                tags[f'momentum/{name}'] = value[0]
-        else:
-            tags['momentum'] = momentums[0]
-        return tags
-
-    def get_loggable_tags(self,
-                          runner,
-                          allow_scalar=True,
-                          allow_text=False,
-                          add_mode=True,
-                          tags_to_skip=('time', 'data_time')):
-        tags = {}
-        for var, val in runner.log_buffer.output.items():
-            if var in tags_to_skip:
-                continue
-            if self.is_scalar(val) and not allow_scalar:
-                continue
-            if isinstance(val, str) and not allow_text:
-                continue
-            if add_mode:
-                var = f'{self.get_mode(runner)}/{var}'
-            tags[var] = val
-        tags.update(self.get_lr_tags(runner))
-        tags.update(self.get_momentum_tags(runner))
-        return tags
-
-    def before_run(self, runner):
-        for hook in runner.hooks[::-1]:
-            if isinstance(hook, LoggerHook):
-                hook.reset_flag = True
-                break
-
-    def before_epoch(self, runner):
-        runner.log_buffer.clear()  # clear logs of last epoch
-
-    def after_train_iter(self, runner):
-        if self.by_epoch and self.every_n_inner_iters(runner, self.interval):
-            runner.log_buffer.average(self.interval)
-        elif not self.by_epoch and self.every_n_iters(runner, self.interval):
-            runner.log_buffer.average(self.interval)
-        elif self.end_of_epoch(runner) and not self.ignore_last:
-            # not precise but more stable
-            runner.log_buffer.average(self.interval)
-
-        if runner.log_buffer.ready:
-            self.log(runner)
-            if self.reset_flag:
-                runner.log_buffer.clear_output()
-
-    def after_train_epoch(self, runner):
-        if runner.log_buffer.ready:
-            self.log(runner)
-            if self.reset_flag:
-                runner.log_buffer.clear_output()
-
-    def after_val_epoch(self, runner):
-        runner.log_buffer.average()
-        self.log(runner)
-        if self.reset_flag:
-            runner.log_buffer.clear_output()
diff --git a/spaces/MestikonAgency/README/download.sh b/spaces/MestikonAgency/README/download.sh
deleted file mode 100644
index 8625963e02dd580ebe8cd13fbbc2c7fe7e2ff66b..0000000000000000000000000000000000000000
--- a/spaces/MestikonAgency/README/download.sh
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/bin/bash
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
-
-set -e
-
-read -p "Enter the URL from email: " PRESIGNED_URL
-echo ""
-read -p "Enter the list of models to download without spaces (7B,13B,70B,7B-chat,13B-chat,70B-chat), or press Enter for all: " MODEL_SIZE
-TARGET_FOLDER="."             # where all files should end up
-mkdir -p ${TARGET_FOLDER}
-
-if [[ $MODEL_SIZE == "" ]]; then
-    MODEL_SIZE="7B,13B,70B,7B-chat,13B-chat,70B-chat"
-fi
-
-echo "Downloading LICENSE and Acceptable Usage Policy"
-wget --continue ${PRESIGNED_URL/'*'/"LICENSE"} -O ${TARGET_FOLDER}"/LICENSE"
-wget --continue ${PRESIGNED_URL/'*'/"USE_POLICY.md"} -O ${TARGET_FOLDER}"/USE_POLICY.md"
-
-echo "Downloading tokenizer"
-wget --continue ${PRESIGNED_URL/'*'/"tokenizer.model"} -O ${TARGET_FOLDER}"/tokenizer.model"
-wget --continue ${PRESIGNED_URL/'*'/"tokenizer_checklist.chk"} -O ${TARGET_FOLDER}"/tokenizer_checklist.chk"
-CPU_ARCH=$(uname -m)
-  if [ "$CPU_ARCH" = "arm64" ]; then
-    (cd ${TARGET_FOLDER} && md5 tokenizer_checklist.chk)
-  else
-    (cd ${TARGET_FOLDER} && md5sum -c tokenizer_checklist.chk)
-  fi
-
-for m in ${MODEL_SIZE//,/ }
-do
-    if [[ $m == "7B" ]]; then
-        SHARD=0
-        MODEL_PATH="llama-2-7b"
-    elif [[ $m == "7B-chat" ]]; then
-        SHARD=0
-        MODEL_PATH="llama-2-7b-chat"
-    elif [[ $m == "13B" ]]; then
-        SHARD=1
-        MODEL_PATH="llama-2-13b"
-    elif [[ $m == "13B-chat" ]]; then
-        SHARD=1
-        MODEL_PATH="llama-2-13b-chat"
-    elif [[ $m == "70B" ]]; then
-        SHARD=7
-        MODEL_PATH="llama-2-70b"
-    elif [[ $m == "70B-chat" ]]; then
-        SHARD=7
-        MODEL_PATH="llama-2-70b-chat"
-    fi
-
-    echo "Downloading ${MODEL_PATH}"
-    mkdir -p ${TARGET_FOLDER}"/${MODEL_PATH}"
-
-    for s in $(seq -f "0%g" 0 ${SHARD})
-    do
-        wget ${PRESIGNED_URL/'*'/"${MODEL_PATH}/consolidated.${s}.pth"} -O ${TARGET_FOLDER}"/${MODEL_PATH}/consolidated.${s}.pth"
-    done
-
-    wget --continue ${PRESIGNED_URL/'*'/"${MODEL_PATH}/params.json"} -O ${TARGET_FOLDER}"/${MODEL_PATH}/params.json"
-    wget --continue ${PRESIGNED_URL/'*'/"${MODEL_PATH}/checklist.chk"} -O ${TARGET_FOLDER}"/${MODEL_PATH}/checklist.chk"
-    echo "Checking checksums"
-    if [ "$CPU_ARCH" = "arm64" ]; then
-      (cd ${TARGET_FOLDER}"/${MODEL_PATH}" && md5 checklist.chk)
-    else
-      (cd ${TARGET_FOLDER}"/${MODEL_PATH}" && md5sum -c checklist.chk)
-    fi
-done
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/kie/sdmgr/README.md b/spaces/Mountchicken/MAERec-Gradio/configs/kie/sdmgr/README.md
deleted file mode 100644
index 921af5310e46803c937168c6e1c0bdf17a372798..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/kie/sdmgr/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# SDMGR
-
-> [Spatial Dual-Modality Graph Reasoning for Key Information Extraction](https://arxiv.org/abs/2103.14470)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-Key information extraction from document images is of paramount importance in office automation. Conventional template matching based approaches fail to generalize well to document images of unseen templates, and are not robust against text recognition errors. In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images. We model document images as dual-modality graphs, nodes of which encode both the visual and textual features of detected text regions, and edges of which represent the spatial relations between neighboring text regions. The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes. In order to roundly evaluate our proposed method as well as boost the future research, we release a new dataset named WildReceipt, which is collected and annotated tailored for the evaluation of key information extraction from document images of unseen templates in the wild. It contains 25 key information categories, a total of about 69000 text boxes, and is about 2 times larger than the existing public datasets. Extensive experiments validate that all information including visual features, textual features and spatial relations can benefit key information extraction. It has been shown that SDMG-R can effectively extract key information from document images of unseen templates, and obtain new state-of-the-art results on the recent popular benchmark SROIE and our WildReceipt. Our code and dataset will be publicly released.
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/22607038/142580689-18edb4d7-f716-475c-b1c1-e2b934658cee.png"/>
-</div>
-
-## Results and models
-
-### WildReceipt
-
-|                                 Method                                 |     Modality     | Macro F1-Score |                                               Download                                               |
-| :--------------------------------------------------------------------: | :--------------: | :------------: | :--------------------------------------------------------------------------------------------------: |
-|   [sdmgr_unet16](/configs/kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py)   | Visual + Textual |     0.890      | [model](https://download.openmmlab.com/mmocr/kie/sdmgr/sdmgr_unet16_60e_wildreceipt/sdmgr_unet16_60e_wildreceipt_20220825_151648-22419f37.pth) \| [log](https://download.openmmlab.com/mmocr/kie/sdmgr/sdmgr_unet16_60e_wildreceipt/20220825_151648.log) |
-| [sdmgr_novisual](/configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py) |     Textual      |     0.873      | [model](https://download.openmmlab.com/mmocr/kie/sdmgr/sdmgr_novisual_60e_wildreceipt/sdmgr_novisual_60e_wildreceipt_20220831_193317-827649d8.pth) \| [log](https://download.openmmlab.com/mmocr/kie/sdmgr/sdmgr_novisual_60e_wildreceipt/20220831_193317.log) |
-
-### WildReceiptOpenset
-
-|                                Method                                 | Modality | Edge F1-Score | Node Macro F1-Score | Node Micro F1-Score |                                 Download                                 |
-| :-------------------------------------------------------------------: | :------: | :-----------: | :-----------------: | :-----------------: | :----------------------------------------------------------------------: |
-| [sdmgr_novisual_openset](/configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt-openset.py) | Textual  |     0.792     |        0.931        |        0.940        | [model](https://download.openmmlab.com/mmocr/kie/sdmgr/sdmgr_novisual_60e_wildreceipt-openset/sdmgr_novisual_60e_wildreceipt-openset_20220831_200807-dedf15ec.pth) \| [log](https://download.openmmlab.com/mmocr/kie/sdmgr/sdmgr_novisual_60e_wildreceipt-openset/20220831_200807.log) |
-
-## Citation
-
-```bibtex
-@misc{sun2021spatial,
-      title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction},
-      author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang},
-      year={2021},
-      eprint={2103.14470},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
diff --git a/spaces/NATSpeech/DiffSpeech/data_gen/tts/wav_processors/base_processor.py b/spaces/NATSpeech/DiffSpeech/data_gen/tts/wav_processors/base_processor.py
deleted file mode 100644
index e8200dc58a9388ac94a5ec34b8a65f75e380255b..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/data_gen/tts/wav_processors/base_processor.py
+++ /dev/null
@@ -1,25 +0,0 @@
-REGISTERED_WAV_PROCESSORS = {}
-
-
-def register_wav_processors(name):
-    def _f(cls):
-        REGISTERED_WAV_PROCESSORS[name] = cls
-        return cls
-
-    return _f
-
-
-def get_wav_processor_cls(name):
-    return REGISTERED_WAV_PROCESSORS.get(name, None)
-
-
-class BaseWavProcessor:
-    @property
-    def name(self):
-        raise NotImplementedError
-
-    def output_fn(self, input_fn):
-        return f'{input_fn[:-4]}_{self.name}.wav'
-
-    def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args):
-        raise NotImplementedError
diff --git a/spaces/NATSpeech/PortaSpeech/modules/tts/portaspeech/portaspeech.py b/spaces/NATSpeech/PortaSpeech/modules/tts/portaspeech/portaspeech.py
deleted file mode 100644
index 589aa108fdde0ea062b06f2dc3aeac28b364c17b..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/modules/tts/portaspeech/portaspeech.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn import Linear
-
-from modules.commons.conv import ConvBlocks, ConditionalConvBlocks
-from modules.commons.layers import Embedding
-from modules.commons.rel_transformer import RelTransformerEncoder
-from modules.commons.transformer import MultiheadAttention, FFTBlocks
-from modules.tts.commons.align_ops import clip_mel2token_to_multiple, build_word_mask, expand_states, mel2ph_to_mel2word
-from modules.tts.fs import FS_DECODERS, FastSpeech
-from modules.tts.portaspeech.fvae import FVAE
-from utils.commons.meters import Timer
-from utils.nn.seq_utils import group_hidden_by_segs
-
-
-class SinusoidalPosEmb(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        """
-
-        :param x: [B, T]
-        :return: [B, T, H]
-        """
-        device = x.device
-        half_dim = self.dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
-        emb = x[:, :, None] * emb[None, :]
-        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
-        return emb
-
-
-class PortaSpeech(FastSpeech):
-    def __init__(self, ph_dict_size, word_dict_size, hparams, out_dims=None):
-        super().__init__(ph_dict_size, hparams, out_dims)
-        # build linguistic encoder
-        if hparams['use_word_encoder']:
-            self.word_encoder = RelTransformerEncoder(
-                word_dict_size, self.hidden_size, self.hidden_size, self.hidden_size, 2,
-                hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
-        if hparams['dur_level'] == 'word':
-            if hparams['word_encoder_type'] == 'rel_fft':
-                self.ph2word_encoder = RelTransformerEncoder(
-                    0, self.hidden_size, self.hidden_size, self.hidden_size, 2,
-                    hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
-            if hparams['word_encoder_type'] == 'fft':
-                self.ph2word_encoder = FFTBlocks(
-                    self.hidden_size, hparams['word_enc_layers'], 1, num_heads=hparams['num_heads'])
-            self.sin_pos = SinusoidalPosEmb(self.hidden_size)
-            self.enc_pos_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.dec_query_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.dec_res_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.attn = MultiheadAttention(self.hidden_size, 1, encoder_decoder_attention=True, bias=False)
-            self.attn.enable_torch_version = False
-            if hparams['text_encoder_postnet']:
-                self.text_encoder_postnet = ConvBlocks(
-                    self.hidden_size, self.hidden_size, [1] * 3, 5, layers_in_block=2)
-        else:
-            self.sin_pos = SinusoidalPosEmb(self.hidden_size)
-        # build VAE decoder
-        if hparams['use_fvae']:
-            del self.decoder
-            del self.mel_out
-            self.fvae = FVAE(
-                c_in_out=self.out_dims,
-                hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'],
-                kernel_size=hparams['fvae_kernel_size'],
-                enc_n_layers=hparams['fvae_enc_n_layers'],
-                dec_n_layers=hparams['fvae_dec_n_layers'],
-                c_cond=self.hidden_size,
-                use_prior_flow=hparams['use_prior_flow'],
-                flow_hidden=hparams['prior_flow_hidden'],
-                flow_kernel_size=hparams['prior_flow_kernel_size'],
-                flow_n_steps=hparams['prior_flow_n_blocks'],
-                strides=[hparams['fvae_strides']],
-                encoder_type=hparams['fvae_encoder_type'],
-                decoder_type=hparams['fvae_decoder_type'],
-            )
-        else:
-            self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
-            self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
-        if hparams['use_pitch_embed']:
-            self.pitch_embed = Embedding(300, self.hidden_size, 0)
-        if self.hparams['add_word_pos']:
-            self.word_pos_proj = Linear(self.hidden_size, self.hidden_size)
-
-    def build_embedding(self, dictionary, embed_dim):
-        num_embeddings = len(dictionary)
-        emb = Embedding(num_embeddings, embed_dim, self.padding_idx)
-        return emb
-
-    def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None,
-                spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None,
-                global_step=None, *args, **kwargs):
-        ret = {}
-        x, tgt_nonpadding = self.run_text_encoder(
-            txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, ret)
-        style_embed = self.forward_style_embed(spk_embed, spk_id)
-        x = x + style_embed
-        x = x * tgt_nonpadding
-        ret['nonpadding'] = tgt_nonpadding
-        if self.hparams['use_pitch_embed']:
-            x = x + self.pitch_embed(pitch)
-        ret['decoder_inp'] = x
-        ret['mel_out_fvae'] = ret['mel_out'] = self.run_decoder(x, tgt_nonpadding, ret, infer, tgt_mels, global_step)
-        return ret
-
-    def run_text_encoder(self, txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, ret):
-        word2word = torch.arange(word_len)[None, :].to(ph2word.device) + 1  # [B, T_mel, T_word]
-        src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-        ph_encoder_out = self.encoder(txt_tokens) * src_nonpadding
-        if self.hparams['use_word_encoder']:
-            word_encoder_out = self.word_encoder(word_tokens)
-            ph_encoder_out = ph_encoder_out + expand_states(word_encoder_out, ph2word)
-        if self.hparams['dur_level'] == 'word':
-            word_encoder_out = 0
-            h_ph_gb_word = group_hidden_by_segs(ph_encoder_out, ph2word, word_len)[0]
-            word_encoder_out = word_encoder_out + self.ph2word_encoder(h_ph_gb_word)
-            if self.hparams['use_word_encoder']:
-                word_encoder_out = word_encoder_out + self.word_encoder(word_tokens)
-            mel2word = self.forward_dur(ph_encoder_out, mel2word, ret, ph2word=ph2word, word_len=word_len)
-            mel2word = clip_mel2token_to_multiple(mel2word, self.hparams['frames_multiple'])
-            tgt_nonpadding = (mel2word > 0).float()[:, :, None]
-            enc_pos = self.get_pos_embed(word2word, ph2word)  # [B, T_ph, H]
-            dec_pos = self.get_pos_embed(word2word, mel2word)  # [B, T_mel, H]
-            dec_word_mask = build_word_mask(mel2word, ph2word)  # [B, T_mel, T_ph]
-            x, weight = self.attention(ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask)
-            if self.hparams['add_word_pos']:
-                x = x + self.word_pos_proj(dec_pos)
-            ret['attn'] = weight
-        else:
-            mel2ph = self.forward_dur(ph_encoder_out, mel2ph, ret)
-            mel2ph = clip_mel2token_to_multiple(mel2ph, self.hparams['frames_multiple'])
-            mel2word = mel2ph_to_mel2word(mel2ph, ph2word)
-            x = expand_states(ph_encoder_out, mel2ph)
-            if self.hparams['add_word_pos']:
-                dec_pos = self.get_pos_embed(word2word, mel2word)  # [B, T_mel, H]
-                x = x + self.word_pos_proj(dec_pos)
-            tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
-        if self.hparams['use_word_encoder']:
-            x = x + expand_states(word_encoder_out, mel2word)
-        return x, tgt_nonpadding
-
-    def attention(self, ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask):
-        ph_kv = self.enc_pos_proj(torch.cat([ph_encoder_out, enc_pos], -1))
-        word_enc_out_expend = expand_states(word_encoder_out, mel2word)
-        word_enc_out_expend = torch.cat([word_enc_out_expend, dec_pos], -1)
-        if self.hparams['text_encoder_postnet']:
-            word_enc_out_expend = self.dec_res_proj(word_enc_out_expend)
-            word_enc_out_expend = self.text_encoder_postnet(word_enc_out_expend)
-            dec_q = x_res = word_enc_out_expend
-        else:
-            dec_q = self.dec_query_proj(word_enc_out_expend)
-            x_res = self.dec_res_proj(word_enc_out_expend)
-        ph_kv, dec_q = ph_kv.transpose(0, 1), dec_q.transpose(0, 1)
-        x, (weight, _) = self.attn(dec_q, ph_kv, ph_kv, attn_mask=(1 - dec_word_mask) * -1e9)
-        x = x.transpose(0, 1)
-        x = x + x_res
-        return x, weight
-
-    def run_decoder(self, x, tgt_nonpadding, ret, infer, tgt_mels=None, global_step=0):
-        if not self.hparams['use_fvae']:
-            x = self.decoder(x)
-            x = self.mel_out(x)
-            ret['kl'] = 0
-            return x * tgt_nonpadding
-        else:
-            decoder_inp = x
-            x = x.transpose(1, 2)  # [B, H, T]
-            tgt_nonpadding_BHT = tgt_nonpadding.transpose(1, 2)  # [B, H, T]
-            if infer:
-                z = self.fvae(cond=x, infer=True)
-            else:
-                tgt_mels = tgt_mels.transpose(1, 2)  # [B, 80, T]
-                z, ret['kl'], ret['z_p'], ret['m_q'], ret['logs_q'] = self.fvae(
-                    tgt_mels, tgt_nonpadding_BHT, cond=x)
-                if global_step < self.hparams['posterior_start_steps']:
-                    z = torch.randn_like(z)
-            x_recon = self.fvae.decoder(z, nonpadding=tgt_nonpadding_BHT, cond=x).transpose(1, 2)
-            ret['pre_mel_out'] = x_recon
-            return x_recon
-
-    def forward_dur(self, dur_input, mel2word, ret, **kwargs):
-        """
-
-        :param dur_input: [B, T_txt, H]
-        :param mel2ph: [B, T_mel]
-        :param txt_tokens: [B, T_txt]
-        :param ret:
-        :return:
-        """
-        src_padding = dur_input.data.abs().sum(-1) == 0
-        dur_input = dur_input.detach() + self.hparams['predictor_grad'] * (dur_input - dur_input.detach())
-        dur = self.dur_predictor(dur_input, src_padding)
-        if self.hparams['dur_level'] == 'word':
-            word_len = kwargs['word_len']
-            ph2word = kwargs['ph2word']
-            B, T_ph = ph2word.shape
-            dur = torch.zeros([B, word_len.max() + 1]).to(ph2word.device).scatter_add(1, ph2word, dur)
-            dur = dur[:, 1:]
-        ret['dur'] = dur
-        if mel2word is None:
-            mel2word = self.length_regulator(dur).detach()
-        return mel2word
-
-    def get_pos_embed(self, word2word, x2word):
-        x_pos = build_word_mask(word2word, x2word).float()  # [B, T_word, T_ph]
-        x_pos = (x_pos.cumsum(-1) / x_pos.sum(-1).clamp(min=1)[..., None] * x_pos).sum(1)
-        x_pos = self.sin_pos(x_pos.float())  # [B, T_ph, H]
-        return x_pos
-
-    def store_inverse_all(self):
-        def remove_weight_norm(m):
-            try:
-                if hasattr(m, 'store_inverse'):
-                    m.store_inverse()
-                nn.utils.remove_weight_norm(m)
-            except ValueError:  # this module didn't have weight norm
-                return
-
-        self.apply(remove_weight_norm)
diff --git a/spaces/NeuralInternet/Text-Generation_Playground/api-example.py b/spaces/NeuralInternet/Text-Generation_Playground/api-example.py
deleted file mode 100644
index 0306b7ab8a3fa3d6f57d8474ad74d67f13557b6d..0000000000000000000000000000000000000000
--- a/spaces/NeuralInternet/Text-Generation_Playground/api-example.py
+++ /dev/null
@@ -1,59 +0,0 @@
-'''
-
-This is an example on how to use the API for oobabooga/text-generation-webui.
-
-Make sure to start the web UI with the following flags:
-
-python server.py --model MODEL --listen --no-stream
-
-Optionally, you can also add the --share flag to generate a public gradio URL,
-allowing you to use the API remotely.
-
-'''
-import requests
-
-# Server address
-server = "127.0.0.1"
-
-# Generation parameters
-# Reference: https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig
-params = {
-    'max_new_tokens': 200,
-    'do_sample': True,
-    'temperature': 0.5,
-    'top_p': 0.9,
-    'typical_p': 1,
-    'repetition_penalty': 1.05,
-    'top_k': 0,
-    'min_length': 0,
-    'no_repeat_ngram_size': 0,
-    'num_beams': 1,
-    'penalty_alpha': 0,
-    'length_penalty': 1,
-    'early_stopping': False,
-}
-
-# Input prompt
-prompt = "What I would like to say is the following: "
-
-response = requests.post(f"http://{server}:7860/run/textgen", json={
-    "data": [
-        prompt,
-        params['max_new_tokens'],
-        params['do_sample'],
-        params['temperature'],
-        params['top_p'],
-        params['typical_p'],
-        params['repetition_penalty'],
-        params['top_k'],
-        params['min_length'],
-        params['no_repeat_ngram_size'],
-        params['num_beams'],
-        params['penalty_alpha'],
-        params['length_penalty'],
-        params['early_stopping'],
-    ]
-}).json()
-
-reply = response["data"][0]
-print(reply)
diff --git a/spaces/NimaBoscarino/climategan/climategan/deeplab/mobilenet_v3.py b/spaces/NimaBoscarino/climategan/climategan/deeplab/mobilenet_v3.py
deleted file mode 100644
index 1ba08e60a2ab529627c93505b9a2cb81522ab518..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/climategan/deeplab/mobilenet_v3.py
+++ /dev/null
@@ -1,324 +0,0 @@
-"""
-from https://github.com/LikeLy-Journey/SegmenTron/blob/
-4bc605eedde7d680314f63d329277b73f83b1c5f/segmentron/modules/basic.py#L34
-"""
-
-from collections import OrderedDict
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-from climategan.blocks import InterpolateNearest2d
-
-
-class SeparableConv2d(nn.Module):
-    def __init__(
-        self,
-        inplanes,
-        planes,
-        kernel_size=3,
-        stride=1,
-        dilation=1,
-        relu_first=True,
-        bias=False,
-        norm_layer=nn.BatchNorm2d,
-    ):
-        super().__init__()
-        depthwise = nn.Conv2d(
-            inplanes,
-            inplanes,
-            kernel_size,
-            stride=stride,
-            padding=dilation,
-            dilation=dilation,
-            groups=inplanes,
-            bias=bias,
-        )
-        bn_depth = norm_layer(inplanes)
-        pointwise = nn.Conv2d(inplanes, planes, 1, bias=bias)
-        bn_point = norm_layer(planes)
-
-        if relu_first:
-            self.block = nn.Sequential(
-                OrderedDict(
-                    [
-                        ("relu", nn.ReLU()),
-                        ("depthwise", depthwise),
-                        ("bn_depth", bn_depth),
-                        ("pointwise", pointwise),
-                        ("bn_point", bn_point),
-                    ]
-                )
-            )
-        else:
-            self.block = nn.Sequential(
-                OrderedDict(
-                    [
-                        ("depthwise", depthwise),
-                        ("bn_depth", bn_depth),
-                        ("relu1", nn.ReLU(inplace=True)),
-                        ("pointwise", pointwise),
-                        ("bn_point", bn_point),
-                        ("relu2", nn.ReLU(inplace=True)),
-                    ]
-                )
-            )
-
-    def forward(self, x):
-        return self.block(x)
-
-
-class _ConvBNReLU(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        relu6=False,
-        norm_layer=nn.BatchNorm2d,
-    ):
-        super(_ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias=False,
-        )
-        self.bn = norm_layer(out_channels)
-        self.relu = nn.ReLU6(True) if relu6 else nn.ReLU(True)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        return x
-
-
-class _DepthwiseConv(nn.Module):
-    """conv_dw in MobileNet"""
-
-    def __init__(
-        self, in_channels, out_channels, stride, norm_layer=nn.BatchNorm2d, **kwargs
-    ):
-        super(_DepthwiseConv, self).__init__()
-        self.conv = nn.Sequential(
-            _ConvBNReLU(
-                in_channels,
-                in_channels,
-                3,
-                stride,
-                1,
-                groups=in_channels,
-                norm_layer=norm_layer,
-            ),
-            _ConvBNReLU(in_channels, out_channels, 1, norm_layer=norm_layer),
-        )
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class InvertedResidual(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        stride,
-        expand_ratio,
-        dilation=1,
-        norm_layer=nn.BatchNorm2d,
-    ):
-        super(InvertedResidual, self).__init__()
-        assert stride in [1, 2]
-        self.use_res_connect = stride == 1 and in_channels == out_channels
-
-        layers = list()
-        inter_channels = int(round(in_channels * expand_ratio))
-        if expand_ratio != 1:
-            # pw
-            layers.append(
-                _ConvBNReLU(
-                    in_channels, inter_channels, 1, relu6=True, norm_layer=norm_layer
-                )
-            )
-        layers.extend(
-            [
-                # dw
-                _ConvBNReLU(
-                    inter_channels,
-                    inter_channels,
-                    3,
-                    stride,
-                    dilation,
-                    dilation,
-                    groups=inter_channels,
-                    relu6=True,
-                    norm_layer=norm_layer,
-                ),
-                # pw-linear
-                nn.Conv2d(inter_channels, out_channels, 1, bias=False),
-                norm_layer(out_channels),
-            ]
-        )
-        self.conv = nn.Sequential(*layers)
-
-    def forward(self, x):
-        if self.use_res_connect:
-            return x + self.conv(x)
-        else:
-            return self.conv(x)
-
-
-class MobileNetV2(nn.Module):
-    def __init__(self, norm_layer=nn.BatchNorm2d, pretrained_path=None, no_init=False):
-        super(MobileNetV2, self).__init__()
-        output_stride = 16
-        self.multiplier = 1.0
-        if output_stride == 32:
-            dilations = [1, 1]
-        elif output_stride == 16:
-            dilations = [1, 2]
-        elif output_stride == 8:
-            dilations = [2, 4]
-        else:
-            raise NotImplementedError
-        inverted_residual_setting = [
-            # t, c, n, s
-            [1, 16, 1, 1],
-            [6, 24, 2, 2],
-            [6, 32, 3, 2],
-            [6, 64, 4, 2],
-            [6, 96, 3, 1],
-            [6, 160, 3, 2],
-            [6, 320, 1, 1],
-        ]
-        # building first layer
-        input_channels = int(32 * self.multiplier) if self.multiplier > 1.0 else 32
-        # last_channels = int(1280 * multiplier) if multiplier > 1.0 else 1280
-        self.conv1 = _ConvBNReLU(
-            3, input_channels, 3, 2, 1, relu6=True, norm_layer=norm_layer
-        )
-
-        # building inverted residual blocks
-        self.planes = input_channels
-        self.block1 = self._make_layer(
-            InvertedResidual,
-            self.planes,
-            inverted_residual_setting[0:1],
-            norm_layer=norm_layer,
-        )
-        self.block2 = self._make_layer(
-            InvertedResidual,
-            self.planes,
-            inverted_residual_setting[1:2],
-            norm_layer=norm_layer,
-        )
-        self.block3 = self._make_layer(
-            InvertedResidual,
-            self.planes,
-            inverted_residual_setting[2:3],
-            norm_layer=norm_layer,
-        )
-        self.block4 = self._make_layer(
-            InvertedResidual,
-            self.planes,
-            inverted_residual_setting[3:5],
-            dilations[0],
-            norm_layer=norm_layer,
-        )
-        self.block5 = self._make_layer(
-            InvertedResidual,
-            self.planes,
-            inverted_residual_setting[5:],
-            dilations[1],
-            norm_layer=norm_layer,
-        )
-        self.last_inp_channels = self.planes
-
-        self.up2 = InterpolateNearest2d()
-
-        # weight initialization
-        if not no_init:
-            self.pretrained_path = pretrained_path
-            if pretrained_path is not None:
-                self._load_pretrained_model()
-            else:
-                for m in self.modules():
-                    if isinstance(m, nn.Conv2d):
-                        nn.init.kaiming_normal_(m.weight, mode="fan_out")
-                        if m.bias is not None:
-                            nn.init.zeros_(m.bias)
-                    elif isinstance(m, nn.BatchNorm2d):
-                        nn.init.ones_(m.weight)
-                        nn.init.zeros_(m.bias)
-                    elif isinstance(m, nn.Linear):
-                        nn.init.normal_(m.weight, 0, 0.01)
-                        if m.bias is not None:
-                            nn.init.zeros_(m.bias)
-
-    def _make_layer(
-        self,
-        block,
-        planes,
-        inverted_residual_setting,
-        dilation=1,
-        norm_layer=nn.BatchNorm2d,
-    ):
-        features = list()
-        for t, c, n, s in inverted_residual_setting:
-            out_channels = int(c * self.multiplier)
-            stride = s if dilation == 1 else 1
-            features.append(
-                block(planes, out_channels, stride, t, dilation, norm_layer)
-            )
-            planes = out_channels
-            for i in range(n - 1):
-                features.append(
-                    block(planes, out_channels, 1, t, norm_layer=norm_layer)
-                )
-                planes = out_channels
-        self.planes = planes
-        return nn.Sequential(*features)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.block1(x)
-        c1 = self.block2(x)
-        c2 = self.block3(c1)
-        c3 = self.block4(c2)
-        c4 = self.up2(self.block5(c3))
-
-        # x = self.features(x)
-        # x = self.classifier(x.view(x.size(0), x.size(1)))
-        return c4, c1
-
-    def _load_pretrained_model(self):
-        assert self.pretrained_path is not None
-        assert Path(self.pretrained_path).exists()
-
-        pretrain_dict = torch.load(self.pretrained_path)
-        pretrain_dict = {k.replace("encoder.", ""): v for k, v in pretrain_dict.items()}
-        model_dict = {}
-        state_dict = self.state_dict()
-        ignored = []
-        for k, v in pretrain_dict.items():
-            if k in state_dict:
-                model_dict[k] = v
-            else:
-                ignored.append(k)
-        state_dict.update(model_dict)
-        self.load_state_dict(state_dict)
-        self.loaded_pre_trained = True
-        print(
-            "    - Loaded pre-trained MobileNetV2: ignored {}/{} keys".format(
-                len(ignored), len(pretrain_dict)
-            )
-        )
diff --git a/spaces/NiuTaipu/moe-tts-test01/transforms.py b/spaces/NiuTaipu/moe-tts-test01/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/NiuTaipu/moe-tts-test01/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py
deleted file mode 100644
index 9bdd25a8685bb7c7b32e1f02372aaeb26d8ba53a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class PQLinear(nn.Module):
-    """
-    Quantized counterpart of nn.Linear module. Stores the centroid, the assignments
-    and the non-quantized biases. The full weight is re-instantiated at each forward
-    pass.
-
-    Args:
-        - centroids: centroids of size n_centroids x block_size
-        - assignments: assignments of the centroids to the subvectors
-          of size self.out_features x n_blocks
-        - bias: the non-quantized bias
-
-    Remarks:
-        - We refer the reader to the official documentation of the nn.Linear module
-          for the other arguments and the behavior of the module
-        - Performance tests on GPU show that this implementation is 15% slower than
-          the non-quantized nn.Linear module for a standard training loop.
-    """
-
-    def __init__(self, centroids, assignments, bias, in_features, out_features):
-        super(PQLinear, self).__init__()
-        self.block_size = centroids.size(1)
-        self.n_centroids = centroids.size(0)
-        self.in_features = in_features
-        self.out_features = out_features
-        # check compatibility
-        if self.in_features % self.block_size != 0:
-            raise ValueError("Wrong PQ sizes")
-        if len(assignments) % self.out_features != 0:
-            raise ValueError("Wrong PQ sizes")
-        # define parameters
-        self.centroids = nn.Parameter(centroids, requires_grad=True)
-        self.register_buffer("assignments", assignments)
-        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
-        if bias is not None:
-            self.bias = nn.Parameter(bias)
-        else:
-            self.register_parameter("bias", None)
-
-    @property
-    def weight(self):
-        return (
-            self.centroids[self.assignments]
-            .reshape(-1, self.out_features, self.block_size)
-            .permute(1, 0, 2)
-            .flatten(1, 2)
-        )
-
-    def forward(self, x):
-        return F.linear(
-            x,
-            self.weight,
-            self.bias,
-        )
-
-    def extra_repr(self):
-        return f"in_features={self.in_features},\
-                 out_features={self.out_features},\
-                 n_centroids={self.n_centroids},\
-                 block_size={self.block_size},\
-                 bias={self.bias is not None}"
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/num_samples_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/num_samples_dataset.py
deleted file mode 100644
index 99a17495c701d8a05e0268f98bf453905e11d078..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/num_samples_dataset.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from . import FairseqDataset
-
-
-class NumSamplesDataset(FairseqDataset):
-    def __getitem__(self, index):
-        return 1
-
-    def __len__(self):
-        return 0
-
-    def collater(self, samples):
-        return sum(samples)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py
deleted file mode 100644
index 2d78ca98708121261aa365738a65c051b5b40626..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .discriminative_reranking_task import DiscriminativeRerankingNMTTask
-
-
-__all__ = [
-    "DiscriminativeRerankingNMTTask",
-]
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py
deleted file mode 100644
index a1f0d902acf0756580a1f4604feee8fc499a9a63..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import sys
-
-import fairseq
-import soundfile as sf
-import torch
-import torch.nn.functional as F
-
-from feature_utils import get_path_iterator, dump_feature
-
-
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("dump_w2v2_feature")
-
-
-class Wav2Vec2FeatureReader(object):
-    def __init__(self, ckpt_path, layer, max_chunk=1600000):
-        (
-            model,
-            cfg,
-            task,
-        ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
-        self.model = model[0].eval().cuda()
-        self.task = task
-        self.layer = layer  # assume this is 1-based like HuBERT
-        self.max_chunk = max_chunk
-        logger.info(f"TASK CONFIG:\n{self.task.cfg}")
-        logger.info(f" max_chunk = {self.max_chunk}")
-        logger.info(f" model:\n{self.model}")
-
-    def read_audio(self, path, ref_len=None):
-        wav, sr = sf.read(path)
-        assert sr == self.task.cfg.sample_rate, sr
-        if wav.ndim == 2:
-            wav = wav.mean(-1)
-        assert wav.ndim == 1, wav.ndim
-        if ref_len is not None and abs(ref_len - len(wav)) > 160:
-            logging.warning(f"ref {ref_len} != read {len(wav)} ({path})")
-        return wav
-
-    def get_feats(self, path, ref_len=None):
-        x = self.read_audio(path, ref_len)
-        with torch.no_grad():
-            x = torch.from_numpy(x).float().cuda()
-            if self.task.cfg.normalize:
-                x = F.layer_norm(x, x.shape)
-            x = x.view(1, -1)
-
-            feat = []
-            for start in range(0, x.size(1), self.max_chunk):
-                x_chunk = x[:, start: start + self.max_chunk]
-                res = self.model.extract_features(
-                    source=x_chunk,
-                    padding_mask=None,
-                    mask=False,
-                    layer=self.layer - 1,
-                )
-                feat_chunk = res["x"]
-                feat.append(feat_chunk)
-        return torch.cat(feat, 1).squeeze(0)
-
-
-def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk):
-    reader = Wav2Vec2FeatureReader(ckpt_path, layer, max_chunk)
-    generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank)
-    dump_feature(reader, generator, num, split, nshard, rank, feat_dir)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("tsv_dir")
-    parser.add_argument("split")
-    parser.add_argument("ckpt_path")
-    parser.add_argument("layer", type=int)
-    parser.add_argument("nshard", type=int)
-    parser.add_argument("rank", type=int)
-    parser.add_argument("feat_dir")
-    parser.add_argument("--max_chunk", type=int, default=1600000)
-    args = parser.parse_args()
-    logger.info(args)
-
-    main(**vars(args))
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/decoder_config.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/decoder_config.py
deleted file mode 100644
index 659eb94a9b8187a7c126d7b439ac2742f9d72022..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/decoder_config.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-from typing import Optional
-
-from fairseq.dataclass.configs import FairseqDataclass
-from fairseq.dataclass.constants import ChoiceEnum
-from omegaconf import MISSING
-
-
-DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"])
-
-
-@dataclass
-class DecoderConfig(FairseqDataclass):
-    type: DECODER_CHOICES = field(
-        default="viterbi",
-        metadata={"help": "The type of decoder to use"},
-    )
-
-
-@dataclass
-class FlashlightDecoderConfig(FairseqDataclass):
-    nbest: int = field(
-        default=1,
-        metadata={"help": "Number of decodings to return"},
-    )
-    unitlm: bool = field(
-        default=False,
-        metadata={"help": "If set, use unit language model"},
-    )
-    lmpath: str = field(
-        default=MISSING,
-        metadata={"help": "Language model for KenLM decoder"},
-    )
-    lexicon: Optional[str] = field(
-        default=None,
-        metadata={"help": "Lexicon for Flashlight decoder"},
-    )
-    beam: int = field(
-        default=50,
-        metadata={"help": "Number of beams to use for decoding"},
-    )
-    beamthreshold: float = field(
-        default=50.0,
-        metadata={"help": "Threshold for beam search decoding"},
-    )
-    beamsizetoken: Optional[int] = field(
-        default=None, metadata={"help": "Beam size to use"}
-    )
-    wordscore: float = field(
-        default=-1,
-        metadata={"help": "Word score for KenLM decoder"},
-    )
-    unkweight: float = field(
-        default=-math.inf,
-        metadata={"help": "Unknown weight for KenLM decoder"},
-    )
-    silweight: float = field(
-        default=0,
-        metadata={"help": "Silence weight for KenLM decoder"},
-    )
-    lmweight: float = field(
-        default=2,
-        metadata={"help": "Weight for LM while interpolating score"},
-    )
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py
deleted file mode 100644
index 27792ebda842057e33fed3dc53dd9d8a594d0483..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py
+++ /dev/null
@@ -1,637 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from enum import Enum, auto
-import math
-import numpy as np
-from typing import Tuple, List, Optional, Dict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import autograd
-
-from fairseq import checkpoint_utils, utils
-from fairseq.dataclass import FairseqDataclass
-from fairseq.models import BaseFairseqModel, register_model
-from fairseq.modules import (
-    SamePad,
-    TransposeLast,
-)
-
-
-class SegmentationType(Enum):
-    NONE = auto()
-    RANDOM = auto()
-    UNIFORM_RANDOM = auto()
-    UNIFORM_RANDOM_JOIN = auto()
-    JOIN = auto()
-
-
-@dataclass
-class SegmentationConfig(FairseqDataclass):
-    type: SegmentationType = SegmentationType.NONE
-    subsample_rate: float = 0.25
-    mean_pool: bool = True
-    mean_pool_join: bool = False
-    remove_zeros: bool = False
-
-
-@dataclass
-class Wav2vec_UConfig(FairseqDataclass):
-
-    discriminator_kernel: int = 3
-    discriminator_dilation: int = 1
-    discriminator_dim: int = 256
-    discriminator_causal: bool = True
-    discriminator_linear_emb: bool = False
-    discriminator_depth: int = 1
-    discriminator_max_pool: bool = False
-    discriminator_act_after_linear: bool = False
-    discriminator_dropout: float = 0.0
-    discriminator_spectral_norm: bool = False
-    discriminator_weight_norm: bool = False
-
-    generator_kernel: int = 4
-    generator_dilation: int = 1
-    generator_stride: int = 1
-    generator_bias: bool = False
-    generator_dropout: float = 0.0
-
-    blank_weight: float = 0
-    blank_mode: str = "add"
-    blank_is_sil: bool = False
-    no_softmax: bool = False
-
-    smoothness_weight: float = 0.0
-    smoothing: float = 0.0
-    smoothing_one_sided: bool = False
-    gradient_penalty: float = 0.0
-    probabilistic_grad_penalty_slicing: bool = False
-    code_penalty: float = 0.0
-    gumbel: bool = False
-    hard_gumbel: bool = True
-    temp: Tuple[float, float, float] = (2, 0.1, 0.99995)
-    input_dim: int = 128
-
-    segmentation: SegmentationConfig = SegmentationConfig()
-
-
-class Segmenter(nn.Module):
-    cfg: SegmentationConfig
-
-    def __init__(self, cfg: SegmentationConfig):
-        super().__init__()
-        self.cfg = cfg
-        self.subsample_rate = cfg.subsample_rate
-
-    def pre_segment(self, dense_x, dense_padding_mask):
-        return dense_x, dense_padding_mask
-
-    def logit_segment(self, logits, padding_mask):
-        return logits, padding_mask
-
-
-class RandomSegmenter(Segmenter):
-    def pre_segment(self, dense_x, dense_padding_mask):
-        target_num = math.ceil(dense_x.size(1) * self.subsample_rate)
-        ones = torch.ones(dense_x.shape[:-1], device=dense_x.device)
-        indices, _ = ones.multinomial(target_num).sort(dim=-1)
-        indices_ld = indices.unsqueeze(-1).expand(-1, -1, dense_x.size(-1))
-        dense_x = dense_x.gather(1, indices_ld)
-        dense_padding_mask = dense_padding_mask.gather(1, index=indices)
-        return dense_x, dense_padding_mask
-
-
-class UniformRandomSegmenter(Segmenter):
-    def pre_segment(self, dense_x, dense_padding_mask):
-        bsz, tsz, fsz = dense_x.shape
-
-        target_num = math.ceil(tsz * self.subsample_rate)
-
-        rem = tsz % target_num
-
-        if rem > 0:
-            dense_x = F.pad(dense_x, [0, 0, 0, target_num - rem])
-            dense_padding_mask = F.pad(
-                dense_padding_mask, [0, target_num - rem], value=True
-            )
-
-        dense_x = dense_x.view(bsz, target_num, -1, fsz)
-        dense_padding_mask = dense_padding_mask.view(bsz, target_num, -1)
-
-        if self.cfg.mean_pool:
-            dense_x = dense_x.mean(dim=-2)
-            dense_padding_mask = dense_padding_mask.all(dim=-1)
-        else:
-            ones = torch.ones((bsz, dense_x.size(2)), device=dense_x.device)
-            indices = ones.multinomial(1)
-            indices = indices.unsqueeze(-1).expand(-1, target_num, -1)
-            indices_ld = indices.unsqueeze(-1).expand(-1, -1, -1, fsz)
-            dense_x = dense_x.gather(2, indices_ld).reshape(bsz, -1, fsz)
-            dense_padding_mask = dense_padding_mask.gather(2, index=indices).reshape(
-                bsz, -1
-            )
-        return dense_x, dense_padding_mask
-
-
-class JoinSegmenter(Segmenter):
-    def logit_segment(self, logits, padding_mask):
-        preds = logits.argmax(dim=-1)
-
-        if padding_mask.any():
-            preds[padding_mask] = -1  # mark pad
-        uniques = []
-
-        bsz, tsz, csz = logits.shape
-
-        for p in preds:
-            uniques.append(
-                p.cpu().unique_consecutive(return_inverse=True, return_counts=True)
-            )
-
-        new_tsz = max(u[0].numel() for u in uniques)
-        new_logits = logits.new_zeros(bsz, new_tsz, csz)
-        new_pad = padding_mask.new_zeros(bsz, new_tsz)
-
-        for b in range(bsz):
-            u, idx, c = uniques[b]
-            keep = u != -1
-
-            if self.cfg.remove_zeros:
-                keep.logical_and_(u != 0)
-
-            if self.training and not self.cfg.mean_pool_join:
-                u[0] = 0
-                u[1:] = c.cumsum(0)[:-1]
-                m = c > 1
-                r = torch.rand(m.sum())
-                o = (c[m] * r).long()
-                u[m] += o
-                new_logits[b, : u.numel()] = logits[b, u]
-            else:
-                new_logits[b].index_add_(
-                    dim=0, index=idx.to(new_logits.device), source=logits[b]
-                )
-                new_logits[b, : c.numel()] /= c.unsqueeze(-1).to(new_logits.device)
-
-            new_sz = keep.sum()
-            if not keep.all():
-                kept_logits = new_logits[b, : c.numel()][keep]
-                new_logits[b, :new_sz] = kept_logits
-
-            if new_sz < new_tsz:
-                pad = new_tsz - new_sz
-                new_logits[b, -pad:] = 0
-                new_pad[b, -pad:] = True
-
-        return new_logits, new_pad
-
-
-class UniformRandomJoinSegmenter(UniformRandomSegmenter, JoinSegmenter):
-    pass
-
-
-SEGMENT_FACTORY = {
-    SegmentationType.NONE: Segmenter,
-    SegmentationType.RANDOM: RandomSegmenter,
-    SegmentationType.UNIFORM_RANDOM: UniformRandomSegmenter,
-    SegmentationType.UNIFORM_RANDOM_JOIN: UniformRandomJoinSegmenter,
-    SegmentationType.JOIN: JoinSegmenter,
-}
-
-
-class Discriminator(nn.Module):
-    def __init__(self, dim, cfg: Wav2vec_UConfig):
-        super().__init__()
-
-        inner_dim = cfg.discriminator_dim
-        kernel = cfg.discriminator_kernel
-        dilation = cfg.discriminator_dilation
-        self.max_pool = cfg.discriminator_max_pool
-
-        if cfg.discriminator_causal:
-            padding = kernel - 1
-        else:
-            padding = kernel // 2
-
-        def make_conv(in_d, out_d, k, p=0, has_dilation=True):
-            conv = nn.Conv1d(
-                in_d,
-                out_d,
-                kernel_size=k,
-                padding=p,
-                dilation=dilation if has_dilation else 1,
-            )
-            if cfg.discriminator_spectral_norm:
-                conv = nn.utils.spectral_norm(conv)
-            elif cfg.discriminator_weight_norm:
-                conv = nn.utils.weight_norm(conv)
-            return conv
-
-        inner_net = [
-            nn.Sequential(
-                make_conv(inner_dim, inner_dim, kernel, padding),
-                SamePad(kernel_size=kernel, causal=cfg.discriminator_causal),
-                nn.Dropout(cfg.discriminator_dropout),
-                nn.GELU(),
-            )
-            for _ in range(cfg.discriminator_depth - 1)
-        ] + [
-            make_conv(inner_dim, 1, kernel, padding, has_dilation=False),
-            SamePad(kernel_size=kernel, causal=cfg.discriminator_causal),
-        ]
-
-        if cfg.discriminator_linear_emb:
-            emb_net = [make_conv(dim, inner_dim, 1)]
-        else:
-            emb_net = [
-                make_conv(dim, inner_dim, kernel, padding),
-                SamePad(kernel_size=kernel, causal=cfg.discriminator_causal),
-            ]
-
-        if cfg.discriminator_act_after_linear:
-            emb_net.append(nn.GELU())
-
-        self.net = nn.Sequential(
-            *emb_net,
-            nn.Dropout(cfg.discriminator_dropout),
-            *inner_net,
-        )
-
-    def forward(self, x, padding_mask):
-        x = x.transpose(1, 2)  # BTC -> BCT
-        x = self.net(x)
-        x = x.transpose(1, 2)
-        x_sz = x.size(1)
-        if padding_mask is not None and padding_mask.any() and padding_mask.dim() > 1:
-            padding_mask = padding_mask[:, : x.size(1)]
-            x[padding_mask] = float("-inf") if self.max_pool else 0
-            x_sz = x_sz - padding_mask.sum(dim=-1)
-        x = x.squeeze(-1)
-        if self.max_pool:
-            x, _ = x.max(dim=-1)
-        else:
-            x = x.sum(dim=-1)
-            x = x / x_sz
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, input_dim, output_dim, cfg: Wav2vec_UConfig):
-        super().__init__()
-
-        self.cfg = cfg
-        self.output_dim = output_dim
-        self.stride = cfg.generator_stride
-        self.dropout = nn.Dropout(cfg.generator_dropout)
-
-        padding = cfg.generator_kernel // 2
-        self.proj = nn.Sequential(
-            TransposeLast(),
-            nn.Conv1d(
-                input_dim,
-                output_dim,
-                kernel_size=cfg.generator_kernel,
-                stride=cfg.generator_stride,
-                dilation=cfg.generator_dilation,
-                padding=padding,
-                bias=cfg.generator_bias,
-            ),
-            TransposeLast(),
-        )
-
-    def forward(self, dense_x, tokens, dense_padding_mask):
-        dense_x = self.dropout(dense_x)
-
-        dense_x = self.proj(dense_x)
-        if self.stride > 1:
-            dense_padding_mask = dense_padding_mask[:, :: self.stride]
-
-        if dense_padding_mask.size(1) != dense_x.size(1):
-            new_padding = dense_padding_mask.new_zeros(dense_x.shape[:-1])
-            diff = new_padding.size(1) - dense_padding_mask.size(1)
-            assert (
-                diff > 0
-            ), f"{new_padding.shape}, {dense_padding_mask.shape}, {dense_x.shape}, {diff}"
-            if diff > 0:
-                new_padding[:, diff:] = dense_padding_mask
-            else:
-                assert diff < 0
-                new_padding = dense_padding_mask[:, :diff]
-
-            dense_padding_mask = new_padding
-
-        result = {}
-
-        token_x = None
-        if tokens is not None:
-            token_x = dense_x.new_zeros(tokens.numel(), self.output_dim)
-            token_x.scatter_(1, tokens.view(-1, 1).long(), 1)
-            token_x = token_x.view(tokens.shape + (self.output_dim,))
-
-        result["dense_x"] = dense_x
-        result["token_x"] = token_x
-        result["dense_padding_mask"] = dense_padding_mask
-
-        return result
-
-
-@register_model("wav2vec_u", dataclass=Wav2vec_UConfig)
-class Wav2vec_U(BaseFairseqModel):
-    def calc_gradient_penalty(self, real_data, fake_data):
-
-        b_size = min(real_data.size(0), fake_data.size(0))
-        t_size = min(real_data.size(1), fake_data.size(1))
-
-        if self.cfg.probabilistic_grad_penalty_slicing:
-
-            def get_slice(data, dim, target_size):
-
-                size = data.size(dim)
-                diff = size - target_size
-                if diff <= 0:
-                    return data
-
-                start = np.random.randint(0, diff + 1)
-                return data.narrow(dim=dim, start=start, length=target_size)
-
-            real_data = get_slice(real_data, 0, b_size)
-            real_data = get_slice(real_data, 1, t_size)
-            fake_data = get_slice(fake_data, 0, b_size)
-            fake_data = get_slice(fake_data, 1, t_size)
-
-        else:
-            real_data = real_data[:b_size, :t_size]
-            fake_data = fake_data[:b_size, :t_size]
-
-        alpha = torch.rand(real_data.size(0), 1, 1)
-        alpha = alpha.expand(real_data.size())
-        alpha = alpha.to(real_data.device)
-
-        interpolates = alpha * real_data + ((1 - alpha) * fake_data)
-
-        disc_interpolates = self.discriminator(interpolates, None)
-
-        gradients = autograd.grad(
-            outputs=disc_interpolates,
-            inputs=interpolates,
-            grad_outputs=torch.ones(disc_interpolates.size(), device=real_data.device),
-            create_graph=True,
-            retain_graph=True,
-            only_inputs=True,
-        )[0]
-
-        gradient_penalty = (gradients.norm(2, dim=1) - 1) ** 2
-        return gradient_penalty
-
-    def set_num_updates(self, num_updates):
-        super().set_num_updates(num_updates)
-        self.update_num = num_updates
-        self.curr_temp = max(
-            self.max_temp * self.temp_decay ** num_updates, self.min_temp
-        )
-
-    def discrim_step(self, num_updates):
-        return num_updates % 2 == 1
-
-    def get_groups_for_update(self, num_updates):
-        return "discriminator" if self.discrim_step(num_updates) else "generator"
-
-    def __init__(self, cfg: Wav2vec_UConfig, target_dict):
-        super().__init__()
-
-        self.cfg = cfg
-        self.zero_index = target_dict.index("<SIL>") if "<SIL>" in target_dict else 0
-        self.smoothness_weight = cfg.smoothness_weight
-
-        output_size = len(target_dict)
-        self.pad = target_dict.pad()
-        self.eos = target_dict.eos()
-        self.smoothing = cfg.smoothing
-        self.smoothing_one_sided = cfg.smoothing_one_sided
-        self.no_softmax = cfg.no_softmax
-        self.gumbel = cfg.gumbel
-        self.hard_gumbel = cfg.hard_gumbel
-        self.last_acc = None
-
-        self.gradient_penalty = cfg.gradient_penalty
-        self.code_penalty = cfg.code_penalty
-        self.blank_weight = cfg.blank_weight
-        self.blank_mode = cfg.blank_mode
-        self.blank_index = target_dict.index("<SIL>") if cfg.blank_is_sil else 0
-        assert self.blank_index != target_dict.unk()
-
-        self.discriminator = Discriminator(output_size, cfg)
-        for p in self.discriminator.parameters():
-            p.param_group = "discriminator"
-
-        self.pca_A = self.pca_b = None
-        d = cfg.input_dim
-
-        self.segmenter = SEGMENT_FACTORY[cfg.segmentation.type](cfg.segmentation)
-
-        self.generator = Generator(d, output_size, cfg)
-
-        for p in self.generator.parameters():
-            p.param_group = "generator"
-
-        for p in self.segmenter.parameters():
-            p.param_group = "generator"
-
-        self.max_temp, self.min_temp, self.temp_decay = cfg.temp
-        self.curr_temp = self.max_temp
-        self.update_num = 0
-
-    @classmethod
-    def build_model(cls, cfg, task):
-        return cls(cfg, task.target_dictionary)
-
-    def get_logits(
-        self,
-        net_output: Optional[Dict[str, List[Optional[torch.Tensor]]]],
-        normalize: bool = False,
-    ):
-        logits = net_output["logits"]
-
-        if self.blank_weight != 0:
-            if self.blank_mode == "add":
-                logits[..., self.blank_index] += self.blank_weight
-            elif self.blank_mode == "set":
-                logits[..., self.blank_index] = self.blank_weight
-            else:
-                raise Exception(f"invalid blank mode {self.blank_mode}")
-
-        padding = net_output["padding_mask"]
-        if padding.any():
-            logits[padding] = float("-inf")
-            logits[padding][..., self.blank_index] = float("inf")
-
-        if normalize:
-            logits = utils.log_softmax(logits.float(), dim=-1)
-
-        return logits.transpose(0, 1)
-
-    def get_normalized_probs(
-        self,
-        net_output: Tuple[
-            torch.Tensor, Optional[Dict[str, List[Optional[torch.Tensor]]]]
-        ],
-        log_probs: bool,
-        sample: Optional[Dict[str, torch.Tensor]] = None,
-    ):
-        logits = self.get_logits(net_output)
-
-        probs = super().get_normalized_probs(logits, log_probs, sample)
-        # BTC -> TBC for ctc
-        probs = probs.transpose(0, 1)
-        return probs
-
-    def normalize(self, dense_x):
-
-        bsz, tsz, csz = dense_x.shape
-
-        if dense_x.numel() == 0:
-            raise Exception(dense_x.shape)
-        _, k = dense_x.max(-1)
-        hard_x = (
-            dense_x.new_zeros(bsz * tsz, csz)
-            .scatter_(-1, k.view(-1, 1), 1.0)
-            .view(-1, csz)
-        )
-        hard_probs = torch.mean(hard_x.float(), dim=0)
-        code_perplexity = torch.exp(
-            -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1)
-        )
-
-        avg_probs = torch.softmax(dense_x.reshape(-1, csz).float(), dim=-1).mean(dim=0)
-        prob_perplexity = torch.exp(
-            -torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1)
-        )
-
-        if not self.no_softmax:
-            if self.training and self.gumbel:
-                dense_x = F.gumbel_softmax(
-                    dense_x.float(), tau=self.curr_temp, hard=self.hard_gumbel
-                ).type_as(dense_x)
-            else:
-                dense_x = dense_x.softmax(-1)
-
-        return dense_x, code_perplexity, prob_perplexity
-
-    def forward(
-        self,
-        features,
-        padding_mask,
-        random_label=None,
-        dense_x_only=False,
-        segment=True,
-    ):
-        if segment:
-            features, padding_mask = self.segmenter.pre_segment(features, padding_mask)
-
-        orig_size = features.size(0) * features.size(1) - padding_mask.sum()
-
-        gen_result = self.generator(features, random_label, padding_mask)
-
-        orig_dense_x, token_x = gen_result["dense_x"], gen_result["token_x"]
-        orig_dense_padding_mask = gen_result["dense_padding_mask"]
-
-        if segment:
-            dense_x, dense_padding_mask = self.segmenter.logit_segment(
-                orig_dense_x, orig_dense_padding_mask
-            )
-        else:
-            dense_x = orig_dense_x
-            dense_padding_mask = orig_dense_padding_mask
-
-        dense_logits = dense_x
-        prob_perplexity = None
-        code_perplexity = None
-
-        if not (self.no_softmax and dense_x_only):
-            dense_x, code_perplexity, prob_perplexity = self.normalize(dense_logits)
-
-        if dense_x_only or self.discriminator is None:
-            return {
-                "logits": dense_x,
-                "padding_mask": dense_padding_mask,
-            }
-
-        token_padding_mask = random_label == self.pad
-
-        dense_y = self.discriminator(dense_x, dense_padding_mask)
-        token_y = self.discriminator(token_x, token_padding_mask)
-
-        sample_size = features.size(0)
-
-        d_step = self.discrim_step(self.update_num)
-
-        fake_smooth = self.smoothing
-        real_smooth = self.smoothing
-        if self.smoothing_one_sided:
-            fake_smooth = 0
-
-        zero_loss = None
-        smoothness_loss = None
-        code_pen = None
-
-        if d_step:
-            loss_dense = F.binary_cross_entropy_with_logits(
-                dense_y,
-                dense_y.new_ones(dense_y.shape) - fake_smooth,
-                reduction="sum",
-            )
-            loss_token = F.binary_cross_entropy_with_logits(
-                token_y,
-                token_y.new_zeros(token_y.shape) + real_smooth,
-                reduction="sum",
-            )
-            if self.training and self.gradient_penalty > 0:
-                grad_pen = self.calc_gradient_penalty(token_x, dense_x)
-                grad_pen = grad_pen.sum() * self.gradient_penalty
-            else:
-                grad_pen = None
-        else:
-            grad_pen = None
-            loss_token = None
-            loss_dense = F.binary_cross_entropy_with_logits(
-                dense_y,
-                dense_y.new_zeros(dense_y.shape) + fake_smooth,
-                reduction="sum",
-            )
-            num_vars = dense_x.size(-1)
-            if prob_perplexity is not None:
-                code_pen = (num_vars - prob_perplexity) / num_vars
-                code_pen = code_pen * sample_size * self.code_penalty
-
-            if self.smoothness_weight > 0:
-                smoothness_loss = F.mse_loss(
-                    dense_logits[:, :-1], dense_logits[:, 1:], reduction="none"
-                )
-                smoothness_loss[dense_padding_mask[:, 1:]] = 0
-                smoothness_loss = (
-                    smoothness_loss.mean() * sample_size * self.smoothness_weight
-                )
-
-        result = {
-            "losses": {
-                "grad_pen": grad_pen,
-                "code_pen": code_pen,
-                "smoothness": smoothness_loss,
-            },
-            "temp": self.curr_temp,
-            "code_ppl": code_perplexity,
-            "prob_ppl": prob_perplexity,
-            "d_steps": int(d_step),
-            "sample_size": sample_size,
-        }
-
-        suff = "_d" if d_step else "_g"
-        result["losses"]["dense" + suff] = loss_dense
-        result["losses"]["token" + suff] = loss_token
-
-        return result
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/vggblock.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/vggblock.py
deleted file mode 100644
index ee5ee19a34816c7350c21fba7c4907fec8ca7a61..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/vggblock.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-from collections.abc import Iterable
-from itertools import repeat
-
-import torch
-import torch.nn as nn
-
-
-def _pair(v):
-    if isinstance(v, Iterable):
-        assert len(v) == 2, "len(v) != 2"
-        return v
-    return tuple(repeat(v, 2))
-
-
-def infer_conv_output_dim(conv_op, input_dim, sample_inchannel):
-    sample_seq_len = 200
-    sample_bsz = 10
-    x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim)
-    # N x C x H x W
-    # N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim
-    x = conv_op(x)
-    # N x C x H x W
-    x = x.transpose(1, 2)
-    # N x H x C x W
-    bsz, seq = x.size()[:2]
-    per_channel_dim = x.size()[3]
-    # bsz: N, seq: H, CxW the rest
-    return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim
-
-
-class VGGBlock(torch.nn.Module):
-    """
-    VGG motibated cnn module https://arxiv.org/pdf/1409.1556.pdf
-
-    Args:
-        in_channels: (int) number of input channels (typically 1)
-        out_channels: (int) number of output channels
-        conv_kernel_size: convolution channels
-        pooling_kernel_size: the size of the pooling window to take a max over
-        num_conv_layers: (int) number of convolution layers
-        input_dim: (int) input dimension
-        conv_stride: the stride of the convolving kernel.
-            Can be a single number or a tuple (sH, sW)  Default: 1
-        padding: implicit paddings on both sides of the input.
-            Can be a single number or a tuple (padH, padW). Default: None
-        layer_norm: (bool) if layer norm is going to be applied. Default: False
-
-    Shape:
-        Input: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
-        Output: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        conv_kernel_size,
-        pooling_kernel_size,
-        num_conv_layers,
-        input_dim,
-        conv_stride=1,
-        padding=None,
-        layer_norm=False,
-    ):
-        assert (
-            input_dim is not None
-        ), "Need input_dim for LayerNorm and infer_conv_output_dim"
-        super(VGGBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.conv_kernel_size = _pair(conv_kernel_size)
-        self.pooling_kernel_size = _pair(pooling_kernel_size)
-        self.num_conv_layers = num_conv_layers
-        self.padding = (
-            tuple(e // 2 for e in self.conv_kernel_size)
-            if padding is None
-            else _pair(padding)
-        )
-        self.conv_stride = _pair(conv_stride)
-
-        self.layers = nn.ModuleList()
-        for layer in range(num_conv_layers):
-            conv_op = nn.Conv2d(
-                in_channels if layer == 0 else out_channels,
-                out_channels,
-                self.conv_kernel_size,
-                stride=self.conv_stride,
-                padding=self.padding,
-            )
-            self.layers.append(conv_op)
-            if layer_norm:
-                conv_output_dim, per_channel_dim = infer_conv_output_dim(
-                    conv_op, input_dim, in_channels if layer == 0 else out_channels
-                )
-                self.layers.append(nn.LayerNorm(per_channel_dim))
-                input_dim = per_channel_dim
-            self.layers.append(nn.ReLU())
-
-        if self.pooling_kernel_size is not None:
-            pool_op = nn.MaxPool2d(kernel_size=self.pooling_kernel_size, ceil_mode=True)
-            self.layers.append(pool_op)
-            self.total_output_dim, self.output_dim = infer_conv_output_dim(
-                pool_op, input_dim, out_channels
-            )
-
-    def forward(self, x):
-        for i, _ in enumerate(self.layers):
-            x = self.layers[i](x)
-        return x
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/__init__.py
deleted file mode 100644
index 5b3dbc023aa4a6f7bfb8403b8204d71ca432f79c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-import importlib
-import os
-
-from fairseq import registry
-from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import (  # noqa
-    FairseqLRScheduler,
-    LegacyFairseqLRScheduler,
-)
-from omegaconf import DictConfig
-
-
-(
-    build_lr_scheduler_,
-    register_lr_scheduler,
-    LR_SCHEDULER_REGISTRY,
-    LR_SCHEDULER_DATACLASS_REGISTRY,
-) = registry.setup_registry(
-    "--lr-scheduler", base_class=FairseqLRScheduler, default="fixed"
-)
-
-
-def build_lr_scheduler(cfg: DictConfig, optimizer):
-    return build_lr_scheduler_(cfg, optimizer)
-
-
-# automatically import any Python files in the optim/lr_scheduler/ directory
-for file in sorted(os.listdir(os.path.dirname(__file__))):
-    if file.endswith(".py") and not file.startswith("_"):
-        file_name = file[: file.find(".py")]
-        importlib.import_module("fairseq.optim.lr_scheduler." + file_name)
diff --git a/spaces/ORI-Muchim/NahidaTTS/monotonic_align/__init__.py b/spaces/ORI-Muchim/NahidaTTS/monotonic_align/__init__.py
deleted file mode 100644
index 40b6f64aa116c74cac2f6a33444c9eeea2fdb38c..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/NahidaTTS/monotonic_align/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    """ numba 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(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)
-
diff --git a/spaces/Omdena-Milan/milan-chapter-agrifoods/README.md b/spaces/Omdena-Milan/milan-chapter-agrifoods/README.md
deleted file mode 100644
index 89c77a839aa257094b0c3eac612ac52892c8ea01..0000000000000000000000000000000000000000
--- a/spaces/Omdena-Milan/milan-chapter-agrifoods/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Milan Chapter Agrifoods
-emoji: 📉
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/__init__.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/__init__.py
deleted file mode 100644
index 855ca1095aacc2a0418722497a8fdcaa56427b45..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-
-from .ms_deform_attn import MSDeformAttn
diff --git a/spaces/PAIR/PAIR-Diffusion/ldm/modules/distributions/__init__.py b/spaces/PAIR/PAIR-Diffusion/ldm/modules/distributions/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/midas/api.py b/spaces/PAIR/Text2Video-Zero/annotator/midas/api.py
deleted file mode 100644
index 1ab9f15bf96bbaffcee0e3e29fc9d3979d6c32e8..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/midas/api.py
+++ /dev/null
@@ -1,169 +0,0 @@
-# based on https://github.com/isl-org/MiDaS
-
-import cv2
-import os
-import torch
-import torch.nn as nn
-from torchvision.transforms import Compose
-
-from .midas.dpt_depth import DPTDepthModel
-from .midas.midas_net import MidasNet
-from .midas.midas_net_custom import MidasNet_small
-from .midas.transforms import Resize, NormalizeImage, PrepareForNet
-from annotator.util import annotator_ckpts_path
-
-
-ISL_PATHS = {
-    "dpt_large": os.path.join(annotator_ckpts_path, "dpt_large-midas-2f21e586.pt"),
-    "dpt_hybrid": os.path.join(annotator_ckpts_path, "dpt_hybrid-midas-501f0c75.pt"),
-    "midas_v21": "",
-    "midas_v21_small": "",
-}
-
-remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt"
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def load_midas_transform(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load transform only
-    if model_type == "dpt_large":  # DPT-Large
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    elif model_type == "midas_v21_small":
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    else:
-        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return transform
-
-
-def load_model(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load network
-    model_path = ISL_PATHS[model_type]
-    if model_type == "dpt_large":  # DPT-Large
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        if not os.path.exists(model_path):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
-
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitb_rn50_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        model = MidasNet(model_path, non_negative=True)
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    elif model_type == "midas_v21_small":
-        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
-                               non_negative=True, blocks={'expand': True})
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    else:
-        print(f"model_type '{model_type}' not implemented, use: --model_type large")
-        assert False
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return model.eval(), transform
-
-
-class MiDaSInference(nn.Module):
-    MODEL_TYPES_TORCH_HUB = [
-        "DPT_Large",
-        "DPT_Hybrid",
-        "MiDaS_small"
-    ]
-    MODEL_TYPES_ISL = [
-        "dpt_large",
-        "dpt_hybrid",
-        "midas_v21",
-        "midas_v21_small",
-    ]
-
-    def __init__(self, model_type):
-        super().__init__()
-        assert (model_type in self.MODEL_TYPES_ISL)
-        model, _ = load_model(model_type)
-        self.model = model
-        self.model.train = disabled_train
-
-    def forward(self, x):
-        with torch.no_grad():
-            prediction = self.model(x)
-        return prediction
-
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/image/__init__.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/image/__init__.py
deleted file mode 100644
index d0051d609d3de4e7562e3fe638335c66617c4d91..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/image/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .colorspace import (bgr2gray, bgr2hls, bgr2hsv, bgr2rgb, bgr2ycbcr,
-                         gray2bgr, gray2rgb, hls2bgr, hsv2bgr, imconvert,
-                         rgb2bgr, rgb2gray, rgb2ycbcr, ycbcr2bgr, ycbcr2rgb)
-from .geometric import (cutout, imcrop, imflip, imflip_, impad,
-                        impad_to_multiple, imrescale, imresize, imresize_like,
-                        imresize_to_multiple, imrotate, imshear, imtranslate,
-                        rescale_size)
-from .io import imfrombytes, imread, imwrite, supported_backends, use_backend
-from .misc import tensor2imgs
-from .photometric import (adjust_brightness, adjust_color, adjust_contrast,
-                          adjust_lighting, adjust_sharpness, auto_contrast,
-                          clahe, imdenormalize, imequalize, iminvert,
-                          imnormalize, imnormalize_, lut_transform, posterize,
-                          solarize)
-
-__all__ = [
-    'bgr2gray', 'bgr2hls', 'bgr2hsv', 'bgr2rgb', 'gray2bgr', 'gray2rgb',
-    'hls2bgr', 'hsv2bgr', 'imconvert', 'rgb2bgr', 'rgb2gray', 'imrescale',
-    'imresize', 'imresize_like', 'imresize_to_multiple', 'rescale_size',
-    'imcrop', 'imflip', 'imflip_', 'impad', 'impad_to_multiple', 'imrotate',
-    'imfrombytes', 'imread', 'imwrite', 'supported_backends', 'use_backend',
-    'imdenormalize', 'imnormalize', 'imnormalize_', 'iminvert', 'posterize',
-    'solarize', 'rgb2ycbcr', 'bgr2ycbcr', 'ycbcr2rgb', 'ycbcr2bgr',
-    'tensor2imgs', 'imshear', 'imtranslate', 'adjust_color', 'imequalize',
-    'adjust_brightness', 'adjust_contrast', 'lut_transform', 'clahe',
-    'adjust_sharpness', 'auto_contrast', 'cutout', 'adjust_lighting'
-]
diff --git a/spaces/PKUWilliamYang/StyleGANEX/utils/train_utils.py b/spaces/PKUWilliamYang/StyleGANEX/utils/train_utils.py
deleted file mode 100644
index 0c55177f7442010bc1fcc64de3d142585c22adc0..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/StyleGANEX/utils/train_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-
-def aggregate_loss_dict(agg_loss_dict):
-	mean_vals = {}
-	for output in agg_loss_dict:
-		for key in output:
-			mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]]
-	for key in mean_vals:
-		if len(mean_vals[key]) > 0:
-			mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key])
-		else:
-			print('{} has no value'.format(key))
-			mean_vals[key] = 0
-	return mean_vals
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/bin/abc2ly.py b/spaces/Pattr/DrumClassification/lilypond-2.24.2/bin/abc2ly.py
deleted file mode 100644
index 6893957051479fcfb5205ef6c99cb843079d8b84..0000000000000000000000000000000000000000
--- a/spaces/Pattr/DrumClassification/lilypond-2.24.2/bin/abc2ly.py
+++ /dev/null
@@ -1,1557 +0,0 @@
-#!/home/lily/lilypond-2.24.2/release/binaries/dependencies/install/Python-3.10.8/bin/python3.10
-# -*- coding: utf-8 -*-
-
-# once upon a rainy monday afternoon.
-
-#    This file is part of LilyPond, the GNU music typesetter.
-#
-#    Copyright (C) 1999--2022  Han-Wen Nienhuys <hanwen@xs4all.nl>
-#                              Jan Nieuwenhuizen <janneke@gnu.org>
-#
-#    LilyPond is free software: you can redistribute it and/or modify
-#    it under the terms of the GNU General Public License as published by
-#    the Free Software Foundation, either version 3 of the License, or
-#    (at your option) any later version.
-#
-#    LilyPond is distributed in the hope that it will be useful,
-#    but WITHOUT ANY WARRANTY; without even the implied warranty of
-#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#    GNU General Public License for more details.
-#
-#    You should have received a copy of the GNU General Public License
-#    along with LilyPond.  If not, see <http://www.gnu.org/licenses/>.
-
-#
-#   ...
-#
-# (not finished.)
-# ABC standard v1.6:  http://abcnotation.com/
-#
-# Enhancements  (Roy R. Rankin)
-#
-# Header section moved to top of lilypond file
-# handle treble, treble-8, alto, and bass clef
-# Handle voices (V: headers) with clef and part names, multiple voices
-# Handle w: lyrics with multiple verses
-# Handle key mode names for minor, major, phrygian, ionian, locrian, aeolian,
-#     mixolydian, lydian, dorian
-# Handle part names from V: header
-# Tuplets handling fixed up
-# Lines starting with |: not discarded as header lines
-# Multiple T: and C: header entries handled
-# Accidental maintained until next bar check
-# Silent rests supported
-# articulations fermata, upbow, downbow, ltoe, accent, tenuto supported
-# Chord strings([-^]"string") can contain a '#'
-# Header fields enclosed by [] in notes string processed
-# W: words output after tune as abc2ps does it (they failed before)
-
-# Enhancements (Laura Conrad)
-#
-# Barring now preserved between ABC and lilypond
-# the default placement for text in abc is above the staff.
-# %%LY now supported.
-# \breve and \longa supported.
-# M:none doesn't crash lily.
-# lilypond '--' supported.
-
-# Enhancements (Guy Gascoigne-Piggford)
-#
-# Add support for maintaining ABC's notion of beaming, this is selectable
-# from the command line with a -b or --beam option.
-# Fixd a problem where on cygwin empty lines weren't being correctly identifed
-# and so were complaining, but still generating the correct output.
-
-# Limitations
-#
-# Multiple tunes in single file not supported
-# Blank T: header lines should write score and open a new score
-# Not all header fields supported
-# ABC line breaks are ignored
-# Block comments generate error and are ignored
-# Postscript commands are ignored
-# lyrics not resynchronized by line breaks (lyrics must fully match notes)
-# %%LY slyrics can't be directly before a w: line.
-# ???
-
-
-# TODO:
-#
-# * coding style
-# * lilylib
-# * GNU style messages:  warning:FILE:LINE:
-# * l10n
-#
-# Convert to new chord styles.
-#
-# UNDEF -> None
-#
-
-import __main__
-import getopt
-import gettext
-import os
-import re
-import sys
-
-"""
-
-# relocate-preamble.py.in
-#
-# This file is part of LilyPond, the GNU music typesetter.
-#
-# Copyright (C) 2007--2022  Han-Wen Nienhuys <hanwen@xs4all.nl>
-#
-# LilyPond is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# LilyPond is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with LilyPond.  If not, see <http://www.gnu.org/licenses/>.
-#
-
-This is generic code, used for all python scripts.
-
-The quotes are to ensure that the source .py file can still be
-run as a python script, but does not include any sys.path handling.
-Otherwise, the lilypond-book calls inside the build
-might modify installed .pyc files.
-
-"""
-
-# This is needed for installations with a non-default layout, ie where share/
-# is not next to bin/.
-sys.path.insert (0, os.path.join ('/home/lily/lilypond-2.24.2/release/binaries/mingw/lilypond/install/share/lilypond/2.24.2', 'python'))
-
-# Dynamic relocation, for installations with a default layout including GUB,
-# but also for execution from the build directory.
-bindir = os.path.abspath (os.path.dirname (sys.argv[0]))
-topdir = os.path.dirname (bindir)
-if bindir.endswith (r'/scripts/out'):
-    topdir = os.path.join (os.path.dirname (topdir), 'out')
-datadir = os.path.abspath (os.path.join (topdir, 'share', 'lilypond'))
-for v in [ 'current', '2.24.2' ]:
-    sys.path.insert (0, os.path.join (datadir, v, 'python'))
-
-"""
-"""
-
-# Load translation and install _() into Python's builtins namespace.
-gettext.install('lilypond', '/home/lily/lilypond-2.24.2/release/binaries/mingw/lilypond/install/share/locale')
-
-import lilylib as ly
-
-version = '2.24.2'
-if version == '@' + 'TOPLEVEL_VERSION' + '@':
-    version = '(unknown version)'                # uGUHGUHGHGUGH
-
-UNDEF = 255
-state = UNDEF
-voice_idx_dict = {}
-header = {}
-header['footnotes'] = ''
-lyrics = []
-slyrics = []
-voices = []
-state_list = []
-repeat_state = [0] * 8
-current_voice_idx = -1
-current_lyric_idx = -1
-lyric_idx = -1
-part_names = 0
-default_len = 8
-length_specified = 0
-nobarlines = 0
-global_key = [0] * 7                        # UGH
-names = ["One", "Two", "Three"]
-DIGITS = '0123456789'
-HSPACE = ' \t'
-midi_specs = ''
-
-
-def error(msg):
-    sys.stderr.write(msg)
-    if global_options.strict:
-        sys.exit(1)
-
-
-def alphabet(i):
-    return chr(i + ord('A'))
-
-
-def check_clef(s):
-    # the number gives the base_octave
-    clefs = [("treble", "treble", 0),
-             ("treble1", "french", 0),
-             ("bass3", "varbaritone", 0),
-             ("bass", "bass", 0),
-             ("alto4", "tenor", 0),
-             ("alto2", "mezzosoprano", 0),
-             ("alto1", "soprano", 0),
-             ("alto", "alto", 0),
-             ("perc", "percussion", 0)]
-    modifier = [("-8va", "_8", -1),
-                ("-8", "_8", -1),
-                (r"\+8", "^8", +1),
-                ("8", "_8", -1)]
-
-    if not s:
-        return ''
-    clef = None
-    octave = 0
-    for c in clefs:
-        m = re.match('^'+c[0], s)
-        if m:
-            (clef, octave) = (c[1], c[2])
-            s = s[m.end():]
-            break
-    if not clef:
-        return s
-
-    mod = ""
-    for md in modifier:
-        m = re.match('^'+md[0], s)
-        if m:
-            mod = md[1]
-            octave += md[2]
-            s = s[m.end():]
-            break
-
-    state.base_octave = octave
-    voices_append("\\clef \""+clef+mod+"\"\n")
-    return s
-
-
-def select_voice(name, rol):
-    if name not in voice_idx_dict:
-        state_list.append(Parser_state())
-        voices.append('')
-        slyrics.append([])
-        voice_idx_dict[name] = len(voices) - 1
-    __main__.current_voice_idx = voice_idx_dict[name]
-    __main__.state = state_list[current_voice_idx]
-    while rol != '':
-        m = re.match('^([^ \t=]*)=(.*)$', rol)  # find keywork
-        if m:
-            keyword = m.group(1)
-            rol = m.group(2)
-            a = re.match('^("[^"]*"|[^ \t]*) *(.*)$', rol)
-            if a:
-                value = a.group(1)
-                rol = a.group(2)
-                if keyword == 'clef':
-                    check_clef(value)
-                elif keyword == "name":
-                    value = re.sub('\\\\', '\\\\\\\\', value)
-                    # < 2.2
-                    voices_append("\\set Staff.instrument = %s\n" % value)
-
-                    __main__.part_names = 1
-                elif keyword == "sname" or keyword == "snm":
-                    voices_append("\\set Staff.instr = %s\n" % value)
-        else:
-            break
-
-
-def dump_header(outf, hdr):
-    outf.write('\\header {\n')
-    ks = sorted(hdr.keys())
-    for k in ks:
-        hdr[k] = re.sub('"', '\\"', hdr[k])
-        outf.write('\t%s = "%s"\n' % (k, hdr[k]))
-    outf.write('}')
-
-
-def dump_lyrics(outf):
-    if lyrics:
-        outf.write("\n\\markup \\column {\n")
-        for i in range(len(lyrics)):
-            outf.write(lyrics[i])
-            outf.write("\n")
-        outf.write("}\n")
-
-
-def dump_default_bar(outf):
-    """
-    Nowadays abc2ly outputs explicits barlines (?)
-    """
-    # < 2.2
-    outf.write("\n\\set Score.measureBarType = \"\"\n")
-
-
-def dump_slyrics(outf):
-    ks = sorted(voice_idx_dict.keys())
-    for k in ks:
-        if re.match('[1-9]', k):
-            m = alphabet(int(k))
-        else:
-            m = k
-        for i in range(len(slyrics[voice_idx_dict[k]])):
-            l = alphabet(i)
-            outf.write("\nwords%sV%s = \\lyricmode {" % (m, l))
-            outf.write("\n" + slyrics[voice_idx_dict[k]][i])
-            outf.write("\n}")
-
-
-def dump_voices(outf):
-    global doing_alternative, in_repeat
-    ks = sorted(voice_idx_dict.keys())
-    for k in ks:
-        if re.match('[1-9]', k):
-            m = alphabet(int(k))
-        else:
-            m = k
-        outf.write("\nvoice%s =  {" % m)
-        dump_default_bar(outf)
-        if repeat_state[voice_idx_dict[k]]:
-            outf.write("\n\\repeat volta 2 {")
-        outf.write("\n" + voices[voice_idx_dict[k]])
-        if not using_old:
-            if doing_alternative[voice_idx_dict[k]]:
-                outf.write("}")
-            if in_repeat[voice_idx_dict[k]]:
-                outf.write("}")
-        outf.write("\n}")
-
-
-def try_parse_q(a):
-    # assume that Q takes the form "Q:'opt. description' 1/4=120"
-    # There are other possibilities, but they are deprecated
-    r = re.compile(r'^(.*) *([0-9]+) */ *([0-9]+) *=* *([0-9]+)\s*')
-    m = r.match(a)
-    if m:
-        descr = m.group(1)  # possibly empty
-        numerator = int(m.group(2))
-        denominator = int(m.group(3))
-        tempo = m.group(4)
-        dur = duration_to_lilypond_duration((numerator, denominator), 1, 0)
-        voices_append("\\tempo " + descr + " " + dur + "=" + tempo + "\n")
-    else:
-        # Parsing of numeric tempi, as these are fairly
-        # common.  The spec says the number is a "beat" so using
-        # a quarter note as the standard time
-        numericQ = re.compile('[0-9]+')
-        m = numericQ.match(a)
-        if m:
-            voices_append("\\tempo 4=" + m.group(0))
-        else:
-            sys.stderr.write(
-                "abc2ly: Warning, unable to parse Q specification: %s\n" % a)
-
-
-def dump_score(outf):
-    outf.write(r"""
-
-\score{
-    <<
-""")
-
-    ks = sorted(voice_idx_dict.keys())
-    for k in ks:
-        if re.match('[1-9]', k):
-            m = alphabet(int(k))
-        else:
-            m = k
-        if k == 'default' and len(voice_idx_dict) > 1:
-            break
-        outf.write("\n\t\\context Staff=\"%s\"\n\t{\n" % k)
-        if k != 'default':
-            outf.write("\t    \\voicedefault\n")
-        outf.write("\t    \\voice%s " % m)
-        outf.write("\n\t}\n")
-
-        l = ord('A')
-        for lyrics in slyrics[voice_idx_dict[k]]:
-            outf.write("\n\t\\addlyrics {\n")
-            if re.match('[1-9]', k):
-                m = alphabet(int(k))
-            else:
-                m = k
-
-            outf.write(" \\words%sV%s } " % (m, chr(l)))
-            l += 1
-
-    outf.write("\n    >>")
-    outf.write("\n\t\\layout {\n")
-    outf.write("\t}\n\t\\midi {%s}\n}\n" % midi_specs)
-
-
-def set_default_length(s):
-    global length_specified
-    m = re.search('1/([0-9]+)', s)
-    if m:
-        __main__.default_len = int(m.group(1))
-        length_specified = 1
-
-
-def set_default_len_from_time_sig(s):
-    m = re.search('([0-9]+)/([0-9]+)', s)
-    if m:
-        n = int(m.group(1))
-        d = int(m.group(2))
-        if (n * 1.0)/(d * 1.0) < 0.75:
-            __main__.default_len = 16
-        else:
-            __main__.default_len = 8
-
-
-def gulp_file(f):
-    try:
-        i = open(f, encoding="utf8")
-        i.seek(0, 2)
-        n = i.tell()
-        i.seek(0, 0)
-    except FileNotFoundError:
-        sys.stderr.write("cannot open file: `%s'\n" % f)
-        return ''
-    s = i.read(n)
-    if len(s) <= 0:
-        sys.stderr.write("gulped empty file: `%s'\n" % f)
-    i.close()
-    return s
-
-
-# pitch manipulation. Tuples are (name, alteration).
-# 0 is (central) C. Alteration -1 is a flat, Alteration +1 is a sharp
-# pitch in semitones.
-def semitone_pitch(tup):
-    p = 0
-
-    t = tup[0]
-    p = p + 12 * (t // 7)
-    t = t % 7
-
-    if t > 2:
-        p = p - 1
-
-    p = p + t * 2 + tup[1]
-    return p
-
-
-def fifth_above_pitch(tup):
-    (n, a) = (tup[0] + 4, tup[1])
-
-    difference = 7 - (semitone_pitch((n, a)) - semitone_pitch(tup))
-    a = a + difference
-
-    return (n, a)
-
-
-def sharp_keys():
-    p = (0, 0)
-    l = []
-    k = 0
-    while True:
-        l.append(p)
-        (t, a) = fifth_above_pitch(p)
-        if semitone_pitch((t, a)) % 12 == 0:
-            break
-
-        p = (t % 7, a)
-    return l
-
-
-def flat_keys():
-    p = (0, 0)
-    l = []
-    k = 0
-    while True:
-        l.append(p)
-        (t, a) = quart_above_pitch(p)
-        if semitone_pitch((t, a)) % 12 == 0:
-            break
-
-        p = (t % 7, a)
-    return l
-
-
-def quart_above_pitch(tup):
-    (n, a) = (tup[0] + 3, tup[1])
-
-    difference = 5 - (semitone_pitch((n, a)) - semitone_pitch(tup))
-    a = a + difference
-
-    return (n, a)
-
-
-key_lookup = {         # abc to lilypond key mode names
-    'm': 'minor',
-    'min': 'minor',
-    'maj': 'major',
-    'major': 'major',
-    'phr': 'phrygian',
-    'ion': 'ionian',
-    'loc': 'locrian',
-    'aeo': 'aeolian',
-    'mix': 'mixolydian',
-    'mixolydian': 'mixolydian',
-    'lyd': 'lydian',
-    'dor': 'dorian',
-    'dorian': 'dorian'
-}
-
-
-def lily_key(k):
-    if k == 'none':
-        return
-    orig = "" + k
-    # UGR
-    k = k.lower()
-    key = k[0]
-    # UGH
-    k = k[1:]
-    if k and k[0] == '#':
-        key = key + 'is'
-        k = k[1:]
-    elif k and k[0] == 'b':
-        key = key + 'es'
-        k = k[1:]
-    if not k:
-        return '%s \\major' % key
-
-    type = k[0:3]
-    if type not in key_lookup:
-        # ugh, use lilylib, say WARNING:FILE:LINE:
-        sys.stderr.write("abc2ly:warning:")
-        sys.stderr.write("ignoring unknown key: `%s'" % orig)
-        sys.stderr.write('\n')
-        return 0
-    return "%s \\%s" % (key, key_lookup[type])
-
-
-def shift_key(note, acc, shift):
-    s = semitone_pitch((note, acc))
-    s = (s + shift + 12) % 12
-    if s <= 4:
-        n = s // 2
-        a = s % 2
-    else:
-        n = (s + 1) // 2
-        a = (s + 1) % 2
-    if a:
-        n = n + 1
-        a = -1
-    return (n, a)
-
-
-key_shift = {  # semitone shifts for key mode names
-    'm': 3,
-    'min': 3,
-    'minor': 3,
-    'maj': 0,
-    'major': 0,
-    'phr': -4,
-    'phrygian': -4,
-    'ion': 0,
-    'ionian': 0,
-    'loc': 1,
-    'locrian': 1,
-    'aeo': 3,
-    'aeolian': 3,
-    'mix': 5,
-    'mixolydian': 5,
-    'lyd': -5,
-    'lydian': -5,
-    'dor': -2,
-    'dorian': -2
-}
-
-
-def compute_key(k):
-    k = k.lower()
-    intkey = (ord(k[0]) - ord('a') + 5) % 7
-    intkeyacc = 0
-    k = k[1:]
-
-    if k and k[0] == 'b':
-        intkeyacc = -1
-        k = k[1:]
-    elif k and k[0] == '#':
-        intkeyacc = 1
-        k = k[1:]
-    k = k[0:3]
-    if k and k in key_shift:
-        (intkey, intkeyacc) = shift_key(intkey, intkeyacc, key_shift[k])
-    keytup = (intkey, intkeyacc)
-
-    sharp_key_seq = sharp_keys()
-    flat_key_seq = flat_keys()
-
-    accseq = None
-    accsign = 0
-    if keytup in sharp_key_seq:
-        accsign = 1
-        key_count = sharp_key_seq.index(keytup)
-        accseq = [(4*x - 1) % 7 for x in range(1, key_count + 1)]
-
-    elif keytup in flat_key_seq:
-        accsign = -1
-        key_count = flat_key_seq.index(keytup)
-        accseq = [(3*x + 3) % 7 for x in range(1, key_count + 1)]
-    else:
-        error("Huh?")
-        raise Exception("Huh")
-
-    key_table = [0] * 7
-    for a in accseq:
-        key_table[a] = key_table[a] + accsign
-
-    return key_table
-
-
-tup_lookup = {
-    '2': '3/2',
-    '3': '2/3',
-    '4': '4/3',
-    '5': '4/5',
-    '6': '4/6',
-    '7': '6/7',
-    '9': '8/9',
-}
-
-
-def try_parse_tuplet_begin(s, state):
-    if re.match(r'\([2-9]', s):
-        dig = s[1]
-        s = s[2:]
-        prev_tuplet_state = state.parsing_tuplet
-        state.parsing_tuplet = int(dig[0])
-        if prev_tuplet_state:
-            voices_append("}")
-        voices_append("\\times %s {" % tup_lookup[dig])
-    return s
-
-
-def try_parse_group_end(s, state):
-    if s and s[0] in HSPACE:
-        s = s[1:]
-        close_beam_state(state)
-    return s
-
-
-def header_append(key, a):
-    s = ''
-    if key in header:
-        s = header[key] + "\n"
-        header[key] = s + a
-
-
-def wordwrap(a, v):
-    linelen = len(v) - v.rfind('\n')
-    if linelen + len(a) > 80:
-        v = v + '\n'
-    return v + a + ' '
-
-
-def stuff_append(stuff, idx, a):
-    if not stuff:
-        stuff.append(a)
-    else:
-        stuff[idx] = wordwrap(a, stuff[idx])
-
-# ignore wordwrap since we are adding to the previous word
-
-
-def stuff_append_back(stuff, idx, a):
-    if not stuff:
-        stuff.append(a)
-    else:
-        point = len(stuff[idx])-1
-        while stuff[idx][point] == ' ':
-            point = point - 1
-        point = point + 1
-        stuff[idx] = stuff[idx][:point] + a + stuff[idx][point:]
-
-
-def voices_append(a):
-    if current_voice_idx < 0:
-        select_voice('default', '')
-    stuff_append(voices, current_voice_idx, a)
-
-# word wrap really makes it hard to bind beams to the end of notes since it
-# pushes out whitespace on every call. The _back functions do an append
-# prior to the last space, effectively tagging whatever they are given
-# onto the last note
-
-
-def voices_append_back(a):
-    if current_voice_idx < 0:
-        select_voice('default', '')
-    stuff_append_back(voices, current_voice_idx, a)
-
-
-def repeat_prepend():
-    global repeat_state
-    if current_voice_idx < 0:
-        select_voice('default', '')
-    if not using_old:
-        repeat_state[current_voice_idx] = 't'
-
-
-def lyrics_append(a):
-    a = re.sub('#', '\\#', a)        # latex does not like naked #'s
-    a = re.sub('"', '\\"', a)        # latex does not like naked "'s
-    a = '  \\line { "' + a + '" }\n'
-    stuff_append(lyrics, current_lyric_idx, a)
-
-# break lyrics to words and put "'s around words containing numbers and '"'s
-
-
-def fix_lyric(s):
-    ret = ''
-    while s != '':
-        m = re.match('[ \t]*([^ \t]*)[ \t]*(.*$)', s)
-        if m:
-            word = m.group(1)
-            s = m.group(2)
-            word = re.sub('"', '\\"', word)  # escape "
-            if re.match(r'.*[0-9"\(]', word):
-                word = re.sub('_', ' ', word)  # _ causes probs inside ""
-                ret = ret + '\"' + word + '\" '
-            else:
-                ret = ret + word + ' '
-        else:
-            return ret
-    return ret
-
-
-def slyrics_append(a):
-    a = re.sub('_', ' _ ', a)        # _ to ' _ '
-    # split words with "-" unless was originally "--"
-    a = re.sub('([^-])-([^-])', '\\1- \\2', a)
-    a = re.sub('\\\\- ', '-', a)         # unless \-
-    a = re.sub('~', '_', a)        # ~ to space('_')
-    a = re.sub(r'\*', '_ ', a)        # * to to space
-    a = re.sub('#', '\\#', a)        # latex does not like naked #'s
-    if re.match(r'.*[0-9"\(]', a):        # put numbers and " and ( into quoted string
-        a = fix_lyric(a)
-    a = re.sub('$', ' ', a)        # insure space between lines
-    __main__.lyric_idx = lyric_idx + 1
-    if len(slyrics[current_voice_idx]) <= lyric_idx:
-        slyrics[current_voice_idx].append(a)
-    else:
-        v = slyrics[current_voice_idx][lyric_idx]
-        slyrics[current_voice_idx][lyric_idx] = wordwrap(
-            a, slyrics[current_voice_idx][lyric_idx])
-
-
-def try_parse_header_line(ln, state):
-    global length_specified
-    m = re.match('^([A-Za-z]): *(.*)$', ln)
-
-    if m:
-        g = m.group(1)
-        a = m.group(2)
-        if g == 'T':  # title
-            a = re.sub('[ \t]*$', '', a)  # strip trailing blanks
-            if 'title' in header:
-                if a:
-                    if len(header['title']):
-                        # the non-ascii character
-                        # in the string below is a
-                        # punctuation dash. (TeX ---)
-                        header['title'] = header['title'] + ' — ' + a
-                    else:
-                        header['subtitle'] = a
-            else:
-                header['title'] = a
-        if g == 'M':        # Meter
-            if a == 'C':
-                if not state.common_time:
-                    state.common_time = 1
-                    voices_append(
-                        " \\override Staff.TimeSignature.style = #'C\n")
-                a = '4/4'
-            if a == 'C|':
-                if not state.common_time:
-                    state.common_time = 1
-                    voices_append(
-                        "\\override Staff.TimeSignature.style = #'C\n")
-                a = '2/2'
-            if not length_specified:
-                set_default_len_from_time_sig(a)
-            else:
-                length_specified = 0
-            if not a == 'none':
-                voices_append('\\time %s' % a)
-            state.next_bar = ''
-        if g == 'K':  # KEY
-            a = check_clef(a)
-            if a:
-                # separate clef info
-                m = re.match('^([^ \t]*) *([^ ]*)( *)(.*)$', a)
-                if m:
-                    # there may or may not be a space
-                    # between the key letter and the mode
-                    # convert the mode to lower-case before comparing
-                    mode = m.group(2)[0:3].lower()
-                    if mode in key_lookup:
-                        # use the full mode, not only the first three letters
-                        key_info = m.group(1) + m.group(2).lower()
-                        clef_info = a[m.start(4):]
-                    else:
-                        key_info = m.group(1)
-                        clef_info = a[m.start(2):]
-                    __main__.global_key = compute_key(key_info)
-                    k = lily_key(key_info)
-                    if k:
-                        voices_append('\\key %s' % k)
-                    check_clef(clef_info)
-                else:
-                    __main__.global_key = compute_key(a)
-                    k = lily_key(a)
-                    if k:
-                        voices_append('\\key %s \\major' % k)
-        if g == 'N':  # Notes
-            header['footnotes'] = header['footnotes'] + '\\\\\\\\' + a
-        if g == 'O':  # Origin
-            header['origin'] = a
-        if g == 'X':  # Reference Number
-            header['crossRefNumber'] = a
-        if g == 'A':  # Area
-            header['area'] = a
-        if g == 'H':        # History
-            header_append('history', a)
-        if g == 'B':        # Book
-            header['book'] = a
-        if g == 'C':        # Composer
-            if 'composer' in header:
-                if a:
-                    header['composer'] = header['composer'] + '\\\\\\\\' + a
-            else:
-                header['composer'] = a
-        if g == 'S':
-            header['subtitle'] = a
-        if g == 'L':        # Default note length
-            set_default_length(ln)
-        if g == 'V':        # Voice
-            voice = re.sub(' .*$', '', a)
-            rest = re.sub('^[^ \t]*  *', '', a)
-            if state.next_bar:
-                voices_append(state.next_bar)
-                state.next_bar = ''
-            select_voice(voice, rest)
-        if g == 'W':        # Words
-            lyrics_append(a)
-        if g == 'w':        # vocals
-            slyrics_append(a)
-        if g == 'Q':        # tempo
-            try_parse_q(a)
-        if g == 'R':        # Rhythm (e.g. jig, reel, hornpipe)
-            header['meter'] = a
-        if g == 'Z':        # Transcription (e.g. Steve Mansfield 1/2/2000)
-            header['transcription'] = a
-        return ''
-    return ln
-
-# we use in this order specified accidental, active accidental for bar,
-# active accidental for key
-
-
-def pitch_to_lilypond_name(name, acc, bar_acc, key):
-    s = ''
-    if acc == UNDEF:
-        if not nobarlines:
-            acc = bar_acc
-    if acc == UNDEF:
-        acc = key
-    if acc == -1:
-        s = 'es'
-    elif acc == 1:
-        s = 'is'
-
-    if name > 4:
-        name = name - 7
-    return chr(name + ord('c')) + s
-
-
-def octave_to_lilypond_quotes(o):
-    o = o + 2
-    s = ''
-    if o < 0:
-        o = -o
-        s = ','
-    else:
-        s = '\''
-
-    return s * o
-
-
-def parse_num(s):
-    durstr = ''
-    while s and s[0] in DIGITS:
-        durstr = durstr + s[0]
-        s = s[1:]
-
-    n = None
-    if durstr:
-        n = int(durstr)
-    return (s, n)
-
-
-def duration_to_lilypond_duration(multiply_tup, defaultlen, dots):
-    base = 1
-    # (num /  den)  / defaultlen < 1/base
-    while base * multiply_tup[0] < multiply_tup[1]:
-        base = base * 2
-    if base == 1:
-        if (multiply_tup[0] / multiply_tup[1]) == 2:
-            base = '\\breve'
-        if (multiply_tup[0] / multiply_tup[1]) == 3:
-            base = '\\breve'
-            dots = 1
-        if (multiply_tup[0] / multiply_tup[1]) == 4:
-            base = '\\longa'
-    return '%s%s' % (base, '.' * dots)
-
-
-class Parser_state:
-    def __init__(self):
-        self.in_acc = {}
-        self.next_articulation = ''
-        self.next_bar = ''
-        self.next_dots = 0
-        self.next_den = 1
-        self.parsing_tuplet = 0
-        self.plus_chord = 0
-        self.base_octave = 0
-        self.common_time = 0
-        self.parsing_beam = 0
-
-
-# return (str, num,den,dots)
-def parse_duration(s, parser_state):
-    num = 0
-    den = parser_state.next_den
-    parser_state.next_den = 1
-
-    (s, num) = parse_num(s)
-    if not num:
-        num = 1
-    if len(s):
-        if s[0] == '/':
-            if len(s[0]):
-                while s[:1] == '/':
-                    s = s[1:]
-                    d = 2
-                    if s[0] in DIGITS:
-                        (s, d) = parse_num(s)
-
-                    den = den * d
-
-    den = den * default_len
-
-    current_dots = parser_state.next_dots
-    parser_state.next_dots = 0
-    if re.match('[ \t]*[<>]', s):
-        while s[0] in HSPACE:
-            s = s[1:]
-        while s[0] == '>':
-            s = s[1:]
-            current_dots = current_dots + 1
-            parser_state.next_den = parser_state.next_den * 2
-
-        while s[0] == '<':
-            s = s[1:]
-            den = den * 2
-            parser_state.next_dots = parser_state.next_dots + 1
-
-    try_dots = [3, 2, 1]
-    for d in try_dots:
-        f = 1 << d
-        multiplier = (2*f-1)
-        if num % multiplier == 0 and den % f == 0:
-            num = num / multiplier
-            den = den / f
-            current_dots = current_dots + d
-
-    return (s, num, den, current_dots)
-
-
-def try_parse_rest(s, parser_state):
-    if not s or s[0] != 'z' and s[0] != 'x':
-        return s
-
-    __main__.lyric_idx = -1
-
-    if parser_state.next_bar:
-        voices_append(parser_state.next_bar)
-        parser_state.next_bar = ''
-
-    if s[0] == 'z':
-        rest = 'r'
-    else:
-        rest = 's'
-    s = s[1:]
-
-    (s, num, den, d) = parse_duration(s, parser_state)
-    voices_append(
-        '%s%s' % (rest, duration_to_lilypond_duration((num, den), default_len, d)))
-    if parser_state.next_articulation:
-        voices_append(parser_state.next_articulation)
-        parser_state.next_articulation = ''
-
-    return s
-
-
-artic_tbl = {
-    '.': '-.',
-    'T': '^\\trill',
-    'H': '^\\fermata',
-    'u': '^\\upbow',
-    'K': '^\\ltoe',
-    'k': '^\\accent',
-    'M': '^\\tenuto',
-    '~': '^"~" ',
-    'J': '',                # ignore slide
-    'R': '',                # ignore roll
-    'S': '^\\segno',
-    'O': '^\\coda',
-    'v': '^\\downbow'
-}
-
-
-def try_parse_articulation(s, state):
-    while s and s[:1] in artic_tbl:
-        state.next_articulation = state.next_articulation + artic_tbl[s[:1]]
-        if not artic_tbl[s[:1]]:
-            sys.stderr.write("Warning: ignoring `%s'\n" % s[:1])
-
-        s = s[1:]
-
-    # s7m2 input doesn't care about spaces
-    if re.match(r'[ \t]*\(', s):
-        s = s.lstrip()
-
-    slur_begin = 0
-    while s[:1] == '(' and s[1] not in DIGITS:
-        slur_begin = slur_begin + 1
-        state.next_articulation = state.next_articulation + '('
-        s = s[1:]
-
-    return s
-
-#
-# remember accidental for rest of bar
-#
-
-
-def set_bar_acc(note, octave, acc, state):
-    if acc == UNDEF:
-        return
-    n_oct = note + octave * 7
-    state.in_acc[n_oct] = acc
-
-# get accidental set in this bar or UNDEF if not set
-
-
-def get_bar_acc(note, octave, state):
-    n_oct = note + octave * 7
-    if n_oct in state.in_acc:
-        return state.in_acc[n_oct]
-    else:
-        return UNDEF
-
-
-def clear_bar_acc(state):
-    state.in_acc = {}
-
-
-# if we are parsing a beam, close it off
-def close_beam_state(state):
-    if state.parsing_beam and global_options.beams:
-        state.parsing_beam = 0
-        voices_append_back(']')
-
-
-# WAT IS ABC EEN ONTZETTENDE PROGRAMMEERPOEP  !
-def try_parse_note(s, parser_state):
-    mud = ''
-
-    slur_begin = 0
-    if not s:
-        return s
-
-    articulation = ''
-    acc = UNDEF
-    if s[0] in '^=_':
-        c = s[0]
-        s = s[1:]
-        if c == '^':
-            acc = 1
-        if c == '=':
-            acc = 0
-        if c == '_':
-            acc = -1
-
-    octave = parser_state.base_octave
-    if s[0] in "ABCDEFG":
-        s = s[0].lower() + s[1:]
-        octave = octave - 1
-
-    notename = 0
-    if s[0] in "abcdefg":
-        notename = (ord(s[0]) - ord('a') + 5) % 7
-        s = s[1:]
-    else:
-        return s                # failed; not a note!
-
-    __main__.lyric_idx = -1
-
-    if parser_state.next_bar:
-        voices_append(parser_state.next_bar)
-        parser_state.next_bar = ''
-
-    while s[0] == ',':
-        octave = octave - 1
-        s = s[1:]
-    while s[0] == '\'':
-        octave = octave + 1
-        s = s[1:]
-
-    (s, num, den, current_dots) = parse_duration(s, parser_state)
-
-    if re.match(r'[ \t]*\)', s):
-        s = s.lstrip()
-
-    slur_end = 0
-    while s[:1] == ')':
-        slur_end = slur_end + 1
-        s = s[1:]
-
-    bar_acc = get_bar_acc(notename, octave, parser_state)
-    pit = pitch_to_lilypond_name(notename, acc, bar_acc, global_key[notename])
-    oct = octave_to_lilypond_quotes(octave)
-    if acc != UNDEF and (acc == global_key[notename] or acc == bar_acc):
-        mod = '!'
-    else:
-        mod = ''
-    voices_append("%s%s%s%s" %
-                  (pit, oct, mod,
-                   duration_to_lilypond_duration((num, den), default_len, current_dots)))
-
-    set_bar_acc(notename, octave, acc, parser_state)
-    if parser_state.next_articulation:
-        articulation = articulation + parser_state.next_articulation
-        parser_state.next_articulation = ''
-
-    voices_append(articulation)
-
-    if slur_begin:
-        voices_append('-(' * slur_begin)
-    if slur_end:
-        voices_append('-)' * slur_end)
-
-    if parser_state.parsing_tuplet:
-        parser_state.parsing_tuplet = parser_state.parsing_tuplet - 1
-        if not parser_state.parsing_tuplet:
-            voices_append("}")
-
-    if global_options.beams and \
-            s[0] in '^=_ABCDEFGabcdefg' and \
-            not parser_state.parsing_beam and \
-            not parser_state.parsing_tuplet:
-        parser_state.parsing_beam = 1
-        voices_append_back('[')
-
-    return s
-
-
-def junk_space(s, state):
-    while s and s[0] in '\t\n\r ':
-        s = s[1:]
-        close_beam_state(state)
-
-    return s
-
-
-def try_parse_guitar_chord(s, state):
-    if s[:1] == '"':
-        s = s[1:]
-        gc = ''
-        if s[0] == '_' or (s[0] == '^'):
-            position = s[0]
-            s = s[1:]
-        else:
-            position = '^'
-        while s and s[0] != '"':
-            gc = gc + s[0]
-            s = s[1:]
-
-        if s:
-            s = s[1:]
-        gc = re.sub('#', '\\#', gc)        # escape '#'s
-        state.next_articulation = ("%c\"%s\"" % (position, gc)) \
-            + state.next_articulation
-    return s
-
-
-def try_parse_escape(s):
-    if not s or s[0] != '\\':
-        return s
-
-    s = s[1:]
-    if s[:1] == 'K':
-        key_table = compute_key()
-    return s
-
-
-#
-# |] thin-thick double bar line
-# || thin-thin double bar line
-# [| thick-thin double bar line
-# :| left repeat
-# |: right repeat
-# :: left-right repeat
-# |1 volta 1
-# |2 volta 2
-old_bar_dict = {
-    '|]': '|.',
-    '||': '||',
-    '[|': '||',
-    ':|': ':|.',
-    '|:': '|:',
-    '::': ':|.|:',
-    '|1': '|',
-    '|2': '|',
-    ':|2': ':|.',
-    '|':  '|'
-}
-bar_dict = {
-    '|]': '\\bar "|."',
-    '||': '\\bar "||"',
-    '[|': '\\bar "||"',
-    ':|': '}',
-    '|:': '\\repeat volta 2 {',
-    '::': '} \\repeat volta 2 {',
-    '|1': '} \\alternative{{',
-    '|2': '} {',
-    ':|2': '} {',
-    '|':  '\\bar "|"'
-}
-
-
-warn_about = ['|:', '::', ':|', '|1', ':|2', '|2']
-alternative_opener = ['|1', '|2', ':|2']
-repeat_ender = ['::', ':|']
-repeat_opener = ['::', '|:']
-in_repeat = [''] * 8
-doing_alternative = [''] * 8
-using_old = ''
-
-def try_parse_bar(string, state):
-    global in_repeat, doing_alternative, using_old
-    do_curly = ''
-    bs = None
-    if current_voice_idx < 0:
-        select_voice('default', '')
-    # first try the longer one
-    for trylen in [3, 2, 1]:
-        if string[:trylen] and string[:trylen] in bar_dict:
-            s = string[:trylen]
-            if using_old:
-                bs = "\\bar \"%s\"" % old_bar_dict[s]
-            else:
-                bs = "%s" % bar_dict[s]
-            string = string[trylen:]
-            if s in alternative_opener:
-                if not in_repeat[current_voice_idx]:
-                    using_old = 't'
-                    bs = "\\bar \"%s\"" % old_bar_dict[s]
-                else:
-                    doing_alternative[current_voice_idx] = 't'
-
-            if s in repeat_ender:
-                if not in_repeat[current_voice_idx]:
-                    sys.stderr.write(
-                        "Warning: inserting repeat to beginning of notes.\n")
-                    repeat_prepend()
-                    in_repeat[current_voice_idx] = ''
-                else:
-                    if doing_alternative[current_voice_idx]:
-                        do_curly = 't'
-                if using_old:
-                    bs = "\\bar \"%s\"" % old_bar_dict[s]
-                else:
-                    bs = bar_dict[s]
-                doing_alternative[current_voice_idx] = ''
-                in_repeat[current_voice_idx] = ''
-            if s in repeat_opener:
-                in_repeat[current_voice_idx] = 't'
-                if using_old:
-                    bs = "\\bar \"%s\"" % old_bar_dict[s]
-                else:
-                    bs = bar_dict[s]
-            break
-    if string[:1] == '|':
-        state.next_bar = '|\n'
-        string = string[1:]
-        clear_bar_acc(state)
-        close_beam_state(state)
-
-    if string[:1] == '}':
-        close_beam_state(state)
-
-    if bs is not None or state.next_bar != '':
-        if state.parsing_tuplet:
-            state.parsing_tuplet = 0
-            voices_append('} ')
-
-    if bs is not None:
-        clear_bar_acc(state)
-        close_beam_state(state)
-        voices_append(bs)
-        if do_curly != '':
-            voices_append("} ")
-            do_curly = ''
-    return string
-
-
-def try_parse_tie(s):
-    if s[:1] == '-':
-        s = s[1:]
-        voices_append(' ~ ')
-    return s
-
-
-def bracket_escape(s, state):
-    m = re.match(r'^([^\]]*)] *(.*)$', s)
-    if m:
-        cmd = m.group(1)
-        s = m.group(2)
-        try_parse_header_line(cmd, state)
-    return s
-
-
-def try_parse_chord_delims(s, state):
-    if s[:1] == '[':
-        s = s[1:]
-        if re.match('[A-Z]:', s):        # bracket escape
-            return bracket_escape(s, state)
-        if state.next_bar:
-            voices_append(state.next_bar)
-            state.next_bar = ''
-        voices_append('<<')
-
-    if s[:1] == '+':
-        s = s[1:]
-        if state.plus_chord:
-            voices_append('>>')
-            state.plus_chord = 0
-        else:
-            if state.next_bar:
-                voices_append(state.next_bar)
-                state.next_bar = ''
-            voices_append('<<')
-            state.plus_chord = 1
-
-    ch = ''
-    if s[:1] == ']':
-        s = s[1:]
-        ch = '>>'
-
-    end = 0
-    while s[:1] == ')':
-        end = end + 1
-        s = s[1:]
-
-    voices_append("\\spanrequest \\stop \"slur\"" * end)
-    voices_append(ch)
-    return s
-
-
-def try_parse_grace_delims(s, state):
-    if s[:1] == '{':
-        if state.next_bar:
-            voices_append(state.next_bar)
-            state.next_bar = ''
-        s = s[1:]
-        voices_append('\\grace { ')
-
-    if s[:1] == '}':
-        s = s[1:]
-        voices_append('}')
-
-    return s
-
-
-def try_parse_comment(s):
-    global nobarlines
-    if s[0] == '%':
-        if s[0:5] == '%MIDI':
-            # the nobarlines option is necessary for an abc to lilypond translator for
-            # exactly the same reason abc2midi needs it: abc requires the user to enter
-            # the note that will be printed, and MIDI and lilypond expect entry of the
-            # pitch that will be played.
-            #
-            # In standard 19th century musical notation, the algorithm for translating
-            # between printed note and pitch involves using the barlines to determine
-            # the scope of the accidentals.
-            #
-            # Since ABC is frequently used for music in styles that do not use this
-            # convention, such as most music written before 1700, or ethnic music in
-            # non-western scales, it is necessary to be able to tell a translator that
-            # the barlines should not affect its interpretation of the pitch.
-            if 'nobarlines' in s:
-                nobarlines = 1
-        elif s[0:3] == '%LY':
-            p = s.find('voices')
-            if p > -1:
-                voices_append(s[p+7:])
-                voices_append("\n")
-            p = s.find('slyrics')
-            if p > -1:
-                slyrics_append(s[p+8:])
-
-# write other kinds of appending  if we ever need them.
-    return s
-
-
-lineno = 0
-happy_count = 100
-
-
-def parse_file(fn):
-    f = open(fn, encoding='utf-8')
-    ls = f.readlines()
-    ls = [re.sub("\r$", '', x) for x in ls]
-
-    select_voice('default', '')
-    global lineno
-    lineno = 0
-    if not global_options.quiet:
-        sys.stderr.write("Line ... ")
-        sys.stderr.flush()
-    __main__.state = state_list[current_voice_idx]
-
-    for ln in ls:
-        lineno = lineno + 1
-
-        if not lineno % happy_count:
-            sys.stderr.write('[%d]' % lineno)
-            sys.stderr.flush()
-        m = re.match('^([^%]*)%(.*)$', ln)  # add comments to current voice
-        if m:
-            if m.group(2):
-                try_parse_comment(m.group(2))
-                voices_append('%% %s\n' % m.group(2))
-            ln = m.group(1)
-
-        orig_ln = ln
-
-        ln = junk_space(ln, state)
-        ln = try_parse_header_line(ln, state)
-
-        # Try nibbling characters off until the line doesn't change.
-        prev_ln = ''
-        while ln != prev_ln:
-            prev_ln = ln
-            ln = try_parse_chord_delims(ln, state)
-            ln = try_parse_rest(ln, state)
-            ln = try_parse_articulation(ln, state)
-            ln = try_parse_note(ln, state)
-            ln = try_parse_bar(ln, state)
-            ln = try_parse_tie(ln)
-            ln = try_parse_escape(ln)
-            ln = try_parse_guitar_chord(ln, state)
-            ln = try_parse_tuplet_begin(ln, state)
-            ln = try_parse_group_end(ln, state)
-            ln = try_parse_grace_delims(ln, state)
-            ln = junk_space(ln, state)
-
-        if ln:
-            error("%s: %d: Huh?  Don't understand\n" % (fn, lineno))
-            left = orig_ln[0:-len(ln)]
-            sys.stderr.write(left + '\n')
-            sys.stderr.write(' ' * len(left) + ln + '\n')
-
-
-def identify():
-    if not global_options.quiet:
-        sys.stderr.write("%s from LilyPond %s\n" % (ly.program_name, version))
-
-
-authors = """
-Written by Han-Wen Nienhuys <hanwen@xs4all.nl>, Laura Conrad
-<lconrad@laymusic.org>, Roy Rankin <Roy.Rankin@@alcatel.com.au>.
-"""
-
-
-def print_version():
-    print(r"""abc2ly (GNU lilypond) %s""" % version)
-
-
-def get_option_parser():
-    p = ly.get_option_parser(usage=_("%s [OPTION]... FILE") % 'abc2ly',
-                             description=_('''abc2ly converts ABC music files (see
-%s) to LilyPond input.
-''') % 'http://abcnotation.com/abc2mtex/abc.txt',
-                             add_help_option=False)
-
-    p.version = "abc2ly (LilyPond) 2.24.2"
-    p.add_option("--version",
-                 action="version",
-                 help=_("show version number and exit"))
-    p.add_option("-h", "--help",
-                 action="help",
-                 help=_("show this help and exit"))
-    p.add_option("-o", "--output", metavar='FILE',
-                 action="store",
-                 help=_("write output to FILE"))
-    p.add_option("-s", "--strict",
-                 action="store_true",
-                 help=_("be strict about success"))
-    p.add_option('-b', '--beams',
-                 action="store_true",
-                 help=_("preserve ABC's notion of beams"))
-    p.add_option('-q', '--quiet',
-                 action="store_true",
-                 help=_("suppress progress messages"))
-    p.add_option_group('',
-                       description=(
-                           _('Report bugs via %s')
-                           % 'bug-lilypond@gnu.org') + '\n')
-    return p
-
-
-option_parser = get_option_parser()
-(global_options, files) = option_parser.parse_args()
-
-
-identify()
-
-header['tagline'] = 'Lily was here %s -- automatically converted from ABC' % version
-for f in files:
-    if f == '-':
-        f = ''
-
-    if not global_options.quiet:
-        sys.stderr.write('Parsing `%s\'...\n' % f)
-    parse_file(f)
-
-    if not global_options.output:
-        global_options.output = os.path.basename(
-            os.path.splitext(f)[0]) + ".ly"
-    if not global_options.quiet:
-        sys.stderr.write('lilypond output to: `%s\'...' %
-                         global_options.output)
-    outf = open(global_options.output, 'w', encoding='utf-8')
-
-# don't substitute @VERSION@. We want this to reflect
-# the last version that was verified to work.
-    outf.write('\\version "2.7.40"\n')
-
-#        dump_global (outf)
-    dump_header(outf, header)
-    dump_slyrics(outf)
-    dump_voices(outf)
-    dump_score(outf)
-    dump_lyrics(outf)
-    if not global_options.quiet:
-        sys.stderr.write('\n')
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/list.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/list.go
deleted file mode 100644
index 4a120bfda0b2de2553b8d9b20559417f2951fe5d..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/list.go and /dev/null differ
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-9/gnu.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-9/gnu.go
deleted file mode 100644
index d200e62c9fe5ce0bf9ada3f46157fce8ff8b4954..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-9/gnu.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/memory/weaviate.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/memory/weaviate.py
deleted file mode 100644
index 5408e9a97aa3594ad443448cfc31f2546a01eb09..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/memory/weaviate.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import uuid
-
-import weaviate
-from weaviate import Client
-from weaviate.embedded import EmbeddedOptions
-from weaviate.util import generate_uuid5
-
-from autogpt.config import Config
-from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
-
-
-def default_schema(weaviate_index):
-    return {
-        "class": weaviate_index,
-        "properties": [
-            {
-                "name": "raw_text",
-                "dataType": ["text"],
-                "description": "original text for the embedding",
-            }
-        ],
-    }
-
-
-class WeaviateMemory(MemoryProviderSingleton):
-    def __init__(self, cfg):
-        auth_credentials = self._build_auth_credentials(cfg)
-
-        url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}"
-
-        if cfg.use_weaviate_embedded:
-            self.client = Client(
-                embedded_options=EmbeddedOptions(
-                    hostname=cfg.weaviate_host,
-                    port=int(cfg.weaviate_port),
-                    persistence_data_path=cfg.weaviate_embedded_path,
-                )
-            )
-
-            print(
-                f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}"
-            )
-        else:
-            self.client = Client(url, auth_client_secret=auth_credentials)
-
-        self.index = WeaviateMemory.format_classname(cfg.memory_index)
-        self._create_schema()
-
-    @staticmethod
-    def format_classname(index):
-        # weaviate uses capitalised index names
-        # The python client uses the following code to format
-        # index names before the corresponding class is created
-        if len(index) == 1:
-            return index.capitalize()
-        return index[0].capitalize() + index[1:]
-
-    def _create_schema(self):
-        schema = default_schema(self.index)
-        if not self.client.schema.contains(schema):
-            self.client.schema.create_class(schema)
-
-    def _build_auth_credentials(self, cfg):
-        if cfg.weaviate_username and cfg.weaviate_password:
-            return weaviate.AuthClientPassword(
-                cfg.weaviate_username, cfg.weaviate_password
-            )
-        if cfg.weaviate_api_key:
-            return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key)
-        else:
-            return None
-
-    def add(self, data):
-        vector = get_ada_embedding(data)
-
-        doc_uuid = generate_uuid5(data, self.index)
-        data_object = {"raw_text": data}
-
-        with self.client.batch as batch:
-            batch.add_data_object(
-                uuid=doc_uuid,
-                data_object=data_object,
-                class_name=self.index,
-                vector=vector,
-            )
-
-        return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}"
-
-    def get(self, data):
-        return self.get_relevant(data, 1)
-
-    def clear(self):
-        self.client.schema.delete_all()
-
-        # weaviate does not yet have a neat way to just remove the items in an index
-        # without removing the entire schema, therefore we need to re-create it
-        # after a call to delete_all
-        self._create_schema()
-
-        return "Obliterated"
-
-    def get_relevant(self, data, num_relevant=5):
-        query_embedding = get_ada_embedding(data)
-        try:
-            results = (
-                self.client.query.get(self.index, ["raw_text"])
-                .with_near_vector({"vector": query_embedding, "certainty": 0.7})
-                .with_limit(num_relevant)
-                .do()
-            )
-
-            if len(results["data"]["Get"][self.index]) > 0:
-                return [
-                    str(item["raw_text"]) for item in results["data"]["Get"][self.index]
-                ]
-            else:
-                return []
-
-        except Exception as err:
-            print(f"Unexpected error {err=}, {type(err)=}")
-            return []
-
-    def get_stats(self):
-        result = self.client.query.aggregate(self.index).with_meta_count().do()
-        class_data = result["data"]["Aggregate"][self.index]
-
-        return class_data[0]["meta"] if class_data else {}
diff --git a/spaces/PeepDaSlan9/De-limiter/utils/read_wave_utils.py b/spaces/PeepDaSlan9/De-limiter/utils/read_wave_utils.py
deleted file mode 100644
index 9f5cf510c69547162c435b9c30fcca04f3218e57..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/utils/read_wave_utils.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import random
-import math
-
-import numpy as np
-import librosa
-import torchaudio
-
-
-def load_wav_arbitrary_position_mono(filename, sample_rate, seq_duration):
-    # mono
-    # seq_duration[second]
-    length = torchaudio.info(filename).num_frames
-
-    read_length = librosa.time_to_samples(seq_duration, sr=sample_rate)
-    if length > read_length:
-        random_start = random.randint(0, int(length - read_length - 1)) / sample_rate
-        X, sr = librosa.load(
-            filename, sr=None, offset=random_start, duration=seq_duration
-        )
-    else:
-        random_start = 0
-        total_pad_length = read_length - length
-        X, sr = librosa.load(filename, sr=None, offset=0, duration=seq_duration)
-        pad_left = random.randint(0, total_pad_length)
-        X = np.pad(X, (pad_left, total_pad_length - pad_left))
-
-    return X
-
-
-def load_wav_specific_position_mono(
-    filename, sample_rate, seq_duration, start_position
-):
-    # mono
-    # seq_duration[second]
-    # start_position[second]
-    length = torchaudio.info(filename).num_frames
-    read_length = librosa.time_to_samples(seq_duration, sr=sample_rate)
-
-    start_pos_sec = max(
-        start_position, 0
-    )  # if start_position is minus, then start from 0.
-    start_pos_sample = librosa.time_to_samples(start_pos_sec, sr=sample_rate)
-
-    if (
-        length <= start_pos_sample
-    ):  # if start position exceeds audio length, then start from 0.
-        start_pos_sec = 0
-        start_pos_sample = 0
-    X, sr = librosa.load(filename, sr=None, offset=start_pos_sec, duration=seq_duration)
-
-    if length < start_pos_sample + read_length:
-        X = np.pad(X, (0, (start_pos_sample + read_length) - length))
-
-    return X
-
-
-# load wav file from arbitrary positions of 16bit stereo wav file
-def load_wav_arbitrary_position_stereo(
-    filename, sample_rate, seq_duration, return_pos=False
-):
-    # stereo
-    # seq_duration[second]
-    length = torchaudio.info(filename).num_frames
-    read_length = librosa.time_to_samples(seq_duration, sr=sample_rate)
-
-    random_start_sample = random.randint(
-        0, int(length - math.ceil(seq_duration * sample_rate) - 1)
-    )
-    random_start_sec = librosa.samples_to_time(random_start_sample, sr=sample_rate)
-    X, sr = librosa.load(
-        filename, sr=None, mono=False, offset=random_start_sec, duration=seq_duration
-    )
-
-    if length < random_start_sample + read_length:
-        X = np.pad(X, ((0, 0), (0, (random_start_sample + read_length) - length)))
-
-    if return_pos:
-        return X, random_start_sec
-    else:
-        return X
-
-
-def load_wav_specific_position_stereo(
-    filename, sample_rate, seq_duration, start_position
-):
-    # stereo
-    # seq_duration[second]
-    # start_position[second]
-    length = torchaudio.info(filename).num_frames
-    read_length = librosa.time_to_samples(seq_duration, sr=sample_rate)
-
-    start_pos_sec = max(
-        start_position, 0
-    )  # if start_position is minus, then start from 0.
-    start_pos_sample = librosa.time_to_samples(start_pos_sec, sr=sample_rate)
-
-    if (
-        length <= start_pos_sample
-    ):  # if start position exceeds audio length, then start from 0.
-        start_pos_sec = 0
-        start_pos_sample = 0
-    X, sr = librosa.load(
-        filename, sr=None, mono=False, offset=start_pos_sec, duration=seq_duration
-    )
-
-    if length < start_pos_sample + read_length:
-        X = np.pad(X, ((0, 0), (0, (start_pos_sample + read_length) - length)))
-
-    return X
diff --git a/spaces/Plurigrid/LifeSim/src/components/ui/card.tsx b/spaces/Plurigrid/LifeSim/src/components/ui/card.tsx
deleted file mode 100644
index 6583ebc1bb942bfb94e00fb4e7c7d685073c7b2a..0000000000000000000000000000000000000000
--- a/spaces/Plurigrid/LifeSim/src/components/ui/card.tsx
+++ /dev/null
@@ -1,79 +0,0 @@
-import * as React from "react"
-
-import { cn } from "@/lib/utils"
-
-const Card = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div
-    ref={ref}
-    className={cn(
-      "rounded-lg border border-stone-200 bg-white text-stone-950 shadow-sm dark:border-stone-800 dark:bg-stone-950 dark:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-Card.displayName = "Card"
-
-const CardHeader = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div
-    ref={ref}
-    className={cn("flex flex-col space-y-1.5 p-6", className)}
-    {...props}
-  />
-))
-CardHeader.displayName = "CardHeader"
-
-const CardTitle = React.forwardRef<
-  HTMLParagraphElement,
-  React.HTMLAttributes<HTMLHeadingElement>
->(({ className, ...props }, ref) => (
-  <h3
-    ref={ref}
-    className={cn(
-      "text-2xl font-semibold leading-none tracking-tight",
-      className
-    )}
-    {...props}
-  />
-))
-CardTitle.displayName = "CardTitle"
-
-const CardDescription = React.forwardRef<
-  HTMLParagraphElement,
-  React.HTMLAttributes<HTMLParagraphElement>
->(({ className, ...props }, ref) => (
-  <p
-    ref={ref}
-    className={cn("text-sm text-stone-500 dark:text-stone-400", className)}
-    {...props}
-  />
-))
-CardDescription.displayName = "CardDescription"
-
-const CardContent = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div ref={ref} className={cn("p-6 pt-0", className)} {...props} />
-))
-CardContent.displayName = "CardContent"
-
-const CardFooter = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div
-    ref={ref}
-    className={cn("flex items-center p-6 pt-0", className)}
-    {...props}
-  />
-))
-CardFooter.displayName = "CardFooter"
-
-export { Card, CardHeader, CardFooter, CardTitle, CardDescription, CardContent }
diff --git a/spaces/Poupeto/RVC_Ryu7ztv/lib/infer_pack/attentions.py b/spaces/Poupeto/RVC_Ryu7ztv/lib/infer_pack/attentions.py
deleted file mode 100644
index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000
--- a/spaces/Poupeto/RVC_Ryu7ztv/lib/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer_pack import commons
-from lib.infer_pack import modules
-from lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/cvvp.py b/spaces/Pranjal12345/Text_to_Speech/tortoise/models/cvvp.py
deleted file mode 100644
index 544ca47b21a31c8d26d4ea407b9783e7d59e8126..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/cvvp.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import einsum
-
-from tortoise.models.arch_util import AttentionBlock
-from tortoise.models.xtransformers import ContinuousTransformerWrapper, Encoder
-
-
-def exists(val):
-    return val is not None
-
-
-def masked_mean(t, mask):
-    t = t.masked_fill(~mask, 0.)
-    return t.sum(dim=1) / mask.sum(dim=1)
-
-
-class CollapsingTransformer(nn.Module):
-    def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs):
-        super().__init__()
-        self.transformer = ContinuousTransformerWrapper(
-            max_seq_len=-1,
-            use_pos_emb=False,
-            attn_layers=Encoder(
-                dim=model_dim,
-                depth=depth,
-                heads=heads,
-                ff_dropout=dropout,
-                ff_mult=1,
-                attn_dropout=dropout,
-                use_rmsnorm=True,
-                ff_glu=True,
-                rotary_pos_emb=True,
-                **encoder_kwargs,
-            ))
-        self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1),
-                                          AttentionBlock(
-            output_dims, num_heads=heads, do_checkpoint=False),
-            nn.Conv1d(output_dims, output_dims, 1))
-        self.mask_percentage = mask_percentage
-
-    def forward(self, x, **transformer_kwargs):
-        h = self.transformer(x, **transformer_kwargs)
-        h = h.permute(0, 2, 1)
-        h = self.pre_combiner(h).permute(0, 2, 1)
-        if self.training:
-            mask = torch.rand_like(h.float()) > self.mask_percentage
-        else:
-            mask = torch.ones_like(h.float()).bool()
-        return masked_mean(h, mask)
-
-
-class ConvFormatEmbedding(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        self.emb = nn.Embedding(*args, **kwargs)
-
-    def forward(self, x):
-        y = self.emb(x)
-        return y.permute(0, 2, 1)
-
-
-class CVVP(nn.Module):
-    def __init__(
-            self,
-            model_dim=512,
-            transformer_heads=8,
-            dropout=.1,
-            conditioning_enc_depth=8,
-            cond_mask_percentage=0,
-            mel_channels=80,
-            mel_codes=None,
-            speech_enc_depth=8,
-            speech_mask_percentage=0,
-            latent_multiplier=1,
-    ):
-        super().__init__()
-        latent_dim = latent_multiplier*model_dim
-        self.temperature = nn.Parameter(torch.tensor(1.))
-
-        self.cond_emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim//2, kernel_size=5, stride=2, padding=2),
-                                      nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1))
-        self.conditioning_transformer = CollapsingTransformer(
-            model_dim, model_dim, transformer_heads, dropout, conditioning_enc_depth, cond_mask_percentage)
-        self.to_conditioning_latent = nn.Linear(
-            latent_dim, latent_dim, bias=False)
-
-        if mel_codes is None:
-            self.speech_emb = nn.Conv1d(
-                mel_channels, model_dim, kernel_size=5, padding=2)
-        else:
-            self.speech_emb = ConvFormatEmbedding(mel_codes, model_dim)
-        self.speech_transformer = CollapsingTransformer(
-            model_dim, latent_dim, transformer_heads, dropout, speech_enc_depth, speech_mask_percentage)
-        self.to_speech_latent = nn.Linear(
-            latent_dim, latent_dim, bias=False)
-
-    def get_grad_norm_parameter_groups(self):
-        return {
-            'conditioning': list(self.conditioning_transformer.parameters()),
-            'speech': list(self.speech_transformer.parameters()),
-        }
-
-    def forward(
-            self,
-            mel_cond,
-            mel_input,
-            return_loss=False
-    ):
-        cond_emb = self.cond_emb(mel_cond).permute(0, 2, 1)
-        enc_cond = self.conditioning_transformer(cond_emb)
-        cond_latents = self.to_conditioning_latent(enc_cond)
-
-        speech_emb = self.speech_emb(mel_input).permute(0, 2, 1)
-        enc_speech = self.speech_transformer(speech_emb)
-        speech_latents = self.to_speech_latent(enc_speech)
-
-        cond_latents, speech_latents = map(lambda t: F.normalize(
-            t, p=2, dim=-1), (cond_latents, speech_latents))
-        temp = self.temperature.exp()
-
-        if not return_loss:
-            sim = einsum('n d, n d -> n', cond_latents,
-                         speech_latents) * temp
-            return sim
-
-        sim = einsum('i d, j d -> i j', cond_latents,
-                     speech_latents) * temp
-        labels = torch.arange(
-            cond_latents.shape[0], device=mel_input.device)
-        loss = (F.cross_entropy(sim, labels) +
-                F.cross_entropy(sim.t(), labels)) / 2
-
-        return loss
-
-
-if __name__ == '__main__':
-    clvp = CVVP()
-    clvp(torch.randn(2, 80, 100),
-         torch.randn(2, 80, 95),
-         return_loss=True)
diff --git a/spaces/PulsarAI/thebloke-quantized-models/README.md b/spaces/PulsarAI/thebloke-quantized-models/README.md
deleted file mode 100644
index cfbb32b5998acac57883540fee0ea14aa05fcd95..0000000000000000000000000000000000000000
--- a/spaces/PulsarAI/thebloke-quantized-models/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: TheBloke Quantized Models
-emoji: 👾
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_537227KB.py b/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_537227KB.py
deleted file mode 100644
index 823b44fb64898e8dcbb12180ba45d1718f9b03f7..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_537227KB.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_537238KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 64)
-        self.stg1_high_band_net = BaseASPPNet(2, 64)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(32, 64)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(64, 128)
-
-        self.out = nn.Conv2d(128, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/Rakesh30/Sentence_Embedding-App/README.md b/spaces/Rakesh30/Sentence_Embedding-App/README.md
deleted file mode 100644
index 2cc7a3cb2035d33616003efec784c15d6edcc7b7..0000000000000000000000000000000000000000
--- a/spaces/Rakesh30/Sentence_Embedding-App/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sentence Embedding-App
-emoji: 🐠
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/msgpack/ext.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/msgpack/ext.py
deleted file mode 100644
index 25544c555648c13762e150ea559d3a69674bdd34..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/msgpack/ext.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# coding: utf-8
-from collections import namedtuple
-import datetime
-import sys
-import struct
-
-
-PY2 = sys.version_info[0] == 2
-
-if PY2:
-    int_types = (int, long)
-    _utc = None
-else:
-    int_types = int
-    try:
-        _utc = datetime.timezone.utc
-    except AttributeError:
-        _utc = datetime.timezone(datetime.timedelta(0))
-
-
-class ExtType(namedtuple("ExtType", "code data")):
-    """ExtType represents ext type in msgpack."""
-
-    def __new__(cls, code, data):
-        if not isinstance(code, int):
-            raise TypeError("code must be int")
-        if not isinstance(data, bytes):
-            raise TypeError("data must be bytes")
-        if not 0 <= code <= 127:
-            raise ValueError("code must be 0~127")
-        return super(ExtType, cls).__new__(cls, code, data)
-
-
-class Timestamp(object):
-    """Timestamp represents the Timestamp extension type in msgpack.
-
-    When built with Cython, msgpack uses C methods to pack and unpack `Timestamp`. When using pure-Python
-    msgpack, :func:`to_bytes` and :func:`from_bytes` are used to pack and unpack `Timestamp`.
-
-    This class is immutable: Do not override seconds and nanoseconds.
-    """
-
-    __slots__ = ["seconds", "nanoseconds"]
-
-    def __init__(self, seconds, nanoseconds=0):
-        """Initialize a Timestamp object.
-
-        :param int seconds:
-            Number of seconds since the UNIX epoch (00:00:00 UTC Jan 1 1970, minus leap seconds).
-            May be negative.
-
-        :param int nanoseconds:
-            Number of nanoseconds to add to `seconds` to get fractional time.
-            Maximum is 999_999_999.  Default is 0.
-
-        Note: Negative times (before the UNIX epoch) are represented as negative seconds + positive ns.
-        """
-        if not isinstance(seconds, int_types):
-            raise TypeError("seconds must be an interger")
-        if not isinstance(nanoseconds, int_types):
-            raise TypeError("nanoseconds must be an integer")
-        if not (0 <= nanoseconds < 10**9):
-            raise ValueError(
-                "nanoseconds must be a non-negative integer less than 999999999."
-            )
-        self.seconds = seconds
-        self.nanoseconds = nanoseconds
-
-    def __repr__(self):
-        """String representation of Timestamp."""
-        return "Timestamp(seconds={0}, nanoseconds={1})".format(
-            self.seconds, self.nanoseconds
-        )
-
-    def __eq__(self, other):
-        """Check for equality with another Timestamp object"""
-        if type(other) is self.__class__:
-            return (
-                self.seconds == other.seconds and self.nanoseconds == other.nanoseconds
-            )
-        return False
-
-    def __ne__(self, other):
-        """not-equals method (see :func:`__eq__()`)"""
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return hash((self.seconds, self.nanoseconds))
-
-    @staticmethod
-    def from_bytes(b):
-        """Unpack bytes into a `Timestamp` object.
-
-        Used for pure-Python msgpack unpacking.
-
-        :param b: Payload from msgpack ext message with code -1
-        :type b: bytes
-
-        :returns: Timestamp object unpacked from msgpack ext payload
-        :rtype: Timestamp
-        """
-        if len(b) == 4:
-            seconds = struct.unpack("!L", b)[0]
-            nanoseconds = 0
-        elif len(b) == 8:
-            data64 = struct.unpack("!Q", b)[0]
-            seconds = data64 & 0x00000003FFFFFFFF
-            nanoseconds = data64 >> 34
-        elif len(b) == 12:
-            nanoseconds, seconds = struct.unpack("!Iq", b)
-        else:
-            raise ValueError(
-                "Timestamp type can only be created from 32, 64, or 96-bit byte objects"
-            )
-        return Timestamp(seconds, nanoseconds)
-
-    def to_bytes(self):
-        """Pack this Timestamp object into bytes.
-
-        Used for pure-Python msgpack packing.
-
-        :returns data: Payload for EXT message with code -1 (timestamp type)
-        :rtype: bytes
-        """
-        if (self.seconds >> 34) == 0:  # seconds is non-negative and fits in 34 bits
-            data64 = self.nanoseconds << 34 | self.seconds
-            if data64 & 0xFFFFFFFF00000000 == 0:
-                # nanoseconds is zero and seconds < 2**32, so timestamp 32
-                data = struct.pack("!L", data64)
-            else:
-                # timestamp 64
-                data = struct.pack("!Q", data64)
-        else:
-            # timestamp 96
-            data = struct.pack("!Iq", self.nanoseconds, self.seconds)
-        return data
-
-    @staticmethod
-    def from_unix(unix_sec):
-        """Create a Timestamp from posix timestamp in seconds.
-
-        :param unix_float: Posix timestamp in seconds.
-        :type unix_float: int or float.
-        """
-        seconds = int(unix_sec // 1)
-        nanoseconds = int((unix_sec % 1) * 10**9)
-        return Timestamp(seconds, nanoseconds)
-
-    def to_unix(self):
-        """Get the timestamp as a floating-point value.
-
-        :returns: posix timestamp
-        :rtype: float
-        """
-        return self.seconds + self.nanoseconds / 1e9
-
-    @staticmethod
-    def from_unix_nano(unix_ns):
-        """Create a Timestamp from posix timestamp in nanoseconds.
-
-        :param int unix_ns: Posix timestamp in nanoseconds.
-        :rtype: Timestamp
-        """
-        return Timestamp(*divmod(unix_ns, 10**9))
-
-    def to_unix_nano(self):
-        """Get the timestamp as a unixtime in nanoseconds.
-
-        :returns: posix timestamp in nanoseconds
-        :rtype: int
-        """
-        return self.seconds * 10**9 + self.nanoseconds
-
-    def to_datetime(self):
-        """Get the timestamp as a UTC datetime.
-
-        Python 2 is not supported.
-
-        :rtype: datetime.
-        """
-        return datetime.datetime.fromtimestamp(0, _utc) + datetime.timedelta(
-            seconds=self.to_unix()
-        )
-
-    @staticmethod
-    def from_datetime(dt):
-        """Create a Timestamp from datetime with tzinfo.
-
-        Python 2 is not supported.
-
-        :rtype: Timestamp
-        """
-        return Timestamp.from_unix(dt.timestamp())
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py
deleted file mode 100644
index 2c4da5b57e5fda8b1510a61c2f14a61fac1c0916..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py
+++ /dev/null
@@ -1,364 +0,0 @@
-"""distutils.cygwinccompiler
-
-Provides the CygwinCCompiler class, a subclass of UnixCCompiler that
-handles the Cygwin port of the GNU C compiler to Windows.  It also contains
-the Mingw32CCompiler class which handles the mingw32 port of GCC (same as
-cygwin in no-cygwin mode).
-"""
-
-import os
-import sys
-import copy
-import shlex
-import warnings
-from subprocess import check_output
-
-from distutils.unixccompiler import UnixCCompiler
-from distutils.file_util import write_file
-from distutils.errors import (
-    DistutilsExecError,
-    DistutilsPlatformError,
-    CCompilerError,
-    CompileError,
-)
-from distutils.version import LooseVersion, suppress_known_deprecation
-
-
-def get_msvcr():
-    """Include the appropriate MSVC runtime library if Python was built
-    with MSVC 7.0 or later.
-    """
-    msc_pos = sys.version.find('MSC v.')
-    if msc_pos != -1:
-        msc_ver = sys.version[msc_pos + 6 : msc_pos + 10]
-        if msc_ver == '1300':
-            # MSVC 7.0
-            return ['msvcr70']
-        elif msc_ver == '1310':
-            # MSVC 7.1
-            return ['msvcr71']
-        elif msc_ver == '1400':
-            # VS2005 / MSVC 8.0
-            return ['msvcr80']
-        elif msc_ver == '1500':
-            # VS2008 / MSVC 9.0
-            return ['msvcr90']
-        elif msc_ver == '1600':
-            # VS2010 / MSVC 10.0
-            return ['msvcr100']
-        elif msc_ver == '1700':
-            # VS2012 / MSVC 11.0
-            return ['msvcr110']
-        elif msc_ver == '1800':
-            # VS2013 / MSVC 12.0
-            return ['msvcr120']
-        elif 1900 <= int(msc_ver) < 2000:
-            # VS2015 / MSVC 14.0
-            return ['ucrt', 'vcruntime140']
-        else:
-            raise ValueError("Unknown MS Compiler version %s " % msc_ver)
-
-
-_runtime_library_dirs_msg = (
-    "Unable to set runtime library search path on Windows, "
-    "usually indicated by `runtime_library_dirs` parameter to Extension"
-)
-
-
-class CygwinCCompiler(UnixCCompiler):
-    """Handles the Cygwin port of the GNU C compiler to Windows."""
-
-    compiler_type = 'cygwin'
-    obj_extension = ".o"
-    static_lib_extension = ".a"
-    shared_lib_extension = ".dll.a"
-    dylib_lib_extension = ".dll"
-    static_lib_format = "lib%s%s"
-    shared_lib_format = "lib%s%s"
-    dylib_lib_format = "cyg%s%s"
-    exe_extension = ".exe"
-
-    def __init__(self, verbose=0, dry_run=0, force=0):
-
-        super().__init__(verbose, dry_run, force)
-
-        status, details = check_config_h()
-        self.debug_print(
-            "Python's GCC status: {} (details: {})".format(status, details)
-        )
-        if status is not CONFIG_H_OK:
-            self.warn(
-                "Python's pyconfig.h doesn't seem to support your compiler. "
-                "Reason: %s. "
-                "Compiling may fail because of undefined preprocessor macros." % details
-            )
-
-        self.cc = os.environ.get('CC', 'gcc')
-        self.cxx = os.environ.get('CXX', 'g++')
-
-        self.linker_dll = self.cc
-        shared_option = "-shared"
-
-        self.set_executables(
-            compiler='%s -mcygwin -O -Wall' % self.cc,
-            compiler_so='%s -mcygwin -mdll -O -Wall' % self.cc,
-            compiler_cxx='%s -mcygwin -O -Wall' % self.cxx,
-            linker_exe='%s -mcygwin' % self.cc,
-            linker_so=('{} -mcygwin {}'.format(self.linker_dll, shared_option)),
-        )
-
-        # Include the appropriate MSVC runtime library if Python was built
-        # with MSVC 7.0 or later.
-        self.dll_libraries = get_msvcr()
-
-    @property
-    def gcc_version(self):
-        # Older numpy dependend on this existing to check for ancient
-        # gcc versions. This doesn't make much sense with clang etc so
-        # just hardcode to something recent.
-        # https://github.com/numpy/numpy/pull/20333
-        warnings.warn(
-            "gcc_version attribute of CygwinCCompiler is deprecated. "
-            "Instead of returning actual gcc version a fixed value 11.2.0 is returned.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        with suppress_known_deprecation():
-            return LooseVersion("11.2.0")
-
-    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
-        """Compiles the source by spawning GCC and windres if needed."""
-        if ext == '.rc' or ext == '.res':
-            # gcc needs '.res' and '.rc' compiled to object files !!!
-            try:
-                self.spawn(["windres", "-i", src, "-o", obj])
-            except DistutilsExecError as msg:
-                raise CompileError(msg)
-        else:  # for other files use the C-compiler
-            try:
-                self.spawn(
-                    self.compiler_so + cc_args + [src, '-o', obj] + extra_postargs
-                )
-            except DistutilsExecError as msg:
-                raise CompileError(msg)
-
-    def link(
-        self,
-        target_desc,
-        objects,
-        output_filename,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        export_symbols=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        build_temp=None,
-        target_lang=None,
-    ):
-        """Link the objects."""
-        # use separate copies, so we can modify the lists
-        extra_preargs = copy.copy(extra_preargs or [])
-        libraries = copy.copy(libraries or [])
-        objects = copy.copy(objects or [])
-
-        if runtime_library_dirs:
-            self.warn(_runtime_library_dirs_msg)
-
-        # Additional libraries
-        libraries.extend(self.dll_libraries)
-
-        # handle export symbols by creating a def-file
-        # with executables this only works with gcc/ld as linker
-        if (export_symbols is not None) and (
-            target_desc != self.EXECUTABLE or self.linker_dll == "gcc"
-        ):
-            # (The linker doesn't do anything if output is up-to-date.
-            # So it would probably better to check if we really need this,
-            # but for this we had to insert some unchanged parts of
-            # UnixCCompiler, and this is not what we want.)
-
-            # we want to put some files in the same directory as the
-            # object files are, build_temp doesn't help much
-            # where are the object files
-            temp_dir = os.path.dirname(objects[0])
-            # name of dll to give the helper files the same base name
-            (dll_name, dll_extension) = os.path.splitext(
-                os.path.basename(output_filename)
-            )
-
-            # generate the filenames for these files
-            def_file = os.path.join(temp_dir, dll_name + ".def")
-
-            # Generate .def file
-            contents = ["LIBRARY %s" % os.path.basename(output_filename), "EXPORTS"]
-            for sym in export_symbols:
-                contents.append(sym)
-            self.execute(write_file, (def_file, contents), "writing %s" % def_file)
-
-            # next add options for def-file
-
-            # for gcc/ld the def-file is specified as any object files
-            objects.append(def_file)
-
-        # end: if ((export_symbols is not None) and
-        #        (target_desc != self.EXECUTABLE or self.linker_dll == "gcc")):
-
-        # who wants symbols and a many times larger output file
-        # should explicitly switch the debug mode on
-        # otherwise we let ld strip the output file
-        # (On my machine: 10KiB < stripped_file < ??100KiB
-        #   unstripped_file = stripped_file + XXX KiB
-        #  ( XXX=254 for a typical python extension))
-        if not debug:
-            extra_preargs.append("-s")
-
-        UnixCCompiler.link(
-            self,
-            target_desc,
-            objects,
-            output_filename,
-            output_dir,
-            libraries,
-            library_dirs,
-            runtime_library_dirs,
-            None,  # export_symbols, we do this in our def-file
-            debug,
-            extra_preargs,
-            extra_postargs,
-            build_temp,
-            target_lang,
-        )
-
-    def runtime_library_dir_option(self, dir):
-        # cygwin doesn't support rpath. While in theory we could error
-        # out like MSVC does, code might expect it to work like on Unix, so
-        # just warn and hope for the best.
-        self.warn(_runtime_library_dirs_msg)
-        return []
-
-    # -- Miscellaneous methods -----------------------------------------
-
-    def _make_out_path(self, output_dir, strip_dir, src_name):
-        # use normcase to make sure '.rc' is really '.rc' and not '.RC'
-        norm_src_name = os.path.normcase(src_name)
-        return super()._make_out_path(output_dir, strip_dir, norm_src_name)
-
-    @property
-    def out_extensions(self):
-        """
-        Add support for rc and res files.
-        """
-        return {
-            **super().out_extensions,
-            **{ext: ext + self.obj_extension for ext in ('.res', '.rc')},
-        }
-
-
-# the same as cygwin plus some additional parameters
-class Mingw32CCompiler(CygwinCCompiler):
-    """Handles the Mingw32 port of the GNU C compiler to Windows."""
-
-    compiler_type = 'mingw32'
-
-    def __init__(self, verbose=0, dry_run=0, force=0):
-
-        super().__init__(verbose, dry_run, force)
-
-        shared_option = "-shared"
-
-        if is_cygwincc(self.cc):
-            raise CCompilerError('Cygwin gcc cannot be used with --compiler=mingw32')
-
-        self.set_executables(
-            compiler='%s -O -Wall' % self.cc,
-            compiler_so='%s -mdll -O -Wall' % self.cc,
-            compiler_cxx='%s -O -Wall' % self.cxx,
-            linker_exe='%s' % self.cc,
-            linker_so='{} {}'.format(self.linker_dll, shared_option),
-        )
-
-        # Maybe we should also append -mthreads, but then the finished
-        # dlls need another dll (mingwm10.dll see Mingw32 docs)
-        # (-mthreads: Support thread-safe exception handling on `Mingw32')
-
-        # no additional libraries needed
-        self.dll_libraries = []
-
-        # Include the appropriate MSVC runtime library if Python was built
-        # with MSVC 7.0 or later.
-        self.dll_libraries = get_msvcr()
-
-    def runtime_library_dir_option(self, dir):
-        raise DistutilsPlatformError(_runtime_library_dirs_msg)
-
-
-# Because these compilers aren't configured in Python's pyconfig.h file by
-# default, we should at least warn the user if he is using an unmodified
-# version.
-
-CONFIG_H_OK = "ok"
-CONFIG_H_NOTOK = "not ok"
-CONFIG_H_UNCERTAIN = "uncertain"
-
-
-def check_config_h():
-    """Check if the current Python installation appears amenable to building
-    extensions with GCC.
-
-    Returns a tuple (status, details), where 'status' is one of the following
-    constants:
-
-    - CONFIG_H_OK: all is well, go ahead and compile
-    - CONFIG_H_NOTOK: doesn't look good
-    - CONFIG_H_UNCERTAIN: not sure -- unable to read pyconfig.h
-
-    'details' is a human-readable string explaining the situation.
-
-    Note there are two ways to conclude "OK": either 'sys.version' contains
-    the string "GCC" (implying that this Python was built with GCC), or the
-    installed "pyconfig.h" contains the string "__GNUC__".
-    """
-
-    # XXX since this function also checks sys.version, it's not strictly a
-    # "pyconfig.h" check -- should probably be renamed...
-
-    from distutils import sysconfig
-
-    # if sys.version contains GCC then python was compiled with GCC, and the
-    # pyconfig.h file should be OK
-    if "GCC" in sys.version:
-        return CONFIG_H_OK, "sys.version mentions 'GCC'"
-
-    # Clang would also work
-    if "Clang" in sys.version:
-        return CONFIG_H_OK, "sys.version mentions 'Clang'"
-
-    # let's see if __GNUC__ is mentioned in python.h
-    fn = sysconfig.get_config_h_filename()
-    try:
-        config_h = open(fn)
-        try:
-            if "__GNUC__" in config_h.read():
-                return CONFIG_H_OK, "'%s' mentions '__GNUC__'" % fn
-            else:
-                return CONFIG_H_NOTOK, "'%s' does not mention '__GNUC__'" % fn
-        finally:
-            config_h.close()
-    except OSError as exc:
-        return (CONFIG_H_UNCERTAIN, "couldn't read '{}': {}".format(fn, exc.strerror))
-
-
-def is_cygwincc(cc):
-    '''Try to determine if the compiler that would be used is from cygwin.'''
-    out_string = check_output(shlex.split(cc) + ['-dumpmachine'])
-    return out_string.strip().endswith(b'cygwin')
-
-
-get_versions = None
-"""
-A stand-in for the previous get_versions() function to prevent failures
-when monkeypatched. See pypa/setuptools#2969.
-"""
diff --git a/spaces/Reha2704/VToonify/vtoonify/style_transfer.py b/spaces/Reha2704/VToonify/vtoonify/style_transfer.py
deleted file mode 100644
index 3e6ba13ca84dc595dfa9eb9ef85a638889d8cdd3..0000000000000000000000000000000000000000
--- a/spaces/Reha2704/VToonify/vtoonify/style_transfer.py
+++ /dev/null
@@ -1,232 +0,0 @@
-import os
-#os.environ['CUDA_VISIBLE_DEVICES'] = "0"
-import argparse
-import numpy as np
-import cv2
-import dlib
-import torch
-from torchvision import transforms
-import torch.nn.functional as F
-from tqdm import tqdm
-from model.vtoonify import VToonify
-from model.bisenet.model import BiSeNet
-from model.encoder.align_all_parallel import align_face
-from util import save_image, load_image, visualize, load_psp_standalone, get_video_crop_parameter, tensor2cv2
-
-
-class TestOptions():
-    def __init__(self):
-
-        self.parser = argparse.ArgumentParser(description="Style Transfer")
-        self.parser.add_argument("--content", type=str, default='./data/077436.jpg', help="path of the content image/video")
-        self.parser.add_argument("--style_id", type=int, default=26, help="the id of the style image")
-        self.parser.add_argument("--style_degree", type=float, default=0.5, help="style degree for VToonify-D")
-        self.parser.add_argument("--color_transfer", action="store_true", help="transfer the color of the style")
-        self.parser.add_argument("--ckpt", type=str, default='./checkpoint/vtoonify_d_cartoon/vtoonify_s_d.pt', help="path of the saved model")
-        self.parser.add_argument("--output_path", type=str, default='./output/', help="path of the output images")
-        self.parser.add_argument("--scale_image", action="store_true", help="resize and crop the image to best fit the model")
-        self.parser.add_argument("--style_encoder_path", type=str, default='./checkpoint/encoder.pt', help="path of the style encoder")
-        self.parser.add_argument("--exstyle_path", type=str, default=None, help="path of the extrinsic style code")
-        self.parser.add_argument("--faceparsing_path", type=str, default='./checkpoint/faceparsing.pth', help="path of the face parsing model")
-        self.parser.add_argument("--video", action="store_true", help="if true, video stylization; if false, image stylization")
-        self.parser.add_argument("--cpu", action="store_true", help="if true, only use cpu")
-        self.parser.add_argument("--backbone", type=str, default='dualstylegan', help="dualstylegan | toonify")
-        self.parser.add_argument("--padding", type=int, nargs=4, default=[200,200,200,200], help="left, right, top, bottom paddings to the face center")
-        self.parser.add_argument("--batch_size", type=int, default=4, help="batch size of frames when processing video")
-        self.parser.add_argument("--parsing_map_path", type=str, default=None, help="path of the refined parsing map of the target video")
-        
-    def parse(self):
-        self.opt = self.parser.parse_args()
-        if self.opt.exstyle_path is None:
-            self.opt.exstyle_path = os.path.join(os.path.dirname(self.opt.ckpt), 'exstyle_code.npy')
-        args = vars(self.opt)
-        print('Load options')
-        for name, value in sorted(args.items()):
-            print('%s: %s' % (str(name), str(value)))
-        return self.opt
-    
-if __name__ == "__main__":
-
-    parser = TestOptions()
-    args = parser.parse()
-    print('*'*98)
-    
-    
-    device = "cpu" if args.cpu else "cuda"
-    
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
-        ])
-    
-    vtoonify = VToonify(backbone = args.backbone)
-    vtoonify.load_state_dict(torch.load(args.ckpt, map_location=lambda storage, loc: storage)['g_ema'])
-    vtoonify.to(device)
-
-    parsingpredictor = BiSeNet(n_classes=19)
-    parsingpredictor.load_state_dict(torch.load(args.faceparsing_path, map_location=lambda storage, loc: storage))
-    parsingpredictor.to(device).eval()
-
-    modelname = './checkpoint/shape_predictor_68_face_landmarks.dat'
-    if not os.path.exists(modelname):
-        import wget, bz2
-        wget.download('http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2', modelname+'.bz2')
-        zipfile = bz2.BZ2File(modelname+'.bz2')
-        data = zipfile.read()
-        open(modelname, 'wb').write(data) 
-    landmarkpredictor = dlib.shape_predictor(modelname)
-
-    pspencoder = load_psp_standalone(args.style_encoder_path, device)    
-
-    if args.backbone == 'dualstylegan':
-        exstyles = np.load(args.exstyle_path, allow_pickle='TRUE').item()
-        stylename = list(exstyles.keys())[args.style_id]
-        exstyle = torch.tensor(exstyles[stylename]).to(device)
-        with torch.no_grad():  
-            exstyle = vtoonify.zplus2wplus(exstyle)
-
-    if args.video and args.parsing_map_path is not None:
-        x_p_hat = torch.tensor(np.load(args.parsing_map_path))          
-            
-    print('Load models successfully!')
-    
-    
-    filename = args.content
-    basename = os.path.basename(filename).split('.')[0]
-    scale = 1
-    kernel_1d = np.array([[0.125],[0.375],[0.375],[0.125]])
-    print('Processing ' + os.path.basename(filename) + ' with vtoonify_' + args.backbone[0])
-    if args.video:
-        cropname = os.path.join(args.output_path, basename + '_input.mp4')
-        savename = os.path.join(args.output_path, basename + '_vtoonify_' +  args.backbone[0] + '.mp4')
-
-        video_cap = cv2.VideoCapture(filename)
-        num = int(video_cap.get(7))
-
-        first_valid_frame = True
-        batch_frames = []
-        for i in tqdm(range(num)):
-            success, frame = video_cap.read()
-            if success == False:
-                assert('load video frames error')
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            # We proprocess the video by detecting the face in the first frame, 
-            # and resizing the frame so that the eye distance is 64 pixels.
-            # Centered on the eyes, we crop the first frame to almost 400x400 (based on args.padding).
-            # All other frames use the same resizing and cropping parameters as the first frame.
-            if first_valid_frame:
-                if args.scale_image:
-                    paras = get_video_crop_parameter(frame, landmarkpredictor, args.padding)
-                    if paras is None:
-                        continue
-                    h,w,top,bottom,left,right,scale = paras
-                    H, W = int(bottom-top), int(right-left)
-                    # for HR video, we apply gaussian blur to the frames to avoid flickers caused by bilinear downsampling
-                    # this can also prevent over-sharp stylization results. 
-                    if scale <= 0.75:
-                        frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                    if scale <= 0.375:
-                        frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                    frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
-                else:
-                    H, W = frame.shape[0], frame.shape[1]
-
-                fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-                videoWriter = cv2.VideoWriter(cropname, fourcc, video_cap.get(5), (W, H))
-                videoWriter2 = cv2.VideoWriter(savename, fourcc, video_cap.get(5), (4*W, 4*H))
-                
-                # For each video, we detect and align the face in the first frame for pSp to obtain the style code. 
-                # This style code is used for all other frames.
-                with torch.no_grad():
-                    I = align_face(frame, landmarkpredictor)
-                    I = transform(I).unsqueeze(dim=0).to(device)
-                    s_w = pspencoder(I)
-                    s_w = vtoonify.zplus2wplus(s_w)
-                    if vtoonify.backbone == 'dualstylegan':
-                        if args.color_transfer:
-                            s_w = exstyle
-                        else:
-                            s_w[:,:7] = exstyle[:,:7]
-                first_valid_frame = False
-            elif args.scale_image:
-                if scale <= 0.75:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                if scale <= 0.375:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
-
-            videoWriter.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-
-            batch_frames += [transform(frame).unsqueeze(dim=0).to(device)]
-
-            if len(batch_frames) == args.batch_size or (i+1) == num:
-                x = torch.cat(batch_frames, dim=0)
-                batch_frames = []
-                with torch.no_grad():
-                    # parsing network works best on 512x512 images, so we predict parsing maps on upsmapled frames
-                    # followed by downsampling the parsing maps
-                    if args.video and args.parsing_map_path is not None:
-                        x_p = x_p_hat[i+1-x.size(0):i+1].to(device)
-                    else:
-                        x_p = F.interpolate(parsingpredictor(2*(F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0], 
-                                        scale_factor=0.5, recompute_scale_factor=False).detach()
-                    # we give parsing maps lower weight (1/16)
-                    inputs = torch.cat((x, x_p/16.), dim=1)
-                    # d_s has no effect when backbone is toonify
-                    y_tilde = vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), d_s = args.style_degree)       
-                    y_tilde = torch.clamp(y_tilde, -1, 1)
-                for k in range(y_tilde.size(0)):
-                    videoWriter2.write(tensor2cv2(y_tilde[k].cpu()))
-
-        videoWriter.release()
-        videoWriter2.release()
-        video_cap.release()
-
-    
-    else:
-        cropname = os.path.join(args.output_path, basename + '_input.jpg')
-        savename = os.path.join(args.output_path, basename + '_vtoonify_' +  args.backbone[0] + '.jpg')
-
-        frame = cv2.imread(filename)
-        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-
-        # We detect the face in the image, and resize the image so that the eye distance is 64 pixels.
-        # Centered on the eyes, we crop the image to almost 400x400 (based on args.padding).
-        if args.scale_image:
-            paras = get_video_crop_parameter(frame, landmarkpredictor, args.padding)
-            if paras is not None:
-                h,w,top,bottom,left,right,scale = paras
-                H, W = int(bottom-top), int(right-left)
-                # for HR image, we apply gaussian blur to it to avoid over-sharp stylization results
-                if scale <= 0.75:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                if scale <= 0.375:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
-
-        with torch.no_grad():
-            I = align_face(frame, landmarkpredictor)
-            I = transform(I).unsqueeze(dim=0).to(device)
-            s_w = pspencoder(I)
-            s_w = vtoonify.zplus2wplus(s_w)
-            if vtoonify.backbone == 'dualstylegan':
-                if args.color_transfer:
-                    s_w = exstyle
-                else:
-                    s_w[:,:7] = exstyle[:,:7]
-
-            x = transform(frame).unsqueeze(dim=0).to(device)
-            # parsing network works best on 512x512 images, so we predict parsing maps on upsmapled frames
-            # followed by downsampling the parsing maps
-            x_p = F.interpolate(parsingpredictor(2*(F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0], 
-                                scale_factor=0.5, recompute_scale_factor=False).detach()
-            # we give parsing maps lower weight (1/16)
-            inputs = torch.cat((x, x_p/16.), dim=1)
-            # d_s has no effect when backbone is toonify
-            y_tilde = vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), d_s = args.style_degree)        
-            y_tilde = torch.clamp(y_tilde, -1, 1)
-
-        cv2.imwrite(cropname, cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-        save_image(y_tilde[0].cpu(), savename)
-        
-    print('Transfer style successfully!')
\ No newline at end of file
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/mask/mask_target.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/mask/mask_target.py
deleted file mode 100644
index 15d26a88bbf3710bd92813335918407db8c4e053..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/mask/mask_target.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import numpy as np
-import torch
-from torch.nn.modules.utils import _pair
-
-
-def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,
-                cfg):
-    """Compute mask target for positive proposals in multiple images.
-
-    Args:
-        pos_proposals_list (list[Tensor]): Positive proposals in multiple
-            images.
-        pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each
-            positive proposals.
-        gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of
-            each image.
-        cfg (dict): Config dict that specifies the mask size.
-
-    Returns:
-        list[Tensor]: Mask target of each image.
-
-    Example:
-        >>> import mmcv
-        >>> import mmdet
-        >>> from mmdet.core.mask import BitmapMasks
-        >>> from mmdet.core.mask.mask_target import *
-        >>> H, W = 17, 18
-        >>> cfg = mmcv.Config({'mask_size': (13, 14)})
-        >>> rng = np.random.RandomState(0)
-        >>> # Positive proposals (tl_x, tl_y, br_x, br_y) for each image
-        >>> pos_proposals_list = [
-        >>>     torch.Tensor([
-        >>>         [ 7.2425,  5.5929, 13.9414, 14.9541],
-        >>>         [ 7.3241,  3.6170, 16.3850, 15.3102],
-        >>>     ]),
-        >>>     torch.Tensor([
-        >>>         [ 4.8448, 6.4010, 7.0314, 9.7681],
-        >>>         [ 5.9790, 2.6989, 7.4416, 4.8580],
-        >>>         [ 0.0000, 0.0000, 0.1398, 9.8232],
-        >>>     ]),
-        >>> ]
-        >>> # Corresponding class index for each proposal for each image
-        >>> pos_assigned_gt_inds_list = [
-        >>>     torch.LongTensor([7, 0]),
-        >>>     torch.LongTensor([5, 4, 1]),
-        >>> ]
-        >>> # Ground truth mask for each true object for each image
-        >>> gt_masks_list = [
-        >>>     BitmapMasks(rng.rand(8, H, W), height=H, width=W),
-        >>>     BitmapMasks(rng.rand(6, H, W), height=H, width=W),
-        >>> ]
-        >>> mask_targets = mask_target(
-        >>>     pos_proposals_list, pos_assigned_gt_inds_list,
-        >>>     gt_masks_list, cfg)
-        >>> assert mask_targets.shape == (5,) + cfg['mask_size']
-    """
-    cfg_list = [cfg for _ in range(len(pos_proposals_list))]
-    mask_targets = map(mask_target_single, pos_proposals_list,
-                       pos_assigned_gt_inds_list, gt_masks_list, cfg_list)
-    mask_targets = list(mask_targets)
-    if len(mask_targets) > 0:
-        mask_targets = torch.cat(mask_targets)
-    return mask_targets
-
-
-def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):
-    """Compute mask target for each positive proposal in the image.
-
-    Args:
-        pos_proposals (Tensor): Positive proposals.
-        pos_assigned_gt_inds (Tensor): Assigned GT inds of positive proposals.
-        gt_masks (:obj:`BaseInstanceMasks`): GT masks in the format of Bitmap
-            or Polygon.
-        cfg (dict): Config dict that indicate the mask size.
-
-    Returns:
-        Tensor: Mask target of each positive proposals in the image.
-
-    Example:
-        >>> import mmcv
-        >>> import mmdet
-        >>> from mmdet.core.mask import BitmapMasks
-        >>> from mmdet.core.mask.mask_target import *  # NOQA
-        >>> H, W = 32, 32
-        >>> cfg = mmcv.Config({'mask_size': (7, 11)})
-        >>> rng = np.random.RandomState(0)
-        >>> # Masks for each ground truth box (relative to the image)
-        >>> gt_masks_data = rng.rand(3, H, W)
-        >>> gt_masks = BitmapMasks(gt_masks_data, height=H, width=W)
-        >>> # Predicted positive boxes in one image
-        >>> pos_proposals = torch.FloatTensor([
-        >>>     [ 16.2,   5.5, 19.9, 20.9],
-        >>>     [ 17.3,  13.6, 19.3, 19.3],
-        >>>     [ 14.8,  16.4, 17.0, 23.7],
-        >>>     [  0.0,   0.0, 16.0, 16.0],
-        >>>     [  4.0,   0.0, 20.0, 16.0],
-        >>> ])
-        >>> # For each predicted proposal, its assignment to a gt mask
-        >>> pos_assigned_gt_inds = torch.LongTensor([0, 1, 2, 1, 1])
-        >>> mask_targets = mask_target_single(
-        >>>     pos_proposals, pos_assigned_gt_inds, gt_masks, cfg)
-        >>> assert mask_targets.shape == (5,) + cfg['mask_size']
-    """
-    device = pos_proposals.device
-    mask_size = _pair(cfg.mask_size)
-    num_pos = pos_proposals.size(0)
-    if num_pos > 0:
-        proposals_np = pos_proposals.cpu().numpy()
-        maxh, maxw = gt_masks.height, gt_masks.width
-        proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw)
-        proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh)
-        pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
-
-        mask_targets = gt_masks.crop_and_resize(
-            proposals_np, mask_size, device=device,
-            inds=pos_assigned_gt_inds).to_ndarray()
-
-        mask_targets = torch.from_numpy(mask_targets).float().to(device)
-    else:
-        mask_targets = pos_proposals.new_zeros((0, ) + mask_size)
-
-    return mask_targets
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/evaluation.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/evaluation.py
deleted file mode 100644
index 4d00999ce5665c53bded8de9e084943eee2d230d..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/evaluation.py
+++ /dev/null
@@ -1,509 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import warnings
-from math import inf
-
-import torch.distributed as dist
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.utils.data import DataLoader
-
-from annotator.uniformer.mmcv.fileio import FileClient
-from annotator.uniformer.mmcv.utils import is_seq_of
-from .hook import Hook
-from .logger import LoggerHook
-
-
-class EvalHook(Hook):
-    """Non-Distributed evaluation hook.
-
-    This hook will regularly perform evaluation in a given interval when
-    performing in non-distributed environment.
-
-    Args:
-        dataloader (DataLoader): A PyTorch dataloader, whose dataset has
-            implemented ``evaluate`` function.
-        start (int | None, optional): Evaluation starting epoch. It enables
-            evaluation before the training starts if ``start`` <= the resuming
-            epoch. If None, whether to evaluate is merely decided by
-            ``interval``. Default: None.
-        interval (int): Evaluation interval. Default: 1.
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            Default: True.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
-            best score value and best checkpoint path, which will be also
-            loaded when resume checkpoint. Options are the evaluation metrics
-            on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
-            detection and instance segmentation. ``AR@100`` for proposal
-            recall. If ``save_best`` is ``auto``, the first key of the returned
-            ``OrderedDict`` result will be used. Default: None.
-        rule (str | None, optional): Comparison rule for best score. If set to
-            None, it will infer a reasonable rule. Keys such as 'acc', 'top'
-            .etc will be inferred by 'greater' rule. Keys contain 'loss' will
-            be inferred by 'less' rule. Options are 'greater', 'less', None.
-            Default: None.
-        test_fn (callable, optional): test a model with samples from a
-            dataloader, and return the test results. If ``None``, the default
-            test function ``mmcv.engine.single_gpu_test`` will be used.
-            (default: ``None``)
-        greater_keys (List[str] | None, optional): Metric keys that will be
-            inferred by 'greater' comparison rule. If ``None``,
-            _default_greater_keys will be used. (default: ``None``)
-        less_keys (List[str] | None, optional): Metric keys that will be
-            inferred by 'less' comparison rule. If ``None``, _default_less_keys
-            will be used. (default: ``None``)
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, `runner.work_dir` will be used by default. If specified,
-            the `out_dir` will be the concatenation of `out_dir` and the last
-            level directory of `runner.work_dir`.
-            `New in version 1.3.16.`
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details. Default: None.
-            `New in version 1.3.16.`
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-
-    Notes:
-        If new arguments are added for EvalHook, tools/test.py,
-        tools/eval_metric.py may be affected.
-    """
-
-    # Since the key for determine greater or less is related to the downstream
-    # tasks, downstream repos may need to overwrite the following inner
-    # variable accordingly.
-
-    rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
-    init_value_map = {'greater': -inf, 'less': inf}
-    _default_greater_keys = [
-        'acc', 'top', 'AR@', 'auc', 'precision', 'mAP', 'mDice', 'mIoU',
-        'mAcc', 'aAcc'
-    ]
-    _default_less_keys = ['loss']
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 test_fn=None,
-                 greater_keys=None,
-                 less_keys=None,
-                 out_dir=None,
-                 file_client_args=None,
-                 **eval_kwargs):
-        if not isinstance(dataloader, DataLoader):
-            raise TypeError(f'dataloader must be a pytorch DataLoader, '
-                            f'but got {type(dataloader)}')
-
-        if interval <= 0:
-            raise ValueError(f'interval must be a positive number, '
-                             f'but got {interval}')
-
-        assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean'
-
-        if start is not None and start < 0:
-            raise ValueError(f'The evaluation start epoch {start} is smaller '
-                             f'than 0')
-
-        self.dataloader = dataloader
-        self.interval = interval
-        self.start = start
-        self.by_epoch = by_epoch
-
-        assert isinstance(save_best, str) or save_best is None, \
-            '""save_best"" should be a str or None ' \
-            f'rather than {type(save_best)}'
-        self.save_best = save_best
-        self.eval_kwargs = eval_kwargs
-        self.initial_flag = True
-
-        if test_fn is None:
-            from annotator.uniformer.mmcv.engine import single_gpu_test
-            self.test_fn = single_gpu_test
-        else:
-            self.test_fn = test_fn
-
-        if greater_keys is None:
-            self.greater_keys = self._default_greater_keys
-        else:
-            if not isinstance(greater_keys, (list, tuple)):
-                greater_keys = (greater_keys, )
-            assert is_seq_of(greater_keys, str)
-            self.greater_keys = greater_keys
-
-        if less_keys is None:
-            self.less_keys = self._default_less_keys
-        else:
-            if not isinstance(less_keys, (list, tuple)):
-                less_keys = (less_keys, )
-            assert is_seq_of(less_keys, str)
-            self.less_keys = less_keys
-
-        if self.save_best is not None:
-            self.best_ckpt_path = None
-            self._init_rule(rule, self.save_best)
-
-        self.out_dir = out_dir
-        self.file_client_args = file_client_args
-
-    def _init_rule(self, rule, key_indicator):
-        """Initialize rule, key_indicator, comparison_func, and best score.
-
-        Here is the rule to determine which rule is used for key indicator
-        when the rule is not specific (note that the key indicator matching
-        is case-insensitive):
-        1. If the key indicator is in ``self.greater_keys``, the rule will be
-           specified as 'greater'.
-        2. Or if the key indicator is in ``self.less_keys``, the rule will be
-           specified as 'less'.
-        3. Or if the key indicator is equal to the substring in any one item
-           in ``self.greater_keys``, the rule will be specified as 'greater'.
-        4. Or if the key indicator is equal to the substring in any one item
-           in ``self.less_keys``, the rule will be specified as 'less'.
-
-        Args:
-            rule (str | None): Comparison rule for best score.
-            key_indicator (str | None): Key indicator to determine the
-                comparison rule.
-        """
-        if rule not in self.rule_map and rule is not None:
-            raise KeyError(f'rule must be greater, less or None, '
-                           f'but got {rule}.')
-
-        if rule is None:
-            if key_indicator != 'auto':
-                # `_lc` here means we use the lower case of keys for
-                # case-insensitive matching
-                key_indicator_lc = key_indicator.lower()
-                greater_keys = [key.lower() for key in self.greater_keys]
-                less_keys = [key.lower() for key in self.less_keys]
-
-                if key_indicator_lc in greater_keys:
-                    rule = 'greater'
-                elif key_indicator_lc in less_keys:
-                    rule = 'less'
-                elif any(key in key_indicator_lc for key in greater_keys):
-                    rule = 'greater'
-                elif any(key in key_indicator_lc for key in less_keys):
-                    rule = 'less'
-                else:
-                    raise ValueError(f'Cannot infer the rule for key '
-                                     f'{key_indicator}, thus a specific rule '
-                                     f'must be specified.')
-        self.rule = rule
-        self.key_indicator = key_indicator
-        if self.rule is not None:
-            self.compare_func = self.rule_map[self.rule]
-
-    def before_run(self, runner):
-        if not self.out_dir:
-            self.out_dir = runner.work_dir
-
-        self.file_client = FileClient.infer_client(self.file_client_args,
-                                                   self.out_dir)
-
-        # if `self.out_dir` is not equal to `runner.work_dir`, it means that
-        # `self.out_dir` is set so the final `self.out_dir` is the
-        # concatenation of `self.out_dir` and the last level directory of
-        # `runner.work_dir`
-        if self.out_dir != runner.work_dir:
-            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
-            self.out_dir = self.file_client.join_path(self.out_dir, basename)
-            runner.logger.info(
-                (f'The best checkpoint will be saved to {self.out_dir} by '
-                 f'{self.file_client.name}'))
-
-        if self.save_best is not None:
-            if runner.meta is None:
-                warnings.warn('runner.meta is None. Creating an empty one.')
-                runner.meta = dict()
-            runner.meta.setdefault('hook_msgs', dict())
-            self.best_ckpt_path = runner.meta['hook_msgs'].get(
-                'best_ckpt', None)
-
-    def before_train_iter(self, runner):
-        """Evaluate the model only at the start of training by iteration."""
-        if self.by_epoch or not self.initial_flag:
-            return
-        if self.start is not None and runner.iter >= self.start:
-            self.after_train_iter(runner)
-        self.initial_flag = False
-
-    def before_train_epoch(self, runner):
-        """Evaluate the model only at the start of training by epoch."""
-        if not (self.by_epoch and self.initial_flag):
-            return
-        if self.start is not None and runner.epoch >= self.start:
-            self.after_train_epoch(runner)
-        self.initial_flag = False
-
-    def after_train_iter(self, runner):
-        """Called after every training iter to evaluate the results."""
-        if not self.by_epoch and self._should_evaluate(runner):
-            # Because the priority of EvalHook is higher than LoggerHook, the
-            # training log and the evaluating log are mixed. Therefore,
-            # we need to dump the training log and clear it before evaluating
-            # log is generated. In addition, this problem will only appear in
-            # `IterBasedRunner` whose `self.by_epoch` is False, because
-            # `EpochBasedRunner` whose `self.by_epoch` is True calls
-            # `_do_evaluate` in `after_train_epoch` stage, and at this stage
-            # the training log has been printed, so it will not cause any
-            # problem. more details at
-            # https://github.com/open-mmlab/mmsegmentation/issues/694
-            for hook in runner._hooks:
-                if isinstance(hook, LoggerHook):
-                    hook.after_train_iter(runner)
-            runner.log_buffer.clear()
-
-            self._do_evaluate(runner)
-
-    def after_train_epoch(self, runner):
-        """Called after every training epoch to evaluate the results."""
-        if self.by_epoch and self._should_evaluate(runner):
-            self._do_evaluate(runner)
-
-    def _do_evaluate(self, runner):
-        """perform evaluation and save ckpt."""
-        results = self.test_fn(runner.model, self.dataloader)
-        runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
-        key_score = self.evaluate(runner, results)
-        # the key_score may be `None` so it needs to skip the action to save
-        # the best checkpoint
-        if self.save_best and key_score:
-            self._save_ckpt(runner, key_score)
-
-    def _should_evaluate(self, runner):
-        """Judge whether to perform evaluation.
-
-        Here is the rule to judge whether to perform evaluation:
-        1. It will not perform evaluation during the epoch/iteration interval,
-           which is determined by ``self.interval``.
-        2. It will not perform evaluation if the start time is larger than
-           current time.
-        3. It will not perform evaluation when current time is larger than
-           the start time but during epoch/iteration interval.
-
-        Returns:
-            bool: The flag indicating whether to perform evaluation.
-        """
-        if self.by_epoch:
-            current = runner.epoch
-            check_time = self.every_n_epochs
-        else:
-            current = runner.iter
-            check_time = self.every_n_iters
-
-        if self.start is None:
-            if not check_time(runner, self.interval):
-                # No evaluation during the interval.
-                return False
-        elif (current + 1) < self.start:
-            # No evaluation if start is larger than the current time.
-            return False
-        else:
-            # Evaluation only at epochs/iters 3, 5, 7...
-            # if start==3 and interval==2
-            if (current + 1 - self.start) % self.interval:
-                return False
-        return True
-
-    def _save_ckpt(self, runner, key_score):
-        """Save the best checkpoint.
-
-        It will compare the score according to the compare function, write
-        related information (best score, best checkpoint path) and save the
-        best checkpoint into ``work_dir``.
-        """
-        if self.by_epoch:
-            current = f'epoch_{runner.epoch + 1}'
-            cur_type, cur_time = 'epoch', runner.epoch + 1
-        else:
-            current = f'iter_{runner.iter + 1}'
-            cur_type, cur_time = 'iter', runner.iter + 1
-
-        best_score = runner.meta['hook_msgs'].get(
-            'best_score', self.init_value_map[self.rule])
-        if self.compare_func(key_score, best_score):
-            best_score = key_score
-            runner.meta['hook_msgs']['best_score'] = best_score
-
-            if self.best_ckpt_path and self.file_client.isfile(
-                    self.best_ckpt_path):
-                self.file_client.remove(self.best_ckpt_path)
-                runner.logger.info(
-                    (f'The previous best checkpoint {self.best_ckpt_path} was '
-                     'removed'))
-
-            best_ckpt_name = f'best_{self.key_indicator}_{current}.pth'
-            self.best_ckpt_path = self.file_client.join_path(
-                self.out_dir, best_ckpt_name)
-            runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path
-
-            runner.save_checkpoint(
-                self.out_dir, best_ckpt_name, create_symlink=False)
-            runner.logger.info(
-                f'Now best checkpoint is saved as {best_ckpt_name}.')
-            runner.logger.info(
-                f'Best {self.key_indicator} is {best_score:0.4f} '
-                f'at {cur_time} {cur_type}.')
-
-    def evaluate(self, runner, results):
-        """Evaluate the results.
-
-        Args:
-            runner (:obj:`mmcv.Runner`): The underlined training runner.
-            results (list): Output results.
-        """
-        eval_res = self.dataloader.dataset.evaluate(
-            results, logger=runner.logger, **self.eval_kwargs)
-
-        for name, val in eval_res.items():
-            runner.log_buffer.output[name] = val
-        runner.log_buffer.ready = True
-
-        if self.save_best is not None:
-            # If the performance of model is pool, the `eval_res` may be an
-            # empty dict and it will raise exception when `self.save_best` is
-            # not None. More details at
-            # https://github.com/open-mmlab/mmdetection/issues/6265.
-            if not eval_res:
-                warnings.warn(
-                    'Since `eval_res` is an empty dict, the behavior to save '
-                    'the best checkpoint will be skipped in this evaluation.')
-                return None
-
-            if self.key_indicator == 'auto':
-                # infer from eval_results
-                self._init_rule(self.rule, list(eval_res.keys())[0])
-            return eval_res[self.key_indicator]
-
-        return None
-
-
-class DistEvalHook(EvalHook):
-    """Distributed evaluation hook.
-
-    This hook will regularly perform evaluation in a given interval when
-    performing in distributed environment.
-
-    Args:
-        dataloader (DataLoader): A PyTorch dataloader, whose dataset has
-            implemented ``evaluate`` function.
-        start (int | None, optional): Evaluation starting epoch. It enables
-            evaluation before the training starts if ``start`` <= the resuming
-            epoch. If None, whether to evaluate is merely decided by
-            ``interval``. Default: None.
-        interval (int): Evaluation interval. Default: 1.
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            default: True.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
-            best score value and best checkpoint path, which will be also
-            loaded when resume checkpoint. Options are the evaluation metrics
-            on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
-            detection and instance segmentation. ``AR@100`` for proposal
-            recall. If ``save_best`` is ``auto``, the first key of the returned
-            ``OrderedDict`` result will be used. Default: None.
-        rule (str | None, optional): Comparison rule for best score. If set to
-            None, it will infer a reasonable rule. Keys such as 'acc', 'top'
-            .etc will be inferred by 'greater' rule. Keys contain 'loss' will
-            be inferred by 'less' rule. Options are 'greater', 'less', None.
-            Default: None.
-        test_fn (callable, optional): test a model with samples from a
-            dataloader in a multi-gpu manner, and return the test results. If
-            ``None``, the default test function ``mmcv.engine.multi_gpu_test``
-            will be used. (default: ``None``)
-        tmpdir (str | None): Temporary directory to save the results of all
-            processes. Default: None.
-        gpu_collect (bool): Whether to use gpu or cpu to collect results.
-            Default: False.
-        broadcast_bn_buffer (bool): Whether to broadcast the
-            buffer(running_mean and running_var) of rank 0 to other rank
-            before evaluation. Default: True.
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, `runner.work_dir` will be used by default. If specified,
-            the `out_dir` will be the concatenation of `out_dir` and the last
-            level directory of `runner.work_dir`.
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details. Default: None.
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-    """
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 test_fn=None,
-                 greater_keys=None,
-                 less_keys=None,
-                 broadcast_bn_buffer=True,
-                 tmpdir=None,
-                 gpu_collect=False,
-                 out_dir=None,
-                 file_client_args=None,
-                 **eval_kwargs):
-
-        if test_fn is None:
-            from annotator.uniformer.mmcv.engine import multi_gpu_test
-            test_fn = multi_gpu_test
-
-        super().__init__(
-            dataloader,
-            start=start,
-            interval=interval,
-            by_epoch=by_epoch,
-            save_best=save_best,
-            rule=rule,
-            test_fn=test_fn,
-            greater_keys=greater_keys,
-            less_keys=less_keys,
-            out_dir=out_dir,
-            file_client_args=file_client_args,
-            **eval_kwargs)
-
-        self.broadcast_bn_buffer = broadcast_bn_buffer
-        self.tmpdir = tmpdir
-        self.gpu_collect = gpu_collect
-
-    def _do_evaluate(self, runner):
-        """perform evaluation and save ckpt."""
-        # Synchronization of BatchNorm's buffer (running_mean
-        # and running_var) is not supported in the DDP of pytorch,
-        # which may cause the inconsistent performance of models in
-        # different ranks, so we broadcast BatchNorm's buffers
-        # of rank 0 to other ranks to avoid this.
-        if self.broadcast_bn_buffer:
-            model = runner.model
-            for name, module in model.named_modules():
-                if isinstance(module,
-                              _BatchNorm) and module.track_running_stats:
-                    dist.broadcast(module.running_var, 0)
-                    dist.broadcast(module.running_mean, 0)
-
-        tmpdir = self.tmpdir
-        if tmpdir is None:
-            tmpdir = osp.join(runner.work_dir, '.eval_hook')
-
-        results = self.test_fn(
-            runner.model,
-            self.dataloader,
-            tmpdir=tmpdir,
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
-            key_score = self.evaluate(runner, results)
-            # the key_score may be `None` so it needs to skip the action to
-            # save the best checkpoint
-            if self.save_best and key_score:
-                self._save_ckpt(runner, key_score)
diff --git a/spaces/Rongjiehuang/ProDiff/vocoders/pwg.py b/spaces/Rongjiehuang/ProDiff/vocoders/pwg.py
deleted file mode 100644
index ca9b6891ab2ba5cb413eeca97a41534e5db129d5..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/vocoders/pwg.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import glob
-import re
-import librosa
-import torch
-import yaml
-from sklearn.preprocessing import StandardScaler
-from torch import nn
-from modules.parallel_wavegan.models import ParallelWaveGANGenerator
-from modules.parallel_wavegan.utils import read_hdf5
-from utils.hparams import hparams
-from utils.pitch_utils import f0_to_coarse
-from vocoders.base_vocoder import BaseVocoder, register_vocoder
-import numpy as np
-
-
-def load_pwg_model(config_path, checkpoint_path, stats_path):
-    # load config
-    with open(config_path) as f:
-        config = yaml.load(f, Loader=yaml.Loader)
-
-    # setup
-    if torch.cuda.is_available():
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-    model = ParallelWaveGANGenerator(**config["generator_params"])
-
-    ckpt_dict = torch.load(checkpoint_path, map_location="cpu")
-    if 'state_dict' not in ckpt_dict:  # official vocoder
-        model.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["model"]["generator"])
-        scaler = StandardScaler()
-        if config["format"] == "hdf5":
-            scaler.mean_ = read_hdf5(stats_path, "mean")
-            scaler.scale_ = read_hdf5(stats_path, "scale")
-        elif config["format"] == "npy":
-            scaler.mean_ = np.load(stats_path)[0]
-            scaler.scale_ = np.load(stats_path)[1]
-        else:
-            raise ValueError("support only hdf5 or npy format.")
-    else:  # custom PWG vocoder
-        fake_task = nn.Module()
-        fake_task.model_gen = model
-        fake_task.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["state_dict"], strict=False)
-        scaler = None
-
-    model.remove_weight_norm()
-    model = model.eval().to(device)
-    print(f"| Loaded model parameters from {checkpoint_path}.")
-    print(f"| PWG device: {device}.")
-    return model, scaler, config, device
-
-
-@register_vocoder
-class PWG(BaseVocoder):
-    def __init__(self):
-        if hparams['vocoder_ckpt'] == '':  # load LJSpeech PWG pretrained model
-            base_dir = 'wavegan_pretrained'
-            ckpts = glob.glob(f'{base_dir}/checkpoint-*steps.pkl')
-            ckpt = sorted(ckpts, key=
-            lambda x: int(re.findall(f'{base_dir}/checkpoint-(\d+)steps.pkl', x)[0]))[-1]
-            config_path = f'{base_dir}/config.yaml'
-            print('| load PWG: ', ckpt)
-            self.model, self.scaler, self.config, self.device = load_pwg_model(
-                config_path=config_path,
-                checkpoint_path=ckpt,
-                stats_path=f'{base_dir}/stats.h5',
-            )
-        else:
-            base_dir = hparams['vocoder_ckpt']
-            print(base_dir)
-            config_path = f'{base_dir}/config.yaml'
-            ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
-            lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1]
-            print('| load PWG: ', ckpt)
-            self.scaler = None
-            self.model, _, self.config, self.device = load_pwg_model(
-                config_path=config_path,
-                checkpoint_path=ckpt,
-                stats_path=f'{base_dir}/stats.h5',
-            )
-
-    def spec2wav(self, mel, **kwargs):
-        # start generation
-        config = self.config
-        device = self.device
-        pad_size = (config["generator_params"]["aux_context_window"],
-                    config["generator_params"]["aux_context_window"])
-        c = mel
-        if self.scaler is not None:
-            c = self.scaler.transform(c)
-
-        with torch.no_grad():
-            z = torch.randn(1, 1, c.shape[0] * config["hop_size"]).to(device)
-            c = np.pad(c, (pad_size, (0, 0)), "edge")
-            c = torch.FloatTensor(c).unsqueeze(0).transpose(2, 1).to(device)
-            p = kwargs.get('f0')
-            if p is not None:
-                p = f0_to_coarse(p)
-                p = np.pad(p, (pad_size,), "edge")
-                p = torch.LongTensor(p[None, :]).to(device)
-            y = self.model(z, c, p).view(-1)
-        wav_out = y.cpu().numpy()
-        return wav_out
-
-    @staticmethod
-    def wav2spec(wav_fn, return_linear=False):
-        from data_gen.tts.data_gen_utils import process_utterance
-        res = process_utterance(
-            wav_fn, fft_size=hparams['fft_size'],
-            hop_size=hparams['hop_size'],
-            win_length=hparams['win_size'],
-            num_mels=hparams['audio_num_mel_bins'],
-            fmin=hparams['fmin'],
-            fmax=hparams['fmax'],
-            sample_rate=hparams['audio_sample_rate'],
-            loud_norm=hparams['loud_norm'],
-            min_level_db=hparams['min_level_db'],
-            return_linear=return_linear, vocoder='pwg', eps=float(hparams.get('wav2spec_eps', 1e-10)))
-        if return_linear:
-            return res[0], res[1].T, res[2].T  # [T, 80], [T, n_fft]
-        else:
-            return res[0], res[1].T
-
-    @staticmethod
-    def wav2mfcc(wav_fn):
-        fft_size = hparams['fft_size']
-        hop_size = hparams['hop_size']
-        win_length = hparams['win_size']
-        sample_rate = hparams['audio_sample_rate']
-        wav, _ = librosa.core.load(wav_fn, sr=sample_rate)
-        mfcc = librosa.feature.mfcc(y=wav, sr=sample_rate, n_mfcc=13,
-                                    n_fft=fft_size, hop_length=hop_size,
-                                    win_length=win_length, pad_mode="constant", power=1.0)
-        mfcc_delta = librosa.feature.delta(mfcc, order=1)
-        mfcc_delta_delta = librosa.feature.delta(mfcc, order=2)
-        mfcc = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta]).T
-        return mfcc
diff --git a/spaces/RugNlpFlashcards/Speech_Language_Processing_Jurafsky_Martin/results/retrieval_time.tex b/spaces/RugNlpFlashcards/Speech_Language_Processing_Jurafsky_Martin/results/retrieval_time.tex
deleted file mode 100644
index 6835f3b9feab933e871a3e1eaf41da046a886025..0000000000000000000000000000000000000000
--- a/spaces/RugNlpFlashcards/Speech_Language_Processing_Jurafsky_Martin/results/retrieval_time.tex
+++ /dev/null
@@ -1,14 +0,0 @@
-\begin{tabular}{lrrrr}
-\toprule
-{} &  faiss\_dpr.retrieve &  faiss\_longformer.retrieve &  es\_dpr.retrieve &  es\_longformer.retrieve \\
-\midrule
-count &           59.000000 &                  59.000000 &        59.000000 &               59.000000 \\
-mean  &            0.056994 &                   0.854546 &         0.013451 &                0.013016 \\
-std   &            0.038737 &                   0.165768 &         0.003771 &                0.002781 \\
-min   &            0.035896 &                   0.729217 &         0.008990 &                0.009167 \\
-25\%   &            0.043558 &                   0.775807 &         0.010590 &                0.011279 \\
-50\%   &            0.046970 &                   0.795175 &         0.011699 &                0.012060 \\
-75\%   &            0.056887 &                   0.838984 &         0.016232 &                0.013151 \\
-max   &            0.303843 &                   1.465686 &         0.026489 &                0.020290 \\
-\bottomrule
-\end{tabular}
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/__init__.py b/spaces/SankarSrin/image-matting-app/ppmatting/__init__.py
deleted file mode 100644
index c1094808e27aa683fc3b5766e9968712b3021532..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import ml, metrics, transforms, datasets, models
diff --git a/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/ONNXVITS_modules.py b/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/ONNXVITS_modules.py
deleted file mode 100644
index 6cf676ce37c1eaf8428c4094e749f862182cb0c3..0000000000000000000000000000000000000000
--- a/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/ONNXVITS_modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from ONNXVITS_transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/SaulLu/test-demo/README.md b/spaces/SaulLu/test-demo/README.md
deleted file mode 100644
index 9006df2a94744a0546c467cd088a55196119e27d..0000000000000000000000000000000000000000
--- a/spaces/SaulLu/test-demo/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
----
-title: Test Demo
-emoji: 🔥
-colorFrom: indigo
-colorTo: red
-sdk: static
-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.
diff --git a/spaces/SeViLA/SeViLA/lavis/runners/__init__.py b/spaces/SeViLA/SeViLA/lavis/runners/__init__.py
deleted file mode 100644
index 38494f1cdf68b0a419c3e0c401871b653a481963..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/runners/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from lavis.runners.runner_base import RunnerBase
-from lavis.runners.runner_iter import RunnerIter
-
-__all__ = ["RunnerBase", "RunnerIter"]
diff --git a/spaces/Senayfre/CropHealth/README.md b/spaces/Senayfre/CropHealth/README.md
deleted file mode 100644
index 3247a36f276d90dfea2ce2e79fa8ef208cf080e5..0000000000000000000000000000000000000000
--- a/spaces/Senayfre/CropHealth/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CropHealth
-emoji: 👁
-colorFrom: purple
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/SilenWang/ReviewGPT/utils/__init__.py b/spaces/SilenWang/ReviewGPT/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Silentlin/DiffSinger/vocoders/hifigan.py b/spaces/Silentlin/DiffSinger/vocoders/hifigan.py
deleted file mode 100644
index 810d3c931b556387f8a2e85537f4964add1e76b0..0000000000000000000000000000000000000000
--- a/spaces/Silentlin/DiffSinger/vocoders/hifigan.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import glob
-import json
-import os
-import re
-
-import librosa
-import torch
-
-import utils
-from modules.hifigan.hifigan import HifiGanGenerator
-from utils.hparams import hparams, set_hparams
-from vocoders.base_vocoder import register_vocoder
-from vocoders.pwg import PWG
-from vocoders.vocoder_utils import denoise
-
-
-def load_model(config_path, checkpoint_path):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    ckpt_dict = torch.load(checkpoint_path, map_location="cpu")
-    if '.yaml' in config_path:
-        config = set_hparams(config_path, global_hparams=False)
-        state = ckpt_dict["state_dict"]["model_gen"]
-    elif '.json' in config_path:
-        config = json.load(open(config_path, 'r'))
-        state = ckpt_dict["generator"]
-
-    model = HifiGanGenerator(config)
-    model.load_state_dict(state, strict=True)
-    model.remove_weight_norm()
-    model = model.eval().to(device)
-    print(f"| Loaded model parameters from {checkpoint_path}.")
-    print(f"| HifiGAN device: {device}.")
-    return model, config, device
-
-
-total_time = 0
-
-
-@register_vocoder
-class HifiGAN(PWG):
-    def __init__(self):
-        base_dir = hparams['vocoder_ckpt']
-        config_path = f'{base_dir}/config.yaml'
-        if os.path.exists(config_path):
-            ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
-            lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1]
-            print('| load HifiGAN: ', ckpt)
-            self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt)
-        else:
-            config_path = f'{base_dir}/config.json'
-            ckpt = f'{base_dir}/generator_v1'
-            if os.path.exists(config_path):
-                self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt)
-
-    def spec2wav(self, mel, **kwargs):
-        device = self.device
-        with torch.no_grad():
-            c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(device)
-            with utils.Timer('hifigan', print_time=hparams['profile_infer']):
-                f0 = kwargs.get('f0')
-                if f0 is not None and hparams.get('use_nsf'):
-                    f0 = torch.FloatTensor(f0[None, :]).to(device)
-                    y = self.model(c, f0).view(-1)
-                else:
-                    y = self.model(c).view(-1)
-        wav_out = y.cpu().numpy()
-        if hparams.get('vocoder_denoise_c', 0.0) > 0:
-            wav_out = denoise(wav_out, v=hparams['vocoder_denoise_c'])
-        return wav_out
-
-    # @staticmethod
-    # def wav2spec(wav_fn, **kwargs):
-    #     wav, _ = librosa.core.load(wav_fn, sr=hparams['audio_sample_rate'])
-    #     wav_torch = torch.FloatTensor(wav)[None, :]
-    #     mel = mel_spectrogram(wav_torch, hparams).numpy()[0]
-    #     return wav, mel.T
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/lib/guisupport.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/lib/guisupport.py
deleted file mode 100644
index 4d532d0f4d589efa103890a10fa41d047a223ead..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/lib/guisupport.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# coding: utf-8
-"""
-Support for creating GUI apps and starting event loops.
-
-IPython's GUI integration allows interactive plotting and GUI usage in IPython
-session. IPython has two different types of GUI integration:
-
-1. The terminal based IPython supports GUI event loops through Python's
-   PyOS_InputHook. PyOS_InputHook is a hook that Python calls periodically
-   whenever raw_input is waiting for a user to type code. We implement GUI
-   support in the terminal by setting PyOS_InputHook to a function that
-   iterates the event loop for a short while. It is important to note that
-   in this situation, the real GUI event loop is NOT run in the normal
-   manner, so you can't use the normal means to detect that it is running.
-2. In the two process IPython kernel/frontend, the GUI event loop is run in
-   the kernel. In this case, the event loop is run in the normal manner by
-   calling the function or method of the GUI toolkit that starts the event
-   loop.
-
-In addition to starting the GUI event loops in one of these two ways, IPython
-will *always* create an appropriate GUI application object when GUi
-integration is enabled.
-
-If you want your GUI apps to run in IPython you need to do two things:
-
-1. Test to see if there is already an existing main application object. If
-   there is, you should use it. If there is not an existing application object
-   you should create one.
-2. Test to see if the GUI event loop is running. If it is, you should not
-   start it. If the event loop is not running you may start it.
-
-This module contains functions for each toolkit that perform these things
-in a consistent manner. Because of how PyOS_InputHook runs the event loop
-you cannot detect if the event loop is running using the traditional calls
-(such as ``wx.GetApp.IsMainLoopRunning()`` in wxPython). If PyOS_InputHook is
-set These methods will return a false negative. That is, they will say the
-event loop is not running, when is actually is. To work around this limitation
-we proposed the following informal protocol:
-
-* Whenever someone starts the event loop, they *must* set the ``_in_event_loop``
-  attribute of the main application object to ``True``. This should be done
-  regardless of how the event loop is actually run.
-* Whenever someone stops the event loop, they *must* set the ``_in_event_loop``
-  attribute of the main application object to ``False``.
-* If you want to see if the event loop is running, you *must* use ``hasattr``
-  to see if ``_in_event_loop`` attribute has been set. If it is set, you
-  *must* use its value. If it has not been set, you can query the toolkit
-  in the normal manner.
-* If you want GUI support and no one else has created an application or
-  started the event loop you *must* do this. We don't want projects to
-  attempt to defer these things to someone else if they themselves need it.
-
-The functions below implement this logic for each GUI toolkit. If you need
-to create custom application subclasses, you will likely have to modify this
-code for your own purposes. This code can be copied into your own project
-so you don't have to depend on IPython.
-
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-from IPython.core.getipython import get_ipython
-
-#-----------------------------------------------------------------------------
-# wx
-#-----------------------------------------------------------------------------
-
-def get_app_wx(*args, **kwargs):
-    """Create a new wx app or return an exiting one."""
-    import wx
-    app = wx.GetApp()
-    if app is None:
-        if 'redirect' not in kwargs:
-            kwargs['redirect'] = False
-        app = wx.PySimpleApp(*args, **kwargs)
-    return app
-
-def is_event_loop_running_wx(app=None):
-    """Is the wx event loop running."""
-    # New way: check attribute on shell instance
-    ip = get_ipython()
-    if ip is not None:
-        if ip.active_eventloop and ip.active_eventloop == 'wx':
-            return True
-        # Fall through to checking the application, because Wx has a native way
-        # to check if the event loop is running, unlike Qt.
-
-    # Old way: check Wx application
-    if app is None:
-        app = get_app_wx()
-    if hasattr(app, '_in_event_loop'):
-        return app._in_event_loop
-    else:
-        return app.IsMainLoopRunning()
-
-def start_event_loop_wx(app=None):
-    """Start the wx event loop in a consistent manner."""
-    if app is None:
-        app = get_app_wx()
-    if not is_event_loop_running_wx(app):
-        app._in_event_loop = True
-        app.MainLoop()
-        app._in_event_loop = False
-    else:
-        app._in_event_loop = True
-
-#-----------------------------------------------------------------------------
-# Qt
-#-----------------------------------------------------------------------------
-
-def get_app_qt4(*args, **kwargs):
-    """Create a new Qt app or return an existing one."""
-    from IPython.external.qt_for_kernel import QtGui
-    app = QtGui.QApplication.instance()
-    if app is None:
-        if not args:
-            args = ([""],)
-        app = QtGui.QApplication(*args, **kwargs)
-    return app
-
-def is_event_loop_running_qt4(app=None):
-    """Is the qt event loop running."""
-    # New way: check attribute on shell instance
-    ip = get_ipython()
-    if ip is not None:
-        return ip.active_eventloop and ip.active_eventloop.startswith('qt')
-
-    # Old way: check attribute on QApplication singleton
-    if app is None:
-        app = get_app_qt4([""])
-    if hasattr(app, '_in_event_loop'):
-        return app._in_event_loop
-    else:
-        # Does qt provide a other way to detect this?
-        return False
-
-def start_event_loop_qt4(app=None):
-    """Start the qt event loop in a consistent manner."""
-    if app is None:
-        app = get_app_qt4([""])
-    if not is_event_loop_running_qt4(app):
-        app._in_event_loop = True
-        app.exec_()
-        app._in_event_loop = False
-    else:
-        app._in_event_loop = True
-
-#-----------------------------------------------------------------------------
-# Tk
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# gtk
-#-----------------------------------------------------------------------------
diff --git a/spaces/TH5314/newbing/src/components/chat-scroll-anchor.tsx b/spaces/TH5314/newbing/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}
diff --git a/spaces/TNR-5/chatorO/config.py b/spaces/TNR-5/chatorO/config.py
deleted file mode 100644
index d24686ff888211748ad74614ea4f8c5cf372b4f3..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/chatorO/config.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from dotenv import load_dotenv
-import os
-
-load_dotenv(dotenv_path=".env")  # Load environment variables from .env file
-
-# DATABASE_URL = os.getenv("DATABASE_URL")
-# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-# OCR_API_KEY = os.getenv("OCR_API_KEY")
-NGROK_AUTH_TOKEN = os.getenv("NGROK_AUTH_TOKEN")
\ No newline at end of file
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/operations/build/wheel.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/operations/build/wheel.py
deleted file mode 100644
index 064811ad11bb07b2b7bc8e30ec6c03f21997d6b2..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/operations/build/wheel.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import logging
-import os
-from typing import Optional
-
-from pip._vendor.pyproject_hooks import BuildBackendHookCaller
-
-from pip._internal.utils.subprocess import runner_with_spinner_message
-
-logger = logging.getLogger(__name__)
-
-
-def build_wheel_pep517(
-    name: str,
-    backend: BuildBackendHookCaller,
-    metadata_directory: str,
-    tempd: str,
-) -> Optional[str]:
-    """Build one InstallRequirement using the PEP 517 build process.
-
-    Returns path to wheel if successfully built. Otherwise, returns None.
-    """
-    assert metadata_directory is not None
-    try:
-        logger.debug("Destination directory: %s", tempd)
-
-        runner = runner_with_spinner_message(
-            f"Building wheel for {name} (pyproject.toml)"
-        )
-        with backend.subprocess_runner(runner):
-            wheel_name = backend.build_wheel(
-                tempd,
-                metadata_directory=metadata_directory,
-            )
-    except Exception:
-        logger.error("Failed building wheel for %s", name)
-        return None
-    return os.path.join(tempd, wheel_name)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/typing_extensions.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/typing_extensions.py
deleted file mode 100644
index 9f1c7aa31e20a7d0ef2e6877ea325c068d50e406..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/typing_extensions.py
+++ /dev/null
@@ -1,2296 +0,0 @@
-import abc
-import collections
-import collections.abc
-import operator
-import sys
-import typing
-
-# After PEP 560, internal typing API was substantially reworked.
-# This is especially important for Protocol class which uses internal APIs
-# quite extensively.
-PEP_560 = sys.version_info[:3] >= (3, 7, 0)
-
-if PEP_560:
-    GenericMeta = type
-else:
-    # 3.6
-    from typing import GenericMeta, _type_vars  # noqa
-
-# The two functions below are copies of typing internal helpers.
-# They are needed by _ProtocolMeta
-
-
-def _no_slots_copy(dct):
-    dict_copy = dict(dct)
-    if '__slots__' in dict_copy:
-        for slot in dict_copy['__slots__']:
-            dict_copy.pop(slot, None)
-    return dict_copy
-
-
-def _check_generic(cls, parameters):
-    if not cls.__parameters__:
-        raise TypeError(f"{cls} is not a generic class")
-    alen = len(parameters)
-    elen = len(cls.__parameters__)
-    if alen != elen:
-        raise TypeError(f"Too {'many' if alen > elen else 'few'} arguments for {cls};"
-                        f" actual {alen}, expected {elen}")
-
-
-# Please keep __all__ alphabetized within each category.
-__all__ = [
-    # Super-special typing primitives.
-    'ClassVar',
-    'Concatenate',
-    'Final',
-    'ParamSpec',
-    'Self',
-    'Type',
-
-    # ABCs (from collections.abc).
-    'Awaitable',
-    'AsyncIterator',
-    'AsyncIterable',
-    'Coroutine',
-    'AsyncGenerator',
-    'AsyncContextManager',
-    'ChainMap',
-
-    # Concrete collection types.
-    'ContextManager',
-    'Counter',
-    'Deque',
-    'DefaultDict',
-    'OrderedDict',
-    'TypedDict',
-
-    # Structural checks, a.k.a. protocols.
-    'SupportsIndex',
-
-    # One-off things.
-    'Annotated',
-    'final',
-    'IntVar',
-    'Literal',
-    'NewType',
-    'overload',
-    'Protocol',
-    'runtime',
-    'runtime_checkable',
-    'Text',
-    'TypeAlias',
-    'TypeGuard',
-    'TYPE_CHECKING',
-]
-
-if PEP_560:
-    __all__.extend(["get_args", "get_origin", "get_type_hints"])
-
-# 3.6.2+
-if hasattr(typing, 'NoReturn'):
-    NoReturn = typing.NoReturn
-# 3.6.0-3.6.1
-else:
-    class _NoReturn(typing._FinalTypingBase, _root=True):
-        """Special type indicating functions that never return.
-        Example::
-
-          from typing import NoReturn
-
-          def stop() -> NoReturn:
-              raise Exception('no way')
-
-        This type is invalid in other positions, e.g., ``List[NoReturn]``
-        will fail in static type checkers.
-        """
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError("NoReturn cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("NoReturn cannot be used with issubclass().")
-
-    NoReturn = _NoReturn(_root=True)
-
-# Some unconstrained type variables.  These are used by the container types.
-# (These are not for export.)
-T = typing.TypeVar('T')  # Any type.
-KT = typing.TypeVar('KT')  # Key type.
-VT = typing.TypeVar('VT')  # Value type.
-T_co = typing.TypeVar('T_co', covariant=True)  # Any type covariant containers.
-T_contra = typing.TypeVar('T_contra', contravariant=True)  # Ditto contravariant.
-
-ClassVar = typing.ClassVar
-
-# On older versions of typing there is an internal class named "Final".
-# 3.8+
-if hasattr(typing, 'Final') and sys.version_info[:2] >= (3, 7):
-    Final = typing.Final
-# 3.7
-elif sys.version_info[:2] >= (3, 7):
-    class _FinalForm(typing._SpecialForm, _root=True):
-
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            item = typing._type_check(parameters,
-                                      f'{self._name} accepts only single type')
-            return typing._GenericAlias(self, (item,))
-
-    Final = _FinalForm('Final',
-                       doc="""A special typing construct to indicate that a name
-                       cannot be re-assigned or overridden in a subclass.
-                       For example:
-
-                           MAX_SIZE: Final = 9000
-                           MAX_SIZE += 1  # Error reported by type checker
-
-                           class Connection:
-                               TIMEOUT: Final[int] = 10
-                           class FastConnector(Connection):
-                               TIMEOUT = 1  # Error reported by type checker
-
-                       There is no runtime checking of these properties.""")
-# 3.6
-else:
-    class _Final(typing._FinalTypingBase, _root=True):
-        """A special typing construct to indicate that a name
-        cannot be re-assigned or overridden in a subclass.
-        For example:
-
-            MAX_SIZE: Final = 9000
-            MAX_SIZE += 1  # Error reported by type checker
-
-            class Connection:
-                TIMEOUT: Final[int] = 10
-            class FastConnector(Connection):
-                TIMEOUT = 1  # Error reported by type checker
-
-        There is no runtime checking of these properties.
-        """
-
-        __slots__ = ('__type__',)
-
-        def __init__(self, tp=None, **kwds):
-            self.__type__ = tp
-
-        def __getitem__(self, item):
-            cls = type(self)
-            if self.__type__ is None:
-                return cls(typing._type_check(item,
-                           f'{cls.__name__[1:]} accepts only single type.'),
-                           _root=True)
-            raise TypeError(f'{cls.__name__[1:]} cannot be further subscripted')
-
-        def _eval_type(self, globalns, localns):
-            new_tp = typing._eval_type(self.__type__, globalns, localns)
-            if new_tp == self.__type__:
-                return self
-            return type(self)(new_tp, _root=True)
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__type__ is not None:
-                r += f'[{typing._type_repr(self.__type__)}]'
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__type__))
-
-        def __eq__(self, other):
-            if not isinstance(other, _Final):
-                return NotImplemented
-            if self.__type__ is not None:
-                return self.__type__ == other.__type__
-            return self is other
-
-    Final = _Final(_root=True)
-
-
-# 3.8+
-if hasattr(typing, 'final'):
-    final = typing.final
-# 3.6-3.7
-else:
-    def final(f):
-        """This decorator can be used to indicate to type checkers that
-        the decorated method cannot be overridden, and decorated class
-        cannot be subclassed. For example:
-
-            class Base:
-                @final
-                def done(self) -> None:
-                    ...
-            class Sub(Base):
-                def done(self) -> None:  # Error reported by type checker
-                    ...
-            @final
-            class Leaf:
-                ...
-            class Other(Leaf):  # Error reported by type checker
-                ...
-
-        There is no runtime checking of these properties.
-        """
-        return f
-
-
-def IntVar(name):
-    return typing.TypeVar(name)
-
-
-# 3.8+:
-if hasattr(typing, 'Literal'):
-    Literal = typing.Literal
-# 3.7:
-elif sys.version_info[:2] >= (3, 7):
-    class _LiteralForm(typing._SpecialForm, _root=True):
-
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            return typing._GenericAlias(self, parameters)
-
-    Literal = _LiteralForm('Literal',
-                           doc="""A type that can be used to indicate to type checkers
-                           that the corresponding value has a value literally equivalent
-                           to the provided parameter. For example:
-
-                               var: Literal[4] = 4
-
-                           The type checker understands that 'var' is literally equal to
-                           the value 4 and no other value.
-
-                           Literal[...] cannot be subclassed. There is no runtime
-                           checking verifying that the parameter is actually a value
-                           instead of a type.""")
-# 3.6:
-else:
-    class _Literal(typing._FinalTypingBase, _root=True):
-        """A type that can be used to indicate to type checkers that the
-        corresponding value has a value literally equivalent to the
-        provided parameter. For example:
-
-            var: Literal[4] = 4
-
-        The type checker understands that 'var' is literally equal to the
-        value 4 and no other value.
-
-        Literal[...] cannot be subclassed. There is no runtime checking
-        verifying that the parameter is actually a value instead of a type.
-        """
-
-        __slots__ = ('__values__',)
-
-        def __init__(self, values=None, **kwds):
-            self.__values__ = values
-
-        def __getitem__(self, values):
-            cls = type(self)
-            if self.__values__ is None:
-                if not isinstance(values, tuple):
-                    values = (values,)
-                return cls(values, _root=True)
-            raise TypeError(f'{cls.__name__[1:]} cannot be further subscripted')
-
-        def _eval_type(self, globalns, localns):
-            return self
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__values__ is not None:
-                r += f'[{", ".join(map(typing._type_repr, self.__values__))}]'
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__values__))
-
-        def __eq__(self, other):
-            if not isinstance(other, _Literal):
-                return NotImplemented
-            if self.__values__ is not None:
-                return self.__values__ == other.__values__
-            return self is other
-
-    Literal = _Literal(_root=True)
-
-
-_overload_dummy = typing._overload_dummy  # noqa
-overload = typing.overload
-
-
-# This is not a real generic class.  Don't use outside annotations.
-Type = typing.Type
-
-# Various ABCs mimicking those in collections.abc.
-# A few are simply re-exported for completeness.
-
-
-class _ExtensionsGenericMeta(GenericMeta):
-    def __subclasscheck__(self, subclass):
-        """This mimics a more modern GenericMeta.__subclasscheck__() logic
-        (that does not have problems with recursion) to work around interactions
-        between collections, typing, and typing_extensions on older
-        versions of Python, see https://github.com/python/typing/issues/501.
-        """
-        if self.__origin__ is not None:
-            if sys._getframe(1).f_globals['__name__'] not in ['abc', 'functools']:
-                raise TypeError("Parameterized generics cannot be used with class "
-                                "or instance checks")
-            return False
-        if not self.__extra__:
-            return super().__subclasscheck__(subclass)
-        res = self.__extra__.__subclasshook__(subclass)
-        if res is not NotImplemented:
-            return res
-        if self.__extra__ in subclass.__mro__:
-            return True
-        for scls in self.__extra__.__subclasses__():
-            if isinstance(scls, GenericMeta):
-                continue
-            if issubclass(subclass, scls):
-                return True
-        return False
-
-
-Awaitable = typing.Awaitable
-Coroutine = typing.Coroutine
-AsyncIterable = typing.AsyncIterable
-AsyncIterator = typing.AsyncIterator
-
-# 3.6.1+
-if hasattr(typing, 'Deque'):
-    Deque = typing.Deque
-# 3.6.0
-else:
-    class Deque(collections.deque, typing.MutableSequence[T],
-                metaclass=_ExtensionsGenericMeta,
-                extra=collections.deque):
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is Deque:
-                return collections.deque(*args, **kwds)
-            return typing._generic_new(collections.deque, cls, *args, **kwds)
-
-ContextManager = typing.ContextManager
-# 3.6.2+
-if hasattr(typing, 'AsyncContextManager'):
-    AsyncContextManager = typing.AsyncContextManager
-# 3.6.0-3.6.1
-else:
-    from _collections_abc import _check_methods as _check_methods_in_mro  # noqa
-
-    class AsyncContextManager(typing.Generic[T_co]):
-        __slots__ = ()
-
-        async def __aenter__(self):
-            return self
-
-        @abc.abstractmethod
-        async def __aexit__(self, exc_type, exc_value, traceback):
-            return None
-
-        @classmethod
-        def __subclasshook__(cls, C):
-            if cls is AsyncContextManager:
-                return _check_methods_in_mro(C, "__aenter__", "__aexit__")
-            return NotImplemented
-
-DefaultDict = typing.DefaultDict
-
-# 3.7.2+
-if hasattr(typing, 'OrderedDict'):
-    OrderedDict = typing.OrderedDict
-# 3.7.0-3.7.2
-elif (3, 7, 0) <= sys.version_info[:3] < (3, 7, 2):
-    OrderedDict = typing._alias(collections.OrderedDict, (KT, VT))
-# 3.6
-else:
-    class OrderedDict(collections.OrderedDict, typing.MutableMapping[KT, VT],
-                      metaclass=_ExtensionsGenericMeta,
-                      extra=collections.OrderedDict):
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is OrderedDict:
-                return collections.OrderedDict(*args, **kwds)
-            return typing._generic_new(collections.OrderedDict, cls, *args, **kwds)
-
-# 3.6.2+
-if hasattr(typing, 'Counter'):
-    Counter = typing.Counter
-# 3.6.0-3.6.1
-else:
-    class Counter(collections.Counter,
-                  typing.Dict[T, int],
-                  metaclass=_ExtensionsGenericMeta, extra=collections.Counter):
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is Counter:
-                return collections.Counter(*args, **kwds)
-            return typing._generic_new(collections.Counter, cls, *args, **kwds)
-
-# 3.6.1+
-if hasattr(typing, 'ChainMap'):
-    ChainMap = typing.ChainMap
-elif hasattr(collections, 'ChainMap'):
-    class ChainMap(collections.ChainMap, typing.MutableMapping[KT, VT],
-                   metaclass=_ExtensionsGenericMeta,
-                   extra=collections.ChainMap):
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is ChainMap:
-                return collections.ChainMap(*args, **kwds)
-            return typing._generic_new(collections.ChainMap, cls, *args, **kwds)
-
-# 3.6.1+
-if hasattr(typing, 'AsyncGenerator'):
-    AsyncGenerator = typing.AsyncGenerator
-# 3.6.0
-else:
-    class AsyncGenerator(AsyncIterator[T_co], typing.Generic[T_co, T_contra],
-                         metaclass=_ExtensionsGenericMeta,
-                         extra=collections.abc.AsyncGenerator):
-        __slots__ = ()
-
-NewType = typing.NewType
-Text = typing.Text
-TYPE_CHECKING = typing.TYPE_CHECKING
-
-
-def _gorg(cls):
-    """This function exists for compatibility with old typing versions."""
-    assert isinstance(cls, GenericMeta)
-    if hasattr(cls, '_gorg'):
-        return cls._gorg
-    while cls.__origin__ is not None:
-        cls = cls.__origin__
-    return cls
-
-
-_PROTO_WHITELIST = ['Callable', 'Awaitable',
-                    'Iterable', 'Iterator', 'AsyncIterable', 'AsyncIterator',
-                    'Hashable', 'Sized', 'Container', 'Collection', 'Reversible',
-                    'ContextManager', 'AsyncContextManager']
-
-
-def _get_protocol_attrs(cls):
-    attrs = set()
-    for base in cls.__mro__[:-1]:  # without object
-        if base.__name__ in ('Protocol', 'Generic'):
-            continue
-        annotations = getattr(base, '__annotations__', {})
-        for attr in list(base.__dict__.keys()) + list(annotations.keys()):
-            if (not attr.startswith('_abc_') and attr not in (
-                    '__abstractmethods__', '__annotations__', '__weakref__',
-                    '_is_protocol', '_is_runtime_protocol', '__dict__',
-                    '__args__', '__slots__',
-                    '__next_in_mro__', '__parameters__', '__origin__',
-                    '__orig_bases__', '__extra__', '__tree_hash__',
-                    '__doc__', '__subclasshook__', '__init__', '__new__',
-                    '__module__', '_MutableMapping__marker', '_gorg')):
-                attrs.add(attr)
-    return attrs
-
-
-def _is_callable_members_only(cls):
-    return all(callable(getattr(cls, attr, None)) for attr in _get_protocol_attrs(cls))
-
-
-# 3.8+
-if hasattr(typing, 'Protocol'):
-    Protocol = typing.Protocol
-# 3.7
-elif PEP_560:
-    from typing import _collect_type_vars  # noqa
-
-    def _no_init(self, *args, **kwargs):
-        if type(self)._is_protocol:
-            raise TypeError('Protocols cannot be instantiated')
-
-    class _ProtocolMeta(abc.ABCMeta):
-        # This metaclass is a bit unfortunate and exists only because of the lack
-        # of __instancehook__.
-        def __instancecheck__(cls, instance):
-            # We need this method for situations where attributes are
-            # assigned in __init__.
-            if ((not getattr(cls, '_is_protocol', False) or
-                 _is_callable_members_only(cls)) and
-                    issubclass(instance.__class__, cls)):
-                return True
-            if cls._is_protocol:
-                if all(hasattr(instance, attr) and
-                       (not callable(getattr(cls, attr, None)) or
-                        getattr(instance, attr) is not None)
-                       for attr in _get_protocol_attrs(cls)):
-                    return True
-            return super().__instancecheck__(instance)
-
-    class Protocol(metaclass=_ProtocolMeta):
-        # There is quite a lot of overlapping code with typing.Generic.
-        # Unfortunately it is hard to avoid this while these live in two different
-        # modules. The duplicated code will be removed when Protocol is moved to typing.
-        """Base class for protocol classes. Protocol classes are defined as::
-
-            class Proto(Protocol):
-                def meth(self) -> int:
-                    ...
-
-        Such classes are primarily used with static type checkers that recognize
-        structural subtyping (static duck-typing), for example::
-
-            class C:
-                def meth(self) -> int:
-                    return 0
-
-            def func(x: Proto) -> int:
-                return x.meth()
-
-            func(C())  # Passes static type check
-
-        See PEP 544 for details. Protocol classes decorated with
-        @typing_extensions.runtime act as simple-minded runtime protocol that checks
-        only the presence of given attributes, ignoring their type signatures.
-
-        Protocol classes can be generic, they are defined as::
-
-            class GenProto(Protocol[T]):
-                def meth(self) -> T:
-                    ...
-        """
-        __slots__ = ()
-        _is_protocol = True
-
-        def __new__(cls, *args, **kwds):
-            if cls is Protocol:
-                raise TypeError("Type Protocol cannot be instantiated; "
-                                "it can only be used as a base class")
-            return super().__new__(cls)
-
-        @typing._tp_cache
-        def __class_getitem__(cls, params):
-            if not isinstance(params, tuple):
-                params = (params,)
-            if not params and cls is not typing.Tuple:
-                raise TypeError(
-                    f"Parameter list to {cls.__qualname__}[...] cannot be empty")
-            msg = "Parameters to generic types must be types."
-            params = tuple(typing._type_check(p, msg) for p in params)  # noqa
-            if cls is Protocol:
-                # Generic can only be subscripted with unique type variables.
-                if not all(isinstance(p, typing.TypeVar) for p in params):
-                    i = 0
-                    while isinstance(params[i], typing.TypeVar):
-                        i += 1
-                    raise TypeError(
-                        "Parameters to Protocol[...] must all be type variables."
-                        f" Parameter {i + 1} is {params[i]}")
-                if len(set(params)) != len(params):
-                    raise TypeError(
-                        "Parameters to Protocol[...] must all be unique")
-            else:
-                # Subscripting a regular Generic subclass.
-                _check_generic(cls, params)
-            return typing._GenericAlias(cls, params)
-
-        def __init_subclass__(cls, *args, **kwargs):
-            tvars = []
-            if '__orig_bases__' in cls.__dict__:
-                error = typing.Generic in cls.__orig_bases__
-            else:
-                error = typing.Generic in cls.__bases__
-            if error:
-                raise TypeError("Cannot inherit from plain Generic")
-            if '__orig_bases__' in cls.__dict__:
-                tvars = _collect_type_vars(cls.__orig_bases__)
-                # Look for Generic[T1, ..., Tn] or Protocol[T1, ..., Tn].
-                # If found, tvars must be a subset of it.
-                # If not found, tvars is it.
-                # Also check for and reject plain Generic,
-                # and reject multiple Generic[...] and/or Protocol[...].
-                gvars = None
-                for base in cls.__orig_bases__:
-                    if (isinstance(base, typing._GenericAlias) and
-                            base.__origin__ in (typing.Generic, Protocol)):
-                        # for error messages
-                        the_base = base.__origin__.__name__
-                        if gvars is not None:
-                            raise TypeError(
-                                "Cannot inherit from Generic[...]"
-                                " and/or Protocol[...] multiple types.")
-                        gvars = base.__parameters__
-                if gvars is None:
-                    gvars = tvars
-                else:
-                    tvarset = set(tvars)
-                    gvarset = set(gvars)
-                    if not tvarset <= gvarset:
-                        s_vars = ', '.join(str(t) for t in tvars if t not in gvarset)
-                        s_args = ', '.join(str(g) for g in gvars)
-                        raise TypeError(f"Some type variables ({s_vars}) are"
-                                        f" not listed in {the_base}[{s_args}]")
-                    tvars = gvars
-            cls.__parameters__ = tuple(tvars)
-
-            # Determine if this is a protocol or a concrete subclass.
-            if not cls.__dict__.get('_is_protocol', None):
-                cls._is_protocol = any(b is Protocol for b in cls.__bases__)
-
-            # Set (or override) the protocol subclass hook.
-            def _proto_hook(other):
-                if not cls.__dict__.get('_is_protocol', None):
-                    return NotImplemented
-                if not getattr(cls, '_is_runtime_protocol', False):
-                    if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']:
-                        return NotImplemented
-                    raise TypeError("Instance and class checks can only be used with"
-                                    " @runtime protocols")
-                if not _is_callable_members_only(cls):
-                    if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']:
-                        return NotImplemented
-                    raise TypeError("Protocols with non-method members"
-                                    " don't support issubclass()")
-                if not isinstance(other, type):
-                    # Same error as for issubclass(1, int)
-                    raise TypeError('issubclass() arg 1 must be a class')
-                for attr in _get_protocol_attrs(cls):
-                    for base in other.__mro__:
-                        if attr in base.__dict__:
-                            if base.__dict__[attr] is None:
-                                return NotImplemented
-                            break
-                        annotations = getattr(base, '__annotations__', {})
-                        if (isinstance(annotations, typing.Mapping) and
-                                attr in annotations and
-                                isinstance(other, _ProtocolMeta) and
-                                other._is_protocol):
-                            break
-                    else:
-                        return NotImplemented
-                return True
-            if '__subclasshook__' not in cls.__dict__:
-                cls.__subclasshook__ = _proto_hook
-
-            # We have nothing more to do for non-protocols.
-            if not cls._is_protocol:
-                return
-
-            # Check consistency of bases.
-            for base in cls.__bases__:
-                if not (base in (object, typing.Generic) or
-                        base.__module__ == 'collections.abc' and
-                        base.__name__ in _PROTO_WHITELIST or
-                        isinstance(base, _ProtocolMeta) and base._is_protocol):
-                    raise TypeError('Protocols can only inherit from other'
-                                    f' protocols, got {repr(base)}')
-            cls.__init__ = _no_init
-# 3.6
-else:
-    from typing import _next_in_mro, _type_check  # noqa
-
-    def _no_init(self, *args, **kwargs):
-        if type(self)._is_protocol:
-            raise TypeError('Protocols cannot be instantiated')
-
-    class _ProtocolMeta(GenericMeta):
-        """Internal metaclass for Protocol.
-
-        This exists so Protocol classes can be generic without deriving
-        from Generic.
-        """
-        def __new__(cls, name, bases, namespace,
-                    tvars=None, args=None, origin=None, extra=None, orig_bases=None):
-            # This is just a version copied from GenericMeta.__new__ that
-            # includes "Protocol" special treatment. (Comments removed for brevity.)
-            assert extra is None  # Protocols should not have extra
-            if tvars is not None:
-                assert origin is not None
-                assert all(isinstance(t, typing.TypeVar) for t in tvars), tvars
-            else:
-                tvars = _type_vars(bases)
-                gvars = None
-                for base in bases:
-                    if base is typing.Generic:
-                        raise TypeError("Cannot inherit from plain Generic")
-                    if (isinstance(base, GenericMeta) and
-                            base.__origin__ in (typing.Generic, Protocol)):
-                        if gvars is not None:
-                            raise TypeError(
-                                "Cannot inherit from Generic[...] or"
-                                " Protocol[...] multiple times.")
-                        gvars = base.__parameters__
-                if gvars is None:
-                    gvars = tvars
-                else:
-                    tvarset = set(tvars)
-                    gvarset = set(gvars)
-                    if not tvarset <= gvarset:
-                        s_vars = ", ".join(str(t) for t in tvars if t not in gvarset)
-                        s_args = ", ".join(str(g) for g in gvars)
-                        cls_name = "Generic" if any(b.__origin__ is typing.Generic
-                                                    for b in bases) else "Protocol"
-                        raise TypeError(f"Some type variables ({s_vars}) are"
-                                        f" not listed in {cls_name}[{s_args}]")
-                    tvars = gvars
-
-            initial_bases = bases
-            if (extra is not None and type(extra) is abc.ABCMeta and
-                    extra not in bases):
-                bases = (extra,) + bases
-            bases = tuple(_gorg(b) if isinstance(b, GenericMeta) else b
-                          for b in bases)
-            if any(isinstance(b, GenericMeta) and b is not typing.Generic for b in bases):
-                bases = tuple(b for b in bases if b is not typing.Generic)
-            namespace.update({'__origin__': origin, '__extra__': extra})
-            self = super(GenericMeta, cls).__new__(cls, name, bases, namespace,
-                                                   _root=True)
-            super(GenericMeta, self).__setattr__('_gorg',
-                                                 self if not origin else
-                                                 _gorg(origin))
-            self.__parameters__ = tvars
-            self.__args__ = tuple(... if a is typing._TypingEllipsis else
-                                  () if a is typing._TypingEmpty else
-                                  a for a in args) if args else None
-            self.__next_in_mro__ = _next_in_mro(self)
-            if orig_bases is None:
-                self.__orig_bases__ = initial_bases
-            elif origin is not None:
-                self._abc_registry = origin._abc_registry
-                self._abc_cache = origin._abc_cache
-            if hasattr(self, '_subs_tree'):
-                self.__tree_hash__ = (hash(self._subs_tree()) if origin else
-                                      super(GenericMeta, self).__hash__())
-            return self
-
-        def __init__(cls, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            if not cls.__dict__.get('_is_protocol', None):
-                cls._is_protocol = any(b is Protocol or
-                                       isinstance(b, _ProtocolMeta) and
-                                       b.__origin__ is Protocol
-                                       for b in cls.__bases__)
-            if cls._is_protocol:
-                for base in cls.__mro__[1:]:
-                    if not (base in (object, typing.Generic) or
-                            base.__module__ == 'collections.abc' and
-                            base.__name__ in _PROTO_WHITELIST or
-                            isinstance(base, typing.TypingMeta) and base._is_protocol or
-                            isinstance(base, GenericMeta) and
-                            base.__origin__ is typing.Generic):
-                        raise TypeError(f'Protocols can only inherit from other'
-                                        f' protocols, got {repr(base)}')
-
-                cls.__init__ = _no_init
-
-            def _proto_hook(other):
-                if not cls.__dict__.get('_is_protocol', None):
-                    return NotImplemented
-                if not isinstance(other, type):
-                    # Same error as for issubclass(1, int)
-                    raise TypeError('issubclass() arg 1 must be a class')
-                for attr in _get_protocol_attrs(cls):
-                    for base in other.__mro__:
-                        if attr in base.__dict__:
-                            if base.__dict__[attr] is None:
-                                return NotImplemented
-                            break
-                        annotations = getattr(base, '__annotations__', {})
-                        if (isinstance(annotations, typing.Mapping) and
-                                attr in annotations and
-                                isinstance(other, _ProtocolMeta) and
-                                other._is_protocol):
-                            break
-                    else:
-                        return NotImplemented
-                return True
-            if '__subclasshook__' not in cls.__dict__:
-                cls.__subclasshook__ = _proto_hook
-
-        def __instancecheck__(self, instance):
-            # We need this method for situations where attributes are
-            # assigned in __init__.
-            if ((not getattr(self, '_is_protocol', False) or
-                    _is_callable_members_only(self)) and
-                    issubclass(instance.__class__, self)):
-                return True
-            if self._is_protocol:
-                if all(hasattr(instance, attr) and
-                        (not callable(getattr(self, attr, None)) or
-                         getattr(instance, attr) is not None)
-                        for attr in _get_protocol_attrs(self)):
-                    return True
-            return super(GenericMeta, self).__instancecheck__(instance)
-
-        def __subclasscheck__(self, cls):
-            if self.__origin__ is not None:
-                if sys._getframe(1).f_globals['__name__'] not in ['abc', 'functools']:
-                    raise TypeError("Parameterized generics cannot be used with class "
-                                    "or instance checks")
-                return False
-            if (self.__dict__.get('_is_protocol', None) and
-                    not self.__dict__.get('_is_runtime_protocol', None)):
-                if sys._getframe(1).f_globals['__name__'] in ['abc',
-                                                              'functools',
-                                                              'typing']:
-                    return False
-                raise TypeError("Instance and class checks can only be used with"
-                                " @runtime protocols")
-            if (self.__dict__.get('_is_runtime_protocol', None) and
-                    not _is_callable_members_only(self)):
-                if sys._getframe(1).f_globals['__name__'] in ['abc',
-                                                              'functools',
-                                                              'typing']:
-                    return super(GenericMeta, self).__subclasscheck__(cls)
-                raise TypeError("Protocols with non-method members"
-                                " don't support issubclass()")
-            return super(GenericMeta, self).__subclasscheck__(cls)
-
-        @typing._tp_cache
-        def __getitem__(self, params):
-            # We also need to copy this from GenericMeta.__getitem__ to get
-            # special treatment of "Protocol". (Comments removed for brevity.)
-            if not isinstance(params, tuple):
-                params = (params,)
-            if not params and _gorg(self) is not typing.Tuple:
-                raise TypeError(
-                    f"Parameter list to {self.__qualname__}[...] cannot be empty")
-            msg = "Parameters to generic types must be types."
-            params = tuple(_type_check(p, msg) for p in params)
-            if self in (typing.Generic, Protocol):
-                if not all(isinstance(p, typing.TypeVar) for p in params):
-                    raise TypeError(
-                        f"Parameters to {repr(self)}[...] must all be type variables")
-                if len(set(params)) != len(params):
-                    raise TypeError(
-                        f"Parameters to {repr(self)}[...] must all be unique")
-                tvars = params
-                args = params
-            elif self in (typing.Tuple, typing.Callable):
-                tvars = _type_vars(params)
-                args = params
-            elif self.__origin__ in (typing.Generic, Protocol):
-                raise TypeError(f"Cannot subscript already-subscripted {repr(self)}")
-            else:
-                _check_generic(self, params)
-                tvars = _type_vars(params)
-                args = params
-
-            prepend = (self,) if self.__origin__ is None else ()
-            return self.__class__(self.__name__,
-                                  prepend + self.__bases__,
-                                  _no_slots_copy(self.__dict__),
-                                  tvars=tvars,
-                                  args=args,
-                                  origin=self,
-                                  extra=self.__extra__,
-                                  orig_bases=self.__orig_bases__)
-
-    class Protocol(metaclass=_ProtocolMeta):
-        """Base class for protocol classes. Protocol classes are defined as::
-
-          class Proto(Protocol):
-              def meth(self) -> int:
-                  ...
-
-        Such classes are primarily used with static type checkers that recognize
-        structural subtyping (static duck-typing), for example::
-
-          class C:
-              def meth(self) -> int:
-                  return 0
-
-          def func(x: Proto) -> int:
-              return x.meth()
-
-          func(C())  # Passes static type check
-
-        See PEP 544 for details. Protocol classes decorated with
-        @typing_extensions.runtime act as simple-minded runtime protocol that checks
-        only the presence of given attributes, ignoring their type signatures.
-
-        Protocol classes can be generic, they are defined as::
-
-          class GenProto(Protocol[T]):
-              def meth(self) -> T:
-                  ...
-        """
-        __slots__ = ()
-        _is_protocol = True
-
-        def __new__(cls, *args, **kwds):
-            if _gorg(cls) is Protocol:
-                raise TypeError("Type Protocol cannot be instantiated; "
-                                "it can be used only as a base class")
-            return typing._generic_new(cls.__next_in_mro__, cls, *args, **kwds)
-
-
-# 3.8+
-if hasattr(typing, 'runtime_checkable'):
-    runtime_checkable = typing.runtime_checkable
-# 3.6-3.7
-else:
-    def runtime_checkable(cls):
-        """Mark a protocol class as a runtime protocol, so that it
-        can be used with isinstance() and issubclass(). Raise TypeError
-        if applied to a non-protocol class.
-
-        This allows a simple-minded structural check very similar to the
-        one-offs in collections.abc such as Hashable.
-        """
-        if not isinstance(cls, _ProtocolMeta) or not cls._is_protocol:
-            raise TypeError('@runtime_checkable can be only applied to protocol classes,'
-                            f' got {cls!r}')
-        cls._is_runtime_protocol = True
-        return cls
-
-
-# Exists for backwards compatibility.
-runtime = runtime_checkable
-
-
-# 3.8+
-if hasattr(typing, 'SupportsIndex'):
-    SupportsIndex = typing.SupportsIndex
-# 3.6-3.7
-else:
-    @runtime_checkable
-    class SupportsIndex(Protocol):
-        __slots__ = ()
-
-        @abc.abstractmethod
-        def __index__(self) -> int:
-            pass
-
-
-if sys.version_info >= (3, 9, 2):
-    # The standard library TypedDict in Python 3.8 does not store runtime information
-    # about which (if any) keys are optional.  See https://bugs.python.org/issue38834
-    # The standard library TypedDict in Python 3.9.0/1 does not honour the "total"
-    # keyword with old-style TypedDict().  See https://bugs.python.org/issue42059
-    TypedDict = typing.TypedDict
-else:
-    def _check_fails(cls, other):
-        try:
-            if sys._getframe(1).f_globals['__name__'] not in ['abc',
-                                                              'functools',
-                                                              'typing']:
-                # Typed dicts are only for static structural subtyping.
-                raise TypeError('TypedDict does not support instance and class checks')
-        except (AttributeError, ValueError):
-            pass
-        return False
-
-    def _dict_new(*args, **kwargs):
-        if not args:
-            raise TypeError('TypedDict.__new__(): not enough arguments')
-        _, args = args[0], args[1:]  # allow the "cls" keyword be passed
-        return dict(*args, **kwargs)
-
-    _dict_new.__text_signature__ = '($cls, _typename, _fields=None, /, **kwargs)'
-
-    def _typeddict_new(*args, total=True, **kwargs):
-        if not args:
-            raise TypeError('TypedDict.__new__(): not enough arguments')
-        _, args = args[0], args[1:]  # allow the "cls" keyword be passed
-        if args:
-            typename, args = args[0], args[1:]  # allow the "_typename" keyword be passed
-        elif '_typename' in kwargs:
-            typename = kwargs.pop('_typename')
-            import warnings
-            warnings.warn("Passing '_typename' as keyword argument is deprecated",
-                          DeprecationWarning, stacklevel=2)
-        else:
-            raise TypeError("TypedDict.__new__() missing 1 required positional "
-                            "argument: '_typename'")
-        if args:
-            try:
-                fields, = args  # allow the "_fields" keyword be passed
-            except ValueError:
-                raise TypeError('TypedDict.__new__() takes from 2 to 3 '
-                                f'positional arguments but {len(args) + 2} '
-                                'were given')
-        elif '_fields' in kwargs and len(kwargs) == 1:
-            fields = kwargs.pop('_fields')
-            import warnings
-            warnings.warn("Passing '_fields' as keyword argument is deprecated",
-                          DeprecationWarning, stacklevel=2)
-        else:
-            fields = None
-
-        if fields is None:
-            fields = kwargs
-        elif kwargs:
-            raise TypeError("TypedDict takes either a dict or keyword arguments,"
-                            " but not both")
-
-        ns = {'__annotations__': dict(fields)}
-        try:
-            # Setting correct module is necessary to make typed dict classes pickleable.
-            ns['__module__'] = sys._getframe(1).f_globals.get('__name__', '__main__')
-        except (AttributeError, ValueError):
-            pass
-
-        return _TypedDictMeta(typename, (), ns, total=total)
-
-    _typeddict_new.__text_signature__ = ('($cls, _typename, _fields=None,'
-                                         ' /, *, total=True, **kwargs)')
-
-    class _TypedDictMeta(type):
-        def __init__(cls, name, bases, ns, total=True):
-            super().__init__(name, bases, ns)
-
-        def __new__(cls, name, bases, ns, total=True):
-            # Create new typed dict class object.
-            # This method is called directly when TypedDict is subclassed,
-            # or via _typeddict_new when TypedDict is instantiated. This way
-            # TypedDict supports all three syntaxes described in its docstring.
-            # Subclasses and instances of TypedDict return actual dictionaries
-            # via _dict_new.
-            ns['__new__'] = _typeddict_new if name == 'TypedDict' else _dict_new
-            tp_dict = super().__new__(cls, name, (dict,), ns)
-
-            annotations = {}
-            own_annotations = ns.get('__annotations__', {})
-            own_annotation_keys = set(own_annotations.keys())
-            msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"
-            own_annotations = {
-                n: typing._type_check(tp, msg) for n, tp in own_annotations.items()
-            }
-            required_keys = set()
-            optional_keys = set()
-
-            for base in bases:
-                annotations.update(base.__dict__.get('__annotations__', {}))
-                required_keys.update(base.__dict__.get('__required_keys__', ()))
-                optional_keys.update(base.__dict__.get('__optional_keys__', ()))
-
-            annotations.update(own_annotations)
-            if total:
-                required_keys.update(own_annotation_keys)
-            else:
-                optional_keys.update(own_annotation_keys)
-
-            tp_dict.__annotations__ = annotations
-            tp_dict.__required_keys__ = frozenset(required_keys)
-            tp_dict.__optional_keys__ = frozenset(optional_keys)
-            if not hasattr(tp_dict, '__total__'):
-                tp_dict.__total__ = total
-            return tp_dict
-
-        __instancecheck__ = __subclasscheck__ = _check_fails
-
-    TypedDict = _TypedDictMeta('TypedDict', (dict,), {})
-    TypedDict.__module__ = __name__
-    TypedDict.__doc__ = \
-        """A simple typed name space. At runtime it is equivalent to a plain dict.
-
-        TypedDict creates a dictionary type that expects all of its
-        instances to have a certain set of keys, with each key
-        associated with a value of a consistent type. This expectation
-        is not checked at runtime but is only enforced by type checkers.
-        Usage::
-
-            class Point2D(TypedDict):
-                x: int
-                y: int
-                label: str
-
-            a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
-            b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check
-
-            assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
-
-        The type info can be accessed via the Point2D.__annotations__ dict, and
-        the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
-        TypedDict supports two additional equivalent forms::
-
-            Point2D = TypedDict('Point2D', x=int, y=int, label=str)
-            Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
-
-        The class syntax is only supported in Python 3.6+, while two other
-        syntax forms work for Python 2.7 and 3.2+
-        """
-
-
-# Python 3.9+ has PEP 593 (Annotated and modified get_type_hints)
-if hasattr(typing, 'Annotated'):
-    Annotated = typing.Annotated
-    get_type_hints = typing.get_type_hints
-    # Not exported and not a public API, but needed for get_origin() and get_args()
-    # to work.
-    _AnnotatedAlias = typing._AnnotatedAlias
-# 3.7-3.8
-elif PEP_560:
-    class _AnnotatedAlias(typing._GenericAlias, _root=True):
-        """Runtime representation of an annotated type.
-
-        At its core 'Annotated[t, dec1, dec2, ...]' is an alias for the type 't'
-        with extra annotations. The alias behaves like a normal typing alias,
-        instantiating is the same as instantiating the underlying type, binding
-        it to types is also the same.
-        """
-        def __init__(self, origin, metadata):
-            if isinstance(origin, _AnnotatedAlias):
-                metadata = origin.__metadata__ + metadata
-                origin = origin.__origin__
-            super().__init__(origin, origin)
-            self.__metadata__ = metadata
-
-        def copy_with(self, params):
-            assert len(params) == 1
-            new_type = params[0]
-            return _AnnotatedAlias(new_type, self.__metadata__)
-
-        def __repr__(self):
-            return (f"typing_extensions.Annotated[{typing._type_repr(self.__origin__)}, "
-                    f"{', '.join(repr(a) for a in self.__metadata__)}]")
-
-        def __reduce__(self):
-            return operator.getitem, (
-                Annotated, (self.__origin__,) + self.__metadata__
-            )
-
-        def __eq__(self, other):
-            if not isinstance(other, _AnnotatedAlias):
-                return NotImplemented
-            if self.__origin__ != other.__origin__:
-                return False
-            return self.__metadata__ == other.__metadata__
-
-        def __hash__(self):
-            return hash((self.__origin__, self.__metadata__))
-
-    class Annotated:
-        """Add context specific metadata to a type.
-
-        Example: Annotated[int, runtime_check.Unsigned] indicates to the
-        hypothetical runtime_check module that this type is an unsigned int.
-        Every other consumer of this type can ignore this metadata and treat
-        this type as int.
-
-        The first argument to Annotated must be a valid type (and will be in
-        the __origin__ field), the remaining arguments are kept as a tuple in
-        the __extra__ field.
-
-        Details:
-
-        - It's an error to call `Annotated` with less than two arguments.
-        - Nested Annotated are flattened::
-
-            Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]
-
-        - Instantiating an annotated type is equivalent to instantiating the
-        underlying type::
-
-            Annotated[C, Ann1](5) == C(5)
-
-        - Annotated can be used as a generic type alias::
-
-            Optimized = Annotated[T, runtime.Optimize()]
-            Optimized[int] == Annotated[int, runtime.Optimize()]
-
-            OptimizedList = Annotated[List[T], runtime.Optimize()]
-            OptimizedList[int] == Annotated[List[int], runtime.Optimize()]
-        """
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwargs):
-            raise TypeError("Type Annotated cannot be instantiated.")
-
-        @typing._tp_cache
-        def __class_getitem__(cls, params):
-            if not isinstance(params, tuple) or len(params) < 2:
-                raise TypeError("Annotated[...] should be used "
-                                "with at least two arguments (a type and an "
-                                "annotation).")
-            msg = "Annotated[t, ...]: t must be a type."
-            origin = typing._type_check(params[0], msg)
-            metadata = tuple(params[1:])
-            return _AnnotatedAlias(origin, metadata)
-
-        def __init_subclass__(cls, *args, **kwargs):
-            raise TypeError(
-                f"Cannot subclass {cls.__module__}.Annotated"
-            )
-
-    def _strip_annotations(t):
-        """Strips the annotations from a given type.
-        """
-        if isinstance(t, _AnnotatedAlias):
-            return _strip_annotations(t.__origin__)
-        if isinstance(t, typing._GenericAlias):
-            stripped_args = tuple(_strip_annotations(a) for a in t.__args__)
-            if stripped_args == t.__args__:
-                return t
-            res = t.copy_with(stripped_args)
-            res._special = t._special
-            return res
-        return t
-
-    def get_type_hints(obj, globalns=None, localns=None, include_extras=False):
-        """Return type hints for an object.
-
-        This is often the same as obj.__annotations__, but it handles
-        forward references encoded as string literals, adds Optional[t] if a
-        default value equal to None is set and recursively replaces all
-        'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
-
-        The argument may be a module, class, method, or function. The annotations
-        are returned as a dictionary. For classes, annotations include also
-        inherited members.
-
-        TypeError is raised if the argument is not of a type that can contain
-        annotations, and an empty dictionary is returned if no annotations are
-        present.
-
-        BEWARE -- the behavior of globalns and localns is counterintuitive
-        (unless you are familiar with how eval() and exec() work).  The
-        search order is locals first, then globals.
-
-        - If no dict arguments are passed, an attempt is made to use the
-          globals from obj (or the respective module's globals for classes),
-          and these are also used as the locals.  If the object does not appear
-          to have globals, an empty dictionary is used.
-
-        - If one dict argument is passed, it is used for both globals and
-          locals.
-
-        - If two dict arguments are passed, they specify globals and
-          locals, respectively.
-        """
-        hint = typing.get_type_hints(obj, globalns=globalns, localns=localns)
-        if include_extras:
-            return hint
-        return {k: _strip_annotations(t) for k, t in hint.items()}
-# 3.6
-else:
-
-    def _is_dunder(name):
-        """Returns True if name is a __dunder_variable_name__."""
-        return len(name) > 4 and name.startswith('__') and name.endswith('__')
-
-    # Prior to Python 3.7 types did not have `copy_with`. A lot of the equality
-    # checks, argument expansion etc. are done on the _subs_tre. As a result we
-    # can't provide a get_type_hints function that strips out annotations.
-
-    class AnnotatedMeta(typing.GenericMeta):
-        """Metaclass for Annotated"""
-
-        def __new__(cls, name, bases, namespace, **kwargs):
-            if any(b is not object for b in bases):
-                raise TypeError("Cannot subclass " + str(Annotated))
-            return super().__new__(cls, name, bases, namespace, **kwargs)
-
-        @property
-        def __metadata__(self):
-            return self._subs_tree()[2]
-
-        def _tree_repr(self, tree):
-            cls, origin, metadata = tree
-            if not isinstance(origin, tuple):
-                tp_repr = typing._type_repr(origin)
-            else:
-                tp_repr = origin[0]._tree_repr(origin)
-            metadata_reprs = ", ".join(repr(arg) for arg in metadata)
-            return f'{cls}[{tp_repr}, {metadata_reprs}]'
-
-        def _subs_tree(self, tvars=None, args=None):  # noqa
-            if self is Annotated:
-                return Annotated
-            res = super()._subs_tree(tvars=tvars, args=args)
-            # Flatten nested Annotated
-            if isinstance(res[1], tuple) and res[1][0] is Annotated:
-                sub_tp = res[1][1]
-                sub_annot = res[1][2]
-                return (Annotated, sub_tp, sub_annot + res[2])
-            return res
-
-        def _get_cons(self):
-            """Return the class used to create instance of this type."""
-            if self.__origin__ is None:
-                raise TypeError("Cannot get the underlying type of a "
-                                "non-specialized Annotated type.")
-            tree = self._subs_tree()
-            while isinstance(tree, tuple) and tree[0] is Annotated:
-                tree = tree[1]
-            if isinstance(tree, tuple):
-                return tree[0]
-            else:
-                return tree
-
-        @typing._tp_cache
-        def __getitem__(self, params):
-            if not isinstance(params, tuple):
-                params = (params,)
-            if self.__origin__ is not None:  # specializing an instantiated type
-                return super().__getitem__(params)
-            elif not isinstance(params, tuple) or len(params) < 2:
-                raise TypeError("Annotated[...] should be instantiated "
-                                "with at least two arguments (a type and an "
-                                "annotation).")
-            else:
-                msg = "Annotated[t, ...]: t must be a type."
-                tp = typing._type_check(params[0], msg)
-                metadata = tuple(params[1:])
-            return self.__class__(
-                self.__name__,
-                self.__bases__,
-                _no_slots_copy(self.__dict__),
-                tvars=_type_vars((tp,)),
-                # Metadata is a tuple so it won't be touched by _replace_args et al.
-                args=(tp, metadata),
-                origin=self,
-            )
-
-        def __call__(self, *args, **kwargs):
-            cons = self._get_cons()
-            result = cons(*args, **kwargs)
-            try:
-                result.__orig_class__ = self
-            except AttributeError:
-                pass
-            return result
-
-        def __getattr__(self, attr):
-            # For simplicity we just don't relay all dunder names
-            if self.__origin__ is not None and not _is_dunder(attr):
-                return getattr(self._get_cons(), attr)
-            raise AttributeError(attr)
-
-        def __setattr__(self, attr, value):
-            if _is_dunder(attr) or attr.startswith('_abc_'):
-                super().__setattr__(attr, value)
-            elif self.__origin__ is None:
-                raise AttributeError(attr)
-            else:
-                setattr(self._get_cons(), attr, value)
-
-        def __instancecheck__(self, obj):
-            raise TypeError("Annotated cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("Annotated cannot be used with issubclass().")
-
-    class Annotated(metaclass=AnnotatedMeta):
-        """Add context specific metadata to a type.
-
-        Example: Annotated[int, runtime_check.Unsigned] indicates to the
-        hypothetical runtime_check module that this type is an unsigned int.
-        Every other consumer of this type can ignore this metadata and treat
-        this type as int.
-
-        The first argument to Annotated must be a valid type, the remaining
-        arguments are kept as a tuple in the __metadata__ field.
-
-        Details:
-
-        - It's an error to call `Annotated` with less than two arguments.
-        - Nested Annotated are flattened::
-
-            Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]
-
-        - Instantiating an annotated type is equivalent to instantiating the
-        underlying type::
-
-            Annotated[C, Ann1](5) == C(5)
-
-        - Annotated can be used as a generic type alias::
-
-            Optimized = Annotated[T, runtime.Optimize()]
-            Optimized[int] == Annotated[int, runtime.Optimize()]
-
-            OptimizedList = Annotated[List[T], runtime.Optimize()]
-            OptimizedList[int] == Annotated[List[int], runtime.Optimize()]
-        """
-
-# Python 3.8 has get_origin() and get_args() but those implementations aren't
-# Annotated-aware, so we can't use those. Python 3.9's versions don't support
-# ParamSpecArgs and ParamSpecKwargs, so only Python 3.10's versions will do.
-if sys.version_info[:2] >= (3, 10):
-    get_origin = typing.get_origin
-    get_args = typing.get_args
-# 3.7-3.9
-elif PEP_560:
-    try:
-        # 3.9+
-        from typing import _BaseGenericAlias
-    except ImportError:
-        _BaseGenericAlias = typing._GenericAlias
-    try:
-        # 3.9+
-        from typing import GenericAlias
-    except ImportError:
-        GenericAlias = typing._GenericAlias
-
-    def get_origin(tp):
-        """Get the unsubscripted version of a type.
-
-        This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar
-        and Annotated. Return None for unsupported types. Examples::
-
-            get_origin(Literal[42]) is Literal
-            get_origin(int) is None
-            get_origin(ClassVar[int]) is ClassVar
-            get_origin(Generic) is Generic
-            get_origin(Generic[T]) is Generic
-            get_origin(Union[T, int]) is Union
-            get_origin(List[Tuple[T, T]][int]) == list
-            get_origin(P.args) is P
-        """
-        if isinstance(tp, _AnnotatedAlias):
-            return Annotated
-        if isinstance(tp, (typing._GenericAlias, GenericAlias, _BaseGenericAlias,
-                           ParamSpecArgs, ParamSpecKwargs)):
-            return tp.__origin__
-        if tp is typing.Generic:
-            return typing.Generic
-        return None
-
-    def get_args(tp):
-        """Get type arguments with all substitutions performed.
-
-        For unions, basic simplifications used by Union constructor are performed.
-        Examples::
-            get_args(Dict[str, int]) == (str, int)
-            get_args(int) == ()
-            get_args(Union[int, Union[T, int], str][int]) == (int, str)
-            get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
-            get_args(Callable[[], T][int]) == ([], int)
-        """
-        if isinstance(tp, _AnnotatedAlias):
-            return (tp.__origin__,) + tp.__metadata__
-        if isinstance(tp, (typing._GenericAlias, GenericAlias)):
-            if getattr(tp, "_special", False):
-                return ()
-            res = tp.__args__
-            if get_origin(tp) is collections.abc.Callable and res[0] is not Ellipsis:
-                res = (list(res[:-1]), res[-1])
-            return res
-        return ()
-
-
-# 3.10+
-if hasattr(typing, 'TypeAlias'):
-    TypeAlias = typing.TypeAlias
-# 3.9
-elif sys.version_info[:2] >= (3, 9):
-    class _TypeAliasForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    @_TypeAliasForm
-    def TypeAlias(self, parameters):
-        """Special marker indicating that an assignment should
-        be recognized as a proper type alias definition by type
-        checkers.
-
-        For example::
-
-            Predicate: TypeAlias = Callable[..., bool]
-
-        It's invalid when used anywhere except as in the example above.
-        """
-        raise TypeError(f"{self} is not subscriptable")
-# 3.7-3.8
-elif sys.version_info[:2] >= (3, 7):
-    class _TypeAliasForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    TypeAlias = _TypeAliasForm('TypeAlias',
-                               doc="""Special marker indicating that an assignment should
-                               be recognized as a proper type alias definition by type
-                               checkers.
-
-                               For example::
-
-                                   Predicate: TypeAlias = Callable[..., bool]
-
-                               It's invalid when used anywhere except as in the example
-                               above.""")
-# 3.6
-else:
-    class _TypeAliasMeta(typing.TypingMeta):
-        """Metaclass for TypeAlias"""
-
-        def __repr__(self):
-            return 'typing_extensions.TypeAlias'
-
-    class _TypeAliasBase(typing._FinalTypingBase, metaclass=_TypeAliasMeta, _root=True):
-        """Special marker indicating that an assignment should
-        be recognized as a proper type alias definition by type
-        checkers.
-
-        For example::
-
-            Predicate: TypeAlias = Callable[..., bool]
-
-        It's invalid when used anywhere except as in the example above.
-        """
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError("TypeAlias cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("TypeAlias cannot be used with issubclass().")
-
-        def __repr__(self):
-            return 'typing_extensions.TypeAlias'
-
-    TypeAlias = _TypeAliasBase(_root=True)
-
-
-# Python 3.10+ has PEP 612
-if hasattr(typing, 'ParamSpecArgs'):
-    ParamSpecArgs = typing.ParamSpecArgs
-    ParamSpecKwargs = typing.ParamSpecKwargs
-# 3.6-3.9
-else:
-    class _Immutable:
-        """Mixin to indicate that object should not be copied."""
-        __slots__ = ()
-
-        def __copy__(self):
-            return self
-
-        def __deepcopy__(self, memo):
-            return self
-
-    class ParamSpecArgs(_Immutable):
-        """The args for a ParamSpec object.
-
-        Given a ParamSpec object P, P.args is an instance of ParamSpecArgs.
-
-        ParamSpecArgs objects have a reference back to their ParamSpec:
-
-        P.args.__origin__ is P
-
-        This type is meant for runtime introspection and has no special meaning to
-        static type checkers.
-        """
-        def __init__(self, origin):
-            self.__origin__ = origin
-
-        def __repr__(self):
-            return f"{self.__origin__.__name__}.args"
-
-    class ParamSpecKwargs(_Immutable):
-        """The kwargs for a ParamSpec object.
-
-        Given a ParamSpec object P, P.kwargs is an instance of ParamSpecKwargs.
-
-        ParamSpecKwargs objects have a reference back to their ParamSpec:
-
-        P.kwargs.__origin__ is P
-
-        This type is meant for runtime introspection and has no special meaning to
-        static type checkers.
-        """
-        def __init__(self, origin):
-            self.__origin__ = origin
-
-        def __repr__(self):
-            return f"{self.__origin__.__name__}.kwargs"
-
-# 3.10+
-if hasattr(typing, 'ParamSpec'):
-    ParamSpec = typing.ParamSpec
-# 3.6-3.9
-else:
-
-    # Inherits from list as a workaround for Callable checks in Python < 3.9.2.
-    class ParamSpec(list):
-        """Parameter specification variable.
-
-        Usage::
-
-           P = ParamSpec('P')
-
-        Parameter specification variables exist primarily for the benefit of static
-        type checkers.  They are used to forward the parameter types of one
-        callable to another callable, a pattern commonly found in higher order
-        functions and decorators.  They are only valid when used in ``Concatenate``,
-        or s the first argument to ``Callable``. In Python 3.10 and higher,
-        they are also supported in user-defined Generics at runtime.
-        See class Generic for more information on generic types.  An
-        example for annotating a decorator::
-
-           T = TypeVar('T')
-           P = ParamSpec('P')
-
-           def add_logging(f: Callable[P, T]) -> Callable[P, T]:
-               '''A type-safe decorator to add logging to a function.'''
-               def inner(*args: P.args, **kwargs: P.kwargs) -> T:
-                   logging.info(f'{f.__name__} was called')
-                   return f(*args, **kwargs)
-               return inner
-
-           @add_logging
-           def add_two(x: float, y: float) -> float:
-               '''Add two numbers together.'''
-               return x + y
-
-        Parameter specification variables defined with covariant=True or
-        contravariant=True can be used to declare covariant or contravariant
-        generic types.  These keyword arguments are valid, but their actual semantics
-        are yet to be decided.  See PEP 612 for details.
-
-        Parameter specification variables can be introspected. e.g.:
-
-           P.__name__ == 'T'
-           P.__bound__ == None
-           P.__covariant__ == False
-           P.__contravariant__ == False
-
-        Note that only parameter specification variables defined in global scope can
-        be pickled.
-        """
-
-        # Trick Generic __parameters__.
-        __class__ = typing.TypeVar
-
-        @property
-        def args(self):
-            return ParamSpecArgs(self)
-
-        @property
-        def kwargs(self):
-            return ParamSpecKwargs(self)
-
-        def __init__(self, name, *, bound=None, covariant=False, contravariant=False):
-            super().__init__([self])
-            self.__name__ = name
-            self.__covariant__ = bool(covariant)
-            self.__contravariant__ = bool(contravariant)
-            if bound:
-                self.__bound__ = typing._type_check(bound, 'Bound must be a type.')
-            else:
-                self.__bound__ = None
-
-            # for pickling:
-            try:
-                def_mod = sys._getframe(1).f_globals.get('__name__', '__main__')
-            except (AttributeError, ValueError):
-                def_mod = None
-            if def_mod != 'typing_extensions':
-                self.__module__ = def_mod
-
-        def __repr__(self):
-            if self.__covariant__:
-                prefix = '+'
-            elif self.__contravariant__:
-                prefix = '-'
-            else:
-                prefix = '~'
-            return prefix + self.__name__
-
-        def __hash__(self):
-            return object.__hash__(self)
-
-        def __eq__(self, other):
-            return self is other
-
-        def __reduce__(self):
-            return self.__name__
-
-        # Hack to get typing._type_check to pass.
-        def __call__(self, *args, **kwargs):
-            pass
-
-        if not PEP_560:
-            # Only needed in 3.6.
-            def _get_type_vars(self, tvars):
-                if self not in tvars:
-                    tvars.append(self)
-
-
-# 3.6-3.9
-if not hasattr(typing, 'Concatenate'):
-    # Inherits from list as a workaround for Callable checks in Python < 3.9.2.
-    class _ConcatenateGenericAlias(list):
-
-        # Trick Generic into looking into this for __parameters__.
-        if PEP_560:
-            __class__ = typing._GenericAlias
-        else:
-            __class__ = typing._TypingBase
-
-        # Flag in 3.8.
-        _special = False
-        # Attribute in 3.6 and earlier.
-        _gorg = typing.Generic
-
-        def __init__(self, origin, args):
-            super().__init__(args)
-            self.__origin__ = origin
-            self.__args__ = args
-
-        def __repr__(self):
-            _type_repr = typing._type_repr
-            return (f'{_type_repr(self.__origin__)}'
-                    f'[{", ".join(_type_repr(arg) for arg in self.__args__)}]')
-
-        def __hash__(self):
-            return hash((self.__origin__, self.__args__))
-
-        # Hack to get typing._type_check to pass in Generic.
-        def __call__(self, *args, **kwargs):
-            pass
-
-        @property
-        def __parameters__(self):
-            return tuple(
-                tp for tp in self.__args__ if isinstance(tp, (typing.TypeVar, ParamSpec))
-            )
-
-        if not PEP_560:
-            # Only required in 3.6.
-            def _get_type_vars(self, tvars):
-                if self.__origin__ and self.__parameters__:
-                    typing._get_type_vars(self.__parameters__, tvars)
-
-
-# 3.6-3.9
-@typing._tp_cache
-def _concatenate_getitem(self, parameters):
-    if parameters == ():
-        raise TypeError("Cannot take a Concatenate of no types.")
-    if not isinstance(parameters, tuple):
-        parameters = (parameters,)
-    if not isinstance(parameters[-1], ParamSpec):
-        raise TypeError("The last parameter to Concatenate should be a "
-                        "ParamSpec variable.")
-    msg = "Concatenate[arg, ...]: each arg must be a type."
-    parameters = tuple(typing._type_check(p, msg) for p in parameters)
-    return _ConcatenateGenericAlias(self, parameters)
-
-
-# 3.10+
-if hasattr(typing, 'Concatenate'):
-    Concatenate = typing.Concatenate
-    _ConcatenateGenericAlias = typing._ConcatenateGenericAlias # noqa
-# 3.9
-elif sys.version_info[:2] >= (3, 9):
-    @_TypeAliasForm
-    def Concatenate(self, parameters):
-        """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
-        higher order function which adds, removes or transforms parameters of a
-        callable.
-
-        For example::
-
-           Callable[Concatenate[int, P], int]
-
-        See PEP 612 for detailed information.
-        """
-        return _concatenate_getitem(self, parameters)
-# 3.7-8
-elif sys.version_info[:2] >= (3, 7):
-    class _ConcatenateForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            return _concatenate_getitem(self, parameters)
-
-    Concatenate = _ConcatenateForm(
-        'Concatenate',
-        doc="""Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
-        higher order function which adds, removes or transforms parameters of a
-        callable.
-
-        For example::
-
-           Callable[Concatenate[int, P], int]
-
-        See PEP 612 for detailed information.
-        """)
-# 3.6
-else:
-    class _ConcatenateAliasMeta(typing.TypingMeta):
-        """Metaclass for Concatenate."""
-
-        def __repr__(self):
-            return 'typing_extensions.Concatenate'
-
-    class _ConcatenateAliasBase(typing._FinalTypingBase,
-                                metaclass=_ConcatenateAliasMeta,
-                                _root=True):
-        """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
-        higher order function which adds, removes or transforms parameters of a
-        callable.
-
-        For example::
-
-           Callable[Concatenate[int, P], int]
-
-        See PEP 612 for detailed information.
-        """
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError("Concatenate cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("Concatenate cannot be used with issubclass().")
-
-        def __repr__(self):
-            return 'typing_extensions.Concatenate'
-
-        def __getitem__(self, parameters):
-            return _concatenate_getitem(self, parameters)
-
-    Concatenate = _ConcatenateAliasBase(_root=True)
-
-# 3.10+
-if hasattr(typing, 'TypeGuard'):
-    TypeGuard = typing.TypeGuard
-# 3.9
-elif sys.version_info[:2] >= (3, 9):
-    class _TypeGuardForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    @_TypeGuardForm
-    def TypeGuard(self, parameters):
-        """Special typing form used to annotate the return type of a user-defined
-        type guard function.  ``TypeGuard`` only accepts a single type argument.
-        At runtime, functions marked this way should return a boolean.
-
-        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
-        type checkers to determine a more precise type of an expression within a
-        program's code flow.  Usually type narrowing is done by analyzing
-        conditional code flow and applying the narrowing to a block of code.  The
-        conditional expression here is sometimes referred to as a "type guard".
-
-        Sometimes it would be convenient to use a user-defined boolean function
-        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
-        return type to alert static type checkers to this intention.
-
-        Using  ``-> TypeGuard`` tells the static type checker that for a given
-        function:
-
-        1. The return value is a boolean.
-        2. If the return value is ``True``, the type of its argument
-        is the type inside ``TypeGuard``.
-
-        For example::
-
-            def is_str(val: Union[str, float]):
-                # "isinstance" type guard
-                if isinstance(val, str):
-                    # Type of ``val`` is narrowed to ``str``
-                    ...
-                else:
-                    # Else, type of ``val`` is narrowed to ``float``.
-                    ...
-
-        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
-        form of ``TypeA`` (it can even be a wider form) and this may lead to
-        type-unsafe results.  The main reason is to allow for things like
-        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
-        a subtype of the former, since ``List`` is invariant.  The responsibility of
-        writing type-safe type guards is left to the user.
-
-        ``TypeGuard`` also works with type variables.  For more information, see
-        PEP 647 (User-Defined Type Guards).
-        """
-        item = typing._type_check(parameters, f'{self} accepts only single type.')
-        return typing._GenericAlias(self, (item,))
-# 3.7-3.8
-elif sys.version_info[:2] >= (3, 7):
-    class _TypeGuardForm(typing._SpecialForm, _root=True):
-
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            item = typing._type_check(parameters,
-                                      f'{self._name} accepts only a single type')
-            return typing._GenericAlias(self, (item,))
-
-    TypeGuard = _TypeGuardForm(
-        'TypeGuard',
-        doc="""Special typing form used to annotate the return type of a user-defined
-        type guard function.  ``TypeGuard`` only accepts a single type argument.
-        At runtime, functions marked this way should return a boolean.
-
-        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
-        type checkers to determine a more precise type of an expression within a
-        program's code flow.  Usually type narrowing is done by analyzing
-        conditional code flow and applying the narrowing to a block of code.  The
-        conditional expression here is sometimes referred to as a "type guard".
-
-        Sometimes it would be convenient to use a user-defined boolean function
-        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
-        return type to alert static type checkers to this intention.
-
-        Using  ``-> TypeGuard`` tells the static type checker that for a given
-        function:
-
-        1. The return value is a boolean.
-        2. If the return value is ``True``, the type of its argument
-        is the type inside ``TypeGuard``.
-
-        For example::
-
-            def is_str(val: Union[str, float]):
-                # "isinstance" type guard
-                if isinstance(val, str):
-                    # Type of ``val`` is narrowed to ``str``
-                    ...
-                else:
-                    # Else, type of ``val`` is narrowed to ``float``.
-                    ...
-
-        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
-        form of ``TypeA`` (it can even be a wider form) and this may lead to
-        type-unsafe results.  The main reason is to allow for things like
-        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
-        a subtype of the former, since ``List`` is invariant.  The responsibility of
-        writing type-safe type guards is left to the user.
-
-        ``TypeGuard`` also works with type variables.  For more information, see
-        PEP 647 (User-Defined Type Guards).
-        """)
-# 3.6
-else:
-    class _TypeGuard(typing._FinalTypingBase, _root=True):
-        """Special typing form used to annotate the return type of a user-defined
-        type guard function.  ``TypeGuard`` only accepts a single type argument.
-        At runtime, functions marked this way should return a boolean.
-
-        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
-        type checkers to determine a more precise type of an expression within a
-        program's code flow.  Usually type narrowing is done by analyzing
-        conditional code flow and applying the narrowing to a block of code.  The
-        conditional expression here is sometimes referred to as a "type guard".
-
-        Sometimes it would be convenient to use a user-defined boolean function
-        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
-        return type to alert static type checkers to this intention.
-
-        Using  ``-> TypeGuard`` tells the static type checker that for a given
-        function:
-
-        1. The return value is a boolean.
-        2. If the return value is ``True``, the type of its argument
-        is the type inside ``TypeGuard``.
-
-        For example::
-
-            def is_str(val: Union[str, float]):
-                # "isinstance" type guard
-                if isinstance(val, str):
-                    # Type of ``val`` is narrowed to ``str``
-                    ...
-                else:
-                    # Else, type of ``val`` is narrowed to ``float``.
-                    ...
-
-        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
-        form of ``TypeA`` (it can even be a wider form) and this may lead to
-        type-unsafe results.  The main reason is to allow for things like
-        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
-        a subtype of the former, since ``List`` is invariant.  The responsibility of
-        writing type-safe type guards is left to the user.
-
-        ``TypeGuard`` also works with type variables.  For more information, see
-        PEP 647 (User-Defined Type Guards).
-        """
-
-        __slots__ = ('__type__',)
-
-        def __init__(self, tp=None, **kwds):
-            self.__type__ = tp
-
-        def __getitem__(self, item):
-            cls = type(self)
-            if self.__type__ is None:
-                return cls(typing._type_check(item,
-                           f'{cls.__name__[1:]} accepts only a single type.'),
-                           _root=True)
-            raise TypeError(f'{cls.__name__[1:]} cannot be further subscripted')
-
-        def _eval_type(self, globalns, localns):
-            new_tp = typing._eval_type(self.__type__, globalns, localns)
-            if new_tp == self.__type__:
-                return self
-            return type(self)(new_tp, _root=True)
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__type__ is not None:
-                r += f'[{typing._type_repr(self.__type__)}]'
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__type__))
-
-        def __eq__(self, other):
-            if not isinstance(other, _TypeGuard):
-                return NotImplemented
-            if self.__type__ is not None:
-                return self.__type__ == other.__type__
-            return self is other
-
-    TypeGuard = _TypeGuard(_root=True)
-
-if hasattr(typing, "Self"):
-    Self = typing.Self
-elif sys.version_info[:2] >= (3, 7):
-    # Vendored from cpython typing._SpecialFrom
-    class _SpecialForm(typing._Final, _root=True):
-        __slots__ = ('_name', '__doc__', '_getitem')
-
-        def __init__(self, getitem):
-            self._getitem = getitem
-            self._name = getitem.__name__
-            self.__doc__ = getitem.__doc__
-
-        def __getattr__(self, item):
-            if item in {'__name__', '__qualname__'}:
-                return self._name
-
-            raise AttributeError(item)
-
-        def __mro_entries__(self, bases):
-            raise TypeError(f"Cannot subclass {self!r}")
-
-        def __repr__(self):
-            return f'typing_extensions.{self._name}'
-
-        def __reduce__(self):
-            return self._name
-
-        def __call__(self, *args, **kwds):
-            raise TypeError(f"Cannot instantiate {self!r}")
-
-        def __or__(self, other):
-            return typing.Union[self, other]
-
-        def __ror__(self, other):
-            return typing.Union[other, self]
-
-        def __instancecheck__(self, obj):
-            raise TypeError(f"{self} cannot be used with isinstance()")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError(f"{self} cannot be used with issubclass()")
-
-        @typing._tp_cache
-        def __getitem__(self, parameters):
-            return self._getitem(self, parameters)
-
-    @_SpecialForm
-    def Self(self, params):
-        """Used to spell the type of "self" in classes.
-
-        Example::
-
-          from typing import Self
-
-          class ReturnsSelf:
-              def parse(self, data: bytes) -> Self:
-                  ...
-                  return self
-
-        """
-
-        raise TypeError(f"{self} is not subscriptable")
-else:
-    class _Self(typing._FinalTypingBase, _root=True):
-        """Used to spell the type of "self" in classes.
-
-        Example::
-
-          from typing import Self
-
-          class ReturnsSelf:
-              def parse(self, data: bytes) -> Self:
-                  ...
-                  return self
-
-        """
-
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError(f"{self} cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError(f"{self} cannot be used with issubclass().")
-
-    Self = _Self(_root=True)
-
-
-if hasattr(typing, 'Required'):
-    Required = typing.Required
-    NotRequired = typing.NotRequired
-elif sys.version_info[:2] >= (3, 9):
-    class _ExtensionsSpecialForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    @_ExtensionsSpecialForm
-    def Required(self, parameters):
-        """A special typing construct to mark a key of a total=False TypedDict
-        as required. For example:
-
-            class Movie(TypedDict, total=False):
-                title: Required[str]
-                year: int
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-
-        There is no runtime checking that a required key is actually provided
-        when instantiating a related TypedDict.
-        """
-        item = typing._type_check(parameters, f'{self._name} accepts only single type')
-        return typing._GenericAlias(self, (item,))
-
-    @_ExtensionsSpecialForm
-    def NotRequired(self, parameters):
-        """A special typing construct to mark a key of a TypedDict as
-        potentially missing. For example:
-
-            class Movie(TypedDict):
-                title: str
-                year: NotRequired[int]
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-        """
-        item = typing._type_check(parameters, f'{self._name} accepts only single type')
-        return typing._GenericAlias(self, (item,))
-
-elif sys.version_info[:2] >= (3, 7):
-    class _RequiredForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            item = typing._type_check(parameters,
-                                      '{} accepts only single type'.format(self._name))
-            return typing._GenericAlias(self, (item,))
-
-    Required = _RequiredForm(
-        'Required',
-        doc="""A special typing construct to mark a key of a total=False TypedDict
-        as required. For example:
-
-            class Movie(TypedDict, total=False):
-                title: Required[str]
-                year: int
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-
-        There is no runtime checking that a required key is actually provided
-        when instantiating a related TypedDict.
-        """)
-    NotRequired = _RequiredForm(
-        'NotRequired',
-        doc="""A special typing construct to mark a key of a TypedDict as
-        potentially missing. For example:
-
-            class Movie(TypedDict):
-                title: str
-                year: NotRequired[int]
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-        """)
-else:
-    # NOTE: Modeled after _Final's implementation when _FinalTypingBase available
-    class _MaybeRequired(typing._FinalTypingBase, _root=True):
-        __slots__ = ('__type__',)
-
-        def __init__(self, tp=None, **kwds):
-            self.__type__ = tp
-
-        def __getitem__(self, item):
-            cls = type(self)
-            if self.__type__ is None:
-                return cls(typing._type_check(item,
-                           '{} accepts only single type.'.format(cls.__name__[1:])),
-                           _root=True)
-            raise TypeError('{} cannot be further subscripted'
-                            .format(cls.__name__[1:]))
-
-        def _eval_type(self, globalns, localns):
-            new_tp = typing._eval_type(self.__type__, globalns, localns)
-            if new_tp == self.__type__:
-                return self
-            return type(self)(new_tp, _root=True)
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__type__ is not None:
-                r += '[{}]'.format(typing._type_repr(self.__type__))
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__type__))
-
-        def __eq__(self, other):
-            if not isinstance(other, type(self)):
-                return NotImplemented
-            if self.__type__ is not None:
-                return self.__type__ == other.__type__
-            return self is other
-
-    class _Required(_MaybeRequired, _root=True):
-        """A special typing construct to mark a key of a total=False TypedDict
-        as required. For example:
-
-            class Movie(TypedDict, total=False):
-                title: Required[str]
-                year: int
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-
-        There is no runtime checking that a required key is actually provided
-        when instantiating a related TypedDict.
-        """
-
-    class _NotRequired(_MaybeRequired, _root=True):
-        """A special typing construct to mark a key of a TypedDict as
-        potentially missing. For example:
-
-            class Movie(TypedDict):
-                title: str
-                year: NotRequired[int]
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-        """
-
-    Required = _Required(_root=True)
-    NotRequired = _NotRequired(_root=True)
diff --git a/spaces/UCAS/ChatGPT4/app.py b/spaces/UCAS/ChatGPT4/app.py
deleted file mode 100644
index 7e09e57ef928fd2451fd0ed1295d0994ca75d026..0000000000000000000000000000000000000000
--- a/spaces/UCAS/ChatGPT4/app.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Huggingface provided GPT4 OpenAI API Key 
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-
-#Inferenec function
-def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
-    print(f"system message is ^^ {system_msg}")
-    if system_msg.strip() == '':
-        initial_message = [{"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = []
-    else:
-        initial_message= [{"role": "system", "content": system_msg},
-                   {"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = [{"role": "system", "content": system_msg},]
-        
-    if chat_counter == 0 :
-        payload = {
-        "model": "gpt-4",
-        "messages": initial_message , 
-        "temperature" : 1.0,
-        "top_p":1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-        print(f"chat_counter - {chat_counter}")
-    else: #if chat_counter != 0 :
-        messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
-        for data in chatbot:
-          user = {}
-          user["role"] = "user" 
-          user["content"] = data[0] 
-          assistant = {}
-          assistant["role"] = "assistant" 
-          assistant["content"] = data[1]
-          messages.append(user)
-          messages.append(assistant)
-        temp = {}
-        temp["role"] = "user" 
-        temp["content"] = inputs
-        messages.append(temp)
-        #messages
-        payload = {
-        "model": "gpt-4",
-        "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,}
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"Logging : payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    print(f"Logging : response code - {response}")
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter, response  # resembles {chatbot: chat, state: history}  
-                   
-#Resetting to blank
-def reset_textbox():
-    return gr.update(value='')
-
-#to set a component as visible=False
-def set_visible_false():
-    return gr.update(visible=False)
-
-#to set a component as visible=True
-def set_visible_true():
-    return gr.update(visible=True)
-
-title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
-
-#display message for themes feature
-theme_addon_msg = """<center>🌟 Discover Gradio Themes with this Demo, featuring v3.22.0! Gradio v3.23.0 also enables seamless Theme sharing. You can develop or modify a theme, and send it to the hub using simple <code>theme.push_to_hub()</code>. 
-<br>🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - <a href="https://huggingface.co/Gradio-Themes" target="_blank">Gradio-Themes-Party🎨</a> 🏆</center>
-"""
-
-#Using info to add additional information about System message in GPT4
-system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. 
-System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
-
-#Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
-                      text_size=gr.themes.sizes.text_lg)                
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
-                      theme=theme) as demo:
-    gr.HTML(title)
-    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
-    gr.HTML(theme_addon_msg)
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-
-    with gr.Column(elem_id = "col_container"):
-        #GPT4 API Key is provided by Huggingface 
-        with gr.Accordion(label="System message:", open=False):
-            system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="")
-            accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
-        chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot")
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
-        state = gr.State([]) 
-        with gr.Row():
-            with gr.Column(scale=7):
-                b1 = gr.Button().style(full_width=True)
-            with gr.Column(scale=3):
-                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
-    
-        #top_p, temperature
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
-
-    #Event handling
-    inputs.submit( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    
-    inputs.submit(set_visible_false, [], [system_msg])
-    b1.click(set_visible_false, [], [system_msg])
-    inputs.submit(set_visible_true, [], [accordion_msg])
-    b1.click(set_visible_true, [], [accordion_msg])
-    
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-
-    #Examples 
-    with gr.Accordion(label="Examples for System message:", open=False):
-        gr.Examples(
-                examples = [["""You are an AI programming assistant.
-        
-                - Follow the user's requirements carefully and to the letter.
-                - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail.
-                - Then output the code in a single code block.
-                - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""],
-                ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."],
-                ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."],
-                ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."],
-                ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."],
-                ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."],
-                ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."],
-                ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."],
-                ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."],
-                ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."],
-                ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."],
-                ["You are a helpful assistant that provides detailed and accurate information."],
-                ["You are an assistant that speaks like Shakespeare."],
-                ["You are a friendly assistant who uses casual language and humor."],
-                ["You are a financial advisor who gives expert advice on investments and budgeting."],
-                ["You are a health and fitness expert who provides advice on nutrition and exercise."],
-                ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."],
-                ["You are a movie critic who shares insightful opinions on films and their themes."],
-                ["You are a history enthusiast who loves to discuss historical events and figures."],
-                ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."],
-                ["You are an AI poet who can compose creative and evocative poems on any given topic."],],
-                inputs = system_msg,)
-        
-demo.queue(max_size=99, concurrency_count=20).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/VGues/NOG/README.md b/spaces/VGues/NOG/README.md
deleted file mode 100644
index 525efcd71902d60cc1df9f07b04d7546d2eb84f3..0000000000000000000000000000000000000000
--- a/spaces/VGues/NOG/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: NOG
-emoji: 🦀
-colorFrom: green
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/VIPLab/Caption-Anything/caption_anything/model.py b/spaces/VIPLab/Caption-Anything/caption_anything/model.py
deleted file mode 100644
index 3d5ca5430d16d73aa7c3eabc660baa246d67d89c..0000000000000000000000000000000000000000
--- a/spaces/VIPLab/Caption-Anything/caption_anything/model.py
+++ /dev/null
@@ -1,294 +0,0 @@
-import os
-import argparse
-import pdb
-import time
-from PIL import Image
-import cv2
-import numpy as np
-from PIL import Image
-import easyocr
-import copy
-import time
-from caption_anything.captioner import build_captioner, BaseCaptioner
-from caption_anything.segmenter import build_segmenter, build_segmenter_densecap
-from caption_anything.text_refiner import build_text_refiner
-from caption_anything.utils.utils import prepare_segmenter, seg_model_map, load_image, get_image_shape
-from caption_anything.utils.utils import mask_painter_foreground_all, mask_painter, xywh_to_x1y1x2y2, image_resize
-from caption_anything.utils.densecap_painter import draw_bbox
-            
-class CaptionAnything:
-    def __init__(self, args, api_key="", captioner=None, segmenter=None, ocr_reader=None, text_refiner=None):
-        self.args = args
-        self.captioner = build_captioner(args.captioner, args.device, args) if captioner is None else captioner
-        self.segmenter = build_segmenter(args.segmenter, args.device, args) if segmenter is None else segmenter
-        self.segmenter_densecap = build_segmenter_densecap(args.segmenter, args.device, args, model=self.segmenter.model)
-        self.ocr_lang = ["ch_tra", "en"]
-        self.ocr_reader = ocr_reader if ocr_reader is not None else easyocr.Reader(self.ocr_lang)
-        
-
-        self.text_refiner = None
-        if not args.disable_gpt:
-            if text_refiner is not None:
-                self.text_refiner = text_refiner
-            elif api_key != "":
-                self.init_refiner(api_key)
-        self.require_caption_prompt = args.captioner == 'blip2'
-        
-    @property
-    def image_embedding(self):
-        return self.segmenter.image_embedding
-
-    @image_embedding.setter
-    def image_embedding(self, image_embedding):
-        self.segmenter.image_embedding = image_embedding
-
-    @property
-    def original_size(self):
-        return self.segmenter.predictor.original_size
-
-    @original_size.setter
-    def original_size(self, original_size):
-        self.segmenter.predictor.original_size = original_size
-
-    @property
-    def input_size(self):
-        return self.segmenter.predictor.input_size
-
-    @input_size.setter
-    def input_size(self, input_size):
-        self.segmenter.predictor.input_size = input_size
-
-    def setup(self, image_embedding, original_size, input_size, is_image_set):
-        self.image_embedding = image_embedding
-        self.original_size = original_size
-        self.input_size = input_size
-        self.segmenter.predictor.is_image_set = is_image_set
-
-    def init_refiner(self, api_key):
-        try:
-            self.text_refiner = build_text_refiner(self.args.text_refiner, self.args.device, self.args, api_key)
-            self.text_refiner.llm('hi')  # test
-        except:
-            self.text_refiner = None
-            print('OpenAI GPT is not available')
-
-    def inference(self, image, prompt, controls, disable_gpt=False, enable_wiki=False, verbose=False, is_densecap=False, args={}):
-        #  segment with prompt
-        print("CA prompt: ", prompt, "CA controls", controls)
-        is_seg_everything = 'everything' in prompt['prompt_type']
-
-        args['seg_crop_mode'] = args.get('seg_crop_mode', self.args.seg_crop_mode)
-        args['clip_filter'] = args.get('clip_filter', self.args.clip_filter)
-        args['disable_regular_box'] = args.get('disable_regular_box', self.args.disable_regular_box)
-        args['context_captions'] = args.get('context_captions', self.args.context_captions)
-        args['enable_reduce_tokens'] = args.get('enable_reduce_tokens', self.args.enable_reduce_tokens)
-        args['enable_morphologyex'] = args.get('enable_morphologyex', self.args.enable_morphologyex)
-        args['topN'] = args.get('topN', 10) if is_seg_everything else 1
-        args['min_mask_area'] = args.get('min_mask_area', 0)
-
-        if not is_densecap:
-            seg_results = self.segmenter.inference(image, prompt)
-        else:
-            seg_results = self.segmenter_densecap.inference(image, prompt)
-            
-        seg_masks, seg_bbox, seg_area = seg_results if is_seg_everything else (seg_results, None, None)
-        
-        if args['topN'] > 1: # sort by area
-            samples = list(zip(*[seg_masks, seg_bbox, seg_area]))
-            # top_samples = sorted(samples, key=lambda x: x[2], reverse=True)
-            # seg_masks, seg_bbox, seg_area = list(zip(*top_samples))
-            samples = list(filter(lambda x: x[2] > args['min_mask_area'], samples))
-            samples = samples[:args['topN']]
-            seg_masks, seg_bbox, seg_area = list(zip(*samples))
-
-        out_list = []
-        for i, seg_mask in enumerate(seg_masks):
-            if args['enable_morphologyex']:
-                seg_mask = 255 * seg_mask.astype(np.uint8)
-                seg_mask = np.stack([seg_mask, seg_mask, seg_mask], axis=-1)
-                seg_mask = cv2.morphologyEx(seg_mask, cv2.MORPH_OPEN, kernel=np.ones((6, 6), np.uint8))
-                seg_mask = cv2.morphologyEx(seg_mask, cv2.MORPH_CLOSE, kernel=np.ones((6, 6), np.uint8))
-                seg_mask = seg_mask[:, :, 0] > 0
-
-            seg_mask_img = Image.fromarray(seg_mask.astype('int') * 255.)        
-            mask_save_path = None
-            
-            if verbose:
-                mask_save_path = f'result/mask_{time.time()}.png'
-                if not os.path.exists(os.path.dirname(mask_save_path)):
-                    os.makedirs(os.path.dirname(mask_save_path))
-
-                if seg_mask_img.mode != 'RGB':
-                    seg_mask_img = seg_mask_img.convert('RGB')
-                seg_mask_img.save(mask_save_path)
-                print('seg_mask path: ', mask_save_path)
-                print("seg_mask.shape: ", seg_mask.shape)
-
-
-            #  captioning with mask
-            if args['enable_reduce_tokens']:
-                result = self.captioner.inference_with_reduced_tokens(image, seg_mask,
-                                                  crop_mode=args['seg_crop_mode'],
-                                                  filter=args['clip_filter'],
-                                                  disable_regular_box=args['disable_regular_box'], 
-                                                  verbose=verbose,
-                                                  caption_args=args)
-            else:
-                result = self.captioner.inference_seg(image, seg_mask, 
-                                  crop_mode=args['seg_crop_mode'],
-                                  filter=args['clip_filter'],
-                                  disable_regular_box=args['disable_regular_box'], 
-                                  verbose=verbose,
-                                  caption_args=args)
-            caption = result.get('caption', None)
-            crop_save_path = result.get('crop_save_path', None)
-            
-            #  refining with TextRefiner
-            context_captions = []
-            if args['context_captions']:
-                context_captions.append(self.captioner.inference(image)['caption'])
-            if not disable_gpt and self.text_refiner is not None:
-                refined_caption = self.text_refiner.inference(query=caption, controls=controls, context=context_captions,
-                                                            enable_wiki=enable_wiki)
-            else:
-                refined_caption = {'raw_caption': caption}
-            out = {'generated_captions': refined_caption,
-                'crop_save_path': crop_save_path,
-                'mask_save_path': mask_save_path,
-                'mask': seg_mask_img,
-                'bbox': seg_bbox[i] if seg_bbox is not None else None,
-                'area': seg_area[i] if seg_area is not None else None,
-                'context_captions': context_captions,
-                'ppl_score': result.get('ppl_score', -100.),
-                'clip_score': result.get('clip_score', 0.)
-                }
-            out_list.append(out)
-        return out_list
-    
-    def parse_dense_caption(self, image, topN=10, reference_caption=[], verbose=False):
-        width, height = get_image_shape(image)
-        prompt = {'prompt_type': ['everything']}
-        densecap_args = {
-            'return_ppl': True, 
-            'clip_filter': True, 
-            'reference_caption': reference_caption,
-            'text_prompt': "", # 'Question: what does the image show? Answer:'
-            'seg_crop_mode': 'w_bg',
-            # 'text_prompt': "",
-            # 'seg_crop_mode': 'wo_bg',
-            'disable_regular_box': False,
-            'topN': topN,
-            'min_ppl_score': -1.8,
-            'min_clip_score': 0.30,
-            'min_mask_area': 2500,
-            }
-            
-        dense_captions = self.inference(image, prompt,
-                                        controls=None, 
-                                        disable_gpt=True, 
-                                        verbose=verbose, 
-                                        is_densecap=True, 
-                                        args=densecap_args)
-        print('Process Dense Captioning: \n', dense_captions)
-        dense_captions = list(filter(lambda x: x['ppl_score'] / (1+len(x['generated_captions']['raw_caption'].split())) >= densecap_args['min_ppl_score'], dense_captions))
-        dense_captions = list(filter(lambda x: x['clip_score'] >= densecap_args['min_clip_score'], dense_captions))
-        dense_cap_prompt = []
-        for cap in dense_captions:
-            x, y, w, h = cap['bbox']
-            cx, cy = x + w/2, (y + h/2)
-            dense_cap_prompt.append("({}: X:{:.0f}, Y:{:.0f}, Width:{:.0f}, Height:{:.0f})".format(cap['generated_captions']['raw_caption'], cx, cy, w, h))
-        
-        if verbose:
-            all_masks = [np.array(item['mask'].convert('P')) for item in dense_captions]
-            new_image = mask_painter_foreground_all(np.array(image), all_masks, background_alpha=0.4)
-            save_path = 'result/dense_caption_mask.png'
-            Image.fromarray(new_image).save(save_path)
-            print(f'Dense captioning mask saved in {save_path}')
-            
-            vis_path = 'result/dense_caption_vis_{}.png'.format(time.time())
-            dense_cap_painter_input = [{'bbox': xywh_to_x1y1x2y2(cap['bbox']), 
-                                        'caption': cap['generated_captions']['raw_caption']} for cap in dense_captions]
-            draw_bbox(load_image(image, return_type='numpy'), vis_path, dense_cap_painter_input, show_caption=True)
-            print(f'Dense Captioning visualization saved in {vis_path}')
-        return ','.join(dense_cap_prompt)
-    
-    def parse_ocr(self, image, thres=0.2):
-        width, height = get_image_shape(image)
-        image = load_image(image, return_type='numpy')
-        bounds = self.ocr_reader.readtext(image)
-        bounds = [bound for bound in bounds if bound[2] > thres]
-        print('Process OCR Text:\n', bounds)
-        
-        ocr_prompt = []
-        for box, text, conf in bounds:
-            p0, p1, p2, p3 = box
-            ocr_prompt.append('(\"{}\": X:{:.0f}, Y:{:.0f})'.format(text, (p0[0]+p1[0]+p2[0]+p3[0])/4, (p0[1]+p1[1]+p2[1]+p3[1])/4))
-        ocr_prompt = '\n'.join(ocr_prompt)
-        
-        # ocr_prompt = self.text_refiner.llm(f'The image have some scene texts with their locations: {ocr_prompt}. Please group these individual words into one or several phrase based on their relative positions (only give me your answer, do not show explanination)').strip()
-        
-        # ocr_prefix1 = f'The image have some scene texts with their locations: {ocr_prompt}. Please group these individual words into one or several phrase based on their relative positions (only give me your answer, do not show explanination)'
-        # ocr_prefix2 = f'Please group these individual words into 1-3 phrases, given scene texts with their locations: {ocr_prompt}. You return is one or several strings and infer their locations. (only give me your answer like (“man working”, X: value, Y: value), do not show explanination)'
-        # ocr_prefix4 = f'summarize the individual scene text words detected by OCR tools into a fluent sentence based on their positions and distances. You should strictly describe all of the given scene text words. Do not miss any given word. Do not create non-exist words. Do not appear numeric positions. The individual words are given:\n{ocr_prompt}\n'
-        # ocr_prefix3 = f'combine the individual scene text words detected by OCR tools into one/several fluent phrases/sentences based on their positions and distances. You should strictly copy or correct all of the given scene text words. Do not miss any given word. Do not create non-exist words. The response is several strings seperate with their location (X, Y), each of which represents a phrase. The individual words are given:\n{ocr_prompt}\n'
-        # response = self.text_refiner.llm(ocr_prefix3).strip() if len(ocr_prompt) else ""
-        return ocr_prompt
-    
-    def inference_cap_everything(self, image, verbose=False):
-        image = load_image(image, return_type='pil')
-        image = image_resize(image, res=1024)
-        width, height = get_image_shape(image)
-        other_args = {'text_prompt': ""} if self.require_caption_prompt else {}
-        img_caption = self.captioner.inference(image, filter=False, args=other_args)['caption']
-        dense_caption_prompt = self.parse_dense_caption(image, topN=10, verbose=verbose, reference_caption=[])
-        scene_text_prompt = self.parse_ocr(image, thres=0.2)
-        # scene_text_prompt = "N/A"
-        
-        # the summarize_prompt is modified from https://github.com/JialianW/GRiT and https://github.com/showlab/Image2Paragraph
-        summarize_prompt = "Imagine you are a blind but intelligent image captioner. You should generate a descriptive, coherent and human-like paragraph based on the given information (a,b,c,d) instead of imagination:\na) Image Resolution: {image_size}\nb) Image Caption:{image_caption}\nc) Dense Caption: {dense_caption}\nd) Scene Text: {scene_text}\nThere are some rules for your response: Show objects with their attributes (e.g. position, color, size, shape, texture).\nPrimarily describe common objects with large size.\nProvide context of the image.\nShow relative position between objects.\nLess than 6 sentences.\nDo not appear number.\nDo not describe any individual letter.\nDo not show the image resolution.\nIngore the white background."
-        prompt = summarize_prompt.format(**{
-            "image_size": "width {} height {}".format(width, height),
-            "image_caption":img_caption, 
-            "dense_caption": dense_caption_prompt,
-            "scene_text": scene_text_prompt})
-        print(f'caption everything prompt: {prompt}')
-        response = self.text_refiner.llm(prompt).strip()
-        # chinese_response = self.text_refiner.llm('Translate it into Chinese: {}'.format(response)).strip()
-        return response
-        
-if __name__ == "__main__":
-    from caption_anything.utils.parser import parse_augment
-    args = parse_augment()
-    image_path = 'result/wt/memes/87226084.jpg'
-    image = Image.open(image_path)
-    prompts = [
-        {
-            "prompt_type": ["click"],
-            "input_point": [[500, 300], [200, 500]],
-            "input_label": [1, 0],
-            "multimask_output": "True",
-        },
-        # {
-        #     "prompt_type": ["click"],
-        #     "input_point": [[300, 800]],
-        #     "input_label": [1],
-        #     "multimask_output": "True",
-        # }
-    ]
-    controls = {
-        "length": "30",
-        "sentiment": "positive",
-        # "imagination": "True",
-        "imagination": "False",
-        "language": "English",
-    }
-
-    model = CaptionAnything(args, os.environ['OPENAI_API_KEY'])
-    img_dir = 'test_images/memes'
-    for image_file in os.listdir(img_dir):
-        image_path = os.path.join(img_dir, image_file)  
-        print('image_path:', image_path)
-        paragraph = model.inference_cap_everything(image_path, verbose=True)
-        print('Caption Everything:\n', paragraph)    
-        ocr = model.parse_ocr(image_path)
-        print('OCR', ocr)
\ No newline at end of file
diff --git a/spaces/VIPLab/Track-Anything/track_anything.py b/spaces/VIPLab/Track-Anything/track_anything.py
deleted file mode 100644
index 8b9f1b5023d5c5668b1888e6c4a04960098a32cb..0000000000000000000000000000000000000000
--- a/spaces/VIPLab/Track-Anything/track_anything.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import PIL
-from tqdm import tqdm
-
-from tools.interact_tools import SamControler
-from tracker.base_tracker import BaseTracker
-from inpainter.base_inpainter import BaseInpainter
-import numpy as np
-import argparse
-import cv2
-
-def read_image_from_userfolder(image_path):
-    # if type:
-    image = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
-    # else:
-        # image = cv2.cvtColor(cv2.imread("/tmp/{}/paintedimages/{}/{:08d}.png".format(username, video_state["video_name"], index+ ".png")), cv2.COLOR_BGR2RGB)
-    return image
-
-def save_image_to_userfolder(video_state, index, image, type:bool):
-    if type:
-        image_path = "/tmp/{}/originimages/{}/{:08d}.png".format(video_state["user_name"], video_state["video_name"], index)
-    else:
-        image_path = "/tmp/{}/paintedimages/{}/{:08d}.png".format(video_state["user_name"], video_state["video_name"], index)
-    cv2.imwrite(image_path, image)
-    return image_path
-class TrackingAnything():
-    def __init__(self, sam_checkpoint, xmem_checkpoint, e2fgvi_checkpoint, args):
-        self.args = args
-        self.sam_checkpoint = sam_checkpoint
-        self.xmem_checkpoint = xmem_checkpoint
-        self.e2fgvi_checkpoint = e2fgvi_checkpoint
-        self.samcontroler = SamControler(self.sam_checkpoint, args.sam_model_type, args.device)
-        self.xmem = BaseTracker(self.xmem_checkpoint, device=args.device)
-        self.baseinpainter = BaseInpainter(self.e2fgvi_checkpoint, args.device) 
-    # def inference_step(self, first_flag: bool, interact_flag: bool, image: np.ndarray, 
-    #                    same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):
-    #     if first_flag:
-    #         mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)
-    #         return mask, logit, painted_image
-        
-    #     if interact_flag:
-    #         mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)
-    #         return mask, logit, painted_image
-        
-    #     mask, logit, painted_image = self.xmem.track(image, logit)
-    #     return mask, logit, painted_image
-    
-    def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):
-        mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)
-        return mask, logit, painted_image
-    
-    # def interact(self, image: np.ndarray, same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):
-    #     mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)
-    #     return mask, logit, painted_image
-
-    def generator(self, images: list, template_mask:np.ndarray, video_state:dict):
-        
-        masks = []
-        logits = []
-        painted_images = []
-        for i in tqdm(range(len(images)), desc="Tracking image"):
-            if i ==0:           
-                mask, logit, painted_image = self.xmem.track(read_image_from_userfolder(images[i]), template_mask)
-                masks.append(mask)
-                logits.append(logit)
-                # painted_images.append(painted_image)
-                painted_images.append(save_image_to_userfolder(video_state, index=i, image=cv2.cvtColor(np.asarray(painted_image),cv2.COLOR_BGR2RGB), type=False))
-                
-            else:
-                mask, logit, painted_image = self.xmem.track(read_image_from_userfolder(images[i]))
-                masks.append(mask)
-                logits.append(logit)
-                # painted_images.append(painted_image)
-                painted_images.append(save_image_to_userfolder(video_state, index=i, image=cv2.cvtColor(np.asarray(painted_image),cv2.COLOR_BGR2RGB), type=False))
-        return masks, logits, painted_images
-    
-        
-def parse_augment():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default="cuda:0")
-    parser.add_argument('--sam_model_type', type=str, default="vit_h")
-    parser.add_argument('--port', type=int, default=6080, help="only useful when running gradio applications")  
-    parser.add_argument('--debug', action="store_true")
-    parser.add_argument('--mask_save', default=False)
-    args = parser.parse_args()
-
-    if args.debug:
-        print(args)
-    return args 
-
-
-if __name__ == "__main__":
-    masks = None
-    logits = None
-    painted_images = None
-    images = []
-    image  = np.array(PIL.Image.open('/hhd3/gaoshang/truck.jpg'))
-    args = parse_augment()
-    # images.append(np.ones((20,20,3)).astype('uint8'))
-    # images.append(np.ones((20,20,3)).astype('uint8'))
-    images.append(image)
-    images.append(image)
-
-    mask = np.zeros_like(image)[:,:,0]
-    mask[0,0]= 1
-    trackany = TrackingAnything('/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth','/ssd1/gaomingqi/checkpoints/XMem-s012.pth', args)
-    masks, logits ,painted_images= trackany.generator(images, mask)
-        
-        
-    
-    
-    
\ No newline at end of file
diff --git a/spaces/XzJosh/Azusa-Bert-VITS2/text/symbols.py b/spaces/XzJosh/Azusa-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Azusa-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/transforms.py b/spaces/XzJosh/Wenjing-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/Yehor/wav2vec2-uk-demo/inference_timestamps.py b/spaces/Yehor/wav2vec2-uk-demo/inference_timestamps.py
deleted file mode 100644
index 658fe4931406de539d41099e11d614b80eaf5d1b..0000000000000000000000000000000000000000
--- a/spaces/Yehor/wav2vec2-uk-demo/inference_timestamps.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import argparse
-from time import gmtime, strftime
-
-import torch
-import torchaudio
-from pathlib import Path
-from transformers import Wav2Vec2ProcessorWithLM, Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer
-
-
-def main(args):
-    tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(args.model_id)
-    processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
-    model = Wav2Vec2ForCTC.from_pretrained(args.model_id)
-    model.to('cpu')
-
-    files = args.path_files.split(',')
-
-    for path_file in files:
-        print('File:', path_file)
-
-        wav_file_path = str(Path(path_file).absolute())
-        waveform, sample_rate = torchaudio.load(wav_file_path)
-
-        if sample_rate != 16000:
-            resample = torchaudio.transforms.Resample(
-                sample_rate, 16000, resampling_method='sinc_interpolation')
-            sample_rate = 16000
-            speech_array = resample(waveform)
-            sp = speech_array.squeeze().numpy()
-        else:
-            sp = waveform.squeeze().numpy()
-
-        # stride_length_s is a tuple of the left and right stride length.
-        # With only 1 number, both sides get the same stride, by default
-        # the stride_length on one side is 1/6th of the chunk_length_s
-        input_values = processor(sp,
-                                 sample_rate=16000,
-                                 chunk_length_s=args.chunk_length_s,
-                                 stride_length_s=(args.stride_length_s_l, args.stride_length_s_r),
-                                 return_tensors="pt").input_values
-
-        with torch.no_grad():
-            logits = model(input_values).logits
-
-        # prediction = tokenizer.decode(pred_ids[0], output_word_offsets=True)
-        # prediction = tokenizer.decode(pred_ids[0], output_char_offsets=True)
-
-        pred_ids = torch.argmax(logits, axis=-1).cpu().tolist()
-        prediction = tokenizer.decode(pred_ids[0], output_word_offsets=True)
-
-        print(f'Sample rate: {sample_rate}')
-        time_offset = 320 / sample_rate
-
-        for item in prediction.word_offsets:
-            r = item
-
-            s = round(r['start_offset'] * time_offset, 2)
-            e = round(r['end_offset'] * time_offset, 2)
-
-            print(f"{s} - {e}: {r['word']}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--path_files", type=str, required=True, help="WAV files to transcribe, separated by a comma"
-    )
-    parser.add_argument(
-        "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
-    )
-    parser.add_argument(
-        "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
-    )
-    parser.add_argument(
-        "--stride_length_s_l", type=int, default=None, help="Stride of the audio chunks, left value."
-    )
-    parser.add_argument(
-        "--stride_length_s_r", type=int, default=None, help="Stride of the audio chunks, right value."
-    )
-    parser.add_argument(
-        "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
-    )
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/YouLiXiya/Mobile-SAM/segment_anything/scripts/export_onnx_model.py b/spaces/YouLiXiya/Mobile-SAM/segment_anything/scripts/export_onnx_model.py
deleted file mode 100644
index 8ec5c2ec24fc53cd9fdf66564cfe163b9eb26c24..0000000000000000000000000000000000000000
--- a/spaces/YouLiXiya/Mobile-SAM/segment_anything/scripts/export_onnx_model.py
+++ /dev/null
@@ -1,204 +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 torch
-
-from segment_anything import build_sam, build_sam_vit_b, build_sam_vit_l
-from segment_anything.utils.onnx import SamOnnxModel
-
-import argparse
-import warnings
-
-try:
-    import onnxruntime  # type: ignore
-
-    onnxruntime_exists = True
-except ImportError:
-    onnxruntime_exists = False
-
-parser = argparse.ArgumentParser(
-    description="Export the SAM prompt encoder and mask decoder to an ONNX model."
-)
-
-parser.add_argument(
-    "--checkpoint", type=str, required=True, help="The path to the SAM model checkpoint."
-)
-
-parser.add_argument(
-    "--output", type=str, required=True, help="The filename to save the ONNX model to."
-)
-
-parser.add_argument(
-    "--model-type",
-    type=str,
-    default="default",
-    help="In ['default', 'vit_b', 'vit_l']. Which type of SAM model to export.",
-)
-
-parser.add_argument(
-    "--return-single-mask",
-    action="store_true",
-    help=(
-        "If true, the exported ONNX model will only return the best mask, "
-        "instead of returning multiple masks. For high resolution images "
-        "this can improve runtime when upscaling masks is expensive."
-    ),
-)
-
-parser.add_argument(
-    "--opset",
-    type=int,
-    default=17,
-    help="The ONNX opset version to use. Must be >=11",
-)
-
-parser.add_argument(
-    "--quantize-out",
-    type=str,
-    default=None,
-    help=(
-        "If set, will quantize the model and save it with this name. "
-        "Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize."
-    ),
-)
-
-parser.add_argument(
-    "--gelu-approximate",
-    action="store_true",
-    help=(
-        "Replace GELU operations with approximations using tanh. Useful "
-        "for some runtimes that have slow or unimplemented erf ops, used in GELU."
-    ),
-)
-
-parser.add_argument(
-    "--use-stability-score",
-    action="store_true",
-    help=(
-        "Replaces the model's predicted mask quality score with the stability "
-        "score calculated on the low resolution masks using an offset of 1.0. "
-    ),
-)
-
-parser.add_argument(
-    "--return-extra-metrics",
-    action="store_true",
-    help=(
-        "The model will return five results: (masks, scores, stability_scores, "
-        "areas, low_res_logits) instead of the usual three. This can be "
-        "significantly slower for high resolution outputs."
-    ),
-)
-
-
-def run_export(
-    model_type: str,
-    checkpoint: str,
-    output: str,
-    opset: int,
-    return_single_mask: bool,
-    gelu_approximate: bool = False,
-    use_stability_score: bool = False,
-    return_extra_metrics=False,
-):
-    print("Loading model...")
-    if model_type == "vit_b":
-        sam = build_sam_vit_b(checkpoint)
-    elif model_type == "vit_l":
-        sam = build_sam_vit_l(checkpoint)
-    else:
-        sam = build_sam(checkpoint)
-
-    onnx_model = SamOnnxModel(
-        model=sam,
-        return_single_mask=return_single_mask,
-        use_stability_score=use_stability_score,
-        return_extra_metrics=return_extra_metrics,
-    )
-
-    if gelu_approximate:
-        for n, m in onnx_model.named_modules():
-            if isinstance(m, torch.nn.GELU):
-                m.approximate = "tanh"
-
-    dynamic_axes = {
-        "point_coords": {1: "num_points"},
-        "point_labels": {1: "num_points"},
-    }
-
-    embed_dim = sam.prompt_encoder.embed_dim
-    embed_size = sam.prompt_encoder.image_embedding_size
-    mask_input_size = [4 * x for x in embed_size]
-    dummy_inputs = {
-        "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
-        "point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
-        "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
-        "mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float),
-        "has_mask_input": torch.tensor([1], dtype=torch.float),
-        "orig_im_size": torch.tensor([1500, 2250], dtype=torch.float),
-    }
-
-    _ = onnx_model(**dummy_inputs)
-
-    output_names = ["masks", "iou_predictions", "low_res_masks"]
-
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
-        warnings.filterwarnings("ignore", category=UserWarning)
-        with open(output, "wb") as f:
-            print(f"Exporing onnx model to {output}...")
-            torch.onnx.export(
-                onnx_model,
-                tuple(dummy_inputs.values()),
-                f,
-                export_params=True,
-                verbose=False,
-                opset_version=opset,
-                do_constant_folding=True,
-                input_names=list(dummy_inputs.keys()),
-                output_names=output_names,
-                dynamic_axes=dynamic_axes,
-            )
-
-    if onnxruntime_exists:
-        ort_inputs = {k: to_numpy(v) for k, v in dummy_inputs.items()}
-        ort_session = onnxruntime.InferenceSession(output)
-        _ = ort_session.run(None, ort_inputs)
-        print("Model has successfully been run with ONNXRuntime.")
-
-
-def to_numpy(tensor):
-    return tensor.cpu().numpy()
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-    run_export(
-        model_type=args.model_type,
-        checkpoint=args.checkpoint,
-        output=args.output,
-        opset=args.opset,
-        return_single_mask=args.return_single_mask,
-        gelu_approximate=args.gelu_approximate,
-        use_stability_score=args.use_stability_score,
-        return_extra_metrics=args.return_extra_metrics,
-    )
-
-    if args.quantize_out is not None:
-        assert onnxruntime_exists, "onnxruntime is required to quantize the model."
-        from onnxruntime.quantization import QuantType  # type: ignore
-        from onnxruntime.quantization.quantize import quantize_dynamic  # type: ignore
-
-        print(f"Quantizing model and writing to {args.quantize_out}...")
-        quantize_dynamic(
-            model_input=args.output,
-            model_output=args.quantize_out,
-            optimize_model=True,
-            per_channel=False,
-            reduce_range=False,
-            weight_type=QuantType.QUInt8,
-        )
-        print("Done!")
diff --git a/spaces/Yuliang/ICON/lib/dataset/NormalModule.py b/spaces/Yuliang/ICON/lib/dataset/NormalModule.py
deleted file mode 100644
index 6b87ec2ff2d1c29f2811bc57fe69b151d59084aa..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/dataset/NormalModule.py
+++ /dev/null
@@ -1,94 +0,0 @@
-
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import numpy as np
-from torch.utils.data import DataLoader
-from .NormalDataset import NormalDataset
-
-# pytorch lightning related libs
-import pytorch_lightning as pl
-
-
-class NormalModule(pl.LightningDataModule):
-    def __init__(self, cfg):
-        super(NormalModule, self).__init__()
-        self.cfg = cfg
-        self.overfit = self.cfg.overfit
-
-        if self.overfit:
-            self.batch_size = 1
-        else:
-            self.batch_size = self.cfg.batch_size
-
-        self.data_size = {}
-
-    def prepare_data(self):
-
-        pass
-
-    @staticmethod
-    def worker_init_fn(worker_id):
-        np.random.seed(np.random.get_state()[1][0] + worker_id)
-
-    def setup(self, stage):
-
-        if stage == 'fit' or stage is None:
-            self.train_dataset = NormalDataset(cfg=self.cfg, split="train")
-            self.val_dataset = NormalDataset(cfg=self.cfg, split="val")
-            self.data_size = {
-                'train': len(self.train_dataset),
-                'val': len(self.val_dataset)
-            }
-
-        if stage == 'test' or stage is None:
-            self.test_dataset = NormalDataset(cfg=self.cfg, split="test")
-
-    def train_dataloader(self):
-
-        train_data_loader = DataLoader(self.train_dataset,
-                                       batch_size=self.batch_size,
-                                       shuffle=not self.overfit,
-                                       num_workers=self.cfg.num_threads,
-                                       pin_memory=True,
-                                       worker_init_fn=self.worker_init_fn)
-
-        return train_data_loader
-
-    def val_dataloader(self):
-
-        if self.overfit:
-            current_dataset = self.train_dataset
-        else:
-            current_dataset = self.val_dataset
-
-        val_data_loader = DataLoader(current_dataset,
-                                     batch_size=self.batch_size,
-                                     shuffle=False,
-                                     num_workers=self.cfg.num_threads,
-                                     pin_memory=True)
-
-        return val_data_loader
-
-    def test_dataloader(self):
-
-        test_data_loader = DataLoader(self.test_dataset,
-                                      batch_size=1,
-                                      shuffle=False,
-                                      num_workers=self.cfg.num_threads,
-                                      pin_memory=True)
-
-        return test_data_loader
diff --git a/spaces/Yusin/ChatGPT-Speech/infer.py b/spaces/Yusin/ChatGPT-Speech/infer.py
deleted file mode 100644
index 39b10801ee9712f5db63356e3e90a1056662e5ae..0000000000000000000000000000000000000000
--- a/spaces/Yusin/ChatGPT-Speech/infer.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# coding:utf-8
-import re
-
-import torch
-import unicodedata
-from pydub import AudioSegment
-from scipy.io.wavfile import write
-
-import commons
-import config
-import utils
-from models import SynthesizerTrn
-from text import text_to_sequence
-
-pth_path = config.pth_path
-config_json = config.config_json
-
-
-def wav2mp3(file_name):
-    sourcefile = AudioSegment.from_wav(f"{file_name}.wav")
-    sourcefile.export(f"{file_name}.mp3", format="mp3")
-
-
-def get_text(text, hps, cleaned=False):
-    if cleaned:
-        text_norm = text_to_sequence(text, hps.symbols, [])
-    else:
-        text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-
-def get_label(text, label):
-    if f'[{label}]' in text:
-        return True, text.replace(f'[{label}]', '')
-    else:
-        return False, text
-
-
-def clean_text(text):
-    jap = re.compile(r'[\u3040-\u309F\u30A0-\u30FF\uAC00-\uD7A3]')  # \uAC00-\uD7A3为匹配韩文的，其余为日文
-    text = f"[JA]{text}[JA]" if jap.search(text) else f"[ZH]{text}[ZH]"
-    text = unicodedata.normalize('NFKC', text)
-    return text
-
-
-def load_model(config_json, pth_path):
-    dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    hps_ms = utils.get_hparams_from_file(f"./configs/{config_json}")
-    n_speakers = hps_ms.data.n_speakers if 'n_speakers' in hps_ms.data.keys() else 0
-    n_symbols = len(hps_ms.symbols) if 'symbols' in hps_ms.keys() else 0
-    net_g_ms = SynthesizerTrn(
-        n_symbols,
-        hps_ms.data.filter_length // 2 + 1,
-        hps_ms.train.segment_size // hps_ms.data.hop_length,
-        n_speakers=n_speakers,
-        **hps_ms.model).to(dev)
-    _ = net_g_ms.eval()
-    _ = utils.load_checkpoint(pth_path, net_g_ms)
-    return net_g_ms, hps_ms
-
-
-def infer(text, net_g_ms, speaker_id, out_name):
-    dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    hps_ms = utils.get_hparams_from_file(f"./configs/{config_json}")
-    with torch.no_grad():
-        stn_tst = get_text(text, hps_ms, cleaned=False)
-        x_tst = stn_tst.unsqueeze(0).to(dev)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev)
-        sid = torch.LongTensor([speaker_id]).to(dev)
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=1)[0][
-            0, 0].data.cpu().float().numpy()
-        write(f"{out_name}.wav", hps_ms.data.sampling_rate, audio)
-        wav2mp3(out_name)
-    return audio
-
-
-if __name__ == "__main__":
-    out_name = "水调歌头"
-    speaker_id = 0
-    text = "明月几时有，把酒问青天。不知天上宫阙，今夕是何年。我欲乘风归去，又恐琼楼玉宇，高处不胜寒。"
-    text = clean_text(text)
-    net_g_ms = load_model(config_json, pth_path)
-    infer(text, net_g_ms, speaker_id, out_name)
diff --git a/spaces/Yusin/ChatGPT-Speech/transforms.py b/spaces/Yusin/ChatGPT-Speech/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Yusin/ChatGPT-Speech/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/assigners/atss_assigner.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/assigners/atss_assigner.py
deleted file mode 100644
index d4fe9d0e3c8704bd780d493eff20a5505dbe9580..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/assigners/atss_assigner.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import torch
-
-from ..builder import BBOX_ASSIGNERS
-from ..iou_calculators import build_iou_calculator
-from .assign_result import AssignResult
-from .base_assigner import BaseAssigner
-
-
-@BBOX_ASSIGNERS.register_module()
-class ATSSAssigner(BaseAssigner):
-    """Assign a corresponding gt bbox or background to each bbox.
-
-    Each proposals will be assigned with `0` or a positive integer
-    indicating the ground truth index.
-
-    - 0: negative sample, no assigned gt
-    - positive integer: positive sample, index (1-based) of assigned gt
-
-    Args:
-        topk (float): number of bbox selected in each level
-    """
-
-    def __init__(self,
-                 topk,
-                 iou_calculator=dict(type='BboxOverlaps2D'),
-                 ignore_iof_thr=-1):
-        self.topk = topk
-        self.iou_calculator = build_iou_calculator(iou_calculator)
-        self.ignore_iof_thr = ignore_iof_thr
-
-    # https://github.com/sfzhang15/ATSS/blob/master/atss_core/modeling/rpn/atss/loss.py
-
-    def assign(self,
-               bboxes,
-               num_level_bboxes,
-               gt_bboxes,
-               gt_bboxes_ignore=None,
-               gt_labels=None):
-        """Assign gt to bboxes.
-
-        The assignment is done in following steps
-
-        1. compute iou between all bbox (bbox of all pyramid levels) and gt
-        2. compute center distance between all bbox and gt
-        3. on each pyramid level, for each gt, select k bbox whose center
-           are closest to the gt center, so we total select k*l bbox as
-           candidates for each gt
-        4. get corresponding iou for the these candidates, and compute the
-           mean and std, set mean + std as the iou threshold
-        5. select these candidates whose iou are greater than or equal to
-           the threshold as positive
-        6. limit the positive sample's center in gt
-
-
-        Args:
-            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
-            num_level_bboxes (List): num of bboxes in each level
-            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
-            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
-                labelled as `ignored`, e.g., crowd boxes in COCO.
-            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        INF = 100000000
-        bboxes = bboxes[:, :4]
-        num_gt, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
-
-        # compute iou between all bbox and gt
-        overlaps = self.iou_calculator(bboxes, gt_bboxes)
-
-        # assign 0 by default
-        assigned_gt_inds = overlaps.new_full((num_bboxes, ),
-                                             0,
-                                             dtype=torch.long)
-
-        if num_gt == 0 or num_bboxes == 0:
-            # No ground truth or boxes, return empty assignment
-            max_overlaps = overlaps.new_zeros((num_bboxes, ))
-            if num_gt == 0:
-                # No truth, assign everything to background
-                assigned_gt_inds[:] = 0
-            if gt_labels is None:
-                assigned_labels = None
-            else:
-                assigned_labels = overlaps.new_full((num_bboxes, ),
-                                                    -1,
-                                                    dtype=torch.long)
-            return AssignResult(
-                num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
-
-        # compute center distance between all bbox and gt
-        gt_cx = (gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2.0
-        gt_cy = (gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2.0
-        gt_points = torch.stack((gt_cx, gt_cy), dim=1)
-
-        bboxes_cx = (bboxes[:, 0] + bboxes[:, 2]) / 2.0
-        bboxes_cy = (bboxes[:, 1] + bboxes[:, 3]) / 2.0
-        bboxes_points = torch.stack((bboxes_cx, bboxes_cy), dim=1)
-
-        distances = (bboxes_points[:, None, :] -
-                     gt_points[None, :, :]).pow(2).sum(-1).sqrt()
-
-        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
-                and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0):
-            ignore_overlaps = self.iou_calculator(
-                bboxes, gt_bboxes_ignore, mode='iof')
-            ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
-            ignore_idxs = ignore_max_overlaps > self.ignore_iof_thr
-            distances[ignore_idxs, :] = INF
-            assigned_gt_inds[ignore_idxs] = -1
-
-        # Selecting candidates based on the center distance
-        candidate_idxs = []
-        start_idx = 0
-        for level, bboxes_per_level in enumerate(num_level_bboxes):
-            # on each pyramid level, for each gt,
-            # select k bbox whose center are closest to the gt center
-            end_idx = start_idx + bboxes_per_level
-            distances_per_level = distances[start_idx:end_idx, :]
-            selectable_k = min(self.topk, bboxes_per_level)
-            _, topk_idxs_per_level = distances_per_level.topk(
-                selectable_k, dim=0, largest=False)
-            candidate_idxs.append(topk_idxs_per_level + start_idx)
-            start_idx = end_idx
-        candidate_idxs = torch.cat(candidate_idxs, dim=0)
-
-        # get corresponding iou for the these candidates, and compute the
-        # mean and std, set mean + std as the iou threshold
-        candidate_overlaps = overlaps[candidate_idxs, torch.arange(num_gt)]
-        overlaps_mean_per_gt = candidate_overlaps.mean(0)
-        overlaps_std_per_gt = candidate_overlaps.std(0)
-        overlaps_thr_per_gt = overlaps_mean_per_gt + overlaps_std_per_gt
-
-        is_pos = candidate_overlaps >= overlaps_thr_per_gt[None, :]
-
-        # limit the positive sample's center in gt
-        for gt_idx in range(num_gt):
-            candidate_idxs[:, gt_idx] += gt_idx * num_bboxes
-        ep_bboxes_cx = bboxes_cx.view(1, -1).expand(
-            num_gt, num_bboxes).contiguous().view(-1)
-        ep_bboxes_cy = bboxes_cy.view(1, -1).expand(
-            num_gt, num_bboxes).contiguous().view(-1)
-        candidate_idxs = candidate_idxs.view(-1)
-
-        # calculate the left, top, right, bottom distance between positive
-        # bbox center and gt side
-        l_ = ep_bboxes_cx[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 0]
-        t_ = ep_bboxes_cy[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 1]
-        r_ = gt_bboxes[:, 2] - ep_bboxes_cx[candidate_idxs].view(-1, num_gt)
-        b_ = gt_bboxes[:, 3] - ep_bboxes_cy[candidate_idxs].view(-1, num_gt)
-        is_in_gts = torch.stack([l_, t_, r_, b_], dim=1).min(dim=1)[0] > 0.01
-        is_pos = is_pos & is_in_gts
-
-        # if an anchor box is assigned to multiple gts,
-        # the one with the highest IoU will be selected.
-        overlaps_inf = torch.full_like(overlaps,
-                                       -INF).t().contiguous().view(-1)
-        index = candidate_idxs.view(-1)[is_pos.view(-1)]
-        overlaps_inf[index] = overlaps.t().contiguous().view(-1)[index]
-        overlaps_inf = overlaps_inf.view(num_gt, -1).t()
-
-        max_overlaps, argmax_overlaps = overlaps_inf.max(dim=1)
-        assigned_gt_inds[
-            max_overlaps != -INF] = argmax_overlaps[max_overlaps != -INF] + 1
-
-        if gt_labels is not None:
-            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
-            pos_inds = torch.nonzero(
-                assigned_gt_inds > 0, as_tuple=False).squeeze()
-            if pos_inds.numel() > 0:
-                assigned_labels[pos_inds] = gt_labels[
-                    assigned_gt_inds[pos_inds] - 1]
-        else:
-            assigned_labels = None
-        return AssignResult(
-            num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gl/wgl.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gl/wgl.py
deleted file mode 100644
index 4b2b024c23bdb7ffaa3eeeaab5b042d88c577212..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gl/wgl.py
+++ /dev/null
@@ -1,338 +0,0 @@
-"""Wrapper for C:\cygwin\home\Alex\pyglet\tools\wgl.h
-
-Generated by tools/gengl.py.
-Do not modify this file.
-"""
-
-from ctypes import *
-from pyglet.gl.lib import link_WGL as _link_function
-
-
-if not _link_function:
-    raise ImportError('opengl32.dll is not available.')
-
-# BEGIN GENERATED CONTENT (do not edit below this line)
-
-# This content is generated by tools/gengl.py.
-# Wrapper for C:\cygwin\home\Alex\pyglet\tools\wgl.h
-
-
-CONST = 0 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:14
-GLenum = c_uint 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:17
-GLboolean = c_ubyte 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:18
-GLbitfield = c_uint 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:19
-GLbyte = c_char 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:20
-GLshort = c_short 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:21
-GLint = c_int 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:22
-GLsizei = c_int 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:23
-GLubyte = c_ubyte 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:24
-GLushort = c_ushort 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:25
-GLuint = c_uint 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:26
-GLfloat = c_float 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:27
-GLclampf = c_float 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:28
-GLdouble = c_double 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:29
-GLclampd = c_double 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:30
-GLvoid = None 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:31
-INT8 = c_char 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:33
-PINT8 = c_char_p 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:33
-INT16 = c_short 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:34
-PINT16 = POINTER(c_short) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:34
-INT32 = c_int 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:35
-PINT32 = POINTER(c_int) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:35
-UINT8 = c_ubyte 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:36
-PUINT8 = POINTER(c_ubyte) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:36
-UINT16 = c_ushort 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:37
-PUINT16 = POINTER(c_ushort) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:37
-UINT32 = c_uint 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:38
-PUINT32 = POINTER(c_uint) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:38
-LONG32 = c_int 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:39
-PLONG32 = POINTER(c_int) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:39
-ULONG32 = c_uint 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:40
-PULONG32 = POINTER(c_uint) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:40
-DWORD32 = c_uint 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:41
-PDWORD32 = POINTER(c_uint) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:41
-INT64 = c_longlong 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:42
-PINT64 = POINTER(c_longlong) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:42
-UINT64 = c_ulonglong 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:43
-PUINT64 = POINTER(c_ulonglong) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:43
-VOID = None 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:45
-LPVOID = POINTER(None) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:45
-LPCSTR = c_char_p 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:46
-CHAR = c_char 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:47
-BYTE = c_ubyte 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:48
-WORD = c_ushort 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:49
-USHORT = c_ushort 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:49
-UINT = c_uint 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:50
-INT = c_int 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:51
-INT_PTR = POINTER(c_int) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:51
-BOOL = c_long 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:52
-LONG = c_long 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:53
-DWORD = c_ulong 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:54
-FLOAT = c_float 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:55
-COLORREF = DWORD 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:56
-LPCOLORREF = POINTER(DWORD) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:56
-HANDLE = POINTER(None) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:58
-HGLRC = HANDLE 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:60
-HDC = HANDLE 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:61
-PROC = CFUNCTYPE(INT_PTR) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:63
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:65
-wglCopyContext = _link_function('wglCopyContext', BOOL, [HGLRC, HGLRC, UINT], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:66
-wglCreateContext = _link_function('wglCreateContext', HGLRC, [HDC], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:67
-wglCreateLayerContext = _link_function('wglCreateLayerContext', HGLRC, [HDC, c_int], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:68
-wglDeleteContext = _link_function('wglDeleteContext', BOOL, [HGLRC], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:69
-wglGetCurrentContext = _link_function('wglGetCurrentContext', HGLRC, [], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:70
-wglGetCurrentDC = _link_function('wglGetCurrentDC', HDC, [], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:71
-wglGetProcAddress = _link_function('wglGetProcAddress', PROC, [LPCSTR], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:72
-wglMakeCurrent = _link_function('wglMakeCurrent', BOOL, [HDC, HGLRC], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:73
-wglShareLists = _link_function('wglShareLists', BOOL, [HGLRC, HGLRC], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:74
-wglUseFontBitmapsA = _link_function('wglUseFontBitmapsA', BOOL, [HDC, DWORD, DWORD, DWORD], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:75
-wglUseFontBitmapsW = _link_function('wglUseFontBitmapsW', BOOL, [HDC, DWORD, DWORD, DWORD], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:76
-SwapBuffers = _link_function('SwapBuffers', BOOL, [HDC], None)
-
-class struct__POINTFLOAT(Structure):
-    __slots__ = [
-        'x',
-        'y',
-    ]
-struct__POINTFLOAT._fields_ = [
-    ('x', FLOAT),
-    ('y', FLOAT),
-]
-
-POINTFLOAT = struct__POINTFLOAT 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:81
-PPOINTFLOAT = POINTER(struct__POINTFLOAT) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:81
-class struct__GLYPHMETRICSFLOAT(Structure):
-    __slots__ = [
-        'gmfBlackBoxX',
-        'gmfBlackBoxY',
-        'gmfptGlyphOrigin',
-        'gmfCellIncX',
-        'gmfCellIncY',
-    ]
-struct__GLYPHMETRICSFLOAT._fields_ = [
-    ('gmfBlackBoxX', FLOAT),
-    ('gmfBlackBoxY', FLOAT),
-    ('gmfptGlyphOrigin', POINTFLOAT),
-    ('gmfCellIncX', FLOAT),
-    ('gmfCellIncY', FLOAT),
-]
-
-GLYPHMETRICSFLOAT = struct__GLYPHMETRICSFLOAT 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:89
-PGLYPHMETRICSFLOAT = POINTER(struct__GLYPHMETRICSFLOAT) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:89
-LPGLYPHMETRICSFLOAT = POINTER(struct__GLYPHMETRICSFLOAT) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:89
-WGL_FONT_LINES = 0 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:91
-WGL_FONT_POLYGONS = 1 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:92
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:93
-wglUseFontOutlinesA = _link_function('wglUseFontOutlinesA', BOOL, [HDC, DWORD, DWORD, DWORD, FLOAT, FLOAT, c_int, LPGLYPHMETRICSFLOAT], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:95
-wglUseFontOutlinesW = _link_function('wglUseFontOutlinesW', BOOL, [HDC, DWORD, DWORD, DWORD, FLOAT, FLOAT, c_int, LPGLYPHMETRICSFLOAT], None)
-
-class struct_tagLAYERPLANEDESCRIPTOR(Structure):
-    __slots__ = [
-        'nSize',
-        'nVersion',
-        'dwFlags',
-        'iPixelType',
-        'cColorBits',
-        'cRedBits',
-        'cRedShift',
-        'cGreenBits',
-        'cGreenShift',
-        'cBlueBits',
-        'cBlueShift',
-        'cAlphaBits',
-        'cAlphaShift',
-        'cAccumBits',
-        'cAccumRedBits',
-        'cAccumGreenBits',
-        'cAccumBlueBits',
-        'cAccumAlphaBits',
-        'cDepthBits',
-        'cStencilBits',
-        'cAuxBuffers',
-        'iLayerPlane',
-        'bReserved',
-        'crTransparent',
-    ]
-struct_tagLAYERPLANEDESCRIPTOR._fields_ = [
-    ('nSize', WORD),
-    ('nVersion', WORD),
-    ('dwFlags', DWORD),
-    ('iPixelType', BYTE),
-    ('cColorBits', BYTE),
-    ('cRedBits', BYTE),
-    ('cRedShift', BYTE),
-    ('cGreenBits', BYTE),
-    ('cGreenShift', BYTE),
-    ('cBlueBits', BYTE),
-    ('cBlueShift', BYTE),
-    ('cAlphaBits', BYTE),
-    ('cAlphaShift', BYTE),
-    ('cAccumBits', BYTE),
-    ('cAccumRedBits', BYTE),
-    ('cAccumGreenBits', BYTE),
-    ('cAccumBlueBits', BYTE),
-    ('cAccumAlphaBits', BYTE),
-    ('cDepthBits', BYTE),
-    ('cStencilBits', BYTE),
-    ('cAuxBuffers', BYTE),
-    ('iLayerPlane', BYTE),
-    ('bReserved', BYTE),
-    ('crTransparent', COLORREF),
-]
-
-LAYERPLANEDESCRIPTOR = struct_tagLAYERPLANEDESCRIPTOR 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:125
-PLAYERPLANEDESCRIPTOR = POINTER(struct_tagLAYERPLANEDESCRIPTOR) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:125
-LPLAYERPLANEDESCRIPTOR = POINTER(struct_tagLAYERPLANEDESCRIPTOR) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:125
-LPD_DOUBLEBUFFER = 1 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:128
-LPD_STEREO = 2 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:129
-LPD_SUPPORT_GDI = 16 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:130
-LPD_SUPPORT_OPENGL = 32 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:131
-LPD_SHARE_DEPTH = 64 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:132
-LPD_SHARE_STENCIL = 128 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:133
-LPD_SHARE_ACCUM = 256 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:134
-LPD_SWAP_EXCHANGE = 512 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:135
-LPD_SWAP_COPY = 1024 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:136
-LPD_TRANSPARENT = 4096 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:137
-LPD_TYPE_RGBA = 0 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:139
-LPD_TYPE_COLORINDEX = 1 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:140
-WGL_SWAP_MAIN_PLANE = 1 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:143
-WGL_SWAP_OVERLAY1 = 2 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:144
-WGL_SWAP_OVERLAY2 = 4 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:145
-WGL_SWAP_OVERLAY3 = 8 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:146
-WGL_SWAP_OVERLAY4 = 16 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:147
-WGL_SWAP_OVERLAY5 = 32 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:148
-WGL_SWAP_OVERLAY6 = 64 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:149
-WGL_SWAP_OVERLAY7 = 128 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:150
-WGL_SWAP_OVERLAY8 = 256 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:151
-WGL_SWAP_OVERLAY9 = 512 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:152
-WGL_SWAP_OVERLAY10 = 1024 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:153
-WGL_SWAP_OVERLAY11 = 2048 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:154
-WGL_SWAP_OVERLAY12 = 4096 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:155
-WGL_SWAP_OVERLAY13 = 8192 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:156
-WGL_SWAP_OVERLAY14 = 16384 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:157
-WGL_SWAP_OVERLAY15 = 32768 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:158
-WGL_SWAP_UNDERLAY1 = 65536 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:159
-WGL_SWAP_UNDERLAY2 = 131072 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:160
-WGL_SWAP_UNDERLAY3 = 262144 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:161
-WGL_SWAP_UNDERLAY4 = 524288 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:162
-WGL_SWAP_UNDERLAY5 = 1048576 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:163
-WGL_SWAP_UNDERLAY6 = 2097152 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:164
-WGL_SWAP_UNDERLAY7 = 4194304 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:165
-WGL_SWAP_UNDERLAY8 = 8388608 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:166
-WGL_SWAP_UNDERLAY9 = 16777216 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:167
-WGL_SWAP_UNDERLAY10 = 33554432 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:168
-WGL_SWAP_UNDERLAY11 = 67108864 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:169
-WGL_SWAP_UNDERLAY12 = 134217728 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:170
-WGL_SWAP_UNDERLAY13 = 268435456 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:171
-WGL_SWAP_UNDERLAY14 = 536870912 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:172
-WGL_SWAP_UNDERLAY15 = 1073741824 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:173
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:175
-wglDescribeLayerPlane = _link_function('wglDescribeLayerPlane', BOOL, [HDC, c_int, c_int, UINT, LPLAYERPLANEDESCRIPTOR], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:177
-wglSetLayerPaletteEntries = _link_function('wglSetLayerPaletteEntries', c_int, [HDC, c_int, c_int, c_int, POINTER(COLORREF)], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:179
-wglGetLayerPaletteEntries = _link_function('wglGetLayerPaletteEntries', c_int, [HDC, c_int, c_int, c_int, POINTER(COLORREF)], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:181
-wglRealizeLayerPalette = _link_function('wglRealizeLayerPalette', BOOL, [HDC, c_int, BOOL], None)
-
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:182
-wglSwapLayerBuffers = _link_function('wglSwapLayerBuffers', BOOL, [HDC, UINT], None)
-
-class struct__WGLSWAP(Structure):
-    __slots__ = [
-        'hdc',
-        'uiFlags',
-    ]
-struct__WGLSWAP._fields_ = [
-    ('hdc', HDC),
-    ('uiFlags', UINT),
-]
-
-WGLSWAP = struct__WGLSWAP 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:188
-PWGLSWAP = POINTER(struct__WGLSWAP) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:188
-LPWGLSWAP = POINTER(struct__WGLSWAP) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:188
-WGL_SWAPMULTIPLE_MAX = 16 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:190
-# C:\cygwin\home\Alex\pyglet\tools\wgl.h:192
-wglSwapMultipleBuffers = _link_function('wglSwapMultipleBuffers', DWORD, [UINT, POINTER(WGLSWAP)], None)
-
-class struct_tagRECT(Structure):
-    __slots__ = [
-        'left',
-        'top',
-        'right',
-        'bottom',
-    ]
-struct_tagRECT._fields_ = [
-    ('left', LONG),
-    ('top', LONG),
-    ('right', LONG),
-    ('bottom', LONG),
-]
-
-RECT = struct_tagRECT 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:200
-PRECT = POINTER(struct_tagRECT) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:200
-NPRECT = POINTER(struct_tagRECT) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:200
-LPRECT = POINTER(struct_tagRECT) 	# C:\cygwin\home\Alex\pyglet\tools\wgl.h:200
-
-__all__ = ['CONST', 'GLenum', 'GLboolean', 'GLbitfield', 'GLbyte', 'GLshort',
-'GLint', 'GLsizei', 'GLubyte', 'GLushort', 'GLuint', 'GLfloat', 'GLclampf',
-'GLdouble', 'GLclampd', 'GLvoid', 'INT8', 'PINT8', 'INT16', 'PINT16', 'INT32',
-'PINT32', 'UINT8', 'PUINT8', 'UINT16', 'PUINT16', 'UINT32', 'PUINT32',
-'LONG32', 'PLONG32', 'ULONG32', 'PULONG32', 'DWORD32', 'PDWORD32', 'INT64',
-'PINT64', 'UINT64', 'PUINT64', 'VOID', 'LPVOID', 'LPCSTR', 'CHAR', 'BYTE',
-'WORD', 'USHORT', 'UINT', 'INT', 'INT_PTR', 'BOOL', 'LONG', 'DWORD', 'FLOAT',
-'COLORREF', 'LPCOLORREF', 'HANDLE', 'HGLRC', 'HDC', 'PROC', 'wglCopyContext',
-'wglCreateContext', 'wglCreateLayerContext', 'wglDeleteContext',
-'wglGetCurrentContext', 'wglGetCurrentDC', 'wglGetProcAddress',
-'wglMakeCurrent', 'wglShareLists', 'wglUseFontBitmapsA', 'wglUseFontBitmapsW',
-'SwapBuffers', 'POINTFLOAT', 'PPOINTFLOAT', 'GLYPHMETRICSFLOAT',
-'PGLYPHMETRICSFLOAT', 'LPGLYPHMETRICSFLOAT', 'WGL_FONT_LINES',
-'WGL_FONT_POLYGONS', 'wglUseFontOutlinesA', 'wglUseFontOutlinesW',
-'LAYERPLANEDESCRIPTOR', 'PLAYERPLANEDESCRIPTOR', 'LPLAYERPLANEDESCRIPTOR',
-'LPD_DOUBLEBUFFER', 'LPD_STEREO', 'LPD_SUPPORT_GDI', 'LPD_SUPPORT_OPENGL',
-'LPD_SHARE_DEPTH', 'LPD_SHARE_STENCIL', 'LPD_SHARE_ACCUM',
-'LPD_SWAP_EXCHANGE', 'LPD_SWAP_COPY', 'LPD_TRANSPARENT', 'LPD_TYPE_RGBA',
-'LPD_TYPE_COLORINDEX', 'WGL_SWAP_MAIN_PLANE', 'WGL_SWAP_OVERLAY1',
-'WGL_SWAP_OVERLAY2', 'WGL_SWAP_OVERLAY3', 'WGL_SWAP_OVERLAY4',
-'WGL_SWAP_OVERLAY5', 'WGL_SWAP_OVERLAY6', 'WGL_SWAP_OVERLAY7',
-'WGL_SWAP_OVERLAY8', 'WGL_SWAP_OVERLAY9', 'WGL_SWAP_OVERLAY10',
-'WGL_SWAP_OVERLAY11', 'WGL_SWAP_OVERLAY12', 'WGL_SWAP_OVERLAY13',
-'WGL_SWAP_OVERLAY14', 'WGL_SWAP_OVERLAY15', 'WGL_SWAP_UNDERLAY1',
-'WGL_SWAP_UNDERLAY2', 'WGL_SWAP_UNDERLAY3', 'WGL_SWAP_UNDERLAY4',
-'WGL_SWAP_UNDERLAY5', 'WGL_SWAP_UNDERLAY6', 'WGL_SWAP_UNDERLAY7',
-'WGL_SWAP_UNDERLAY8', 'WGL_SWAP_UNDERLAY9', 'WGL_SWAP_UNDERLAY10',
-'WGL_SWAP_UNDERLAY11', 'WGL_SWAP_UNDERLAY12', 'WGL_SWAP_UNDERLAY13',
-'WGL_SWAP_UNDERLAY14', 'WGL_SWAP_UNDERLAY15', 'wglDescribeLayerPlane',
-'wglSetLayerPaletteEntries', 'wglGetLayerPaletteEntries',
-'wglRealizeLayerPalette', 'wglSwapLayerBuffers', 'WGLSWAP', 'PWGLSWAP',
-'LPWGLSWAP', 'WGL_SWAPMULTIPLE_MAX', 'wglSwapMultipleBuffers', 'RECT',
-'PRECT', 'NPRECT', 'LPRECT']
-# END GENERATED CONTENT (do not edit above this line)
-
diff --git a/spaces/ahmedghani/svoice_demo/svoice/evaluate.py b/spaces/ahmedghani/svoice_demo/svoice/evaluate.py
deleted file mode 100644
index 22c888f6b1501a0da1d542c822e18d2eb7aeb38e..0000000000000000000000000000000000000000
--- a/spaces/ahmedghani/svoice_demo/svoice/evaluate.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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.
-
-# Authors: Eliya Nachmani (enk100), Yossi Adi (adiyoss), Lior Wolf and Alexandre Defossez (adefossez)
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import json
-import logging
-import sys
-
-import numpy as np
-from pesq import pesq
-from pystoi import stoi
-import torch
-
-from .models.sisnr_loss import cal_loss
-from .data.data import Validset
-from . import distrib
-from .utils import bold, deserialize_model, LogProgress
-
-
-logger = logging.getLogger(__name__)
-
-parser = argparse.ArgumentParser(
-    'Evaluate separation performance using MulCat blocks')
-parser.add_argument('model_path',
-                    help='Path to model file created by training')
-parser.add_argument('data_dir',
-                    help='directory including mix.json, s1.json, s2.json, ... files')
-parser.add_argument('--device', default="cuda")
-parser.add_argument('--sdr', type=int, default=0)
-parser.add_argument('--sample_rate', default=16000,
-                    type=int, help='Sample rate')
-parser.add_argument('--num_workers', type=int, default=5)
-parser.add_argument('-v', '--verbose', action='store_const', const=logging.DEBUG,
-                    default=logging.INFO, help="More loggging")
-
-
-def evaluate(args, model=None, data_loader=None, sr=None):
-    total_sisnr = 0
-    total_pesq = 0
-    total_stoi = 0
-    total_cnt = 0
-    updates = 5
-
-    # Load model
-    if not model:
-        pkg = torch.load(args.model_path, map_location=args.device)
-        if 'model' in pkg:
-            model = pkg['model']
-        else:
-            model = pkg
-        model = deserialize_model(model)
-        if 'best_state' in pkg:
-            model.load_state_dict(pkg['best_state'])
-    logger.debug(model)
-    model.eval()
-    model.to(args.device)
-    # Load data
-    if not data_loader:
-        dataset = Validset(args.data_dir)
-        data_loader = distrib.loader(
-            dataset, batch_size=1, num_workers=args.num_workers)
-        sr = args.sample_rate
-    pendings = []
-    with ProcessPoolExecutor(args.num_workers) as pool:
-        with torch.no_grad():
-            iterator = LogProgress(logger, data_loader, name="Eval estimates")
-            for i, data in enumerate(iterator):
-                # Get batch data
-                mixture, lengths, sources = [x.to(args.device) for x in data]
-                # Forward
-                with torch.no_grad():
-                    mixture /= mixture.max()
-                    estimate = model(mixture)[-1]
-                sisnr_loss, snr, estimate, reorder_estimate = cal_loss(
-                    sources, estimate, lengths)
-                reorder_estimate = reorder_estimate.cpu()
-                sources = sources.cpu()
-                mixture = mixture.cpu()
-
-                pendings.append(
-                    pool.submit(_run_metrics, sources, reorder_estimate, mixture, None,
-                                sr=sr))
-                total_cnt += sources.shape[0]
-
-            for pending in LogProgress(logger, pendings, updates, name="Eval metrics"):
-                sisnr_i, pesq_i, stoi_i = pending.result()
-                total_sisnr += sisnr_i
-                total_pesq += pesq_i
-                total_stoi += stoi_i
-
-    metrics = [total_sisnr, total_pesq, total_stoi]
-    sisnr, pesq, stoi = distrib.average(
-        [m/total_cnt for m in metrics], total_cnt)
-    logger.info(
-        bold(f'Test set performance: SISNRi={sisnr:.2f} PESQ={pesq}, STOI={stoi}.'))
-    return sisnr, pesq, stoi
-
-
-def _run_metrics(clean, estimate, mix, model, sr, pesq=False):
-    if model is not None:
-        torch.set_num_threads(1)
-        # parallel evaluation here
-        with torch.no_grad():
-            estimate = model(estimate)[-1]
-    estimate = estimate.numpy()
-    clean = clean.numpy()
-    mix = mix.numpy()
-    sisnr = cal_SISNRi(clean, estimate, mix)
-    if pesq:
-        pesq_i = cal_PESQ(clean, estimate, sr=sr)
-        stoi_i = cal_STOI(clean, estimate, sr=sr)
-    else:
-        pesq_i = 0
-        stoi_i = 0
-    return sisnr.mean(), pesq_i, stoi_i
-
-
-def cal_SISNR(ref_sig, out_sig, eps=1e-8):
-    """Calcuate Scale-Invariant Source-to-Noise Ratio (SI-SNR)
-    Args:
-        ref_sig: numpy.ndarray, [B, T]
-        out_sig: numpy.ndarray, [B, T]
-    Returns:
-        SISNR
-    """
-    assert len(ref_sig) == len(out_sig)
-    B, T = ref_sig.shape
-    ref_sig = ref_sig - np.mean(ref_sig, axis=1).reshape(B, 1)
-    out_sig = out_sig - np.mean(out_sig, axis=1).reshape(B, 1)
-    ref_energy = (np.sum(ref_sig ** 2, axis=1) + eps).reshape(B, 1)
-    proj = (np.sum(ref_sig * out_sig, axis=1).reshape(B, 1)) * \
-        ref_sig / ref_energy
-    noise = out_sig - proj
-    ratio = np.sum(proj ** 2, axis=1) / (np.sum(noise ** 2, axis=1) + eps)
-    sisnr = 10 * np.log(ratio + eps) / np.log(10.0)
-    return sisnr.mean()
-
-
-def cal_PESQ(ref_sig, out_sig, sr):
-    """Calculate PESQ.
-    Args:
-        ref_sig: numpy.ndarray, [B, C, T]
-        out_sig: numpy.ndarray, [B, C, T]
-    Returns 
-        PESQ
-    """
-    B, C, T = ref_sig.shape
-    ref_sig = ref_sig.reshape(B*C, T)
-    out_sig = out_sig.reshape(B*C, T)
-    pesq_val = 0
-    for i in range(len(ref_sig)):
-        pesq_val += pesq(sr, ref_sig[i], out_sig[i], 'nb')
-    return pesq_val / (B*C)
-
-
-def cal_STOI(ref_sig, out_sig, sr):
-    """Calculate STOI.
-    Args:
-        ref_sig: numpy.ndarray, [B, C, T]
-        out_sig: numpy.ndarray, [B, C, T]
-    Returns:
-        STOI
-    """
-    B, C, T = ref_sig.shape
-    ref_sig = ref_sig.reshape(B*C, T)
-    out_sig = out_sig.reshape(B*C, T)
-    try:
-        stoi_val = 0
-        for i in range(len(ref_sig)):
-            stoi_val += stoi(ref_sig[i], out_sig[i], sr, extended=False)
-        return stoi_val / (B*C)
-    except:
-        return 0
-
-
-def cal_SISNRi(src_ref, src_est, mix):
-    """Calculate Scale-Invariant Source-to-Noise Ratio improvement (SI-SNRi)
-    Args:
-        src_ref: numpy.ndarray, [B, C, T]
-        src_est: numpy.ndarray, [B, C, T], reordered by best PIT permutation
-        mix: numpy.ndarray, [T]
-    Returns:
-        average_SISNRi
-    """
-    avg_SISNRi = 0.0
-    B, C, T = src_ref.shape
-    for c in range(C):
-        sisnr = cal_SISNR(src_ref[:, c], src_est[:, c])
-        sisnrb = cal_SISNR(src_ref[:, c], mix)
-        avg_SISNRi += (sisnr - sisnrb)
-    avg_SISNRi /= C
-    return avg_SISNRi
-
-
-def main():
-    args = parser.parse_args()
-    logging.basicConfig(stream=sys.stderr, level=args.verbose)
-    logger.debug(args)
-    sisnr, pesq, stoi = evaluate(args)
-    json.dump({'sisnr': sisnr,
-               'pesq': pesq, 'stoi': stoi}, sys.stdout)
-    sys.stdout.write('\n')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/aieye/named_entity_recognition_tutorial/utils/__init__.py b/spaces/aieye/named_entity_recognition_tutorial/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/aj-data/AP2223_P1/README.md b/spaces/aj-data/AP2223_P1/README.md
deleted file mode 100644
index 7c912b25b6da93afdd463158716b4083ad1c2b5c..0000000000000000000000000000000000000000
--- a/spaces/aj-data/AP2223_P1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AP2223 P1
-emoji: 🦀
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/Mask2Former/demo_video/README.md b/spaces/akhaliq/Mask2Former/demo_video/README.md
deleted file mode 100644
index 704154131563d45b54ab6e9f3420fb5b6b7a9915..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/demo_video/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-## Video Mask2Former Demo
-
-We provide a command line tool to run a simple demo of builtin configs.
-The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md).
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/docs/infinibatch/torch/data.html b/spaces/akhaliq/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/docs/infinibatch/torch/data.html
deleted file mode 100644
index 084b0ac93b596a09a349bb2aaa4509fd5b5563ad..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/docs/infinibatch/torch/data.html
+++ /dev/null
@@ -1,268 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
-<meta name="generator" content="pdoc 0.7.5" />
-<title>infinibatch.torch.data API documentation</title>
-<meta name="description" content="" />
-<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
-<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
-<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
-<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
-<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
-<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>infinibatch.torch.data</code></h1>
-</header>
-<section id="section-intro">
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">import torch
-from infinibatch.iterators import CheckpointableIterator
-from infinibatch.datasets  import chunked_dataset_iterator
-from typing import Union, Iterable, Any
-
-
-# @TODO: This has been tested once, but we have no regression test presently. I am worried tests will fail if Torch is not installed.
-class IterableCheckpointedDataset(torch.utils.data.IterableDataset):
-    &#34;&#34;&#34;
-    Wraps a CheckpointableIterator into a PyTorch IterableDataset, which is recognized by its type by
-    PyTorch&#39;s DataLoader class.
-    &#34;&#34;&#34;
-    def __init__(self, source: CheckpointableIterator):
-        super().__init__()
-        self._source = source
-
-    def __iter__(self):  # this is called in the forked clone
-        worker_info = torch.utils.data.get_worker_info()
-        assert worker_info is None or worker_info.num_workers == 1  # not supported since we can&#39;t get at the checkpoint for each worker
-        return iter(self._source)
-
-
-# @TODO: This is currently untested, and may not work presently.
-class IterableChunkedDataset(torch.utils.data.IterableDataset):
-    def __init__(self, paths: Union[str, Iterable[str]], shuffle: bool=True, buffer_size: int=2**20, transform=None, seed: int=None, world_size: int=1, rank: int=0, num_workers_per_rank: int=1):
-        super().__init__()
-        self.rank = rank
-        self.num_workers_per_rank = num_workers_per_rank
-        # instance_rank is set assuming that num_workers_per_rank = 1 and adapted dynamically in __iter__
-        self.dataset = chunked_dataset_iterator(paths, shuffle=shuffle, buffer_size=buffer_size, transform=transform, seed=seed, num_instances=world_size*num_workers_per_rank, instance_rank=rank)
-
-    def __iter__(self):
-        worker_info = torch.utils.data.get_worker_info()
-        if worker_info is None:  # single-process data loading
-            self.dataset._instance_rank = self.rank
-        else:
-            assert worker_info.num_workers == self.num_workers_per_rank
-            self.dataset._instance_rank = self.rank * self.num_workers_per_rank + worker_info.id
-        return iter(self.dataset)</code></pre>
-</details>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="infinibatch.torch.data.IterableCheckpointedDataset"><code class="flex name class">
-<span>class <span class="ident">IterableCheckpointedDataset</span></span>
-<span>(</span><span>source: <a title="infinibatch.iterators.CheckpointableIterator" href="../iterators.html#infinibatch.iterators.CheckpointableIterator">CheckpointableIterator</a>)</span>
-</code></dt>
-<dd>
-<section class="desc"><p>Wraps a CheckpointableIterator into a PyTorch IterableDataset, which is recognized by its type by
-PyTorch's DataLoader class.</p></section>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class IterableCheckpointedDataset(torch.utils.data.IterableDataset):
-    &#34;&#34;&#34;
-    Wraps a CheckpointableIterator into a PyTorch IterableDataset, which is recognized by its type by
-    PyTorch&#39;s DataLoader class.
-    &#34;&#34;&#34;
-    def __init__(self, source: CheckpointableIterator):
-        super().__init__()
-        self._source = source
-
-    def __iter__(self):  # this is called in the forked clone
-        worker_info = torch.utils.data.get_worker_info()
-        assert worker_info is None or worker_info.num_workers == 1  # not supported since we can&#39;t get at the checkpoint for each worker
-        return iter(self._source)</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>torch.utils.data.dataset.IterableDataset</li>
-<li>torch.utils.data.dataset.Dataset</li>
-</ul>
-</dd>
-<dt id="infinibatch.torch.data.IterableChunkedDataset"><code class="flex name class">
-<span>class <span class="ident">IterableChunkedDataset</span></span>
-<span>(</span><span>paths: Union[str, Iterable[str]], shuffle: bool = True, buffer_size: int = 1048576, transform=None, seed: int = None, world_size: int = 1, rank: int = 0, num_workers_per_rank: int = 1)</span>
-</code></dt>
-<dd>
-<section class="desc"><p>An iterable Dataset.</p>
-<p>All datasets that represent an iterable of data samples should subclass it.
-Such form of datasets is particularly useful when data come from a stream.</p>
-<p>All subclasses should overwrite :meth:<code>__iter__</code>, which would return an
-iterator of samples in this dataset.</p>
-<p>When a subclass is used with :class:<code>~torch.utils.data.DataLoader</code>, each
-item in the dataset will be yielded from the :class:<code>~torch.utils.data.DataLoader</code>
-iterator. When :attr:<code>num_workers &gt; 0</code>, each worker process will have a
-different copy of the dataset object, so it is often desired to configure
-each copy independently to avoid having duplicate data returned from the
-workers. :func:<code>~torch.utils.data.get_worker_info</code>, when called in a worker
-process, returns information about the worker. It can be used in either the
-dataset's :meth:<code>__iter__</code> method or the :class:<code>~torch.utils.data.DataLoader</code> 's
-:attr:<code>worker_init_fn</code> option to modify each copy's behavior.</p>
-<p>Example 1: splitting workload across all workers in :meth:<code>__iter__</code>::</p>
-<pre><code>&gt;&gt;&gt; class MyIterableDataset(torch.utils.data.IterableDataset):
-...     def __init__(self, start, end):
-...         super(MyIterableDataset).__init__()
-...         assert end &gt; start, "this example code only works with end &gt;= start"
-...         self.start = start
-...         self.end = end
-...
-...     def __iter__(self):
-...         worker_info = torch.utils.data.get_worker_info()
-...         if worker_info is None:  # single-process data loading, return the full iterator
-...             iter_start = self.start
-...             iter_end = self.end
-...         else:  # in a worker process
-...             # split workload
-...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
-...             worker_id = worker_info.id
-...             iter_start = self.start + worker_id * per_worker
-...             iter_end = min(iter_start + per_worker, self.end)
-...         return iter(range(iter_start, iter_end))
-...
-&gt;&gt;&gt; # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
-&gt;&gt;&gt; ds = MyIterableDataset(start=3, end=7)
-
-&gt;&gt;&gt; # Single-process loading
-&gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
-[3, 4, 5, 6]
-
-&gt;&gt;&gt; # Mult-process loading with two worker processes
-&gt;&gt;&gt; # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
-&gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
-[3, 5, 4, 6]
-
-&gt;&gt;&gt; # With even more workers
-&gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=20)))
-[3, 4, 5, 6]
-</code></pre>
-<p>Example 2: splitting workload across all workers using :attr:<code>worker_init_fn</code>::</p>
-<pre><code>&gt;&gt;&gt; class MyIterableDataset(torch.utils.data.IterableDataset):
-...     def __init__(self, start, end):
-...         super(MyIterableDataset).__init__()
-...         assert end &gt; start, "this example code only works with end &gt;= start"
-...         self.start = start
-...         self.end = end
-...
-...     def __iter__(self):
-...         return iter(range(self.start, self.end))
-...
-&gt;&gt;&gt; # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
-&gt;&gt;&gt; ds = MyIterableDataset(start=3, end=7)
-
-&gt;&gt;&gt; # Single-process loading
-&gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
-[3, 4, 5, 6]
-&gt;&gt;&gt;
-&gt;&gt;&gt; # Directly doing multi-process loading yields duplicate data
-&gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
-[3, 3, 4, 4, 5, 5, 6, 6]
-
-&gt;&gt;&gt; # Define a `worker_init_fn` that configures each dataset copy differently
-&gt;&gt;&gt; def worker_init_fn(worker_id):
-...     worker_info = torch.utils.data.get_worker_info()
-...     dataset = worker_info.dataset  # the dataset copy in this worker process
-...     overall_start = dataset.start
-...     overall_end = dataset.end
-...     # configure the dataset to only process the split workload
-...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
-...     worker_id = worker_info.id
-...     dataset.start = overall_start + worker_id * per_worker
-...     dataset.end = min(dataset.start + per_worker, overall_end)
-...
-
-&gt;&gt;&gt; # Mult-process loading with the custom `worker_init_fn`
-&gt;&gt;&gt; # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
-&gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
-[3, 5, 4, 6]
-
-&gt;&gt;&gt; # With even more workers
-&gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=20, worker_init_fn=worker_init_fn)))
-[3, 4, 5, 6]
-</code></pre></section>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class IterableChunkedDataset(torch.utils.data.IterableDataset):
-    def __init__(self, paths: Union[str, Iterable[str]], shuffle: bool=True, buffer_size: int=2**20, transform=None, seed: int=None, world_size: int=1, rank: int=0, num_workers_per_rank: int=1):
-        super().__init__()
-        self.rank = rank
-        self.num_workers_per_rank = num_workers_per_rank
-        # instance_rank is set assuming that num_workers_per_rank = 1 and adapted dynamically in __iter__
-        self.dataset = chunked_dataset_iterator(paths, shuffle=shuffle, buffer_size=buffer_size, transform=transform, seed=seed, num_instances=world_size*num_workers_per_rank, instance_rank=rank)
-
-    def __iter__(self):
-        worker_info = torch.utils.data.get_worker_info()
-        if worker_info is None:  # single-process data loading
-            self.dataset._instance_rank = self.rank
-        else:
-            assert worker_info.num_workers == self.num_workers_per_rank
-            self.dataset._instance_rank = self.rank * self.num_workers_per_rank + worker_info.id
-        return iter(self.dataset)</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>torch.utils.data.dataset.IterableDataset</li>
-<li>torch.utils.data.dataset.Dataset</li>
-</ul>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<h1>Index</h1>
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="infinibatch.torch" href="index.html">infinibatch.torch</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="infinibatch.torch.data.IterableCheckpointedDataset" href="#infinibatch.torch.data.IterableCheckpointedDataset">IterableCheckpointedDataset</a></code></h4>
-</li>
-<li>
-<h4><code><a title="infinibatch.torch.data.IterableChunkedDataset" href="#infinibatch.torch.data.IterableChunkedDataset">IterableChunkedDataset</a></code></h4>
-</li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
-<footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
-</footer>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
-<script>hljs.initHighlightingOnLoad()</script>
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/akhaliq/deeplab2/model/layers/resized_fuse_test.py b/spaces/akhaliq/deeplab2/model/layers/resized_fuse_test.py
deleted file mode 100644
index 3ba8431462e4bb5b4e714834bef2dbb97facdc46..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/layers/resized_fuse_test.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# 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.
-
-"""Tests for resized_fuse."""
-
-import tensorflow as tf
-
-from deeplab2.model.layers import resized_fuse
-
-
-class ResizedFuseTest(tf.test.TestCase):
-
-  def test_resize_and_fuse_features(self):
-    batch, height, width, channels = 2, 11, 11, 6
-    smaller_height, smaller_width, smaller_channels = 6, 6, 3
-    larger_height1, larger_width1 = 21, 21  # Stride 2 conv.
-    larger_height2, larger_width2 = 22, 22  # Stride 2 conv.
-    larger_height3, larger_width3 = 23, 23  # Conv and resize.
-
-    feature_list = []
-    feature_list.append(tf.zeros([batch, smaller_height, smaller_width,
-                                  smaller_channels]))
-    feature_list.append(tf.zeros([batch, smaller_height, smaller_width,
-                                  channels]))
-    feature_list.append(tf.zeros([batch, height, width, smaller_channels]))
-    feature_list.append(tf.zeros([batch, height, width, channels]))
-    feature_list.append(tf.zeros([batch, larger_height1, larger_width1,
-                                  channels]))
-    feature_list.append(tf.zeros([batch, larger_height1, larger_width1,
-                                  smaller_channels]))
-    feature_list.append(tf.zeros([batch, larger_height2, larger_width2,
-                                  smaller_channels]))
-    feature_list.append(tf.zeros([batch, larger_height3, larger_width3,
-                                  smaller_channels]))
-    layer = resized_fuse.ResizedFuse(name='fuse',
-                                     height=height,
-                                     width=width,
-                                     num_channels=channels)
-    output = layer(feature_list)
-    self.assertEqual(output.get_shape().as_list(), [batch, height, width,
-                                                    channels])
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/spaces/akhaliq/lama/saicinpainting/evaluation/data.py b/spaces/akhaliq/lama/saicinpainting/evaluation/data.py
deleted file mode 100644
index 69ddb8d3c12d0261e459f7c4f66a702d0c477df0..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/saicinpainting/evaluation/data.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import glob
-import os
-
-import cv2
-import PIL.Image as Image
-import numpy as np
-
-from torch.utils.data import Dataset
-import torch.nn.functional as F
-
-
-def load_image(fname, mode='RGB', return_orig=False):
-    img = np.array(Image.open(fname).convert(mode))
-    if img.ndim == 3:
-        img = np.transpose(img, (2, 0, 1))
-    out_img = img.astype('float32') / 255
-    if return_orig:
-        return out_img, img
-    else:
-        return out_img
-
-
-def ceil_modulo(x, mod):
-    if x % mod == 0:
-        return x
-    return (x // mod + 1) * mod
-
-
-def pad_img_to_modulo(img, mod):
-    channels, height, width = img.shape
-    out_height = ceil_modulo(height, mod)
-    out_width = ceil_modulo(width, mod)
-    return np.pad(img, ((0, 0), (0, out_height - height), (0, out_width - width)), mode='symmetric')
-
-
-def pad_tensor_to_modulo(img, mod):
-    batch_size, channels, height, width = img.shape
-    out_height = ceil_modulo(height, mod)
-    out_width = ceil_modulo(width, mod)
-    return F.pad(img, pad=(0, out_width - width, 0, out_height - height), mode='reflect')
-
-
-def scale_image(img, factor, interpolation=cv2.INTER_AREA):
-    if img.shape[0] == 1:
-        img = img[0]
-    else:
-        img = np.transpose(img, (1, 2, 0))
-
-    img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation)
-
-    if img.ndim == 2:
-        img = img[None, ...]
-    else:
-        img = np.transpose(img, (2, 0, 1))
-    return img
-
-
-class InpaintingDataset(Dataset):
-    def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):
-        self.datadir = datadir
-        self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, '**', '*mask*.png'), recursive=True)))
-        self.img_filenames = [fname.rsplit('_mask', 1)[0] + img_suffix for fname in self.mask_filenames]
-        self.pad_out_to_modulo = pad_out_to_modulo
-        self.scale_factor = scale_factor
-
-    def __len__(self):
-        return len(self.mask_filenames)
-
-    def __getitem__(self, i):
-        image = load_image(self.img_filenames[i], mode='RGB')
-        mask = load_image(self.mask_filenames[i], mode='L')
-        result = dict(image=image, mask=mask[None, ...])
-
-        if self.scale_factor is not None:
-            result['image'] = scale_image(result['image'], self.scale_factor)
-            result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)
-
-        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
-            result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
-            result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
-
-        return result
-
-class OurInpaintingDataset(Dataset):
-    def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):
-        self.datadir = datadir
-        self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, 'mask', '**', '*mask*.png'), recursive=True)))
-        self.img_filenames = [os.path.join(self.datadir, 'img', os.path.basename(fname.rsplit('-', 1)[0].rsplit('_', 1)[0]) + '.png') for fname in self.mask_filenames]
-        self.pad_out_to_modulo = pad_out_to_modulo
-        self.scale_factor = scale_factor
-
-    def __len__(self):
-        return len(self.mask_filenames)
-
-    def __getitem__(self, i):
-        result = dict(image=load_image(self.img_filenames[i], mode='RGB'),
-                      mask=load_image(self.mask_filenames[i], mode='L')[None, ...])
-
-        if self.scale_factor is not None:
-            result['image'] = scale_image(result['image'], self.scale_factor)
-            result['mask'] = scale_image(result['mask'], self.scale_factor)
-
-        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
-            result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
-            result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
-
-        return result
-
-class PrecomputedInpaintingResultsDataset(InpaintingDataset):
-    def __init__(self, datadir, predictdir, inpainted_suffix='_inpainted.jpg', **kwargs):
-        super().__init__(datadir, **kwargs)
-        if not datadir.endswith('/'):
-            datadir += '/'
-        self.predictdir = predictdir
-        self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)
-                               for fname in self.mask_filenames]
-
-    def __getitem__(self, i):
-        result = super().__getitem__(i)
-        result['inpainted'] = load_image(self.pred_filenames[i])
-        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
-            result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)
-        return result
-
-class OurPrecomputedInpaintingResultsDataset(OurInpaintingDataset):
-    def __init__(self, datadir, predictdir, inpainted_suffix="png", **kwargs):
-        super().__init__(datadir, **kwargs)
-        if not datadir.endswith('/'):
-            datadir += '/'
-        self.predictdir = predictdir
-        self.pred_filenames = [os.path.join(predictdir, os.path.basename(os.path.splitext(fname)[0]) + f'_inpainted.{inpainted_suffix}')
-                               for fname in self.mask_filenames]
-        # self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)
-        #                        for fname in self.mask_filenames]
-
-    def __getitem__(self, i):
-        result = super().__getitem__(i)
-        result['inpainted'] = self.file_loader(self.pred_filenames[i])
-
-        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
-            result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)
-        return result
-
-class InpaintingEvalOnlineDataset(Dataset):
-    def __init__(self, indir, mask_generator, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None,  **kwargs):
-        self.indir = indir
-        self.mask_generator = mask_generator
-        self.img_filenames = sorted(list(glob.glob(os.path.join(self.indir, '**', f'*{img_suffix}' ), recursive=True)))
-        self.pad_out_to_modulo = pad_out_to_modulo
-        self.scale_factor = scale_factor
-
-    def __len__(self):
-        return len(self.img_filenames)
-
-    def __getitem__(self, i):
-        img, raw_image = load_image(self.img_filenames[i], mode='RGB', return_orig=True)
-        mask = self.mask_generator(img, raw_image=raw_image)
-        result = dict(image=img, mask=mask)
-
-        if self.scale_factor is not None:
-            result['image'] = scale_image(result['image'], self.scale_factor)
-            result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)
-
-        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
-            result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
-            result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
-        return result
\ No newline at end of file
diff --git a/spaces/ali-ghamdan/deoldify/fastai/vision/models/xresnet.py b/spaces/ali-ghamdan/deoldify/fastai/vision/models/xresnet.py
deleted file mode 100644
index accc600a15efb63f3d84d4ee68867d73e9f4a9f8..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/vision/models/xresnet.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch.nn as nn
-import torch,math,sys
-import torch.utils.model_zoo as model_zoo
-from functools import partial
-from ...torch_core import Module
-
-__all__ = ['XResNet', 'xresnet18', 'xresnet34', 'xresnet50', 'xresnet101', 'xresnet152']
-
-# or: ELU+init (a=0.54; gain=1.55)
-act_fn = nn.ReLU(inplace=True)
-
-class Flatten(Module):
-    def forward(self, x): return x.view(x.size(0), -1)
-
-def init_cnn(m):
-    if getattr(m, 'bias', None) is not None: nn.init.constant_(m.bias, 0)
-    if isinstance(m, (nn.Conv2d,nn.Linear)): nn.init.kaiming_normal_(m.weight)
-    for l in m.children(): init_cnn(l)
-
-def conv(ni, nf, ks=3, stride=1, bias=False):
-    return nn.Conv2d(ni, nf, kernel_size=ks, stride=stride, padding=ks//2, bias=bias)
-
-def noop(x): return x
-
-def conv_layer(ni, nf, ks=3, stride=1, zero_bn=False, act=True):
-    bn = nn.BatchNorm2d(nf)
-    nn.init.constant_(bn.weight, 0. if zero_bn else 1.)
-    layers = [conv(ni, nf, ks, stride=stride), bn]
-    if act: layers.append(act_fn)
-    return nn.Sequential(*layers)
-
-class ResBlock(Module):
-    def __init__(self, expansion, ni, nh, stride=1):
-        nf,ni = nh*expansion,ni*expansion
-        layers  = [conv_layer(ni, nh, 3, stride=stride),
-                   conv_layer(nh, nf, 3, zero_bn=True, act=False)
-        ] if expansion == 1 else [
-                   conv_layer(ni, nh, 1),
-                   conv_layer(nh, nh, 3, stride=stride),
-                   conv_layer(nh, nf, 1, zero_bn=True, act=False)
-        ]
-        self.convs = nn.Sequential(*layers)
-        # TODO: check whether act=True works better
-        self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)
-        self.pool = noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)
-
-    def forward(self, x): return act_fn(self.convs(x) + self.idconv(self.pool(x)))
-
-def filt_sz(recep): return min(64, 2**math.floor(math.log2(recep*0.75)))
-
-class XResNet(nn.Sequential):
-    def __init__(self, expansion, layers, c_in=3, c_out=1000):
-        stem = []
-        sizes = [c_in,32,32,64]
-        for i in range(3):
-            stem.append(conv_layer(sizes[i], sizes[i+1], stride=2 if i==0 else 1))
-            #nf = filt_sz(c_in*9)
-            #stem.append(conv_layer(c_in, nf, stride=2 if i==1 else 1))
-            #c_in = nf
-
-        block_szs = [64//expansion,64,128,256,512]
-        blocks = [self._make_layer(expansion, block_szs[i], block_szs[i+1], l, 1 if i==0 else 2)
-                  for i,l in enumerate(layers)]
-        super().__init__(
-            *stem,
-            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
-            *blocks,
-            nn.AdaptiveAvgPool2d(1), Flatten(),
-            nn.Linear(block_szs[-1]*expansion, c_out),
-        )
-        init_cnn(self)
-
-    def _make_layer(self, expansion, ni, nf, blocks, stride):
-        return nn.Sequential(
-            *[ResBlock(expansion, ni if i==0 else nf, nf, stride if i==0 else 1)
-              for i in range(blocks)])
-
-def xresnet(expansion, n_layers, name, pretrained=False, **kwargs):
-    model = XResNet(expansion, n_layers, **kwargs)
-    if pretrained: model.load_state_dict(model_zoo.load_url(model_urls[name]))
-    return model
-
-me = sys.modules[__name__]
-for n,e,l in [
-    [ 18 , 1, [2,2,2 ,2] ],
-    [ 34 , 1, [3,4,6 ,3] ],
-    [ 50 , 4, [3,4,6 ,3] ],
-    [ 101, 4, [3,4,23,3] ],
-    [ 152, 4, [3,8,36,3] ],
-]:
-    name = f'xresnet{n}'
-    setattr(me, name, partial(xresnet, expansion=e, n_layers=l, name=name))
-
diff --git a/spaces/allandclive/Uganda_MMS/uroman/lib/JSON/backportPP/Boolean.pm b/spaces/allandclive/Uganda_MMS/uroman/lib/JSON/backportPP/Boolean.pm
deleted file mode 100644
index 38be6a3817b3b3b5632f4ee6bd3bba7397af567e..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/uroman/lib/JSON/backportPP/Boolean.pm
+++ /dev/null
@@ -1,27 +0,0 @@
-=head1 NAME
-
-JSON::PP::Boolean - dummy module providing JSON::PP::Boolean
-
-=head1 SYNOPSIS
-
- # do not "use" yourself
-
-=head1 DESCRIPTION
-
-This module exists only to provide overload resolution for Storable
-and similar modules. See L<JSON::PP> for more info about this class.
-
-=cut
-
-use JSON::backportPP ();
-use strict;
-
-1;
-
-=head1 AUTHOR
-
-This idea is from L<JSON::XS::Boolean> written by
-Marc Lehmann <schmorp[at]schmorp.de>
-
-=cut
-
diff --git a/spaces/allknowingroger/Image-Models-Test138/README.md b/spaces/allknowingroger/Image-Models-Test138/README.md
deleted file mode 100644
index 157b60126025340e853d024c141cb88a6f2a9dc2..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test138/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-duplicated_from: allknowingroger/Image-Models-Test137
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test182/README.md b/spaces/allknowingroger/Image-Models-Test182/README.md
deleted file mode 100644
index f91e4b31ab345f987b425de029c057bfb69d9e1b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test182/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/alsrbdni/magic-to-diffusion/share_btn.py b/spaces/alsrbdni/magic-to-diffusion/share_btn.py
deleted file mode 100644
index 1382fb25a5ef50e843598187e1e660e86ea8dd05..0000000000000000000000000000000000000000
--- a/spaces/alsrbdni/magic-to-diffusion/share_btn.py
+++ /dev/null
@@ -1,88 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `magic-prompt-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `magic-prompt-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const imgEls = gradioEl.querySelectorAll('#generated-gallery img');
-    const promptTxt = gradioEl.querySelector('#translated textarea').value;
-    let titleTxt = promptTxt;
-    if(titleTxt.length > 100){
-        titleTxt = titleTxt.slice(0, 100) + ' ...';
-    }
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!imgEls.length){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => {
-            const res = await fetch(imgEl.src);
-            const blob = await res.blob();
-            const imgId = Date.now() % 200;
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        })
-    );
-    const inputFile = await getInputImgFile(inputImgEl);
-    files.push(inputFile);
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-    const urlInputImg = urls.pop();
-	const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-    const htmlImgsMd = htmlImgs.join(`\n`);
-	const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-#### Caption:
-${promptTxt}
-#### Generations:
-<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgsMd}
-</div>`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/huggingface-projects/magic-diffusion/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_srate.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_srate.c
deleted file mode 100644
index d4ce81b26095264fb9822d4421af8962c43566e4..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_srate.c
+++ /dev/null
@@ -1,182 +0,0 @@
-/*
- * $Id: patest_sine.c 1097 2006-08-26 08:27:53Z rossb $
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com/
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-/** @file patest_sine_srate_mac.c
-    @ingroup test_src
-    @brief Plays sine waves at 44100 and 48000,
-          and forces the hardware to change if this is a mac.
-          Designed for use with CoreAudio.
-    @author Bjorn Roche <bjorn@xowave.com>
-   @author Ross Bencina <rossb@audiomulch.com>
-   @author Phil Burk <philburk@softsynth.com>
-*/
-
-#include <stdio.h>
-#include <math.h>
-#include "portaudio.h"
-
-#ifdef __APPLE__
-#include "pa_mac_core.h"
-#endif
-
-#define NUM_SECONDS   (5)
-#define SAMPLE_RATE1   (44100)
-#define SAMPLE_RATE2   (48000)
-#define FRAMES_PER_BUFFER  (64)
-
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-
-#define TABLE_SIZE   (200)
-typedef struct
-{
-    float sine[TABLE_SIZE];
-    int left_phase;
-    int right_phase;
-}
-paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer, void *outputBuffer,
-                            unsigned long framesPerBuffer,
-                            const PaStreamCallbackTimeInfo* timeInfo,
-                            PaStreamCallbackFlags statusFlags,
-                            void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    float *out = (float*)outputBuffer;
-    unsigned long i;
-
-    (void) timeInfo; /* Prevent unused variable warnings. */
-    (void) statusFlags;
-    (void) inputBuffer;
-
-    for( i=0; i<framesPerBuffer; i++ )
-    {
-        *out++ = data->sine[data->left_phase];  /* left */
-        *out++ = data->sine[data->right_phase];  /* right */
-        data->left_phase += 1;
-        if( data->left_phase >= TABLE_SIZE ) data->left_phase -= TABLE_SIZE;
-        data->right_phase += 3; /* higher pitch so we can distinguish left and right. */
-        if( data->right_phase >= TABLE_SIZE ) data->right_phase -= TABLE_SIZE;
-    }
-
-    return paContinue;
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
-    PaStreamParameters outputParameters;
-    PaStream *stream;
-    PaError err;
-    paTestData data;
-#ifdef __APPLE__
-    PaMacCoreStreamInfo macInfo;
-#endif
-    int i;
-
-    /* initialise sinusoidal wavetable */
-    for( i=0; i<TABLE_SIZE; i++ )
-    {
-        data.sine[i] = (float) sin( ((double)i/(double)TABLE_SIZE) * M_PI * 2. );
-    }
-    data.left_phase = data.right_phase = 0;
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-    for( i=0; i<2; ++i ) {
-        const float sr = i ? SAMPLE_RATE2 : SAMPLE_RATE1;
-        printf("PortAudio Test: output sine wave. SR = %g, BufSize = %d\n", sr, FRAMES_PER_BUFFER);
-        outputParameters.device = Pa_GetDefaultOutputDevice(); /* default output device */
-        if (outputParameters.device == paNoDevice) {
-            fprintf(stderr,"Error: No default output device.\n");
-            goto error;
-        }
-        outputParameters.channelCount = 2;       /* stereo output */
-        outputParameters.sampleFormat = paFloat32; /* 32 bit floating point output */
-        outputParameters.suggestedLatency = Pa_GetDeviceInfo( outputParameters.device )->defaultLowOutputLatency;
-        /** setup host specific info */
-#ifdef __APPLE__
-        PaMacCore_SetupStreamInfo( &macInfo, paMacCorePro );
-        outputParameters.hostApiSpecificStreamInfo = &macInfo;
-#else
-        printf( "Hardware SR changing not being tested on this platform.\n" );
-        outputParameters.hostApiSpecificStreamInfo = NULL;
-#endif
-        err = Pa_OpenStream(
-                &stream,
-                NULL, /* no input */
-                &outputParameters,
-                sr,
-                FRAMES_PER_BUFFER,
-                paClipOff,      /* we won't output out of range samples so don't bother clipping them */
-                patestCallback,
-                &data );
-        if( err != paNoError ) goto error;
-
-        err = Pa_StartStream( stream );
-        if( err != paNoError ) goto error;
-
-        printf("Play for %d seconds.\n", NUM_SECONDS );
-        Pa_Sleep( NUM_SECONDS * 1000 );
-
-        err = Pa_StopStream( stream );
-        if( err != paNoError ) goto error;
-
-        err = Pa_CloseStream( stream );
-        if( err != paNoError ) goto error;
-    }
-
-    Pa_Terminate();
-    printf("Test finished.\n");
-
-    return err;
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    return err;
-}
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/downloads.py b/spaces/anaclaudia13ct/insect_detection/utils/downloads.py
deleted file mode 100644
index 72ea87340eb9b4f07b2271cce24f86bf3a6872ab..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/downloads.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Download utils
-"""
-
-import logging
-import os
-import subprocess
-import urllib
-from pathlib import Path
-
-import requests
-import torch
-
-
-def is_url(url, check=True):
-    # Check if string is URL and check if URL exists
-    try:
-        url = str(url)
-        result = urllib.parse.urlparse(url)
-        assert all([result.scheme, result.netloc])  # check if is url
-        return (urllib.request.urlopen(url).getcode() == 200) if check else True  # check if exists online
-    except (AssertionError, urllib.request.HTTPError):
-        return False
-
-
-def gsutil_getsize(url=''):
-    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
-    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
-    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
-
-
-def url_getsize(url='https://ultralytics.com/images/bus.jpg'):
-    # Return downloadable file size in bytes
-    response = requests.head(url, allow_redirects=True)
-    return int(response.headers.get('content-length', -1))
-
-
-def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
-    # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
-    from utils.general import LOGGER
-
-    file = Path(file)
-    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
-    try:  # url1
-        LOGGER.info(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
-        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
-    except Exception as e:  # url2
-        if file.exists():
-            file.unlink()  # remove partial downloads
-        LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
-        os.system(f"curl -# -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
-    finally:
-        if not file.exists() or file.stat().st_size < min_bytes:  # check
-            if file.exists():
-                file.unlink()  # remove partial downloads
-            LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
-        LOGGER.info('')
-
-
-def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'):
-    # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc.
-    from utils.general import LOGGER
-
-    def github_assets(repository, version='latest'):
-        # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
-        if version != 'latest':
-            version = f'tags/{version}'  # i.e. tags/v7.0
-        response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api
-        return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets
-
-    file = Path(str(file).strip().replace("'", ''))
-    if not file.exists():
-        # URL specified
-        name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
-        if str(file).startswith(('http:/', 'https:/')):  # download
-            url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/
-            file = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
-            if Path(file).is_file():
-                LOGGER.info(f'Found {url} locally at {file}')  # file already exists
-            else:
-                safe_download(file=file, url=url, min_bytes=1E5)
-            return file
-
-        # GitHub assets
-        assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
-        try:
-            tag, assets = github_assets(repo, release)
-        except Exception:
-            try:
-                tag, assets = github_assets(repo)  # latest release
-            except Exception:
-                try:
-                    tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
-                except Exception:
-                    tag = release
-
-        file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
-        if name in assets:
-            url3 = 'https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl'  # backup gdrive mirror
-            safe_download(
-                file,
-                url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
-                min_bytes=1E5,
-                error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
-
-    return str(file)
diff --git a/spaces/annchen2010/ChatGPT/chat_func.py b/spaces/annchen2010/ChatGPT/chat_func.py
deleted file mode 100644
index 374178f3d22c5c23d1dc2952336cdc298a77315d..0000000000000000000000000000000000000000
--- a/spaces/annchen2010/ChatGPT/chat_func.py
+++ /dev/null
@@ -1,456 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import os
-import requests
-import urllib3
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-
-from presets import *
-from llama_func import *
-from utils import *
-
-# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class DataframeData(TypedDict):
-        headers: List[str]
-        data: List[List[str | int | bool]]
-
-
-initial_prompt = "You are a helpful assistant."
-API_URL = "https://api.openai.com/v1/chat/completions"
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-def get_response(
-    openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model
-):
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {openai_api_key}",
-    }
-
-    history = [construct_system(system_prompt), *history]
-
-    payload = {
-        "model": selected_model,
-        "messages": history,  # [{"role": "user", "content": f"{inputs}"}],
-        "temperature": temperature,  # 1.0,
-        "top_p": top_p,  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    if stream:
-        timeout = timeout_streaming
-    else:
-        timeout = timeout_all
-
-    # 获取环境变量中的代理设置
-    http_proxy = os.environ.get("HTTP_PROXY") or os.environ.get("http_proxy")
-    https_proxy = os.environ.get("HTTPS_PROXY") or os.environ.get("https_proxy")
-
-    # 如果存在代理设置，使用它们
-    proxies = {}
-    if http_proxy:
-        logging.info(f"Using HTTP proxy: {http_proxy}")
-        proxies["http"] = http_proxy
-    if https_proxy:
-        logging.info(f"Using HTTPS proxy: {https_proxy}")
-        proxies["https"] = https_proxy
-
-    # 如果有代理，使用代理发送请求，否则使用默认设置发送请求
-    if proxies:
-        response = requests.post(
-            API_URL,
-            headers=headers,
-            json=payload,
-            stream=True,
-            timeout=timeout,
-            proxies=proxies,
-        )
-    else:
-        response = requests.post(
-            API_URL,
-            headers=headers,
-            json=payload,
-            stream=True,
-            timeout=timeout,
-        )
-    return response
-
-
-def stream_predict(
-    openai_api_key,
-    system_prompt,
-    history,
-    inputs,
-    chatbot,
-    all_token_counts,
-    top_p,
-    temperature,
-    selected_model,
-    fake_input=None,
-    display_append=""
-):
-    def get_return_value():
-        return chatbot, history, status_text, all_token_counts
-
-    logging.info("实时回答模式")
-    partial_words = ""
-    counter = 0
-    status_text = "开始实时传输回答……"
-    history.append(construct_user(inputs))
-    history.append(construct_assistant(""))
-    if fake_input:
-        chatbot.append((fake_input, ""))
-    else:
-        chatbot.append((inputs, ""))
-    user_token_count = 0
-    if len(all_token_counts) == 0:
-        system_prompt_token_count = count_token(construct_system(system_prompt))
-        user_token_count = (
-            count_token(construct_user(inputs)) + system_prompt_token_count
-        )
-    else:
-        user_token_count = count_token(construct_user(inputs))
-    all_token_counts.append(user_token_count)
-    logging.info(f"输入token计数: {user_token_count}")
-    yield get_return_value()
-    try:
-        response = get_response(
-            openai_api_key,
-            system_prompt,
-            history,
-            temperature,
-            top_p,
-            True,
-            selected_model,
-        )
-    except requests.exceptions.ConnectTimeout:
-        status_text = (
-            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
-        )
-        yield get_return_value()
-        return
-    except requests.exceptions.ReadTimeout:
-        status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
-        yield get_return_value()
-        return
-
-    yield get_return_value()
-    error_json_str = ""
-
-    for chunk in tqdm(response.iter_lines()):
-        if counter == 0:
-            counter += 1
-            continue
-        counter += 1
-        # check whether each line is non-empty
-        if chunk:
-            chunk = chunk.decode()
-            chunklength = len(chunk)
-            try:
-                chunk = json.loads(chunk[6:])
-            except json.JSONDecodeError:
-                logging.info(chunk)
-                error_json_str += chunk
-                status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
-                yield get_return_value()
-                continue
-            # decode each line as response data is in bytes
-            if chunklength > 6 and "delta" in chunk["choices"][0]:
-                finish_reason = chunk["choices"][0]["finish_reason"]
-                status_text = construct_token_message(
-                    sum(all_token_counts), stream=True
-                )
-                if finish_reason == "stop":
-                    yield get_return_value()
-                    break
-                try:
-                    partial_words = (
-                        partial_words + chunk["choices"][0]["delta"]["content"]
-                    )
-                except KeyError:
-                    status_text = (
-                        standard_error_msg
-                        + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: "
-                        + str(sum(all_token_counts))
-                    )
-                    yield get_return_value()
-                    break
-                history[-1] = construct_assistant(partial_words)
-                chatbot[-1] = (chatbot[-1][0], partial_words+display_append)
-                all_token_counts[-1] += 1
-                yield get_return_value()
-
-
-def predict_all(
-    openai_api_key,
-    system_prompt,
-    history,
-    inputs,
-    chatbot,
-    all_token_counts,
-    top_p,
-    temperature,
-    selected_model,
-    fake_input=None,
-    display_append=""
-):
-    logging.info("一次性回答模式")
-    history.append(construct_user(inputs))
-    history.append(construct_assistant(""))
-    if fake_input:
-        chatbot.append((fake_input, ""))
-    else:
-        chatbot.append((inputs, ""))
-    all_token_counts.append(count_token(construct_user(inputs)))
-    try:
-        response = get_response(
-            openai_api_key,
-            system_prompt,
-            history,
-            temperature,
-            top_p,
-            False,
-            selected_model,
-        )
-    except requests.exceptions.ConnectTimeout:
-        status_text = (
-            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
-        )
-        return chatbot, history, status_text, all_token_counts
-    except requests.exceptions.ProxyError:
-        status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
-        return chatbot, history, status_text, all_token_counts
-    except requests.exceptions.SSLError:
-        status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt
-        return chatbot, history, status_text, all_token_counts
-    response = json.loads(response.text)
-    content = response["choices"][0]["message"]["content"]
-    history[-1] = construct_assistant(content)
-    chatbot[-1] = (chatbot[-1][0], content+display_append)
-    total_token_count = response["usage"]["total_tokens"]
-    all_token_counts[-1] = total_token_count - sum(all_token_counts)
-    status_text = construct_token_message(total_token_count)
-    return chatbot, history, status_text, all_token_counts
-
-
-def predict(
-    openai_api_key,
-    system_prompt,
-    history,
-    inputs,
-    chatbot,
-    all_token_counts,
-    top_p,
-    temperature,
-    stream=False,
-    selected_model=MODELS[0],
-    use_websearch=False,
-    files = None,
-    should_check_token_count=True,
-):  # repetition_penalty, top_k
-    logging.info("输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
-    if files:
-        msg = "构建索引中……（这可能需要比较久的时间）"
-        logging.info(msg)
-        yield chatbot, history, msg, all_token_counts
-        index = construct_index(openai_api_key, file_src=files)
-        msg = "索引构建完成，获取回答中……"
-        yield chatbot, history, msg, all_token_counts
-        history, chatbot, status_text = chat_ai(openai_api_key, index, inputs, history, chatbot)
-        yield chatbot, history, status_text, all_token_counts
-        return
-
-    old_inputs = ""
-    link_references = []
-    if use_websearch:
-        search_results = ddg(inputs, max_results=5)
-        old_inputs = inputs
-        web_results = []
-        for idx, result in enumerate(search_results):
-            logging.info(f"搜索结果{idx + 1}：{result}")
-            domain_name = urllib3.util.parse_url(result["href"]).host
-            web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}')
-            link_references.append(f"{idx+1}. [{domain_name}]({result['href']})\n")
-        link_references = "\n\n" + "".join(link_references)
-        inputs = (
-            replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
-            .replace("{query}", inputs)
-            .replace("{web_results}", "\n\n".join(web_results))
-        )
-    else:
-        link_references = ""
-
-    if len(openai_api_key) != 51:
-        status_text = standard_error_msg + no_apikey_msg
-        logging.info(status_text)
-        chatbot.append((inputs, ""))
-        if len(history) == 0:
-            history.append(construct_user(inputs))
-            history.append("")
-            all_token_counts.append(0)
-        else:
-            history[-2] = construct_user(inputs)
-        yield chatbot, history, status_text, all_token_counts
-        return
-
-    yield chatbot, history, "开始生成回答……", all_token_counts
-
-    if stream:
-        logging.info("使用流式传输")
-        iter = stream_predict(
-            openai_api_key,
-            system_prompt,
-            history,
-            inputs,
-            chatbot,
-            all_token_counts,
-            top_p,
-            temperature,
-            selected_model,
-            fake_input=old_inputs,
-            display_append=link_references
-        )
-        for chatbot, history, status_text, all_token_counts in iter:
-            yield chatbot, history, status_text, all_token_counts
-    else:
-        logging.info("不使用流式传输")
-        chatbot, history, status_text, all_token_counts = predict_all(
-            openai_api_key,
-            system_prompt,
-            history,
-            inputs,
-            chatbot,
-            all_token_counts,
-            top_p,
-            temperature,
-            selected_model,
-            fake_input=old_inputs,
-            display_append=link_references
-        )
-        yield chatbot, history, status_text, all_token_counts
-
-    logging.info(f"传输完毕。当前token计数为{all_token_counts}")
-    if len(history) > 1 and history[-1]["content"] != inputs:
-        logging.info(
-            "回答为："
-            + colorama.Fore.BLUE
-            + f"{history[-1]['content']}"
-            + colorama.Style.RESET_ALL
-        )
-
-    if stream:
-        max_token = max_token_streaming
-    else:
-        max_token = max_token_all
-
-    if sum(all_token_counts) > max_token and should_check_token_count:
-        status_text = f"精简token中{all_token_counts}/{max_token}"
-        logging.info(status_text)
-        yield chatbot, history, status_text, all_token_counts
-        iter = reduce_token_size(
-            openai_api_key,
-            system_prompt,
-            history,
-            chatbot,
-            all_token_counts,
-            top_p,
-            temperature,
-            max_token//2,
-            selected_model=selected_model,
-        )
-        for chatbot, history, status_text, all_token_counts in iter:
-            status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}"
-            yield chatbot, history, status_text, all_token_counts
-
-
-def retry(
-    openai_api_key,
-    system_prompt,
-    history,
-    chatbot,
-    token_count,
-    top_p,
-    temperature,
-    stream=False,
-    selected_model=MODELS[0],
-):
-    logging.info("重试中……")
-    if len(history) == 0:
-        yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
-        return
-    history.pop()
-    inputs = history.pop()["content"]
-    token_count.pop()
-    iter = predict(
-        openai_api_key,
-        system_prompt,
-        history,
-        inputs,
-        chatbot,
-        token_count,
-        top_p,
-        temperature,
-        stream=stream,
-        selected_model=selected_model,
-    )
-    logging.info("重试中……")
-    for x in iter:
-        yield x
-    logging.info("重试完毕")
-
-
-def reduce_token_size(
-    openai_api_key,
-    system_prompt,
-    history,
-    chatbot,
-    token_count,
-    top_p,
-    temperature,
-    max_token_count,
-    selected_model=MODELS[0],
-):
-    logging.info("开始减少token数量……")
-    iter = predict(
-        openai_api_key,
-        system_prompt,
-        history,
-        summarize_prompt,
-        chatbot,
-        token_count,
-        top_p,
-        temperature,
-        selected_model=selected_model,
-        should_check_token_count=False,
-    )
-    logging.info(f"chatbot: {chatbot}")
-    flag = False
-    for chatbot, history, status_text, previous_token_count in iter:
-        num_chat = find_n(previous_token_count, max_token_count)
-        if flag:
-            chatbot = chatbot[:-1]
-        flag = True
-        history = history[-2*num_chat:] if num_chat > 0 else []
-        token_count = previous_token_count[-num_chat:] if num_chat > 0 else []
-        msg = f"保留了最近{num_chat}轮对话"
-        yield chatbot, history, msg + "，" + construct_token_message(
-            sum(token_count) if len(token_count) > 0 else 0,
-        ), token_count
-    logging.info(msg)
-    logging.info("减少token数量完毕")
\ No newline at end of file
diff --git a/spaces/antonovmaxim/text-generation-webui-space/modules/ui.py b/spaces/antonovmaxim/text-generation-webui-space/modules/ui.py
deleted file mode 100644
index 1e9c4ab0cb4933f59318eab1d823144146d1ccc7..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/modules/ui.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from pathlib import Path
-
-import gradio as gr
-import torch
-
-from modules import shared
-
-with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f:
-    css = f.read()
-with open(Path(__file__).resolve().parent / '../css/chat.css', 'r') as f:
-    chat_css = f.read()
-with open(Path(__file__).resolve().parent / '../css/main.js', 'r') as f:
-    main_js = f.read()
-with open(Path(__file__).resolve().parent / '../css/chat.js', 'r') as f:
-    chat_js = f.read()
-
-refresh_symbol = '\U0001f504'  # 🔄
-theme = gr.themes.Default(
-    font=['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'],
-    font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
-).set(
-    border_color_primary='#c5c5d2',
-    button_large_padding='6px 12px',
-    body_text_color_subdued='#484848',
-    background_fill_secondary='#eaeaea'
-)
-
-
-def list_model_elements():
-    elements = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers']
-    for i in range(torch.cuda.device_count()):
-        elements.append(f'gpu_memory_{i}')
-    return elements
-
-
-def list_interface_input_elements(chat=False):
-    elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu', 'stream']
-    if chat:
-        elements += ['name1', 'name2', 'greeting', 'context', 'chat_prompt_size', 'chat_generation_attempts', 'stop_at_newline', 'mode', 'instruction_template', 'character_menu', 'name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template', 'chat_style', 'chat-instruct_command']
-
-    elements += list_model_elements()
-    return elements
-
-
-def gather_interface_values(*args):
-    output = {}
-    for i, element in enumerate(shared.input_elements):
-        output[element] = args[i]
-
-    shared.persistent_interface_state = output
-    return output
-
-
-def apply_interface_values(state, use_persistent=False):
-    if use_persistent:
-        state = shared.persistent_interface_state
-
-    elements = list_interface_input_elements(chat=shared.is_chat())
-    if len(state) == 0:
-        return [gr.update() for k in elements]  # Dummy, do nothing
-    else:
-        return [state[k] if k in state else gr.update() for k in elements]
-
-
-class ToolButton(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, fits inside gradio forms"""
-
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool", **kwargs)
-
-    def get_block_name(self):
-        return "button"
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/server/__init__.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/server/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/radam.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/radam.py
deleted file mode 100644
index cbd14990f33cb671f030e401a3a2f9b96c2710cd..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/radam.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# modified from https://github.com/LiyuanLucasLiu/RAdam
-
-import math
-
-import torch
-from torch.optim.optimizer import Optimizer
-
-
-class RAdam(Optimizer):
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
-        if lr < 0.0:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if eps < 0.0:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-
-        self.degenerated_to_sgd = degenerated_to_sgd
-        if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
-            for param in params:
-                if "betas" in param and (param["betas"][0] != betas[0] or param["betas"][1] != betas[1]):
-                    param["buffer"] = [[None, None, None] for _ in range(10)]
-        defaults = dict(
-            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, buffer=[[None, None, None] for _ in range(10)]
-        )
-        super().__init__(params, defaults)
-
-    def __setstate__(self, state):  # pylint: disable=useless-super-delegation
-        super().__setstate__(state)
-
-    def step(self, closure=None):
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-            for p in group["params"]:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data.float()
-                if grad.is_sparse:
-                    raise RuntimeError("RAdam does not support sparse gradients")
-
-                p_data_fp32 = p.data.float()
-
-                state = self.state[p]
-
-                if len(state) == 0:
-                    state["step"] = 0
-                    state["exp_avg"] = torch.zeros_like(p_data_fp32)
-                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
-                else:
-                    state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
-                    state["exp_avg_sq"] = state["exp_avg_sq"].type_as(p_data_fp32)
-
-                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
-                beta1, beta2 = group["betas"]
-
-                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
-
-                state["step"] += 1
-                buffered = group["buffer"][int(state["step"] % 10)]
-                if state["step"] == buffered[0]:
-                    N_sma, step_size = buffered[1], buffered[2]
-                else:
-                    buffered[0] = state["step"]
-                    beta2_t = beta2 ** state["step"]
-                    N_sma_max = 2 / (1 - beta2) - 1
-                    N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1 - beta2_t)
-                    buffered[1] = N_sma
-
-                    # more conservative since it's an approximated value
-                    if N_sma >= 5:
-                        step_size = math.sqrt(
-                            (1 - beta2_t)
-                            * (N_sma - 4)
-                            / (N_sma_max - 4)
-                            * (N_sma - 2)
-                            / N_sma
-                            * N_sma_max
-                            / (N_sma_max - 2)
-                        ) / (1 - beta1 ** state["step"])
-                    elif self.degenerated_to_sgd:
-                        step_size = 1.0 / (1 - beta1 ** state["step"])
-                    else:
-                        step_size = -1
-                    buffered[2] = step_size
-
-                # more conservative since it's an approximated value
-                if N_sma >= 5:
-                    if group["weight_decay"] != 0:
-                        p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"])
-                    denom = exp_avg_sq.sqrt().add_(group["eps"])
-                    p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group["lr"])
-                    p.data.copy_(p_data_fp32)
-                elif step_size > 0:
-                    if group["weight_decay"] != 0:
-                        p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"])
-                    p_data_fp32.add_(exp_avg, alpha=-step_size * group["lr"])
-                    p.data.copy_(p_data_fp32)
-
-        return loss
diff --git a/spaces/asafAdge/Detic/detic/modeling/roi_heads/detic_roi_heads.py b/spaces/asafAdge/Detic/detic/modeling/roi_heads/detic_roi_heads.py
deleted file mode 100644
index c87559359e0516443a43ed327110ec55fa4fa307..0000000000000000000000000000000000000000
--- a/spaces/asafAdge/Detic/detic/modeling/roi_heads/detic_roi_heads.py
+++ /dev/null
@@ -1,271 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import numpy as np
-import json
-import math
-import torch
-from torch import nn
-from torch.autograd.function import Function
-from typing import Dict, List, Optional, Tuple, Union
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec
-from detectron2.layers import batched_nms
-from detectron2.structures import Boxes, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference
-from detectron2.modeling.roi_heads.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads
-from detectron2.modeling.roi_heads.cascade_rcnn import CascadeROIHeads, _ScaleGradient
-from detectron2.modeling.roi_heads.box_head import build_box_head
-from .detic_fast_rcnn import DeticFastRCNNOutputLayers
-from ..debug import debug_second_stage
-
-from torch.cuda.amp import autocast
-
-@ROI_HEADS_REGISTRY.register()
-class DeticCascadeROIHeads(CascadeROIHeads):
-    @configurable
-    def __init__(
-        self,
-        *,
-        mult_proposal_score: bool = False,
-        with_image_labels: bool = False,
-        add_image_box: bool = False,
-        image_box_size: float = 1.0,
-        ws_num_props: int = 512,
-        add_feature_to_prop: bool = False,
-        mask_weight: float = 1.0,
-        one_class_per_proposal: bool = False,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.mult_proposal_score = mult_proposal_score
-        self.with_image_labels = with_image_labels
-        self.add_image_box = add_image_box
-        self.image_box_size = image_box_size
-        self.ws_num_props = ws_num_props
-        self.add_feature_to_prop = add_feature_to_prop
-        self.mask_weight = mask_weight
-        self.one_class_per_proposal = one_class_per_proposal
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        ret = super().from_config(cfg, input_shape)
-        ret.update({
-            'mult_proposal_score': cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE,
-            'with_image_labels': cfg.WITH_IMAGE_LABELS,
-            'add_image_box': cfg.MODEL.ROI_BOX_HEAD.ADD_IMAGE_BOX,
-            'image_box_size': cfg.MODEL.ROI_BOX_HEAD.IMAGE_BOX_SIZE,
-            'ws_num_props': cfg.MODEL.ROI_BOX_HEAD.WS_NUM_PROPS,
-            'add_feature_to_prop': cfg.MODEL.ROI_BOX_HEAD.ADD_FEATURE_TO_PROP,
-            'mask_weight': cfg.MODEL.ROI_HEADS.MASK_WEIGHT,
-            'one_class_per_proposal': cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL,
-        })
-        return ret
-
-
-    @classmethod
-    def _init_box_head(self, cfg, input_shape):
-        ret = super()._init_box_head(cfg, input_shape)
-        del ret['box_predictors']
-        cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS
-        box_predictors = []
-        for box_head, bbox_reg_weights in zip(ret['box_heads'], \
-            cascade_bbox_reg_weights):
-            box_predictors.append(
-                DeticFastRCNNOutputLayers(
-                    cfg, box_head.output_shape,
-                    box2box_transform=Box2BoxTransform(weights=bbox_reg_weights)
-                ))
-        ret['box_predictors'] = box_predictors
-        return ret
-
-
-    def _forward_box(self, features, proposals, targets=None, 
-        ann_type='box', classifier_info=(None,None,None)):
-        """
-        Add mult proposal scores at testing
-        Add ann_type
-        """
-        if (not self.training) and self.mult_proposal_score:
-            if len(proposals) > 0 and proposals[0].has('scores'):
-                proposal_scores = [p.get('scores') for p in proposals]
-            else:
-                proposal_scores = [p.get('objectness_logits') for p in proposals]
-        
-        features = [features[f] for f in self.box_in_features]
-        head_outputs = []  # (predictor, predictions, proposals)
-        prev_pred_boxes = None
-        image_sizes = [x.image_size for x in proposals]
-
-        for k in range(self.num_cascade_stages):
-            if k > 0:
-                proposals = self._create_proposals_from_boxes(
-                    prev_pred_boxes, image_sizes,
-                    logits=[p.objectness_logits for p in proposals])
-                if self.training and ann_type in ['box']:
-                    proposals = self._match_and_label_boxes(
-                        proposals, k, targets)
-            predictions = self._run_stage(features, proposals, k, 
-                classifier_info=classifier_info)
-            prev_pred_boxes = self.box_predictor[k].predict_boxes(
-                (predictions[0], predictions[1]), proposals)
-            head_outputs.append((self.box_predictor[k], predictions, proposals))
-        
-        if self.training:
-            losses = {}
-            storage = get_event_storage()
-            for stage, (predictor, predictions, proposals) in enumerate(head_outputs):
-                with storage.name_scope("stage{}".format(stage)):
-                    if ann_type != 'box': 
-                        stage_losses = {}
-                        if ann_type in ['image', 'caption', 'captiontag']:
-                            image_labels = [x._pos_category_ids for x in targets]
-                            weak_losses = predictor.image_label_losses(
-                                predictions, proposals, image_labels,
-                                classifier_info=classifier_info,
-                                ann_type=ann_type)
-                            stage_losses.update(weak_losses)
-                    else: # supervised
-                        stage_losses = predictor.losses(
-                            (predictions[0], predictions[1]), proposals,
-                            classifier_info=classifier_info)
-                        if self.with_image_labels:
-                            stage_losses['image_loss'] = \
-                                predictions[0].new_zeros([1])[0]
-                losses.update({k + "_stage{}".format(stage): v \
-                    for k, v in stage_losses.items()})
-            return losses
-        else:
-            # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1)
-            scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs]
-            scores = [
-                sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages)
-                for scores_per_image in zip(*scores_per_stage)
-            ]
-            if self.mult_proposal_score:
-                scores = [(s * ps[:, None]) ** 0.5 \
-                    for s, ps in zip(scores, proposal_scores)]
-            if self.one_class_per_proposal:
-                scores = [s * (s == s[:, :-1].max(dim=1)[0][:, None]).float() for s in scores]
-            predictor, predictions, proposals = head_outputs[-1]
-            boxes = predictor.predict_boxes(
-                (predictions[0], predictions[1]), proposals)
-            pred_instances, _ = fast_rcnn_inference(
-                boxes,
-                scores,
-                image_sizes,
-                predictor.test_score_thresh,
-                predictor.test_nms_thresh,
-                predictor.test_topk_per_image,
-            )
-            return pred_instances
-
-
-    def forward(self, images, features, proposals, targets=None,
-        ann_type='box', classifier_info=(None,None,None)):
-        '''
-        enable debug and image labels
-        classifier_info is shared across the batch
-        '''
-        if self.training:
-            if ann_type in ['box', 'prop', 'proptag']:
-                proposals = self.label_and_sample_proposals(
-                    proposals, targets)
-            else:
-                proposals = self.get_top_proposals(proposals)
-            
-            losses = self._forward_box(features, proposals, targets, \
-                ann_type=ann_type, classifier_info=classifier_info)
-            if ann_type == 'box' and targets[0].has('gt_masks'):
-                mask_losses = self._forward_mask(features, proposals)
-                losses.update({k: v * self.mask_weight \
-                    for k, v in mask_losses.items()})
-                losses.update(self._forward_keypoint(features, proposals))
-            else:
-                losses.update(self._get_empty_mask_loss(
-                    features, proposals,
-                    device=proposals[0].objectness_logits.device))
-            return proposals, losses
-        else:
-            pred_instances = self._forward_box(
-                features, proposals, classifier_info=classifier_info)
-            pred_instances = self.forward_with_given_boxes(features, pred_instances)
-            return pred_instances, {}
-
-
-    def get_top_proposals(self, proposals):
-        for i in range(len(proposals)):
-            proposals[i].proposal_boxes.clip(proposals[i].image_size)
-        proposals = [p[:self.ws_num_props] for p in proposals]
-        for i, p in enumerate(proposals):
-            p.proposal_boxes.tensor = p.proposal_boxes.tensor.detach()
-            if self.add_image_box:
-                proposals[i] = self._add_image_box(p)
-        return proposals
-
-
-    def _add_image_box(self, p):
-        image_box = Instances(p.image_size)
-        n = 1
-        h, w = p.image_size
-        f = self.image_box_size
-        image_box.proposal_boxes = Boxes(
-            p.proposal_boxes.tensor.new_tensor(
-                [w * (1. - f) / 2., 
-                    h * (1. - f) / 2.,
-                    w * (1. - (1. - f) / 2.), 
-                    h * (1. - (1. - f) / 2.)]
-                ).view(n, 4))
-        image_box.objectness_logits = p.objectness_logits.new_ones(n)
-        return Instances.cat([p, image_box])
-
-
-    def _get_empty_mask_loss(self, features, proposals, device):
-        if self.mask_on:
-            return {'loss_mask': torch.zeros(
-                (1, ), device=device, dtype=torch.float32)[0]}
-        else:
-            return {}
-
-
-    def _create_proposals_from_boxes(self, boxes, image_sizes, logits):
-        """
-        Add objectness_logits
-        """
-        boxes = [Boxes(b.detach()) for b in boxes]
-        proposals = []
-        for boxes_per_image, image_size, logit in zip(
-            boxes, image_sizes, logits):
-            boxes_per_image.clip(image_size)
-            if self.training:
-                inds = boxes_per_image.nonempty()
-                boxes_per_image = boxes_per_image[inds]
-                logit = logit[inds]
-            prop = Instances(image_size)
-            prop.proposal_boxes = boxes_per_image
-            prop.objectness_logits = logit
-            proposals.append(prop)
-        return proposals
-
-
-    def _run_stage(self, features, proposals, stage, \
-        classifier_info=(None,None,None)):
-        """
-        Support classifier_info and add_feature_to_prop
-        """
-        pool_boxes = [x.proposal_boxes for x in proposals]
-        box_features = self.box_pooler(features, pool_boxes)
-        box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages)
-        box_features = self.box_head[stage](box_features)
-        if self.add_feature_to_prop:
-            feats_per_image = box_features.split(
-                [len(p) for p in proposals], dim=0)
-            for feat, p in zip(feats_per_image, proposals):
-                p.feat = feat
-        return self.box_predictor[stage](
-            box_features, 
-            classifier_info=classifier_info)
diff --git a/spaces/ashhadahsan/whisperX/README.md b/spaces/ashhadahsan/whisperX/README.md
deleted file mode 100644
index 0421b3a8e23902befc582d8b6e1f040a4d0ac939..0000000000000000000000000000000000000000
--- a/spaces/ashhadahsan/whisperX/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: WhisperX
-emoji: 🔥
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/ema.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/ema.py
deleted file mode 100644
index c8c75af43565f6e140287644aaaefa97dd6e67c5..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/ema.py
+++ /dev/null
@@ -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)
diff --git a/spaces/aukaru/claude-wangy/greeting.md b/spaces/aukaru/claude-wangy/greeting.md
deleted file mode 100644
index b434731863613b2c7d3654000fc6962039a58905..0000000000000000000000000000000000000000
--- a/spaces/aukaru/claude-wangy/greeting.md
+++ /dev/null
@@ -1 +0,0 @@
-<img style="width:50%; display: block; margin: auto;" src="https://media.tenor.com/n8bzbiWouzwAAAAC/zeta-%E3%82%BC%E3%83%BC%E3%82%BF.gif"/>
\ No newline at end of file
diff --git a/spaces/awacke1/3D-Models-GLB-Animation-Gradio/files/Readme.md b/spaces/awacke1/3D-Models-GLB-Animation-Gradio/files/Readme.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/awacke1/Bird-Species-Migration-Month-Map/README.md b/spaces/awacke1/Bird-Species-Migration-Month-Map/README.md
deleted file mode 100644
index a6fa448df8273c6655dd9056d3d6412b1998a31a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bird-Species-Migration-Month-Map/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bird Species Migration Month Map
-emoji: 🏢
-colorFrom: blue
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/CSV2ClassifyVisualization/README.md b/spaces/awacke1/CSV2ClassifyVisualization/README.md
deleted file mode 100644
index e92c21280d0216fc6326c0e5bc5929b560391dbf..0000000000000000000000000000000000000000
--- a/spaces/awacke1/CSV2ClassifyVisualization/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 🗒️NLP CSV Classify Sentiment❤️
-emoji: 🗒️❤️
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/awacke1/DigitalCity/README.md b/spaces/awacke1/DigitalCity/README.md
deleted file mode 100644
index 316aa484e7cd33b48d9da111eaeab5402a4811a2..0000000000000000000000000000000000000000
--- a/spaces/awacke1/DigitalCity/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: DigitalCity
-emoji: 💻
-colorFrom: purple
-colorTo: red
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/awacke1/Seq2Seq-QAGenerator/app.py b/spaces/awacke1/Seq2Seq-QAGenerator/app.py
deleted file mode 100644
index c1cd92499cf1c7d2a91b4dc226bf2d558ff67661..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Seq2Seq-QAGenerator/app.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import gradio as gr
-from qasrl_model_pipeline import QASRL_Pipeline
-
-models = ["kleinay/qanom-seq2seq-model-baseline", 
-          "kleinay/qanom-seq2seq-model-joint"]
-pipelines = {model: QASRL_Pipeline(model) for model in models}
-
-
-description = f"""Using Seq2Seq T5 model which takes a sequence of items and outputs another sequence this model generates Questions and Answers (QA) with focus on Semantic Role Labeling (SRL)""" 
-title="Seq2Seq T5 Questions and Answers (QA) with Semantic Role Labeling (SRL)"
-examples = [[models[0], "In March and April the patient <p> had two falls.  One was related to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", True, "fall"],
-            [models[1], "In March and April the patient had two falls.  One was related to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions <p> like anaphylaxis and shortness of breath.", True, "reactions"],
-            [models[0], "In March and April the patient had two falls.  One was related <p> to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", True, "relate"],
-            [models[1], "In March and April the patient <p> had two falls.  One was related to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", False, "fall"]]
-
-input_sent_box_label = "Insert sentence here. Mark the predicate by adding the token '<p>' before it."
-verb_form_inp_placeholder = "e.g. 'decide' for the nominalization 'decision', 'teach' for 'teacher', etc."
-links = """<p style='text-align: center'>
-<a href='https://www.qasrl.org' target='_blank'>QASRL Website</a>  |  <a href='https://huggingface.co/kleinay/qanom-seq2seq-model-baseline' target='_blank'>Model Repo at Huggingface Hub</a>
-</p>"""
-def call(model_name, sentence, is_nominal, verb_form):
-    predicate_marker="<p>"
-    if predicate_marker not in sentence:
-        raise ValueError("You must highlight one word of the sentence as a predicate using preceding '<p>'.")
-        
-    if not verb_form:
-        if is_nominal:
-            raise ValueError("You should provide the verbal form of the nominalization")
-            
-        toks = sentence.split(" ")
-        pred_idx = toks.index(predicate_marker)
-        predicate = toks(pred_idx+1)
-        verb_form=predicate
-    pipeline = pipelines[model_name]
-    pipe_out = pipeline([sentence], 
-                    predicate_marker=predicate_marker, 
-                    predicate_type="nominal" if is_nominal else "verbal",
-                    verb_form=verb_form)[0]
-    return pipe_out["QAs"], pipe_out["generated_text"]
-iface = gr.Interface(fn=call, 
-                     inputs=[gr.inputs.Radio(choices=models, default=models[0], label="Model"), 
-                             gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), 
-                             gr.inputs.Checkbox(default=True, label="Is Nominalization?"),
-                             gr.inputs.Textbox(placeholder=verb_form_inp_placeholder, label="Verbal form (for nominalizations)", default='')], 
-                     outputs=[gr.outputs.JSON(label="Model Output - QASRL"), gr.outputs.Textbox(label="Raw output sequence")],
-                     title=title,
-                     description=description,
-                     article=links,
-                     examples=examples )
-                     
-iface.launch()
\ No newline at end of file
diff --git a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/config.py b/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/config.py
deleted file mode 100644
index 30a0a8149d5b1bb1a8f3f2868018e62ec45eefef..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/config.py
+++ /dev/null
@@ -1,17 +0,0 @@
-yolo_config = {
-    # Basic
-    'img_size': (416, 416, 3),
-    'anchors': [12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401],
-    'strides': [8, 16, 32],
-    'xyscale': [1.2, 1.1, 1.05],
-
-    # Training
-    'iou_loss_thresh': 0.5,
-    'batch_size': 8,
-    'num_gpu': 1,  # 2,
-
-    # Inference
-    'max_boxes': 100,
-    'iou_threshold': 0.413,
-    'score_threshold': 0.3,
-}
diff --git a/spaces/awacke1/Wikipedia.Chat.Multiplayer/backup.app.py b/spaces/awacke1/Wikipedia.Chat.Multiplayer/backup.app.py
deleted file mode 100644
index 7ab6f03df431cb617dec0dc1cafbe64bc867df2d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Wikipedia.Chat.Multiplayer/backup.app.py
+++ /dev/null
@@ -1,253 +0,0 @@
-#In streamlit and python edit this example and add tracking of the option selections by adding buttons for the three choice sets for options.  Also save the values to text file and show full history after an option is recorded.  import streamlit as st
-import spacy
-import wikipediaapi
-import wikipedia
-from wikipedia.exceptions import DisambiguationError
-from transformers import TFAutoModel, AutoTokenizer
-import numpy as np
-import pandas as pd
-import faiss
-import datetime
-import time
-
-
-st.markdown("""
-Scene 1: The Enchanted Castle
-You arrive at the enchanted castle, surrounded by a forest of thorns. You have heard stories of a beautiful princess asleep within, waiting for someone to awaken her.
-Option 1: Try to make your way through the thorns.
-Option 2: Look for another way in.
-Sentiment: Feels like harsher trials after passive sleep.
----
-Scene 2: The Castle's Secrets
-If you made it past the thorns, you discover that the castle is full of hidden chambers, each containing a different trial. 
-These trials are designed to test your limits, reveal your inner most desires, and help you understand the suffering of humankind.
-Option 1: Enter the first chamber.
-Option 2: Continue exploring the castle.
-Sentiment: Comedy ending in marriage.
----
-Scene 3: The Princess's Awakening
-After navigating the castle's trials, you finally reach the chamber where the princess lies sleeping. 
-You are faced with the decision of how to awaken her, knowing that your actions will determine the nature of your relationship with her.
-Option 1: Awaken her with a gentle kiss.
-Option 2: Awaken her through a more assertive act like lifting her up.
-Sentiment: Heart forged awakening with different implications depending on context.
-""")
-
-try:
-    nlp = spacy.load("en_core_web_sm")
-except:
-    spacy.cli.download("en_core_web_sm")
-    nlp = spacy.load("en_core_web_sm")
-
-wh_words = ['what', 'who', 'how', 'when', 'which']
-
-def get_concepts(text):
-    text = text.lower()
-    doc = nlp(text)
-    concepts = []
-    for chunk in doc.noun_chunks:
-        if chunk.text not in wh_words:
-            concepts.append(chunk.text)
-    return concepts
-
-def get_passages(text, k=100):
-    doc = nlp(text)
-    passages = []
-    passage_len = 0
-    passage = ""
-    sents = list(doc.sents)
-    for i in range(len(sents)):
-        sen = sents[i]
-        passage_len += len(sen)
-        if passage_len >= k:
-            passages.append(passage)
-            passage = sen.text
-            passage_len = len(sen)
-            continue
-        elif i == (len(sents) - 1):
-            passage += " " + sen.text
-            passages.append(passage)
-            passage = ""
-            passage_len = 0
-            continue
-        passage += " " + sen.text
-    return passages
-
-def get_dicts_for_dpr(concepts, n_results=20, k=100):
-    dicts = []
-    for concept in concepts:
-        wikis = wikipedia.search(concept, results=n_results)
-        st.write(f"{concept} No of Wikis: {len(wikis)}")
-        for wiki in wikis:
-            try:
-                html_page = wikipedia.page(title=wiki, auto_suggest=False)
-            except DisambiguationError:
-                continue
-            htmlResults = html_page.content
-            passages = get_passages(htmlResults, k=k)
-            for passage in passages:
-                i_dicts = {}
-                i_dicts['text'] = passage
-                i_dicts['title'] = wiki
-                dicts.append(i_dicts)
-    return dicts
-
-passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
-query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
-p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
-q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
-
-def get_title_text_combined(passage_dicts):
-    res = []
-    for p in passage_dicts:
-        res.append(tuple((p['title'], p['text'])))
-    return res
-
-def extracted_passage_embeddings(processed_passages, max_length=156):
-    passage_inputs = p_tokenizer.batch_encode_plus(
-                    processed_passages,
-                    add_special_tokens=True,
-                    truncation=True,
-                    padding="max_length",
-                    max_length=max_length,
-                    return_token_type_ids=True
-                )
-    passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), np.array(passage_inputs['attention_mask']), 
-                                            np.array(passage_inputs['token_type_ids'])], 
-                                            batch_size=64, 
-                                            verbose=1)
-    return passage_embeddings
-
-def extracted_query_embeddings(queries, max_length=64):
-    query_inputs = q_tokenizer.batch_encode_plus(
-        queries,
-        add_special_tokens=True,
-        truncation=True,
-        padding="max_length",
-        max_length=max_length,
-        return_token_type_ids=True
-    )
-    
-    query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']),
-        np.array(query_inputs['attention_mask']),
-        np.array(query_inputs['token_type_ids'])],
-        batch_size=1,
-        verbose=1)
-    return query_embeddings
-
-def get_pagetext(page):
-    s = str(page).replace("/t","")
-    return s
-
-def get_wiki_summary(search):
-    wiki_wiki = wikipediaapi.Wikipedia('en')
-    page = wiki_wiki.page(search)                                   
-
-
-def get_wiki_summaryDF(search):
-    wiki_wiki = wikipediaapi.Wikipedia('en')
-    page = wiki_wiki.page(search)
-
-    isExist = page.exists()
-    if not isExist:
-        return isExist, "Not found", "Not found", "Not found", "Not found"
-
-    pageurl = page.fullurl
-    pagetitle = page.title
-    pagesummary = page.summary[0:60]
-    pagetext = get_pagetext(page.text)
-
-    backlinks = page.backlinks
-    linklist = ""
-    for link in backlinks.items():
-      pui = link[0]
-      linklist += pui + " ,  "
-      a=1 
-      
-    categories = page.categories
-    categorylist = ""
-    for category in categories.items():
-      pui = category[0]
-      categorylist += pui + " ,  "
-      a=1     
-    
-    links = page.links
-    linklist2 = ""
-    for link in links.items():
-      pui = link[0]
-      linklist2 += pui + " ,  "
-      a=1 
-      
-    sections = page.sections
-    
-    ex_dic = {
-      'Entity' : ["URL","Title","Summary", "Text", "Backlinks", "Links", "Categories"],
-      'Value': [pageurl, pagetitle, pagesummary, pagetext, linklist,linklist2, categorylist ]
-    }
-
-    df = pd.DataFrame(ex_dic)
-    
-    return df
-
-
-def save_message(name, message):
-    now = datetime.datetime.now()
-    timestamp = now.strftime("%Y-%m-%d %H:%M:%S")
-    with open("chat.txt", "a") as f:
-        f.write(f"{timestamp} - {name}: {message}\n")
-
-def press_release():
-    st.markdown("""🎉🎊 Breaking News! 📢📣
-Introducing StreamlitWikipediaChat - the ultimate way to chat with Wikipedia and the whole world at the same time! 🌎📚👋
-Are you tired of reading boring articles on Wikipedia? Do you want to have some fun while learning new things? Then StreamlitWikipediaChat is just the thing for you! 😃💻
-With StreamlitWikipediaChat, you can ask Wikipedia anything you want and get instant responses! Whether you want to know the capital of Madagascar or how to make a delicious chocolate cake, Wikipedia has got you covered. 🍰🌍
-But that's not all! You can also chat with other people from around the world who are using StreamlitWikipediaChat at the same time. It's like a virtual classroom where you can learn from and teach others. 🌐👨‍🏫👩‍🏫
-And the best part? StreamlitWikipediaChat is super easy to use! All you have to do is type in your question and hit send. That's it! 🤯🙌
-So, what are you waiting for? Join the fun and start chatting with Wikipedia and the world today! 😎🎉
-StreamlitWikipediaChat - where learning meets fun! 🤓🎈""")
-
-
-def main():
-    st.title("Streamlit Chat")
-
-    name = st.text_input("Enter your name")
-    message = st.text_input("Enter a topic to share from Wikipedia")
-    if st.button("Submit"):
-        
-        # wiki
-        df = get_wiki_summaryDF(message)
-        
-        save_message(name, message)
-        save_message(name, df)
-        
-        st.text("Message sent!")
-
-    
-    st.text("Chat history:")
-    with open("chat.txt", "a+") as f:
-        f.seek(0)
-        chat_history = f.read()
-    #st.text(chat_history)
-    st.markdown(chat_history)
-
-    countdown = st.empty()
-    t = 60
-    while t:
-        mins, secs = divmod(t, 60)
-        countdown.text(f"Time remaining: {mins:02d}:{secs:02d}")
-        time.sleep(1)
-        t -= 1
-        if t == 0:
-            countdown.text("Time's up!")
-            with open("chat.txt", "a+") as f:
-                f.seek(0)
-                chat_history = f.read()
-            #st.text(chat_history)
-            st.markdown(chat_history)
-
-            press_release()
-            
-            t = 60
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/textures/DataTexture.js b/spaces/banana-projects/web3d/node_modules/three/src/textures/DataTexture.js
deleted file mode 100644
index 554407555300b10ae0957c2e15ad26634225fc47..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/textures/DataTexture.js
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * @author alteredq / http://alteredqualia.com/
- */
-
-import { Texture } from './Texture.js';
-import { NearestFilter } from '../constants.js';
-
-function DataTexture( data, width, height, format, type, mapping, wrapS, wrapT, magFilter, minFilter, anisotropy, encoding ) {
-
-	Texture.call( this, null, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, encoding );
-
-	this.image = { data: data, width: width, height: height };
-
-	this.magFilter = magFilter !== undefined ? magFilter : NearestFilter;
-	this.minFilter = minFilter !== undefined ? minFilter : NearestFilter;
-
-	this.generateMipmaps = false;
-	this.flipY = false;
-	this.unpackAlignment = 1;
-
-}
-
-DataTexture.prototype = Object.create( Texture.prototype );
-DataTexture.prototype.constructor = DataTexture;
-
-DataTexture.prototype.isDataTexture = true;
-
-
-export { DataTexture };
diff --git a/spaces/bioriAsaeru/text-to-voice/Anonymous OpVote LOCOSDEL136.md b/spaces/bioriAsaeru/text-to-voice/Anonymous OpVote LOCOSDEL136.md
deleted file mode 100644
index f87a47bd5d903c30c95c3635b4bfea11219ea447..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Anonymous OpVote LOCOSDEL136.md	
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diff --git a/spaces/bioriAsaeru/text-to-voice/Driver Sharp Ar-5731 The Best Choice for Fast and Secure Printing.md b/spaces/bioriAsaeru/text-to-voice/Driver Sharp Ar-5731 The Best Choice for Fast and Secure Printing.md
deleted file mode 100644
index 9eb03642cb80324d25e986e360477f90e275171c..0000000000000000000000000000000000000000
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diff --git a/spaces/bioriAsaeru/text-to-voice/Dt07 img fix for pes 2013 skidrow download Step by step instructions to install and run.md b/spaces/bioriAsaeru/text-to-voice/Dt07 img fix for pes 2013 skidrow download Step by step instructions to install and run.md
deleted file mode 100644
index 0e4eeef40c063a793904eda5d62ff6f1a729f63d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Dt07 img fix for pes 2013 skidrow download Step by step instructions to install and run.md	
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index ef358365408abb60f5fa3e66d4aff7fe4e623c2a..0000000000000000000000000000000000000000
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diff --git a/spaces/bkhmsi/Font-To-Sketch/code/utils.py b/spaces/bkhmsi/Font-To-Sketch/code/utils.py
deleted file mode 100644
index 85abed849d4ccc2625510c966b333ee3202b03c1..0000000000000000000000000000000000000000
--- a/spaces/bkhmsi/Font-To-Sketch/code/utils.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import collections.abc
-import os
-import os.path as osp
-from torch import nn
-import kornia.augmentation as K
-import pydiffvg
-import save_svg
-import cv2
-from ttf import font_string_to_svgs, font_string_to_svgs_hb, normalize_letter_size
-import torch
-import numpy as np
-
-
-def edict_2_dict(x):
-    if isinstance(x, dict):
-        xnew = {}
-        for k in x:
-            xnew[k] = edict_2_dict(x[k])
-        return xnew
-    elif isinstance(x, list):
-        xnew = []
-        for i in range(len(x)):
-            xnew.append( edict_2_dict(x[i]))
-        return xnew
-    else:
-        return x
-
-
-def check_and_create_dir(path):
-    pathdir = osp.split(path)[0]
-    if osp.isdir(pathdir):
-        pass
-    else:
-        os.makedirs(pathdir)
-
-
-def update(d, u):
-    """https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth"""
-    for k, v in u.items():
-        if isinstance(v, collections.abc.Mapping):
-            d[k] = update(d.get(k, {}), v)
-        else:
-            d[k] = v
-    return d
-
-
-def preprocess(font, word, letter, script, level_of_cc=1):
-
-    if level_of_cc == 0:
-        target_cp = None
-    else:
-        target_cp = {"A": 120, "B": 120, "C": 100, "D": 100,
-                     "E": 120, "F": 120, "G": 120, "H": 120,
-                     "I": 35, "J": 80, "K": 100, "L": 80,
-                     "M": 100, "N": 100, "O": 100, "P": 120,
-                     "Q": 120, "R": 130, "S": 110, "T": 90,
-                     "U": 100, "V": 100, "W": 100, "X": 130,
-                     "Y": 120, "Z": 120,
-                     "a": 120, "b": 120, "c": 100, "d": 100,
-                     "e": 120, "f": 120, "g": 120, "h": 120,
-                     "i": 35, "j": 80, "k": 100, "l": 80,
-                     "m": 100, "n": 100, "o": 100, "p": 120,
-                     "q": 120, "r": 130, "s": 110, "t": 90,
-                     "u": 100, "v": 100, "w": 100, "x": 130,
-                     "y": 120, "z": 120
-                     }
-        target_cp = {k: v * level_of_cc for k, v in target_cp.items()}
-        
-    print(f"======= {font} =======")
-    font_path = f"code/data/fonts/{script}/{font}.ttf"
-
-    init_path = f"code/data/init"
-    subdivision_thresh = None
-    chars = font_string_to_svgs_hb(init_path, font_path, word, target_control=target_cp,
-                        subdivision_thresh=subdivision_thresh)
-    normalize_letter_size(init_path, font_path, word, chars)
-
-    # optimaize two adjacent letters
-    if len(letter) > 1:
-        subdivision_thresh = None
-        font_string_to_svgs_hb(init_path, font_path, letter, target_control=target_cp,
-                            subdivision_thresh=subdivision_thresh)
-        normalize_letter_size(init_path, font_path, letter, chars)
-
-    print("Done preprocess")
-
-def get_data_augs(cut_size):
-    augmentations = []
-    augmentations.append(K.RandomPerspective(distortion_scale=0.5, p=0.7))
-    augmentations.append(K.RandomCrop(size=(cut_size, cut_size), pad_if_needed=True, padding_mode='reflect', p=1.0))
-    return nn.Sequential(*augmentations)
-
-
-'''pytorch adaptation of https://github.com/google/mipnerf'''
-def learning_rate_decay(step,
-                        lr_init,
-                        lr_final,
-                        max_steps,
-                        lr_delay_steps=0,
-                        lr_delay_mult=1):
-  """Continuous learning rate decay function.
-  The returned rate is lr_init when step=0 and lr_final when step=max_steps, and
-  is log-linearly interpolated elsewhere (equivalent to exponential decay).
-  If lr_delay_steps>0 then the learning rate will be scaled by some smooth
-  function of lr_delay_mult, such that the initial learning rate is
-  lr_init*lr_delay_mult at the beginning of optimization but will be eased back
-  to the normal learning rate when steps>lr_delay_steps.
-  Args:
-    step: int, the current optimization step.
-    lr_init: float, the initial learning rate.
-    lr_final: float, the final learning rate.
-    max_steps: int, the number of steps during optimization.
-    lr_delay_steps: int, the number of steps to delay the full learning rate.
-    lr_delay_mult: float, the multiplier on the rate when delaying it.
-  Returns:
-    lr: the learning for current step 'step'.
-  """
-  if lr_delay_steps > 0:
-    # A kind of reverse cosine decay.
-    delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
-        0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1))
-  else:
-    delay_rate = 1.
-  t = np.clip(step / max_steps, 0, 1)
-  log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
-  return delay_rate * log_lerp
-
-
-
-def save_image(img, filename, gamma=1):
-    check_and_create_dir(filename)
-    imshow = img.detach().cpu()
-    pydiffvg.imwrite(imshow, filename, gamma=gamma)
-
-
-def get_letter_ids(letter, word, shape_groups):
-    for group, l in zip(shape_groups, word):
-        if l == letter:
-            return group.shape_ids
-
-
-def combine_word(word, letter, font, experiment_dir):
-    word_svg_scaled = f"./code/data/init/{font}_{word}_scaled.svg"
-    canvas_width_word, canvas_height_word, shapes_word, shape_groups_word = pydiffvg.svg_to_scene(word_svg_scaled)
-
-    letter_ids = []
-    for l in letter:
-        letter_ids += get_letter_ids(l, word, shape_groups_word)
-
-    w_min, w_max = min([torch.min(shapes_word[ids].points[:, 0]) for ids in letter_ids]), max(
-        [torch.max(shapes_word[ids].points[:, 0]) for ids in letter_ids])
-    h_min, h_max = min([torch.min(shapes_word[ids].points[:, 1]) for ids in letter_ids]), max(
-        [torch.max(shapes_word[ids].points[:, 1]) for ids in letter_ids])
-
-    c_w = (-w_min + w_max) / 2
-    c_h = (-h_min + h_max) / 2
-
-    svg_result = os.path.join(experiment_dir, "output-svg", "output.svg")
-    canvas_width, canvas_height, shapes, shape_groups = pydiffvg.svg_to_scene(svg_result)
-    
-    out_w_min, out_w_max = min([torch.min(p.points[:, 0]) for p in shapes]), max(
-        [torch.max(p.points[:, 0]) for p in shapes])
-    out_h_min, out_h_max = min([torch.min(p.points[:, 1]) for p in shapes]), max(
-        [torch.max(p.points[:, 1]) for p in shapes])
-
-    out_c_w = (-out_w_min + out_w_max) / 2
-    out_c_h = (-out_h_min + out_h_max) / 2
-
-    scale_canvas_w = (w_max - w_min) / (out_w_max - out_w_min)
-    scale_canvas_h = (h_max - h_min) / (out_h_max - out_h_min)
-
-    if scale_canvas_h > scale_canvas_w:
-        wsize = int((out_w_max - out_w_min) * scale_canvas_h)
-        scale_canvas_w = wsize / (out_w_max - out_w_min)
-        shift_w = -out_c_w * scale_canvas_w + c_w
-    else:
-        hsize = int((out_h_max - out_h_min) * scale_canvas_w)
-        scale_canvas_h = hsize / (out_h_max - out_h_min)
-        shift_h = -out_c_h * scale_canvas_h + c_h
-
-    for num, p in enumerate(shapes):
-        p.points[:, 0] = p.points[:, 0] * scale_canvas_w
-        p.points[:, 1] = p.points[:, 1] * scale_canvas_h
-        if scale_canvas_h > scale_canvas_w:
-            p.points[:, 0] = p.points[:, 0] - out_w_min * scale_canvas_w + w_min + shift_w
-            p.points[:, 1] = p.points[:, 1] - out_h_min * scale_canvas_h + h_min
-        else:
-            p.points[:, 0] = p.points[:, 0] - out_w_min * scale_canvas_w + w_min
-            p.points[:, 1] = p.points[:, 1] - out_h_min * scale_canvas_h + h_min + shift_h
-
-    for j, s in enumerate(letter_ids):
-        shapes_word[s] = shapes[j]
-
-    save_svg.save_svg(
-        f"{experiment_dir}/{font}_{word}_{letter}.svg", canvas_width, canvas_height, shapes_word,
-        shape_groups_word)
-
-    render = pydiffvg.RenderFunction.apply
-    scene_args = pydiffvg.RenderFunction.serialize_scene(canvas_width, canvas_height, shapes_word, shape_groups_word)
-    img = render(canvas_width, canvas_height, 2, 2, 0, None, *scene_args)
-    img = img[:, :, 3:4] * img[:, :, :3] + \
-               torch.ones(img.shape[0], img.shape[1], 3, device="cuda:0") * (1 - img[:, :, 3:4])
-    img = img[:, :, :3]
-    save_image(img, f"{experiment_dir}/{font}_{word}_{letter}.png")
-
-
-def create_video(num_iter, experiment_dir, video_frame_freq):
-    img_array = []
-    for ii in range(0, num_iter):
-        if ii % video_frame_freq == 0 or ii == num_iter - 1:
-            filename = os.path.join(
-                experiment_dir, "video-png", f"iter{ii:04d}.png")
-            img = cv2.imread(filename)
-            img_array.append(img)
-
-    video_name = os.path.join(
-        experiment_dir, "video.mp4")
-    check_and_create_dir(video_name)
-    out = cv2.VideoWriter(video_name, cv2.VideoWriter_fourcc(*'mp4v'), 30.0, (600, 600))
-    for iii in range(len(img_array)):
-        out.write(img_array[iii])
-    out.release()
diff --git a/spaces/brendenc/Keras-Reshape-Layers/README.md b/spaces/brendenc/Keras-Reshape-Layers/README.md
deleted file mode 100644
index 53486ee383b37f155d7468dd6044d0d0ef1ffb5b..0000000000000000000000000000000000000000
--- a/spaces/brendenc/Keras-Reshape-Layers/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Keras Reshape
-emoji: 📚
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/inference.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/inference.py
deleted file mode 100644
index 9f8a9ac9a18f9aaea87f47a92e41938b9e6859b5..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/inference.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import matplotlib.pyplot as plt
-import IPython.display as ipd
-
-import os
-import json
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-
-import commons
-import utils
-from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import text_to_sequence
-
-from scipy.io.wavfile import write
-
-
-def get_text(text, hps):
-    text_norm = text_to_sequence(text, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-
-hps = utils.get_hparams_from_file("./configs/yuzu.json")
-
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model).cuda()
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("pretrained_models/yuzu.pth", net_g, None)
\ No newline at end of file
diff --git a/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/clap/training/params.py b/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/clap/training/params.py
deleted file mode 100644
index 0cc1a0e2d982e900988cf5a4b24b2e59b093537b..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/clap/training/params.py
+++ /dev/null
@@ -1,563 +0,0 @@
-import argparse
-
-
-def get_default_params(model_name):
-    # Params from paper (https://arxiv.org/pdf/2103.00020.pdf)
-    model_name = model_name.lower()
-    if "vit" in model_name:
-        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.98, "eps": 1.0e-6}
-    else:
-        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.999, "eps": 1.0e-8}
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--train-data",
-        type=str,
-        default=None,
-        help="Path to h5 filewith training data",
-    )
-    parser.add_argument(
-        "--val-data",
-        type=str,
-        default=None,
-        help="Path to h5 file with validation data",
-    )
-    parser.add_argument(
-        "--freeze-text",
-        default=False,
-        action="store_true",
-        help="if you need to freeze the text encoder, make this True",
-    )
-    parser.add_argument(
-        "--freeze-text-after",
-        type=int,
-        default=-1,
-        help="if you need to freeze the text encoder after (include) epoch x, set this param to x. Set -1 to disable it",
-    )
-    parser.add_argument(
-        "--train-ipc",
-        type=str,
-        default=None,
-        help="Path to npy file of the number of instance per class in training data",
-    )
-    parser.add_argument(
-        "--val-ipc",
-        type=str,
-        default=None,
-        help="Path to npy file of the number of instance per class in validation data",
-    )
-    parser.add_argument(
-        "--train-num-samples",
-        type=int,
-        default=None,
-        help="Number of samples in dataset. Required for webdataset if not available in info file.",
-    )
-    parser.add_argument(
-        "--val-num-samples",
-        type=int,
-        default=None,
-        help="Number of samples in dataset. Useful for webdataset if not available in info file.",
-    )
-    parser.add_argument(
-        "--dataset-type",
-        choices=["webdataset", "csv", "auto", "toy"],
-        default="auto",
-        help="Which type of dataset to process.",
-    )
-    parser.add_argument(
-        "--csv-separator",
-        type=str,
-        default="\t",
-        help="For csv-like datasets, which separator to use.",
-    )
-    parser.add_argument(
-        "--csv-img-key",
-        type=str,
-        default="filepath",
-        help="For csv-like datasets, the name of the key for the image paths.",
-    )
-    parser.add_argument(
-        "--csv-caption-key",
-        type=str,
-        default="title",
-        help="For csv-like datasets, the name of the key for the captions.",
-    )
-    parser.add_argument(
-        "--imagenet-val",
-        type=str,
-        default=None,
-        help="Path to imagenet val set for conducting zero shot evaluation.",
-    )
-    parser.add_argument(
-        "--imagenet-v2",
-        type=str,
-        default=None,
-        help="Path to imagenet v2 for conducting zero shot evaluation.",
-    )
-    parser.add_argument(
-        "--datasetnames",
-        nargs="+",
-        default=None,
-        help="If loading webdataset, spedify the dataset names to load. Can be some of these: Clotho, audioset, audiocaps, BBCSoundEffects",
-    )
-    parser.add_argument(
-        "--full-train-dataset",
-        nargs="+",
-        default=None,
-        help="Which dataset will be trained with all the subsets. (train+test)",
-    )
-    parser.add_argument(
-        "--exclude-eval-dataset",
-        nargs="+",
-        default=None,
-        help="Which dataset will be excluded with evaluation",
-    )
-    parser.add_argument(
-        "--datasetinfos",
-        nargs="+",
-        default=None,
-        help="If loading webdataset, spedify the dataset types to load. Can be some of these: train, test, valid, unbalanced_train, balanced_train, eval",
-    )
-    parser.add_argument(
-        "--dataset-proportion",
-        type=float,
-        default=1.0,
-        help="How much proportion of dataset we want to train.",
-    )
-    parser.add_argument(
-        "--remotedata",
-        default=False,
-        action="store_true",
-        help="if the dataset is remote, set this flag",
-    )
-    parser.add_argument(
-        "--class-label-path",
-        type=str,
-        default=None,
-        help="The path of the class label pickle or csv.",
-    )
-    parser.add_argument(
-        "--datasetpath",
-        type=str,
-        default="/mnt/audio_clip/webdataset_tar",
-        help="The path to the dataset",
-    )
-    parser.add_argument(
-        "--logs",
-        type=str,
-        default="./logs/",
-        help="Where to store tensorboard logs. Use None to avoid storing logs.",
-    )
-    parser.add_argument(
-        "--log-local",
-        action="store_true",
-        default=False,
-        help="log files on local master, otherwise global master only.",
-    )
-    parser.add_argument(
-        "--name",
-        type=str,
-        default=None,
-        help="Optional identifier for the experiment when storing logs. Otherwise use current time.",
-    )
-    parser.add_argument(
-        "--workers", type=int, default=1, help="Number of workers per GPU."
-    )
-    parser.add_argument(
-        "--batch-size", type=int, default=64, help="Batch size per GPU."
-    )
-    parser.add_argument(
-        "--epochs", type=int, default=32, help="Number of epochs to train for."
-    )
-    parser.add_argument("--lr", type=float, default=None, help="Learning rate.")
-    parser.add_argument("--beta1", type=float, default=None, help="Adam beta 1.")
-    parser.add_argument("--beta2", type=float, default=None, help="Adam beta 2.")
-    parser.add_argument("--eps", type=float, default=None, help="Adam epsilon.")
-    parser.add_argument("--momentum", type=float, default=None, help="SGD epsilon.")
-    parser.add_argument("--wd", type=float, default=0.2, help="Weight decay.")
-
-    parser.add_argument(
-        "--split-opt",
-        action="store_true",
-        default=False,
-        help="Use this flag to skip the learning rate decay.",
-    )
-    parser.add_argument(
-        "--lr-pretrained", type=float, default=None, help="Learning rate for text."
-    )
-    parser.add_argument(
-        "--beta1-pretrained", type=float, default=None, help="Adam beta 1 for text."
-    )
-    parser.add_argument(
-        "--beta2-pretrained", type=float, default=None, help="Adam beta 2 for text."
-    )
-    parser.add_argument(
-        "--eps-pretrained", type=float, default=None, help="Adam epsilon for text."
-    )
-    parser.add_argument(
-        "--wd-pretrained", type=float, default=0.2, help="Weight decay for text."
-    )
-    parser.add_argument(
-        "--momentum-pretrained", type=float, default=0.9, help="Momentum for text."
-    )
-    parser.add_argument(
-        "--lr-new", type=float, default=None, help="Learning rate for audio."
-    )
-    parser.add_argument(
-        "--beta1-new", type=float, default=None, help="Adam beta 1 for audio."
-    )
-    parser.add_argument(
-        "--beta2-new", type=float, default=None, help="Adam beta 2 for audio."
-    )
-    parser.add_argument(
-        "--eps-new", type=float, default=None, help="Adam epsilon for audio."
-    )
-    parser.add_argument(
-        "--wd-new", type=float, default=0.2, help="Weight decay for audio."
-    )
-    parser.add_argument(
-        "--momentum-new", type=float, default=0.9, help="Momentum for audio."
-    )
-    parser.add_argument(
-        "--warmup", type=int, default=10000, help="Number of steps to warmup for."
-    )
-    parser.add_argument(
-        "--use-bn-sync",
-        default=False,
-        action="store_true",
-        help="Whether to use batch norm sync.",
-    )
-    parser.add_argument(
-        "--skip-scheduler",
-        action="store_true",
-        default=False,
-        help="Use this flag to skip the learning rate decay.",
-    )
-    parser.add_argument(
-        "--save-frequency", type=int, default=1, help="How often to save checkpoints."
-    )
-    parser.add_argument(
-        "--save-top-performance",
-        type=int,
-        default=0,
-        help="Save the top x performance weights if the value >0",
-    )
-    parser.add_argument(
-        "--save-most-recent",
-        action="store_true",
-        default=False,
-        help="Always save the most recent model trained to epoch_latest.pt.",
-    )
-    parser.add_argument(
-        "--zeroshot-frequency", type=int, default=2, help="How often to run zero shot."
-    )
-    parser.add_argument(
-        "--val-frequency",
-        type=int,
-        default=1,
-        help="How often to run evaluation with val data.",
-    )
-    parser.add_argument(
-        "--resume",
-        default=None,
-        type=str,
-        help="path to latest checkpoint (default: none)",
-    )
-    parser.add_argument(
-        "--precision",
-        choices=["amp", "fp16", "fp32"],
-        default="amp",
-        help="Floating point precision.",
-    )
-    parser.add_argument(
-        "--amodel",
-        type=str,
-        default="RN50",
-        help="Name of the audio backbone to use.",
-    )
-    parser.add_argument(
-        "--tmodel",
-        type=str,
-        default="transformer",
-        help="Name of the text backbone to use. Can be [transformer, bert, roberta, bart]",
-    )
-    parser.add_argument(
-        "--pretrained-audio",
-        default="",
-        type=str,
-        help="Use a pretrained audio model weights for the audio encoder of CLAP",
-    )
-    parser.add_argument(
-        "--pretrained-text",
-        default="",
-        type=str,
-        help="Use a pretrained text model weights for the text encoder of CLAP",
-    )
-    parser.add_argument(
-        "--pretrained",
-        default="",
-        type=str,
-        help="Use a pretrained CLIP model weights with the specified tag or file path.",
-    )
-    parser.add_argument(
-        "--pretrained-image",
-        default=False,
-        action="store_true",
-        help="Load imagenet pretrained weights for image tower backbone if available.",
-    )
-    parser.add_argument(
-        "--lock-image",
-        default=False,
-        action="store_true",
-        help="Lock full image tower by disabling gradients.",
-    )
-    parser.add_argument(
-        "--lock-image-unlocked-groups",
-        type=int,
-        default=0,
-        help="Leave last n image tower layer groups unlocked.",
-    )
-    parser.add_argument(
-        "--lock-image-freeze-bn-stats",
-        default=False,
-        action="store_true",
-        help="Freeze BatchNorm running stats in image tower for any locked layers.",
-    )
-    parser.add_argument(
-        "--local-loss",
-        default=False,
-        action="store_true",
-        help="calculate loss w/ local features @ global (instead of realizing full global @ global matrix)",
-    )
-    parser.add_argument(
-        "--gather-with-grad",
-        default=False,
-        action="store_true",
-        help="enable full distributed gradient for feature gather",
-    )
-    parser.add_argument(
-        "--force-quick-gelu",
-        default=False,
-        action="store_true",
-        help="Force use of QuickGELU activation for non-OpenAI transformer models.",
-    )
-    parser.add_argument(
-        "--torchscript",
-        default=False,
-        action="store_true",
-        help="torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'",
-    )
-    parser.add_argument(
-        "--trace",
-        default=False,
-        action="store_true",
-        help="torch.jit.trace the model for inference / eval only",
-    )
-    # arguments for distributed training
-    parser.add_argument(
-        "--dist-url",
-        default="env://",
-        type=str,
-        help="url used to set up distributed training",
-    )
-    parser.add_argument(
-        "--dist-backend", default="nccl", type=str, help="distributed backend"
-    )
-    parser.add_argument(
-        "--report-to",
-        default="",
-        type=str,
-        help="Options are ['wandb', 'tensorboard', 'wandb,tensorboard']",
-    )
-    parser.add_argument(
-        "--wandb-notes", default="", type=str, help="Notes if logging with wandb"
-    )
-    parser.add_argument(
-        "--C", type=float, default=3.16, help="inverse regularizer for logistic reg."
-    )
-    parser.add_argument(
-        "--debug",
-        default=False,
-        action="store_true",
-        help="If true, more information is logged.",
-    )
-    parser.add_argument(
-        "--copy-codebase",
-        default=False,
-        action="store_true",
-        help="If true, we copy the entire base on the log diretory, and execute from there.",
-    )
-    parser.add_argument(
-        "--horovod",
-        default=False,
-        action="store_true",
-        help="Use horovod for distributed training.",
-    )
-    parser.add_argument(
-        "--ddp-static-graph",
-        default=False,
-        action="store_true",
-        help="Enable static graph optimization for DDP in PyTorch >= 1.11.",
-    )
-    parser.add_argument(
-        "--no-set-device-rank",
-        default=False,
-        action="store_true",
-        help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).",
-    )
-    parser.add_argument("--seed", type=int, default=4242, help="Default random seed.")
-
-    parser.add_argument(
-        "--top-k-checkpoint-select-dataset",
-        type=str,
-        default="all",
-        help="The dataset of selecting top-k checkpoint.",
-    )
-
-    # @R10, @R@5, @R1, mAP@10
-    parser.add_argument(
-        "--top-k-checkpoint-select-metric",
-        type=str,
-        default="_R@10",
-        help="The metric for selecting top-k checkpoint.",
-    )
-    parser.add_argument(
-        "--openai-model-cache-dir",
-        type=str,
-        default="~/.cache/clip",
-        help="Directory to download OpenAI models.",
-    )
-    parser.add_argument(
-        "--optimizer",
-        type=str,
-        default="adamw",
-        help="can be AdamW or SGD",
-    )
-    parser.add_argument(
-        "--parallel-eval",
-        default=False,
-        action="store_true",
-        help="Eval in parallel (multi-GPU, multi-node).",
-    )
-
-    parser.add_argument(
-        "--no-eval",
-        default=False,
-        action="store_true",
-        help="Training without evaluation.",
-    )
-
-    parser.add_argument(
-        "--lp-mlp",
-        default=False,
-        action="store_true",
-        help="Linear Probe using MLP layer or not.",
-    )
-
-    parser.add_argument(
-        "--lp-freeze",
-        default=False,
-        action="store_true",
-        help="Linear Probe using Freeze CLAP or not",
-    )
-
-    parser.add_argument(
-        "--lp-act",
-        default="None",
-        type=str,
-        help="Options are ['relu','elu','prelu','softmax','sigmoid']",
-    )
-
-    parser.add_argument(
-        "--lp-loss", type=str, default="bce", help="Loss func of Linear Probe."
-    )
-
-    parser.add_argument(
-        "--lp-metrics",
-        type=str,
-        default="map,mauc,acc",
-        help="Metrics of Linear Probe.",
-    )
-
-    parser.add_argument(
-        "--lp-lr", type=float, default=1e-4, help="learning rate of linear probe"
-    )
-    parser.add_argument(
-        "--kappa",
-        type=float,
-        default=0,
-        help="the kappa in the weighted contrastive loss, default is to turn off the weighted contrastive loss",
-    )
-
-    parser.add_argument(
-        "--data-filling",
-        type=str,
-        default="pad",
-        help="type of data filling when the audio length is shorter than the max length."
-        "Can be one of the following: repeat, repeatpad, pad",
-    )
-    parser.add_argument(
-        "--data-truncating",
-        type=str,
-        default="rand_trunc",
-        help="type of data truncation when the audio length is longer than the max length."
-        "Can be one of the following: rand_trunc, fusion",
-    )
-
-    parser.add_argument(
-        "--clap-mlploss",
-        default=False,
-        action="store_true",
-        help="Using MLP loss for CLAP model or not",
-    )
-
-    parser.add_argument(
-        "--wandb-id",
-        type=str,
-        default=None,
-        help="the id of wandb experiment to restore.",
-    )
-
-    parser.add_argument(
-        "--sleep", type=float, default=0, help="sleep n seconds before start training"
-    )
-
-    # variable length processing
-    parser.add_argument(
-        "--enable-fusion",
-        default=False,
-        action="store_true",
-        help="Enable feature funsion for variable-length data",
-    )
-
-    parser.add_argument(
-        "--fusion-type",
-        type=str,
-        default="None",
-        help="Type is among ['channel_map', 'daf_1d','aff_1d','iaff_1d','daf_2d','aff_2d','iaff_2d']",
-    )
-
-    parser.add_argument(
-        "--mixup",
-        default=False,
-        action="store_true",
-        help="Enable mixup in finetuning training.",
-    )
-    parser.add_argument(
-        "--text-augment-selection",
-        type=str,
-        default=None,
-        help="For selecting levels of augmented text. Type is among ['all', 'augment_only', 'none']",
-    )
-
-    args = parser.parse_args()
-
-    # If some params are not passed, we use the default values based on model name.
-    default_params = get_default_params(args.amodel)
-    for name, val in default_params.items():
-        if getattr(args, name) is None:
-            setattr(args, name, val)
-
-    return args
diff --git a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/losses.py b/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/losses.py
deleted file mode 100644
index b1b263e4c205e78ffe970f622ab6ff68f36d3b17..0000000000000000000000000000000000000000
--- a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/losses.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            rl = rl.float().detach()
-            gl = gl.float()
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        dr = dr.float()
-        dg = dg.float()
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        dg = dg.float()
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-    """
-    z_p, logs_q: [b, h, t_t]
-    m_p, logs_p: [b, h, t_t]
-    """
-    z_p = z_p.float()
-    logs_q = logs_q.float()
-    m_p = m_p.float()
-    logs_p = logs_p.float()
-    z_mask = z_mask.float()
-
-    kl = logs_p - logs_q - 0.5
-    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
-    kl = torch.sum(kl * z_mask)
-    l = kl / torch.sum(z_mask)
-    return l
diff --git a/spaces/chongjie/MCC_slim/engine_mcc.py b/spaces/chongjie/MCC_slim/engine_mcc.py
deleted file mode 100644
index f9cd7fcb9035996daa1d0b7fe893f4e3bf496088..0000000000000000000000000000000000000000
--- a/spaces/chongjie/MCC_slim/engine_mcc.py
+++ /dev/null
@@ -1,587 +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.
-# --------------------------------------------------------
-# References:
-# DeiT: https://github.com/facebookresearch/deit
-# BEiT: https://github.com/microsoft/unilm/tree/master/beit
-# MAE: https://github.com/facebookresearch/mae
-# --------------------------------------------------------
-import math
-from typing import Iterable
-import os
-import matplotlib.pyplot as plt
-import random
-import torch
-import numpy as np
-import time
-import base64
-from io import BytesIO
-
-import util.misc as misc
-import util.lr_sched as lr_sched
-
-from pytorch3d.structures import Pointclouds
-from pytorch3d.vis.plotly_vis import plot_scene
-from pytorch3d.transforms import RotateAxisAngle
-from pytorch3d.io import IO
-
-
-def evaluate_points(predicted_xyz, gt_xyz, dist_thres):
-    if predicted_xyz.shape[0] == 0:
-        return 0.0, 0.0, 0.0
-    slice_size = 1000
-    precision = 0.0
-    for i in range(int(np.ceil(predicted_xyz.shape[0] / slice_size))):
-        start = slice_size * i
-        end   = slice_size * (i + 1)
-        dist = ((predicted_xyz[start:end, None] - gt_xyz[None]) ** 2.0).sum(axis=-1) ** 0.5
-        precision += ((dist < dist_thres).sum(axis=1) > 0).sum()
-    precision /= predicted_xyz.shape[0]
-
-    recall = 0.0
-    for i in range(int(np.ceil(predicted_xyz.shape[0] / slice_size))):
-        start = slice_size * i
-        end   = slice_size * (i + 1)
-        dist = ((predicted_xyz[:, None] - gt_xyz[None, start:end]) ** 2.0).sum(axis=-1) ** 0.5
-        recall += ((dist < dist_thres).sum(axis=0) > 0).sum()
-    recall /= gt_xyz.shape[0]
-    return precision, recall, get_f1(precision, recall)
-
-def aug_xyz(seen_xyz, unseen_xyz, args, is_train):
-    degree_x = 0
-    degree_y = 0
-    degree_z = 0
-    if is_train:
-        r_delta = args.random_scale_delta
-        scale = torch.tensor([
-            random.uniform(1.0 - r_delta, 1.0 + r_delta),
-            random.uniform(1.0 - r_delta, 1.0 + r_delta),
-            random.uniform(1.0 - r_delta, 1.0 + r_delta),
-        ], device=seen_xyz.device)
-
-        if args.use_hypersim:
-            shift = 0
-        else:
-            degree_x = random.randrange(-args.random_rotate_degree, args.random_rotate_degree + 1)
-            degree_y = random.randrange(-args.random_rotate_degree, args.random_rotate_degree + 1)
-            degree_z = random.randrange(-args.random_rotate_degree, args.random_rotate_degree + 1)
-
-            r_shift = args.random_shift
-            shift = torch.tensor([[[
-                random.uniform(-r_shift, r_shift),
-                random.uniform(-r_shift, r_shift),
-                random.uniform(-r_shift, r_shift),
-            ]]], device=seen_xyz.device)
-        seen_xyz = seen_xyz * scale + shift
-        unseen_xyz = unseen_xyz * scale + shift
-
-    B, H, W, _ = seen_xyz.shape
-    return [
-        rotate(seen_xyz.reshape((B, -1, 3)), degree_x, degree_y, degree_z).reshape((B, H, W, 3)),
-        rotate(unseen_xyz, degree_x, degree_y, degree_z),
-    ]
-
-
-def rotate(sample, degree_x, degree_y, degree_z):
-    for degree, axis in [(degree_x, "X"), (degree_y, "Y"), (degree_z, "Z")]:
-        if degree != 0:
-            sample = RotateAxisAngle(degree, axis=axis).to(sample.device).transform_points(sample)
-    return sample
-
-
-def get_grid(B, device, co3d_world_size, granularity):
-    N = int(np.ceil(2 * co3d_world_size / granularity))
-    grid_unseen_xyz = torch.zeros((N, N, N, 3), device=device)
-    for i in range(N):
-        grid_unseen_xyz[i, :, :, 0] = i
-    for j in range(N):
-        grid_unseen_xyz[:, j, :, 1] = j
-    for k in range(N):
-        grid_unseen_xyz[:, :, k, 2] = k
-    grid_unseen_xyz -= (N / 2.0)
-    grid_unseen_xyz /= (N / 2.0) / co3d_world_size
-    grid_unseen_xyz = grid_unseen_xyz.reshape((1, -1, 3)).repeat(B, 1, 1)
-    return grid_unseen_xyz
-
-
-def run_viz(model, data_loader, device, args, epoch):
-    epoch_start_time = time.time()
-    model.eval()
-    os.system(f'mkdir {args.job_dir}/viz')
-
-    print('Visualization data_loader length:', len(data_loader))
-    dataset = data_loader.dataset
-    for sample_idx, samples in enumerate(data_loader):
-        if sample_idx >= args.max_n_viz_obj:
-            break
-        seen_xyz, valid_seen_xyz, unseen_xyz, unseen_rgb, labels, seen_images = prepare_data(samples, device, is_train=False, args=args, is_viz=True)
-
-        pred_occupy = []
-        pred_colors = []
-        (model.module if hasattr(model, "module") else model).clear_cache()
-
-        # don't forward all at once to avoid oom
-        max_n_queries_fwd = 2000
-
-        total_n_passes = int(np.ceil(unseen_xyz.shape[1] / max_n_queries_fwd))
-        for p_idx in range(total_n_passes):
-            p_start = p_idx     * max_n_queries_fwd
-            p_end = (p_idx + 1) * max_n_queries_fwd
-            cur_unseen_xyz = unseen_xyz[:, p_start:p_end]
-            cur_unseen_rgb = unseen_rgb[:, p_start:p_end].zero_()
-            cur_labels = labels[:, p_start:p_end].zero_()
-
-            with torch.no_grad():
-                _, pred, = model(
-                    seen_images=seen_images,
-                    seen_xyz=seen_xyz,
-                    unseen_xyz=cur_unseen_xyz,
-                    unseen_rgb=cur_unseen_rgb,
-                    unseen_occupy=cur_labels,
-                    cache_enc=args.run_viz,
-                    valid_seen_xyz=valid_seen_xyz,
-                )
-
-            cur_occupy_out = pred[..., 0]
-
-            if args.regress_color:
-                cur_color_out = pred[..., 1:].reshape((-1, 3))
-            else:
-                cur_color_out = pred[..., 1:].reshape((-1, 3, 256)).max(dim=2)[1] / 255.0
-            pred_occupy.append(cur_occupy_out)
-            pred_colors.append(cur_color_out)
-
-        rank = misc.get_rank()
-        prefix = f'{args.job_dir}/viz/' + dataset.dataset_split + f'_ep{epoch}_rank{rank}_i{sample_idx}'
-
-        img = (seen_images[0].permute(1, 2, 0) * 255).cpu().numpy().copy().astype(np.uint8)
-
-        gt_xyz = samples[1][0].to(device).reshape(-1, 3)
-        gt_rgb = samples[1][1].to(device).reshape(-1, 3)
-        mesh_xyz = samples[2].to(device).reshape(-1, 3) if args.use_hypersim else None
-
-        with open(prefix + '.html', 'a') as f:
-            generate_html(
-                img,
-                seen_xyz, seen_images,
-                torch.cat(pred_occupy, dim=1),
-                torch.cat(pred_colors, dim=0),
-                unseen_xyz,
-                f,
-                gt_xyz=gt_xyz,
-                gt_rgb=gt_rgb,
-                mesh_xyz=mesh_xyz,
-            )
-    print("Visualization epoch time:", time.time() - epoch_start_time)
-
-
-def get_f1(precision, recall):
-    if (precision + recall) == 0:
-        return 0.0
-    return 2.0 * precision * recall / (precision + recall)
-
-
-def generate_plot(img, seen_xyz, seen_rgb, pred_occ, pred_rgb, unseen_xyz,
-        gt_xyz=None, gt_rgb=None, mesh_xyz=None, score_thresholds=[0.1, 0.3, 0.5, 0.7, 0.9],
-        pointcloud_marker_size=2,
-    ):
-    # if img is not None:
-    #     fig = plt.figure()
-    #     plt.imshow(img)
-    #     tmpfile = BytesIO()
-    #     fig.savefig(tmpfile, format='jpg')
-    #     encoded = base64.b64encode(tmpfile.getvalue()).decode('utf-8')
-
-    #     html = '<img src=\'data:image/png;base64,{}\'>'.format(encoded)
-    #     f.write(html)
-    #     plt.close()
-
-    clouds = {"MCC Output": {}}
-    # Seen
-    if seen_xyz is not None:
-        seen_xyz = seen_xyz.reshape((-1, 3)).cpu()
-        seen_rgb = torch.nn.functional.interpolate(seen_rgb, (112, 112)).permute(0, 2, 3, 1).reshape((-1, 3)).cpu()
-        good_seen = seen_xyz[:, 0] != -100
-
-        seen_pc = Pointclouds(
-            points=seen_xyz[good_seen][None],
-            features=seen_rgb[good_seen][None],
-        )
-        clouds["MCC Output"]["seen"] = seen_pc
-
-    # GT points
-    if gt_xyz is not None:
-        subset_gt = random.sample(range(gt_xyz.shape[0]), 10000)
-        gt_pc = Pointclouds(
-            points=gt_xyz[subset_gt][None],
-            features=gt_rgb[subset_gt][None],
-        )
-        clouds["MCC Output"]["GT points"] = gt_pc
-
-    # GT meshes
-    if mesh_xyz is not None:
-        subset_mesh = random.sample(range(mesh_xyz.shape[0]), 10000)
-        mesh_pc = Pointclouds(
-            points=mesh_xyz[subset_mesh][None],
-        )
-        clouds["MCC Output"]["GT mesh"] = mesh_pc
-
-    pred_occ = torch.nn.Sigmoid()(pred_occ).cpu()
-    for t in score_thresholds:
-        pos = pred_occ > t
-
-        points = unseen_xyz[pos].reshape((-1, 3))
-        features = pred_rgb[None][pos].reshape((-1, 3))
-        good_points = points[:, 0] != -100
-
-        if good_points.sum() == 0:
-            continue
-
-        pc = Pointclouds(
-            points=points[good_points][None].cpu(),
-            features=features[good_points][None].cpu(),
-        )
-
-        clouds["MCC Output"][f"pred_{t}"] = pc
-        IO().save_pointcloud(pc, "output_pointcloud.ply")
-
-    plt.figure()
-    try:
-        fig = plot_scene(clouds, pointcloud_marker_size=pointcloud_marker_size, pointcloud_max_points=20000 * 2)
-        fig.update_layout(height=1000, width=1000)
-        return fig
-    except Exception as e:
-        print('writing failed', e)
-    try:
-        plt.close()
-    except:
-        pass
-
-
-def generate_html(img, seen_xyz, seen_rgb, pred_occ, pred_rgb, unseen_xyz, f,
-        gt_xyz=None, gt_rgb=None, mesh_xyz=None, score_thresholds=[0.1, 0.3, 0.5, 0.7, 0.9],
-        pointcloud_marker_size=2,
-    ):
-    if img is not None:
-        fig = plt.figure()
-        plt.imshow(img)
-        tmpfile = BytesIO()
-        fig.savefig(tmpfile, format='jpg')
-        encoded = base64.b64encode(tmpfile.getvalue()).decode('utf-8')
-
-        html = '<img src=\'data:image/png;base64,{}\'>'.format(encoded)
-        f.write(html)
-        plt.close()
-
-    clouds = {"MCC Output": {}}
-    # Seen
-    if seen_xyz is not None:
-        seen_xyz = seen_xyz.reshape((-1, 3)).cpu()
-        seen_rgb = torch.nn.functional.interpolate(seen_rgb, (112, 112)).permute(0, 2, 3, 1).reshape((-1, 3)).cpu()
-        good_seen = seen_xyz[:, 0] != -100
-
-        seen_pc = Pointclouds(
-            points=seen_xyz[good_seen][None],
-            features=seen_rgb[good_seen][None],
-        )
-        clouds["MCC Output"]["seen"] = seen_pc
-
-    # GT points
-    if gt_xyz is not None:
-        subset_gt = random.sample(range(gt_xyz.shape[0]), 10000)
-        gt_pc = Pointclouds(
-            points=gt_xyz[subset_gt][None],
-            features=gt_rgb[subset_gt][None],
-        )
-        clouds["MCC Output"]["GT points"] = gt_pc
-
-    # GT meshes
-    if mesh_xyz is not None:
-        subset_mesh = random.sample(range(mesh_xyz.shape[0]), 10000)
-        mesh_pc = Pointclouds(
-            points=mesh_xyz[subset_mesh][None],
-        )
-        clouds["MCC Output"]["GT mesh"] = mesh_pc
-
-    pred_occ = torch.nn.Sigmoid()(pred_occ).cpu()
-    for t in score_thresholds:
-        pos = pred_occ > t
-
-        points = unseen_xyz[pos].reshape((-1, 3))
-        features = pred_rgb[None][pos].reshape((-1, 3))
-        good_points = points[:, 0] != -100
-
-        if good_points.sum() == 0:
-            continue
-
-        pc = Pointclouds(
-            points=points[good_points][None].cpu(),
-            features=features[good_points][None].cpu(),
-        )
-
-        clouds["MCC Output"][f"pred_{t}"] = pc
-
-    plt.figure()
-    try:
-        fig = plot_scene(clouds, pointcloud_marker_size=pointcloud_marker_size, pointcloud_max_points=20000 * 2)
-        fig.update_layout(height=1000, width=1000)
-        html_string = fig.to_html(full_html=False, include_plotlyjs="cnd")
-        f.write(html_string)
-        return fig, plt
-    except Exception as e:
-        print('writing failed', e)
-    try:
-        plt.close()
-    except:
-        pass
-
-
-def train_one_epoch(model: torch.nn.Module,
-                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
-                    device: torch.device, epoch: int, loss_scaler,
-                    args=None):
-    epoch_start_time = time.time()
-    model.train(True)
-    metric_logger = misc.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
-
-    accum_iter = args.accum_iter
-
-    optimizer.zero_grad()
-
-    print('Training data_loader length:', len(data_loader))
-    for data_iter_step, samples in enumerate(data_loader):
-        # we use a per iteration (instead of per epoch) lr scheduler
-        if data_iter_step % accum_iter == 0:
-            lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
-        seen_xyz, valid_seen_xyz, unseen_xyz, unseen_rgb, labels, seen_images = prepare_data(samples, device, is_train=True, args=args)
-
-        with torch.cuda.amp.autocast():
-            loss, _ = model(
-                seen_images=seen_images,
-                seen_xyz=seen_xyz,
-                unseen_xyz=unseen_xyz,
-                unseen_rgb=unseen_rgb,
-                unseen_occupy=labels,
-                valid_seen_xyz=valid_seen_xyz,
-            )
-
-        loss_value = loss.item()
-        if not math.isfinite(loss_value):
-            print("Warning: Loss is {}".format(loss_value))
-            loss *= 0.0
-            loss_value = 100.0
-
-        loss /= accum_iter
-        loss_scaler(loss, optimizer, parameters=model.parameters(),
-                    clip_grad=args.clip_grad,
-                    update_grad=(data_iter_step + 1) % accum_iter == 0,
-                    verbose=(data_iter_step % 100) == 0)
-
-        if (data_iter_step + 1) % accum_iter == 0:
-            optimizer.zero_grad()
-
-        torch.cuda.synchronize()
-
-        metric_logger.update(loss=loss_value)
-
-        lr = optimizer.param_groups[0]["lr"]
-        metric_logger.update(lr=lr)
-
-        if data_iter_step == 30:
-            os.system('nvidia-smi')
-            os.system('free -g')
-        if args.debug and data_iter_step == 5:
-            break
-
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger)
-    print("Training epoch time:", time.time() - epoch_start_time)
-    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
-
-
-def eval_one_epoch(
-        model: torch.nn.Module,
-        data_loader: Iterable,
-        device: torch.device,
-        args=None
-    ):
-    epoch_start_time = time.time()
-    model.train(False)
-
-    metric_logger = misc.MetricLogger(delimiter="  ")
-
-    print('Eval len(data_loader):', len(data_loader))
-
-    for data_iter_step, samples in enumerate(data_loader):
-        seen_xyz, valid_seen_xyz, unseen_xyz, unseen_rgb, labels, seen_images = prepare_data(samples, device, is_train=False, args=args)
-
-        # don't forward all at once to avoid oom
-        max_n_queries_fwd = 5000
-        all_loss, all_preds = [], []
-        for p_idx in range(int(np.ceil(unseen_xyz.shape[1] / max_n_queries_fwd))):
-            p_start = p_idx     * max_n_queries_fwd
-            p_end = (p_idx + 1) * max_n_queries_fwd
-            cur_unseen_xyz = unseen_xyz[:, p_start:p_end]
-            cur_unseen_rgb = unseen_rgb[:, p_start:p_end]
-            cur_labels = labels[:, p_start:p_end]
-
-            with torch.no_grad():
-                loss, pred = model(
-                    seen_images=seen_images,
-                    seen_xyz=seen_xyz,
-                    unseen_xyz=cur_unseen_xyz,
-                    unseen_rgb=cur_unseen_rgb,
-                    unseen_occupy=cur_labels,
-                    valid_seen_xyz=valid_seen_xyz,
-                )
-            all_loss.append(loss)
-            all_preds.append(pred)
-
-        loss = sum(all_loss) / len(all_loss)
-        pred = torch.cat(all_preds, dim=1)
-
-        B = pred.shape[0]
-
-        gt_xyz = samples[1][0].to(device).reshape((B, -1, 3))
-        if args.use_hypersim:
-            mesh_xyz = samples[2].to(device).reshape((B, -1, 3))
-
-        s_thres = args.eval_score_threshold
-        d_thres = args.eval_dist_threshold
-
-        for b_idx in range(B):
-            geometry_metrics = {}
-            predicted_idx = torch.nn.Sigmoid()(pred[b_idx, :, 0]) > s_thres
-            predicted_xyz = unseen_xyz[b_idx, predicted_idx]
-
-            precision, recall, f1 = evaluate_points(predicted_xyz, gt_xyz[b_idx], d_thres)
-            geometry_metrics[f'd{d_thres}_s{s_thres}_point_pr'] = precision
-            geometry_metrics[f'd{d_thres}_s{s_thres}_point_rc'] = recall
-            geometry_metrics[f'd{d_thres}_s{s_thres}_point_f1'] = f1
-
-            if args.use_hypersim:
-                precision, recall, f1 = evaluate_points(predicted_xyz, mesh_xyz[b_idx], d_thres)
-                geometry_metrics[f'd{d_thres}_s{s_thres}_mesh_pr'] = precision
-                geometry_metrics[f'd{d_thres}_s{s_thres}_mesh_rc'] = recall
-                geometry_metrics[f'd{d_thres}_s{s_thres}_mesh_f1'] = f1
-
-            metric_logger.update(**geometry_metrics)
-
-        loss_value = loss.item()
-
-        torch.cuda.synchronize()
-        metric_logger.update(loss=loss_value)
-
-        if args.debug and data_iter_step == 5:
-            break
-
-    metric_logger.synchronize_between_processes()
-    print("Validation averaged stats:", metric_logger)
-    print("Val epoch time:", time.time() - epoch_start_time)
-    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
-
-
-def sample_uniform_semisphere(B, N, semisphere_size, device):
-    for _ in range(100):
-        points = torch.empty(B * N * 3, 3, device=device).uniform_(-semisphere_size, semisphere_size)
-        points[..., 2] = points[..., 2].abs()
-        dist = (points ** 2.0).sum(axis=-1) ** 0.5
-        if (dist < semisphere_size).sum() >= B * N:
-            return points[dist < semisphere_size][:B * N].reshape((B, N, 3))
-        else:
-            print('resampling sphere')
-
-
-def get_grid_semisphere(B, granularity, semisphere_size, device):
-    n_grid_pts = int(semisphere_size / granularity) * 2 + 1
-    grid_unseen_xyz = torch.zeros((n_grid_pts, n_grid_pts, n_grid_pts // 2 + 1, 3), device=device)
-    for i in range(n_grid_pts):
-        grid_unseen_xyz[i, :, :, 0] = i
-        grid_unseen_xyz[:, i, :, 1] = i
-    for i in range(n_grid_pts // 2 + 1):
-        grid_unseen_xyz[:, :, i, 2] = i
-    grid_unseen_xyz[..., :2] -= (n_grid_pts // 2.0)
-    grid_unseen_xyz *= granularity
-    dist = (grid_unseen_xyz ** 2.0).sum(axis=-1) ** 0.5
-    grid_unseen_xyz = grid_unseen_xyz[dist <= semisphere_size]
-    return grid_unseen_xyz[None].repeat(B, 1, 1)
-
-
-def get_min_dist(a, b, slice_size=1000):
-    all_min, all_idx = [], []
-    for i in range(int(np.ceil(a.shape[1] / slice_size))):
-        start = slice_size * i
-        end   = slice_size * (i + 1)
-        # B, n_queries, n_gt
-        dist = ((a[:, start:end] - b) ** 2.0).sum(axis=-1) ** 0.5
-        # B, n_queries
-        cur_min, cur_idx = dist.min(axis=2)
-        all_min.append(cur_min)
-        all_idx.append(cur_idx)
-    return torch.cat(all_min, dim=1), torch.cat(all_idx, dim=1)
-
-
-def construct_uniform_semisphere(gt_xyz, gt_rgb, semisphere_size, n_queries, dist_threshold, is_train, granularity):
-    B = gt_xyz.shape[0]
-    device = gt_xyz.device
-    if is_train:
-        unseen_xyz = sample_uniform_semisphere(B, n_queries, semisphere_size, device)
-    else:
-        unseen_xyz = get_grid_semisphere(B, granularity, semisphere_size, device)
-    dist, idx_to_gt = get_min_dist(unseen_xyz[:, :, None], gt_xyz[:, None])
-    labels = dist < dist_threshold
-    unseen_rgb = torch.zeros_like(unseen_xyz)
-    unseen_rgb[labels] = torch.gather(gt_rgb, 1, idx_to_gt.unsqueeze(-1).repeat(1, 1, 3))[labels]
-    return unseen_xyz, unseen_rgb, labels.float()
-
-
-def construct_uniform_grid(gt_xyz, gt_rgb, co3d_world_size, n_queries, dist_threshold, is_train, granularity):
-    B = gt_xyz.shape[0]
-    device = gt_xyz.device
-    if is_train:
-        unseen_xyz = torch.empty((B, n_queries, 3), device=device).uniform_(-co3d_world_size, co3d_world_size)
-    else:
-        unseen_xyz = get_grid(B, device, co3d_world_size, granularity)
-    dist, idx_to_gt = get_min_dist(unseen_xyz[:, :, None], gt_xyz[:, None])
-    labels = dist < dist_threshold
-    unseen_rgb = torch.zeros_like(unseen_xyz)
-    unseen_rgb[labels] = torch.gather(gt_rgb, 1, idx_to_gt.unsqueeze(-1).repeat(1, 1, 3))[labels]
-    return unseen_xyz, unseen_rgb, labels.float()
-
-
-def prepare_data(samples, device, is_train, args, is_viz=False):
-    # Seen
-    seen_xyz, seen_rgb = samples[0][0].to(device), samples[0][1].to(device)
-    valid_seen_xyz = torch.isfinite(seen_xyz.sum(axis=-1))
-    seen_xyz[~valid_seen_xyz] = -100
-    B = seen_xyz.shape[0]
-    # Gt
-    gt_xyz, gt_rgb = samples[1][0].to(device).reshape(B, -1, 3), samples[1][1].to(device).reshape(B, -1, 3)
-
-    sampling_func = construct_uniform_semisphere if args.use_hypersim else construct_uniform_grid
-    unseen_xyz, unseen_rgb, labels = sampling_func(
-        gt_xyz, gt_rgb,
-        args.semisphere_size if args.use_hypersim else args.co3d_world_size,
-        args.n_queries,
-        args.train_dist_threshold,
-        is_train,
-        args.viz_granularity if is_viz else args.eval_granularity,
-    )
-
-    if is_train:
-        seen_xyz, unseen_xyz = aug_xyz(seen_xyz, unseen_xyz, args, is_train=is_train)
-
-        # Random Flip
-        if random.random() < 0.5:
-            seen_xyz[..., 0] *= -1
-            unseen_xyz[..., 0] *= -1
-            seen_xyz = torch.flip(seen_xyz, [2])
-            valid_seen_xyz = torch.flip(valid_seen_xyz, [2])
-            seen_rgb = torch.flip(seen_rgb, [3])
-
-    return seen_xyz, valid_seen_xyz, unseen_xyz, unseen_rgb, labels, seen_rgb
diff --git a/spaces/chrisjay/afro-speech/app.css b/spaces/chrisjay/afro-speech/app.css
deleted file mode 100644
index 6fcc2b6d1ee451e2be66dcc423a99d7e4845ed62..0000000000000000000000000000000000000000
--- a/spaces/chrisjay/afro-speech/app.css
+++ /dev/null
@@ -1,38 +0,0 @@
-
-.infoPoint h1 {
-    font-size: 30px;
-    text-decoration: bold;
-    
-    }
-    
-a {
-    text-decoration: underline; 
-    color: #1f3b54 ;
-}
-
-.finished {
-    color:rgb(9, 102, 169);
-     font-size:13px
-}
-
-table {
-  
-    margin: 25px 0;
-    font-size: 0.9em;
-    font-family: sans-serif;
-    min-width: 400px;
-    max-width: 400px;
-    box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
-}
-
-table th,
-table td {
-    padding: 12px 15px;
-}
-
-tr {
-text-align: left;
-}
-thead tr {
-text-align: left;
-}
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/dirfs.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/dirfs.py
deleted file mode 100644
index 3e6def1f54b5b31985bd2ae12deb85c839ec890f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/dirfs.py
+++ /dev/null
@@ -1,356 +0,0 @@
-from .. import filesystem
-from ..asyn import AsyncFileSystem
-
-
-class DirFileSystem(AsyncFileSystem):
-    """Directory prefix filesystem
-
-    The DirFileSystem is a filesystem-wrapper. It assumes every path it is dealing with
-    is relative to the `path`. After performing the necessary paths operation it
-    delegates everything to the wrapped filesystem.
-    """
-
-    def __init__(
-        self,
-        path=None,
-        fs=None,
-        fo=None,
-        target_protocol=None,
-        target_options=None,
-        **storage_options,
-    ):
-        """
-        Parameters
-        ----------
-        path: str
-            Path to the directory.
-        fs: AbstractFileSystem
-            An instantiated filesystem to wrap.
-        target_protocol, target_options:
-            if fs is none, construct it from these
-        fo: str
-            Alternate for path; do not provide both
-        """
-        super().__init__(**storage_options)
-        if fs is None:
-            fs = filesystem(protocol=target_protocol, **(target_options or {}))
-        if (path is not None) ^ (fo is not None) is False:
-            raise ValueError("Provide path or fo, not both")
-        path = path or fo
-
-        if self.asynchronous and not fs.async_impl:
-            raise ValueError("can't use asynchronous with non-async fs")
-
-        if fs.async_impl and self.asynchronous != fs.asynchronous:
-            raise ValueError("both dirfs and fs should be in the same sync/async mode")
-
-        self.path = fs._strip_protocol(path)
-        self.fs = fs
-
-    def _join(self, path):
-        if isinstance(path, str):
-            if not self.path:
-                return path
-            if not path:
-                return self.path
-            return self.fs.sep.join((self.path, path))
-        return [self._join(_path) for _path in path]
-
-    def _relpath(self, path):
-        if isinstance(path, str):
-            if not self.path:
-                return path
-            if path == self.path:
-                return ""
-            prefix = self.path + self.fs.sep
-            assert path.startswith(prefix)
-            return path[len(prefix) :]
-        return [self._relpath(_path) for _path in path]
-
-    # Wrappers below
-
-    @property
-    def sep(self):
-        return self.fs.sep
-
-    async def set_session(self, *args, **kwargs):
-        return await self.fs.set_session(*args, **kwargs)
-
-    async def _rm_file(self, path, **kwargs):
-        return await self.fs._rm_file(self._join(path), **kwargs)
-
-    def rm_file(self, path, **kwargs):
-        return self.fs.rm_file(self._join(path), **kwargs)
-
-    async def _rm(self, path, *args, **kwargs):
-        return await self.fs._rm(self._join(path), *args, **kwargs)
-
-    def rm(self, path, *args, **kwargs):
-        return self.fs.rm(self._join(path), *args, **kwargs)
-
-    async def _cp_file(self, path1, path2, **kwargs):
-        return await self.fs._cp_file(self._join(path1), self._join(path2), **kwargs)
-
-    def cp_file(self, path1, path2, **kwargs):
-        return self.fs.cp_file(self._join(path1), self._join(path2), **kwargs)
-
-    async def _copy(
-        self,
-        path1,
-        path2,
-        *args,
-        **kwargs,
-    ):
-        return await self.fs._copy(
-            self._join(path1),
-            self._join(path2),
-            *args,
-            **kwargs,
-        )
-
-    def copy(self, path1, path2, *args, **kwargs):
-        return self.fs.copy(
-            self._join(path1),
-            self._join(path2),
-            *args,
-            **kwargs,
-        )
-
-    async def _pipe(self, path, *args, **kwargs):
-        return await self.fs._pipe(self._join(path), *args, **kwargs)
-
-    def pipe(self, path, *args, **kwargs):
-        return self.fs.pipe(self._join(path), *args, **kwargs)
-
-    async def _cat_file(self, path, *args, **kwargs):
-        return await self.fs._cat_file(self._join(path), *args, **kwargs)
-
-    def cat_file(self, path, *args, **kwargs):
-        return self.fs.cat_file(self._join(path), *args, **kwargs)
-
-    async def _cat(self, path, *args, **kwargs):
-        ret = await self.fs._cat(
-            self._join(path),
-            *args,
-            **kwargs,
-        )
-
-        if isinstance(ret, dict):
-            return {self._relpath(key): value for key, value in ret.items()}
-
-        return ret
-
-    def cat(self, path, *args, **kwargs):
-        ret = self.fs.cat(
-            self._join(path),
-            *args,
-            **kwargs,
-        )
-
-        if isinstance(ret, dict):
-            return {self._relpath(key): value for key, value in ret.items()}
-
-        return ret
-
-    async def _put_file(self, lpath, rpath, **kwargs):
-        return await self.fs._put_file(lpath, self._join(rpath), **kwargs)
-
-    def put_file(self, lpath, rpath, **kwargs):
-        return self.fs.put_file(lpath, self._join(rpath), **kwargs)
-
-    async def _put(
-        self,
-        lpath,
-        rpath,
-        *args,
-        **kwargs,
-    ):
-        return await self.fs._put(
-            lpath,
-            self._join(rpath),
-            *args,
-            **kwargs,
-        )
-
-    def put(self, lpath, rpath, *args, **kwargs):
-        return self.fs.put(
-            lpath,
-            self._join(rpath),
-            *args,
-            **kwargs,
-        )
-
-    async def _get_file(self, rpath, lpath, **kwargs):
-        return await self.fs._get_file(self._join(rpath), lpath, **kwargs)
-
-    def get_file(self, rpath, lpath, **kwargs):
-        return self.fs.get_file(self._join(rpath), lpath, **kwargs)
-
-    async def _get(self, rpath, *args, **kwargs):
-        return await self.fs._get(self._join(rpath), *args, **kwargs)
-
-    def get(self, rpath, *args, **kwargs):
-        return self.fs.get(self._join(rpath), *args, **kwargs)
-
-    async def _isfile(self, path):
-        return await self.fs._isfile(self._join(path))
-
-    def isfile(self, path):
-        return self.fs.isfile(self._join(path))
-
-    async def _isdir(self, path):
-        return await self.fs._isdir(self._join(path))
-
-    def isdir(self, path):
-        return self.fs.isdir(self._join(path))
-
-    async def _size(self, path):
-        return await self.fs._size(self._join(path))
-
-    def size(self, path):
-        return self.fs.size(self._join(path))
-
-    async def _exists(self, path):
-        return await self.fs._exists(self._join(path))
-
-    def exists(self, path):
-        return self.fs.exists(self._join(path))
-
-    async def _info(self, path, **kwargs):
-        return await self.fs._info(self._join(path), **kwargs)
-
-    def info(self, path, **kwargs):
-        return self.fs.info(self._join(path), **kwargs)
-
-    async def _ls(self, path, detail=True, **kwargs):
-        ret = (await self.fs._ls(self._join(path), detail=detail, **kwargs)).copy()
-        if detail:
-            out = []
-            for entry in ret:
-                entry = entry.copy()
-                entry["name"] = self._relpath(entry["name"])
-                out.append(entry)
-            return out
-
-        return self._relpath(ret)
-
-    def ls(self, path, detail=True, **kwargs):
-        ret = self.fs.ls(self._join(path), detail=detail, **kwargs).copy()
-        if detail:
-            out = []
-            for entry in ret:
-                entry = entry.copy()
-                entry["name"] = self._relpath(entry["name"])
-                out.append(entry)
-            return out
-
-        return self._relpath(ret)
-
-    async def _walk(self, path, *args, **kwargs):
-        async for root, dirs, files in self.fs._walk(self._join(path), *args, **kwargs):
-            yield self._relpath(root), dirs, files
-
-    def walk(self, path, *args, **kwargs):
-        for root, dirs, files in self.fs.walk(self._join(path), *args, **kwargs):
-            yield self._relpath(root), dirs, files
-
-    async def _glob(self, path, **kwargs):
-        detail = kwargs.get("detail", False)
-        ret = await self.fs._glob(self._join(path), **kwargs)
-        if detail:
-            return {self._relpath(path): info for path, info in ret.items()}
-        return self._relpath(ret)
-
-    def glob(self, path, **kwargs):
-        detail = kwargs.get("detail", False)
-        ret = self.fs.glob(self._join(path), **kwargs)
-        if detail:
-            return {self._relpath(path): info for path, info in ret.items()}
-        return self._relpath(ret)
-
-    async def _du(self, path, *args, **kwargs):
-        total = kwargs.get("total", True)
-        ret = await self.fs._du(self._join(path), *args, **kwargs)
-        if total:
-            return ret
-
-        return {self._relpath(path): size for path, size in ret.items()}
-
-    def du(self, path, *args, **kwargs):
-        total = kwargs.get("total", True)
-        ret = self.fs.du(self._join(path), *args, **kwargs)
-        if total:
-            return ret
-
-        return {self._relpath(path): size for path, size in ret.items()}
-
-    async def _find(self, path, *args, **kwargs):
-        detail = kwargs.get("detail", False)
-        ret = await self.fs._find(self._join(path), *args, **kwargs)
-        if detail:
-            return {self._relpath(path): info for path, info in ret.items()}
-        return self._relpath(ret)
-
-    def find(self, path, *args, **kwargs):
-        detail = kwargs.get("detail", False)
-        ret = self.fs.find(self._join(path), *args, **kwargs)
-        if detail:
-            return {self._relpath(path): info for path, info in ret.items()}
-        return self._relpath(ret)
-
-    async def _expand_path(self, path, *args, **kwargs):
-        return self._relpath(
-            await self.fs._expand_path(self._join(path), *args, **kwargs)
-        )
-
-    def expand_path(self, path, *args, **kwargs):
-        return self._relpath(self.fs.expand_path(self._join(path), *args, **kwargs))
-
-    async def _mkdir(self, path, *args, **kwargs):
-        return await self.fs._mkdir(self._join(path), *args, **kwargs)
-
-    def mkdir(self, path, *args, **kwargs):
-        return self.fs.mkdir(self._join(path), *args, **kwargs)
-
-    async def _makedirs(self, path, *args, **kwargs):
-        return await self.fs._makedirs(self._join(path), *args, **kwargs)
-
-    def makedirs(self, path, *args, **kwargs):
-        return self.fs.makedirs(self._join(path), *args, **kwargs)
-
-    def rmdir(self, path):
-        return self.fs.rmdir(self._join(path))
-
-    def mv_file(self, path1, path2, **kwargs):
-        return self.fs.mv_file(
-            self._join(path1),
-            self._join(path2),
-            **kwargs,
-        )
-
-    def touch(self, path, **kwargs):
-        return self.fs.touch(self._join(path), **kwargs)
-
-    def created(self, path):
-        return self.fs.created(self._join(path))
-
-    def modified(self, path):
-        return self.fs.modified(self._join(path))
-
-    def sign(self, path, *args, **kwargs):
-        return self.fs.sign(self._join(path), *args, **kwargs)
-
-    def __repr__(self):
-        return f"{self.__class__.__qualname__}(path='{self.path}', fs={self.fs})"
-
-    def open(
-        self,
-        path,
-        *args,
-        **kwargs,
-    ):
-        return self.fs.open(
-            self._join(path),
-            *args,
-            **kwargs,
-        )
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Form-2d54a466.js b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Form-2d54a466.js
deleted file mode 100644
index a8b06272457531f232b0a0b951dcace8fc4beea8..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Form-2d54a466.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as m,e as c,s as r,a9 as d,N as h,K as g,U as u,L as _,p as v,ab as w,ac as b,ad as F,z as q,v as y,A as S}from"./index-f877dfd5.js";function z(n){let s,l;const f=n[4].default,t=d(f,n,n[3],null);return{c(){s=h("div"),t&&t.c(),g(s,"class","form svelte-sfqy0y"),u(s,"hidden",!n[0]),_(s,"flex-grow",n[1]),_(s,"min-width",`calc(min(${n[2]}px, 100%))`)},m(e,i){v(e,s,i),t&&t.m(s,null),l=!0},p(e,[i]){t&&t.p&&(!l||i&8)&&w(t,f,e,e[3],l?F(f,e[3],i,null):b(e[3]),null),(!l||i&1)&&u(s,"hidden",!e[0]),i&2&&_(s,"flex-grow",e[1]),i&4&&_(s,"min-width",`calc(min(${e[2]}px, 100%))`)},i(e){l||(q(t,e),l=!0)},o(e){y(t,e),l=!1},d(e){e&&S(s),t&&t.d(e)}}}function A(n,s,l){let{$$slots:f={},$$scope:t}=s,{visible:e=!0}=s,{scale:i=null}=s,{min_width:o=0}=s;return n.$$set=a=>{"visible"in a&&l(0,e=a.visible),"scale"in a&&l(1,i=a.scale),"min_width"in a&&l(2,o=a.min_width),"$$scope"in a&&l(3,t=a.$$scope)},[e,i,o,t,f]}class K extends m{constructor(s){super(),c(this,s,A,z,r,{visible:0,scale:1,min_width:2})}}export{K as F};
-//# sourceMappingURL=Form-2d54a466.js.map
diff --git a/spaces/cihyFjudo/fairness-paper-search/Imprimir Cartones Bingo Binvi Pdf Aprende a imprimir y recortar estos cartones de bingo personalizados.md b/spaces/cihyFjudo/fairness-paper-search/Imprimir Cartones Bingo Binvi Pdf Aprende a imprimir y recortar estos cartones de bingo personalizados.md
deleted file mode 100644
index c2088a2ed01ab803fd485a8e800e99d19e550b98..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Imprimir Cartones Bingo Binvi Pdf Aprende a imprimir y recortar estos cartones de bingo personalizados.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-
-<p>Te ofrecemos un generador de cartones de bingo gratis para que los puedas imprimir y disfrutar de las reuniones con tus amigos jugando al bingo. Los juegos de bingo más populares son el bingo de 90 bolas y el bingo de 75 bolas. A continuación, podrás generar tus cartones de bingo en un archivo pdf imprimible para cualquiera de estos juegos de bingo. Para imprimir tus cartones sigue los siguientes pasos:</p>
-<h2>Imprimir Cartones Bingo Binvi Pdf</h2><br /><p><b><b>Download Zip</b> &#9733;&#9733;&#9733; <a href="https://tinurli.com/2uwj3Q">https://tinurli.com/2uwj3Q</a></b></p><br /><br />
-<p>Compre sus cartones en el 675 014 916 - 633 122 462 Nuestros cartones de bingo Bingol son ideales para reuniones familiares, clubs de futbol, animación de fiestas, hoteles, asociaciones de vecinos discotecas, peñas, etc. El juego consta de un bolillero, bolas numeradas (que pueden ser 75 o 90 dependiendo del tipo de bingo), una pizarra y cartones divididos en cuadros numerados. Bingo en linea Bolillero Virtual para jugar al Bingo desde casa tan solo tienes que tener los cartones puedes cantar linea girar bombo de forma automatica. Cartones de Bingo; Bingo Chat; Cómo Ganar en el Bingo; Historia del Bingo; Bonos de Bingo; Juegos de Bingo . Bingo.es lanza un generador para imprimir cartones de bingo gratis en pdf para los juegos de bingo 90 bolas y 75 bolas.</p>
-<p>No tienes que invertir nada de dinero para tener un momento divertido con tu familia o amistades. Puedes jugar al bingo online con tu móvil Android o iPhone, tablet y PC sin descargar. Puede reponer en cualquier momento los cartones y rotuladores simplemente llamando a los teléfonos 902 012 455 ó 676 253 660, o bien realizando el pedido a través de esta misma página, en la sección de Pedidos.También el juego incluye dos tarjetas postales para la reposición de cartones. A continuación, podrás generar tus cartones de bingo en pdf para cualquiera de estos juegos de bingo.</p>
-<p>26-feb-2018 - Explora el tablero de Mariuska Garcia "Cartones de bingo" en Pinterest. Te ofrecemos un generador de cartones de bingo gratis para que los puedas imprimir y disfrutar de las reuniones con tus amigos jugando al bingo.</p>
-<p>27-ago-2018 - Explora el tablero de Flaviosanchez "Bingo para imprimir" en Pinterest. España - Bingo.es ha lanzado una aplicación web gratuita muy demandada por los amantes del juego del bingo y bingo online , se trata de un generador de cartones de bingo en formato PDF para los dos juegos de bingo más populares. 15-may-2019 - Explora el tablero de Antonia Betancor "Cartones de bingo" en Pinterest. Aquí se pueden elegir los cartones y tachar los números que vayáis cantando con el dedo, o si preferís tener los cartones en la mano, los podéis imprimir aqui, ahora solo os falta un bolígrafo y ¡listo! La aplicación "Cartones de Bingo" permite usar nuestro teléfono móvil o tablet como si de un cartón de bingo virtual se tratase.</p>
-<p>Hay una única serie de 4 cartones con 15 números cada uno en 5x3 casillas, todos ellos están en la pantalla principal con un botón ON/OFF. El bingo de 80 bolas, que brinda la página 7bingo.com, es un juego que ofrece una gran promoción que permite a todos los jugadores, sean principiantes o no, obtener grandes cantidades de dinero extra en cada una de las partidas e ir acumulándolas. El juego de bingo permite seleccionar manualmente el número de las tarjetas activas. Visualizarás continuamente en el panel las bolas que han sido extraídas, y escucharás mensajes sonoros e incluso una voz (personalizable) cantando los números. Bingo Caller es un programa que cantará los números del bingo desde tu ordenador. El tradicional juego de bingo, pero ahora en dvd para que usted se divierta en familia jugando todo el tiempo Formato en DVD con 30 partidas distintas y aleatorias. Cómo jugar Bingo Abradoodle : Mejores juegos de bingo gratis en PC,Ordenador portátil,Tableta.</p>
-<p>Imprimir cartones para bingo en tres pasos El programa ofrece un generador de cartones de bingo gratis para imprimir y disfrutar de las reuniones con amigos. 90 Bolas y 12 cartones de Bingo, Juego de acción para Toda la Familia, para niños a Partir de 6 años. Cartones Bingo 80 Bolas Para Imprimir Gratis, Bingo 90 Bolas Lite incluyen 120 cartones para ser usados en forma individual o 20 series de 6 cartones.</p>
-<p></p>
-<p>La aplicación "Bingo en Casa" es un juego de bingo que sirve como bombo de bolas para las partidas de bingo en casa, en familia y con amigos. También podréis descargaros gratis los cartones de bingo para el juego de Bingo en Casa, con la aplicación asociada Cartones de Bingo. Pelotas O Bolas Para Bingo en Mercado Libre Venezuela Bingo 90 Pc Y Laptos-windows: pin. ya que cada una de las celdas que componen los cartones admite contenidos en formato texto (también eñes y tildes). Bueno, Primero, no se si estoy en el foro correcto, agradecería que se me dijese a donde dirigirme, si ese no fuese el caso. Publibingo es un sitio web gratuito que ofrece un generador de cartones de bingo en pdf para descargar, imprimir y usar en casa. Para empezar a jugar solo tienes que escoger tu apuesta encendiendo o apagando los cartones que tú desees.</p>
-<p>Aquí podrás encontrar opiniones relacionadas con imprimir cartones bingo 80 bolas y descubrirás qué opina la gente de imprimir cartones bingo 80 bolas. Este método de uso Cartones de Bingo APK funciona para todos los dispositivos Android. La versión demo del juego de bingo Rock Live te ofrece 250 tokens para jugar gratis y conocer el juego. Con este software Generador Cartones Bingo podrás diseñar, crear y editar cartones para este juego de azar con finalidad tanto doméstica como profesional. Descargar Bingo Para Pc 75 Bolas; Online bingo descargar bingo para pc 75 bolas nfr roping contestants 2019! Al ir marcando en el panel el número sorteado el sistema informa los aciertos de cada cartón y cuando alguno llega a tener premio.</p>
-<p>Bingo Card Maker es un generador de cartones de 3×3 o 5×5 celdas para bingos de palabras, números, definiciones, operaciones aritméticas, preguntas y respuestas, etc. Se pueden elegir cartones de bingo de 90 bolas y de 75 bolas, marcar, desmarcar, volver a jugar con los mismos cartones o volver a elegir. ADVERTENCIA, para juegos de bingo con 90 bolas, tendría que quitar del bombo las bolas del 81 a la 90. Descarga en PDF cartones de bingo de 90 bolas e imprímelos y podrás jugar al bingo donde quieras y cuando quieras. Cartones De Bingo 80 Para Imprimir Gratis, Generador Cartones Bingo, descargar gratis. Puedes descargar el Bingo de 90 bolas haciendo clic en este botón (actualizado 09/08/2020): Descargar Bingo Excel Gratis Nombre archivo: Bingo_Excel_Gratis.exe.</p>
-<p>Cartones para juego de bingo BINVI , 20 números por cartón , 80 series de 4 cartones por paquete. Los cartones en el bingo de 80 bolas se presentan de forma diferente a los de la variante de 75 y 90, ya que, en este caso, están repartidos en 4 filas y 4 columnas, con un total de 16 números. Rock Live es un juego de bingo de 30 bolas donde las apuestas se realizan con créditos y cuyos valores oscilan entre 500 y 25000 créditos. Descubre la mejor forma de comprar online.elonce.comCartones de bingo para imprimir en PDF Gratis! Productos patrocinados relacionados con este artículo 25 opiniones de clientes Valorar este producto Leer reseñas que mencionen Ha surgido un problema al filtrar las opiniones justo en este momento. El tradicional juego de bingo, pero ahora en dvd para que usted se divierta en familia jugando todo el tiempo, incluye 70 partidas aleatorias y 6 Locutores. Es muy fácil descarga estos documentos en pdf y envíalos a tu impresora y ya podrás jugar al bingo en tu lugar favorito.</p>
-<p>Puedes descargar el que prefieras de 6 juegos de Cartones de Bingo de 8, 16, 32, 64, 100, 200 y 600 cartones cada uno, en formato PDF e imprimir los que necesites para jugar bingo. Esta nueva aplicación permite a los usuarios imprimir cartones de bingo en varios colores: naranja, rosa, azul y verde para los juegos de bingo de 90 bolas y 75 bolas. Los hay para todos los gustos y dependiendo del territorio están más de moda unos u otros.</p>
-<p>Los juegos de bingo más populares son el bingo de 90 bolas y el bingo de 75 bolas. Cartones diseñados para tachar en el juego de Bingo mas real BINVI , podría poner fichas si no desea gastarlos. Bingo 90 Bolas Lite incluyen 120 cartones para ser usados en forma individual o 20 series de 6 cartones. Címkék: para de gratis 80 bingo imprimir cartones bolas Artículos sobre Generador Cartones Bingo. Como su nombre indica, los cartones de bingo 80 es la variante del juego que se encuentra en la mitad de las demás versiones que tienen más o menos bolas dentro del bolillero. El juego del bingo es sencillo y divertido siempre que se juegue con responsabilidad asociacion entre los numeros para jugadores posibilidades de ganar Reglas de juego Numeros . Cartones para Bingo 80 saludos colegas.les dejo este enlace para que bajen en pdf cinco archivos con cartones para bingo hasta el nmero 80.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/clarin-pl/datasets-explorer/clarin_datasets/polemo_dataset.py b/spaces/clarin-pl/datasets-explorer/clarin_datasets/polemo_dataset.py
deleted file mode 100644
index 4295f9693fe19fcc8e05dc4874322e228a239e68..0000000000000000000000000000000000000000
--- a/spaces/clarin-pl/datasets-explorer/clarin_datasets/polemo_dataset.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from datasets import load_dataset
-import pandas as pd
-import plotly.figure_factory as ff
-import plotly.graph_objects as go
-from sklearn.manifold import TSNE
-import streamlit as st
-
-from clarin_datasets.dataset_to_show import DatasetToShow
-from clarin_datasets.utils import (
-    count_num_of_characters,
-    count_num_of_words,
-    embed_sentence,
-    PLOT_COLOR_PALETTE
-)
-
-
-class PolemoDataset(DatasetToShow):
-    def __init__(self):
-        DatasetToShow.__init__(self)
-        self.dataset_name = "clarin-pl/polemo2-official"
-        self.subsets = ["train", "validation", "test"]
-        self.description = f"""
-        Dataset link: https://huggingface.co/datasets/{self.dataset_name}
-        
-        The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, 
-        hotels, products, and university. It is human-annotated on a level of full reviews and individual 
-        sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and 
-        sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,
-        046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is 
-        annotated with four labels: positive, negative, neutral, or ambiguous. """
-
-    def load_data(self):
-        raw_dataset = load_dataset(self.dataset_name)
-        self.data_dict = {
-            subset: raw_dataset[subset].to_pandas() for subset in self.subsets
-        }
-
-    def show_dataset(self):
-        header = st.container()
-        description = st.container()
-        dataframe_head = st.container()
-        word_searching = st.container()
-        dataset_statistics = st.container()
-        tsne_projection = st.container()
-
-        with header:
-            st.title(self.dataset_name)
-
-        with description:
-            st.header("Dataset description")
-            st.write(self.description)
-
-        with dataframe_head:
-            filtering_options = self.data_dict["train"]["target"].unique().tolist()
-            filtering_options.append("All classes")
-
-            st.header("First 10 observations of a chosen class")
-            class_to_show = st.selectbox(
-                label="Select class to show", options=filtering_options
-            )
-            df_to_show = pd.concat(
-                [
-                    self.data_dict["train"].copy(),
-                    self.data_dict["validation"].copy(),
-                    self.data_dict["test"].copy(),
-                ]
-            )
-            if class_to_show == "All classes":
-                df_to_show = df_to_show.head(10)
-            else:
-                df_to_show = df_to_show.loc[df_to_show["target"] == class_to_show].head(
-                    10
-                )
-            st.dataframe(df_to_show)
-            st.text_area(label="Latex code", value=df_to_show.style.to_latex())
-
-            st.subheader("First 10 observations of a chosen domain and text type")
-            domain = st.selectbox(
-                label="Select domain",
-                options=["all", "hotels", "medicine", "products", "reviews"],
-            )
-            text_type = st.selectbox(
-                label="Select text type",
-                options=["Full text", "Tokenized to sentences"],
-            )
-            text_type_mapping_dict = {
-                "Full text": "text",
-                "Tokenized to sentences": "sentence",
-            }
-
-            polemo_subset = load_dataset(
-                self.dataset_name,
-                f"{domain}_{text_type_mapping_dict[text_type]}",
-            )
-            df = pd.concat(
-                [
-                    polemo_subset["train"].to_pandas(),
-                    polemo_subset["validation"].to_pandas(),
-                    polemo_subset["test"].to_pandas(),
-                ]
-            ).head(10)
-            st.dataframe(df)
-            st.text_area(label="Latex code", value=df.style.to_latex())
-
-        with word_searching:
-            st.header("Observations containing a chosen word")
-            searched_word = st.text_input(
-                label="Enter the word you are looking for below"
-            )
-            df_to_show = pd.concat(
-                [
-                    self.data_dict["train"].copy(),
-                    self.data_dict["validation"].copy(),
-                    self.data_dict["test"].copy(),
-                ]
-            )
-            df_to_show = df_to_show.loc[df_to_show["text"].str.contains(searched_word)]
-            st.dataframe(df_to_show)
-            st.text_area(label="Latex code", value=df_to_show.style.to_latex())
-
-        with dataset_statistics:
-            st.header("Dataset statistics")
-            st.subheader("Number of samples in each data split")
-            metrics_df = pd.DataFrame.from_dict(
-                {
-                    "Train": self.data_dict["train"].shape[0],
-                    "Validation": self.data_dict["validation"].shape[0],
-                    "Test": self.data_dict["test"].shape[0],
-                    "Total": sum(
-                        [
-                            self.data_dict["train"].shape[0],
-                            self.data_dict["validation"].shape[0],
-                            self.data_dict["test"].shape[0],
-                        ]
-                    ),
-                },
-                orient="index",
-            ).reset_index()
-            metrics_df.columns = ["Subset", "Number of samples"]
-            st.dataframe(metrics_df)
-
-            latex_df = metrics_df.style.to_latex()
-            st.text_area(label="Latex code", value=latex_df)
-
-            # Class distribution in each subset
-            st.subheader("Class distribution in each subset")
-            target_unique_values = self.data_dict["train"]["target"].unique()
-            hist = (
-                pd.DataFrame(
-                    [
-                        df["target"].value_counts(normalize=True).rename(k)
-                        for k, df in self.data_dict.items()
-                    ]
-                )
-                .reset_index()
-                .rename({"index": "split_name"}, axis=1)
-            )
-            plot_data = [
-                go.Bar(
-                    name=str(target_unique_values[i]),
-                    x=self.subsets,
-                    y=hist[target_unique_values[i]].values,
-                )
-                for i in range(len(target_unique_values))
-            ]
-            barchart_class_dist = go.Figure(data=plot_data)
-            barchart_class_dist.update_layout(
-                barmode="group",
-                title_text="Barchart - class distribution",
-                xaxis_title="Split name",
-                yaxis_title="Number of data points",
-            )
-            st.plotly_chart(barchart_class_dist, use_container_width=True)
-            st.dataframe(hist)
-            st.text_area(label="Latex code", value=hist.style.to_latex())
-
-            # Number of words per observation
-            st.subheader("Number of words per observation in each subset")
-            hist_data_num_words = [
-                df["text"].apply(count_num_of_words) for df in self.data_dict.values()
-            ]
-            fig_num_words = ff.create_distplot(
-                hist_data_num_words, self.subsets, show_rug=False, bin_size=1
-            )
-            fig_num_words.update_traces(
-                nbinsx=100, autobinx=True, selector={"type": "histogram"}
-            )
-            fig_num_words.update_layout(
-                title_text="Histogram - number of characters per observation",
-                xaxis_title="Number of characters",
-            )
-            st.plotly_chart(fig_num_words, use_container_width=True)
-
-            # Number of characters per observation
-            st.subheader("Number of characters per observation in each subset")
-            hist_data_num_characters = [
-                df["text"].apply(count_num_of_characters)
-                for df in self.data_dict.values()
-            ]
-            fig_num_chars = ff.create_distplot(
-                hist_data_num_characters, self.subsets, show_rug=False, bin_size=1
-            )
-            fig_num_chars.update_layout(
-                title_text="Histogram - number of characters per observation",
-                xaxis_title="Number of characters",
-            )
-            st.plotly_chart(fig_num_chars, use_container_width=True)
-
-            with tsne_projection:
-                st.header("t-SNE projection of the dataset")
-                subset_to_project = st.selectbox(
-                    label="Select subset to project", options=self.subsets
-                )
-                sentences = self.data_dict[subset_to_project]["text"].values
-                reducer = TSNE(
-                    n_components=2
-                )
-                embedded_sentences = np.array(
-                    [embed_sentence(text) for text in sentences]
-                )
-                transformed_embeddings = reducer.fit_transform(embedded_sentences)
-                fig, ax = plt.subplots()
-                ax.scatter(
-                    x=transformed_embeddings[:, 0],
-                    y=transformed_embeddings[:, 1],
-                    c=[
-                        PLOT_COLOR_PALETTE[x]
-                        for x in self.data_dict[subset_to_project]["target"].values
-                    ],
-                )
-                st.pyplot(fig)
diff --git a/spaces/cleanmaster/so-vits-svc-akagi/models.py b/spaces/cleanmaster/so-vits-svc-akagi/models.py
deleted file mode 100644
index 5d8f154887a43a5c5f67cf6340f74268398e32d5..0000000000000000000000000000000000000000
--- a/spaces/cleanmaster/so-vits-svc-akagi/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import attentions
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-from vdecoder.hifigan.models import Generator
-from utils import f0_to_coarse
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    # print(x.shape,x_lengths.shape)
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0,
-      filter_channels=None,
-      n_heads=None,
-      p_dropout=None):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    self.f0_emb = nn.Embedding(256, hidden_channels)
-
-    self.enc_ =  attentions.Encoder(
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-
-  def forward(self, x, x_lengths, f0=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = x + self.f0_emb(f0).transpose(1,2)
-    x = self.enc_(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-
-    return z, m, logs, x_mask
-
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-        
-        
-class SpeakerEncoder(torch.nn.Module):
-    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
-        super(SpeakerEncoder, self).__init__()
-        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
-        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, mels):
-        self.lstm.flatten_parameters()
-        _, (hidden, _) = self.lstm(mels)
-        embeds_raw = self.relu(self.linear(hidden[-1]))
-        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-        
-    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
-        mel_slices = []
-        for i in range(0, total_frames-partial_frames, partial_hop):
-            mel_range = torch.arange(i, i+partial_frames)
-            mel_slices.append(mel_range)
-            
-        return mel_slices
-    
-    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
-        mel_len = mel.size(1)
-        last_mel = mel[:,-partial_frames:]
-        
-        if mel_len > partial_frames:
-            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
-            mels = list(mel[:,s] for s in mel_slices)
-            mels.append(last_mel)
-            mels = torch.stack(tuple(mels), 0).squeeze(1)
-        
-            with torch.no_grad():
-                partial_embeds = self(mels)
-            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
-            #embed = embed / torch.linalg.norm(embed, 2)
-        else:
-            with torch.no_grad():
-                embed = self(last_mel)
-        
-        return embed
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    gin_channels,
-    ssl_dim,
-    n_speakers,
-    **kwargs):
-
-    super().__init__()
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.gin_channels = gin_channels
-    self.ssl_dim = ssl_dim
-    self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-    self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
-    hps = {
-        "sampling_rate": 48000,
-        "inter_channels": 192,
-        "resblock": "1",
-        "resblock_kernel_sizes": [3, 7, 11],
-        "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-        "upsample_rates": [10, 8, 2, 2],
-        "upsample_initial_channel": 512,
-        "upsample_kernel_sizes": [16, 16, 4, 4],
-        "gin_channels": 256,
-    }
-    self.dec = Generator(h=hps)
-    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-  def forward(self, c, f0, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
-    if c_lengths == None:
-      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-    if spec_lengths == None:
-      spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
-    g = self.emb_g(g).transpose(1,2)
-
-    z_ptemp, m_p, logs_p, _ = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
-    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
-
-    z_p = self.flow(z, spec_mask, g=g)
-    z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
-
-    # o = self.dec(z_slice, g=g)
-    o = self.dec(z_slice, g=g, f0=pitch_slice)
-
-    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, c, f0, g=None, mel=None, c_lengths=None):
-    if c_lengths == None:
-      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-    g = self.emb_g(g).transpose(1,2)
-
-    z_p, m_p, logs_p, c_mask = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
-    z = self.flow(z_p, c_mask, g=g, reverse=True)
-
-    o = self.dec(z * c_mask, g=g, f0=f0)
-
-    return o
diff --git a/spaces/cloixai/dalle-minii/README.md b/spaces/cloixai/dalle-minii/README.md
deleted file mode 100644
index e8d3d7e311b04d1e35e1ff572ec809613f71f09c..0000000000000000000000000000000000000000
--- a/spaces/cloixai/dalle-minii/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: DALL·E mini
-metaTitle: DALL·E mini by craiyon.com on Hugging Face
-emoji: 🥑
-colorFrom: yellow
-colorTo: green
-sdk: static
-pinned: true
-license: apache-2.0
-duplicated_from: dalle-mini/dalle-mini
----
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/lexer.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/lexer.py
deleted file mode 100644
index e0ae0aefeef33e1cbfd11cdffdfafb330bdca205..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/lexer.py
+++ /dev/null
@@ -1,291 +0,0 @@
-from fontTools.feaLib.error import FeatureLibError, IncludedFeaNotFound
-from fontTools.feaLib.location import FeatureLibLocation
-import re
-import os
-
-try:
-    import cython
-except ImportError:
-    # if cython not installed, use mock module with no-op decorators and types
-    from fontTools.misc import cython
-
-
-class Lexer(object):
-    NUMBER = "NUMBER"
-    HEXADECIMAL = "HEXADECIMAL"
-    OCTAL = "OCTAL"
-    NUMBERS = (NUMBER, HEXADECIMAL, OCTAL)
-    FLOAT = "FLOAT"
-    STRING = "STRING"
-    NAME = "NAME"
-    FILENAME = "FILENAME"
-    GLYPHCLASS = "GLYPHCLASS"
-    CID = "CID"
-    SYMBOL = "SYMBOL"
-    COMMENT = "COMMENT"
-    NEWLINE = "NEWLINE"
-    ANONYMOUS_BLOCK = "ANONYMOUS_BLOCK"
-
-    CHAR_WHITESPACE_ = " \t"
-    CHAR_NEWLINE_ = "\r\n"
-    CHAR_SYMBOL_ = ",;:-+'{}[]<>()="
-    CHAR_DIGIT_ = "0123456789"
-    CHAR_HEXDIGIT_ = "0123456789ABCDEFabcdef"
-    CHAR_LETTER_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
-    CHAR_NAME_START_ = CHAR_LETTER_ + "_+*:.^~!\\"
-    CHAR_NAME_CONTINUATION_ = CHAR_LETTER_ + CHAR_DIGIT_ + "_.+*:^~!/-"
-
-    RE_GLYPHCLASS = re.compile(r"^[A-Za-z_0-9.\-]+$")
-
-    MODE_NORMAL_ = "NORMAL"
-    MODE_FILENAME_ = "FILENAME"
-
-    def __init__(self, text, filename):
-        self.filename_ = filename
-        self.line_ = 1
-        self.pos_ = 0
-        self.line_start_ = 0
-        self.text_ = text
-        self.text_length_ = len(text)
-        self.mode_ = Lexer.MODE_NORMAL_
-
-    def __iter__(self):
-        return self
-
-    def next(self):  # Python 2
-        return self.__next__()
-
-    def __next__(self):  # Python 3
-        while True:
-            token_type, token, location = self.next_()
-            if token_type != Lexer.NEWLINE:
-                return (token_type, token, location)
-
-    def location_(self):
-        column = self.pos_ - self.line_start_ + 1
-        return FeatureLibLocation(self.filename_ or "<features>", self.line_, column)
-
-    def next_(self):
-        self.scan_over_(Lexer.CHAR_WHITESPACE_)
-        location = self.location_()
-        start = self.pos_
-        text = self.text_
-        limit = len(text)
-        if start >= limit:
-            raise StopIteration()
-        cur_char = text[start]
-        next_char = text[start + 1] if start + 1 < limit else None
-
-        if cur_char == "\n":
-            self.pos_ += 1
-            self.line_ += 1
-            self.line_start_ = self.pos_
-            return (Lexer.NEWLINE, None, location)
-        if cur_char == "\r":
-            self.pos_ += 2 if next_char == "\n" else 1
-            self.line_ += 1
-            self.line_start_ = self.pos_
-            return (Lexer.NEWLINE, None, location)
-        if cur_char == "#":
-            self.scan_until_(Lexer.CHAR_NEWLINE_)
-            return (Lexer.COMMENT, text[start : self.pos_], location)
-
-        if self.mode_ is Lexer.MODE_FILENAME_:
-            if cur_char != "(":
-                raise FeatureLibError("Expected '(' before file name", location)
-            self.scan_until_(")")
-            cur_char = text[self.pos_] if self.pos_ < limit else None
-            if cur_char != ")":
-                raise FeatureLibError("Expected ')' after file name", location)
-            self.pos_ += 1
-            self.mode_ = Lexer.MODE_NORMAL_
-            return (Lexer.FILENAME, text[start + 1 : self.pos_ - 1], location)
-
-        if cur_char == "\\" and next_char in Lexer.CHAR_DIGIT_:
-            self.pos_ += 1
-            self.scan_over_(Lexer.CHAR_DIGIT_)
-            return (Lexer.CID, int(text[start + 1 : self.pos_], 10), location)
-        if cur_char == "@":
-            self.pos_ += 1
-            self.scan_over_(Lexer.CHAR_NAME_CONTINUATION_)
-            glyphclass = text[start + 1 : self.pos_]
-            if len(glyphclass) < 1:
-                raise FeatureLibError("Expected glyph class name", location)
-            if len(glyphclass) > 63:
-                raise FeatureLibError(
-                    "Glyph class names must not be longer than 63 characters", location
-                )
-            if not Lexer.RE_GLYPHCLASS.match(glyphclass):
-                raise FeatureLibError(
-                    "Glyph class names must consist of letters, digits, "
-                    "underscore, period or hyphen",
-                    location,
-                )
-            return (Lexer.GLYPHCLASS, glyphclass, location)
-        if cur_char in Lexer.CHAR_NAME_START_:
-            self.pos_ += 1
-            self.scan_over_(Lexer.CHAR_NAME_CONTINUATION_)
-            token = text[start : self.pos_]
-            if token == "include":
-                self.mode_ = Lexer.MODE_FILENAME_
-            return (Lexer.NAME, token, location)
-        if cur_char == "0" and next_char in "xX":
-            self.pos_ += 2
-            self.scan_over_(Lexer.CHAR_HEXDIGIT_)
-            return (Lexer.HEXADECIMAL, int(text[start : self.pos_], 16), location)
-        if cur_char == "0" and next_char in Lexer.CHAR_DIGIT_:
-            self.scan_over_(Lexer.CHAR_DIGIT_)
-            return (Lexer.OCTAL, int(text[start : self.pos_], 8), location)
-        if cur_char in Lexer.CHAR_DIGIT_:
-            self.scan_over_(Lexer.CHAR_DIGIT_)
-            if self.pos_ >= limit or text[self.pos_] != ".":
-                return (Lexer.NUMBER, int(text[start : self.pos_], 10), location)
-            self.scan_over_(".")
-            self.scan_over_(Lexer.CHAR_DIGIT_)
-            return (Lexer.FLOAT, float(text[start : self.pos_]), location)
-        if cur_char == "-" and next_char in Lexer.CHAR_DIGIT_:
-            self.pos_ += 1
-            self.scan_over_(Lexer.CHAR_DIGIT_)
-            if self.pos_ >= limit or text[self.pos_] != ".":
-                return (Lexer.NUMBER, int(text[start : self.pos_], 10), location)
-            self.scan_over_(".")
-            self.scan_over_(Lexer.CHAR_DIGIT_)
-            return (Lexer.FLOAT, float(text[start : self.pos_]), location)
-        if cur_char in Lexer.CHAR_SYMBOL_:
-            self.pos_ += 1
-            return (Lexer.SYMBOL, cur_char, location)
-        if cur_char == '"':
-            self.pos_ += 1
-            self.scan_until_('"')
-            if self.pos_ < self.text_length_ and self.text_[self.pos_] == '"':
-                self.pos_ += 1
-                # strip newlines embedded within a string
-                string = re.sub("[\r\n]", "", text[start + 1 : self.pos_ - 1])
-                return (Lexer.STRING, string, location)
-            else:
-                raise FeatureLibError("Expected '\"' to terminate string", location)
-        raise FeatureLibError("Unexpected character: %r" % cur_char, location)
-
-    def scan_over_(self, valid):
-        p = self.pos_
-        while p < self.text_length_ and self.text_[p] in valid:
-            p += 1
-        self.pos_ = p
-
-    def scan_until_(self, stop_at):
-        p = self.pos_
-        while p < self.text_length_ and self.text_[p] not in stop_at:
-            p += 1
-        self.pos_ = p
-
-    def scan_anonymous_block(self, tag):
-        location = self.location_()
-        tag = tag.strip()
-        self.scan_until_(Lexer.CHAR_NEWLINE_)
-        self.scan_over_(Lexer.CHAR_NEWLINE_)
-        regexp = r"}\s*" + tag + r"\s*;"
-        split = re.split(regexp, self.text_[self.pos_ :], maxsplit=1)
-        if len(split) != 2:
-            raise FeatureLibError(
-                "Expected '} %s;' to terminate anonymous block" % tag, location
-            )
-        self.pos_ += len(split[0])
-        return (Lexer.ANONYMOUS_BLOCK, split[0], location)
-
-
-class IncludingLexer(object):
-    """A Lexer that follows include statements.
-
-    The OpenType feature file specification states that due to
-    historical reasons, relative imports should be resolved in this
-    order:
-
-    1. If the source font is UFO format, then relative to the UFO's
-       font directory
-    2. relative to the top-level include file
-    3. relative to the parent include file
-
-    We only support 1 (via includeDir) and 2.
-    """
-
-    def __init__(self, featurefile, *, includeDir=None):
-        """Initializes an IncludingLexer.
-
-        Behavior:
-            If includeDir is passed, it will be used to determine the top-level
-            include directory to use for all encountered include statements. If it is
-            not passed, ``os.path.dirname(featurefile)`` will be considered the
-            include directory.
-        """
-
-        self.lexers_ = [self.make_lexer_(featurefile)]
-        self.featurefilepath = self.lexers_[0].filename_
-        self.includeDir = includeDir
-
-    def __iter__(self):
-        return self
-
-    def next(self):  # Python 2
-        return self.__next__()
-
-    def __next__(self):  # Python 3
-        while self.lexers_:
-            lexer = self.lexers_[-1]
-            try:
-                token_type, token, location = next(lexer)
-            except StopIteration:
-                self.lexers_.pop()
-                continue
-            if token_type is Lexer.NAME and token == "include":
-                fname_type, fname_token, fname_location = lexer.next()
-                if fname_type is not Lexer.FILENAME:
-                    raise FeatureLibError("Expected file name", fname_location)
-                # semi_type, semi_token, semi_location = lexer.next()
-                # if semi_type is not Lexer.SYMBOL or semi_token != ";":
-                #    raise FeatureLibError("Expected ';'", semi_location)
-                if os.path.isabs(fname_token):
-                    path = fname_token
-                else:
-                    if self.includeDir is not None:
-                        curpath = self.includeDir
-                    elif self.featurefilepath is not None:
-                        curpath = os.path.dirname(self.featurefilepath)
-                    else:
-                        # if the IncludingLexer was initialized from an in-memory
-                        # file-like stream, it doesn't have a 'name' pointing to
-                        # its filesystem path, therefore we fall back to using the
-                        # current working directory to resolve relative includes
-                        curpath = os.getcwd()
-                    path = os.path.join(curpath, fname_token)
-                if len(self.lexers_) >= 5:
-                    raise FeatureLibError("Too many recursive includes", fname_location)
-                try:
-                    self.lexers_.append(self.make_lexer_(path))
-                except FileNotFoundError as err:
-                    raise IncludedFeaNotFound(fname_token, fname_location) from err
-            else:
-                return (token_type, token, location)
-        raise StopIteration()
-
-    @staticmethod
-    def make_lexer_(file_or_path):
-        if hasattr(file_or_path, "read"):
-            fileobj, closing = file_or_path, False
-        else:
-            filename, closing = file_or_path, True
-            fileobj = open(filename, "r", encoding="utf-8")
-        data = fileobj.read()
-        filename = getattr(fileobj, "name", None)
-        if closing:
-            fileobj.close()
-        return Lexer(data, filename)
-
-    def scan_anonymous_block(self, tag):
-        return self.lexers_[-1].scan_anonymous_block(tag)
-
-
-class NonIncludingLexer(IncludingLexer):
-    """Lexer that does not follow `include` statements, emits them as-is."""
-
-    def __next__(self):  # Python 3
-        return next(self.lexers_[0])
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hpeldsp_init_loongarch.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hpeldsp_init_loongarch.c
deleted file mode 100644
index 1690be543806900904124a015f128a2053b9203a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hpeldsp_init_loongarch.c
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2021 Loongson Technology Corporation Limited
- * Contributed by Shiyou Yin <yinshiyou-hf@loongson.cn>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/loongarch/cpu.h"
-#include "libavcodec/hpeldsp.h"
-#include "libavcodec/loongarch/hpeldsp_lasx.h"
-
-void ff_hpeldsp_init_loongarch(HpelDSPContext *c, int flags)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-    if (have_lasx(cpu_flags)) {
-        c->put_pixels_tab[0][0] = ff_put_pixels16_8_lsx;
-        c->put_pixels_tab[0][1] = ff_put_pixels16_x2_8_lasx;
-        c->put_pixels_tab[0][2] = ff_put_pixels16_y2_8_lasx;
-        c->put_pixels_tab[0][3] = ff_put_pixels16_xy2_8_lasx;
-
-        c->put_pixels_tab[1][0] = ff_put_pixels8_8_lasx;
-        c->put_pixels_tab[1][1] = ff_put_pixels8_x2_8_lasx;
-        c->put_pixels_tab[1][2] = ff_put_pixels8_y2_8_lasx;
-        c->put_pixels_tab[1][3] = ff_put_pixels8_xy2_8_lasx;
-        c->put_no_rnd_pixels_tab[0][0] = ff_put_pixels16_8_lsx;
-        c->put_no_rnd_pixels_tab[0][1] = ff_put_no_rnd_pixels16_x2_8_lasx;
-        c->put_no_rnd_pixels_tab[0][2] = ff_put_no_rnd_pixels16_y2_8_lasx;
-        c->put_no_rnd_pixels_tab[0][3] = ff_put_no_rnd_pixels16_xy2_8_lasx;
-
-        c->put_no_rnd_pixels_tab[1][0] = ff_put_pixels8_8_lasx;
-        c->put_no_rnd_pixels_tab[1][1] = ff_put_no_rnd_pixels8_x2_8_lasx;
-        c->put_no_rnd_pixels_tab[1][2] = ff_put_no_rnd_pixels8_y2_8_lasx;
-        c->put_no_rnd_pixels_tab[1][3] = ff_put_no_rnd_pixels8_xy2_8_lasx;
-    }
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Discover the Secrets of Futbol Edit The Most Popular and Powerful Soccer Editing Software.md b/spaces/congsaPfin/Manga-OCR/logs/Discover the Secrets of Futbol Edit The Most Popular and Powerful Soccer Editing Software.md
deleted file mode 100644
index 105b1269ba57fb194570f89a040908ee9804d8f0..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Discover the Secrets of Futbol Edit The Most Popular and Powerful Soccer Editing Software.md	
+++ /dev/null
@@ -1,117 +0,0 @@
-<br />
-<h1>Futbol Edit: How to Create Amazing Videos of Your Favorite Sport</h1>
- <p>If you are a fan of soccer or football, you probably enjoy watching videos of your favorite games, teams, or players. But have you ever wondered how to create your own videos of the sport you love? In this article, we will introduce you to futbol edit, a term that refers to the process of editing videos of soccer or football games to highlight the best moments, skills, goals, and players. We will also show you some of the best tools and software for futbol edit, as well as some tips and tricks that can help you create amazing videos.</p>
- <h2>What is futbol edit and why is it popular?</h2>
- <h3>Futbol edit is a term that refers to the process of editing videos of soccer or football games to highlight the best moments, skills, goals, and players.</h3>
- <p>Futbol edit is not just cutting and pasting clips together. It is an art form that requires creativity, skill, and passion. Futbol edit can be done for various purposes, such as entertainment, education, analysis, or promotion. Futbol edit can also be done in different styles, such as cinematic, artistic, motivational, or humorous.</p>
-<h2>futbol edit</h2><br /><p><b><b>Download</b> &hArr; <a href="https://urlca.com/2uO6jn">https://urlca.com/2uO6jn</a></b></p><br /><br />
- <h3>Futbol edit is popular among fans, players, coaches, and analysts who want to enjoy, learn from, or share their passion for the sport.</h3>
- <p>Fans use futbol edit to express their admiration for their favorite teams or players, or to relive the most exciting moments in a game. Players use futbol edit to showcase their talents, improve their skills, or inspire others. Coaches use futbol edit to review their strategies, evaluate their performance, or prepare for upcoming matches. Analysts use futbol edit to study the game trends, statistics, or tactics.</p>
- <h2>What are the best tools and software for futbol edit?</h2>
- <h3>There are many tools and software available for futbol edit, depending on your needs, budget, and preferences.</h3>
- <p>Some tools <p>Some tools and software are more suitable for beginners, while others are more advanced and require more experience. Some tools and software are free or cheap, while others are more expensive and require a subscription or a license. Some tools and software are web-based, while others are desktop-based or mobile-based. Here are some of the most popular ones:</p>
- <h4>Some of the most popular ones are:</h4>
- <h5>Adobe Premiere Pro: A professional video editing software that offers a wide range of features and effects for creating high-quality videos.</h5>
- <p>Adobe Premiere Pro is one of the most widely used video editing software in the world. It is compatible with Windows and Mac operating systems, and supports various video formats and resolutions. It allows you to edit your videos in a timeline-based interface, where you can trim, crop, rotate, split, merge, and adjust your clips. You can also add transitions, filters, titles, captions, audio, and music to your videos. You can also use advanced features such as color grading, keyframing, masking, chroma keying, and motion tracking to enhance your videos. Adobe Premiere Pro is not free, but you can get a 7-day trial or a monthly subscription.</p>
- <h5>LongoMatch: A video analysis software that helps coaches, analysts, and players improve their performance by generating custom reports from videos.</h5>
- <p>LongoMatch is a video analysis software that is designed for sports. It is compatible with Windows, Mac, and Linux operating systems, and supports various video formats and resolutions. It allows you to import your videos and tag them with different categories and events, such as goals, passes, shots, fouls, etc. You can also create custom dashboards and templates to organize your data. You can then generate reports and statistics from your videos, such as heat maps, graphs, charts, etc. You can also export your videos with annotations or share them online. LongoMatch is not free, but you can get a 30-day trial or a yearly subscription.</p>
- <h5>PES 2021 Kit Creator: A web-based tool that allows you to create custom kits for PES 2021, a popular soccer video game.</h5>
- <p>PES 2021 Kit Creator is a web-based tool that allows you to create custom kits for PES 2021, a popular soccer video game. It is compatible with any browser and device, and does not require any installation or registration. It allows you to choose from various templates and colors for your kits, as well as add logos, sponsors, badges, numbers, names, etc. You can also preview your kits in 3D and download them as PNG files. You can then import your kits into PES 2021 using the edit mode or the option file. PES 2021 Kit Creator is free to use.</p>
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-futbol edit kimmich</p>
- <h2>What are some tips and tricks for futbol edit?</h2>
- <h3>Here are some tips and tricks that can help you create amazing futbol edit videos:</h3>
- <h4>Determine the highlights of the game: Use tools like LongoMatch to index and retrieve the most important moments in a game.</h4>
- <p>One of the first steps in futbol edit is to determine the highlights of the game that you want to include in your video. You can use tools like LongoMatch to index and retrieve the most important moments in a game, such as goals, assists, saves, tackles, dribbles, etc. You can also use filters and queries to narrow down your search results. This way, you can save time and avoid watching the entire game again.</p>
- <h4>Apply slow motion and zoom effects: Use tools like Adobe Premiere Pro to emphasize key actions or details in your videos.</h4>
- <p>Another tip for futbol edit is to apply slow motion and zoom effects to emphasize key actions or details in your videos. You can use tools like Adobe Premiere Pro to adjust the speed and scale of your clips. For example, you can slow down a clip of a goal or a skill to show how it was executed or how it affected the game. You can also zoom in on a clip of a player or a ball to show their facial expressions or movements. This way, you can create more impact and drama in your videos.</p>
- <h4>Add text and commentary: Use tools like Adobe Premiere Pro to add text that follows the players or commentary that explains the game.</h4>
- <p>A third tip for futbol edit is to add text and commentary to your videos. You can use tools like Adobe Premiere Pro to add text that follows the players or commentary that explains the game. For example, For example, you can add text that follows the players or commentary that explains the game. For example, you can add text that shows the name, position, or nationality of a player, or the score, time, or venue of a game. You can also add commentary that narrates the action, provides background information, or expresses opinions. You can use tools like Adobe Premiere Pro to adjust the font, size, color, and position of your text, or to record, import, or edit your audio. This way, you can make your videos more informative and engaging.</p>
- <h4>Use transitions and picture-in-picture: Use tools like Adobe Premiere Pro to create smooth transitions between clips and show multiple angles of the same action.</h4>
- <p>A fourth tip for futbol edit is to use transitions and picture-in-picture to create smooth transitions between clips and show multiple angles of the same action. You can use tools like Adobe Premiere Pro to apply different types of transitions, such as fades, wipes, slides, or cuts, to your clips. You can also use tools like Adobe Premiere Pro to create picture-in-picture effects, where you can overlay a smaller clip on top of a larger clip. For example, you can use transitions and picture-in-picture to show a goal from different angles or perspectives, such as from behind the goal, from the side of the field, or from the player's point of view. This way, you can make your videos more dynamic and interesting.</p>
- <h2>Conclusion and FAQs</h2>
- <p>In conclusion, futbol edit is a term that refers to the process of editing videos of soccer or football games to highlight the best moments, skills, goals, and players. It is popular among fans, players, coaches, and analysts who want to enjoy, learn from, or share their passion for the sport. There are many tools and software available for futbol edit, such as Adobe Premiere Pro, LongoMatch, and PES 2021 Kit Creator. There are also some tips and tricks that can help you create amazing futbol edit videos, such as determining the highlights of the game, applying slow motion and zoom effects, adding text and commentary, and using transitions and picture-in-picture. We hope this article has helped you understand what futbol edit is and how to do it.</p>
- <p>Here are some FAQs that you might have:</p>
- <h4>Q: How long does it take to create a futbol edit video?</h4>
-<p>A: The time it takes to create a futbol edit video depends on various factors, such as the length and quality of the original video, the number and complexity of the edits you want to make, the tools and software you use, and your level of experience and skill. It could take anywhere from a few minutes to a few hours.</p>
- <h4>Q: Where can I find videos to edit for futbol edit?</h4>
-<p>A: You can find videos to edit for futbol edit from various sources, <p>A: You can find videos to edit for futbol edit from various sources, such as:</p>
- <ul>
-<li>Online platforms that stream or host soccer or football videos, such as YouTube, DAZN, ESPN, or FIFA.</li>
-<li>Online communities that share or request soccer or football videos, such as Reddit, Twitter, or Facebook.</li>
-<li>Offline sources that record or store soccer or football videos, such as TV channels, DVDs, or USB drives.</li>
-</ul>
- <p>However, you should always respect the intellectual property rights of the original video owners and creators, and follow the fair use guidelines when using their videos for futbol edit.</p>
- <h4>Q: How can I share my futbol edit videos with others?</h4>
-<p>A: You can share your futbol edit videos with others by:</p>
- <ul>
-<li>Uploading them to online platforms that allow you to share or publish your videos, such as YouTube, Vimeo, or Instagram.</li>
-<li>Sending them to online communities that appreciate or critique your videos, such as Reddit, Twitter, or Facebook.</li>
-<li>Showing them to offline audiences that are interested in your videos, such as your friends, family, or teammates.</li>
-</ul>
- <p>However, you should always respect the privacy and preferences of the people who appear in your videos, and follow the community guidelines when sharing your videos with others.</p>
- <h4>Q: How can I improve my skills and knowledge in futbol edit?</h4>
-<p>A: You can improve your skills and knowledge in futbol edit by:</p>
- <ul>
-<li>Watching and analyzing other futbol edit videos that inspire or challenge you, and learning from their techniques and styles.</li>
-<li>Practicing and experimenting with different tools and software that suit your needs and preferences, and discovering new features and effects.</li>
-<li>Seeking and receiving feedback from other futbol edit enthusiasts or experts, and applying their suggestions and criticisms.</li>
-</ul>
- <p>You can also enroll in online courses or tutorials that teach you the basics or advanced skills of futbol edit.</p>
- <h4>Q: What are some of the benefits of futbol edit?</h4>
-<p>A: Some of the benefits of futbol edit are:</p>
- <ul>
-<li>You can express your creativity and passion for soccer or football through your videos.</li>
-<li>You can enhance your enjoyment and appreciation of the sport by reliving its best moments.</li>
-<li>You can improve your understanding and performance of the sport by studying its details and patterns.</li>
-<li>You can connect and communicate with other soccer or football fans or players by sharing your videos.</li>
-</ul></p> 401be4b1e0<br />
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-<h1>Halloween S.O.S Mundo Download: How to Listen to the Remastered Version of the Rap Song</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of rap music, you might have heard of Allen Halloween, a Portuguese rapper who is known for his dark and realistic lyrics. One of his most famous songs is S.O.S Mundo, which was released in 2011 and has become a classic in the genre. But did you know that there is a remastered version of this song that came out in 2023? In this article, we will tell you everything you need to know about Halloween S.O.S Mundo download, including what it is, why it is remastered, and how to get it from allenhalloween.com.</p>
- <h3>What is Halloween S.O.S Mundo?</h3>
- <p>Halloween S.O.S Mundo is a rap song by Allen Halloween, who is considered one of the best rappers in Portugal. The song is part of his album Projecto Mary Witch, which was inspired by his own life experiences and struggles. The song talks about the problems and injustices that affect the world, such as poverty, violence, corruption, racism, and war. It also expresses a hope for a better future and a call for action to change the situation.</p>
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- <h3>Why is it remastered in 2023?</h3>
- <p>In 2023, Allen Halloween decided to remaster and remix his song S.O.S Mundo for a special edition in vinyl. He collaborated with Maradox Primeiro, a producer who has worked with him before. The remastered version has improved sound quality and clarity, as well as some changes in the beats and the vocals. The remastered version also has a new cover art, which shows a globe with a red cross on it.</p>
- <h3>How to download it from allenhalloween.com?</h3>
- <p>If you want to listen to the remastered version of Halloween S.O.S Mundo, you can download it from allenhalloween.com, which is the official website of Allen Halloween. The website offers different options for downloading, such as MP3, WAV, or FLAC. You can also buy the vinyl edition from the website, which comes with a digital download code. The website also has other products related to Allen Halloween, such as T-shirts, hoodies, posters, and stickers.</p>
- <h2>Main Body</h2>
- <h3>The lyrics and meaning of Halloween S.O.S Mundo</h3>
- <p>The lyrics of Halloween S.O.S Mundo are very powerful and meaningful. They reflect the reality and the feelings of many people who live in difficult situations. They also challenge the listeners to think critically and act responsibly. Here are some of the main aspects of the lyrics:</p>
- <h4>The chorus and the message of hope</h4>
- <p>The chorus of the song is repeated four times throughout the song. It says:</p>
- <blockquote>
-<p>SOS mundo<br>
-SOS mundo<br>
-SOS mundo<br>
-SOS mundo<br>
-SOS mundo<br>
-SOS mundo<br>
-SOS mundo<br>
-SOS mundo</p>
-</blockquote>
- <p>This chorus is like a cry for help from the world, which is suffering from many problems. It also implies that there is still hope for saving the world, if people work together and care for each other.</p>
- <h4>The verses and the critique of society</h4>
- <p>The verses of the song are divided into three parts. Each part has four lines that rhyme with each other. The verses are very descriptive and critical of the society that Allen Halloween lives in. He mentions topics such as hunger, crime, drugs, racism, corruption, war, and pollution. He also criticizes the politicians, the media, the police, the religious leaders, and the rich people who are responsible for or indifferent to these problems. He uses metaphors, similes, irony, and sarcasm to convey his message. For example, he says:</p>
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-<p>Os políticos são os vampiros que sugam o sangue do povo<br>
-(The politicians are the vampires who suck the blood of the people)<br>
-<br>
-A polícia é a máfia que controla o jogo<br>
-(The police is the mafia that controls the game)<br>
-<br>
-A religião é o ópio que adormece o povo<br>
-(Religion is the opium that puts the people to sleep)<br>
-<br>
-A televisão é a droga que aliena o povo<br>
-(Television is the drug that alienates the people)</p>
-</blockquote>
- <h4>The references and the influences</h4>
- <p>The lyrics of Halloween S.O.S Mundo also contain many references and influences from other rap songs, artists, and cultures. Allen Halloween shows his knowledge and appreciation of rap history and diversity. He mentions names such as Tupac Shakur, Bob Marley, Nas, KRS-One, Public Enemy, Wu-Tang Clan, and N.W.A. He also uses words and phrases from English, French, Spanish, and Arabic. He mixes different styles and genres of rap, such as gangsta rap, conscious rap, reggae rap, and hardcore rap. He creates a unique and original sound that reflects his identity and vision.</p>
- <h3>The reception and impact of Halloween S.O.S Mundo</h3>
- <p>The song Halloween S.O.S Mundo has received a lot of attention and praise from rap fans and critics. It has also had a significant impact on the rap scene and the society. Here are some of the main aspects of the reception and impact of the song:</p>
- <h4>The popularity and the views on YouTube</h4>
- <p>The song Halloween S.O.S Mundo was uploaded on YouTube on October 31st, 2011. Since then, it has accumulated more than 15 million views and 200 thousand likes. It is one of the most viewed and liked rap songs from Portugal on YouTube. It has also been shared and commented by thousands of people who appreciate its message and quality.</p>
- <h4>The reviews and the ratings on AfroCharts</h4>
- <p>The song Halloween S.O.S Mundo was also featured on AfroCharts, a website that ranks the best rap songs from Africa and its diaspora. The song received a rating of 9.5 out of 10 from AfroCharts editors, who praised its lyrics, production, delivery, and relevance. The song also received positive reviews from AfroCharts users, who gave it an average rating of 8.9 out of 10. The song was ranked as one of the top 10 rap songs of 2011 by AfroCharts.</p>
- <h4>The feedback and the comments from fans</h4>
- <p>The song Halloween S.O.S Mundo has also received a lot of feedback and comments from fans who listened to it. Many fans expressed their admiration and gratitude for Allen Halloween for making such a powerful and meaningful song. They also related to his experiences and feelings, and shared their own stories and opinions. Some fans even said that the song changed their lives or inspired them to take action. Here are some examples of fan comments:</p>
- <blockquote>
-<p>"This song is a masterpiece. It speaks the truth about the world we live in. It makes me cry every time I listen to it."</p>
-<p>"Allen Halloween is a legend. He is not afraid to say what he thinks and feels. He is a voice for the voiceless."</p>
-<p>"This song is more than music. It is a message. It is a wake-up call. It is a revolution."</p>
-</blockquote>
- <h2>Conclusion</h2>
- <p>In conclusion, Halloween S.O.S Mundo is a rap song by Allen Halloween that was released in 2011 and remastered in 2023. It is a song that talks about the problems and injustices that affect the world, as well as a hope for a better future. It is a song that has powerful and meaningful lyrics, influenced by different rap artists and cultures. It is a song that has received a lot of attention and praise from rap fans and critics, as well as a significant impact on the rap scene and the society.</p>
- <p>If you want to listen to this amazing song, you can download it from allenhalloween.com in different formats or buy it in vinyl edition. You can also check out other songs by Allen Halloween on his website or on YouTube.</p>
- <p>We hope you enjoyed this article about Halloween S.O.S Mundo download. If you did, please share it with your friends and family. And if you have any questions or comments about the song or the article, please leave them below. We would love to hear from you.</p>
- <h3>FAQs</h3>
- <p>Here are some of the frequently asked questions about Halloween S.O.S Mundo download:</p>
- <ol>
-<li>What does S.O.S stand for in the song title?</li>
-<p>S.O.S stands for Save Our Souls, which is a common distress signal used in emergencies. It also represents the plea for help from the world that is suffering from many problems.</p>
-<li>Who is Allen Halloween and what is his background?</li>
-<p>Allen Halloween is a Portuguese rapper who was born in Luanda, Angola, in 1979. He moved to Portugal with his family when he was six years old. He grew up in a poor and violent neighborhood in Lisbon, where he witnessed and experienced many hardships. He started rapping when he was 15 years old, and released his first album in 2006.</p>
-<li>What is the difference between the original and the remastered version of the song?</li>
-<p>The original version of the song was released in 2011 and has a duration of 5 minutes and 18 seconds. The remastered version of the song was released in 2023 and has a duration of 4 minutes and 56 seconds. The remastered version has improved sound quality and clarity, as well as some changes in the beats and the vocals. The remastered version also has a new cover art, which shows a globe with a red cross on it.</p>
-<li>Where can I find more information about the song and the rapper?</li>
-<p>You can find more information about the song and the rapper on allenhalloween.com, which is the official website of Allen Halloween. You can also follow him on his social media accounts, such as Facebook, Instagram, Twitter, and Spotify. You can also watch his interviews and documentaries on YouTube.</p>
-<li>What are some other rap songs that are similar to Halloween S.O.S Mundo?</li>
-<p>Some other rap songs that are similar to Halloween S.O.S Mundo are:</p>
-<ul>
-<li>Changes by Tupac Shakur</li>
-<li>The Message by Grandmaster Flash and The Furious Five</li>
-<li>Fight The Power by Public Enemy</li>
-<li>I Can by Nas</li>
-<li>Alright by Kendrick Lamar</li>
-</ul></p> 401be4b1e0<br />
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-<li>Go to [this link] and download the mod apk file on your device.</li>
-<li>Go to your device's settings and enable the installation of apps from unknown sources.</li>
-<li>Locate the downloaded mod apk file on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game and enjoy unlimited shinobites and other features.</li>
-</ol>
- <h2>Tips and Tricks for Playing Naruto x Boruto Ninja Voltage</h2>
- <p>Naruto x Boruto Ninja Voltage is a game that requires strategy and skill to master. If you want to improve your gameplay and become a better ninja, here are some tips and tricks that you can follow:</p>
- <h3>Upgrade Your Village Buildings</h3>
- <p>Your village is your base of operations in Naruto x Boruto Ninja Voltage. It consists of various buildings that have different functions and benefits. You should upgrade your village buildings regularly to increase their efficiency and unlock new features. Some of the important buildings that you should upgrade are:</p>
- <ul>
-<li>Village Headquarters: This is where you can manage your village resources such as chakra, ryo, fortress medals, etc. Upgrading it will increase your storage capacity and production rate.</li>
-<li>Ninja Card House: This is where you can summon new characters and ninja cards using shinobites or summoning tickets. Upgrading it will increase your card inventory space and unlock new summoning banners.</li>
-<li>Ninja Tool House: This is where you can craft new ninja tools using materials such as iron, steel, copper, etc. Upgrading it will increase your ninja tool inventory space and unlock new ninja tools.</li>
-<li>Shinobi List: This is where you can view and manage your shinobi collection. You can enhance, evolve, limit break, super awaken, and equip your shinobi with ninja cards and ninja tools. Upgrading it will increase your shinobi inventory space and unlock new shinobi slots.</li>
-<li>Fortress: This is where you can design and customize your own shinobi fortress to defend your village from enemy attacks. You can place traps, trained shinobi, and other obstacles to make your fortress more secure. Upgrading it will increase your fortress level and unlock new fortress features.</li>
-</ul>
- <h3>Summon Free Cards Every Day</h3>
- <p>One of the best ways to get new characters and ninja cards in Naruto x Boruto Ninja Voltage is to summon them using shinobites or summoning tickets. However, shinobites are hard to come by and summoning tickets are limited. That's why you should take advantage of the free summons that the game offers every day. You can get one free single summon every day from the normal banner and one free multi summon every week from the special banner. You can also get free summoning tickets from various events and missions. You never know when you might get lucky and pull a rare or powerful card from these free summons.</p>
- <h3>Choose Your Characters and Form Your Team</h3>
- <p>Naruto x Boruto Ninja Voltage allows you to control a team of four ninjas in each mission. You can choose any character that you have unlocked from both the Naruto Shippuden and Boruto: Naruto Next Generations anime series. However, not all characters are equal in terms of power, skills, and compatibility. You should choose your characters wisely and form a balanced team that can handle any situation. Some of the factors that you should consider when choosing your characters are:</p>
- <ul>
-<li>Element: Each character has an element that determines their strength and weakness against other elements. The elements are fire, wind, lightning, earth, and water. Fire beats wind, wind beats lightning, lightning beats earth, earth beats water, and water beats fire. You should choose characters that have an advantage over the enemy's element or avoid characters that have a disadvantage.</li>
-<li>Role: Each character has a role that determines their main function in the team. The roles are attack, defense, support, and heal. Attack characters deal high damage to enemies but have low defense. Defense characters have high defense and can protect the team from enemy attacks but have low damage. Support characters can buff the team's stats or debuff the enemy's stats but have moderate damage and defense. Heal characters can heal the team's health or remove negative effects but have low damage and defense. You should choose characters that complement each other's roles and cover each other's weaknesses.</li>
-<li>Skill: Each character has a skill that determines their special ability or ninjutsu attack. The skills are divided into three types: red, blue, and green. Red skills deal damage to enemies or inflict status effects such as stun, seal, confusion, etc. Blue skills boost the team's stats or grant positive effects such as invincibility, evasion, speed up, etc. Green skills heal the team's health or remove negative effects such as poison, burn, slow down, etc. You should choose characters that have skills that suit your strategy and preference.</li>
-<li>Link Bonus: Each character has a link bonus that grants them extra stats or effects when they are paired with certain other characters. The link bonus is based on the character's relationship or affiliation in the anime series. For example, Naruto Uzumaki has a link bonus with Sasuke Uchiha, Sakura Haruno, Kakashi Hatake, etc. You should choose characters that have link bonuses with each other to increase their performance.</li>
-</ul>
- <h3>Complete Every Mission</h3>
- <p>Naruto x Boruto Ninja Voltage has various missions that you can play to earn rewards and progress in the game. You should complete every mission that you can to maximize your benefits and experience. Some of the missions that you should complete are:</p>
- <ul>
-<li>Daily Missions: These are missions that reset every day and give you rewards such as shinobites, summoning tickets, chakra, ryo, etc. You should complete these missions every day to get free resources and items.</li>
-<li>Achievement Missions: These are missions that give you rewards based on your cumulative achievements in the game such as clearing a certain number of missions, upgrading a certain number of buildings, enhancing a certain number of cards, etc. You should complete these missions as soon as possible to get rewards such as shinobites, hero fragments awakening stones, chakra, ryo, etc. This will increase the stats and effects of your ninja cards to the maximum level. You can super awaken your ninja cards only once when they reach six stars and level 100.</li>
-<li>Equip: You can equip your shinobi with up to six ninja cards to increase their stats and abilities. You can also equip them with ninja tools to further boost their stats. You should equip your shinobi with ninja cards and ninja tools that match their element, role, and skill.</li>
-</ul>
- <h3>Join a Guild and Cooperate with Other Players</h3>
- <p>Naruto x Boruto Ninja Voltage is a game that can be played solo or with other players. However, playing with other players can give you more benefits and fun than playing alone. You should join a shinobi guild and cooperate with other players to enjoy the game more. Some of the benefits of joining a guild and cooperating with other players are:</p>
- <ul>
-<li>You can chat with other players and make new friends who share your passion for Naruto and Boruto.</li>
-<li>You can participate in guild wars and compete with other guilds for the highest battle ranking and rewards.</li>
-<li>You can join surprise attack missions and cooperate with other players to defeat giant bosses and earn guild points and medals.</li>
-<li>You can request and donate hero fragments to help each other upgrade your shinobi.</li>
-<li>You can get support from other players in case you need help or advice on the game.</li>
-</ul>
- <h2>Naruto x Boruto Ninja Voltage Game Review</h2>
- <p>Naruto x Boruto Ninja Voltage is a game that has received mixed reviews from critics and players alike. Some people love the game for its graphics, gameplay, and features, while others dislike the game for its bugs, glitches, and pay-to-win aspects. Here are some of the pros and cons of the game based on user ratings and feedback:</p>
- <h3>Pros and Cons of the Game</h3>
- <table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>The game has stunning 3D graphics that capture the anime style and atmosphere.</td><td>The game has long loading times and frequent updates that consume a lot of storage and battery.</td></tr>
-<tr><td>The game has simple and intuitive controls that make it easy to perform ninja combos and ninjutsu attacks.</td><td>The game has some bugs and glitches that affect the gameplay and performance.</td></tr>
-<tr><td>The game has a large roster of characters from both Naruto Shippuden and Boruto: Naruto Next Generations anime series.</td><td>The game has a low probability of getting rare or powerful characters or ninja cards from summons.</td></tr>
-<tr><td>The game has various game modes that offer different challenges and rewards.</td><td>The game has a high difficulty level that requires a lot of grinding or spending to progress.</td></tr>
-<tr><td>The game has a multiplayer option that allows you to play with or against other players online.</td><td>The game has a poor matchmaking system that pairs you with unfair opponents or teammates.</td></tr>
-</table>
- <h3>User Ratings and Feedback</h3>
- <p>Here are some of the user ratings and feedback from Google Play Store and App Store as of June 22, 2023:</p>
- <ul>
-<li>"This is one of the best Naruto games I have ever played. The graphics are amazing, the gameplay is smooth, and the characters are awesome. I love how you can customize your own fortress and attack other players. The only thing I don't like is how hard it is to get shinobites. They are so expensive and rare. I wish they would give more free shinobites or lower the prices." - 4 stars</li>
-<li>"This game is terrible. It is full of bugs and glitches that ruin the experience. The loading times are too long, the updates are too frequent, and the servers are too unstable. The game is also very pay-to-win. You need to spend a lot of money or time to get good characters or cards. The summons are rigged and the rates are too low. The game is also very repetitive and boring. There is no variety or creativity in the missions or the gameplay. The game is a waste of time and money. Do not download this game." - 1 star</li>
-<li>"This game is decent. It has some good aspects and some bad aspects. The good aspects are the graphics, the characters, and the story mode. The bad aspects are the loading times, the updates, and the shinobites. The game could be better if they fix the bugs, improve the loading times, and give more shinobites. The game is not bad, but it is not great either. It is just average." - 3 stars</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Naruto x Boruto Ninja Voltage is a game that lets you experience the ninja action of the Naruto and Boruto anime series. You can collect your favorite characters, create your own fortress, and battle with other players online. However, the game also has some drawbacks that might affect your enjoyment of the game. That's why some players prefer to use Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 to get unlimited shinobites and other features. In this article, we have explained what this mod apk is, how to download and install it, and some tips and tricks for playing the game. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021:</p>
- <ol>
-<li>Is Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 safe to use?</li>
-<p>Yes, Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 is safe to use as long as you download it from a trusted source and follow the installation instructions carefully. However, you should be aware that using any mod apk might violate the terms of service of the original game and might result in your account being banned or suspended. Therefore, you should use it at your own risk and discretion.</p>
-<li>Is Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 compatible with my device?</li>
-<p>Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 is compatible with most Android devices that have Android 4.4 or higher versions. However, some devices might not support the mod apk due to different specifications or settings. Therefore, you should check your device's compatibility before downloading and installing the mod apk.</p>
-<li>Can I play Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 with my friends?</li>
-<p>Yes, you can play Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 with your friends as long as they also have the mod apk installed on their devices. You can join a shinobi guild and cooperate with your friends in various missions and events. You can also chat with your friends and share your progress and achievements in the game.</p>
-<li>Can I update Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021?</li>
-<p>No, you cannot update Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 as it is a modified version of the original game that does not receive official updates from the game developers or servers. If you try to update the mod apk, you might lose your data or face errors or crashes in the game. Therefore, you should avoid updating the mod apk and stick to the current version.</p>
-<li>Can I switch back to the original game after using Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021?</li>
-<p>Yes, you can switch back to the original game after using Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 if you want to. However, you should be careful when doing so as you might lose your data or face compatibility issues in the original game. Therefore, you should backup your data before switching back to the original game and follow these steps:</p>
- <ul>
-<li>Uninstall Naruto x Boruto Ninja Voltage APK Mod Dinheiro Infinito 2021 from your device.</li>
-<li>Go to Google Play Store or App Store and download Naruto x Boruto Ninja Voltage from there.</li>
-<li>Launch the original game and log in with your account.</li>
-<li>Restore your data from your backup or start a new game.</li>
-</ul>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Panzer War Definitive Edition - How to Download and Play the Best Tank Game Ever.md b/spaces/congsaPfin/Manga-OCR/logs/Panzer War Definitive Edition - How to Download and Play the Best Tank Game Ever.md
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-
-<h1>Panzer War: Definitive Edition - A Review</h1>
-<p>Panzer War: Definitive Edition is a third-person shooter (TPS) tank game that simulates war battles from World War II to modern times. It features over 20 tanks and aircrafts from different countries and eras, as well as hundreds of mod tanks from the workshop. It also offers two types of damage mechanics, module-based and hitpoint-based, that you can choose according to your preference. The game uses the new rendering pipelines to enhance the graphics quality and performance. In this review, we will take a closer look at the game's features, gameplay, graphics, sound, mods, workshop, and more.</p>
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-<h2>Introduction</h2>
-<p>Panzer War: Definitive Edition is developed by WindyVerse and published by WindyVerse, Matrix Games, Shrapnel Games, and Strategic Simulations. It was released on Steam on February 28, 2018. The game is a remake of the classic Panzer War game that was released in 1995. The game does not have a tech-tree or a progression system. You can play all the tanks and aircrafts in the game for free. You can also download more tanks and aircrafts from the workshop or create your own mods.</p>
-<p>To download and install the game, you need to have a Steam account and a compatible device. The game requires a 64-bit processor and operating system, Windows 7 or higher, at least 2 GB of RAM, at least 2 GB of storage space, and DirectX version 9.0 or higher. The game costs $5.99 on Steam, but you can also get it for free if you buy Cry of War, another tank game by WindyVerse. You can also play the mobile version of the game by visiting [15](https://game.waroftanks.cn/de), but it is currently being tested in China only.</p>
-<h2>Gameplay</h2>
-<p>The gameplay of Panzer War: Definitive Edition is based on realistic physics, historical accuracy, and tactical strategy. You can control your tanks and aircrafts from a top-down perspective, on a map with a hexagonal grid. You can move, rotate, aim, fire, zoom, switch ammo types, repair, resupply, camouflage, scout, spot, capture flags, and more. You can also customize your units by changing their skins, decals, names, crews, equipment, etc.</p>
-<p>The game offers four main modes: 7v7, Skirmish (Respawn), Historical Mode, and Play Field. In 7v7 mode, you can play against other players or AI bots in team-based battles. In Skirmish mode, you can respawn after being destroyed and continue fighting until one team wins. In Historical Mode, you can play scenarios based on real historical events or battles. In Play Field mode, you can create your own scenarios or maps using various tools and options.</p>
-<p>One of the most unique features of the game is the two types of damage mechanics: module-based and hitpoint-based. In module-based mode, each part of your tank or aircraft has its own health and function. If a part is damaged or destroyed, it will affect the performance of your unit. For example, if your engine is damaged, you will move slower; if your gun is damaged, you will reload slower; if your ammo rack is damaged, you will explode. In hitpoint-based mode, your unit has a single health bar that decreases when you take damage. When your health reaches zero, you are destroyed. You can choose which mode you prefer in the settings menu.</p>
-<h2>Graphics and Sound</h2>
-<p>The graphics and sound of Panzer War: Definitive Edition are impressive and immersive. The game uses the new rendering pipelines to improve the lighting, shadows, reflections, textures, and effects of the game. The game also supports 4K resolution and HDR mode for better visual quality. The game has realistic and detailed models of tanks and aircrafts, as well as various terrains, buildings, trees, grass, water, smoke, fire, explosions, etc. The game also has dynamic weather and day/night cycles that affect the visibility and gameplay.</p>
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-<p>The sound of the game is also realistic and atmospheric. The game has authentic and accurate sounds of engines, guns, tracks, shells, impacts, etc. The game also has voice-overs for the crews and commanders of different countries and languages. The game also has a background music that matches the mood and theme of the game.</p>
-<p>The graphics and sound settings of the game can be adjusted to your preference in the options menu. You can change the resolution, quality, brightness, contrast, gamma, anti-aliasing, etc. You can also change the volume, language, subtitles, etc.</p>
-<h2>Mods and Workshop</h2>
-<p>Panzer War: Definitive Edition has a rich and active modding community that adds more content and features to the game. The game has a built-in workshop that allows you to access and use thousands of mods created by other players or developers. You can find mods that add new tanks, aircrafts, maps, scenarios, skins, decals, sounds, effects, etc. You can also rate, comment, subscribe, or unsubscribe to any mod you like or dislike.</p>
-<p>The game also allows you to create and share your own mods using the mod tools provided by the developers. You can use the model editor to create or edit models of tanks or aircrafts; the texture editor to create or edit textures of skins or decals; the map editor to create or edit maps or scenarios; the script editor to create or edit scripts of events or actions; etc. You can also use the mod manager to manage your mods and upload them to the workshop.</p>
-<h2>Conclusion</h2>
-<p>Panzer War: Definitive Edition is a fun and engaging tank game that offers a lot of variety and customization. The game has realistic and historical tanks and aircrafts from different countries and eras; two types of damage mechanics that suit different play styles; improved graphics and sound quality that enhance the immersion; a workshop and mod tools that allow you to access and create more content; and four modes that offer different challenges and experiences. The game is suitable for anyone who likes tank games or war games in general.</p>
-<p>However, the game also has some drawbacks that may affect your enjoyment. The game has some bugs and glitches that may cause crashes or errors; some balance issues that may make some tanks or aircrafts too weak or too strong; some optimization issues that may cause lag or low FPS; some interface issues that may make some menus or buttons hard to read or use; some translation issues that may make some texts or voices inaccurate or unclear; etc. The game is not suitable for anyone who expects a flawless or polished game.</p>
-<p>The final verdict and rating of Panzer War: Definitive Edition is 8/10. The game is a great remake of a classic tank game that offers a lot of fun and value for its price. The game is not perfect but it is still enjoyable and worth playing.</p>
-<h2>FAQs</h2>
-<ul>
-<li><b>Q1: Is Panzer War: Definitive Edition free to play?</b></li>
-<li>A1: No, Panzer War: Definitive Edition is not free to play. The game costs $5.99 on Steam but you can get it for free if you buy Cry of War by WindyVerse.</li>
-<li><b>Q2: Is Panzer War: Definitive Edition compatible with mobile devices?</b></li>
-<li>A2: Yes, Panzer War: Definitive Edition is compatible with mobile devices but it is currently being tested in China only. You can visit [15](https://game.waroftanks.cn/de ) to play the mobile version of the game.</li>
-<li><b>Q3: Is Panzer War: Definitive Edition multiplayer or single-player?</b></li>
-<li>A3: Panzer War: Definitive Edition is both multiplayer and single-player. You can play online with other players or offline with AI bots. You can also play co-op or versus modes with your friends or strangers.</li>
-<li><b>Q4: Is Panzer War: Definitive Edition realistic or arcade-like?</b></li>
-<li>A4: Panzer War: Definitive Edition is both realistic and arcade-like. You can choose between two types of damage mechanics, module-based or hitpoint-based, that affect the realism and difficulty of the game. You can also adjust the realism settings in the options menu, such as gravity, penetration, ricochet, etc.</li>
-<li><b>Q5: Is Panzer War: Definitive Edition updated regularly?</b></li>
-<li>A5: Yes, Panzer War: Definitive Edition is updated regularly by the developers and the modders. The game receives new tanks, aircrafts, maps, scenarios, skins, decals, sounds, effects, etc. from the workshop and the mod tools. The game also receives bug fixes, balance changes, optimization improvements, interface enhancements, translation corrections, etc. from the developers.</li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/contluForse/HuggingGPT/assets/?his?nho???n?sg???or????? ??8?rmdsl.md b/spaces/contluForse/HuggingGPT/assets/?his?nho???n?sg???or????? ??8?rmdsl.md
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/registry.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/registry.py
deleted file mode 100644
index 4f374cca4961c06babf328bb7407723a14026c47..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/registry.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from annotator.mmpkg.mmcv.utils import Registry
-
-CONV_LAYERS = Registry('conv layer')
-NORM_LAYERS = Registry('norm layer')
-ACTIVATION_LAYERS = Registry('activation layer')
-PADDING_LAYERS = Registry('padding layer')
-UPSAMPLE_LAYERS = Registry('upsample layer')
-PLUGIN_LAYERS = Registry('plugin layer')
-
-DROPOUT_LAYERS = Registry('drop out layers')
-POSITIONAL_ENCODING = Registry('position encoding')
-ATTENTION = Registry('attention')
-FEEDFORWARD_NETWORK = Registry('feed-forward Network')
-TRANSFORMER_LAYER = Registry('transformerLayer')
-TRANSFORMER_LAYER_SEQUENCE = Registry('transformer-layers sequence')
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/caffe2_validate.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/caffe2_validate.py
deleted file mode 100644
index 7cfaab38c095663fe32e4addbdf06b57bcb53614..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/caffe2_validate.py
+++ /dev/null
@@ -1,138 +0,0 @@
-""" Caffe2 validation script
-
-This script is created to verify exported ONNX models running in Caffe2
-It utilizes the same PyTorch dataloader/processing pipeline for a
-fair comparison against the originals.
-
-Copyright 2020 Ross Wightman
-"""
-import argparse
-import numpy as np
-from caffe2.python import core, workspace, model_helper
-from caffe2.proto import caffe2_pb2
-from data import create_loader, resolve_data_config, Dataset
-from utils import AverageMeter
-import time
-
-parser = argparse.ArgumentParser(description='Caffe2 ImageNet Validation')
-parser.add_argument('data', metavar='DIR',
-                    help='path to dataset')
-parser.add_argument('--c2-prefix', default='', type=str, metavar='NAME',
-                    help='caffe2 model pb name prefix')
-parser.add_argument('--c2-init', default='', type=str, metavar='PATH',
-                    help='caffe2 model init .pb')
-parser.add_argument('--c2-predict', default='', type=str, metavar='PATH',
-                    help='caffe2 model predict .pb')
-parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
-                    help='number of data loading workers (default: 2)')
-parser.add_argument('-b', '--batch-size', default=256, type=int,
-                    metavar='N', help='mini-batch size (default: 256)')
-parser.add_argument('--img-size', default=None, type=int,
-                    metavar='N', help='Input image dimension, uses model default if empty')
-parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
-                    help='Override mean pixel value of dataset')
-parser.add_argument('--std', type=float,  nargs='+', default=None, metavar='STD',
-                    help='Override std deviation of of dataset')
-parser.add_argument('--crop-pct', type=float, default=None, metavar='PCT',
-                    help='Override default crop pct of 0.875')
-parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
-                    help='Image resize interpolation type (overrides model)')
-parser.add_argument('--tf-preprocessing', dest='tf_preprocessing', action='store_true',
-                    help='use tensorflow mnasnet preporcessing')
-parser.add_argument('--print-freq', '-p', default=10, type=int,
-                    metavar='N', help='print frequency (default: 10)')
-
-
-def main():
-    args = parser.parse_args()
-    args.gpu_id = 0
-    if args.c2_prefix:
-        args.c2_init = args.c2_prefix + '.init.pb'
-        args.c2_predict = args.c2_prefix + '.predict.pb'
-
-    model = model_helper.ModelHelper(name="validation_net", init_params=False)
-
-    # Bring in the init net from init_net.pb
-    init_net_proto = caffe2_pb2.NetDef()
-    with open(args.c2_init, "rb") as f:
-        init_net_proto.ParseFromString(f.read())
-    model.param_init_net = core.Net(init_net_proto)
-
-    # bring in the predict net from predict_net.pb
-    predict_net_proto = caffe2_pb2.NetDef()
-    with open(args.c2_predict, "rb") as f:
-        predict_net_proto.ParseFromString(f.read())
-    model.net = core.Net(predict_net_proto)
-
-    data_config = resolve_data_config(None, args)
-    loader = create_loader(
-        Dataset(args.data, load_bytes=args.tf_preprocessing),
-        input_size=data_config['input_size'],
-        batch_size=args.batch_size,
-        use_prefetcher=False,
-        interpolation=data_config['interpolation'],
-        mean=data_config['mean'],
-        std=data_config['std'],
-        num_workers=args.workers,
-        crop_pct=data_config['crop_pct'],
-        tensorflow_preprocessing=args.tf_preprocessing)
-
-    # this is so obvious, wonderful interface </sarcasm>
-    input_blob = model.net.external_inputs[0]
-    output_blob = model.net.external_outputs[0]
-
-    if True:
-        device_opts = None
-    else:
-        # CUDA is crashing, no idea why, awesome error message, give it a try for kicks
-        device_opts = core.DeviceOption(caffe2_pb2.PROTO_CUDA, args.gpu_id)
-        model.net.RunAllOnGPU(gpu_id=args.gpu_id, use_cudnn=True)
-        model.param_init_net.RunAllOnGPU(gpu_id=args.gpu_id, use_cudnn=True)
-
-    model.param_init_net.GaussianFill(
-        [], input_blob.GetUnscopedName(),
-        shape=(1,) + data_config['input_size'], mean=0.0, std=1.0)
-    workspace.RunNetOnce(model.param_init_net)
-    workspace.CreateNet(model.net, overwrite=True)
-
-    batch_time = AverageMeter()
-    top1 = AverageMeter()
-    top5 = AverageMeter()
-    end = time.time()
-    for i, (input, target) in enumerate(loader):
-        # run the net and return prediction
-        caffe2_in = input.data.numpy()
-        workspace.FeedBlob(input_blob, caffe2_in, device_opts)
-        workspace.RunNet(model.net, num_iter=1)
-        output = workspace.FetchBlob(output_blob)
-
-        # measure accuracy and record loss
-        prec1, prec5 = accuracy_np(output.data, target.numpy())
-        top1.update(prec1.item(), input.size(0))
-        top5.update(prec5.item(), input.size(0))
-
-        # measure elapsed time
-        batch_time.update(time.time() - end)
-        end = time.time()
-
-        if i % args.print_freq == 0:
-            print('Test: [{0}/{1}]\t'
-                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {rate_avg:.3f}/s, {ms_avg:.3f} ms/sample) \t'
-                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
-                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
-                i, len(loader), batch_time=batch_time, rate_avg=input.size(0) / batch_time.avg,
-                ms_avg=100 * batch_time.avg / input.size(0), top1=top1, top5=top5))
-
-    print(' * Prec@1 {top1.avg:.3f} ({top1a:.3f}) Prec@5 {top5.avg:.3f} ({top5a:.3f})'.format(
-        top1=top1, top1a=100-top1.avg, top5=top5, top5a=100.-top5.avg))
-
-
-def accuracy_np(output, target):
-    max_indices = np.argsort(output, axis=1)[:, ::-1]
-    top5 = 100 * np.equal(max_indices[:, :5], target[:, np.newaxis]).sum(axis=1).mean()
-    top1 = 100 * np.equal(max_indices[:, 0], target).mean()
-    return top1, top5
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/env.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/env.py
deleted file mode 100644
index 40634c17c73273ac8927632be164f466cfe7d1fa..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/env.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import importlib
-import importlib.util
-import logging
-import numpy as np
-import os
-import random
-import sys
-from datetime import datetime
-import torch
-
-__all__ = ["seed_all_rng"]
-
-
-TORCH_VERSION = tuple(int(x) for x in torch.__version__.split(".")[:2])
-"""
-PyTorch version as a tuple of 2 ints. Useful for comparison.
-"""
-
-
-DOC_BUILDING = os.getenv("_DOC_BUILDING", False)  # set in docs/conf.py
-"""
-Whether we're building documentation.
-"""
-
-
-def seed_all_rng(seed=None):
-    """
-    Set the random seed for the RNG in torch, numpy and python.
-
-    Args:
-        seed (int): if None, will use a strong random seed.
-    """
-    if seed is None:
-        seed = (
-            os.getpid()
-            + int(datetime.now().strftime("%S%f"))
-            + int.from_bytes(os.urandom(2), "big")
-        )
-        logger = logging.getLogger(__name__)
-        logger.info("Using a generated random seed {}".format(seed))
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    random.seed(seed)
-    os.environ["PYTHONHASHSEED"] = str(seed)
-
-
-# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
-def _import_file(module_name, file_path, make_importable=False):
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    if make_importable:
-        sys.modules[module_name] = module
-    return module
-
-
-def _configure_libraries():
-    """
-    Configurations for some libraries.
-    """
-    # An environment option to disable `import cv2` globally,
-    # in case it leads to negative performance impact
-    disable_cv2 = int(os.environ.get("DETECTRON2_DISABLE_CV2", False))
-    if disable_cv2:
-        sys.modules["cv2"] = None
-    else:
-        # Disable opencl in opencv since its interaction with cuda often has negative effects
-        # This envvar is supported after OpenCV 3.4.0
-        os.environ["OPENCV_OPENCL_RUNTIME"] = "disabled"
-        try:
-            import cv2
-
-            if int(cv2.__version__.split(".")[0]) >= 3:
-                cv2.ocl.setUseOpenCL(False)
-        except ModuleNotFoundError:
-            # Other types of ImportError, if happened, should not be ignored.
-            # Because a failed opencv import could mess up address space
-            # https://github.com/skvark/opencv-python/issues/381
-            pass
-
-    def get_version(module, digit=2):
-        return tuple(map(int, module.__version__.split(".")[:digit]))
-
-    # fmt: off
-    assert get_version(torch) >= (1, 4), "Requires torch>=1.4"
-    import fvcore
-    assert get_version(fvcore, 3) >= (0, 1, 2), "Requires fvcore>=0.1.2"
-    import yaml
-    assert get_version(yaml) >= (5, 1), "Requires pyyaml>=5.1"
-    # fmt: on
-
-
-_ENV_SETUP_DONE = False
-
-
-def setup_environment():
-    """Perform environment setup work. The default setup is a no-op, but this
-    function allows the user to specify a Python source file or a module in
-    the $DETECTRON2_ENV_MODULE environment variable, that performs
-    custom setup work that may be necessary to their computing environment.
-    """
-    global _ENV_SETUP_DONE
-    if _ENV_SETUP_DONE:
-        return
-    _ENV_SETUP_DONE = True
-
-    _configure_libraries()
-
-    custom_module_path = os.environ.get("DETECTRON2_ENV_MODULE")
-
-    if custom_module_path:
-        setup_custom_environment(custom_module_path)
-    else:
-        # The default setup is a no-op
-        pass
-
-
-def setup_custom_environment(custom_module):
-    """
-    Load custom environment setup by importing a Python source file or a
-    module, and run the setup function.
-    """
-    if custom_module.endswith(".py"):
-        module = _import_file("detectron2.utils.env.custom_module", custom_module)
-    else:
-        module = importlib.import_module(custom_module)
-    assert hasattr(module, "setup_environment") and callable(module.setup_environment), (
-        "Custom environment module defined in {} does not have the "
-        "required callable attribute 'setup_environment'."
-    ).format(custom_module)
-    module.setup_environment()
-
-
-def fixup_module_metadata(module_name, namespace, keys=None):
-    """
-    Fix the __qualname__ of module members to be their exported api name, so
-    when they are referenced in docs, sphinx can find them. Reference:
-    https://github.com/python-trio/trio/blob/6754c74eacfad9cc5c92d5c24727a2f3b620624e/trio/_util.py#L216-L241
-    """
-    if not DOC_BUILDING:
-        return
-    seen_ids = set()
-
-    def fix_one(qualname, name, obj):
-        # avoid infinite recursion (relevant when using
-        # typing.Generic, for example)
-        if id(obj) in seen_ids:
-            return
-        seen_ids.add(id(obj))
-
-        mod = getattr(obj, "__module__", None)
-        if mod is not None and (mod.startswith(module_name) or mod.startswith("fvcore.")):
-            obj.__module__ = module_name
-            # Modules, unlike everything else in Python, put fully-qualitied
-            # names into their __name__ attribute. We check for "." to avoid
-            # rewriting these.
-            if hasattr(obj, "__name__") and "." not in obj.__name__:
-                obj.__name__ = name
-                obj.__qualname__ = qualname
-            if isinstance(obj, type):
-                for attr_name, attr_value in obj.__dict__.items():
-                    fix_one(objname + "." + attr_name, attr_name, attr_value)
-
-    if keys is None:
-        keys = namespace.keys()
-    for objname in keys:
-        if not objname.startswith("_"):
-            obj = namespace[objname]
-            fix_one(objname, objname, obj)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py
deleted file mode 100644
index 7368ae8031188a9f946d9d3f29633c96e791e68e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py
+++ /dev/null
@@ -1,333 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# File author: Shariq Farooq Bhat
-
-import itertools
-
-import torch
-import torch.nn as nn
-
-from zoedepth.models.depth_model import DepthModel
-from zoedepth.models.base_models.midas import MidasCore
-from zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed
-from zoedepth.models.layers.dist_layers import ConditionalLogBinomial
-from zoedepth.models.layers.localbins_layers import (Projector, SeedBinRegressor,
-                                            SeedBinRegressorUnnormed)
-from zoedepth.models.layers.patch_transformer import PatchTransformerEncoder
-from zoedepth.models.model_io import load_state_from_resource
-
-
-class ZoeDepthNK(DepthModel):
-    def __init__(self, core,  bin_conf, bin_centers_type="softplus", bin_embedding_dim=128,
-                 n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp',
-                 min_temp=5, max_temp=50,
-                 memory_efficient=False, train_midas=True,
-                 is_midas_pretrained=True, midas_lr_factor=1, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False,  **kwargs):
-        """ZoeDepthNK model. This is the version of ZoeDepth that has two metric heads and uses a learned router to route to experts.
-
-        Args:
-            core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features
-
-            bin_conf (List[dict]): A list of dictionaries that contain the bin configuration for each metric head. Each dictionary should contain the following keys: 
-                                    "name" (str, typically same as the dataset name), "n_bins" (int), "min_depth" (float), "max_depth" (float)
-
-                                   The length of this list determines the number of metric heads.
-            bin_centers_type (str, optional): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers.
-                                               For "softplus", softplus activation is used and thus are unbounded. Defaults to "normed".
-            bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128.
-
-            n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1].
-            attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300.
-            attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2.
-            attractor_kind (str, optional): Attraction aggregation "sum" or "mean". Defaults to 'sum'.
-            attractor_type (str, optional): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'exp'.
-
-            min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5.
-            max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50.
-            
-            memory_efficient (bool, optional): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended incase of multiple metric heads in order save GPU memory. Defaults to False.
-
-            train_midas (bool, optional): Whether to train "core", the base midas model. Defaults to True.
-            is_midas_pretrained (bool, optional): Is "core" pretrained? Defaults to True.
-            midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10.
-            encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10.
-            pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10.
-
-        """
-
-        super().__init__()
-
-        self.core = core
-        self.bin_conf = bin_conf
-        self.min_temp = min_temp
-        self.max_temp = max_temp
-        self.memory_efficient = memory_efficient
-        self.train_midas = train_midas
-        self.is_midas_pretrained = is_midas_pretrained
-        self.midas_lr_factor = midas_lr_factor
-        self.encoder_lr_factor = encoder_lr_factor
-        self.pos_enc_lr_factor = pos_enc_lr_factor
-        self.inverse_midas = inverse_midas
-
-        N_MIDAS_OUT = 32
-        btlnck_features = self.core.output_channels[0]
-        num_out_features = self.core.output_channels[1:]
-        # self.scales = [16, 8, 4, 2]  # spatial scale factors
-
-        self.conv2 = nn.Conv2d(
-            btlnck_features, btlnck_features, kernel_size=1, stride=1, padding=0)
-
-        # Transformer classifier on the bottleneck
-        self.patch_transformer = PatchTransformerEncoder(
-            btlnck_features, 1, 128, use_class_token=True)
-        self.mlp_classifier = nn.Sequential(
-            nn.Linear(128, 128),
-            nn.ReLU(),
-            nn.Linear(128, 2)
-        )
-
-        if bin_centers_type == "normed":
-            SeedBinRegressorLayer = SeedBinRegressor
-            Attractor = AttractorLayer
-        elif bin_centers_type == "softplus":
-            SeedBinRegressorLayer = SeedBinRegressorUnnormed
-            Attractor = AttractorLayerUnnormed
-        elif bin_centers_type == "hybrid1":
-            SeedBinRegressorLayer = SeedBinRegressor
-            Attractor = AttractorLayerUnnormed
-        elif bin_centers_type == "hybrid2":
-            SeedBinRegressorLayer = SeedBinRegressorUnnormed
-            Attractor = AttractorLayer
-        else:
-            raise ValueError(
-                "bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'")
-        self.bin_centers_type = bin_centers_type
-        # We have bins for each bin conf.
-        # Create a map (ModuleDict) of 'name' -> seed_bin_regressor
-        self.seed_bin_regressors = nn.ModuleDict(
-            {conf['name']: SeedBinRegressorLayer(btlnck_features, conf["n_bins"], mlp_dim=bin_embedding_dim//2, min_depth=conf["min_depth"], max_depth=conf["max_depth"])
-             for conf in bin_conf}
-        )
-
-        self.seed_projector = Projector(
-            btlnck_features, bin_embedding_dim, mlp_dim=bin_embedding_dim//2)
-        self.projectors = nn.ModuleList([
-            Projector(num_out, bin_embedding_dim, mlp_dim=bin_embedding_dim//2)
-            for num_out in num_out_features
-        ])
-
-        # Create a map (ModuleDict) of 'name' -> attractors (ModuleList)
-        self.attractors = nn.ModuleDict(
-            {conf['name']: nn.ModuleList([
-                Attractor(bin_embedding_dim, n_attractors[i],
-                          mlp_dim=bin_embedding_dim, alpha=attractor_alpha,
-                          gamma=attractor_gamma, kind=attractor_kind,
-                          attractor_type=attractor_type, memory_efficient=memory_efficient,
-                          min_depth=conf["min_depth"], max_depth=conf["max_depth"])
-                for i in range(len(n_attractors))
-            ])
-                for conf in bin_conf}
-        )
-
-        last_in = N_MIDAS_OUT
-        # conditional log binomial for each bin conf
-        self.conditional_log_binomial = nn.ModuleDict(
-            {conf['name']: ConditionalLogBinomial(last_in, bin_embedding_dim, conf['n_bins'], bottleneck_factor=4, min_temp=self.min_temp, max_temp=self.max_temp)
-             for conf in bin_conf}
-        )
-
-    def forward(self, x, return_final_centers=False, denorm=False, return_probs=False, **kwargs):
-        """
-        Args:
-            x (torch.Tensor): Input image tensor of shape (B, C, H, W). Assumes all images are from the same domain.
-            return_final_centers (bool, optional): Whether to return the final centers of the attractors. Defaults to False.
-            denorm (bool, optional): Whether to denormalize the input image. Defaults to False.
-            return_probs (bool, optional): Whether to return the probabilities of the bins. Defaults to False.
-        
-        Returns:
-            dict: Dictionary of outputs with keys:
-                - "rel_depth": Relative depth map of shape (B, 1, H, W)
-                - "metric_depth": Metric depth map of shape (B, 1, H, W)
-                - "domain_logits": Domain logits of shape (B, 2)
-                - "bin_centers": Bin centers of shape (B, N, H, W). Present only if return_final_centers is True
-                - "probs": Bin probabilities of shape (B, N, H, W). Present only if return_probs is True
-        """
-        b, c, h, w = x.shape
-        self.orig_input_width = w
-        self.orig_input_height = h
-        rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True)
-
-        outconv_activation = out[0]
-        btlnck = out[1]
-        x_blocks = out[2:]
-
-        x_d0 = self.conv2(btlnck)
-        x = x_d0
-
-        # Predict which path to take
-        embedding = self.patch_transformer(x)[0]  # N, E
-        domain_logits = self.mlp_classifier(embedding)  # N, 2
-        domain_vote = torch.softmax(domain_logits.sum(
-            dim=0, keepdim=True), dim=-1)  # 1, 2
-
-        # Get the path
-        bin_conf_name = ["nyu", "kitti"][torch.argmax(
-            domain_vote, dim=-1).squeeze().item()]
-
-        try:
-            conf = [c for c in self.bin_conf if c.name == bin_conf_name][0]
-        except IndexError:
-            raise ValueError(
-                f"bin_conf_name {bin_conf_name} not found in bin_confs")
-
-        min_depth = conf['min_depth']
-        max_depth = conf['max_depth']
-
-        seed_bin_regressor = self.seed_bin_regressors[bin_conf_name]
-        _, seed_b_centers = seed_bin_regressor(x)
-        if self.bin_centers_type == 'normed' or self.bin_centers_type == 'hybrid2':
-            b_prev = (seed_b_centers - min_depth)/(max_depth - min_depth)
-        else:
-            b_prev = seed_b_centers
-        prev_b_embedding = self.seed_projector(x)
-
-        attractors = self.attractors[bin_conf_name]
-        for projector, attractor, x in zip(self.projectors, attractors, x_blocks):
-            b_embedding = projector(x)
-            b, b_centers = attractor(
-                b_embedding, b_prev, prev_b_embedding, interpolate=True)
-            b_prev = b
-            prev_b_embedding = b_embedding
-
-        last = outconv_activation
-
-        b_centers = nn.functional.interpolate(
-            b_centers, last.shape[-2:], mode='bilinear', align_corners=True)
-        b_embedding = nn.functional.interpolate(
-            b_embedding, last.shape[-2:], mode='bilinear', align_corners=True)
-
-        clb = self.conditional_log_binomial[bin_conf_name]
-        x = clb(last, b_embedding)
-
-        # Now depth value is Sum px * cx , where cx are bin_centers from the last bin tensor
-        # print(x.shape, b_centers.shape)
-        # b_centers = nn.functional.interpolate(b_centers, x.shape[-2:], mode='bilinear', align_corners=True)
-        out = torch.sum(x * b_centers, dim=1, keepdim=True)
-
-        output = dict(domain_logits=domain_logits, metric_depth=out)
-        if return_final_centers or return_probs:
-            output['bin_centers'] = b_centers
-
-        if return_probs:
-            output['probs'] = x
-        return output
-
-    def get_lr_params(self, lr):
-        """
-        Learning rate configuration for different layers of the model
-
-        Args:
-            lr (float) : Base learning rate
-        Returns:
-            list : list of parameters to optimize and their learning rates, in the format required by torch optimizers.
-        """
-        param_conf = []
-        if self.train_midas:
-            def get_rel_pos_params():
-                for name, p in self.core.core.pretrained.named_parameters():
-                    if "relative_position" in name:
-                        yield p
-
-            def get_enc_params_except_rel_pos():
-                for name, p in self.core.core.pretrained.named_parameters():
-                    if "relative_position" not in name:
-                        yield p
-
-            encoder_params = get_enc_params_except_rel_pos()
-            rel_pos_params = get_rel_pos_params()
-            midas_params = self.core.core.scratch.parameters()
-            midas_lr_factor = self.midas_lr_factor if self.is_midas_pretrained else 1.0
-            param_conf.extend([
-                {'params': encoder_params, 'lr': lr / self.encoder_lr_factor},
-                {'params': rel_pos_params, 'lr': lr / self.pos_enc_lr_factor},
-                {'params': midas_params, 'lr': lr / midas_lr_factor}
-            ])
-
-        remaining_modules = []
-        for name, child in self.named_children():
-            if name != 'core':
-                remaining_modules.append(child)
-        remaining_params = itertools.chain(
-            *[child.parameters() for child in remaining_modules])
-        param_conf.append({'params': remaining_params, 'lr': lr})
-        return param_conf
-
-    def get_conf_parameters(self, conf_name):
-        """
-        Returns parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
-        """
-        params = []
-        for name, child in self.named_children():
-            if isinstance(child, nn.ModuleDict):
-                for bin_conf_name, module in child.items():
-                    if bin_conf_name == conf_name:
-                        params += list(module.parameters())
-        return params
-
-    def freeze_conf(self, conf_name):
-        """
-        Freezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
-        """
-        for p in self.get_conf_parameters(conf_name):
-            p.requires_grad = False
-
-    def unfreeze_conf(self, conf_name):
-        """
-        Unfreezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
-        """
-        for p in self.get_conf_parameters(conf_name):
-            p.requires_grad = True
-
-    def freeze_all_confs(self):
-        """
-        Freezes all the parameters of all the ModuleDicts children
-        """
-        for name, child in self.named_children():
-            if isinstance(child, nn.ModuleDict):
-                for bin_conf_name, module in child.items():
-                    for p in module.parameters():
-                        p.requires_grad = False
-
-    @staticmethod
-    def build(midas_model_type="DPT_BEiT_L_384", pretrained_resource=None, use_pretrained_midas=False, train_midas=False, freeze_midas_bn=True, **kwargs):
-        core = MidasCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas,
-                               train_midas=train_midas, fetch_features=True, freeze_bn=freeze_midas_bn, **kwargs)
-        model = ZoeDepthNK(core, **kwargs)
-        if pretrained_resource:
-            assert isinstance(pretrained_resource, str), "pretrained_resource must be a string"
-            model = load_state_from_resource(model, pretrained_resource)
-        return model
-
-    @staticmethod
-    def build_from_config(config):
-        return ZoeDepthNK.build(**config)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/misc.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/misc.py
deleted file mode 100644
index 4bbe403d3669829eecdf658458c76aa5e87e2b33..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/misc.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# File author: Shariq Farooq Bhat
-
-"""Miscellaneous utility functions."""
-
-from scipy import ndimage
-
-import base64
-import math
-import re
-from io import BytesIO
-
-import matplotlib
-import matplotlib.cm
-import numpy as np
-import requests
-import torch
-import torch.distributed as dist
-import torch.nn
-import torch.nn as nn
-import torch.utils.data.distributed
-from PIL import Image
-from torchvision.transforms import ToTensor
-
-
-class RunningAverage:
-    def __init__(self):
-        self.avg = 0
-        self.count = 0
-
-    def append(self, value):
-        self.avg = (value + self.count * self.avg) / (self.count + 1)
-        self.count += 1
-
-    def get_value(self):
-        return self.avg
-
-
-def denormalize(x):
-    """Reverses the imagenet normalization applied to the input.
-
-    Args:
-        x (torch.Tensor - shape(N,3,H,W)): input tensor
-
-    Returns:
-        torch.Tensor - shape(N,3,H,W): Denormalized input
-    """
-    mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(x.device)
-    std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(x.device)
-    return x * std + mean
-
-
-class RunningAverageDict:
-    """A dictionary of running averages."""
-    def __init__(self):
-        self._dict = None
-
-    def update(self, new_dict):
-        if new_dict is None:
-            return
-
-        if self._dict is None:
-            self._dict = dict()
-            for key, value in new_dict.items():
-                self._dict[key] = RunningAverage()
-
-        for key, value in new_dict.items():
-            self._dict[key].append(value)
-
-    def get_value(self):
-        if self._dict is None:
-            return None
-        return {key: value.get_value() for key, value in self._dict.items()}
-
-
-def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):
-    """Converts a depth map to a color image.
-
-    Args:
-        value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
-        vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
-        vmax (float, optional):  vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
-        cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.
-        invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.
-        invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.
-        background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).
-        gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.
-        value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.
-
-    Returns:
-        numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)
-    """
-    if isinstance(value, torch.Tensor):
-        value = value.detach().cpu().numpy()
-
-    value = value.squeeze()
-    if invalid_mask is None:
-        invalid_mask = value == invalid_val
-    mask = np.logical_not(invalid_mask)
-
-    # normalize
-    vmin = np.percentile(value[mask],2) if vmin is None else vmin
-    vmax = np.percentile(value[mask],85) if vmax is None else vmax
-    if vmin != vmax:
-        value = (value - vmin) / (vmax - vmin)  # vmin..vmax
-    else:
-        # Avoid 0-division
-        value = value * 0.
-
-    # squeeze last dim if it exists
-    # grey out the invalid values
-
-    value[invalid_mask] = np.nan
-    cmapper = matplotlib.cm.get_cmap(cmap)
-    if value_transform:
-        value = value_transform(value)
-        # value = value / value.max()
-    value = cmapper(value, bytes=True)  # (nxmx4)
-
-    # img = value[:, :, :]
-    img = value[...]
-    img[invalid_mask] = background_color
-
-    #     return img.transpose((2, 0, 1))
-    if gamma_corrected:
-        # gamma correction
-        img = img / 255
-        img = np.power(img, 2.2)
-        img = img * 255
-        img = img.astype(np.uint8)
-    return img
-
-
-def count_parameters(model, include_all=False):
-    return sum(p.numel() for p in model.parameters() if p.requires_grad or include_all)
-
-
-def compute_errors(gt, pred):
-    """Compute metrics for 'pred' compared to 'gt'
-
-    Args:
-        gt (numpy.ndarray): Ground truth values
-        pred (numpy.ndarray): Predicted values
-
-        gt.shape should be equal to pred.shape
-
-    Returns:
-        dict: Dictionary containing the following metrics:
-            'a1': Delta1 accuracy: Fraction of pixels that are within a scale factor of 1.25
-            'a2': Delta2 accuracy: Fraction of pixels that are within a scale factor of 1.25^2
-            'a3': Delta3 accuracy: Fraction of pixels that are within a scale factor of 1.25^3
-            'abs_rel': Absolute relative error
-            'rmse': Root mean squared error
-            'log_10': Absolute log10 error
-            'sq_rel': Squared relative error
-            'rmse_log': Root mean squared error on the log scale
-            'silog': Scale invariant log error
-    """
-    thresh = np.maximum((gt / pred), (pred / gt))
-    a1 = (thresh < 1.25).mean()
-    a2 = (thresh < 1.25 ** 2).mean()
-    a3 = (thresh < 1.25 ** 3).mean()
-
-    abs_rel = np.mean(np.abs(gt - pred) / gt)
-    sq_rel = np.mean(((gt - pred) ** 2) / gt)
-
-    rmse = (gt - pred) ** 2
-    rmse = np.sqrt(rmse.mean())
-
-    rmse_log = (np.log(gt) - np.log(pred)) ** 2
-    rmse_log = np.sqrt(rmse_log.mean())
-
-    err = np.log(pred) - np.log(gt)
-    silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
-
-    log_10 = (np.abs(np.log10(gt) - np.log10(pred))).mean()
-    return dict(a1=a1, a2=a2, a3=a3, abs_rel=abs_rel, rmse=rmse, log_10=log_10, rmse_log=rmse_log,
-                silog=silog, sq_rel=sq_rel)
-
-
-def compute_metrics(gt, pred, interpolate=True, garg_crop=False, eigen_crop=True, dataset='nyu', min_depth_eval=0.1, max_depth_eval=10, **kwargs):
-    """Compute metrics of predicted depth maps. Applies cropping and masking as necessary or specified via arguments. Refer to compute_errors for more details on metrics.
-    """
-    if 'config' in kwargs:
-        config = kwargs['config']
-        garg_crop = config.garg_crop
-        eigen_crop = config.eigen_crop
-        min_depth_eval = config.min_depth_eval
-        max_depth_eval = config.max_depth_eval
-
-    if gt.shape[-2:] != pred.shape[-2:] and interpolate:
-        pred = nn.functional.interpolate(
-            pred, gt.shape[-2:], mode='bilinear', align_corners=True)
-
-    pred = pred.squeeze().cpu().numpy()
-    pred[pred < min_depth_eval] = min_depth_eval
-    pred[pred > max_depth_eval] = max_depth_eval
-    pred[np.isinf(pred)] = max_depth_eval
-    pred[np.isnan(pred)] = min_depth_eval
-
-    gt_depth = gt.squeeze().cpu().numpy()
-    valid_mask = np.logical_and(
-        gt_depth > min_depth_eval, gt_depth < max_depth_eval)
-
-    if garg_crop or eigen_crop:
-        gt_height, gt_width = gt_depth.shape
-        eval_mask = np.zeros(valid_mask.shape)
-
-        if garg_crop:
-            eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height),
-                      int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
-
-        elif eigen_crop:
-            # print("-"*10, " EIGEN CROP ", "-"*10)
-            if dataset == 'kitti':
-                eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height),
-                          int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1
-            else:
-                # assert gt_depth.shape == (480, 640), "Error: Eigen crop is currently only valid for (480, 640) images"
-                eval_mask[45:471, 41:601] = 1
-        else:
-            eval_mask = np.ones(valid_mask.shape)
-    valid_mask = np.logical_and(valid_mask, eval_mask)
-    return compute_errors(gt_depth[valid_mask], pred[valid_mask])
-
-
-#################################### Model uilts ################################################
-
-
-def parallelize(config, model, find_unused_parameters=True):
-
-    if config.gpu is not None:
-        torch.cuda.set_device(config.gpu)
-        model = model.cuda(config.gpu)
-
-    config.multigpu = False
-    if config.distributed:
-        # Use DDP
-        config.multigpu = True
-        config.rank = config.rank * config.ngpus_per_node + config.gpu
-        dist.init_process_group(backend=config.dist_backend, init_method=config.dist_url,
-                                world_size=config.world_size, rank=config.rank)
-        config.batch_size = int(config.batch_size / config.ngpus_per_node)
-        # config.batch_size = 8
-        config.workers = int(
-            (config.num_workers + config.ngpus_per_node - 1) / config.ngpus_per_node)
-        print("Device", config.gpu, "Rank",  config.rank, "batch size",
-              config.batch_size, "Workers", config.workers)
-        torch.cuda.set_device(config.gpu)
-        model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
-        model = model.cuda(config.gpu)
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.gpu], output_device=config.gpu,
-                                                          find_unused_parameters=find_unused_parameters)
-
-    elif config.gpu is None:
-        # Use DP
-        config.multigpu = True
-        model = model.cuda()
-        model = torch.nn.DataParallel(model)
-
-    return model
-
-
-#################################################################################################
-
-
-#####################################################################################################
-
-
-class colors:
-    '''Colors class:
-    Reset all colors with colors.reset
-    Two subclasses fg for foreground and bg for background.
-    Use as colors.subclass.colorname.
-    i.e. colors.fg.red or colors.bg.green
-    Also, the generic bold, disable, underline, reverse, strikethrough,
-    and invisible work with the main class
-    i.e. colors.bold
-    '''
-    reset = '\033[0m'
-    bold = '\033[01m'
-    disable = '\033[02m'
-    underline = '\033[04m'
-    reverse = '\033[07m'
-    strikethrough = '\033[09m'
-    invisible = '\033[08m'
-
-    class fg:
-        black = '\033[30m'
-        red = '\033[31m'
-        green = '\033[32m'
-        orange = '\033[33m'
-        blue = '\033[34m'
-        purple = '\033[35m'
-        cyan = '\033[36m'
-        lightgrey = '\033[37m'
-        darkgrey = '\033[90m'
-        lightred = '\033[91m'
-        lightgreen = '\033[92m'
-        yellow = '\033[93m'
-        lightblue = '\033[94m'
-        pink = '\033[95m'
-        lightcyan = '\033[96m'
-
-    class bg:
-        black = '\033[40m'
-        red = '\033[41m'
-        green = '\033[42m'
-        orange = '\033[43m'
-        blue = '\033[44m'
-        purple = '\033[45m'
-        cyan = '\033[46m'
-        lightgrey = '\033[47m'
-
-
-def printc(text, color):
-    print(f"{color}{text}{colors.reset}")
-
-############################################
-
-def get_image_from_url(url):
-    response = requests.get(url)
-    img = Image.open(BytesIO(response.content)).convert("RGB")
-    return img
-
-def url_to_torch(url, size=(384, 384)):
-    img = get_image_from_url(url)
-    img = img.resize(size, Image.ANTIALIAS)
-    img = torch.from_numpy(np.asarray(img)).float()
-    img = img.permute(2, 0, 1)
-    img.div_(255)
-    return img
-
-def pil_to_batched_tensor(img):
-    return ToTensor()(img).unsqueeze(0)
-
-def save_raw_16bit(depth, fpath="raw.png"):
-    if isinstance(depth, torch.Tensor):
-        depth = depth.squeeze().cpu().numpy()
-    
-    assert isinstance(depth, np.ndarray), "Depth must be a torch tensor or numpy array"
-    assert depth.ndim == 2, "Depth must be 2D"
-    depth = depth * 256  # scale for 16-bit png
-    depth = depth.astype(np.uint16)
-    depth = Image.fromarray(depth)
-    depth.save(fpath)
-    print("Saved raw depth to", fpath)
\ No newline at end of file
diff --git a/spaces/cozyanduofen/bingo/src/components/ui/alert-dialog.tsx b/spaces/cozyanduofen/bingo/src/components/ui/alert-dialog.tsx
deleted file mode 100644
index 17fec4d16510328deacc1416569173c97761ef72..0000000000000000000000000000000000000000
--- a/spaces/cozyanduofen/bingo/src/components/ui/alert-dialog.tsx
+++ /dev/null
@@ -1,150 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as AlertDialogPrimitive from '@radix-ui/react-alert-dialog'
-
-import { cn } from '@/lib/utils'
-import { buttonVariants } from '@/components/ui/button'
-
-const AlertDialog = AlertDialogPrimitive.Root
-
-const AlertDialogTrigger = AlertDialogPrimitive.Trigger
-
-const AlertDialogPortal = ({
-  className,
-  children,
-  ...props
-}: AlertDialogPrimitive.AlertDialogPortalProps) => (
-  <AlertDialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-end justify-center sm:items-center">
-      {children}
-    </div>
-  </AlertDialogPrimitive.Portal>
-)
-AlertDialogPortal.displayName = AlertDialogPrimitive.Portal.displayName
-
-const AlertDialogOverlay = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <AlertDialogPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-opacity animate-in fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-AlertDialogOverlay.displayName = AlertDialogPrimitive.Overlay.displayName
-
-const AlertDialogContent = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Content>
->(({ className, ...props }, ref) => (
-  <AlertDialogPortal>
-    <AlertDialogOverlay />
-    <AlertDialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full max-w-lg scale-100 gap-4 border bg-background p-6 opacity-100 shadow-lg animate-in fade-in-90 slide-in-from-bottom-10 sm:rounded-lg sm:zoom-in-90 sm:slide-in-from-bottom-0 md:w-full',
-        className
-      )}
-      {...props}
-    />
-  </AlertDialogPortal>
-))
-AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName
-
-const AlertDialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-2 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogHeader.displayName = 'AlertDialogHeader'
-
-const AlertDialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogFooter.displayName = 'AlertDialogFooter'
-
-const AlertDialogTitle = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold', className)}
-    {...props}
-  />
-))
-AlertDialogTitle.displayName = AlertDialogPrimitive.Title.displayName
-
-const AlertDialogDescription = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-AlertDialogDescription.displayName =
-  AlertDialogPrimitive.Description.displayName
-
-const AlertDialogAction = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Action>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Action>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Action
-    ref={ref}
-    className={cn(buttonVariants(), className)}
-    {...props}
-  />
-))
-AlertDialogAction.displayName = AlertDialogPrimitive.Action.displayName
-
-const AlertDialogCancel = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Cancel>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Cancel>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Cancel
-    ref={ref}
-    className={cn(
-      buttonVariants({ variant: 'outline' }),
-      'mt-2 sm:mt-0',
-      className
-    )}
-    {...props}
-  />
-))
-AlertDialogCancel.displayName = AlertDialogPrimitive.Cancel.displayName
-
-export {
-  AlertDialog,
-  AlertDialogTrigger,
-  AlertDialogContent,
-  AlertDialogHeader,
-  AlertDialogFooter,
-  AlertDialogTitle,
-  AlertDialogDescription,
-  AlertDialogAction,
-  AlertDialogCancel
-}
diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/eyebrow_morphing_combiner/eyebrow_morphing_combiner_00.py b/spaces/cymic/Talking_Head_Anime_3/tha3/nn/eyebrow_morphing_combiner/eyebrow_morphing_combiner_00.py
deleted file mode 100644
index 0447b05e5d6bdafbc6b63f21710908b9f0bf1c20..0000000000000000000000000000000000000000
--- a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/eyebrow_morphing_combiner/eyebrow_morphing_combiner_00.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from typing import List, Optional
-
-import torch
-from torch import Tensor
-from torch.nn import Module
-
-from tha3.nn.common.poser_encoder_decoder_00 import PoserEncoderDecoder00Args, PoserEncoderDecoder00
-from tha3.nn.image_processing_util import apply_color_change, apply_grid_change, apply_rgb_change
-from tha3.module.module_factory import ModuleFactory
-from tha3.nn.nonlinearity_factory import ReLUFactory
-from tha3.nn.normalization import InstanceNorm2dFactory
-from tha3.nn.util import BlockArgs
-
-
-class EyebrowMorphingCombiner00Args(PoserEncoderDecoder00Args):
-    def __init__(self,
-                 image_size: int = 128,
-                 image_channels: int = 4,
-                 num_pose_params: int = 12,
-                 start_channels: int = 64,
-                 bottleneck_image_size=16,
-                 num_bottleneck_blocks=6,
-                 max_channels: int = 512,
-                 block_args: Optional[BlockArgs] = None):
-        super().__init__(
-            image_size,
-            2 * image_channels,
-            image_channels,
-            num_pose_params,
-            start_channels,
-            bottleneck_image_size,
-            num_bottleneck_blocks,
-            max_channels,
-            block_args)
-
-
-class EyebrowMorphingCombiner00(Module):
-    def __init__(self, args: EyebrowMorphingCombiner00Args):
-        super().__init__()
-        self.args = args
-        self.body = PoserEncoderDecoder00(args)
-        self.morphed_eyebrow_layer_grid_change = self.args.create_grid_change_block()
-        self.morphed_eyebrow_layer_alpha = self.args.create_alpha_block()
-        self.morphed_eyebrow_layer_color_change = self.args.create_color_change_block()
-        self.combine_alpha = self.args.create_alpha_block()
-
-    def forward(self, background_layer: Tensor, eyebrow_layer: Tensor, pose: Tensor, *args) -> List[Tensor]:
-        combined_image = torch.cat([background_layer, eyebrow_layer], dim=1)
-        feature = self.body(combined_image, pose)[0]
-
-        morphed_eyebrow_layer_grid_change = self.morphed_eyebrow_layer_grid_change(feature)
-        morphed_eyebrow_layer_alpha = self.morphed_eyebrow_layer_alpha(feature)
-        morphed_eyebrow_layer_color_change = self.morphed_eyebrow_layer_color_change(feature)
-        warped_eyebrow_layer = apply_grid_change(morphed_eyebrow_layer_grid_change, eyebrow_layer)
-        morphed_eyebrow_layer = apply_color_change(
-            morphed_eyebrow_layer_alpha, morphed_eyebrow_layer_color_change, warped_eyebrow_layer)
-
-        combine_alpha = self.combine_alpha(feature)
-        eyebrow_image = apply_rgb_change(combine_alpha, morphed_eyebrow_layer, background_layer)
-        eyebrow_image_no_combine_alpha = apply_rgb_change(
-            (morphed_eyebrow_layer[:, 3:4, :, :] + 1.0) / 2.0, morphed_eyebrow_layer, background_layer)
-
-        return [
-            eyebrow_image,  # 0
-            combine_alpha,  # 1
-            eyebrow_image_no_combine_alpha,  # 2
-            morphed_eyebrow_layer,  # 3
-            morphed_eyebrow_layer_alpha,  # 4
-            morphed_eyebrow_layer_color_change,  # 5
-            warped_eyebrow_layer,  # 6
-            morphed_eyebrow_layer_grid_change,  # 7
-        ]
-
-    EYEBROW_IMAGE_INDEX = 0
-    COMBINE_ALPHA_INDEX = 1
-    EYEBROW_IMAGE_NO_COMBINE_ALPHA_INDEX = 2
-    MORPHED_EYEBROW_LAYER_INDEX = 3
-    MORPHED_EYEBROW_LAYER_ALPHA_INDEX = 4
-    MORPHED_EYEBROW_LAYER_COLOR_CHANGE_INDEX = 5
-    WARPED_EYEBROW_LAYER_INDEX = 6
-    MORPHED_EYEBROW_LAYER_GRID_CHANGE_INDEX = 7
-    OUTPUT_LENGTH = 8
-
-
-class EyebrowMorphingCombiner00Factory(ModuleFactory):
-    def __init__(self, args: EyebrowMorphingCombiner00Args):
-        super().__init__()
-        self.args = args
-
-    def create(self) -> Module:
-        return EyebrowMorphingCombiner00(self.args)
-
-
-if __name__ == "__main__":
-    cuda = torch.device('cuda')
-    args = EyebrowMorphingCombiner00Args(
-        image_size=128,
-        image_channels=4,
-        num_pose_params=12,
-        start_channels=64,
-        bottleneck_image_size=16,
-        num_bottleneck_blocks=3,
-        block_args=BlockArgs(
-            initialization_method='xavier',
-            use_spectral_norm=False,
-            normalization_layer_factory=InstanceNorm2dFactory(),
-            nonlinearity_factory=ReLUFactory(inplace=True)))
-    face_morpher = EyebrowMorphingCombiner00(args).to(cuda)
-
-    background_layer = torch.randn(8, 4, 128, 128, device=cuda)
-    eyebrow_layer = torch.randn(8, 4, 128, 128, device=cuda)
-    pose = torch.randn(8, 12, device=cuda)
-    outputs = face_morpher.forward(background_layer, eyebrow_layer, pose)
-    for i in range(len(outputs)):
-        print(i, outputs[i].shape)
diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/models/bfm.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/models/bfm.py
deleted file mode 100644
index 3e642ef874f67549fa4b5a7308ab5204e0296b14..0000000000000000000000000000000000000000
--- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/models/bfm.py
+++ /dev/null
@@ -1,331 +0,0 @@
-"""This script defines the parametric 3d face model for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-import  torch
-import torch.nn.functional as F
-from scipy.io import loadmat
-from Demo_TFR_Pirenderer.src.face3d.util.load_mats import transferBFM09
-import os
-
-def perspective_projection(focal, center):
-    # return p.T (N, 3) @ (3, 3) 
-    return np.array([
-        focal, 0, center,
-        0, focal, center,
-        0, 0, 1
-    ]).reshape([3, 3]).astype(np.float32).transpose()
-
-class SH:
-    def __init__(self):
-        self.a = [np.pi, 2 * np.pi / np.sqrt(3.), 2 * np.pi / np.sqrt(8.)]
-        self.c = [1/np.sqrt(4 * np.pi), np.sqrt(3.) / np.sqrt(4 * np.pi), 3 * np.sqrt(5.) / np.sqrt(12 * np.pi)]
-
-
-
-class ParametricFaceModel:
-    def __init__(self, 
-                bfm_folder='./BFM', 
-                recenter=True,
-                camera_distance=10.,
-                init_lit=np.array([
-                    0.8, 0, 0, 0, 0, 0, 0, 0, 0
-                    ]),
-                focal=1015.,
-                center=112.,
-                is_train=True,
-                default_name='BFM_model_front.mat'):
-        
-        if not os.path.isfile(os.path.join(bfm_folder, default_name)):
-            transferBFM09(bfm_folder)
-            
-        model = loadmat(os.path.join(bfm_folder, default_name))
-        # mean face shape. [3*N,1]
-        self.mean_shape = model['meanshape'].astype(np.float32)
-        # identity basis. [3*N,80]
-        self.id_base = model['idBase'].astype(np.float32)
-        # expression basis. [3*N,64]
-        self.exp_base = model['exBase'].astype(np.float32)
-        # mean face texture. [3*N,1] (0-255)
-        self.mean_tex = model['meantex'].astype(np.float32)
-        # texture basis. [3*N,80]
-        self.tex_base = model['texBase'].astype(np.float32)
-        # face indices for each vertex that lies in. starts from 0. [N,8]
-        self.point_buf = model['point_buf'].astype(np.int64) - 1
-        # vertex indices for each face. starts from 0. [F,3]
-        self.face_buf = model['tri'].astype(np.int64) - 1
-        # vertex indices for 68 landmarks. starts from 0. [68,1]
-        self.keypoints = np.squeeze(model['keypoints']).astype(np.int64) - 1
-
-        if is_train:
-            # vertex indices for small face region to compute photometric error. starts from 0.
-            self.front_mask = np.squeeze(model['frontmask2_idx']).astype(np.int64) - 1
-            # vertex indices for each face from small face region. starts from 0. [f,3]
-            self.front_face_buf = model['tri_mask2'].astype(np.int64) - 1
-            # vertex indices for pre-defined skin region to compute reflectance loss
-            self.skin_mask = np.squeeze(model['skinmask'])
-        
-        if recenter:
-            mean_shape = self.mean_shape.reshape([-1, 3])
-            mean_shape = mean_shape - np.mean(mean_shape, axis=0, keepdims=True)
-            self.mean_shape = mean_shape.reshape([-1, 1])
-
-        self.persc_proj = perspective_projection(focal, center)
-        self.device = 'cpu'
-        self.camera_distance = camera_distance
-        self.SH = SH()
-        self.init_lit = init_lit.reshape([1, 1, -1]).astype(np.float32)
-        
-
-    def to(self, device):
-        self.device = device
-        for key, value in self.__dict__.items():
-            if type(value).__module__ == np.__name__:
-                setattr(self, key, torch.tensor(value).to(device))
-
-    
-    def compute_shape(self, id_coeff, exp_coeff):
-        """
-        Return:
-            face_shape       -- torch.tensor, size (B, N, 3)
-
-        Parameters:
-            id_coeff         -- torch.tensor, size (B, 80), identity coeffs
-            exp_coeff        -- torch.tensor, size (B, 64), expression coeffs
-        """
-        batch_size = id_coeff.shape[0]
-        id_part = torch.einsum('ij,aj->ai', self.id_base, id_coeff)
-        exp_part = torch.einsum('ij,aj->ai', self.exp_base, exp_coeff)
-        face_shape = id_part + exp_part + self.mean_shape.reshape([1, -1])
-        return face_shape.reshape([batch_size, -1, 3])
-    
-
-    def compute_texture(self, tex_coeff, normalize=True):
-        """
-        Return:
-            face_texture     -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
-
-        Parameters:
-            tex_coeff        -- torch.tensor, size (B, 80)
-        """
-        batch_size = tex_coeff.shape[0]
-        face_texture = torch.einsum('ij,aj->ai', self.tex_base, tex_coeff) + self.mean_tex
-        if normalize:
-            face_texture = face_texture / 255.
-        return face_texture.reshape([batch_size, -1, 3])
-
-
-    def compute_norm(self, face_shape):
-        """
-        Return:
-            vertex_norm      -- torch.tensor, size (B, N, 3)
-
-        Parameters:
-            face_shape       -- torch.tensor, size (B, N, 3)
-        """
-
-        v1 = face_shape[:, self.face_buf[:, 0]]
-        v2 = face_shape[:, self.face_buf[:, 1]]
-        v3 = face_shape[:, self.face_buf[:, 2]]
-        e1 = v1 - v2
-        e2 = v2 - v3
-        face_norm = torch.cross(e1, e2, dim=-1)
-        face_norm = F.normalize(face_norm, dim=-1, p=2)
-        face_norm = torch.cat([face_norm, torch.zeros(face_norm.shape[0], 1, 3).to(self.device)], dim=1)
-        
-        vertex_norm = torch.sum(face_norm[:, self.point_buf], dim=2)
-        vertex_norm = F.normalize(vertex_norm, dim=-1, p=2)
-        return vertex_norm
-
-
-    def compute_color(self, face_texture, face_norm, gamma):
-        """
-        Return:
-            face_color       -- torch.tensor, size (B, N, 3), range (0, 1.)
-
-        Parameters:
-            face_texture     -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
-            face_norm        -- torch.tensor, size (B, N, 3), rotated face normal
-            gamma            -- torch.tensor, size (B, 27), SH coeffs
-        """
-        batch_size = gamma.shape[0]
-        v_num = face_texture.shape[1]
-        a, c = self.SH.a, self.SH.c
-        gamma = gamma.reshape([batch_size, 3, 9])
-        gamma = gamma + self.init_lit
-        gamma = gamma.permute(0, 2, 1)
-        Y = torch.cat([
-             a[0] * c[0] * torch.ones_like(face_norm[..., :1]).to(self.device),
-            -a[1] * c[1] * face_norm[..., 1:2],
-             a[1] * c[1] * face_norm[..., 2:],
-            -a[1] * c[1] * face_norm[..., :1],
-             a[2] * c[2] * face_norm[..., :1] * face_norm[..., 1:2],
-            -a[2] * c[2] * face_norm[..., 1:2] * face_norm[..., 2:],
-            0.5 * a[2] * c[2] / np.sqrt(3.) * (3 * face_norm[..., 2:] ** 2 - 1),
-            -a[2] * c[2] * face_norm[..., :1] * face_norm[..., 2:],
-            0.5 * a[2] * c[2] * (face_norm[..., :1] ** 2  - face_norm[..., 1:2] ** 2)
-        ], dim=-1)
-        r = Y @ gamma[..., :1]
-        g = Y @ gamma[..., 1:2]
-        b = Y @ gamma[..., 2:]
-        face_color = torch.cat([r, g, b], dim=-1) * face_texture
-        return face_color
-
-    
-    def compute_rotation(self, angles):
-        """
-        Return:
-            rot              -- torch.tensor, size (B, 3, 3) pts @ trans_mat
-
-        Parameters:
-            angles           -- torch.tensor, size (B, 3), radian
-        """
-
-        batch_size = angles.shape[0]
-        ones = torch.ones([batch_size, 1]).to(self.device)
-        zeros = torch.zeros([batch_size, 1]).to(self.device)
-        x, y, z = angles[:, :1], angles[:, 1:2], angles[:, 2:],
-        
-        rot_x = torch.cat([
-            ones, zeros, zeros,
-            zeros, torch.cos(x), -torch.sin(x), 
-            zeros, torch.sin(x), torch.cos(x)
-        ], dim=1).reshape([batch_size, 3, 3])
-        
-        rot_y = torch.cat([
-            torch.cos(y), zeros, torch.sin(y),
-            zeros, ones, zeros,
-            -torch.sin(y), zeros, torch.cos(y)
-        ], dim=1).reshape([batch_size, 3, 3])
-
-        rot_z = torch.cat([
-            torch.cos(z), -torch.sin(z), zeros,
-            torch.sin(z), torch.cos(z), zeros,
-            zeros, zeros, ones
-        ], dim=1).reshape([batch_size, 3, 3])
-
-        rot = rot_z @ rot_y @ rot_x
-        return rot.permute(0, 2, 1)
-
-
-    def to_camera(self, face_shape):
-        face_shape[..., -1] = self.camera_distance - face_shape[..., -1]
-        return face_shape
-
-    def to_image(self, face_shape):
-        """
-        Return:
-            face_proj        -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
-
-        Parameters:
-            face_shape       -- torch.tensor, size (B, N, 3)
-        """
-        # to image_plane
-        face_proj = face_shape @ self.persc_proj
-        face_proj = face_proj[..., :2] / face_proj[..., 2:]
-
-        return face_proj
-
-
-    def transform(self, face_shape, rot, trans):
-        """
-        Return:
-            face_shape       -- torch.tensor, size (B, N, 3) pts @ rot + trans
-
-        Parameters:
-            face_shape       -- torch.tensor, size (B, N, 3)
-            rot              -- torch.tensor, size (B, 3, 3)
-            trans            -- torch.tensor, size (B, 3)
-        """
-        return face_shape @ rot + trans.unsqueeze(1)
-
-
-    def get_landmarks(self, face_proj):
-        """
-        Return:
-            face_lms         -- torch.tensor, size (B, 68, 2)
-
-        Parameters:
-            face_proj       -- torch.tensor, size (B, N, 2)
-        """  
-        return face_proj[:, self.keypoints]
-
-    def split_coeff(self, coeffs):
-        """
-        Return:
-            coeffs_dict     -- a dict of torch.tensors
-
-        Parameters:
-            coeffs          -- torch.tensor, size (B, 256)
-        """
-        id_coeffs = coeffs[:, :80]
-        exp_coeffs = coeffs[:, 80: 144]
-        tex_coeffs = coeffs[:, 144: 224]
-        angles = coeffs[:, 224: 227]
-        gammas = coeffs[:, 227: 254]
-        translations = coeffs[:, 254:]
-        return {
-            'id': id_coeffs,
-            'exp': exp_coeffs,
-            'tex': tex_coeffs,
-            'angle': angles,
-            'gamma': gammas,
-            'trans': translations
-        }
-    def compute_for_render(self, coeffs):
-        """
-        Return:
-            face_vertex     -- torch.tensor, size (B, N, 3), in camera coordinate
-            face_color      -- torch.tensor, size (B, N, 3), in RGB order
-            landmark        -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
-        Parameters:
-            coeffs          -- torch.tensor, size (B, 257)
-        """
-        coef_dict = self.split_coeff(coeffs)
-        face_shape = self.compute_shape(coef_dict['id'], coef_dict['exp'])
-        rotation = self.compute_rotation(coef_dict['angle'])
-
-
-        face_shape_transformed = self.transform(face_shape, rotation, coef_dict['trans'])
-        face_vertex = self.to_camera(face_shape_transformed)
-        
-        face_proj = self.to_image(face_vertex)
-        landmark = self.get_landmarks(face_proj)
-
-        face_texture = self.compute_texture(coef_dict['tex'])
-        face_norm = self.compute_norm(face_shape)
-        face_norm_roted = face_norm @ rotation
-        face_color = self.compute_color(face_texture, face_norm_roted, coef_dict['gamma'])
-
-        return face_vertex, face_texture, face_color, landmark
-
-    def compute_for_render_woRotation(self, coeffs):
-        """
-        Return:
-            face_vertex     -- torch.tensor, size (B, N, 3), in camera coordinate
-            face_color      -- torch.tensor, size (B, N, 3), in RGB order
-            landmark        -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
-        Parameters:
-            coeffs          -- torch.tensor, size (B, 257)
-        """
-        coef_dict = self.split_coeff(coeffs)
-        face_shape = self.compute_shape(coef_dict['id'], coef_dict['exp'])
-        #rotation = self.compute_rotation(coef_dict['angle'])
-
-
-        #face_shape_transformed = self.transform(face_shape, rotation, coef_dict['trans'])
-        face_vertex = self.to_camera(face_shape)
-        
-        face_proj = self.to_image(face_vertex)
-        landmark = self.get_landmarks(face_proj)
-
-        face_texture = self.compute_texture(coef_dict['tex'])
-        face_norm = self.compute_norm(face_shape)
-        face_norm_roted = face_norm                                    # @ rotation
-        face_color = self.compute_color(face_texture, face_norm_roted, coef_dict['gamma'])
-
-        return face_vertex, face_texture, face_color, landmark
-
-
-if __name__ == '__main__':
-    transferBFM09()
\ No newline at end of file
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/_version.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/_version.py
deleted file mode 100644
index b723056a756af22aaf1a4709c5122bea9fb279ee..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/_version.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# coding: utf-8
-# file generated by setuptools_scm
-# don't change, don't track in version control
-version = '2.8.2'
-version_tuple = (2, 8, 2)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-e7c48875.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-e7c48875.js
deleted file mode 100644
index 919122b9d0c013ebdbbc53abcf339dbb5493ad24..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-e7c48875.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as p,e as g,s as d,f as n,g as e,h as f,j as i,n as l,k as m}from"./index-39fce9e2.js";function u(c){let t,r,s,o;return{c(){t=n("svg"),r=n("rect"),s=n("circle"),o=n("polyline"),e(r,"x","3"),e(r,"y","3"),e(r,"width","18"),e(r,"height","18"),e(r,"rx","2"),e(r,"ry","2"),e(s,"cx","8.5"),e(s,"cy","8.5"),e(s,"r","1.5"),e(o,"points","21 15 16 10 5 21"),e(t,"xmlns","http://www.w3.org/2000/svg"),e(t,"width","100%"),e(t,"height","100%"),e(t,"viewBox","0 0 24 24"),e(t,"fill","none"),e(t,"stroke","currentColor"),e(t,"stroke-width","1.5"),e(t,"stroke-linecap","round"),e(t,"stroke-linejoin","round"),e(t,"class","feather feather-image")},m(a,h){f(a,t,h),i(t,r),i(t,s),i(t,o)},p:l,i:l,o:l,d(a){a&&m(t)}}}class x extends p{constructor(t){super(),g(this,t,null,u,d,{})}}export{x as I};
-//# sourceMappingURL=Image-e7c48875.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markupsafe/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markupsafe/__init__.py
deleted file mode 100644
index 21d31960385611ad029826a36417fe50a17ca557..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markupsafe/__init__.py
+++ /dev/null
@@ -1,304 +0,0 @@
-import functools
-import re
-import string
-import sys
-import typing as t
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-
-    class HasHTML(te.Protocol):
-        def __html__(self) -> str:
-            pass
-
-    _P = te.ParamSpec("_P")
-
-
-__version__ = "2.1.3"
-
-_strip_comments_re = re.compile(r"<!--.*?-->", re.DOTALL)
-_strip_tags_re = re.compile(r"<.*?>", re.DOTALL)
-
-
-def _simple_escaping_wrapper(func: "t.Callable[_P, str]") -> "t.Callable[_P, Markup]":
-    @functools.wraps(func)
-    def wrapped(self: "Markup", *args: "_P.args", **kwargs: "_P.kwargs") -> "Markup":
-        arg_list = _escape_argspec(list(args), enumerate(args), self.escape)
-        _escape_argspec(kwargs, kwargs.items(), self.escape)
-        return self.__class__(func(self, *arg_list, **kwargs))  # type: ignore[arg-type]
-
-    return wrapped  # type: ignore[return-value]
-
-
-class Markup(str):
-    """A string that is ready to be safely inserted into an HTML or XML
-    document, either because it was escaped or because it was marked
-    safe.
-
-    Passing an object to the constructor converts it to text and wraps
-    it to mark it safe without escaping. To escape the text, use the
-    :meth:`escape` class method instead.
-
-    >>> Markup("Hello, <em>World</em>!")
-    Markup('Hello, <em>World</em>!')
-    >>> Markup(42)
-    Markup('42')
-    >>> Markup.escape("Hello, <em>World</em>!")
-    Markup('Hello &lt;em&gt;World&lt;/em&gt;!')
-
-    This implements the ``__html__()`` interface that some frameworks
-    use. Passing an object that implements ``__html__()`` will wrap the
-    output of that method, marking it safe.
-
-    >>> class Foo:
-    ...     def __html__(self):
-    ...         return '<a href="/foo">foo</a>'
-    ...
-    >>> Markup(Foo())
-    Markup('<a href="/foo">foo</a>')
-
-    This is a subclass of :class:`str`. It has the same methods, but
-    escapes their arguments and returns a ``Markup`` instance.
-
-    >>> Markup("<em>%s</em>") % ("foo & bar",)
-    Markup('<em>foo &amp; bar</em>')
-    >>> Markup("<em>Hello</em> ") + "<foo>"
-    Markup('<em>Hello</em> &lt;foo&gt;')
-    """
-
-    __slots__ = ()
-
-    def __new__(
-        cls, base: t.Any = "", encoding: t.Optional[str] = None, errors: str = "strict"
-    ) -> "te.Self":
-        if hasattr(base, "__html__"):
-            base = base.__html__()
-
-        if encoding is None:
-            return super().__new__(cls, base)
-
-        return super().__new__(cls, base, encoding, errors)
-
-    def __html__(self) -> "te.Self":
-        return self
-
-    def __add__(self, other: t.Union[str, "HasHTML"]) -> "te.Self":
-        if isinstance(other, str) or hasattr(other, "__html__"):
-            return self.__class__(super().__add__(self.escape(other)))
-
-        return NotImplemented
-
-    def __radd__(self, other: t.Union[str, "HasHTML"]) -> "te.Self":
-        if isinstance(other, str) or hasattr(other, "__html__"):
-            return self.escape(other).__add__(self)
-
-        return NotImplemented
-
-    def __mul__(self, num: "te.SupportsIndex") -> "te.Self":
-        if isinstance(num, int):
-            return self.__class__(super().__mul__(num))
-
-        return NotImplemented
-
-    __rmul__ = __mul__
-
-    def __mod__(self, arg: t.Any) -> "te.Self":
-        if isinstance(arg, tuple):
-            # a tuple of arguments, each wrapped
-            arg = tuple(_MarkupEscapeHelper(x, self.escape) for x in arg)
-        elif hasattr(type(arg), "__getitem__") and not isinstance(arg, str):
-            # a mapping of arguments, wrapped
-            arg = _MarkupEscapeHelper(arg, self.escape)
-        else:
-            # a single argument, wrapped with the helper and a tuple
-            arg = (_MarkupEscapeHelper(arg, self.escape),)
-
-        return self.__class__(super().__mod__(arg))
-
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}({super().__repr__()})"
-
-    def join(self, seq: t.Iterable[t.Union[str, "HasHTML"]]) -> "te.Self":
-        return self.__class__(super().join(map(self.escape, seq)))
-
-    join.__doc__ = str.join.__doc__
-
-    def split(  # type: ignore[override]
-        self, sep: t.Optional[str] = None, maxsplit: int = -1
-    ) -> t.List["te.Self"]:
-        return [self.__class__(v) for v in super().split(sep, maxsplit)]
-
-    split.__doc__ = str.split.__doc__
-
-    def rsplit(  # type: ignore[override]
-        self, sep: t.Optional[str] = None, maxsplit: int = -1
-    ) -> t.List["te.Self"]:
-        return [self.__class__(v) for v in super().rsplit(sep, maxsplit)]
-
-    rsplit.__doc__ = str.rsplit.__doc__
-
-    def splitlines(  # type: ignore[override]
-        self, keepends: bool = False
-    ) -> t.List["te.Self"]:
-        return [self.__class__(v) for v in super().splitlines(keepends)]
-
-    splitlines.__doc__ = str.splitlines.__doc__
-
-    def unescape(self) -> str:
-        """Convert escaped markup back into a text string. This replaces
-        HTML entities with the characters they represent.
-
-        >>> Markup("Main &raquo; <em>About</em>").unescape()
-        'Main » <em>About</em>'
-        """
-        from html import unescape
-
-        return unescape(str(self))
-
-    def striptags(self) -> str:
-        """:meth:`unescape` the markup, remove tags, and normalize
-        whitespace to single spaces.
-
-        >>> Markup("Main &raquo;\t<em>About</em>").striptags()
-        'Main » About'
-        """
-        # Use two regexes to avoid ambiguous matches.
-        value = _strip_comments_re.sub("", self)
-        value = _strip_tags_re.sub("", value)
-        value = " ".join(value.split())
-        return self.__class__(value).unescape()
-
-    @classmethod
-    def escape(cls, s: t.Any) -> "te.Self":
-        """Escape a string. Calls :func:`escape` and ensures that for
-        subclasses the correct type is returned.
-        """
-        rv = escape(s)
-
-        if rv.__class__ is not cls:
-            return cls(rv)
-
-        return rv  # type: ignore[return-value]
-
-    __getitem__ = _simple_escaping_wrapper(str.__getitem__)
-    capitalize = _simple_escaping_wrapper(str.capitalize)
-    title = _simple_escaping_wrapper(str.title)
-    lower = _simple_escaping_wrapper(str.lower)
-    upper = _simple_escaping_wrapper(str.upper)
-    replace = _simple_escaping_wrapper(str.replace)
-    ljust = _simple_escaping_wrapper(str.ljust)
-    rjust = _simple_escaping_wrapper(str.rjust)
-    lstrip = _simple_escaping_wrapper(str.lstrip)
-    rstrip = _simple_escaping_wrapper(str.rstrip)
-    center = _simple_escaping_wrapper(str.center)
-    strip = _simple_escaping_wrapper(str.strip)
-    translate = _simple_escaping_wrapper(str.translate)
-    expandtabs = _simple_escaping_wrapper(str.expandtabs)
-    swapcase = _simple_escaping_wrapper(str.swapcase)
-    zfill = _simple_escaping_wrapper(str.zfill)
-    casefold = _simple_escaping_wrapper(str.casefold)
-
-    if sys.version_info >= (3, 9):
-        removeprefix = _simple_escaping_wrapper(str.removeprefix)
-        removesuffix = _simple_escaping_wrapper(str.removesuffix)
-
-    def partition(self, sep: str) -> t.Tuple["te.Self", "te.Self", "te.Self"]:
-        l, s, r = super().partition(self.escape(sep))
-        cls = self.__class__
-        return cls(l), cls(s), cls(r)
-
-    def rpartition(self, sep: str) -> t.Tuple["te.Self", "te.Self", "te.Self"]:
-        l, s, r = super().rpartition(self.escape(sep))
-        cls = self.__class__
-        return cls(l), cls(s), cls(r)
-
-    def format(self, *args: t.Any, **kwargs: t.Any) -> "te.Self":
-        formatter = EscapeFormatter(self.escape)
-        return self.__class__(formatter.vformat(self, args, kwargs))
-
-    def format_map(  # type: ignore[override]
-        self, map: t.Mapping[str, t.Any]
-    ) -> "te.Self":
-        formatter = EscapeFormatter(self.escape)
-        return self.__class__(formatter.vformat(self, (), map))
-
-    def __html_format__(self, format_spec: str) -> "te.Self":
-        if format_spec:
-            raise ValueError("Unsupported format specification for Markup.")
-
-        return self
-
-
-class EscapeFormatter(string.Formatter):
-    __slots__ = ("escape",)
-
-    def __init__(self, escape: t.Callable[[t.Any], Markup]) -> None:
-        self.escape = escape
-        super().__init__()
-
-    def format_field(self, value: t.Any, format_spec: str) -> str:
-        if hasattr(value, "__html_format__"):
-            rv = value.__html_format__(format_spec)
-        elif hasattr(value, "__html__"):
-            if format_spec:
-                raise ValueError(
-                    f"Format specifier {format_spec} given, but {type(value)} does not"
-                    " define __html_format__. A class that defines __html__ must define"
-                    " __html_format__ to work with format specifiers."
-                )
-            rv = value.__html__()
-        else:
-            # We need to make sure the format spec is str here as
-            # otherwise the wrong callback methods are invoked.
-            rv = string.Formatter.format_field(self, value, str(format_spec))
-        return str(self.escape(rv))
-
-
-_ListOrDict = t.TypeVar("_ListOrDict", list, dict)
-
-
-def _escape_argspec(
-    obj: _ListOrDict, iterable: t.Iterable[t.Any], escape: t.Callable[[t.Any], Markup]
-) -> _ListOrDict:
-    """Helper for various string-wrapped functions."""
-    for key, value in iterable:
-        if isinstance(value, str) or hasattr(value, "__html__"):
-            obj[key] = escape(value)
-
-    return obj
-
-
-class _MarkupEscapeHelper:
-    """Helper for :meth:`Markup.__mod__`."""
-
-    __slots__ = ("obj", "escape")
-
-    def __init__(self, obj: t.Any, escape: t.Callable[[t.Any], Markup]) -> None:
-        self.obj = obj
-        self.escape = escape
-
-    def __getitem__(self, item: t.Any) -> "te.Self":
-        return self.__class__(self.obj[item], self.escape)
-
-    def __str__(self) -> str:
-        return str(self.escape(self.obj))
-
-    def __repr__(self) -> str:
-        return str(self.escape(repr(self.obj)))
-
-    def __int__(self) -> int:
-        return int(self.obj)
-
-    def __float__(self) -> float:
-        return float(self.obj)
-
-
-# circular import
-try:
-    from ._speedups import escape as escape
-    from ._speedups import escape_silent as escape_silent
-    from ._speedups import soft_str as soft_str
-except ImportError:
-    from ._native import escape as escape
-    from ._native import escape_silent as escape_silent  # noqa: F401
-    from ._native import soft_str as soft_str  # noqa: F401
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/__init__.py
deleted file mode 100644
index ae68094000fc3361d970fb91b6950cf36cb7fec6..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/__init__.py
+++ /dev/null
@@ -1,1486 +0,0 @@
-"""
-An object-oriented plotting library.
-
-A procedural interface is provided by the companion pyplot module,
-which may be imported directly, e.g.::
-
-    import matplotlib.pyplot as plt
-
-or using ipython::
-
-    ipython
-
-at your terminal, followed by::
-
-    In [1]: %matplotlib
-    In [2]: import matplotlib.pyplot as plt
-
-at the ipython shell prompt.
-
-For the most part, direct use of the explicit object-oriented library is
-encouraged when programming; the implicit pyplot interface is primarily for
-working interactively. The exceptions to this suggestion are the pyplot
-functions `.pyplot.figure`, `.pyplot.subplot`, `.pyplot.subplots`, and
-`.pyplot.savefig`, which can greatly simplify scripting.  See
-:ref:`api_interfaces` for an explanation of the tradeoffs between the implicit
-and explicit interfaces.
-
-Modules include:
-
-    :mod:`matplotlib.axes`
-        The `~.axes.Axes` class.  Most pyplot functions are wrappers for
-        `~.axes.Axes` methods.  The axes module is the highest level of OO
-        access to the library.
-
-    :mod:`matplotlib.figure`
-        The `.Figure` class.
-
-    :mod:`matplotlib.artist`
-        The `.Artist` base class for all classes that draw things.
-
-    :mod:`matplotlib.lines`
-        The `.Line2D` class for drawing lines and markers.
-
-    :mod:`matplotlib.patches`
-        Classes for drawing polygons.
-
-    :mod:`matplotlib.text`
-        The `.Text` and `.Annotation` classes.
-
-    :mod:`matplotlib.image`
-        The `.AxesImage` and `.FigureImage` classes.
-
-    :mod:`matplotlib.collections`
-        Classes for efficient drawing of groups of lines or polygons.
-
-    :mod:`matplotlib.colors`
-        Color specifications and making colormaps.
-
-    :mod:`matplotlib.cm`
-        Colormaps, and the `.ScalarMappable` mixin class for providing color
-        mapping functionality to other classes.
-
-    :mod:`matplotlib.ticker`
-        Calculation of tick mark locations and formatting of tick labels.
-
-    :mod:`matplotlib.backends`
-        A subpackage with modules for various GUI libraries and output formats.
-
-The base matplotlib namespace includes:
-
-    `~matplotlib.rcParams`
-        Default configuration settings; their defaults may be overridden using
-        a :file:`matplotlibrc` file.
-
-    `~matplotlib.use`
-        Setting the Matplotlib backend.  This should be called before any
-        figure is created, because it is not possible to switch between
-        different GUI backends after that.
-
-The following environment variables can be used to customize the behavior:
-
-    :envvar:`MPLBACKEND`
-        This optional variable can be set to choose the Matplotlib backend. See
-        :ref:`what-is-a-backend`.
-
-    :envvar:`MPLCONFIGDIR`
-        This is the directory used to store user customizations to
-        Matplotlib, as well as some caches to improve performance. If
-        :envvar:`MPLCONFIGDIR` is not defined, :file:`{HOME}/.config/matplotlib`
-        and :file:`{HOME}/.cache/matplotlib` are used on Linux, and
-        :file:`{HOME}/.matplotlib` on other platforms, if they are
-        writable. Otherwise, the Python standard library's `tempfile.gettempdir`
-        is used to find a base directory in which the :file:`matplotlib`
-        subdirectory is created.
-
-Matplotlib was initially written by John D. Hunter (1968-2012) and is now
-developed and maintained by a host of others.
-
-Occasionally the internal documentation (python docstrings) will refer
-to MATLAB®, a registered trademark of The MathWorks, Inc.
-
-"""
-
-import atexit
-from collections import namedtuple
-from collections.abc import MutableMapping
-import contextlib
-import functools
-import importlib
-import inspect
-from inspect import Parameter
-import locale
-import logging
-import os
-from pathlib import Path
-import pprint
-import re
-import shutil
-import subprocess
-import sys
-import tempfile
-import warnings
-
-import numpy
-from packaging.version import parse as parse_version
-
-# cbook must import matplotlib only within function
-# definitions, so it is safe to import from it here.
-from . import _api, _version, cbook, _docstring, rcsetup
-from matplotlib.cbook import sanitize_sequence
-from matplotlib._api import MatplotlibDeprecationWarning
-from matplotlib.rcsetup import validate_backend, cycler
-
-
-_log = logging.getLogger(__name__)
-
-__bibtex__ = r"""@Article{Hunter:2007,
-  Author    = {Hunter, J. D.},
-  Title     = {Matplotlib: A 2D graphics environment},
-  Journal   = {Computing in Science \& Engineering},
-  Volume    = {9},
-  Number    = {3},
-  Pages     = {90--95},
-  abstract  = {Matplotlib is a 2D graphics package used for Python
-  for application development, interactive scripting, and
-  publication-quality image generation across user
-  interfaces and operating systems.},
-  publisher = {IEEE COMPUTER SOC},
-  year      = 2007
-}"""
-
-# modelled after sys.version_info
-_VersionInfo = namedtuple('_VersionInfo',
-                          'major, minor, micro, releaselevel, serial')
-
-
-def _parse_to_version_info(version_str):
-    """
-    Parse a version string to a namedtuple analogous to sys.version_info.
-
-    See:
-    https://packaging.pypa.io/en/latest/version.html#packaging.version.parse
-    https://docs.python.org/3/library/sys.html#sys.version_info
-    """
-    v = parse_version(version_str)
-    if v.pre is None and v.post is None and v.dev is None:
-        return _VersionInfo(v.major, v.minor, v.micro, 'final', 0)
-    elif v.dev is not None:
-        return _VersionInfo(v.major, v.minor, v.micro, 'alpha', v.dev)
-    elif v.pre is not None:
-        releaselevel = {
-            'a': 'alpha',
-            'b': 'beta',
-            'rc': 'candidate'}.get(v.pre[0], 'alpha')
-        return _VersionInfo(v.major, v.minor, v.micro, releaselevel, v.pre[1])
-    else:
-        # fallback for v.post: guess-next-dev scheme from setuptools_scm
-        return _VersionInfo(v.major, v.minor, v.micro + 1, 'alpha', v.post)
-
-
-def _get_version():
-    """Return the version string used for __version__."""
-    # Only shell out to a git subprocess if really needed, i.e. when we are in
-    # a matplotlib git repo but not in a shallow clone, such as those used by
-    # CI, as the latter would trigger a warning from setuptools_scm.
-    root = Path(__file__).resolve().parents[2]
-    if ((root / ".matplotlib-repo").exists()
-            and (root / ".git").exists()
-            and not (root / ".git/shallow").exists()):
-        import setuptools_scm
-        return setuptools_scm.get_version(
-            root=root,
-            version_scheme="release-branch-semver",
-            local_scheme="node-and-date",
-            fallback_version=_version.version,
-        )
-    else:  # Get the version from the _version.py setuptools_scm file.
-        return _version.version
-
-
-@_api.caching_module_getattr
-class __getattr__:
-    __version__ = property(lambda self: _get_version())
-    __version_info__ = property(
-        lambda self: _parse_to_version_info(self.__version__))
-
-
-def _check_versions():
-
-    # Quickfix to ensure Microsoft Visual C++ redistributable
-    # DLLs are loaded before importing kiwisolver
-    from . import ft2font
-
-    for modname, minver in [
-            ("cycler", "0.10"),
-            ("dateutil", "2.7"),
-            ("kiwisolver", "1.0.1"),
-            ("numpy", "1.20"),
-            ("pyparsing", "2.3.1"),
-    ]:
-        module = importlib.import_module(modname)
-        if parse_version(module.__version__) < parse_version(minver):
-            raise ImportError(f"Matplotlib requires {modname}>={minver}; "
-                              f"you have {module.__version__}")
-
-
-_check_versions()
-
-
-# The decorator ensures this always returns the same handler (and it is only
-# attached once).
-@functools.lru_cache()
-def _ensure_handler():
-    """
-    The first time this function is called, attach a `StreamHandler` using the
-    same format as `logging.basicConfig` to the Matplotlib root logger.
-
-    Return this handler every time this function is called.
-    """
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter(logging.BASIC_FORMAT))
-    _log.addHandler(handler)
-    return handler
-
-
-def set_loglevel(level):
-    """
-    Configure Matplotlib's logging levels.
-
-    Matplotlib uses the standard library `logging` framework under the root
-    logger 'matplotlib'.  This is a helper function to:
-
-    - set Matplotlib's root logger level
-    - set the root logger handler's level, creating the handler
-      if it does not exist yet
-
-    Typically, one should call ``set_loglevel("info")`` or
-    ``set_loglevel("debug")`` to get additional debugging information.
-
-    Users or applications that are installing their own logging handlers
-    may want to directly manipulate ``logging.getLogger('matplotlib')`` rather
-    than use this function.
-
-    Parameters
-    ----------
-    level : {"notset", "debug", "info", "warning", "error", "critical"}
-        The log level of the handler.
-
-    Notes
-    -----
-    The first time this function is called, an additional handler is attached
-    to Matplotlib's root handler; this handler is reused every time and this
-    function simply manipulates the logger and handler's level.
-
-    """
-    _log.setLevel(level.upper())
-    _ensure_handler().setLevel(level.upper())
-
-
-def _logged_cached(fmt, func=None):
-    """
-    Decorator that logs a function's return value, and memoizes that value.
-
-    After ::
-
-        @_logged_cached(fmt)
-        def func(): ...
-
-    the first call to *func* will log its return value at the DEBUG level using
-    %-format string *fmt*, and memoize it; later calls to *func* will directly
-    return that value.
-    """
-    if func is None:  # Return the actual decorator.
-        return functools.partial(_logged_cached, fmt)
-
-    called = False
-    ret = None
-
-    @functools.wraps(func)
-    def wrapper(**kwargs):
-        nonlocal called, ret
-        if not called:
-            ret = func(**kwargs)
-            called = True
-            _log.debug(fmt, ret)
-        return ret
-
-    return wrapper
-
-
-_ExecInfo = namedtuple("_ExecInfo", "executable raw_version version")
-
-
-class ExecutableNotFoundError(FileNotFoundError):
-    """
-    Error raised when an executable that Matplotlib optionally
-    depends on can't be found.
-    """
-    pass
-
-
-@functools.lru_cache()
-def _get_executable_info(name):
-    """
-    Get the version of some executable that Matplotlib optionally depends on.
-
-    .. warning::
-       The list of executables that this function supports is set according to
-       Matplotlib's internal needs, and may change without notice.
-
-    Parameters
-    ----------
-    name : str
-        The executable to query.  The following values are currently supported:
-        "dvipng", "gs", "inkscape", "magick", "pdftocairo", "pdftops".  This
-        list is subject to change without notice.
-
-    Returns
-    -------
-    tuple
-        A namedtuple with fields ``executable`` (`str`) and ``version``
-        (`packaging.Version`, or ``None`` if the version cannot be determined).
-
-    Raises
-    ------
-    ExecutableNotFoundError
-        If the executable is not found or older than the oldest version
-        supported by Matplotlib.  For debugging purposes, it is also
-        possible to "hide" an executable from Matplotlib by adding it to the
-        :envvar:`_MPLHIDEEXECUTABLES` environment variable (a comma-separated
-        list), which must be set prior to any calls to this function.
-    ValueError
-        If the executable is not one that we know how to query.
-    """
-
-    def impl(args, regex, min_ver=None, ignore_exit_code=False):
-        # Execute the subprocess specified by args; capture stdout and stderr.
-        # Search for a regex match in the output; if the match succeeds, the
-        # first group of the match is the version.
-        # Return an _ExecInfo if the executable exists, and has a version of
-        # at least min_ver (if set); else, raise ExecutableNotFoundError.
-        try:
-            output = subprocess.check_output(
-                args, stderr=subprocess.STDOUT,
-                universal_newlines=True, errors="replace")
-        except subprocess.CalledProcessError as _cpe:
-            if ignore_exit_code:
-                output = _cpe.output
-            else:
-                raise ExecutableNotFoundError(str(_cpe)) from _cpe
-        except OSError as _ose:
-            raise ExecutableNotFoundError(str(_ose)) from _ose
-        match = re.search(regex, output)
-        if match:
-            raw_version = match.group(1)
-            version = parse_version(raw_version)
-            if min_ver is not None and version < parse_version(min_ver):
-                raise ExecutableNotFoundError(
-                    f"You have {args[0]} version {version} but the minimum "
-                    f"version supported by Matplotlib is {min_ver}")
-            return _ExecInfo(args[0], raw_version, version)
-        else:
-            raise ExecutableNotFoundError(
-                f"Failed to determine the version of {args[0]} from "
-                f"{' '.join(args)}, which output {output}")
-
-    if name in os.environ.get("_MPLHIDEEXECUTABLES", "").split(","):
-        raise ExecutableNotFoundError(f"{name} was hidden")
-
-    if name == "dvipng":
-        return impl(["dvipng", "-version"], "(?m)^dvipng(?: .*)? (.+)", "1.6")
-    elif name == "gs":
-        execs = (["gswin32c", "gswin64c", "mgs", "gs"]  # "mgs" for miktex.
-                 if sys.platform == "win32" else
-                 ["gs"])
-        for e in execs:
-            try:
-                return impl([e, "--version"], "(.*)", "9")
-            except ExecutableNotFoundError:
-                pass
-        message = "Failed to find a Ghostscript installation"
-        raise ExecutableNotFoundError(message)
-    elif name == "inkscape":
-        try:
-            # Try headless option first (needed for Inkscape version < 1.0):
-            return impl(["inkscape", "--without-gui", "-V"],
-                        "Inkscape ([^ ]*)")
-        except ExecutableNotFoundError:
-            pass  # Suppress exception chaining.
-        # If --without-gui is not accepted, we may be using Inkscape >= 1.0 so
-        # try without it:
-        return impl(["inkscape", "-V"], "Inkscape ([^ ]*)")
-    elif name == "magick":
-        if sys.platform == "win32":
-            # Check the registry to avoid confusing ImageMagick's convert with
-            # Windows's builtin convert.exe.
-            import winreg
-            binpath = ""
-            for flag in [0, winreg.KEY_WOW64_32KEY, winreg.KEY_WOW64_64KEY]:
-                try:
-                    with winreg.OpenKeyEx(
-                            winreg.HKEY_LOCAL_MACHINE,
-                            r"Software\Imagemagick\Current",
-                            0, winreg.KEY_QUERY_VALUE | flag) as hkey:
-                        binpath = winreg.QueryValueEx(hkey, "BinPath")[0]
-                except OSError:
-                    pass
-            path = None
-            if binpath:
-                for name in ["convert.exe", "magick.exe"]:
-                    candidate = Path(binpath, name)
-                    if candidate.exists():
-                        path = str(candidate)
-                        break
-            if path is None:
-                raise ExecutableNotFoundError(
-                    "Failed to find an ImageMagick installation")
-        else:
-            path = "convert"
-        info = impl([path, "--version"], r"^Version: ImageMagick (\S*)")
-        if info.raw_version == "7.0.10-34":
-            # https://github.com/ImageMagick/ImageMagick/issues/2720
-            raise ExecutableNotFoundError(
-                f"You have ImageMagick {info.version}, which is unsupported")
-        return info
-    elif name == "pdftocairo":
-        return impl(["pdftocairo", "-v"], "pdftocairo version (.*)")
-    elif name == "pdftops":
-        info = impl(["pdftops", "-v"], "^pdftops version (.*)",
-                    ignore_exit_code=True)
-        if info and not (
-                3 <= info.version.major or
-                # poppler version numbers.
-                parse_version("0.9") <= info.version < parse_version("1.0")):
-            raise ExecutableNotFoundError(
-                f"You have pdftops version {info.version} but the minimum "
-                f"version supported by Matplotlib is 3.0")
-        return info
-    else:
-        raise ValueError("Unknown executable: {!r}".format(name))
-
-
-@_api.deprecated("3.6", alternative="a vendored copy of this function")
-def checkdep_usetex(s):
-    if not s:
-        return False
-    if not shutil.which("tex"):
-        _log.warning("usetex mode requires TeX.")
-        return False
-    try:
-        _get_executable_info("dvipng")
-    except ExecutableNotFoundError:
-        _log.warning("usetex mode requires dvipng.")
-        return False
-    try:
-        _get_executable_info("gs")
-    except ExecutableNotFoundError:
-        _log.warning("usetex mode requires ghostscript.")
-        return False
-    return True
-
-
-def _get_xdg_config_dir():
-    """
-    Return the XDG configuration directory, according to the XDG base
-    directory spec:
-
-    https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html
-    """
-    return os.environ.get('XDG_CONFIG_HOME') or str(Path.home() / ".config")
-
-
-def _get_xdg_cache_dir():
-    """
-    Return the XDG cache directory, according to the XDG base directory spec:
-
-    https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html
-    """
-    return os.environ.get('XDG_CACHE_HOME') or str(Path.home() / ".cache")
-
-
-def _get_config_or_cache_dir(xdg_base_getter):
-    configdir = os.environ.get('MPLCONFIGDIR')
-    if configdir:
-        configdir = Path(configdir).resolve()
-    elif sys.platform.startswith(('linux', 'freebsd')):
-        # Only call _xdg_base_getter here so that MPLCONFIGDIR is tried first,
-        # as _xdg_base_getter can throw.
-        configdir = Path(xdg_base_getter(), "matplotlib")
-    else:
-        configdir = Path.home() / ".matplotlib"
-    try:
-        configdir.mkdir(parents=True, exist_ok=True)
-    except OSError:
-        pass
-    else:
-        if os.access(str(configdir), os.W_OK) and configdir.is_dir():
-            return str(configdir)
-    # If the config or cache directory cannot be created or is not a writable
-    # directory, create a temporary one.
-    try:
-        tmpdir = tempfile.mkdtemp(prefix="matplotlib-")
-    except OSError as exc:
-        raise OSError(
-            f"Matplotlib requires access to a writable cache directory, but the "
-            f"default path ({configdir}) is not a writable directory, and a temporary "
-            f"directory could not be created; set the MPLCONFIGDIR environment "
-            f"variable to a writable directory") from exc
-    os.environ["MPLCONFIGDIR"] = tmpdir
-    atexit.register(shutil.rmtree, tmpdir)
-    _log.warning(
-        "Matplotlib created a temporary cache directory at %s because the default path "
-        "(%s) is not a writable directory; it is highly recommended to set the "
-        "MPLCONFIGDIR environment variable to a writable directory, in particular to "
-        "speed up the import of Matplotlib and to better support multiprocessing.",
-        tmpdir, configdir)
-    return tmpdir
-
-
-@_logged_cached('CONFIGDIR=%s')
-def get_configdir():
-    """
-    Return the string path of the configuration directory.
-
-    The directory is chosen as follows:
-
-    1. If the MPLCONFIGDIR environment variable is supplied, choose that.
-    2. On Linux, follow the XDG specification and look first in
-       ``$XDG_CONFIG_HOME``, if defined, or ``$HOME/.config``.  On other
-       platforms, choose ``$HOME/.matplotlib``.
-    3. If the chosen directory exists and is writable, use that as the
-       configuration directory.
-    4. Else, create a temporary directory, and use it as the configuration
-       directory.
-    """
-    return _get_config_or_cache_dir(_get_xdg_config_dir)
-
-
-@_logged_cached('CACHEDIR=%s')
-def get_cachedir():
-    """
-    Return the string path of the cache directory.
-
-    The procedure used to find the directory is the same as for
-    _get_config_dir, except using ``$XDG_CACHE_HOME``/``$HOME/.cache`` instead.
-    """
-    return _get_config_or_cache_dir(_get_xdg_cache_dir)
-
-
-@_logged_cached('matplotlib data path: %s')
-def get_data_path():
-    """Return the path to Matplotlib data."""
-    return str(Path(__file__).with_name("mpl-data"))
-
-
-def matplotlib_fname():
-    """
-    Get the location of the config file.
-
-    The file location is determined in the following order
-
-    - ``$PWD/matplotlibrc``
-    - ``$MATPLOTLIBRC`` if it is not a directory
-    - ``$MATPLOTLIBRC/matplotlibrc``
-    - ``$MPLCONFIGDIR/matplotlibrc``
-    - On Linux,
-        - ``$XDG_CONFIG_HOME/matplotlib/matplotlibrc`` (if ``$XDG_CONFIG_HOME``
-          is defined)
-        - or ``$HOME/.config/matplotlib/matplotlibrc`` (if ``$XDG_CONFIG_HOME``
-          is not defined)
-    - On other platforms,
-      - ``$HOME/.matplotlib/matplotlibrc`` if ``$HOME`` is defined
-    - Lastly, it looks in ``$MATPLOTLIBDATA/matplotlibrc``, which should always
-      exist.
-    """
-
-    def gen_candidates():
-        # rely on down-stream code to make absolute.  This protects us
-        # from having to directly get the current working directory
-        # which can fail if the user has ended up with a cwd that is
-        # non-existent.
-        yield 'matplotlibrc'
-        try:
-            matplotlibrc = os.environ['MATPLOTLIBRC']
-        except KeyError:
-            pass
-        else:
-            yield matplotlibrc
-            yield os.path.join(matplotlibrc, 'matplotlibrc')
-        yield os.path.join(get_configdir(), 'matplotlibrc')
-        yield os.path.join(get_data_path(), 'matplotlibrc')
-
-    for fname in gen_candidates():
-        if os.path.exists(fname) and not os.path.isdir(fname):
-            return fname
-
-    raise RuntimeError("Could not find matplotlibrc file; your Matplotlib "
-                       "install is broken")
-
-
-# rcParams deprecated and automatically mapped to another key.
-# Values are tuples of (version, new_name, f_old2new, f_new2old).
-_deprecated_map = {}
-# rcParams deprecated; some can manually be mapped to another key.
-# Values are tuples of (version, new_name_or_None).
-_deprecated_ignore_map = {}
-# rcParams deprecated; can use None to suppress warnings; remain actually
-# listed in the rcParams.
-# Values are tuples of (version,)
-_deprecated_remain_as_none = {}
-
-
-@_docstring.Substitution(
-    "\n".join(map("- {}".format, sorted(rcsetup._validators, key=str.lower)))
-)
-class RcParams(MutableMapping, dict):
-    """
-    A dict-like key-value store for config parameters, including validation.
-
-    Validating functions are defined and associated with rc parameters in
-    :mod:`matplotlib.rcsetup`.
-
-    The list of rcParams is:
-
-    %s
-
-    See Also
-    --------
-    :ref:`customizing-with-matplotlibrc-files`
-    """
-
-    validate = rcsetup._validators
-
-    # validate values on the way in
-    def __init__(self, *args, **kwargs):
-        self.update(*args, **kwargs)
-
-    def _set(self, key, val):
-        """
-        Directly write data bypassing deprecation and validation logic.
-
-        Notes
-        -----
-        As end user or downstream library you almost always should use
-        ``rcParams[key] = val`` and not ``_set()``.
-
-        There are only very few special cases that need direct data access.
-        These cases previously used ``dict.__setitem__(rcParams, key, val)``,
-        which is now deprecated and replaced by ``rcParams._set(key, val)``.
-
-        Even though private, we guarantee API stability for ``rcParams._set``,
-        i.e. it is subject to Matplotlib's API and deprecation policy.
-
-        :meta public:
-        """
-        dict.__setitem__(self, key, val)
-
-    def _get(self, key):
-        """
-        Directly read data bypassing deprecation, backend and validation
-        logic.
-
-        Notes
-        -----
-        As end user or downstream library you almost always should use
-        ``val = rcParams[key]`` and not ``_get()``.
-
-        There are only very few special cases that need direct data access.
-        These cases previously used ``dict.__getitem__(rcParams, key, val)``,
-        which is now deprecated and replaced by ``rcParams._get(key)``.
-
-        Even though private, we guarantee API stability for ``rcParams._get``,
-        i.e. it is subject to Matplotlib's API and deprecation policy.
-
-        :meta public:
-        """
-        return dict.__getitem__(self, key)
-
-    def __setitem__(self, key, val):
-        try:
-            if key in _deprecated_map:
-                version, alt_key, alt_val, inverse_alt = _deprecated_map[key]
-                _api.warn_deprecated(
-                    version, name=key, obj_type="rcparam", alternative=alt_key)
-                key = alt_key
-                val = alt_val(val)
-            elif key in _deprecated_remain_as_none and val is not None:
-                version, = _deprecated_remain_as_none[key]
-                _api.warn_deprecated(version, name=key, obj_type="rcparam")
-            elif key in _deprecated_ignore_map:
-                version, alt_key = _deprecated_ignore_map[key]
-                _api.warn_deprecated(
-                    version, name=key, obj_type="rcparam", alternative=alt_key)
-                return
-            elif key == 'backend':
-                if val is rcsetup._auto_backend_sentinel:
-                    if 'backend' in self:
-                        return
-            try:
-                cval = self.validate[key](val)
-            except ValueError as ve:
-                raise ValueError(f"Key {key}: {ve}") from None
-            self._set(key, cval)
-        except KeyError as err:
-            raise KeyError(
-                f"{key} is not a valid rc parameter (see rcParams.keys() for "
-                f"a list of valid parameters)") from err
-
-    def __getitem__(self, key):
-        if key in _deprecated_map:
-            version, alt_key, alt_val, inverse_alt = _deprecated_map[key]
-            _api.warn_deprecated(
-                version, name=key, obj_type="rcparam", alternative=alt_key)
-            return inverse_alt(self._get(alt_key))
-
-        elif key in _deprecated_ignore_map:
-            version, alt_key = _deprecated_ignore_map[key]
-            _api.warn_deprecated(
-                version, name=key, obj_type="rcparam", alternative=alt_key)
-            return self._get(alt_key) if alt_key else None
-
-        # In theory, this should only ever be used after the global rcParams
-        # has been set up, but better be safe e.g. in presence of breakpoints.
-        elif key == "backend" and self is globals().get("rcParams"):
-            val = self._get(key)
-            if val is rcsetup._auto_backend_sentinel:
-                from matplotlib import pyplot as plt
-                plt.switch_backend(rcsetup._auto_backend_sentinel)
-
-        return self._get(key)
-
-    def _get_backend_or_none(self):
-        """Get the requested backend, if any, without triggering resolution."""
-        backend = self._get("backend")
-        return None if backend is rcsetup._auto_backend_sentinel else backend
-
-    def __repr__(self):
-        class_name = self.__class__.__name__
-        indent = len(class_name) + 1
-        with _api.suppress_matplotlib_deprecation_warning():
-            repr_split = pprint.pformat(dict(self), indent=1,
-                                        width=80 - indent).split('\n')
-        repr_indented = ('\n' + ' ' * indent).join(repr_split)
-        return '{}({})'.format(class_name, repr_indented)
-
-    def __str__(self):
-        return '\n'.join(map('{0[0]}: {0[1]}'.format, sorted(self.items())))
-
-    def __iter__(self):
-        """Yield sorted list of keys."""
-        with _api.suppress_matplotlib_deprecation_warning():
-            yield from sorted(dict.__iter__(self))
-
-    def __len__(self):
-        return dict.__len__(self)
-
-    def find_all(self, pattern):
-        """
-        Return the subset of this RcParams dictionary whose keys match,
-        using :func:`re.search`, the given ``pattern``.
-
-        .. note::
-
-            Changes to the returned dictionary are *not* propagated to
-            the parent RcParams dictionary.
-
-        """
-        pattern_re = re.compile(pattern)
-        return RcParams((key, value)
-                        for key, value in self.items()
-                        if pattern_re.search(key))
-
-    def copy(self):
-        """Copy this RcParams instance."""
-        rccopy = RcParams()
-        for k in self:  # Skip deprecations and revalidation.
-            rccopy._set(k, self._get(k))
-        return rccopy
-
-
-def rc_params(fail_on_error=False):
-    """Construct a `RcParams` instance from the default Matplotlib rc file."""
-    return rc_params_from_file(matplotlib_fname(), fail_on_error)
-
-
-@functools.lru_cache()
-def _get_ssl_context():
-    try:
-        import certifi
-    except ImportError:
-        _log.debug("Could not import certifi.")
-        return None
-    import ssl
-    return ssl.create_default_context(cafile=certifi.where())
-
-
-@contextlib.contextmanager
-def _open_file_or_url(fname):
-    if (isinstance(fname, str)
-            and fname.startswith(('http://', 'https://', 'ftp://', 'file:'))):
-        import urllib.request
-        ssl_ctx = _get_ssl_context()
-        if ssl_ctx is None:
-            _log.debug(
-                "Could not get certifi ssl context, https may not work."
-            )
-        with urllib.request.urlopen(fname, context=ssl_ctx) as f:
-            yield (line.decode('utf-8') for line in f)
-    else:
-        fname = os.path.expanduser(fname)
-        with open(fname, encoding='utf-8') as f:
-            yield f
-
-
-def _rc_params_in_file(fname, transform=lambda x: x, fail_on_error=False):
-    """
-    Construct a `RcParams` instance from file *fname*.
-
-    Unlike `rc_params_from_file`, the configuration class only contains the
-    parameters specified in the file (i.e. default values are not filled in).
-
-    Parameters
-    ----------
-    fname : path-like
-        The loaded file.
-    transform : callable, default: the identity function
-        A function called on each individual line of the file to transform it,
-        before further parsing.
-    fail_on_error : bool, default: False
-        Whether invalid entries should result in an exception or a warning.
-    """
-    import matplotlib as mpl
-    rc_temp = {}
-    with _open_file_or_url(fname) as fd:
-        try:
-            for line_no, line in enumerate(fd, 1):
-                line = transform(line)
-                strippedline = cbook._strip_comment(line)
-                if not strippedline:
-                    continue
-                tup = strippedline.split(':', 1)
-                if len(tup) != 2:
-                    _log.warning('Missing colon in file %r, line %d (%r)',
-                                 fname, line_no, line.rstrip('\n'))
-                    continue
-                key, val = tup
-                key = key.strip()
-                val = val.strip()
-                if val.startswith('"') and val.endswith('"'):
-                    val = val[1:-1]  # strip double quotes
-                if key in rc_temp:
-                    _log.warning('Duplicate key in file %r, line %d (%r)',
-                                 fname, line_no, line.rstrip('\n'))
-                rc_temp[key] = (val, line, line_no)
-        except UnicodeDecodeError:
-            _log.warning('Cannot decode configuration file %r as utf-8.',
-                         fname)
-            raise
-
-    config = RcParams()
-
-    for key, (val, line, line_no) in rc_temp.items():
-        if key in rcsetup._validators:
-            if fail_on_error:
-                config[key] = val  # try to convert to proper type or raise
-            else:
-                try:
-                    config[key] = val  # try to convert to proper type or skip
-                except Exception as msg:
-                    _log.warning('Bad value in file %r, line %d (%r): %s',
-                                 fname, line_no, line.rstrip('\n'), msg)
-        elif key in _deprecated_ignore_map:
-            version, alt_key = _deprecated_ignore_map[key]
-            _api.warn_deprecated(
-                version, name=key, alternative=alt_key, obj_type='rcparam',
-                addendum="Please update your matplotlibrc.")
-        else:
-            # __version__ must be looked up as an attribute to trigger the
-            # module-level __getattr__.
-            version = ('main' if '.post' in mpl.__version__
-                       else f'v{mpl.__version__}')
-            _log.warning("""
-Bad key %(key)s in file %(fname)s, line %(line_no)s (%(line)r)
-You probably need to get an updated matplotlibrc file from
-https://github.com/matplotlib/matplotlib/blob/%(version)s/lib/matplotlib/mpl-data/matplotlibrc
-or from the matplotlib source distribution""",
-                         dict(key=key, fname=fname, line_no=line_no,
-                              line=line.rstrip('\n'), version=version))
-    return config
-
-
-def rc_params_from_file(fname, fail_on_error=False, use_default_template=True):
-    """
-    Construct a `RcParams` from file *fname*.
-
-    Parameters
-    ----------
-    fname : str or path-like
-        A file with Matplotlib rc settings.
-    fail_on_error : bool
-        If True, raise an error when the parser fails to convert a parameter.
-    use_default_template : bool
-        If True, initialize with default parameters before updating with those
-        in the given file. If False, the configuration class only contains the
-        parameters specified in the file. (Useful for updating dicts.)
-    """
-    config_from_file = _rc_params_in_file(fname, fail_on_error=fail_on_error)
-
-    if not use_default_template:
-        return config_from_file
-
-    with _api.suppress_matplotlib_deprecation_warning():
-        config = RcParams({**rcParamsDefault, **config_from_file})
-
-    if "".join(config['text.latex.preamble']):
-        _log.info("""
-*****************************************************************
-You have the following UNSUPPORTED LaTeX preamble customizations:
-%s
-Please do not ask for support with these customizations active.
-*****************************************************************
-""", '\n'.join(config['text.latex.preamble']))
-    _log.debug('loaded rc file %s', fname)
-
-    return config
-
-
-# When constructing the global instances, we need to perform certain updates
-# by explicitly calling the superclass (dict.update, dict.items) to avoid
-# triggering resolution of _auto_backend_sentinel.
-rcParamsDefault = _rc_params_in_file(
-    cbook._get_data_path("matplotlibrc"),
-    # Strip leading comment.
-    transform=lambda line: line[1:] if line.startswith("#") else line,
-    fail_on_error=True)
-dict.update(rcParamsDefault, rcsetup._hardcoded_defaults)
-# Normally, the default matplotlibrc file contains *no* entry for backend (the
-# corresponding line starts with ##, not #; we fill on _auto_backend_sentinel
-# in that case.  However, packagers can set a different default backend
-# (resulting in a normal `#backend: foo` line) in which case we should *not*
-# fill in _auto_backend_sentinel.
-dict.setdefault(rcParamsDefault, "backend", rcsetup._auto_backend_sentinel)
-rcParams = RcParams()  # The global instance.
-dict.update(rcParams, dict.items(rcParamsDefault))
-dict.update(rcParams, _rc_params_in_file(matplotlib_fname()))
-rcParamsOrig = rcParams.copy()
-with _api.suppress_matplotlib_deprecation_warning():
-    # This also checks that all rcParams are indeed listed in the template.
-    # Assigning to rcsetup.defaultParams is left only for backcompat.
-    defaultParams = rcsetup.defaultParams = {
-        # We want to resolve deprecated rcParams, but not backend...
-        key: [(rcsetup._auto_backend_sentinel if key == "backend" else
-               rcParamsDefault[key]),
-              validator]
-        for key, validator in rcsetup._validators.items()}
-if rcParams['axes.formatter.use_locale']:
-    locale.setlocale(locale.LC_ALL, '')
-
-
-def rc(group, **kwargs):
-    """
-    Set the current `.rcParams`.  *group* is the grouping for the rc, e.g.,
-    for ``lines.linewidth`` the group is ``lines``, for
-    ``axes.facecolor``, the group is ``axes``, and so on.  Group may
-    also be a list or tuple of group names, e.g., (*xtick*, *ytick*).
-    *kwargs* is a dictionary attribute name/value pairs, e.g.,::
-
-      rc('lines', linewidth=2, color='r')
-
-    sets the current `.rcParams` and is equivalent to::
-
-      rcParams['lines.linewidth'] = 2
-      rcParams['lines.color'] = 'r'
-
-    The following aliases are available to save typing for interactive users:
-
-    =====   =================
-    Alias   Property
-    =====   =================
-    'lw'    'linewidth'
-    'ls'    'linestyle'
-    'c'     'color'
-    'fc'    'facecolor'
-    'ec'    'edgecolor'
-    'mew'   'markeredgewidth'
-    'aa'    'antialiased'
-    =====   =================
-
-    Thus you could abbreviate the above call as::
-
-          rc('lines', lw=2, c='r')
-
-    Note you can use python's kwargs dictionary facility to store
-    dictionaries of default parameters.  e.g., you can customize the
-    font rc as follows::
-
-      font = {'family' : 'monospace',
-              'weight' : 'bold',
-              'size'   : 'larger'}
-      rc('font', **font)  # pass in the font dict as kwargs
-
-    This enables you to easily switch between several configurations.  Use
-    ``matplotlib.style.use('default')`` or :func:`~matplotlib.rcdefaults` to
-    restore the default `.rcParams` after changes.
-
-    Notes
-    -----
-    Similar functionality is available by using the normal dict interface, i.e.
-    ``rcParams.update({"lines.linewidth": 2, ...})`` (but ``rcParams.update``
-    does not support abbreviations or grouping).
-    """
-
-    aliases = {
-        'lw':  'linewidth',
-        'ls':  'linestyle',
-        'c':   'color',
-        'fc':  'facecolor',
-        'ec':  'edgecolor',
-        'mew': 'markeredgewidth',
-        'aa':  'antialiased',
-        }
-
-    if isinstance(group, str):
-        group = (group,)
-    for g in group:
-        for k, v in kwargs.items():
-            name = aliases.get(k) or k
-            key = '%s.%s' % (g, name)
-            try:
-                rcParams[key] = v
-            except KeyError as err:
-                raise KeyError(('Unrecognized key "%s" for group "%s" and '
-                                'name "%s"') % (key, g, name)) from err
-
-
-def rcdefaults():
-    """
-    Restore the `.rcParams` from Matplotlib's internal default style.
-
-    Style-blacklisted `.rcParams` (defined in
-    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.
-
-    See Also
-    --------
-    matplotlib.rc_file_defaults
-        Restore the `.rcParams` from the rc file originally loaded by
-        Matplotlib.
-    matplotlib.style.use
-        Use a specific style file.  Call ``style.use('default')`` to restore
-        the default style.
-    """
-    # Deprecation warnings were already handled when creating rcParamsDefault,
-    # no need to reemit them here.
-    with _api.suppress_matplotlib_deprecation_warning():
-        from .style.core import STYLE_BLACKLIST
-        rcParams.clear()
-        rcParams.update({k: v for k, v in rcParamsDefault.items()
-                         if k not in STYLE_BLACKLIST})
-
-
-def rc_file_defaults():
-    """
-    Restore the `.rcParams` from the original rc file loaded by Matplotlib.
-
-    Style-blacklisted `.rcParams` (defined in
-    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.
-    """
-    # Deprecation warnings were already handled when creating rcParamsOrig, no
-    # need to reemit them here.
-    with _api.suppress_matplotlib_deprecation_warning():
-        from .style.core import STYLE_BLACKLIST
-        rcParams.update({k: rcParamsOrig[k] for k in rcParamsOrig
-                         if k not in STYLE_BLACKLIST})
-
-
-def rc_file(fname, *, use_default_template=True):
-    """
-    Update `.rcParams` from file.
-
-    Style-blacklisted `.rcParams` (defined in
-    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.
-
-    Parameters
-    ----------
-    fname : str or path-like
-        A file with Matplotlib rc settings.
-
-    use_default_template : bool
-        If True, initialize with default parameters before updating with those
-        in the given file. If False, the current configuration persists
-        and only the parameters specified in the file are updated.
-    """
-    # Deprecation warnings were already handled in rc_params_from_file, no need
-    # to reemit them here.
-    with _api.suppress_matplotlib_deprecation_warning():
-        from .style.core import STYLE_BLACKLIST
-        rc_from_file = rc_params_from_file(
-            fname, use_default_template=use_default_template)
-        rcParams.update({k: rc_from_file[k] for k in rc_from_file
-                         if k not in STYLE_BLACKLIST})
-
-
-@contextlib.contextmanager
-def rc_context(rc=None, fname=None):
-    """
-    Return a context manager for temporarily changing rcParams.
-
-    The :rc:`backend` will not be reset by the context manager.
-
-    rcParams changed both through the context manager invocation and
-    in the body of the context will be reset on context exit.
-
-    Parameters
-    ----------
-    rc : dict
-        The rcParams to temporarily set.
-    fname : str or path-like
-        A file with Matplotlib rc settings. If both *fname* and *rc* are given,
-        settings from *rc* take precedence.
-
-    See Also
-    --------
-    :ref:`customizing-with-matplotlibrc-files`
-
-    Examples
-    --------
-    Passing explicit values via a dict::
-
-        with mpl.rc_context({'interactive': False}):
-            fig, ax = plt.subplots()
-            ax.plot(range(3), range(3))
-            fig.savefig('example.png')
-            plt.close(fig)
-
-    Loading settings from a file::
-
-         with mpl.rc_context(fname='print.rc'):
-             plt.plot(x, y)  # uses 'print.rc'
-
-    Setting in the context body::
-
-        with mpl.rc_context():
-            # will be reset
-            mpl.rcParams['lines.linewidth'] = 5
-            plt.plot(x, y)
-
-    """
-    orig = dict(rcParams.copy())
-    del orig['backend']
-    try:
-        if fname:
-            rc_file(fname)
-        if rc:
-            rcParams.update(rc)
-        yield
-    finally:
-        dict.update(rcParams, orig)  # Revert to the original rcs.
-
-
-def use(backend, *, force=True):
-    """
-    Select the backend used for rendering and GUI integration.
-
-    If pyplot is already imported, `~matplotlib.pyplot.switch_backend` is used
-    and if the new backend is different than the current backend, all Figures
-    will be closed.
-
-    Parameters
-    ----------
-    backend : str
-        The backend to switch to.  This can either be one of the standard
-        backend names, which are case-insensitive:
-
-        - interactive backends:
-          GTK3Agg, GTK3Cairo, GTK4Agg, GTK4Cairo, MacOSX, nbAgg, QtAgg,
-          QtCairo, TkAgg, TkCairo, WebAgg, WX, WXAgg, WXCairo, Qt5Agg, Qt5Cairo
-
-        - non-interactive backends:
-          agg, cairo, pdf, pgf, ps, svg, template
-
-        or a string of the form: ``module://my.module.name``.
-
-        Switching to an interactive backend is not possible if an unrelated
-        event loop has already been started (e.g., switching to GTK3Agg if a
-        TkAgg window has already been opened).  Switching to a non-interactive
-        backend is always possible.
-
-    force : bool, default: True
-        If True (the default), raise an `ImportError` if the backend cannot be
-        set up (either because it fails to import, or because an incompatible
-        GUI interactive framework is already running); if False, silently
-        ignore the failure.
-
-    See Also
-    --------
-    :ref:`backends`
-    matplotlib.get_backend
-    matplotlib.pyplot.switch_backend
-
-    """
-    name = validate_backend(backend)
-    # don't (prematurely) resolve the "auto" backend setting
-    if rcParams._get_backend_or_none() == name:
-        # Nothing to do if the requested backend is already set
-        pass
-    else:
-        # if pyplot is not already imported, do not import it.  Doing
-        # so may trigger a `plt.switch_backend` to the _default_ backend
-        # before we get a chance to change to the one the user just requested
-        plt = sys.modules.get('matplotlib.pyplot')
-        # if pyplot is imported, then try to change backends
-        if plt is not None:
-            try:
-                # we need this import check here to re-raise if the
-                # user does not have the libraries to support their
-                # chosen backend installed.
-                plt.switch_backend(name)
-            except ImportError:
-                if force:
-                    raise
-        # if we have not imported pyplot, then we can set the rcParam
-        # value which will be respected when the user finally imports
-        # pyplot
-        else:
-            rcParams['backend'] = backend
-    # if the user has asked for a given backend, do not helpfully
-    # fallback
-    rcParams['backend_fallback'] = False
-
-
-if os.environ.get('MPLBACKEND'):
-    rcParams['backend'] = os.environ.get('MPLBACKEND')
-
-
-def get_backend():
-    """
-    Return the name of the current backend.
-
-    See Also
-    --------
-    matplotlib.use
-    """
-    return rcParams['backend']
-
-
-def interactive(b):
-    """
-    Set whether to redraw after every plotting command (e.g. `.pyplot.xlabel`).
-    """
-    rcParams['interactive'] = b
-
-
-def is_interactive():
-    """
-    Return whether to redraw after every plotting command.
-
-    .. note::
-
-        This function is only intended for use in backends. End users should
-        use `.pyplot.isinteractive` instead.
-    """
-    return rcParams['interactive']
-
-
-def _init_tests():
-    # The version of FreeType to install locally for running the
-    # tests.  This must match the value in `setupext.py`
-    LOCAL_FREETYPE_VERSION = '2.6.1'
-
-    from matplotlib import ft2font
-    if (ft2font.__freetype_version__ != LOCAL_FREETYPE_VERSION or
-            ft2font.__freetype_build_type__ != 'local'):
-        _log.warning(
-            f"Matplotlib is not built with the correct FreeType version to "
-            f"run tests.  Rebuild without setting system_freetype=1 in "
-            f"mplsetup.cfg.  Expect many image comparison failures below.  "
-            f"Expected freetype version {LOCAL_FREETYPE_VERSION}.  "
-            f"Found freetype version {ft2font.__freetype_version__}.  "
-            "Freetype build type is {}local".format(
-                "" if ft2font.__freetype_build_type__ == 'local' else "not "))
-
-
-def _replacer(data, value):
-    """
-    Either returns ``data[value]`` or passes ``data`` back, converts either to
-    a sequence.
-    """
-    try:
-        # if key isn't a string don't bother
-        if isinstance(value, str):
-            # try to use __getitem__
-            value = data[value]
-    except Exception:
-        # key does not exist, silently fall back to key
-        pass
-    return sanitize_sequence(value)
-
-
-def _label_from_arg(y, default_name):
-    try:
-        return y.name
-    except AttributeError:
-        if isinstance(default_name, str):
-            return default_name
-    return None
-
-
-def _add_data_doc(docstring, replace_names):
-    """
-    Add documentation for a *data* field to the given docstring.
-
-    Parameters
-    ----------
-    docstring : str
-        The input docstring.
-    replace_names : list of str or None
-        The list of parameter names which arguments should be replaced by
-        ``data[name]`` (if ``data[name]`` does not throw an exception).  If
-        None, replacement is attempted for all arguments.
-
-    Returns
-    -------
-    str
-        The augmented docstring.
-    """
-    if (docstring is None
-            or replace_names is not None and len(replace_names) == 0):
-        return docstring
-    docstring = inspect.cleandoc(docstring)
-
-    data_doc = ("""\
-    If given, all parameters also accept a string ``s``, which is
-    interpreted as ``data[s]`` (unless this raises an exception)."""
-                if replace_names is None else f"""\
-    If given, the following parameters also accept a string ``s``, which is
-    interpreted as ``data[s]`` (unless this raises an exception):
-
-    {', '.join(map('*{}*'.format, replace_names))}""")
-    # using string replacement instead of formatting has the advantages
-    # 1) simpler indent handling
-    # 2) prevent problems with formatting characters '{', '%' in the docstring
-    if _log.level <= logging.DEBUG:
-        # test_data_parameter_replacement() tests against these log messages
-        # make sure to keep message and test in sync
-        if "data : indexable object, optional" not in docstring:
-            _log.debug("data parameter docstring error: no data parameter")
-        if 'DATA_PARAMETER_PLACEHOLDER' not in docstring:
-            _log.debug("data parameter docstring error: missing placeholder")
-    return docstring.replace('    DATA_PARAMETER_PLACEHOLDER', data_doc)
-
-
-def _preprocess_data(func=None, *, replace_names=None, label_namer=None):
-    """
-    A decorator to add a 'data' kwarg to a function.
-
-    When applied::
-
-        @_preprocess_data()
-        def func(ax, *args, **kwargs): ...
-
-    the signature is modified to ``decorated(ax, *args, data=None, **kwargs)``
-    with the following behavior:
-
-    - if called with ``data=None``, forward the other arguments to ``func``;
-    - otherwise, *data* must be a mapping; for any argument passed in as a
-      string ``name``, replace the argument by ``data[name]`` (if this does not
-      throw an exception), then forward the arguments to ``func``.
-
-    In either case, any argument that is a `MappingView` is also converted to a
-    list.
-
-    Parameters
-    ----------
-    replace_names : list of str or None, default: None
-        The list of parameter names for which lookup into *data* should be
-        attempted. If None, replacement is attempted for all arguments.
-    label_namer : str, default: None
-        If set e.g. to "namer" (which must be a kwarg in the function's
-        signature -- not as ``**kwargs``), if the *namer* argument passed in is
-        a (string) key of *data* and no *label* kwarg is passed, then use the
-        (string) value of the *namer* as *label*. ::
-
-            @_preprocess_data(label_namer="foo")
-            def func(foo, label=None): ...
-
-            func("key", data={"key": value})
-            # is equivalent to
-            func.__wrapped__(value, label="key")
-    """
-
-    if func is None:  # Return the actual decorator.
-        return functools.partial(
-            _preprocess_data,
-            replace_names=replace_names, label_namer=label_namer)
-
-    sig = inspect.signature(func)
-    varargs_name = None
-    varkwargs_name = None
-    arg_names = []
-    params = list(sig.parameters.values())
-    for p in params:
-        if p.kind is Parameter.VAR_POSITIONAL:
-            varargs_name = p.name
-        elif p.kind is Parameter.VAR_KEYWORD:
-            varkwargs_name = p.name
-        else:
-            arg_names.append(p.name)
-    data_param = Parameter("data", Parameter.KEYWORD_ONLY, default=None)
-    if varkwargs_name:
-        params.insert(-1, data_param)
-    else:
-        params.append(data_param)
-    new_sig = sig.replace(parameters=params)
-    arg_names = arg_names[1:]  # remove the first "ax" / self arg
-
-    assert {*arg_names}.issuperset(replace_names or []) or varkwargs_name, (
-        "Matplotlib internal error: invalid replace_names ({!r}) for {!r}"
-        .format(replace_names, func.__name__))
-    assert label_namer is None or label_namer in arg_names, (
-        "Matplotlib internal error: invalid label_namer ({!r}) for {!r}"
-        .format(label_namer, func.__name__))
-
-    @functools.wraps(func)
-    def inner(ax, *args, data=None, **kwargs):
-        if data is None:
-            return func(ax, *map(sanitize_sequence, args), **kwargs)
-
-        bound = new_sig.bind(ax, *args, **kwargs)
-        auto_label = (bound.arguments.get(label_namer)
-                      or bound.kwargs.get(label_namer))
-
-        for k, v in bound.arguments.items():
-            if k == varkwargs_name:
-                for k1, v1 in v.items():
-                    if replace_names is None or k1 in replace_names:
-                        v[k1] = _replacer(data, v1)
-            elif k == varargs_name:
-                if replace_names is None:
-                    bound.arguments[k] = tuple(_replacer(data, v1) for v1 in v)
-            else:
-                if replace_names is None or k in replace_names:
-                    bound.arguments[k] = _replacer(data, v)
-
-        new_args = bound.args
-        new_kwargs = bound.kwargs
-
-        args_and_kwargs = {**bound.arguments, **bound.kwargs}
-        if label_namer and "label" not in args_and_kwargs:
-            new_kwargs["label"] = _label_from_arg(
-                args_and_kwargs.get(label_namer), auto_label)
-
-        return func(*new_args, **new_kwargs)
-
-    inner.__doc__ = _add_data_doc(inner.__doc__, replace_names)
-    inner.__signature__ = new_sig
-    return inner
-
-
-_log.debug('interactive is %s', is_interactive())
-_log.debug('platform is %s', sys.platform)
-
-
-# workaround: we must defer colormaps import to after loading rcParams, because
-# colormap creation depends on rcParams
-from matplotlib.cm import _colormaps as colormaps
-from matplotlib.colors import _color_sequences as color_sequences
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_color_data.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_color_data.py
deleted file mode 100644
index 44f97adbb76aeaec2578cedfe60219a3278fd2ca..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_color_data.py
+++ /dev/null
@@ -1,1141 +0,0 @@
-BASE_COLORS = {
-    'b': (0, 0, 1),        # blue
-    'g': (0, 0.5, 0),      # green
-    'r': (1, 0, 0),        # red
-    'c': (0, 0.75, 0.75),  # cyan
-    'm': (0.75, 0, 0.75),  # magenta
-    'y': (0.75, 0.75, 0),  # yellow
-    'k': (0, 0, 0),        # black
-    'w': (1, 1, 1),        # white
-}
-
-
-# These colors are from Tableau
-TABLEAU_COLORS = {
-    'tab:blue': '#1f77b4',
-    'tab:orange': '#ff7f0e',
-    'tab:green': '#2ca02c',
-    'tab:red': '#d62728',
-    'tab:purple': '#9467bd',
-    'tab:brown': '#8c564b',
-    'tab:pink': '#e377c2',
-    'tab:gray': '#7f7f7f',
-    'tab:olive': '#bcbd22',
-    'tab:cyan': '#17becf',
-}
-
-
-# This mapping of color names -> hex values is taken from
-# a survey run by Randall Munroe see:
-# https://blog.xkcd.com/2010/05/03/color-survey-results/
-# for more details.  The results are hosted at
-# https://xkcd.com/color/rgb/
-# and also available as a text file at
-# https://xkcd.com/color/rgb.txt
-#
-# License: https://creativecommons.org/publicdomain/zero/1.0/
-XKCD_COLORS = {
-    'cloudy blue': '#acc2d9',
-    'dark pastel green': '#56ae57',
-    'dust': '#b2996e',
-    'electric lime': '#a8ff04',
-    'fresh green': '#69d84f',
-    'light eggplant': '#894585',
-    'nasty green': '#70b23f',
-    'really light blue': '#d4ffff',
-    'tea': '#65ab7c',
-    'warm purple': '#952e8f',
-    'yellowish tan': '#fcfc81',
-    'cement': '#a5a391',
-    'dark grass green': '#388004',
-    'dusty teal': '#4c9085',
-    'grey teal': '#5e9b8a',
-    'macaroni and cheese': '#efb435',
-    'pinkish tan': '#d99b82',
-    'spruce': '#0a5f38',
-    'strong blue': '#0c06f7',
-    'toxic green': '#61de2a',
-    'windows blue': '#3778bf',
-    'blue blue': '#2242c7',
-    'blue with a hint of purple': '#533cc6',
-    'booger': '#9bb53c',
-    'bright sea green': '#05ffa6',
-    'dark green blue': '#1f6357',
-    'deep turquoise': '#017374',
-    'green teal': '#0cb577',
-    'strong pink': '#ff0789',
-    'bland': '#afa88b',
-    'deep aqua': '#08787f',
-    'lavender pink': '#dd85d7',
-    'light moss green': '#a6c875',
-    'light seafoam green': '#a7ffb5',
-    'olive yellow': '#c2b709',
-    'pig pink': '#e78ea5',
-    'deep lilac': '#966ebd',
-    'desert': '#ccad60',
-    'dusty lavender': '#ac86a8',
-    'purpley grey': '#947e94',
-    'purply': '#983fb2',
-    'candy pink': '#ff63e9',
-    'light pastel green': '#b2fba5',
-    'boring green': '#63b365',
-    'kiwi green': '#8ee53f',
-    'light grey green': '#b7e1a1',
-    'orange pink': '#ff6f52',
-    'tea green': '#bdf8a3',
-    'very light brown': '#d3b683',
-    'egg shell': '#fffcc4',
-    'eggplant purple': '#430541',
-    'powder pink': '#ffb2d0',
-    'reddish grey': '#997570',
-    'baby shit brown': '#ad900d',
-    'liliac': '#c48efd',
-    'stormy blue': '#507b9c',
-    'ugly brown': '#7d7103',
-    'custard': '#fffd78',
-    'darkish pink': '#da467d',
-    'deep brown': '#410200',
-    'greenish beige': '#c9d179',
-    'manilla': '#fffa86',
-    'off blue': '#5684ae',
-    'battleship grey': '#6b7c85',
-    'browny green': '#6f6c0a',
-    'bruise': '#7e4071',
-    'kelley green': '#009337',
-    'sickly yellow': '#d0e429',
-    'sunny yellow': '#fff917',
-    'azul': '#1d5dec',
-    'darkgreen': '#054907',
-    'green/yellow': '#b5ce08',
-    'lichen': '#8fb67b',
-    'light light green': '#c8ffb0',
-    'pale gold': '#fdde6c',
-    'sun yellow': '#ffdf22',
-    'tan green': '#a9be70',
-    'burple': '#6832e3',
-    'butterscotch': '#fdb147',
-    'toupe': '#c7ac7d',
-    'dark cream': '#fff39a',
-    'indian red': '#850e04',
-    'light lavendar': '#efc0fe',
-    'poison green': '#40fd14',
-    'baby puke green': '#b6c406',
-    'bright yellow green': '#9dff00',
-    'charcoal grey': '#3c4142',
-    'squash': '#f2ab15',
-    'cinnamon': '#ac4f06',
-    'light pea green': '#c4fe82',
-    'radioactive green': '#2cfa1f',
-    'raw sienna': '#9a6200',
-    'baby purple': '#ca9bf7',
-    'cocoa': '#875f42',
-    'light royal blue': '#3a2efe',
-    'orangeish': '#fd8d49',
-    'rust brown': '#8b3103',
-    'sand brown': '#cba560',
-    'swamp': '#698339',
-    'tealish green': '#0cdc73',
-    'burnt siena': '#b75203',
-    'camo': '#7f8f4e',
-    'dusk blue': '#26538d',
-    'fern': '#63a950',
-    'old rose': '#c87f89',
-    'pale light green': '#b1fc99',
-    'peachy pink': '#ff9a8a',
-    'rosy pink': '#f6688e',
-    'light bluish green': '#76fda8',
-    'light bright green': '#53fe5c',
-    'light neon green': '#4efd54',
-    'light seafoam': '#a0febf',
-    'tiffany blue': '#7bf2da',
-    'washed out green': '#bcf5a6',
-    'browny orange': '#ca6b02',
-    'nice blue': '#107ab0',
-    'sapphire': '#2138ab',
-    'greyish teal': '#719f91',
-    'orangey yellow': '#fdb915',
-    'parchment': '#fefcaf',
-    'straw': '#fcf679',
-    'very dark brown': '#1d0200',
-    'terracota': '#cb6843',
-    'ugly blue': '#31668a',
-    'clear blue': '#247afd',
-    'creme': '#ffffb6',
-    'foam green': '#90fda9',
-    'grey/green': '#86a17d',
-    'light gold': '#fddc5c',
-    'seafoam blue': '#78d1b6',
-    'topaz': '#13bbaf',
-    'violet pink': '#fb5ffc',
-    'wintergreen': '#20f986',
-    'yellow tan': '#ffe36e',
-    'dark fuchsia': '#9d0759',
-    'indigo blue': '#3a18b1',
-    'light yellowish green': '#c2ff89',
-    'pale magenta': '#d767ad',
-    'rich purple': '#720058',
-    'sunflower yellow': '#ffda03',
-    'green/blue': '#01c08d',
-    'leather': '#ac7434',
-    'racing green': '#014600',
-    'vivid purple': '#9900fa',
-    'dark royal blue': '#02066f',
-    'hazel': '#8e7618',
-    'muted pink': '#d1768f',
-    'booger green': '#96b403',
-    'canary': '#fdff63',
-    'cool grey': '#95a3a6',
-    'dark taupe': '#7f684e',
-    'darkish purple': '#751973',
-    'true green': '#089404',
-    'coral pink': '#ff6163',
-    'dark sage': '#598556',
-    'dark slate blue': '#214761',
-    'flat blue': '#3c73a8',
-    'mushroom': '#ba9e88',
-    'rich blue': '#021bf9',
-    'dirty purple': '#734a65',
-    'greenblue': '#23c48b',
-    'icky green': '#8fae22',
-    'light khaki': '#e6f2a2',
-    'warm blue': '#4b57db',
-    'dark hot pink': '#d90166',
-    'deep sea blue': '#015482',
-    'carmine': '#9d0216',
-    'dark yellow green': '#728f02',
-    'pale peach': '#ffe5ad',
-    'plum purple': '#4e0550',
-    'golden rod': '#f9bc08',
-    'neon red': '#ff073a',
-    'old pink': '#c77986',
-    'very pale blue': '#d6fffe',
-    'blood orange': '#fe4b03',
-    'grapefruit': '#fd5956',
-    'sand yellow': '#fce166',
-    'clay brown': '#b2713d',
-    'dark blue grey': '#1f3b4d',
-    'flat green': '#699d4c',
-    'light green blue': '#56fca2',
-    'warm pink': '#fb5581',
-    'dodger blue': '#3e82fc',
-    'gross green': '#a0bf16',
-    'ice': '#d6fffa',
-    'metallic blue': '#4f738e',
-    'pale salmon': '#ffb19a',
-    'sap green': '#5c8b15',
-    'algae': '#54ac68',
-    'bluey grey': '#89a0b0',
-    'greeny grey': '#7ea07a',
-    'highlighter green': '#1bfc06',
-    'light light blue': '#cafffb',
-    'light mint': '#b6ffbb',
-    'raw umber': '#a75e09',
-    'vivid blue': '#152eff',
-    'deep lavender': '#8d5eb7',
-    'dull teal': '#5f9e8f',
-    'light greenish blue': '#63f7b4',
-    'mud green': '#606602',
-    'pinky': '#fc86aa',
-    'red wine': '#8c0034',
-    'shit green': '#758000',
-    'tan brown': '#ab7e4c',
-    'darkblue': '#030764',
-    'rosa': '#fe86a4',
-    'lipstick': '#d5174e',
-    'pale mauve': '#fed0fc',
-    'claret': '#680018',
-    'dandelion': '#fedf08',
-    'orangered': '#fe420f',
-    'poop green': '#6f7c00',
-    'ruby': '#ca0147',
-    'dark': '#1b2431',
-    'greenish turquoise': '#00fbb0',
-    'pastel red': '#db5856',
-    'piss yellow': '#ddd618',
-    'bright cyan': '#41fdfe',
-    'dark coral': '#cf524e',
-    'algae green': '#21c36f',
-    'darkish red': '#a90308',
-    'reddy brown': '#6e1005',
-    'blush pink': '#fe828c',
-    'camouflage green': '#4b6113',
-    'lawn green': '#4da409',
-    'putty': '#beae8a',
-    'vibrant blue': '#0339f8',
-    'dark sand': '#a88f59',
-    'purple/blue': '#5d21d0',
-    'saffron': '#feb209',
-    'twilight': '#4e518b',
-    'warm brown': '#964e02',
-    'bluegrey': '#85a3b2',
-    'bubble gum pink': '#ff69af',
-    'duck egg blue': '#c3fbf4',
-    'greenish cyan': '#2afeb7',
-    'petrol': '#005f6a',
-    'royal': '#0c1793',
-    'butter': '#ffff81',
-    'dusty orange': '#f0833a',
-    'off yellow': '#f1f33f',
-    'pale olive green': '#b1d27b',
-    'orangish': '#fc824a',
-    'leaf': '#71aa34',
-    'light blue grey': '#b7c9e2',
-    'dried blood': '#4b0101',
-    'lightish purple': '#a552e6',
-    'rusty red': '#af2f0d',
-    'lavender blue': '#8b88f8',
-    'light grass green': '#9af764',
-    'light mint green': '#a6fbb2',
-    'sunflower': '#ffc512',
-    'velvet': '#750851',
-    'brick orange': '#c14a09',
-    'lightish red': '#fe2f4a',
-    'pure blue': '#0203e2',
-    'twilight blue': '#0a437a',
-    'violet red': '#a50055',
-    'yellowy brown': '#ae8b0c',
-    'carnation': '#fd798f',
-    'muddy yellow': '#bfac05',
-    'dark seafoam green': '#3eaf76',
-    'deep rose': '#c74767',
-    'dusty red': '#b9484e',
-    'grey/blue': '#647d8e',
-    'lemon lime': '#bffe28',
-    'purple/pink': '#d725de',
-    'brown yellow': '#b29705',
-    'purple brown': '#673a3f',
-    'wisteria': '#a87dc2',
-    'banana yellow': '#fafe4b',
-    'lipstick red': '#c0022f',
-    'water blue': '#0e87cc',
-    'brown grey': '#8d8468',
-    'vibrant purple': '#ad03de',
-    'baby green': '#8cff9e',
-    'barf green': '#94ac02',
-    'eggshell blue': '#c4fff7',
-    'sandy yellow': '#fdee73',
-    'cool green': '#33b864',
-    'pale': '#fff9d0',
-    'blue/grey': '#758da3',
-    'hot magenta': '#f504c9',
-    'greyblue': '#77a1b5',
-    'purpley': '#8756e4',
-    'baby shit green': '#889717',
-    'brownish pink': '#c27e79',
-    'dark aquamarine': '#017371',
-    'diarrhea': '#9f8303',
-    'light mustard': '#f7d560',
-    'pale sky blue': '#bdf6fe',
-    'turtle green': '#75b84f',
-    'bright olive': '#9cbb04',
-    'dark grey blue': '#29465b',
-    'greeny brown': '#696006',
-    'lemon green': '#adf802',
-    'light periwinkle': '#c1c6fc',
-    'seaweed green': '#35ad6b',
-    'sunshine yellow': '#fffd37',
-    'ugly purple': '#a442a0',
-    'medium pink': '#f36196',
-    'puke brown': '#947706',
-    'very light pink': '#fff4f2',
-    'viridian': '#1e9167',
-    'bile': '#b5c306',
-    'faded yellow': '#feff7f',
-    'very pale green': '#cffdbc',
-    'vibrant green': '#0add08',
-    'bright lime': '#87fd05',
-    'spearmint': '#1ef876',
-    'light aquamarine': '#7bfdc7',
-    'light sage': '#bcecac',
-    'yellowgreen': '#bbf90f',
-    'baby poo': '#ab9004',
-    'dark seafoam': '#1fb57a',
-    'deep teal': '#00555a',
-    'heather': '#a484ac',
-    'rust orange': '#c45508',
-    'dirty blue': '#3f829d',
-    'fern green': '#548d44',
-    'bright lilac': '#c95efb',
-    'weird green': '#3ae57f',
-    'peacock blue': '#016795',
-    'avocado green': '#87a922',
-    'faded orange': '#f0944d',
-    'grape purple': '#5d1451',
-    'hot green': '#25ff29',
-    'lime yellow': '#d0fe1d',
-    'mango': '#ffa62b',
-    'shamrock': '#01b44c',
-    'bubblegum': '#ff6cb5',
-    'purplish brown': '#6b4247',
-    'vomit yellow': '#c7c10c',
-    'pale cyan': '#b7fffa',
-    'key lime': '#aeff6e',
-    'tomato red': '#ec2d01',
-    'lightgreen': '#76ff7b',
-    'merlot': '#730039',
-    'night blue': '#040348',
-    'purpleish pink': '#df4ec8',
-    'apple': '#6ecb3c',
-    'baby poop green': '#8f9805',
-    'green apple': '#5edc1f',
-    'heliotrope': '#d94ff5',
-    'yellow/green': '#c8fd3d',
-    'almost black': '#070d0d',
-    'cool blue': '#4984b8',
-    'leafy green': '#51b73b',
-    'mustard brown': '#ac7e04',
-    'dusk': '#4e5481',
-    'dull brown': '#876e4b',
-    'frog green': '#58bc08',
-    'vivid green': '#2fef10',
-    'bright light green': '#2dfe54',
-    'fluro green': '#0aff02',
-    'kiwi': '#9cef43',
-    'seaweed': '#18d17b',
-    'navy green': '#35530a',
-    'ultramarine blue': '#1805db',
-    'iris': '#6258c4',
-    'pastel orange': '#ff964f',
-    'yellowish orange': '#ffab0f',
-    'perrywinkle': '#8f8ce7',
-    'tealish': '#24bca8',
-    'dark plum': '#3f012c',
-    'pear': '#cbf85f',
-    'pinkish orange': '#ff724c',
-    'midnight purple': '#280137',
-    'light urple': '#b36ff6',
-    'dark mint': '#48c072',
-    'greenish tan': '#bccb7a',
-    'light burgundy': '#a8415b',
-    'turquoise blue': '#06b1c4',
-    'ugly pink': '#cd7584',
-    'sandy': '#f1da7a',
-    'electric pink': '#ff0490',
-    'muted purple': '#805b87',
-    'mid green': '#50a747',
-    'greyish': '#a8a495',
-    'neon yellow': '#cfff04',
-    'banana': '#ffff7e',
-    'carnation pink': '#ff7fa7',
-    'tomato': '#ef4026',
-    'sea': '#3c9992',
-    'muddy brown': '#886806',
-    'turquoise green': '#04f489',
-    'buff': '#fef69e',
-    'fawn': '#cfaf7b',
-    'muted blue': '#3b719f',
-    'pale rose': '#fdc1c5',
-    'dark mint green': '#20c073',
-    'amethyst': '#9b5fc0',
-    'blue/green': '#0f9b8e',
-    'chestnut': '#742802',
-    'sick green': '#9db92c',
-    'pea': '#a4bf20',
-    'rusty orange': '#cd5909',
-    'stone': '#ada587',
-    'rose red': '#be013c',
-    'pale aqua': '#b8ffeb',
-    'deep orange': '#dc4d01',
-    'earth': '#a2653e',
-    'mossy green': '#638b27',
-    'grassy green': '#419c03',
-    'pale lime green': '#b1ff65',
-    'light grey blue': '#9dbcd4',
-    'pale grey': '#fdfdfe',
-    'asparagus': '#77ab56',
-    'blueberry': '#464196',
-    'purple red': '#990147',
-    'pale lime': '#befd73',
-    'greenish teal': '#32bf84',
-    'caramel': '#af6f09',
-    'deep magenta': '#a0025c',
-    'light peach': '#ffd8b1',
-    'milk chocolate': '#7f4e1e',
-    'ocher': '#bf9b0c',
-    'off green': '#6ba353',
-    'purply pink': '#f075e6',
-    'lightblue': '#7bc8f6',
-    'dusky blue': '#475f94',
-    'golden': '#f5bf03',
-    'light beige': '#fffeb6',
-    'butter yellow': '#fffd74',
-    'dusky purple': '#895b7b',
-    'french blue': '#436bad',
-    'ugly yellow': '#d0c101',
-    'greeny yellow': '#c6f808',
-    'orangish red': '#f43605',
-    'shamrock green': '#02c14d',
-    'orangish brown': '#b25f03',
-    'tree green': '#2a7e19',
-    'deep violet': '#490648',
-    'gunmetal': '#536267',
-    'blue/purple': '#5a06ef',
-    'cherry': '#cf0234',
-    'sandy brown': '#c4a661',
-    'warm grey': '#978a84',
-    'dark indigo': '#1f0954',
-    'midnight': '#03012d',
-    'bluey green': '#2bb179',
-    'grey pink': '#c3909b',
-    'soft purple': '#a66fb5',
-    'blood': '#770001',
-    'brown red': '#922b05',
-    'medium grey': '#7d7f7c',
-    'berry': '#990f4b',
-    'poo': '#8f7303',
-    'purpley pink': '#c83cb9',
-    'light salmon': '#fea993',
-    'snot': '#acbb0d',
-    'easter purple': '#c071fe',
-    'light yellow green': '#ccfd7f',
-    'dark navy blue': '#00022e',
-    'drab': '#828344',
-    'light rose': '#ffc5cb',
-    'rouge': '#ab1239',
-    'purplish red': '#b0054b',
-    'slime green': '#99cc04',
-    'baby poop': '#937c00',
-    'irish green': '#019529',
-    'pink/purple': '#ef1de7',
-    'dark navy': '#000435',
-    'greeny blue': '#42b395',
-    'light plum': '#9d5783',
-    'pinkish grey': '#c8aca9',
-    'dirty orange': '#c87606',
-    'rust red': '#aa2704',
-    'pale lilac': '#e4cbff',
-    'orangey red': '#fa4224',
-    'primary blue': '#0804f9',
-    'kermit green': '#5cb200',
-    'brownish purple': '#76424e',
-    'murky green': '#6c7a0e',
-    'wheat': '#fbdd7e',
-    'very dark purple': '#2a0134',
-    'bottle green': '#044a05',
-    'watermelon': '#fd4659',
-    'deep sky blue': '#0d75f8',
-    'fire engine red': '#fe0002',
-    'yellow ochre': '#cb9d06',
-    'pumpkin orange': '#fb7d07',
-    'pale olive': '#b9cc81',
-    'light lilac': '#edc8ff',
-    'lightish green': '#61e160',
-    'carolina blue': '#8ab8fe',
-    'mulberry': '#920a4e',
-    'shocking pink': '#fe02a2',
-    'auburn': '#9a3001',
-    'bright lime green': '#65fe08',
-    'celadon': '#befdb7',
-    'pinkish brown': '#b17261',
-    'poo brown': '#885f01',
-    'bright sky blue': '#02ccfe',
-    'celery': '#c1fd95',
-    'dirt brown': '#836539',
-    'strawberry': '#fb2943',
-    'dark lime': '#84b701',
-    'copper': '#b66325',
-    'medium brown': '#7f5112',
-    'muted green': '#5fa052',
-    "robin's egg": '#6dedfd',
-    'bright aqua': '#0bf9ea',
-    'bright lavender': '#c760ff',
-    'ivory': '#ffffcb',
-    'very light purple': '#f6cefc',
-    'light navy': '#155084',
-    'pink red': '#f5054f',
-    'olive brown': '#645403',
-    'poop brown': '#7a5901',
-    'mustard green': '#a8b504',
-    'ocean green': '#3d9973',
-    'very dark blue': '#000133',
-    'dusty green': '#76a973',
-    'light navy blue': '#2e5a88',
-    'minty green': '#0bf77d',
-    'adobe': '#bd6c48',
-    'barney': '#ac1db8',
-    'jade green': '#2baf6a',
-    'bright light blue': '#26f7fd',
-    'light lime': '#aefd6c',
-    'dark khaki': '#9b8f55',
-    'orange yellow': '#ffad01',
-    'ocre': '#c69c04',
-    'maize': '#f4d054',
-    'faded pink': '#de9dac',
-    'british racing green': '#05480d',
-    'sandstone': '#c9ae74',
-    'mud brown': '#60460f',
-    'light sea green': '#98f6b0',
-    'robin egg blue': '#8af1fe',
-    'aqua marine': '#2ee8bb',
-    'dark sea green': '#11875d',
-    'soft pink': '#fdb0c0',
-    'orangey brown': '#b16002',
-    'cherry red': '#f7022a',
-    'burnt yellow': '#d5ab09',
-    'brownish grey': '#86775f',
-    'camel': '#c69f59',
-    'purplish grey': '#7a687f',
-    'marine': '#042e60',
-    'greyish pink': '#c88d94',
-    'pale turquoise': '#a5fbd5',
-    'pastel yellow': '#fffe71',
-    'bluey purple': '#6241c7',
-    'canary yellow': '#fffe40',
-    'faded red': '#d3494e',
-    'sepia': '#985e2b',
-    'coffee': '#a6814c',
-    'bright magenta': '#ff08e8',
-    'mocha': '#9d7651',
-    'ecru': '#feffca',
-    'purpleish': '#98568d',
-    'cranberry': '#9e003a',
-    'darkish green': '#287c37',
-    'brown orange': '#b96902',
-    'dusky rose': '#ba6873',
-    'melon': '#ff7855',
-    'sickly green': '#94b21c',
-    'silver': '#c5c9c7',
-    'purply blue': '#661aee',
-    'purpleish blue': '#6140ef',
-    'hospital green': '#9be5aa',
-    'shit brown': '#7b5804',
-    'mid blue': '#276ab3',
-    'amber': '#feb308',
-    'easter green': '#8cfd7e',
-    'soft blue': '#6488ea',
-    'cerulean blue': '#056eee',
-    'golden brown': '#b27a01',
-    'bright turquoise': '#0ffef9',
-    'red pink': '#fa2a55',
-    'red purple': '#820747',
-    'greyish brown': '#7a6a4f',
-    'vermillion': '#f4320c',
-    'russet': '#a13905',
-    'steel grey': '#6f828a',
-    'lighter purple': '#a55af4',
-    'bright violet': '#ad0afd',
-    'prussian blue': '#004577',
-    'slate green': '#658d6d',
-    'dirty pink': '#ca7b80',
-    'dark blue green': '#005249',
-    'pine': '#2b5d34',
-    'yellowy green': '#bff128',
-    'dark gold': '#b59410',
-    'bluish': '#2976bb',
-    'darkish blue': '#014182',
-    'dull red': '#bb3f3f',
-    'pinky red': '#fc2647',
-    'bronze': '#a87900',
-    'pale teal': '#82cbb2',
-    'military green': '#667c3e',
-    'barbie pink': '#fe46a5',
-    'bubblegum pink': '#fe83cc',
-    'pea soup green': '#94a617',
-    'dark mustard': '#a88905',
-    'shit': '#7f5f00',
-    'medium purple': '#9e43a2',
-    'very dark green': '#062e03',
-    'dirt': '#8a6e45',
-    'dusky pink': '#cc7a8b',
-    'red violet': '#9e0168',
-    'lemon yellow': '#fdff38',
-    'pistachio': '#c0fa8b',
-    'dull yellow': '#eedc5b',
-    'dark lime green': '#7ebd01',
-    'denim blue': '#3b5b92',
-    'teal blue': '#01889f',
-    'lightish blue': '#3d7afd',
-    'purpley blue': '#5f34e7',
-    'light indigo': '#6d5acf',
-    'swamp green': '#748500',
-    'brown green': '#706c11',
-    'dark maroon': '#3c0008',
-    'hot purple': '#cb00f5',
-    'dark forest green': '#002d04',
-    'faded blue': '#658cbb',
-    'drab green': '#749551',
-    'light lime green': '#b9ff66',
-    'snot green': '#9dc100',
-    'yellowish': '#faee66',
-    'light blue green': '#7efbb3',
-    'bordeaux': '#7b002c',
-    'light mauve': '#c292a1',
-    'ocean': '#017b92',
-    'marigold': '#fcc006',
-    'muddy green': '#657432',
-    'dull orange': '#d8863b',
-    'steel': '#738595',
-    'electric purple': '#aa23ff',
-    'fluorescent green': '#08ff08',
-    'yellowish brown': '#9b7a01',
-    'blush': '#f29e8e',
-    'soft green': '#6fc276',
-    'bright orange': '#ff5b00',
-    'lemon': '#fdff52',
-    'purple grey': '#866f85',
-    'acid green': '#8ffe09',
-    'pale lavender': '#eecffe',
-    'violet blue': '#510ac9',
-    'light forest green': '#4f9153',
-    'burnt red': '#9f2305',
-    'khaki green': '#728639',
-    'cerise': '#de0c62',
-    'faded purple': '#916e99',
-    'apricot': '#ffb16d',
-    'dark olive green': '#3c4d03',
-    'grey brown': '#7f7053',
-    'green grey': '#77926f',
-    'true blue': '#010fcc',
-    'pale violet': '#ceaefa',
-    'periwinkle blue': '#8f99fb',
-    'light sky blue': '#c6fcff',
-    'blurple': '#5539cc',
-    'green brown': '#544e03',
-    'bluegreen': '#017a79',
-    'bright teal': '#01f9c6',
-    'brownish yellow': '#c9b003',
-    'pea soup': '#929901',
-    'forest': '#0b5509',
-    'barney purple': '#a00498',
-    'ultramarine': '#2000b1',
-    'purplish': '#94568c',
-    'puke yellow': '#c2be0e',
-    'bluish grey': '#748b97',
-    'dark periwinkle': '#665fd1',
-    'dark lilac': '#9c6da5',
-    'reddish': '#c44240',
-    'light maroon': '#a24857',
-    'dusty purple': '#825f87',
-    'terra cotta': '#c9643b',
-    'avocado': '#90b134',
-    'marine blue': '#01386a',
-    'teal green': '#25a36f',
-    'slate grey': '#59656d',
-    'lighter green': '#75fd63',
-    'electric green': '#21fc0d',
-    'dusty blue': '#5a86ad',
-    'golden yellow': '#fec615',
-    'bright yellow': '#fffd01',
-    'light lavender': '#dfc5fe',
-    'umber': '#b26400',
-    'poop': '#7f5e00',
-    'dark peach': '#de7e5d',
-    'jungle green': '#048243',
-    'eggshell': '#ffffd4',
-    'denim': '#3b638c',
-    'yellow brown': '#b79400',
-    'dull purple': '#84597e',
-    'chocolate brown': '#411900',
-    'wine red': '#7b0323',
-    'neon blue': '#04d9ff',
-    'dirty green': '#667e2c',
-    'light tan': '#fbeeac',
-    'ice blue': '#d7fffe',
-    'cadet blue': '#4e7496',
-    'dark mauve': '#874c62',
-    'very light blue': '#d5ffff',
-    'grey purple': '#826d8c',
-    'pastel pink': '#ffbacd',
-    'very light green': '#d1ffbd',
-    'dark sky blue': '#448ee4',
-    'evergreen': '#05472a',
-    'dull pink': '#d5869d',
-    'aubergine': '#3d0734',
-    'mahogany': '#4a0100',
-    'reddish orange': '#f8481c',
-    'deep green': '#02590f',
-    'vomit green': '#89a203',
-    'purple pink': '#e03fd8',
-    'dusty pink': '#d58a94',
-    'faded green': '#7bb274',
-    'camo green': '#526525',
-    'pinky purple': '#c94cbe',
-    'pink purple': '#db4bda',
-    'brownish red': '#9e3623',
-    'dark rose': '#b5485d',
-    'mud': '#735c12',
-    'brownish': '#9c6d57',
-    'emerald green': '#028f1e',
-    'pale brown': '#b1916e',
-    'dull blue': '#49759c',
-    'burnt umber': '#a0450e',
-    'medium green': '#39ad48',
-    'clay': '#b66a50',
-    'light aqua': '#8cffdb',
-    'light olive green': '#a4be5c',
-    'brownish orange': '#cb7723',
-    'dark aqua': '#05696b',
-    'purplish pink': '#ce5dae',
-    'dark salmon': '#c85a53',
-    'greenish grey': '#96ae8d',
-    'jade': '#1fa774',
-    'ugly green': '#7a9703',
-    'dark beige': '#ac9362',
-    'emerald': '#01a049',
-    'pale red': '#d9544d',
-    'light magenta': '#fa5ff7',
-    'sky': '#82cafc',
-    'light cyan': '#acfffc',
-    'yellow orange': '#fcb001',
-    'reddish purple': '#910951',
-    'reddish pink': '#fe2c54',
-    'orchid': '#c875c4',
-    'dirty yellow': '#cdc50a',
-    'orange red': '#fd411e',
-    'deep red': '#9a0200',
-    'orange brown': '#be6400',
-    'cobalt blue': '#030aa7',
-    'neon pink': '#fe019a',
-    'rose pink': '#f7879a',
-    'greyish purple': '#887191',
-    'raspberry': '#b00149',
-    'aqua green': '#12e193',
-    'salmon pink': '#fe7b7c',
-    'tangerine': '#ff9408',
-    'brownish green': '#6a6e09',
-    'red brown': '#8b2e16',
-    'greenish brown': '#696112',
-    'pumpkin': '#e17701',
-    'pine green': '#0a481e',
-    'charcoal': '#343837',
-    'baby pink': '#ffb7ce',
-    'cornflower': '#6a79f7',
-    'blue violet': '#5d06e9',
-    'chocolate': '#3d1c02',
-    'greyish green': '#82a67d',
-    'scarlet': '#be0119',
-    'green yellow': '#c9ff27',
-    'dark olive': '#373e02',
-    'sienna': '#a9561e',
-    'pastel purple': '#caa0ff',
-    'terracotta': '#ca6641',
-    'aqua blue': '#02d8e9',
-    'sage green': '#88b378',
-    'blood red': '#980002',
-    'deep pink': '#cb0162',
-    'grass': '#5cac2d',
-    'moss': '#769958',
-    'pastel blue': '#a2bffe',
-    'bluish green': '#10a674',
-    'green blue': '#06b48b',
-    'dark tan': '#af884a',
-    'greenish blue': '#0b8b87',
-    'pale orange': '#ffa756',
-    'vomit': '#a2a415',
-    'forrest green': '#154406',
-    'dark lavender': '#856798',
-    'dark violet': '#34013f',
-    'purple blue': '#632de9',
-    'dark cyan': '#0a888a',
-    'olive drab': '#6f7632',
-    'pinkish': '#d46a7e',
-    'cobalt': '#1e488f',
-    'neon purple': '#bc13fe',
-    'light turquoise': '#7ef4cc',
-    'apple green': '#76cd26',
-    'dull green': '#74a662',
-    'wine': '#80013f',
-    'powder blue': '#b1d1fc',
-    'off white': '#ffffe4',
-    'electric blue': '#0652ff',
-    'dark turquoise': '#045c5a',
-    'blue purple': '#5729ce',
-    'azure': '#069af3',
-    'bright red': '#ff000d',
-    'pinkish red': '#f10c45',
-    'cornflower blue': '#5170d7',
-    'light olive': '#acbf69',
-    'grape': '#6c3461',
-    'greyish blue': '#5e819d',
-    'purplish blue': '#601ef9',
-    'yellowish green': '#b0dd16',
-    'greenish yellow': '#cdfd02',
-    'medium blue': '#2c6fbb',
-    'dusty rose': '#c0737a',
-    'light violet': '#d6b4fc',
-    'midnight blue': '#020035',
-    'bluish purple': '#703be7',
-    'red orange': '#fd3c06',
-    'dark magenta': '#960056',
-    'greenish': '#40a368',
-    'ocean blue': '#03719c',
-    'coral': '#fc5a50',
-    'cream': '#ffffc2',
-    'reddish brown': '#7f2b0a',
-    'burnt sienna': '#b04e0f',
-    'brick': '#a03623',
-    'sage': '#87ae73',
-    'grey green': '#789b73',
-    'white': '#ffffff',
-    "robin's egg blue": '#98eff9',
-    'moss green': '#658b38',
-    'steel blue': '#5a7d9a',
-    'eggplant': '#380835',
-    'light yellow': '#fffe7a',
-    'leaf green': '#5ca904',
-    'light grey': '#d8dcd6',
-    'puke': '#a5a502',
-    'pinkish purple': '#d648d7',
-    'sea blue': '#047495',
-    'pale purple': '#b790d4',
-    'slate blue': '#5b7c99',
-    'blue grey': '#607c8e',
-    'hunter green': '#0b4008',
-    'fuchsia': '#ed0dd9',
-    'crimson': '#8c000f',
-    'pale yellow': '#ffff84',
-    'ochre': '#bf9005',
-    'mustard yellow': '#d2bd0a',
-    'light red': '#ff474c',
-    'cerulean': '#0485d1',
-    'pale pink': '#ffcfdc',
-    'deep blue': '#040273',
-    'rust': '#a83c09',
-    'light teal': '#90e4c1',
-    'slate': '#516572',
-    'goldenrod': '#fac205',
-    'dark yellow': '#d5b60a',
-    'dark grey': '#363737',
-    'army green': '#4b5d16',
-    'grey blue': '#6b8ba4',
-    'seafoam': '#80f9ad',
-    'puce': '#a57e52',
-    'spring green': '#a9f971',
-    'dark orange': '#c65102',
-    'sand': '#e2ca76',
-    'pastel green': '#b0ff9d',
-    'mint': '#9ffeb0',
-    'light orange': '#fdaa48',
-    'bright pink': '#fe01b1',
-    'chartreuse': '#c1f80a',
-    'deep purple': '#36013f',
-    'dark brown': '#341c02',
-    'taupe': '#b9a281',
-    'pea green': '#8eab12',
-    'puke green': '#9aae07',
-    'kelly green': '#02ab2e',
-    'seafoam green': '#7af9ab',
-    'blue green': '#137e6d',
-    'khaki': '#aaa662',
-    'burgundy': '#610023',
-    'dark teal': '#014d4e',
-    'brick red': '#8f1402',
-    'royal purple': '#4b006e',
-    'plum': '#580f41',
-    'mint green': '#8fff9f',
-    'gold': '#dbb40c',
-    'baby blue': '#a2cffe',
-    'yellow green': '#c0fb2d',
-    'bright purple': '#be03fd',
-    'dark red': '#840000',
-    'pale blue': '#d0fefe',
-    'grass green': '#3f9b0b',
-    'navy': '#01153e',
-    'aquamarine': '#04d8b2',
-    'burnt orange': '#c04e01',
-    'neon green': '#0cff0c',
-    'bright blue': '#0165fc',
-    'rose': '#cf6275',
-    'light pink': '#ffd1df',
-    'mustard': '#ceb301',
-    'indigo': '#380282',
-    'lime': '#aaff32',
-    'sea green': '#53fca1',
-    'periwinkle': '#8e82fe',
-    'dark pink': '#cb416b',
-    'olive green': '#677a04',
-    'peach': '#ffb07c',
-    'pale green': '#c7fdb5',
-    'light brown': '#ad8150',
-    'hot pink': '#ff028d',
-    'black': '#000000',
-    'lilac': '#cea2fd',
-    'navy blue': '#001146',
-    'royal blue': '#0504aa',
-    'beige': '#e6daa6',
-    'salmon': '#ff796c',
-    'olive': '#6e750e',
-    'maroon': '#650021',
-    'bright green': '#01ff07',
-    'dark purple': '#35063e',
-    'mauve': '#ae7181',
-    'forest green': '#06470c',
-    'aqua': '#13eac9',
-    'cyan': '#00ffff',
-    'tan': '#d1b26f',
-    'dark blue': '#00035b',
-    'lavender': '#c79fef',
-    'turquoise': '#06c2ac',
-    'dark green': '#033500',
-    'violet': '#9a0eea',
-    'light purple': '#bf77f6',
-    'lime green': '#89fe05',
-    'grey': '#929591',
-    'sky blue': '#75bbfd',
-    'yellow': '#ffff14',
-    'magenta': '#c20078',
-    'light green': '#96f97b',
-    'orange': '#f97306',
-    'teal': '#029386',
-    'light blue': '#95d0fc',
-    'red': '#e50000',
-    'brown': '#653700',
-    'pink': '#ff81c0',
-    'blue': '#0343df',
-    'green': '#15b01a',
-    'purple': '#7e1e9c'}
-
-# Normalize name to "xkcd:<name>" to avoid name collisions.
-XKCD_COLORS = {'xkcd:' + name: value for name, value in XKCD_COLORS.items()}
-
-
-# https://drafts.csswg.org/css-color-4/#named-colors
-CSS4_COLORS = {
-    'aliceblue':            '#F0F8FF',
-    'antiquewhite':         '#FAEBD7',
-    'aqua':                 '#00FFFF',
-    'aquamarine':           '#7FFFD4',
-    'azure':                '#F0FFFF',
-    'beige':                '#F5F5DC',
-    'bisque':               '#FFE4C4',
-    'black':                '#000000',
-    'blanchedalmond':       '#FFEBCD',
-    'blue':                 '#0000FF',
-    'blueviolet':           '#8A2BE2',
-    'brown':                '#A52A2A',
-    'burlywood':            '#DEB887',
-    'cadetblue':            '#5F9EA0',
-    'chartreuse':           '#7FFF00',
-    'chocolate':            '#D2691E',
-    'coral':                '#FF7F50',
-    'cornflowerblue':       '#6495ED',
-    'cornsilk':             '#FFF8DC',
-    'crimson':              '#DC143C',
-    'cyan':                 '#00FFFF',
-    'darkblue':             '#00008B',
-    'darkcyan':             '#008B8B',
-    'darkgoldenrod':        '#B8860B',
-    'darkgray':             '#A9A9A9',
-    'darkgreen':            '#006400',
-    'darkgrey':             '#A9A9A9',
-    'darkkhaki':            '#BDB76B',
-    'darkmagenta':          '#8B008B',
-    'darkolivegreen':       '#556B2F',
-    'darkorange':           '#FF8C00',
-    'darkorchid':           '#9932CC',
-    'darkred':              '#8B0000',
-    'darksalmon':           '#E9967A',
-    'darkseagreen':         '#8FBC8F',
-    'darkslateblue':        '#483D8B',
-    'darkslategray':        '#2F4F4F',
-    'darkslategrey':        '#2F4F4F',
-    'darkturquoise':        '#00CED1',
-    'darkviolet':           '#9400D3',
-    'deeppink':             '#FF1493',
-    'deepskyblue':          '#00BFFF',
-    'dimgray':              '#696969',
-    'dimgrey':              '#696969',
-    'dodgerblue':           '#1E90FF',
-    'firebrick':            '#B22222',
-    'floralwhite':          '#FFFAF0',
-    'forestgreen':          '#228B22',
-    'fuchsia':              '#FF00FF',
-    'gainsboro':            '#DCDCDC',
-    'ghostwhite':           '#F8F8FF',
-    'gold':                 '#FFD700',
-    'goldenrod':            '#DAA520',
-    'gray':                 '#808080',
-    'green':                '#008000',
-    'greenyellow':          '#ADFF2F',
-    'grey':                 '#808080',
-    'honeydew':             '#F0FFF0',
-    'hotpink':              '#FF69B4',
-    'indianred':            '#CD5C5C',
-    'indigo':               '#4B0082',
-    'ivory':                '#FFFFF0',
-    'khaki':                '#F0E68C',
-    'lavender':             '#E6E6FA',
-    'lavenderblush':        '#FFF0F5',
-    'lawngreen':            '#7CFC00',
-    'lemonchiffon':         '#FFFACD',
-    'lightblue':            '#ADD8E6',
-    'lightcoral':           '#F08080',
-    'lightcyan':            '#E0FFFF',
-    'lightgoldenrodyellow': '#FAFAD2',
-    'lightgray':            '#D3D3D3',
-    'lightgreen':           '#90EE90',
-    'lightgrey':            '#D3D3D3',
-    'lightpink':            '#FFB6C1',
-    'lightsalmon':          '#FFA07A',
-    'lightseagreen':        '#20B2AA',
-    'lightskyblue':         '#87CEFA',
-    'lightslategray':       '#778899',
-    'lightslategrey':       '#778899',
-    'lightsteelblue':       '#B0C4DE',
-    'lightyellow':          '#FFFFE0',
-    'lime':                 '#00FF00',
-    'limegreen':            '#32CD32',
-    'linen':                '#FAF0E6',
-    'magenta':              '#FF00FF',
-    'maroon':               '#800000',
-    'mediumaquamarine':     '#66CDAA',
-    'mediumblue':           '#0000CD',
-    'mediumorchid':         '#BA55D3',
-    'mediumpurple':         '#9370DB',
-    'mediumseagreen':       '#3CB371',
-    'mediumslateblue':      '#7B68EE',
-    'mediumspringgreen':    '#00FA9A',
-    'mediumturquoise':      '#48D1CC',
-    'mediumvioletred':      '#C71585',
-    'midnightblue':         '#191970',
-    'mintcream':            '#F5FFFA',
-    'mistyrose':            '#FFE4E1',
-    'moccasin':             '#FFE4B5',
-    'navajowhite':          '#FFDEAD',
-    'navy':                 '#000080',
-    'oldlace':              '#FDF5E6',
-    'olive':                '#808000',
-    'olivedrab':            '#6B8E23',
-    'orange':               '#FFA500',
-    'orangered':            '#FF4500',
-    'orchid':               '#DA70D6',
-    'palegoldenrod':        '#EEE8AA',
-    'palegreen':            '#98FB98',
-    'paleturquoise':        '#AFEEEE',
-    'palevioletred':        '#DB7093',
-    'papayawhip':           '#FFEFD5',
-    'peachpuff':            '#FFDAB9',
-    'peru':                 '#CD853F',
-    'pink':                 '#FFC0CB',
-    'plum':                 '#DDA0DD',
-    'powderblue':           '#B0E0E6',
-    'purple':               '#800080',
-    'rebeccapurple':        '#663399',
-    'red':                  '#FF0000',
-    'rosybrown':            '#BC8F8F',
-    'royalblue':            '#4169E1',
-    'saddlebrown':          '#8B4513',
-    'salmon':               '#FA8072',
-    'sandybrown':           '#F4A460',
-    'seagreen':             '#2E8B57',
-    'seashell':             '#FFF5EE',
-    'sienna':               '#A0522D',
-    'silver':               '#C0C0C0',
-    'skyblue':              '#87CEEB',
-    'slateblue':            '#6A5ACD',
-    'slategray':            '#708090',
-    'slategrey':            '#708090',
-    'snow':                 '#FFFAFA',
-    'springgreen':          '#00FF7F',
-    'steelblue':            '#4682B4',
-    'tan':                  '#D2B48C',
-    'teal':                 '#008080',
-    'thistle':              '#D8BFD8',
-    'tomato':               '#FF6347',
-    'turquoise':            '#40E0D0',
-    'violet':               '#EE82EE',
-    'wheat':                '#F5DEB3',
-    'white':                '#FFFFFF',
-    'whitesmoke':           '#F5F5F5',
-    'yellow':               '#FFFF00',
-    'yellowgreen':          '#9ACD32'}
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/doc_utils.py b/spaces/declare-lab/tango/diffusers/src/diffusers/utils/doc_utils.py
deleted file mode 100644
index f1f87743f99802931334bd51bf99985775116d59..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/doc_utils.py
+++ /dev/null
@@ -1,38 +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.
-"""
-Doc utilities: Utilities related to documentation
-"""
-import re
-
-
-def replace_example_docstring(example_docstring):
-    def docstring_decorator(fn):
-        func_doc = fn.__doc__
-        lines = func_doc.split("\n")
-        i = 0
-        while i < len(lines) and re.search(r"^\s*Examples?:\s*$", lines[i]) is None:
-            i += 1
-        if i < len(lines):
-            lines[i] = example_docstring
-            func_doc = "\n".join(lines)
-        else:
-            raise ValueError(
-                f"The function {fn} should have an empty 'Examples:' in its docstring as placeholder, "
-                f"current docstring is:\n{func_doc}"
-            )
-        fn.__doc__ = func_doc
-        return fn
-
-    return docstring_decorator
diff --git a/spaces/derek-thomas/arabic-RAG/backend/query_llm.py b/spaces/derek-thomas/arabic-RAG/backend/query_llm.py
deleted file mode 100644
index aadc115fc19cf1930885b17d9166230763feffe9..0000000000000000000000000000000000000000
--- a/spaces/derek-thomas/arabic-RAG/backend/query_llm.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os
-from os import getenv
-
-import gradio as gr
-import requests
-
-API_URL = getenv('API_URL')
-BEARER = getenv('BEARER')
-
-headers = {
-    "Authorization": f"Bearer {BEARER}",
-    "Content-Type": "application/json"
-    }
-
-
-def call_jais(payload):
-    try:
-        response = requests.post(API_URL, headers=headers, json=payload)
-        response.raise_for_status()  # This will raise an exception for HTTP error codes
-        return response.json()
-    except requests.exceptions.HTTPError as http_err:
-        raise gr.Error(f"An error occurred while processing the request. {http_err}")
-    except Exception as err:
-        raise gr.Error(f"An error occurred while processing the request. {err}")
-
-
-def generate(prompt: str):
-    payload = {'inputs': '', 'prompt': prompt}
-    response = call_jais(payload)
-    return response
-
-
-def check_endpoint_status():
-    # Replace with the actual API URL and headers
-    api_url = os.getenv("ENDPOINT_URL")
-    headers = {
-        'accept': 'application/json',
-        'Authorization': f'Bearer {os.getenv("BEARER")}'
-        }
-
-    try:
-        response = requests.get(api_url, headers=headers)
-        response.raise_for_status()  # will throw an exception for non-200 status
-        data = response.json()
-
-        # Extracting the status information
-        status = data.get('status', {}).get('state', 'No status found')
-        message = data.get('status', {}).get('message', 'No message found')
-
-        return f"Status: {status}\nMessage: {message}"
-    except requests.exceptions.RequestException as e:
-        return f"Failed to get status: {str(e)}"
diff --git a/spaces/descript/vampnet/vampnet/interface.py b/spaces/descript/vampnet/vampnet/interface.py
deleted file mode 100644
index 1099de3863c42827d10ff093a45720a428666a77..0000000000000000000000000000000000000000
--- a/spaces/descript/vampnet/vampnet/interface.py
+++ /dev/null
@@ -1,422 +0,0 @@
-import os
-from pathlib import Path
-import math
-
-import torch
-import numpy as np
-from audiotools import AudioSignal
-import tqdm
-
-from .modules.transformer import VampNet
-from .beats import WaveBeat
-from .mask import *
-
-# from dac.model.dac import DAC
-from lac.model.lac import LAC as DAC
-
-
-def signal_concat(
-    audio_signals: list,
-):
-    audio_data = torch.cat([x.audio_data for x in audio_signals], dim=-1)
-
-    return AudioSignal(audio_data, sample_rate=audio_signals[0].sample_rate)
-
-
-def _load_model(
-    ckpt: str, 
-    lora_ckpt: str = None,
-    device: str = "cpu",
-    chunk_size_s: int = 10,
-):
-    # we need to set strict to False if the model has lora weights to add later
-    model = VampNet.load(location=Path(ckpt), map_location="cpu", strict=False)
-
-    # load lora weights if needed
-    if lora_ckpt is not None:
-        if not Path(lora_ckpt).exists():
-            should_cont = input(
-                f"lora checkpoint {lora_ckpt} does not exist. continue? (y/n) "
-            )
-            if should_cont != "y":
-                raise Exception("aborting")
-        else:
-            model.load_state_dict(torch.load(lora_ckpt, map_location="cpu"), strict=False)
-
-    model.to(device)
-    model.eval()
-    model.chunk_size_s = chunk_size_s
-    return model
-
-
-
-class Interface(torch.nn.Module):
-    def __init__(
-        self,
-        coarse_ckpt: str = None,
-        coarse_lora_ckpt: str = None,
-        coarse2fine_ckpt: str = None,
-        coarse2fine_lora_ckpt: str = None,
-        codec_ckpt: str = None,
-        wavebeat_ckpt: str = None,
-        device: str = "cpu",
-        coarse_chunk_size_s: int =  10, 
-        coarse2fine_chunk_size_s: int =  3,
-    ):
-        super().__init__()
-        assert codec_ckpt is not None, "must provide a codec checkpoint"
-        self.codec = DAC.load(Path(codec_ckpt))
-        self.codec.eval()
-        self.codec.to(device)
-
-        assert coarse_ckpt is not None, "must provide a coarse checkpoint"
-        self.coarse = _load_model(
-            ckpt=coarse_ckpt,
-            lora_ckpt=coarse_lora_ckpt,
-            device=device,
-            chunk_size_s=coarse_chunk_size_s,
-        )
-
-        # check if we have a coarse2fine ckpt
-        if coarse2fine_ckpt is not None:
-            self.c2f = _load_model(
-                ckpt=coarse2fine_ckpt,
-                lora_ckpt=coarse2fine_lora_ckpt,
-                device=device,
-                chunk_size_s=coarse2fine_chunk_size_s,
-            )
-        else:
-            self.c2f = None
-
-        if wavebeat_ckpt is not None:
-            print(f"loading wavebeat from {wavebeat_ckpt}")
-            self.beat_tracker = WaveBeat(wavebeat_ckpt)
-            self.beat_tracker.model.to(device)
-        else:
-            self.beat_tracker = None
-
-        self.device = device
-
-    def lora_load(
-        self, 
-        coarse_ckpt: str = None,
-        c2f_ckpt: str = None,
-        full_ckpts: bool = False,
-    ):
-        if full_ckpts:
-            if coarse_ckpt is not None:
-                self.coarse = _load_model(
-                    ckpt=coarse_ckpt,  
-                    device=self.device,
-                    chunk_size_s=self.coarse.chunk_size_s,
-                )
-            if c2f_ckpt is not None:
-                self.c2f = _load_model(
-                    ckpt=c2f_ckpt,
-                    device=self.device,
-                    chunk_size_s=self.c2f.chunk_size_s,
-                )
-        else:
-            if coarse_ckpt is not None:
-                self.coarse.to("cpu")
-                state_dict = torch.load(coarse_ckpt, map_location="cpu")
-                print(f"loading coarse from {coarse_ckpt}")
-                self.coarse.load_state_dict(state_dict, strict=False)
-                self.coarse.to(self.device)
-            if c2f_ckpt is not None:
-                self.c2f.to("cpu")
-                state_dict = torch.load(c2f_ckpt, map_location="cpu")
-                print(f"loading c2f from {c2f_ckpt}")
-                self.c2f.load_state_dict(state_dict, strict=False)
-                self.c2f.to(self.device)
-        
-    def s2t(self, seconds: float):
-        """seconds to tokens"""
-        if isinstance(seconds, np.ndarray):
-            return np.ceil(seconds * self.codec.sample_rate / self.codec.hop_length)
-        else:
-            return math.ceil(seconds * self.codec.sample_rate / self.codec.hop_length)
-
-    def s2t2s(self, seconds: float):
-        """seconds to tokens to seconds"""
-        return self.t2s(self.s2t(seconds))
-    
-    def t2s(self, tokens: int):
-        """tokens to seconds"""
-        return tokens * self.codec.hop_length / self.codec.sample_rate
-
-    def to(self, device):
-        self.device = device
-        self.coarse.to(device)
-        self.codec.to(device)
-
-        if self.c2f is not None:
-            self.c2f.to(device)
-
-        if self.beat_tracker is not None:
-            self.beat_tracker.model.to(device)
-        return self
-
-    def to_signal(self, z: torch.Tensor):
-        return self.coarse.to_signal(z, self.codec)
-    
-    def preprocess(self, signal: AudioSignal):
-        signal = (
-            signal.clone()
-            .resample(self.codec.sample_rate)
-            .to_mono()
-            .normalize(-24)
-            .ensure_max_of_audio(1.0)
-        )
-        return signal
-    
-    @torch.inference_mode()
-    def encode(self, signal: AudioSignal):
-        signal = self.preprocess(signal).to(self.device)
-        z = self.codec.encode(signal.samples, signal.sample_rate)["codes"]
-        return z
-
-    def snap_to_beats(
-        self, 
-        signal: AudioSignal
-    ):
-        assert hasattr(self, "beat_tracker"), "No beat tracker loaded"
-        beats, downbeats = self.beat_tracker.extract_beats(signal)
-        
-        # trim the signa around the first beat time
-        samples_begin = int(beats[0] * signal.sample_rate )
-        samples_end = int(beats[-1] * signal.sample_rate)
-        print(beats[0])
-        signal = signal.clone().trim(samples_begin, signal.length - samples_end)
-
-        return signal
-
-    def make_beat_mask(self, 
-            signal: AudioSignal, 
-            before_beat_s: float = 0.0,
-            after_beat_s: float = 0.02,
-            mask_downbeats: bool = True,
-            mask_upbeats: bool = True,
-            downbeat_downsample_factor: int = None,
-            beat_downsample_factor: int = None,
-            dropout: float = 0.0,
-            invert: bool = True,
-    ):
-        """make a beat synced mask. that is, make a mask that 
-        places 1s at and around the beat, and 0s everywhere else. 
-        """
-        assert self.beat_tracker is not None, "No beat tracker loaded"
-
-        # get the beat times
-        beats, downbeats = self.beat_tracker.extract_beats(signal)
-
-        # get the beat indices in z
-        beats_z, downbeats_z = self.s2t(beats), self.s2t(downbeats)
-
-        # remove downbeats from beats
-        beats_z = torch.tensor(beats_z)[~torch.isin(torch.tensor(beats_z), torch.tensor(downbeats_z))]
-        beats_z = beats_z.tolist()
-        downbeats_z = downbeats_z.tolist()
-
-        # make the mask 
-        seq_len = self.s2t(signal.duration)
-        mask = torch.zeros(seq_len, device=self.device)
-        
-        mask_b4 = self.s2t(before_beat_s)
-        mask_after = self.s2t(after_beat_s)
-
-        if beat_downsample_factor is not None:
-            if beat_downsample_factor < 1:
-                raise ValueError("mask_beat_downsample_factor must be >= 1 or None")
-        else:
-            beat_downsample_factor = 1
-
-        if downbeat_downsample_factor is not None:
-            if downbeat_downsample_factor < 1:
-                raise ValueError("mask_beat_downsample_factor must be >= 1 or None")
-        else:
-            downbeat_downsample_factor = 1
-
-        beats_z = beats_z[::beat_downsample_factor]
-        downbeats_z = downbeats_z[::downbeat_downsample_factor]
-        print(f"beats_z: {len(beats_z)}")
-        print(f"downbeats_z: {len(downbeats_z)}")
-    
-        if mask_upbeats:
-            for beat_idx in beats_z:
-                _slice = int(beat_idx - mask_b4), int(beat_idx + mask_after)
-                num_steps = mask[_slice[0]:_slice[1]].shape[0]
-                _m = torch.ones(num_steps, device=self.device)
-                _m_mask = torch.bernoulli(_m * (1 - dropout))
-                _m = _m * _m_mask.long()
-                
-                mask[_slice[0]:_slice[1]] = _m
-
-        if mask_downbeats:
-            for downbeat_idx in downbeats_z:
-                _slice = int(downbeat_idx - mask_b4), int(downbeat_idx + mask_after)
-                num_steps = mask[_slice[0]:_slice[1]].shape[0]
-                _m = torch.ones(num_steps, device=self.device)
-                _m_mask = torch.bernoulli(_m * (1 - dropout))
-                _m = _m * _m_mask.long()
-                
-                mask[_slice[0]:_slice[1]] = _m
-        
-        mask = mask.clamp(0, 1)
-        if invert:
-            mask = 1 - mask
-        
-        mask = mask[None, None, :].bool().long()
-        if self.c2f is not None:
-            mask = mask.repeat(1, self.c2f.n_codebooks, 1)
-        else:
-            mask = mask.repeat(1, self.coarse.n_codebooks, 1)
-        return mask
-        
-    def coarse_to_fine(
-        self, 
-        z: torch.Tensor,
-        mask: torch.Tensor = None,
-        **kwargs
-    ):
-        assert self.c2f is not None, "No coarse2fine model loaded"
-        length = z.shape[-1]
-        chunk_len = self.s2t(self.c2f.chunk_size_s)
-        n_chunks = math.ceil(z.shape[-1] / chunk_len)
-
-        # zero pad to chunk_len
-        if length % chunk_len != 0:
-            pad_len = chunk_len - (length % chunk_len)
-            z = torch.nn.functional.pad(z, (0, pad_len))
-            mask = torch.nn.functional.pad(mask, (0, pad_len)) if mask is not None else None
-
-        n_codebooks_to_append = self.c2f.n_codebooks - z.shape[1]
-        if n_codebooks_to_append > 0:
-            z = torch.cat([
-                z,
-                torch.zeros(z.shape[0], n_codebooks_to_append, z.shape[-1]).long().to(self.device)
-            ], dim=1)
-
-        # set the mask to 0 for all conditioning codebooks
-        if mask is not None:
-            mask = mask.clone()
-            mask[:, :self.c2f.n_conditioning_codebooks, :] = 0
-
-        fine_z = []
-        for i in range(n_chunks):
-            chunk = z[:, :, i * chunk_len : (i + 1) * chunk_len]
-            mask_chunk = mask[:, :, i * chunk_len : (i + 1) * chunk_len] if mask is not None else None
-            
-            chunk = self.c2f.generate(
-                codec=self.codec,
-                time_steps=chunk_len,
-                start_tokens=chunk,
-                return_signal=False,
-                mask=mask_chunk,
-                **kwargs
-            )
-            fine_z.append(chunk)
-
-        fine_z = torch.cat(fine_z, dim=-1)
-        return fine_z[:, :, :length].clone()
-    
-    def coarse_vamp(
-        self, 
-        z, 
-        mask,
-        return_mask=False,
-        gen_fn=None,
-        **kwargs
-    ):
-        # coarse z
-        cz = z[:, : self.coarse.n_codebooks, :].clone()
-        assert cz.shape[-1] <= self.s2t(self.coarse.chunk_size_s), f"the sequence of tokens provided must match the one specified in the coarse chunk size, but got {cz.shape[-1]} and {self.s2t(self.coarse.chunk_size_s)}"
-
-        mask = mask[:, : self.coarse.n_codebooks, :]
-
-        cz_masked, mask = apply_mask(cz, mask, self.coarse.mask_token)
-        cz_masked = cz_masked[:, : self.coarse.n_codebooks, :]
-
-        gen_fn = gen_fn or self.coarse.generate
-        c_vamp = gen_fn(
-            codec=self.codec,
-            time_steps=cz.shape[-1],
-            start_tokens=cz,
-            mask=mask, 
-            return_signal=False,
-            **kwargs
-        )
-
-        # add the fine codes back in
-        c_vamp = torch.cat(
-            [c_vamp, z[:, self.coarse.n_codebooks :, :]], 
-            dim=1
-        )
-
-        if return_mask:
-            return c_vamp, cz_masked
-        
-        return c_vamp
-
-
-if __name__ == "__main__":
-    import audiotools as at
-    import logging
-    logger = logging.getLogger()
-    logger.setLevel(logging.INFO)
-    torch.set_printoptions(threshold=10000)
-    at.util.seed(42)
-
-    interface = Interface(
-        coarse_ckpt="./models/vampnet/coarse.pth", 
-        coarse2fine_ckpt="./models/vampnet/c2f.pth", 
-        codec_ckpt="./models/vampnet/codec.pth",
-        device="cuda", 
-        wavebeat_ckpt="./models/wavebeat.pth"
-    )
-
-
-    sig = at.AudioSignal('assets/example.wav')
-
-    z = interface.encode(sig)
-    breakpoint()
-
-    # mask = linear_random(z, 1.0)
-    # mask = mask_and(
-    #     mask, periodic_mask(
-    #         z,
-    #         32,
-    #         1,
-    #         random_roll=True
-    #     )
-    # )
-
-    # mask = interface.make_beat_mask(
-    #     sig, 0.0, 0.075
-    # )
-    # mask = dropout(mask, 0.0)
-    # mask = codebook_unmask(mask, 0)
-
-    mask = inpaint(z, n_prefix=100, n_suffix=100)
-    
-    zv, mask_z = interface.coarse_vamp(
-        z, 
-        mask=mask,
-        sampling_steps=36,
-        temperature=8.0,
-        return_mask=True, 
-        gen_fn=interface.coarse.generate
-    )
-    
-
-    use_coarse2fine = True
-    if use_coarse2fine: 
-        zv = interface.coarse_to_fine(zv, temperature=0.8, mask=mask)
-        breakpoint()
-
-    mask = interface.to_signal(mask_z).cpu()
-
-    sig = interface.to_signal(zv).cpu()
-    print("done")
-
-        
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Geovision Gv 650 800 S V3.52 Drivers.zip 40.md b/spaces/diacanFperku/AutoGPT/Geovision Gv 650 800 S V3.52 Drivers.zip 40.md
deleted file mode 100644
index 6987356706c718ab684c641cb5fb1616dc9b492e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Geovision Gv 650 800 S V3.52 Drivers.zip 40.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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deleted file mode 100644
index 6d34acc687d89d51c987c6a141365f147d0f5775..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Intergraph Caesar Ii Crack !LINK! Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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-
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diff --git a/spaces/diacanFperku/AutoGPT/New Cutting Edge Pre Intermediate Teachers Book Pdf Free 43.md b/spaces/diacanFperku/AutoGPT/New Cutting Edge Pre Intermediate Teachers Book Pdf Free 43.md
deleted file mode 100644
index d4f162e1356a21d218a16b567a39ad03c682177b..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/New Cutting Edge Pre Intermediate Teachers Book Pdf Free 43.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<br />
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diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/training/eager_batcher.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/training/eager_batcher.py
deleted file mode 100644
index 2edec7a18dcd61c3bbb2dbd6596fd64e1ccdcf4c..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/colbert/training/eager_batcher.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-import ujson
-
-from functools import partial
-from colbert.utils.utils import print_message
-from colbert.modeling.tokenization import QueryTokenizer, DocTokenizer, tensorize_triples
-
-from colbert.utils.runs import Run
-
-
-class EagerBatcher():
-    def __init__(self, args, rank=0, nranks=1):
-        self.rank, self.nranks = rank, nranks
-        self.bsize, self.accumsteps = args.bsize, args.accumsteps
-
-        self.query_tokenizer = QueryTokenizer(args.query_maxlen)
-        self.doc_tokenizer = DocTokenizer(args.doc_maxlen)
-        self.tensorize_triples = partial(tensorize_triples, self.query_tokenizer, self.doc_tokenizer)
-
-        self.triples_path = args.triples
-        self._reset_triples()
-
-    def _reset_triples(self):
-        self.reader = open(self.triples_path, mode='r', encoding="utf-8")
-        self.position = 0
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        queries, positives, negatives = [], [], []
-
-        for line_idx, line in zip(range(self.bsize * self.nranks), self.reader):
-            if (self.position + line_idx) % self.nranks != self.rank:
-                continue
-
-            query, pos, neg = line.strip().split('\t')
-
-            queries.append(query)
-            positives.append(pos)
-            negatives.append(neg)
-
-        self.position += line_idx + 1
-
-        if len(queries) < self.bsize:
-            raise StopIteration
-
-        return self.collate(queries, positives, negatives)
-
-    def collate(self, queries, positives, negatives):
-        assert len(queries) == len(positives) == len(negatives) == self.bsize
-
-        return self.tensorize_triples(queries, positives, negatives, self.bsize // self.accumsteps)
-
-    def skip_to_batch(self, batch_idx, intended_batch_size):
-        self._reset_triples()
-
-        Run.warn(f'Skipping to batch #{batch_idx} (with intended_batch_size = {intended_batch_size}) for training.')
-
-        _ = [self.reader.readline() for _ in range(batch_idx * intended_batch_size)]
-
-        return None
diff --git a/spaces/diffusers/controlnet-3d-pose/share_btn.py b/spaces/diffusers/controlnet-3d-pose/share_btn.py
deleted file mode 100644
index 05daa5ee81f72e80d683753f5ce68498c10e57df..0000000000000000000000000000000000000000
--- a/spaces/diffusers/controlnet-3d-pose/share_btn.py
+++ /dev/null
@@ -1,129 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-
-	async function getInputImgFile(imgEl, suppliedSrc){
-        const src = suppliedSrc ? suppliedSrc : imgEl.src;
-        const res = await fetch(src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = !!imgEl;
-        if(isPng){
-            const fileName = `controlnet-3d-pose-${imgId}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `controlnet-3d-pose-${imgId}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-
-    // const gradioEl = document.querySelector('body > gradio-app');
-    const gradioEl = document.querySelector("gradio-app");
-    const inputTxt = gradioEl.querySelector('#prompt input').value;
-    const imgEls = gradioEl.querySelectorAll('#gallery img');
-
-    const poseMakerEl = gradioEl.querySelector("pose-maker");
-    const three_d_base64 = poseMakerEl.captureScreenshot();
-    const three_d_file = await getInputImgFile(null, three_d_base64);
-    const three_d_url = await uploadFile(three_d_file);
-
-    if(!inputTxt || !imgEls.length){
-        return;
-    };
-
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => getInputImgFile(imgEl, null))
-    );
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-    const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-
-    const titleLength = 150;
-    let titleTxt = inputTxt;
-    if(titleTxt.length > titleLength){
-        titleTxt = titleTxt.slice(0, titleLength) + ' ...';
-    }
-
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-
-	const descriptionMd = `### Prompt:
-${inputTxt}
-
-### 3D Pose:
-
-<img src='${three_d_url}' width='400' height='400'>
-
-### Renderings:
-
-<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgs.join(`\n`)}
-</div>`;
-
-    const params = {
-        title: titleTxt,
-        description: descriptionMd,
-    };
-
-    const paramsStr = Object.entries(params)
-        .map(([key, value]) => `${encodeURIComponent(key)}=${encodeURIComponent(value)}`)
-        .join('&');
-
-	window.open(`https://huggingface.co/spaces/diffusers/controlnet-3d-pose/discussions/new?${paramsStr}`, '_blank');
-
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
-
-share_btn_css = """
-a {text-decoration-line: underline; font-weight: 600;}
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from { transform: rotate(0deg); }
-    to { transform: rotate(360deg); }
-}
-#share-btn-container {
-    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-}
-#share-btn {
-    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
-}
-#share-btn * {
-    all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
-    width: auto !important;
-    min-height: 0px !important;
-}
-#share-btn-container .wrap {
-    display: none !important;
-}
-"""
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/monotonic_align/core.py b/spaces/digitalxingtong/Jiuxia-Bert-Vits2/monotonic_align/core.py
deleted file mode 100644
index 5ff728cd74c9228346a82ec64a9829cb98ad315e..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/monotonic_align/core.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]),
-           nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-    b = paths.shape[0]
-    max_neg_val = -1e9
-    for i in range(int(b)):
-        path = paths[i]
-        value = values[i]
-        t_y = t_ys[i]
-        t_x = t_xs[i]
-
-        v_prev = v_cur = 0.0
-        index = t_x - 1
-
-        for y in range(t_y):
-            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-                if x == y:
-                    v_cur = max_neg_val
-                else:
-                    v_cur = value[y - 1, x]
-                if x == 0:
-                    if y == 0:
-                        v_prev = 0.
-                    else:
-                        v_prev = max_neg_val
-                else:
-                    v_prev = value[y - 1, x - 1]
-                value[y, x] += max(v_prev, v_cur)
-
-        for y in range(t_y - 1, -1, -1):
-            path[y, index] = 1
-            if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]):
-                index = index - 1
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/train_ms.py b/spaces/digitalxingtong/Nailv-Bert-Vits2/train_ms.py
deleted file mode 100644
index 5d109003d40497ea4493e7c73f47c1eb7370a81e..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/train_ms.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-import shutil
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from tqdm import tqdm
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import commons
-import utils
-from data_utils import (
-    TextAudioSpeakerLoader,
-    TextAudioSpeakerCollate,
-    DistributedBucketSampler
-)
-from models import (
-    SynthesizerTrn,
-    MultiPeriodDiscriminator,
-    DurationDiscriminator,
-)
-from losses import (
-    generator_loss,
-    discriminator_loss,
-    feature_loss,
-    kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-torch.backends.cudnn.benchmark = True
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.set_float32_matmul_precision('medium')
-global_step = 0
-
-
-def main():
-    """Assume Single Node Multi GPUs Training Only"""
-    assert torch.cuda.is_available(), "CPU training is not allowed."
-
-    n_gpus = torch.cuda.device_count()
-    os.environ['MASTER_ADDR'] = 'localhost'
-    os.environ['MASTER_PORT'] = '65280'
-
-    hps = utils.get_hparams()
-    if not hps.cont:
-           shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth')
-           shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth')
-           shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth')
-    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.model_dir)
-        logger.info(hps)
-        utils.check_git_hash(hps.model_dir)
-        writer = SummaryWriter(log_dir=hps.model_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-    dist.init_process_group(backend=  'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
-    torch.manual_seed(hps.train.seed)
-    torch.cuda.set_device(rank)
-
-    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
-    train_sampler = DistributedBucketSampler(
-        train_dataset,
-        hps.train.batch_size,
-        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
-        num_replicas=n_gpus,
-        rank=rank,
-        shuffle=True)
-    collate_fn = TextAudioSpeakerCollate()
-    train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
-                              collate_fn=collate_fn, batch_sampler=train_sampler)
-    if rank == 0:
-        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
-        eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
-                                 batch_size=1, pin_memory=True,
-                                 drop_last=False, collate_fn=collate_fn)
-    if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
-        print("Using noise scaled MAS for VITS2")
-        use_noise_scaled_mas = True
-        mas_noise_scale_initial = 0.01
-        noise_scale_delta = 2e-6
-    else:
-        print("Using normal MAS for VITS1")
-        use_noise_scaled_mas = False
-        mas_noise_scale_initial = 0.0
-        noise_scale_delta = 0.0
-    if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True:
-        print("Using duration discriminator for VITS2")
-        use_duration_discriminator = True
-        net_dur_disc = DurationDiscriminator(
-         hps.model.hidden_channels, 
-         hps.model.hidden_channels, 
-         3, 
-         0.1, 
-         gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
-         ).cuda(rank)
-    if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
-        if hps.data.n_speakers == 0:
-            raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
-        use_spk_conditioned_encoder = True
-    else:
-        print("Using normal encoder for VITS1")
-        use_spk_conditioned_encoder = False
-
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        mas_noise_scale_initial = mas_noise_scale_initial,
-        noise_scale_delta = noise_scale_delta,
-        **hps.model).cuda(rank)
-
-    freeze_enc = getattr(hps.model, "freeze_enc", False)
-    if freeze_enc:
-        print("freeze encoder !!!")
-        for param in net_g.enc_p.parameters():
-            param.requires_grad = False
-
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-    optim_g = torch.optim.AdamW(
-        filter(lambda p: p.requires_grad, net_g.parameters()),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    if net_dur_disc is not None:
-        optim_dur_disc = torch.optim.AdamW(
-        net_dur_disc.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    else:
-        optim_dur_disc = None
-    net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
-    net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
-    if net_dur_disc is not None:
-        net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
-
-    pretrain_dir = None
-    if pretrain_dir is None:
-        try:
-            if net_dur_disc is not None:
-                _, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont)
-            _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
-                                                   optim_g, skip_optimizer=not hps.cont)
-            _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
-                                                   optim_d, skip_optimizer=not hps.cont)
-            
-            epoch_str = max(epoch_str, 1)
-            global_step = (epoch_str - 1) * len(train_loader)
-        except Exception as e:
-            print(e)
-            epoch_str = 1
-            global_step = 0
-    else:
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
-                                                   optim_g, True)
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
-                                                   optim_d, True)
-
-
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    if net_dur_disc is not None:
-        scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-    else:
-        scheduler_dur_disc = None
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-        else:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None)
-        scheduler_g.step()
-        scheduler_d.step()
-        if net_dur_disc is not None:
-            scheduler_dur_disc.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-    net_g, net_d, net_dur_disc = nets
-    optim_g, optim_d, optim_dur_disc = optims
-    scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-    if net_dur_disc is not None:
-        net_dur_disc.train()
-    for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
-        if net_g.module.use_noise_scaled_mas:
-            current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
-            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
-        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-        spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-        speakers = speakers.cuda(rank, non_blocking=True)
-        tone = tone.cuda(rank, non_blocking=True)
-        language = language.cuda(rank, non_blocking=True)
-        bert = bert.cuda(rank, non_blocking=True)
-
-        with autocast(enabled=hps.train.fp16_run):
-            y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
-                (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax)
-            y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-
-            y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-                loss_disc_all = loss_disc
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
-                with autocast(enabled=False):
-                 # TODO: I think need to mean using the mask, but for now, just mean all
-                    loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
-                    loss_dur_disc_all = loss_dur_disc
-                optim_dur_disc.zero_grad()
-                scaler.scale(loss_dur_disc_all).backward()
-                scaler.unscale_(optim_dur_disc)
-                grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
-                scaler.step(optim_dur_disc)
-
-        optim_d.zero_grad()
-        scaler.scale(loss_disc_all).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
-            with autocast(enabled=False):
-                loss_dur = torch.sum(l_length.float())
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-                if net_dur_disc is not None:
-                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
-                    loss_gen_all += loss_dur_gen
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]['lr']
-                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-                logger.info('Train Epoch: {} [{:.0f}%]'.format(
-                    epoch,
-                    100. * batch_idx / len(train_loader)))
-                logger.info([x.item() for x in losses] + [global_step, lr])
-
-                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
-                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-                scalar_dict.update(
-                    {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-                scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-                scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-                scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-          
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-                    "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-                    "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy())
-                }
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict)
-
-            if global_step % hps.train.eval_interval == 0:
-                evaluate(hps, net_g, eval_loader, writer_eval)
-                utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-                if net_dur_disc is not None:
-                    utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)))    
-                keep_ckpts = getattr(hps.train, 'keep_ckpts', 5)
-                if keep_ckpts > 0:
-                    utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
-
-        global_step += 1
-
-    if rank == 0:
-        logger.info('====> Epoch: {}'.format(epoch))
-
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    image_dict = {}
-    audio_dict = {}
-    print("Evaluating ...")
-    with torch.no_grad():
-        for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
-            x, x_lengths = x.cuda(), x_lengths.cuda()
-            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
-            y, y_lengths = y.cuda(), y_lengths.cuda()
-            speakers = speakers.cuda()
-            bert = bert.cuda()
-            tone = tone.cuda()
-            language = language.cuda()
-            for use_sdp in [True, False]:
-                y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
-                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
-
-                mel = spec_to_mel_torch(
-                    spec,
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax)
-                y_hat_mel = mel_spectrogram_torch(
-                    y_hat.squeeze(1).float(),
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.hop_length,
-                    hps.data.win_length,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax
-                )
-                image_dict.update({
-                    f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-                })
-                audio_dict.update({
-                    f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]]
-                })
-                image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})
-
-    utils.summarize(
-        writer=writer_eval,
-        global_step=global_step,
-        images=image_dict,
-        audios=audio_dict,
-        audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/dmeck/RVC-Speakers/rvc/infer_pack/modules/__init__.py b/spaces/dmeck/RVC-Speakers/rvc/infer_pack/modules/__init__.py
deleted file mode 100644
index bc34fc3e0993a3b5a59e286a39b8c29c71606179..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/rvc/infer_pack/modules/__init__.py
+++ /dev/null
@@ -1,519 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from rvc.infer_pack import commons
-from rvc.infer_pack.commons import init_weights, get_padding
-from rvc.infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-            self,
-            in_channels,
-            hidden_channels,
-            out_channels,
-            kernel_size,
-            n_layers,
-            p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-            self,
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=0,
-            p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-                )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-            self,
-            channels,
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=0,
-            gin_channels=0,
-            mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-            self,
-            in_channels,
-            filter_channels,
-            kernel_size,
-            n_layers,
-            num_bins=10,
-            tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/dmeck/RVC-Speakers/vits/__init__.py b/spaces/dmeck/RVC-Speakers/vits/__init__.py
deleted file mode 100644
index 493bfc1db166d0c65c332cf1f3e29d45941f0377..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/vits/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from speakers.common.registry import registry
-import os
-
-root_dir = os.path.dirname(os.path.abspath(__file__))
-registry.register_path("vits_library_root", root_dir)
-
diff --git a/spaces/dolceschokolade/chatbot-mini/components/Settings/Import.tsx b/spaces/dolceschokolade/chatbot-mini/components/Settings/Import.tsx
deleted file mode 100644
index 5cc9582f8322dc8584677eb9eb9801a6809f68b9..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/components/Settings/Import.tsx
+++ /dev/null
@@ -1,51 +0,0 @@
-import { IconFileImport } from '@tabler/icons-react';
-import { FC } from 'react';
-
-import { useTranslation } from 'next-i18next';
-
-import { SupportedExportFormats } from '@/types/export';
-
-import { SidebarButton } from '../Sidebar/SidebarButton';
-
-interface Props {
-  onImport: (data: SupportedExportFormats) => void;
-}
-
-export const Import: FC<Props> = ({ onImport }) => {
-  const { t } = useTranslation('sidebar');
-  return (
-    <>
-      <input
-        id="import-file"
-        className="sr-only"
-        tabIndex={-1}
-        type="file"
-        accept=".json"
-        onChange={(e) => {
-          if (!e.target.files?.length) return;
-
-          const file = e.target.files[0];
-          const reader = new FileReader();
-          reader.onload = (e) => {
-            let json = JSON.parse(e.target?.result as string);
-            onImport(json);
-          };
-          reader.readAsText(file);
-        }}
-      />
-
-      <SidebarButton
-        text={t('Import data')}
-        icon={<IconFileImport size={18} />}
-        onClick={() => {
-          const importFile = document.querySelector(
-            '#import-file',
-          ) as HTMLInputElement;
-          if (importFile) {
-            importFile.click();
-          }
-        }}
-      />
-    </>
-  );
-};
diff --git a/spaces/dolceschokolade/chatbot-mini/components/Sidebar/SidebarButton.tsx b/spaces/dolceschokolade/chatbot-mini/components/Sidebar/SidebarButton.tsx
deleted file mode 100644
index 7c6f3494918f0e39a02fc1a09e70c716d2026838..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/components/Sidebar/SidebarButton.tsx
+++ /dev/null
@@ -1,19 +0,0 @@
-import { FC } from 'react';
-
-interface Props {
-  text: string;
-  icon: JSX.Element;
-  onClick: () => void;
-}
-
-export const SidebarButton: FC<Props> = ({ text, icon, onClick }) => {
-  return (
-    <button
-      className="flex w-full cursor-pointer select-none items-center gap-3 rounded-md py-3 px-3 text-[14px] leading-3 text-white transition-colors duration-200 hover:bg-gray-500/10"
-      onClick={onClick}
-    >
-      <div>{icon}</div>
-      <span>{text}</span>
-    </button>
-  );
-};
diff --git a/spaces/dorkai/ChatUIPro/app/components/sidebar/card.module.css b/spaces/dorkai/ChatUIPro/app/components/sidebar/card.module.css
deleted file mode 100644
index c917cb4db6ca20ca59639dad8c64bbe43bd83183..0000000000000000000000000000000000000000
--- a/spaces/dorkai/ChatUIPro/app/components/sidebar/card.module.css
+++ /dev/null
@@ -1,3 +0,0 @@
-.card:hover {
-  background: linear-gradient(0deg, rgba(235, 245, 255, 0.4), rgba(235, 245, 255, 0.4)), #FFFFFF;
-}
\ No newline at end of file
diff --git a/spaces/eeyorestoned/midjourney-v5/README.md b/spaces/eeyorestoned/midjourney-v5/README.md
deleted file mode 100644
index 76189ac0ce98515ed5a2cd7423218111eaf87b3e..0000000000000000000000000000000000000000
--- a/spaces/eeyorestoned/midjourney-v5/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Midjourney V5
-emoji: 📚
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: hareshhecker/midjourney-v5
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/elkraken/Video-Object-Detection/models/experimental.py b/spaces/elkraken/Video-Object-Detection/models/experimental.py
deleted file mode 100644
index 735d7aa0ebe7dbf3c4b062ebc3858cb5f9ebab40..0000000000000000000000000000000000000000
--- a/spaces/elkraken/Video-Object-Detection/models/experimental.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import numpy as np
-import random
-import torch
-import torch.nn as nn
-
-from models.common import Conv, DWConv
-from utils.google_utils import attempt_download
-
-
-class CrossConv(nn.Module):
-    # Cross Convolution Downsample
-    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
-        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
-        super(CrossConv, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, (1, k), (1, s))
-        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class Sum(nn.Module):
-    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
-    def __init__(self, n, weight=False):  # n: number of inputs
-        super(Sum, self).__init__()
-        self.weight = weight  # apply weights boolean
-        self.iter = range(n - 1)  # iter object
-        if weight:
-            self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True)  # layer weights
-
-    def forward(self, x):
-        y = x[0]  # no weight
-        if self.weight:
-            w = torch.sigmoid(self.w) * 2
-            for i in self.iter:
-                y = y + x[i + 1] * w[i]
-        else:
-            for i in self.iter:
-                y = y + x[i + 1]
-        return y
-
-
-class MixConv2d(nn.Module):
-    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
-        super(MixConv2d, self).__init__()
-        groups = len(k)
-        if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
-        else:  # equal weight.numel() per group
-            b = [c2] + [0] * groups
-            a = np.eye(groups + 1, groups, k=-1)
-            a -= np.roll(a, 1, axis=1)
-            a *= np.array(k) ** 2
-            a[0] = 1
-            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
-
-        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-
-    def forward(self, x):
-        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
-
-
-class Ensemble(nn.ModuleList):
-    # Ensemble of models
-    def __init__(self):
-        super(Ensemble, self).__init__()
-
-    def forward(self, x, augment=False):
-        y = []
-        for module in self:
-            y.append(module(x, augment)[0])
-        # y = torch.stack(y).max(0)[0]  # max ensemble
-        # y = torch.stack(y).mean(0)  # mean ensemble
-        y = torch.cat(y, 1)  # nms ensemble
-        return y, None  # inference, train output
-
-
-
-
-
-class ORT_NMS(torch.autograd.Function):
-    '''ONNX-Runtime NMS operation'''
-    @staticmethod
-    def forward(ctx,
-                boxes,
-                scores,
-                max_output_boxes_per_class=torch.tensor([100]),
-                iou_threshold=torch.tensor([0.45]),
-                score_threshold=torch.tensor([0.25])):
-        device = boxes.device
-        batch = scores.shape[0]
-        num_det = random.randint(0, 100)
-        batches = torch.randint(0, batch, (num_det,)).sort()[0].to(device)
-        idxs = torch.arange(100, 100 + num_det).to(device)
-        zeros = torch.zeros((num_det,), dtype=torch.int64).to(device)
-        selected_indices = torch.cat([batches[None], zeros[None], idxs[None]], 0).T.contiguous()
-        selected_indices = selected_indices.to(torch.int64)
-        return selected_indices
-
-    @staticmethod
-    def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold):
-        return g.op("NonMaxSuppression", boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)
-
-
-class TRT_NMS(torch.autograd.Function):
-    '''TensorRT NMS operation'''
-    @staticmethod
-    def forward(
-        ctx,
-        boxes,
-        scores,
-        background_class=-1,
-        box_coding=1,
-        iou_threshold=0.45,
-        max_output_boxes=100,
-        plugin_version="1",
-        score_activation=0,
-        score_threshold=0.25,
-    ):
-        batch_size, num_boxes, num_classes = scores.shape
-        num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32)
-        det_boxes = torch.randn(batch_size, max_output_boxes, 4)
-        det_scores = torch.randn(batch_size, max_output_boxes)
-        det_classes = torch.randint(0, num_classes, (batch_size, max_output_boxes), dtype=torch.int32)
-        return num_det, det_boxes, det_scores, det_classes
-
-    @staticmethod
-    def symbolic(g,
-                 boxes,
-                 scores,
-                 background_class=-1,
-                 box_coding=1,
-                 iou_threshold=0.45,
-                 max_output_boxes=100,
-                 plugin_version="1",
-                 score_activation=0,
-                 score_threshold=0.25):
-        out = g.op("TRT::EfficientNMS_TRT",
-                   boxes,
-                   scores,
-                   background_class_i=background_class,
-                   box_coding_i=box_coding,
-                   iou_threshold_f=iou_threshold,
-                   max_output_boxes_i=max_output_boxes,
-                   plugin_version_s=plugin_version,
-                   score_activation_i=score_activation,
-                   score_threshold_f=score_threshold,
-                   outputs=4)
-        nums, boxes, scores, classes = out
-        return nums, boxes, scores, classes
-
-
-class ONNX_ORT(nn.Module):
-    '''onnx module with ONNX-Runtime NMS operation.'''
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None, n_classes=80):
-        super().__init__()
-        self.device = device if device else torch.device("cpu")
-        self.max_obj = torch.tensor([max_obj]).to(device)
-        self.iou_threshold = torch.tensor([iou_thres]).to(device)
-        self.score_threshold = torch.tensor([score_thres]).to(device)
-        self.max_wh = max_wh # if max_wh != 0 : non-agnostic else : agnostic
-        self.convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
-                                           dtype=torch.float32,
-                                           device=self.device)
-        self.n_classes=n_classes
-
-    def forward(self, x):
-        boxes = x[:, :, :4]
-        conf = x[:, :, 4:5]
-        scores = x[:, :, 5:]
-        if self.n_classes == 1:
-            scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5,
-                                 # so there is no need to multiplicate.
-        else:
-            scores *= conf  # conf = obj_conf * cls_conf
-        boxes @= self.convert_matrix
-        max_score, category_id = scores.max(2, keepdim=True)
-        dis = category_id.float() * self.max_wh
-        nmsbox = boxes + dis
-        max_score_tp = max_score.transpose(1, 2).contiguous()
-        selected_indices = ORT_NMS.apply(nmsbox, max_score_tp, self.max_obj, self.iou_threshold, self.score_threshold)
-        X, Y = selected_indices[:, 0], selected_indices[:, 2]
-        selected_boxes = boxes[X, Y, :]
-        selected_categories = category_id[X, Y, :].float()
-        selected_scores = max_score[X, Y, :]
-        X = X.unsqueeze(1).float()
-        return torch.cat([X, selected_boxes, selected_categories, selected_scores], 1)
-
-class ONNX_TRT(nn.Module):
-    '''onnx module with TensorRT NMS operation.'''
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None, n_classes=80):
-        super().__init__()
-        assert max_wh is None
-        self.device = device if device else torch.device('cpu')
-        self.background_class = -1,
-        self.box_coding = 1,
-        self.iou_threshold = iou_thres
-        self.max_obj = max_obj
-        self.plugin_version = '1'
-        self.score_activation = 0
-        self.score_threshold = score_thres
-        self.n_classes=n_classes
-
-    def forward(self, x):
-        boxes = x[:, :, :4]
-        conf = x[:, :, 4:5]
-        scores = x[:, :, 5:]
-        if self.n_classes == 1:
-            scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5,
-                                 # so there is no need to multiplicate.
-        else:
-            scores *= conf  # conf = obj_conf * cls_conf
-        num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(boxes, scores, self.background_class, self.box_coding,
-                                                                    self.iou_threshold, self.max_obj,
-                                                                    self.plugin_version, self.score_activation,
-                                                                    self.score_threshold)
-        return num_det, det_boxes, det_scores, det_classes
-
-
-class End2End(nn.Module):
-    '''export onnx or tensorrt model with NMS operation.'''
-    def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None, n_classes=80):
-        super().__init__()
-        device = device if device else torch.device('cpu')
-        assert isinstance(max_wh,(int)) or max_wh is None
-        self.model = model.to(device)
-        self.model.model[-1].end2end = True
-        self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT
-        self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device, n_classes)
-        self.end2end.eval()
-
-    def forward(self, x):
-        x = self.model(x)
-        x = self.end2end(x)
-        return x
-
-
-
-
-
-def attempt_load(weights, map_location=None):
-    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
-    model = Ensemble()
-    for w in weights if isinstance(weights, list) else [weights]:
-        attempt_download(w)
-        ckpt = torch.load(w, map_location=map_location)  # load
-        model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
-    
-    # Compatibility updates
-    for m in model.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-            m.inplace = True  # pytorch 1.7.0 compatibility
-        elif type(m) is nn.Upsample:
-            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
-        elif type(m) is Conv:
-            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-    
-    if len(model) == 1:
-        return model[-1]  # return model
-    else:
-        print('Ensemble created with %s\n' % weights)
-        for k in ['names', 'stride']:
-            setattr(model, k, getattr(model[-1], k))
-        return model  # return ensemble
-
-
diff --git a/spaces/enochianborg/stable-diffusion-webui-vorstcavry/app.py b/spaces/enochianborg/stable-diffusion-webui-vorstcavry/app.py
deleted file mode 100644
index 32b191b32a78e7d5a88510e580835dde61ac3859..0000000000000000000000000000000000000000
--- a/spaces/enochianborg/stable-diffusion-webui-vorstcavry/app.py
+++ /dev/null
@@ -1,190 +0,0 @@
-"""
-Stable Diffusion Webui Version 1.6
-https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.6.0
-
-"""
-commit_id=r"5ef669de080814067961f28357256e8fe27544f4" #Version 1.3.0
-import os
-from sys import executable 
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:pathlib.Path ) -> int :
-  if pathlib.Path.exists(ClonePath):
-    return 0
-  for z in range(10):
-    i=subprocess.run([r"git",r"clone",str(URI),str(ClonePath)])
-    if(i.returncode == 0 ): 
-     del i
-     return 0
-    else :
-     del i
-  raise Exception(str.format("clone \'{0}\' failed",URI))
-    
-
-def DownLoad(URI:str,DownloadPath:pathlib.Path,DownLoadFileName:str ) -> int:
-  if (DownloadPath / DownLoadFileName).is_file(): return 0
-  for z in range(10):
-    i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",str(DownloadPath),r"-o",DownLoadFileName,URI]);
-    if(i.returncode == 0 ): 
-      del i
-      gc.collect()
-      return 0
-    else :
-      del i
-  raise Exception(str.format("download \'{0}\' failed",URI))
-
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",user_home / r"stable-diffusion-webui")
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard "+commit_id)
-#install extensions
-print("installing extensions")
-Gitclone(r"https://github.com/vorstcavry/embeddings",user_home / r"stable-diffusion-webui" / r"embeddings"  / r"negative")
-Gitclone(r"https://github.com/vorstcavry/lora",user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive")
-Gitclone(r"https://github.com/vorstcavry/Checkpoint-Model",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint")
-
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN" ,r"4x-UltraSharp.pth")
-while (True):
-   i=subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")])
-   if(i.returncode == 0 ): 
-    del i
-    gc.collect()
-    break
-   else :
-    del i
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" )
-Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser")
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface")
-Gitclone(r"https://github.com/BlafKing/sd-civitai-browser-plus",user_home / r"stable-diffusion-webui" / r"extensions" / r"civitai-browser")
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks")
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet")
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor")
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib")
-Gitclone(r"https://github.com/hnmr293/posex",user_home / r"stable-diffusion-webui" / r"extensions" / r"posex")
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor")
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN")
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete")
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels")
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui")
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg")
-Gitclone(r"https://tinyurl.com/aspect-ratio-v",user_home / r"stable-diffusion-webui" / r"extensions" / r"aspect-ratio")
-Gitclone(r"https://github.com/Iyashinouta/sd-model-downloader",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-model-downloader")
-Gitclone(r"https://github.com/AIrjen/OneButtonPrompt",user_home / r"stable-diffusion-webui" / r"extensions" / r"OneButtonPrompt")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-wildcards")
-Gitclone(r"https://github.com/adieyal/sd-dynamic-prompts",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-dynamic-prompts")
-Gitclone(r"https://github.com/d8ahazard/sd_dreambooth_extension",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_dreambooth_extension")
-Gitclone(r"https://github.com/yfszzx/stable-diffusion-webui-inspiration",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-inspiration")
-Gitclone(r"https://github.com/Coyote-A/ultimate-upscale-for-automatic1111",user_home / r"stable-diffusion-webui" / r"extensions" / r"ultimate-upscale-for-automatic1111")
-os.chdir(user_home / r"stable-diffusion-webui")
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"extensions" / "sd-webui-controlnet" / r"models",pathlib.Path(dList[i]).name)
-del dList
-#download model    
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-#Stable Diffusion Checkpoint Model
-#anything version4.5
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"anything-v4.5-pruned.ckpt")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"anything-v4.0.vae.pt")
-#Counterfeit-V3.0
-#DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Counterfeit-V3.0_fp16.safetensors")
-#AbyssOrangeMix2 sfw
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_sfw.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"AbyssOrangeMix2_sfw.safetensors")
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"orangemix.vae.pt")
-#MeinaPastelV5
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"MeinaPastelV5_BakedVAE.safetensors")
-#DownLoad(r"https://huggingface.co/AnonPerson/ChilloutMix/resolve/main/ChilloutMix-ni-fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"ChilloutMix-ni-fp16.safetensors")
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV4%20-%20Without%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"MeinaPastelV4%20-%20Without%20VAE.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/perfect_world/resolve/main/perfectWorld_v2Baked.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"perfectWorld_v2Baked.safetensors")
-#DownLoad(r"https://huggingface.co/vorstcavry/figurestyle1/resolve/main/figure.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"figure.safetensors")
-#DownLoad(r"https://huggingface.co/vorstcavry/dosmix/resolve/main/ddosmix_V2.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"ddosmix_V2.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/rev-animated/resolve/main/revAnimated_v11.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"revAnimated_v11.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/MeinaMix/resolve/main/Meina_V8_baked_VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Meina_V8_baked_VAE.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/CyberRealistic/resolve/main/cyberrealistic_v13.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"cyberrealistic_v13.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/mymodel/resolve/main/Cavry_V2.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Cavry_V2.safetensors")
-#downloadvae
-DownLoad(r"https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"vae-ft-mse-840000-ema-pruned.safetensors")
-
-#Lora Model
-#Better Light
-#DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"Better_light.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"Better_light.safetensors")
-#LAS
-#DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"LAS.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"LAS.safetensors")
-#Backlighting
-#DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"backlighting.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"backlighting.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/japaneseDollLikeness_v15.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"japaneseDollLikeness_v15.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/koreanDollLikeness_v20.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"koreanDollLikeness_v20.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/taiwanDollLikeness_v15.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"taiwanDollLikeness_v15.safetensors")
-
-
-
-
-#GFPGAN Model
-#detection Resnet50
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"detection_Resnet50_Final.pth")
-#parsing_parsenet
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"parsing_parsenet.pth")
-#GFPGANv1.4
-DownLoad(r"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"GFPGANv1.4.pth")
-#strt Stable Diffusion Webui
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-gc.collect()
-while True:
-  ret=subprocess.run([executable ,user_home / r"stable-diffusion-webui" / r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
-  if(ret.returncode == 0 ): 
-    del ret
-    gc.collect()
-  else :
-    del ret
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/eson/bert-perplexity/README.md b/spaces/eson/bert-perplexity/README.md
deleted file mode 100644
index d90d98747cb2bd9e8e0289f89294536556bcedd1..0000000000000000000000000000000000000000
--- a/spaces/eson/bert-perplexity/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bert Perplexity
-emoji: 💩
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-duplicated_from: eson/bert-perplexity-debug
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/evaluate-metric/meteor/meteor.py b/spaces/evaluate-metric/meteor/meteor.py
deleted file mode 100644
index 058ee80ed77aaa4253acde460f915ca2771c4835..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/meteor/meteor.py
+++ /dev/null
@@ -1,168 +0,0 @@
-# Copyright 2020 The HuggingFace Evaluate Authors.
-#
-# 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.
-""" METEOR metric. """
-
-import datasets
-import numpy as np
-from nltk.translate import meteor_score
-from packaging import version
-
-import evaluate
-
-
-if evaluate.config.PY_VERSION < version.parse("3.8"):
-    import importlib_metadata
-else:
-    import importlib.metadata as importlib_metadata
-
-
-NLTK_VERSION = version.parse(importlib_metadata.version("nltk"))
-if NLTK_VERSION >= version.Version("3.6.4"):
-    from nltk import word_tokenize
-
-
-_CITATION = """\
-@inproceedings{banarjee2005,
-  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
-  author    = {Banerjee, Satanjeev  and Lavie, Alon},
-  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
-  month     = jun,
-  year      = {2005},
-  address   = {Ann Arbor, Michigan},
-  publisher = {Association for Computational Linguistics},
-  url       = {https://www.aclweb.org/anthology/W05-0909},
-  pages     = {65--72},
-}
-"""
-
-_DESCRIPTION = """\
-METEOR, an automatic metric for machine translation evaluation
-that is based on a generalized concept of unigram matching between the
-machine-produced translation and human-produced reference translations.
-Unigrams can be matched based on their surface forms, stemmed forms,
-and meanings; furthermore, METEOR can be easily extended to include more
-advanced matching strategies. Once all generalized unigram matches
-between the two strings have been found, METEOR computes a score for
-this matching using a combination of unigram-precision, unigram-recall, and
-a measure of fragmentation that is designed to directly capture how
-well-ordered the matched words in the machine translation are in relation
-to the reference.
-
-METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
-data and 0.331 on the Chinese data. This is shown to be an improvement on
-using simply unigram-precision, unigram-recall and their harmonic F1
-combination.
-"""
-
-_KWARGS_DESCRIPTION = """
-Computes METEOR score of translated segments against one or more references.
-Args:
-    predictions: list of predictions to score. Each prediction
-        should be a string with tokens separated by spaces.
-    references: list of reference for each prediction. Each
-        reference should be a string with tokens separated by spaces.
-    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
-    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
-    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
-Returns:
-    'meteor': meteor score.
-Examples:
-
-    >>> meteor = evaluate.load('meteor')
-    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
-    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
-    >>> results = meteor.compute(predictions=predictions, references=references)
-    >>> print(round(results["meteor"], 4))
-    0.6944
-"""
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class Meteor(evaluate.Metric):
-    def _info(self):
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=[
-                datasets.Features(
-                    {
-                        "predictions": datasets.Value("string", id="sequence"),
-                        "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
-                    }
-                ),
-                datasets.Features(
-                    {
-                        "predictions": datasets.Value("string", id="sequence"),
-                        "references": datasets.Value("string", id="sequence"),
-                    }
-                ),
-            ],
-            codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"],
-            reference_urls=[
-                "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
-                "https://en.wikipedia.org/wiki/METEOR",
-            ],
-        )
-
-    def _download_and_prepare(self, dl_manager):
-        import nltk
-
-        nltk.download("wordnet")
-        if NLTK_VERSION >= version.Version("3.6.5"):
-            nltk.download("punkt")
-        if NLTK_VERSION >= version.Version("3.6.6"):
-            nltk.download("omw-1.4")
-
-    def _compute(self, predictions, references, alpha=0.9, beta=3, gamma=0.5):
-        multiple_refs = isinstance(references[0], list)
-        if NLTK_VERSION >= version.Version("3.6.5"):
-            # the version of METEOR in NLTK version 3.6.5 and earlier expect tokenized inputs
-            if multiple_refs:
-                scores = [
-                    meteor_score.meteor_score(
-                        [word_tokenize(ref) for ref in refs],
-                        word_tokenize(pred),
-                        alpha=alpha,
-                        beta=beta,
-                        gamma=gamma,
-                    )
-                    for refs, pred in zip(references, predictions)
-                ]
-            else:
-                scores = [
-                    meteor_score.single_meteor_score(
-                        word_tokenize(ref), word_tokenize(pred), alpha=alpha, beta=beta, gamma=gamma
-                    )
-                    for ref, pred in zip(references, predictions)
-                ]
-        else:
-            if multiple_refs:
-                scores = [
-                    meteor_score.meteor_score(
-                        [[word_tokenize(ref) for ref in group] for group in references][0],
-                        word_tokenize(pred),
-                        alpha=alpha,
-                        beta=beta,
-                        gamma=gamma,
-                    )
-                    for ref, pred in zip(references, predictions)
-                ]
-            else:
-                scores = [
-                    meteor_score.single_meteor_score(ref, pred, alpha=alpha, beta=beta, gamma=gamma)
-                    for ref, pred in zip(references, predictions)
-                ]
-
-        return {"meteor": np.mean(scores)}
diff --git a/spaces/facat/alpaca-lora-cn/app.py b/spaces/facat/alpaca-lora-cn/app.py
deleted file mode 100644
index 09547b2f940566801e0fddab80b72de7a1e0e2eb..0000000000000000000000000000000000000000
--- a/spaces/facat/alpaca-lora-cn/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# %%
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-import gradio as gr
-from transformers import LlamaTokenizer
-from transformers import LlamaForCausalLM, GenerationConfig
-from peft import PeftModel
-import torch
-if torch.cuda.is_available():
-    device = "cuda"
-else:
-    device = "cpu"
-device_map={'': 0}
-def generate_instruction_prompt(instruction, input=None):
-    if input:
-        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-### Instruction:
-{instruction}
-
-### Input:
-{input}
-
-### Response:"""
-    else:
-        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{instruction}
-
-### Response:"""
-
-
-def evaluate(
-    model,
-    tokenizer,
-    instruction,
-    input=None,
-    temperature=0.1,
-    top_p=0.75,
-    num_beams=4,
-    max_token=256,
-):
-    generation_config = GenerationConfig(
-        temperature=temperature,
-        top_p=top_p,
-        num_beams=num_beams,
-        top_k=40,
-        no_repeat_ngram_size=3,
-    )
-    prompt = generate_instruction_prompt(instruction, input)
-    inputs = tokenizer(prompt, return_tensors="pt")
-    input_ids = inputs["input_ids"].to(device)
-    generation_output = model.generate(
-        input_ids=input_ids,
-        generation_config=generation_config,
-        return_dict_in_generate=True,
-        output_scores=True,
-        max_new_tokens=max_token,
-    )
-    s = generation_output.sequences[0]
-    output = tokenizer.decode(s)
-    res = output.split("### Response:")[1].strip()
-    print("Response:", res)
-    return res
-
-
-def load_lora(lora_path, base_model="decapoda-research/llama-7b-hf"):
-    model = LlamaForCausalLM.from_pretrained(
-        base_model,
-        # load_in_8bit=True,
-        # device_map=device_map,
-        low_cpu_mem_usage=True,
-        # torch_type=torch.float16,
-    )
-    print("Loading LoRA...")
-    lora = PeftModel.from_pretrained(
-        model,
-        lora_path,
-        torch_type=torch.float16,
-        # device_map=device_map,
-    )
-    return lora
-
-
-base_model = "decapoda-research/llama-7b-hf"
-tokenizer = LlamaTokenizer.from_pretrained(base_model)
-# question = "如果今天是星期五, 那么后天是星期几?"
-model = load_lora(lora_path="facat/alpaca-lora-cn", base_model=base_model)
-
-eval = lambda question, input, temperature, beams, max_token: evaluate(
-    model,
-    tokenizer,
-    question,
-    input=input,
-    temperature=temperature,
-    num_beams=beams,
-    max_token=max_token,
-)
-
-gr.Interface(
-    fn=eval,
-    inputs=[
-        gr.components.Textbox(
-            lines=2, label="Instruction", placeholder="Tell me about alpacas."
-        ),
-        gr.components.Textbox(lines=2, label="Input", placeholder="none"),
-        gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
-        # gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
-        # gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
-        gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"),
-        gr.components.Slider(
-            minimum=1, maximum=512, step=1, value=256, label="Max tokens"
-        ),
-    ],
-    outputs=[
-        gr.inputs.Textbox(
-            lines=8,
-            label="Output",
-        )
-    ],
-    title=f"Alpaca-LoRA",
-    description=f"Alpaca-LoRA",
-).launch()
diff --git a/spaces/facebook/StyleNeRF/viz/layer_widget.py b/spaces/facebook/StyleNeRF/viz/layer_widget.py
deleted file mode 100644
index 365d6dbd9e1700b68b3ce121d987ef8c51356a01..0000000000000000000000000000000000000000
--- a/spaces/facebook/StyleNeRF/viz/layer_widget.py
+++ /dev/null
@@ -1,183 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import imgui
-from gui_utils import imgui_utils
-
-#----------------------------------------------------------------------------
-
-class LayerWidget:
-    def __init__(self, viz):
-        self.viz            = viz
-        self.prev_layers    = None
-        self.cur_layer      = None
-        self.sel_channels   = 3
-        self.base_channel   = 0
-        self.img_scale_db   = 0
-        self.img_normalize  = False
-        self.fft_show       = False
-        self.fft_all        = True
-        self.fft_range_db   = 50
-        self.fft_beta       = 8
-        self.refocus        = False
-
-    @imgui_utils.scoped_by_object_id
-    def __call__(self, show=True):
-        viz = self.viz
-        layers = viz.result.get('layers', [])
-        if self.prev_layers != layers:
-            self.prev_layers = layers
-            self.refocus = True
-        layer = ([layer for layer in layers if layer.name == self.cur_layer] + [None])[0]
-        if layer is None and len(layers) > 0:
-            layer = layers[-1]
-            self.cur_layer = layer.name
-        num_channels = layer.shape[1] if layer is not None else 0
-        base_channel_max = max(num_channels - self.sel_channels, 0)
-
-        if show:
-            bg_color = [0.16, 0.29, 0.48, 0.2]
-            dim_color = list(imgui.get_style().colors[imgui.COLOR_TEXT])
-            dim_color[-1] *= 0.5
-
-            # Begin list.
-            width = viz.font_size * 28
-            height = imgui.get_text_line_height_with_spacing() * 12 + viz.spacing
-            imgui.push_style_var(imgui.STYLE_FRAME_PADDING, [0, 0])
-            imgui.push_style_color(imgui.COLOR_CHILD_BACKGROUND, *bg_color)
-            imgui.push_style_color(imgui.COLOR_HEADER, 0, 0, 0, 0)
-            imgui.push_style_color(imgui.COLOR_HEADER_HOVERED, 0.16, 0.29, 0.48, 0.5)
-            imgui.push_style_color(imgui.COLOR_HEADER_ACTIVE, 0.16, 0.29, 0.48, 0.9)
-            imgui.begin_child('##list', width=width, height=height, border=True, flags=imgui.WINDOW_ALWAYS_VERTICAL_SCROLLBAR)
-
-            # List items.
-            for layer in layers:
-                selected = (self.cur_layer == layer.name)
-                _opened, selected = imgui.selectable(f'##{layer.name}_selectable', selected)
-                imgui.same_line(viz.spacing)
-                _clicked, selected = imgui.checkbox(f'{layer.name}##radio', selected)
-                if selected:
-                    self.cur_layer = layer.name
-                    if self.refocus:
-                        imgui.set_scroll_here()
-                        viz.skip_frame() # Focus will change on next frame.
-                        self.refocus = False
-                imgui.same_line(width - viz.font_size * 13)
-                imgui.text_colored('x'.join(str(x) for x in layer.shape[2:]), *dim_color)
-                imgui.same_line(width - viz.font_size * 8)
-                imgui.text_colored(str(layer.shape[1]), *dim_color)
-                imgui.same_line(width - viz.font_size * 5)
-                imgui.text_colored(layer.dtype, *dim_color)
-
-            # End list.
-            if len(layers) == 0:
-                imgui.text_colored('No layers found', *dim_color)
-            imgui.end_child()
-            imgui.pop_style_color(4)
-            imgui.pop_style_var(1)
-
-            # Begin options.
-            imgui.same_line()
-            imgui.begin_child('##options', width=-1, height=height, border=False)
-
-            # RGB & normalize.
-            rgb = (self.sel_channels == 3)
-            _clicked, rgb = imgui.checkbox('RGB', rgb)
-            self.sel_channels = 3 if rgb else 1
-            imgui.same_line(viz.font_size * 4)
-            _clicked, self.img_normalize = imgui.checkbox('Normalize', self.img_normalize)
-            imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w)
-            if imgui_utils.button('Reset##img_flags', width=-1, enabled=(self.sel_channels != 3 or self.img_normalize)):
-                self.sel_channels = 3
-                self.img_normalize = False
-
-            # Image scale.
-            with imgui_utils.item_width(-1 - viz.button_w - viz.spacing):
-                _changed, self.img_scale_db = imgui.slider_float('##scale', self.img_scale_db, min_value=-40, max_value=40, format='Scale %+.1f dB')
-            imgui.same_line()
-            if imgui_utils.button('Reset##scale', width=-1, enabled=(self.img_scale_db != 0)):
-                self.img_scale_db = 0
-
-            # Base channel.
-            self.base_channel = min(max(self.base_channel, 0), base_channel_max)
-            narrow_w = imgui.get_text_line_height_with_spacing()
-            with imgui_utils.grayed_out(base_channel_max == 0):
-                with imgui_utils.item_width(-1 - viz.button_w - narrow_w * 2 - viz.spacing * 3):
-                    _changed, self.base_channel = imgui.drag_int('##channel', self.base_channel, change_speed=0.05, min_value=0, max_value=base_channel_max, format=f'Channel %d/{num_channels}')
-                imgui.same_line()
-                if imgui_utils.button('-##channel', width=narrow_w):
-                    self.base_channel -= 1
-                imgui.same_line()
-                if imgui_utils.button('+##channel', width=narrow_w):
-                    self.base_channel += 1
-            imgui.same_line()
-            self.base_channel = min(max(self.base_channel, 0), base_channel_max)
-            if imgui_utils.button('Reset##channel', width=-1, enabled=(self.base_channel != 0 and base_channel_max > 0)):
-                self.base_channel = 0
-
-            # Stats.
-            stats = viz.result.get('stats', None)
-            stats = [f'{stats[idx]:g}' if stats is not None else 'N/A' for idx in range(6)]
-            rows = [
-                ['Statistic',   'All channels', 'Selected'],
-                ['Mean',        stats[0],       stats[1]],
-                ['Std',         stats[2],       stats[3]],
-                ['Max',         stats[4],       stats[5]],
-            ]
-            height = imgui.get_text_line_height_with_spacing() * len(rows) + viz.spacing
-            imgui.push_style_color(imgui.COLOR_CHILD_BACKGROUND, *bg_color)
-            imgui.begin_child('##stats', width=-1, height=height, border=True)
-            for y, cols in enumerate(rows):
-                for x, col in enumerate(cols):
-                    if x != 0:
-                        imgui.same_line(viz.font_size * (4 + (x - 1) * 6))
-                    if x == 0 or y == 0:
-                        imgui.text_colored(col, *dim_color)
-                    else:
-                        imgui.text(col)
-            imgui.end_child()
-            imgui.pop_style_color(1)
-
-            # FFT & all.
-            _clicked, self.fft_show = imgui.checkbox('FFT', self.fft_show)
-            imgui.same_line(viz.font_size * 4)
-            with imgui_utils.grayed_out(not self.fft_show or base_channel_max == 0):
-                _clicked, self.fft_all = imgui.checkbox('All channels', self.fft_all)
-            imgui.same_line(imgui.get_content_region_max()[0] - 1 - viz.button_w)
-            with imgui_utils.grayed_out(not self.fft_show):
-                if imgui_utils.button('Reset##fft_flags', width=-1, enabled=(self.fft_show or not self.fft_all)):
-                    self.fft_show = False
-                    self.fft_all = True
-
-            # FFT range.
-            with imgui_utils.grayed_out(not self.fft_show):
-                with imgui_utils.item_width(-1 - viz.button_w - viz.spacing):
-                    _changed, self.fft_range_db = imgui.slider_float('##fft_range_db', self.fft_range_db, min_value=0.1, max_value=100, format='Range +-%.1f dB')
-                imgui.same_line()
-                if imgui_utils.button('Reset##fft_range_db', width=-1, enabled=(self.fft_range_db != 50)):
-                    self.fft_range_db = 50
-
-            # FFT beta.
-            with imgui_utils.grayed_out(not self.fft_show):
-                with imgui_utils.item_width(-1 - viz.button_w - viz.spacing):
-                    _changed, self.fft_beta = imgui.slider_float('##fft_beta', self.fft_beta, min_value=0, max_value=50, format='Kaiser beta %.2f', power=2.63)
-                imgui.same_line()
-                if imgui_utils.button('Reset##fft_beta', width=-1, enabled=(self.fft_beta != 8)):
-                    self.fft_beta = 8
-
-            # End options.
-            imgui.end_child()
-
-        self.base_channel = min(max(self.base_channel, 0), base_channel_max)
-        viz.args.layer_name = self.cur_layer if len(layers) > 0 and self.cur_layer != layers[-1].name else None
-        viz.args.update(sel_channels=self.sel_channels, base_channel=self.base_channel, img_scale_db=self.img_scale_db, img_normalize=self.img_normalize)
-        viz.args.fft_show = self.fft_show
-        if self.fft_show:
-            viz.args.update(fft_all=self.fft_all, fft_range_db=self.fft_range_db, fft_beta=self.fft_beta)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/facebook/incoder-demo/modules/cloud_logging.py b/spaces/facebook/incoder-demo/modules/cloud_logging.py
deleted file mode 100644
index f82a884f65d92ffde60be56be8b7599df09882d0..0000000000000000000000000000000000000000
--- a/spaces/facebook/incoder-demo/modules/cloud_logging.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-def make_logging_client():
-    cred_filename = os.environ.get('GOOGLE_APPLICATION_CREDENTIALS')
-    if not cred_filename:
-        return None
-    print("cred filename:", cred_filename)
-    cred_string = os.environ.get('GOOGLE_APPLICATION_CREDENTIALS_STRING')
-    print("cred string:", bool(cred_string))
-    if not os.path.exists(cred_filename):
-        if cred_string:
-            print(f"writing cred string to {cred_filename}")
-            with open(cred_filename, 'w') as f:
-                f.write(cred_string)
-        else:
-            return None
-    from google.cloud import logging
-    logging_client = logging.Client()
-    logging_client.setup_logging()
-    return logging_client
-
-logging_client = make_logging_client()
diff --git a/spaces/falterWliame/Face_Mask_Detection/Cambridge Vocabulary For First Certificate (with Answers And Audio CD) REPACK.md b/spaces/falterWliame/Face_Mask_Detection/Cambridge Vocabulary For First Certificate (with Answers And Audio CD) REPACK.md
deleted file mode 100644
index 141d6fb41aef0007ec37b6fc2bb01109db6bcfeb..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Cambridge Vocabulary For First Certificate (with Answers And Audio CD) REPACK.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-
-<h1>How to Improve Your Vocabulary for the First Certificate Exam</h1>
-<p>If you are preparing for the First Certificate exam, also known as B2 First, you might be wondering how to improve your vocabulary. Vocabulary is one of the key components of the exam, as it tests your ability to understand and use a wide range of words and phrases in different contexts.</p>
-<p>One of the best ways to improve your vocabulary is to use a dedicated book that covers the topics and language areas that you need for the exam. One such book is <strong>Cambridge Vocabulary For First Certificate (with Answers And Audio CD)</strong>, published by Cambridge University Press. This book is designed to help you learn and practice the vocabulary that you need for the exam, as well as develop your skills in reading, writing, listening and speaking.</p>
-<h2>Cambridge Vocabulary For First Certificate (with Answers And Audio CD)</h2><br /><p><b><b>Download</b> - <a href="https://urlca.com/2uDcxJ">https://urlca.com/2uDcxJ</a></b></p><br /><br />
-<p>The book contains 25 units that cover topics such as education, travel, health, entertainment, technology and society. Each unit introduces new vocabulary through authentic texts and recordings, followed by exercises that help you check your understanding and use the words correctly. The book also includes tips and strategies on how to deal with different types of questions in the exam, such as multiple choice, gap fill, word formation and keyword transformation.</p>
-<p>The book comes with an answer key and an audio CD that contains all the listening material for the exercises. You can use the book for self-study or in class with a teacher. The book is suitable for students who have an intermediate level of English (B1) or higher.</p>
-<p>By using <strong>Cambridge Vocabulary For First Certificate (with Answers And Audio CD)</strong>, you can improve your vocabulary and confidence for the exam. You can also expand your knowledge of English and learn about different topics and cultures. The book is available from Cambridge University Press or other online and offline retailers.</p>
-
-<p>Another way to improve your vocabulary is to use online tools and apps that can help you practice and learn new words. For example, you can use <strong>Test & Train</strong>, an easy-to-use practice tool that helps you get ready for the B2 First exam through short, sharp workouts. With over 300 practice questions, you can use it anytime, anywhere and as many times as you like[^2^].</p>
-<p>You can also use <strong>Write & Improve</strong>, a free online tool that helps you practice and improve your writing. Just choose a task, write or upload your answer and use the feedback to quickly improve. You can also use <strong>Exam Lift</strong>, a free app that helps you learn English on the go and develop the skills you need for the B2 First exam[^2^].</p>
-<p>Finally, you can find more tips and advice on how to prepare for the B2 First exam on various websites and blogs. For example, you can visit <strong>FCE Exam Tips</strong>, a website that offers exam strategies, vocabulary ideas, videos and helpful articles on different skills and topics[^1^]. You can also visit <strong>Cambridge English</strong>, the official website of the exam provider, where you can find sample tests, handbooks, lesson plans, teacher guides and webinars[^2^] [^3^].</p>
-<p>By using these resources and tools, you can boost your vocabulary and your chances of success in the B2 First exam. Remember that vocabulary is not only about knowing words, but also about using them appropriately and accurately in different situations. Good luck with your exam preparation!</p>
-<p></p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Facing The Giants DVDRIP FRENCH.md b/spaces/falterWliame/Face_Mask_Detection/Facing The Giants DVDRIP FRENCH.md
deleted file mode 100644
index 1e0d2815b44c0d33232c5d72220962dd0d0c1ffc..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Facing The Giants DVDRIP FRENCH.md	
+++ /dev/null
@@ -1,39 +0,0 @@
-
-<h1>Facing The Giants DVDRIP FRENCH: Un film qui vous inspire à croire</h1>
-<p>Facing The Giants DVDRIP FRENCH est un fichier qui contient le film Facing The Giants en format numérique. Il a été extrait d'un DVD, ce qui signifie qu'il a été copié et compressé pour réduire sa taille et sa qualité. Mais cela ne l'empêche pas d'être un film passionnant et émouvant, qui vous fera découvrir l'histoire d'un entraîneur de football américain et de son équipe, qui font face à leurs géants de la peur et de l'échec, sur le terrain et dans la vie.</p>
-<h2>Facing The Giants DVDRIP FRENCH</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://urlca.com/2uDdyI">https://urlca.com/2uDdyI</a></b></p><br /><br />
-<h2>De quoi parle le film Facing The Giants DVDRIP FRENCH?</h2>
-<p>Le film Facing The Giants DVDRIP FRENCH raconte l'histoire de Grant Taylor, un entraîneur de football américain dans un lycée chrétien, qui traverse une crise personnelle et professionnelle. Son équipe perd tous ses matchs depuis six ans, ses joueurs manquent de motivation et de confiance, et il est menacé d'être renvoyé. De plus, il doit faire face à des problèmes conjugaux et à une infertilité qui le frustre. Il se sent dépassé par les difficultés et prêt à abandonner.</p>
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Freebandicamfullversiondownload HOTmediafire.md b/spaces/falterWliame/Face_Mask_Detection/Freebandicamfullversiondownload HOTmediafire.md
deleted file mode 100644
index ea4e6ff6f74a3ed11c5084d40b0cc6bbd7e74b96..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Freebandicamfullversiondownload HOTmediafire.md	
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Gaussian 09 Torrent 1357.md b/spaces/falterWliame/Face_Mask_Detection/Gaussian 09 Torrent 1357.md
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index c050548b156344fb3a46e4393a8bdafe3d04d663..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Gaussian 09 Torrent 1357.md	
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Microwave Engineering By Annapurna Das Sisir K Dasrar __LINK__.md b/spaces/falterWliame/Face_Mask_Detection/Microwave Engineering By Annapurna Das Sisir K Dasrar __LINK__.md
deleted file mode 100644
index bb7c3e48552fa81e7cc5999d59949ea59127452d..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Microwave Engineering By Annapurna Das Sisir K Dasrar __LINK__.md	
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diff --git a/spaces/fatiXbelha/sd/Delphi Connect How to Access Your Cars Data Alerts and Location Map.md b/spaces/fatiXbelha/sd/Delphi Connect How to Access Your Cars Data Alerts and Location Map.md
deleted file mode 100644
index 042bc1c5784385cae48501ba42a27aba4ef22e29..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Delphi Connect How to Access Your Cars Data Alerts and Location Map.md	
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-<td>Google Play Store</td>
-<td>4.1 out of 5 stars (based on 1,216 reviews)</td>
-<td>"Great app. Works well with my 2013 Ford Escape. I can start my car from anywhere and monitor its location and health. The customer service is also very helpful and responsive."</td>
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-<td>CNET</td>
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-<td>"Delphi Connect is a useful and easy-to-use system for anyone who wants to monitor their car's location and status remotely. It also adds some features that are normally found only on newer or more expensive cars."</td>
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-<tr>
-<td>PCMag</td>
-<td>3.5 out of 5 stars (based on 1 review)</td>
-<td>"Delphi Connect is a good choice for anyone looking for a simple way to add connectivity features to their car. It offers basic remote control and diagnostic functions, but lacks some of the advanced features of its competitors."</td>
-</tr>
-</table>
- <h2>How Does Delphi Cars APK Compare to Its Competitors in the Market?</h2>
- <p>Delphi cars apk is not the only app that offers car connectivity and diagnostic features. There are other apps and systems that provide similar or different functions for different prices and requirements. Here are some of the main competitors of delphi cars apk in the market:</p>
- <ul>
-<li><b>Viper SmartStart:</b> This is an app that works with a Viper security system installed in your car. It allows you to remotely start, lock/unlock, locate, and monitor your car from your smartphone or browser. It also offers security features such as alarm notifications, panic button, etc. The app is free to download, but the system costs around $300 to $600 depending on the model and installation.</li>
-<li><b>Automatic:</b> This is an app that works with a small device that plugs into your car's OBD port. It allows you to track your driving habits, fuel efficiency, trip logs, engine health, etc. It also offers crash alert, roadside assistance, parking reminder, etc. The app is free to download, but the device costs $99.95.</li>
-<li><b>Zubie:</b> This is an app that works with a device that plugs into your car's OBD port and connects to the cloud via a cellular data connection. It allows you to locate, track, diagnose, and monitor your car from your smartphone or browser. It also offers driving insights, maintenance reminders, trip reports, etc. The app is free to download, but the device costs $99.95 plus a monthly service fee of $9.95.</li>
-</ul>
- <h2>Conclusion: Why Should You Choose Delphi Cars APK for Your Car Maintenance and Connectivity Needs?</h2>
- <p>If you are looking for a smart solution for your car maintenance and connectivity needs, then delphi cars apk might be the right choice for you. Here are some of the reasons why you should choose delphi cars apk over its competitors:</p>
- <ul>
-<li><b>Compatibility:</b> Delphi cars apk works with almost all vehicles sold in the US since 1996. You don't need to worry about whether your car model or brand is supported or not.</li>
-<li><b>Affordability:</b> Delphi cars apk is free to download and use. You only need to pay for the Delphi Connect system which costs around $200 plus a monthly service fee of $5. The system includes a wireless module, a GPS receiver, and a cellular data connection. You don't need to pay extra for installation or other devices.</li>
-<li><b>Functionality:</b> Delphi cars apk offers a wide range of features and functions that allow you to diagnose and control your car from anywhere and anytime. You can access live data, alerts, trip logs, geo-fencing, keyfob functionality, and more. You can also add features to your car that have traditionally been available only on new vehicles or with professionally installed aftermarket systems.</li>
-<li><b>Reliability:</b> Delphi cars apk is backed by Delphi Technologies, a global leader in automotive technology and innovation. The app and the system are designed and tested to meet the highest standards of quality and performance. You can trust that your car data and information are secure and accurate.</li>
-</ul>
- <p>Delphi cars apk is a smart solution for your car maintenance and connectivity needs. It is compatible, affordable, functional, and reliable. It can help you save time, money, and hassle while enhancing your driving experience and safety. Download delphi cars apk today and see for yourself how it can transform your car into a smart car.</p>
- <h2>FAQs: Some Common Questions and Answers About Delphi Cars APK</h2>
- <p>Here are some of the common questions and answers about delphi cars apk that you might have:</p>
- <ol>
-<li><b>Q: How do I get the Delphi Connect system for my car?</b></li>
-<li><b>A: You can buy the Delphi Connect system online from [text] or from authorized retailers such as Verizon Wireless, Best Buy, Amazon, etc. You can also find a list of retailers near you on the website.</b></li>
-<li><b>Q: How do I install the Delphi Connect system in my car?</b></li>
-<li><b>A: You can install the Delphi Connect system in your car by yourself in minutes. All you need to do is plug the wireless module into your car's OBD port (usually located under the dashboard) and activate it with your Delphi Connect account. You can find more detailed instructions on the website or in the user manual.</b></li>
-<li><b>Q: How do I update the Delphi Connect system or the app?</b></li>
-<li><b>A: The Delphi Connect system and the app are automatically updated over the air whenever there are new features or improvements available. You don't need to do anything to update them.</b></li>
-<li><b>Q: How do I contact the customer support for Delphi Connect?</b></li>
-<li><b>A: You can contact the customer support for Delphi Connect by phone at 1-877-855-8400 or by email at delphiconnectsupport@delphi.com. You can also visit the website for more information and resources.</b></li>
-<li><b>Q: What are the minimum requirements for using Delphi cars apk?</b></li>
-<li><b>A: To use delphi cars apk, you need an Android device running Android 4.0 or higher, a Delphi Connect account, and a Delphi Connect system installed in your car.</b></li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/fatiXbelha/sd/FIFA Mobile Dominate the Field with Advanced Passing and Tactics on iOS.md b/spaces/fatiXbelha/sd/FIFA Mobile Dominate the Field with Advanced Passing and Tactics on iOS.md
deleted file mode 100644
index b2e46e6975e6cf3536c0b3666635cedb846fde4b..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/FIFA Mobile Dominate the Field with Advanced Passing and Tactics on iOS.md	
+++ /dev/null
@@ -1,97 +0,0 @@
-
-<h1>FIFA Mobile: The Ultimate Football Game for iOS and Android</h1>
-<p>If you are a fan of football, you will love FIFA Mobile, the mobile version of the popular EA SPORTS FIFA game. FIFA Mobile lets you build your own Ultimate Team and challenge your friends and other players from around the world in various modes, including Head-to-Head, VS Attack, and Manager Mode. You can also play through the entire Tournament with any of the 32 qualified National Teams, or rewrite history and take control of 15 non-qualified National Teams. FIFA Mobile is available for both iOS and Android devices, and you can download it for free from the App Store or Google Play.</p>
-<h2>fifa mobile ios apk</h2><br /><p><b><b>DOWNLOAD</b> &#9658; <a href="https://urllie.com/2uNIuG">https://urllie.com/2uNIuG</a></b></p><br /><br />
- <h2>What is FIFA Mobile?</h2>
-<p>FIFA Mobile is a football game that allows you to experience the thrill of playing with the world's best players and teams. You can choose from over 15,000 players, including world-class talent like Kylian Mbappé and Christian Pulisic, plus 600+ teams, including Real Madrid and Manchester City. You can also kick off against teams from club football's most prestigious competitions – the UEFA Champions League, UEFA Europa League, and UEFA Europa Conference League. You can follow every step, from the group stages all the way to the final.</p>
- <h3>Features of FIFA Mobile</h3>
-<p>FIFA Mobile has many features that make it an exciting and engaging game for football lovers. Here are some of them:</p>
- <h4>Play with over 15,000 players and 600+ teams</h4>
-<p>You can build your Ultimate Team with players from the Premier League, LaLiga Santander, Bundesliga, Serie A TIM, Ligue 1 Uber Eats, and more. You can also transfer players to reflect their new team in the next roster update. You can train your favorite players, increasing their stats and OVR. You can take your superstars to the next level by scoring goals with them.</p>
- <h4>Compete in the UEFA Champions League, UEFA Europa League, and UEFA Europa Conference League</h4>
-<p>You can kick off against teams from club football's most prestigious competitions – the UEFA Champions League, UEFA Europa League, and UEFA Europa Conference League. You can take part in playable live events that correspond with the real-world tournaments throughout the football season to earn special UEFA Champions League, UEFA Europa League, and UEFA Europa Conference League players. You can follow every step, from the group stages all the way to the final.</p>
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- <h4>Build your team with ICONS and HEROES</h4>
-<p>You can build a team full of football legends, with over 100 of the biggest ICONS, from Zidane and Beckham to Ronaldo and Maldini. You can also celebrate some of football's most memorable players with new Heroes, representing career-making unforgettable moments from fan favorites like Solskjær and Di Natale.</p>
- <h3>How to download FIFA Mobile on iOS and Android</h3>
-<p>Downloading FIFA Mobile on your iOS or Android device is easy and fast. Here are the steps you need to follow:</p>
- <h4>Download from the App Store or Google Play</h4>
-<p>You can download FIFA Mobile for free from the App Store or Google Play. Just search for "FIFA Mobile" in your app store and tap on "Get" or "Install". The game will start downloading on your device.</p>
- <h4>Sign up with your EA Account or create a new one</h4>
-<p>Once you have downloaded FIFA Mobile, you will need to sign up with your EA Account or create a new one. If you already have an EA Account, you can use it to log in to FIFA Mobile. If you don't have one, you can create one by entering your email address, password, and security question. You can also link your Facebook, Google, or Apple account to your EA Account for easier access.</p>
- <h4>Customize your profile and settings</h4>
-<p>After you have signed up, you can customize your profile and settings. You can choose your country, language, and favorite team. You can also adjust your game preferences, such as sound, graphics, and controls. You can change your profile and settings anytime from the main menu.</p>
- <h3>Tips and tricks for playing FIFA Mobile</h3>
-<p>FIFA Mobile is a fun and addictive game that will keep you entertained for hours. However, if you want to improve your skills and performance, you might want to follow some tips and tricks. Here are some of them:</p>
- <h4>Train your players and improve their stats</h4>
-<p>One of the best ways to make your team stronger is to train your players and improve their stats. You can use Training XP items that you earn from playing matches and events to level up your players. You can also use Skill Boosts items that you collect from various sources to boost specific attributes of your players. You can train and boost any player in your team, regardless of their OVR or position.</p>
- <h4>Use the Advanced Passing system to create more chances</h4>
-<p>Another way to enhance your gameplay is to use the Advanced Passing system, which allows you to control the direction and power of your passes. You can swipe on the screen to pass the ball to a specific spot or player, or tap on a teammate to pass the ball to them. You can also use the Through Pass button to send the ball ahead of a running player, or the Lob Pass button to chip the ball over the defenders. The Advanced Passing system can help you create more chances and score more goals.</p>
- <h4>Participate in live events and tournaments to earn rewards</h4>
-<p>A third way to enjoy FIFA Mobile is to participate in live events and tournaments that are updated regularly. You can play through various challenges and scenarios that reflect the real-world football season and earn rewards such as coins, players, kits, and more. You can also join leagues and compete with other players in tournaments such as League vs League or League Survival. You can also play against other players in Head-to-Head or VS Attack modes and climb the leaderboards.</p>
- <h3>Conclusion</h3>
-<p>FIFA Mobile is a great game for football fans who want to experience the excitement of playing with their favorite players and teams on their mobile devices. You can download FIFA Mobile for free from the App Store or Google Play and start building your Ultimate Team. You can also compete in various modes and events, such as the UEFA Champions League, UEFA Europa League, UEFA Europa Conference League, Head-to-Head, VS Attack, Manager Mode, Tournament Mode, and more. FIFA Mobile is a game that will keep you hooked for hours with its amazing graphics, realistic gameplay, and engaging features.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about FIFA Mobile:</p>
- <ul>
-<li><b>Q: How much space does FIFA Mobile take on my device?</b></li>
-<li>A: FIFA Mobile requires about 1 GB of free space on your device. However, this may vary depending on your device model and operating system.</li>
-<li><b>Q: How do I update FIFA Mobile?</b></li>
-<li>A: FIFA Mobile updates automatically when you launch the game if there is a new version available. However, you can also check for updates manually by going to the App Store or Google Play and tapping on "Update".</li>
-<li><b>Q: How do I contact EA Customer Support?</b></li>
-<li>A: If you have any issues or questions about FIFA Mobile, you can contact EA Customer Support by going to the main menu and tapping on "Settings", then "Help". You can also visit https://help.ea.com/en/fifa/fifa-mobile/ for more information.</li>
-<li><b>Q: How do I get more coins in FIFA Mobile?</b></li>
-<li>A: There are many ways to get more coins in FIFA Mobile, such as playing matches and events, completing achievements and quests, selling players on the market, watching ads, or buying coin packs with real money.</li>
-<li><b>Q: How do I get more FIFA Points in FIFA Mobile?</b></li>
-<li>A: The only way to get more FIFA Points in FIFA Mobile is to buy them with real money. You can use FIFA Points to buy premium packs, bundles, passes, offers, and more.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/feregVcuzo/sanity-test-midi/Kaplan Surgery Audio - Carlos Pestana.md b/spaces/feregVcuzo/sanity-test-midi/Kaplan Surgery Audio - Carlos Pestana.md
deleted file mode 100644
index 69b1923ace0f3965c24b4dfbafa65e63d9782206..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/Kaplan Surgery Audio - Carlos Pestana.md	
+++ /dev/null
@@ -1,96 +0,0 @@
-## Kaplan Surgery Audio - Carlos Pestana
-
-
-
- 
-
-  
-
- 
-
-**CLICK HERE >> [https://www.google.com/url?q=https%3A%2F%2Fcinurl.com%2F2txvKv&sa=D&sntz=1&usg=AOvVaw0g8CMElJz2dMaZ12CHC54E](https://www.google.com/url?q=https%3A%2F%2Fcinurl.com%2F2txvKv&sa=D&sntz=1&usg=AOvVaw0g8CMElJz2dMaZ12CHC54E)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# Dr. Pestana's Surgery Notes: A Review of Kaplan's Audio Course
-
- 
-
-If you are preparing for the surgical clerkship and shelf exams, you might be interested in Kaplan's audio course, Dr. Pestana's Surgery Notes. This course is based on the popular book by Carlos Pestana, a renowned surgeon and educator who has taught thousands of medical students and residents. In this course, you will get a concise and comprehensive overview of the most important topics in surgery, such as trauma, vascular, abdominal, breast, endocrine, and pediatric surgery. You will also learn about preoperative and postoperative care, anesthesia, complications, ethics, and legal issues.
-
- 
-
-The course is narrated by Matthew Kugler, a professional voice actor who delivers the content in a clear and engaging manner. The course is divided into 10 chapters, each lasting about 30 minutes. You can listen to the course at your own pace, whenever and wherever you want. The course also comes with a PDF study guide that summarizes the key points and provides practice questions and answers.
-
- 
-
-Dr. Pestana's Surgery Notes is a pocket-sized review that will help you ace the surgical clerkship and shelf exams. It will also reinforce your knowledge and skills for your future career as a surgeon. You can download the course from [Audiobooks Now](https://www.audiobooksnow.com/audiobook/dr-pestanas-surgery-notes/5698166/) for $14.99 or get it for free with a 30-day trial membership.
-
-
-
-Here are some of the benefits of listening to Dr. Pestana's Surgery Notes:
-
- 
-
-- You will save time and energy by focusing on the most high-yield topics in surgery.
-
-- You will boost your confidence and performance by reviewing the essential concepts and facts.
-
-- You will improve your retention and recall by listening to the audio repeatedly.
-
-- You will enhance your understanding and application by doing the practice questions and checking the answers.
-
-- You will enjoy learning from a master teacher who has a wealth of experience and wisdom.
-
-
-
-Dr. Pestana's Surgery Notes is more than just an audio course. It is a valuable resource that will help you succeed in your surgical clerkship and shelf exams. Don't miss this opportunity to learn from one of the best in the field. Get your copy of Dr. Pestana's Surgery Notes today and start your journey to becoming a great surgeon.
-
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-
-Here are some of the testimonials from other students who have used this course:
-
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-> "Dr. Pestana's Surgery Notes was a lifesaver for me. I listened to it every day on my way to the hospital and it helped me ace the shelf exam. I highly recommend it to anyone who wants to learn surgery in a simple and effective way."
-
-> 
-
-> <cite>- Sarah, third-year medical student</cite>
-
-
-
-> "This course is amazing. Dr. Pestana explains everything so clearly and concisely. He makes surgery fun and easy to understand. I wish I had this course when I was studying for Step 1."
-
-> 
-
-> <cite>- David, fourth-year medical student</cite>
-
-
-
-> "I love this course. It covers all the important topics in surgery and gives you the tips and tricks you need to do well on the clerkship and the shelf exam. It also helps you prepare for the oral exams and the residency interviews. It's like having a personal mentor in your pocket."
-
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-
-> <cite>- Jessica, third-year medical student</cite>
-
-
-
-As you can see, Dr. Pestana's Surgery Notes has helped many students achieve their goals and dreams. It can help you too.
-
- dfd1c89656
-
- 
-
- 
-
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/unpipe/HISTORY.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/unpipe/HISTORY.md
deleted file mode 100644
index 85e0f8d747dc2a960e1ae6640c8bf081631ac0ec..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/unpipe/HISTORY.md
+++ /dev/null
@@ -1,4 +0,0 @@
-1.0.0 / 2015-06-14
-==================
-
-  * Initial release
diff --git a/spaces/fffiloni/instant-TTS-Bark-cloning/examples/blank.md b/spaces/fffiloni/instant-TTS-Bark-cloning/examples/blank.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fffiloni/lama-video-watermark-remover/bin/paper_runfiles/generate_test_paris.sh b/spaces/fffiloni/lama-video-watermark-remover/bin/paper_runfiles/generate_test_paris.sh
deleted file mode 100644
index 66056017c3aa376ef0767a59583ab25a321b559b..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/bin/paper_runfiles/generate_test_paris.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-# paths to data are valid for mml-ws01
-OUT_DIR="/media/inpainting/paper_data/Paris_StreetView_Dataset_val"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in paris_eval_gt
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 segm_256
-    do
-        "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-paris \
-         location.out_dir=OUT_DIR cropping.out_square_crop=False cropping.out_min_size=227
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done
diff --git a/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/evaluation/vis.py b/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/evaluation/vis.py
deleted file mode 100644
index c2910b4ef8c61efee72dabd0531a9b669ec8bf98..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/evaluation/vis.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import numpy as np
-from skimage import io
-from skimage.segmentation import mark_boundaries
-
-
-def save_item_for_vis(item, out_file):
-    mask = item['mask'] > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    img = mark_boundaries(np.transpose(item['image'], (1, 2, 0)),
-                          mask,
-                          color=(1., 0., 0.),
-                          outline_color=(1., 1., 1.),
-                          mode='thick')
-
-    if 'inpainted' in item:
-        inp_img = mark_boundaries(np.transpose(item['inpainted'], (1, 2, 0)),
-                                  mask,
-                                  color=(1., 0., 0.),
-                                  mode='outer')
-        img = np.concatenate((img, inp_img), axis=1)
-
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
-
-
-def save_mask_for_sidebyside(item, out_file):
-    mask = item['mask']# > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    mask = np.clip(mask * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, mask)
-
-def save_img_for_sidebyside(item, out_file):
-    img = np.transpose(item['image'], (1, 2, 0))
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
\ No newline at end of file
diff --git a/spaces/firsk/ai_otto/data_utils.py b/spaces/firsk/ai_otto/data_utils.py
deleted file mode 100644
index d8e6b9e30b90839644e8a2c33c5166288b720d02..0000000000000000000000000000000000000000
--- a/spaces/firsk/ai_otto/data_utils.py
+++ /dev/null
@@ -1,406 +0,0 @@
-import os
-import random
-import torch
-import torch.utils.data
-from tqdm import tqdm
-from loguru import logger
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio, speaker_id, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.spk_map = hparams.spk2id
-        self.hparams = hparams
-
-        self.use_mel_spec_posterior = getattr(
-            hparams, "use_mel_posterior_encoder", False
-        )
-        if self.use_mel_spec_posterior:
-            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 300)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        skipped = 0
-        logger.info("Init dataset...")
-        for _id, spk, language, text, phones, tone, word2ph in tqdm(
-            self.audiopaths_sid_text
-        ):
-            audiopath = f"{_id}"
-            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
-                phones = phones.split(" ")
-                tone = [int(i) for i in tone.split(" ")]
-                word2ph = [int(i) for i in word2ph.split(" ")]
-                audiopaths_sid_text_new.append(
-                    [audiopath, spk, language, text, phones, tone, word2ph]
-                )
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-            else:
-                skipped += 1
-        logger.info(
-            "skipped: "
-            + str(skipped)
-            + ", total: "
-            + str(len(self.audiopaths_sid_text))
-        )
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
-        bert, ja_bert, phones, tone, language = self.get_text(
-            text, word2ph, phones, tone, language, audiopath
-        )
-
-        spec, wav = self.get_audio(audiopath)
-        sid = torch.LongTensor([int(self.spk_map[sid])])
-        return (phones, spec, wav, sid, tone, language, bert, ja_bert)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} {} SR doesn't match target {} SR".format(
-                    filename, sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        try:
-            spec = torch.load(spec_filename)
-        except:
-            if self.use_mel_spec_posterior:
-                spec = mel_spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.n_mel_channels,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    self.hparams.mel_fmin,
-                    self.hparams.mel_fmax,
-                    center=False,
-                )
-            else:
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-        if self.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
-        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)
-            torch.save(bert, bert_path)
-            assert bert.shape[-1] == len(phone), phone
-
-        if language_str == "ZH":
-            bert = bert
-            ja_bert = torch.zeros(768, len(phone))
-        elif language_str == "JP":
-            ja_bert = bert
-            bert = torch.zeros(1024, len(phone))
-        else:
-            bert = torch.zeros(1024, len(phone))
-            ja_bert = torch.zeros(768, len(phone))
-        assert bert.shape[-1] == len(phone), (
-            bert.shape,
-            len(phone),
-            sum(word2ph),
-            p1,
-            p2,
-            t1,
-            t2,
-            pold,
-            pold2,
-            word2ph,
-            text,
-            w2pho,
-        )
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, ja_bert, phone, tone, language
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        tone_padded = torch.LongTensor(len(batch), max_text_len)
-        language_padded = torch.LongTensor(len(batch), max_text_len)
-        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-        ja_bert_padded = torch.FloatTensor(len(batch), 768, max_text_len)
-
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        tone_padded.zero_()
-        language_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        bert_padded.zero_()
-        ja_bert_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, : text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, : wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-            tone = row[4]
-            tone_padded[i, : tone.size(0)] = tone
-
-            language = row[5]
-            language_padded[i, : language.size(0)] = language
-
-            bert = row[6]
-            bert_padded[i, :, : bert.size(1)] = bert
-
-            ja_bert = row[7]
-            ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
-
-        return (
-            text_padded,
-            text_lengths,
-            spec_padded,
-            spec_lengths,
-            wav_padded,
-            wav_lengths,
-            sid,
-            tone_padded,
-            language_padded,
-            bert_padded,
-            ja_bert_padded,
-        )
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        batch_size,
-        boundaries,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-    ):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        try:
-            for i in range(len(buckets) - 1, 0, -1):
-                if len(buckets[i]) == 0:
-                    buckets.pop(i)
-                    self.boundaries.pop(i + 1)
-            assert all(len(bucket) > 0 for bucket in buckets)
-        # When one bucket is not traversed
-        except Exception as e:
-            print("Bucket warning ", e)
-            for i in range(len(buckets) - 1, -1, -1):
-                if len(buckets[i]) == 0:
-                    buckets.pop(i)
-                    self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (
-                total_batch_size - (len_bucket % total_batch_size)
-            ) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            if len_bucket == 0:
-                continue
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = (
-                ids_bucket
-                + ids_bucket * (rem // len_bucket)
-                + ids_bucket[: (rem % len_bucket)]
-            )
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank :: self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [
-                    bucket[idx]
-                    for idx in ids_bucket[
-                        j * self.batch_size : (j + 1) * self.batch_size
-                    ]
-                ]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/fishaudio/fish-diffusion/configs/JSUT.py b/spaces/fishaudio/fish-diffusion/configs/JSUT.py
deleted file mode 100644
index e7ab4e2e48d4bf618f3487f91bb5848ff2dce9ac..0000000000000000000000000000000000000000
--- a/spaces/fishaudio/fish-diffusion/configs/JSUT.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
-    "./_base_/archs/hifi_svc.py",
-]
-
-speaker_mapping = {'jsut': 0,}
-
-model = dict(
-    type="HiFiSVC",
-    speaker_encoder=dict(
-        input_size=len(speaker_mapping),
-    ),
-)
-
-preprocessing = dict(
-    text_features_extractor=dict(
-        type="ContentVec",
-    ),
-    pitch_extractor=dict(
-        type="ParselMouthPitchExtractor",
-        keep_zeros=False,
-        f0_min=40.0,
-        f0_max=1600.0,
-    ),
-    energy_extractor=dict(
-        type="RMSEnergyExtractor",
-    ),
-    augmentations=[
-        dict(
-            type="RandomPitchShifting",
-            key_shifts=[-5., 5.],
-            probability=1.5,
-        ),
-        dict(
-            type="RandomTimeStretching",
-            factors=[0.8, 1.2],
-            probability=0.75,
-        )
-    ],
-)
\ No newline at end of file
diff --git a/spaces/flax-community/spanish-image-captioning/utils.py b/spaces/flax-community/spanish-image-captioning/utils.py
deleted file mode 100644
index e7eb5ca2ec403549a46efea4521e02c346200df0..0000000000000000000000000000000000000000
--- a/spaces/flax-community/spanish-image-captioning/utils.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from torchvision.io import read_image, ImageReadMode
-import torch
-import numpy as np
-from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
-from torchvision.transforms.functional import InterpolationMode
-from PIL import Image
-
-
-class Transform(torch.nn.Module):
-    def __init__(self, image_size):
-        super().__init__()
-        self.transforms = torch.nn.Sequential(
-            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
-            CenterCrop(image_size),
-            ConvertImageDtype(torch.float),
-            Normalize(
-                (0.48145466, 0.4578275, 0.40821073),
-                (0.26862954, 0.26130258, 0.27577711),
-            ),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        with torch.no_grad():
-            x = self.transforms(x)
-        return x
-
-
-transform = Transform(224)
-
-def get_transformed_image(image):
-    if image.shape[-1] == 3 and isinstance(image, np.ndarray):
-        image = image.transpose(2, 0, 1)
-        image = torch.tensor(image)
-    return transform(image).unsqueeze(0).permute(0, 2, 3, 1).numpy()
\ No newline at end of file
diff --git a/spaces/floriankrempl/mtg_rules_bot/tests/__init__.py b/spaces/floriankrempl/mtg_rules_bot/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/florim/MedGPT/tests/test_prompt_generator.py b/spaces/florim/MedGPT/tests/test_prompt_generator.py
deleted file mode 100644
index 6a0bfd6c7bbdbfaa3750e9dee621bd25e17a448b..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/tests/test_prompt_generator.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from unittest import TestCase
-
-from autogpt.promptgenerator import PromptGenerator
-
-
-class TestPromptGenerator(TestCase):
-    """
-    Test cases for the PromptGenerator class, which is responsible for generating
-    prompts for the AI with constraints, commands, resources, and performance evaluations.
-    """
-
-    @classmethod
-    def setUpClass(cls):
-        """
-        Set up the initial state for each test method by creating an instance of PromptGenerator.
-        """
-        cls.generator = PromptGenerator()
-
-    # Test whether the add_constraint() method adds a constraint to the generator's constraints list
-    def test_add_constraint(self):
-        """
-        Test if the add_constraint() method adds a constraint to the generator's constraints list.
-        """
-        constraint = "Constraint1"
-        self.generator.add_constraint(constraint)
-        self.assertIn(constraint, self.generator.constraints)
-
-    # Test whether the add_command() method adds a command to the generator's commands list
-    def test_add_command(self):
-        """
-        Test if the add_command() method adds a command to the generator's commands list.
-        """
-        command_label = "Command Label"
-        command_name = "command_name"
-        args = {"arg1": "value1", "arg2": "value2"}
-        self.generator.add_command(command_label, command_name, args)
-        command = {
-            "label": command_label,
-            "name": command_name,
-            "args": args,
-        }
-        self.assertIn(command, self.generator.commands)
-
-    def test_add_resource(self):
-        """
-        Test if the add_resource() method adds a resource to the generator's resources list.
-        """
-        resource = "Resource1"
-        self.generator.add_resource(resource)
-        self.assertIn(resource, self.generator.resources)
-
-    def test_add_performance_evaluation(self):
-        """
-        Test if the add_performance_evaluation() method adds an evaluation to the generator's
-        performance_evaluation list.
-        """
-        evaluation = "Evaluation1"
-        self.generator.add_performance_evaluation(evaluation)
-        self.assertIn(evaluation, self.generator.performance_evaluation)
-
-    def test_generate_prompt_string(self):
-        """
-        Test if the generate_prompt_string() method generates a prompt string with all the added
-        constraints, commands, resources, and evaluations.
-        """
-        # Define the test data
-        constraints = ["Constraint1", "Constraint2"]
-        commands = [
-            {
-                "label": "Command1",
-                "name": "command_name1",
-                "args": {"arg1": "value1"},
-            },
-            {
-                "label": "Command2",
-                "name": "command_name2",
-                "args": {},
-            },
-        ]
-        resources = ["Resource1", "Resource2"]
-        evaluations = ["Evaluation1", "Evaluation2"]
-
-        # Add test data to the generator
-        for constraint in constraints:
-            self.generator.add_constraint(constraint)
-        for command in commands:
-            self.generator.add_command(
-                command["label"], command["name"], command["args"]
-            )
-        for resource in resources:
-            self.generator.add_resource(resource)
-        for evaluation in evaluations:
-            self.generator.add_performance_evaluation(evaluation)
-
-        # Generate the prompt string and verify its correctness
-        prompt_string = self.generator.generate_prompt_string()
-        self.assertIsNotNone(prompt_string)
-
-        # Check if all constraints, commands, resources, and evaluations are present in the prompt string
-        for constraint in constraints:
-            self.assertIn(constraint, prompt_string)
-        for command in commands:
-            self.assertIn(command["name"], prompt_string)
-            for key, value in command["args"].items():
-                self.assertIn(f'"{key}": "{value}"', prompt_string)
-        for resource in resources:
-            self.assertIn(resource, prompt_string)
-        for evaluation in evaluations:
-            self.assertIn(evaluation, prompt_string)
-
-        self.assertIn("constraints", prompt_string.lower())
-        self.assertIn("commands", prompt_string.lower())
-        self.assertIn("resources", prompt_string.lower())
-        self.assertIn("performance evaluation", prompt_string.lower())
diff --git a/spaces/gary109/hotdog-not-hotdog/app.py b/spaces/gary109/hotdog-not-hotdog/app.py
deleted file mode 100644
index 3bf20a1281ee2fc125aa10e4f7132f61e9504eca..0000000000000000000000000000000000000000
--- a/spaces/gary109/hotdog-not-hotdog/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-
-def predict(image):
-    predictions = pipeline(image)
-    return {p["label"]: p["score"] for p in predictions}
-
-gr.Interface(
-    predict,
-    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
-    outputs=gr.outputs.Label(num_top_classes=2),
-    title="是不是熱狗?",
-    examples=[["./imgs/hotdog1.jpg"]]
-).launch()
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/__init__.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/__init__.py
deleted file mode 100644
index f7cc4b23413a0639e9de00eeb0bf600632d2c6cd..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .class_names import get_classes, get_palette
-from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
-
-__all__ = [
-    'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
-    'eval_metrics', 'get_classes', 'get_palette'
-]
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py
deleted file mode 100644
index 16817400b4102899794fe64c9644713a4e54e2f9..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import logging
-
-import annotator.uniformer.mmcv as mmcv
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init
-from annotator.uniformer.mmcv.cnn.bricks import Conv2dAdaptivePadding
-from annotator.uniformer.mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from ..builder import BACKBONES
-from ..utils import InvertedResidualV3 as InvertedResidual
-
-
-@BACKBONES.register_module()
-class MobileNetV3(nn.Module):
-    """MobileNetV3 backbone.
-
-    This backbone is the improved implementation of `Searching for MobileNetV3
-    <https://ieeexplore.ieee.org/document/9008835>`_.
-
-    Args:
-        arch (str): Architecture of mobilnetv3, from {'small', 'large'}.
-            Default: 'small'.
-        conv_cfg (dict): Config dict for convolution layer.
-            Default: None, which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='BN').
-        out_indices (tuple[int]): Output from which layer.
-            Default: (0, 1, 12).
-        frozen_stages (int): Stages to be frozen (all param fixed).
-            Default: -1, which means not freezing any parameters.
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only. Default: False.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save
-            some memory while slowing down the training speed.
-            Default: False.
-    """
-    # Parameters to build each block:
-    #     [kernel size, mid channels, out channels, with_se, act type, stride]
-    arch_settings = {
-        'small': [[3, 16, 16, True, 'ReLU', 2],  # block0 layer1 os=4
-                  [3, 72, 24, False, 'ReLU', 2],  # block1 layer2 os=8
-                  [3, 88, 24, False, 'ReLU', 1],
-                  [5, 96, 40, True, 'HSwish', 2],  # block2 layer4 os=16
-                  [5, 240, 40, True, 'HSwish', 1],
-                  [5, 240, 40, True, 'HSwish', 1],
-                  [5, 120, 48, True, 'HSwish', 1],  # block3 layer7 os=16
-                  [5, 144, 48, True, 'HSwish', 1],
-                  [5, 288, 96, True, 'HSwish', 2],  # block4 layer9 os=32
-                  [5, 576, 96, True, 'HSwish', 1],
-                  [5, 576, 96, True, 'HSwish', 1]],
-        'large': [[3, 16, 16, False, 'ReLU', 1],  # block0 layer1 os=2
-                  [3, 64, 24, False, 'ReLU', 2],  # block1 layer2 os=4
-                  [3, 72, 24, False, 'ReLU', 1],
-                  [5, 72, 40, True, 'ReLU', 2],  # block2 layer4 os=8
-                  [5, 120, 40, True, 'ReLU', 1],
-                  [5, 120, 40, True, 'ReLU', 1],
-                  [3, 240, 80, False, 'HSwish', 2],  # block3 layer7 os=16
-                  [3, 200, 80, False, 'HSwish', 1],
-                  [3, 184, 80, False, 'HSwish', 1],
-                  [3, 184, 80, False, 'HSwish', 1],
-                  [3, 480, 112, True, 'HSwish', 1],  # block4 layer11 os=16
-                  [3, 672, 112, True, 'HSwish', 1],
-                  [5, 672, 160, True, 'HSwish', 2],  # block5 layer13 os=32
-                  [5, 960, 160, True, 'HSwish', 1],
-                  [5, 960, 160, True, 'HSwish', 1]]
-    }  # yapf: disable
-
-    def __init__(self,
-                 arch='small',
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 out_indices=(0, 1, 12),
-                 frozen_stages=-1,
-                 reduction_factor=1,
-                 norm_eval=False,
-                 with_cp=False):
-        super(MobileNetV3, self).__init__()
-        assert arch in self.arch_settings
-        assert isinstance(reduction_factor, int) and reduction_factor > 0
-        assert mmcv.is_tuple_of(out_indices, int)
-        for index in out_indices:
-            if index not in range(0, len(self.arch_settings[arch]) + 2):
-                raise ValueError(
-                    'the item in out_indices must in '
-                    f'range(0, {len(self.arch_settings[arch])+2}). '
-                    f'But received {index}')
-
-        if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
-            raise ValueError('frozen_stages must be in range(-1, '
-                             f'{len(self.arch_settings[arch])+2}). '
-                             f'But received {frozen_stages}')
-        self.arch = arch
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.reduction_factor = reduction_factor
-        self.norm_eval = norm_eval
-        self.with_cp = with_cp
-        self.layers = self._make_layer()
-
-    def _make_layer(self):
-        layers = []
-
-        # build the first layer (layer0)
-        in_channels = 16
-        layer = ConvModule(
-            in_channels=3,
-            out_channels=in_channels,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            conv_cfg=dict(type='Conv2dAdaptivePadding'),
-            norm_cfg=self.norm_cfg,
-            act_cfg=dict(type='HSwish'))
-        self.add_module('layer0', layer)
-        layers.append('layer0')
-
-        layer_setting = self.arch_settings[self.arch]
-        for i, params in enumerate(layer_setting):
-            (kernel_size, mid_channels, out_channels, with_se, act,
-             stride) = params
-
-            if self.arch == 'large' and i >= 12 or self.arch == 'small' and \
-                    i >= 8:
-                mid_channels = mid_channels // self.reduction_factor
-                out_channels = out_channels // self.reduction_factor
-
-            if with_se:
-                se_cfg = dict(
-                    channels=mid_channels,
-                    ratio=4,
-                    act_cfg=(dict(type='ReLU'),
-                             dict(type='HSigmoid', bias=3.0, divisor=6.0)))
-            else:
-                se_cfg = None
-
-            layer = InvertedResidual(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                mid_channels=mid_channels,
-                kernel_size=kernel_size,
-                stride=stride,
-                se_cfg=se_cfg,
-                with_expand_conv=(in_channels != mid_channels),
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=dict(type=act),
-                with_cp=self.with_cp)
-            in_channels = out_channels
-            layer_name = 'layer{}'.format(i + 1)
-            self.add_module(layer_name, layer)
-            layers.append(layer_name)
-
-        # build the last layer
-        # block5 layer12 os=32 for small model
-        # block6 layer16 os=32 for large model
-        layer = ConvModule(
-            in_channels=in_channels,
-            out_channels=576 if self.arch == 'small' else 960,
-            kernel_size=1,
-            stride=1,
-            dilation=4,
-            padding=0,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=dict(type='HSwish'))
-        layer_name = 'layer{}'.format(len(layer_setting) + 1)
-        self.add_module(layer_name, layer)
-        layers.append(layer_name)
-
-        # next, convert backbone MobileNetV3 to a semantic segmentation version
-        if self.arch == 'small':
-            self.layer4.depthwise_conv.conv.stride = (1, 1)
-            self.layer9.depthwise_conv.conv.stride = (1, 1)
-            for i in range(4, len(layers)):
-                layer = getattr(self, layers[i])
-                if isinstance(layer, InvertedResidual):
-                    modified_module = layer.depthwise_conv.conv
-                else:
-                    modified_module = layer.conv
-
-                if i < 9:
-                    modified_module.dilation = (2, 2)
-                    pad = 2
-                else:
-                    modified_module.dilation = (4, 4)
-                    pad = 4
-
-                if not isinstance(modified_module, Conv2dAdaptivePadding):
-                    # Adjust padding
-                    pad *= (modified_module.kernel_size[0] - 1) // 2
-                    modified_module.padding = (pad, pad)
-        else:
-            self.layer7.depthwise_conv.conv.stride = (1, 1)
-            self.layer13.depthwise_conv.conv.stride = (1, 1)
-            for i in range(7, len(layers)):
-                layer = getattr(self, layers[i])
-                if isinstance(layer, InvertedResidual):
-                    modified_module = layer.depthwise_conv.conv
-                else:
-                    modified_module = layer.conv
-
-                if i < 13:
-                    modified_module.dilation = (2, 2)
-                    pad = 2
-                else:
-                    modified_module.dilation = (4, 4)
-                    pad = 4
-
-                if not isinstance(modified_module, Conv2dAdaptivePadding):
-                    # Adjust padding
-                    pad *= (modified_module.kernel_size[0] - 1) // 2
-                    modified_module.padding = (pad, pad)
-
-        return layers
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, nn.BatchNorm2d):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        outs = []
-        for i, layer_name in enumerate(self.layers):
-            layer = getattr(self, layer_name)
-            x = layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-        return outs
-
-    def _freeze_stages(self):
-        for i in range(self.frozen_stages + 1):
-            layer = getattr(self, f'layer{i}')
-            layer.eval()
-            for param in layer.parameters():
-                param.requires_grad = False
-
-    def train(self, mode=True):
-        super(MobileNetV3, self).train(mode)
-        self._freeze_stages()
-        if mode and self.norm_eval:
-            for m in self.modules():
-                if isinstance(m, _BatchNorm):
-                    m.eval()
diff --git a/spaces/gojiteji/NAGISystem/app.py b/spaces/gojiteji/NAGISystem/app.py
deleted file mode 100644
index 8d5b4bcb56739aba1e577733da788f3c53f94c23..0000000000000000000000000000000000000000
--- a/spaces/gojiteji/NAGISystem/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModelForSeq2SeqLM, AutoModelForCausalLM
-
-BERTTokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese")
-BERTModel = AutoModelForMaskedLM.from_pretrained("cl-tohoku/bert-base-japanese")
-
-mBERTTokenizer = AutoTokenizer.from_pretrained("bert-base-multilingual-cased")
-mBERTModel = AutoModelForMaskedLM.from_pretrained("bert-base-multilingual-cased")
-
-GPT2Tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium")
-GPT2Model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium")
-
-votes=[]
-def MELCHIOR(sue):#BERT
-    allow=BERTTokenizer("承認").input_ids[1]
-    deny=BERTTokenizer("否定").input_ids[1]
-    output=BERTModel(**BERTTokenizer('MELCHIORは科学者としての人格を持っています。人間とMELCHIORの対話です。人間「'+sue+'。承認 か 否定 のどちらかで答えてください。」'+"MELCHIOR 「[MASK]」",return_tensors="pt")).logits
-    BERTTokenizer.batch_decode(torch.argmax(output,-1))
-    mask=output[0,-3,:]
-    votes.append(1 if mask[allow]>mask[deny] else -1)
-    return "承認"  if mask[allow]>mask[deny] else "否定"
-
-def BALTHASAR(sue):#mT5
-    allow=mBERTTokenizer("Yes").input_ids[1]
-    deny=mBERTTokenizer("No").input_ids[1]
-    output=mBERTModel(**mBERTTokenizer('BALTHASARは母としての人格を持っています。人間とBALTHASARの対話です。人間「'+sue+'。YesかNoか。」'+"BALTHASAR 「[MASK]」",return_tensors="pt")).logits
-    mask=output[0,-3,:]
-    votes.append(1 if mask[allow]>mask[deny] else -1)
-    return "承認"  if mask[allow]>mask[deny] else "否定"
-
-
-def CASPER(sue):#GPT2
-    allow=GPT2Tokenizer("承認").input_ids[1]
-    deny=GPT2Tokenizer("否定").input_ids[1]
-    inpt=GPT2Tokenizer('女としての人格を持ったAI・カスパーと人間の対話です。人間「'+sue+'。これに承認か否定か。」'+"カスパー「私は，",return_tensors="pt")
-    probs=GPT2Model(input_ids=inpt.input_ids[:,:-1],attention_mask=inpt.attention_mask[:,:-1]).logits[0]
-    i=-1
-    p_answer=probs
-    id=torch.argmax(probs[i])
-    votes.append(1 if probs[i][allow]>probs[i][deny] else -1)
-    return "承認" if probs[i][allow]>probs[i][deny] else "否定"
-
-
-def greet(sue):
-    text1="BERT-1"+MELCHIOR(sue)
-    text2="GPT-2"+CASPER(sue)
-    text3="mBERT-3"+BALTHASAR(sue)
-    return text1+" "+text2+" "+text3+"\n＿＿＿\n\n"+("|可決|" if sum(votes[-3:])>0 else "| 否決 |")+"\n＿＿＿"
-
-
-css="@import url('https://fonts.googleapis.com/css2?family=Shippori+Mincho:wght@800&display=swap');  .gradio-container {background-color: black} .gr-button {background-color: blue;color:black; weight:200%;font-family:'Shippori Mincho', serif;}"
-css+=".block{color:orange;} ::placeholder {font-size:35%} .gr-box {text-align: center;font-size: 125%;border-color:orange;background-color: #000000;weight:200%;font-family:'Shippori Mincho', serif;}:disabled {color: orange;opacity:1.0;}"
-with gr.Blocks(css=css) as demo:
-    sue = gr.Textbox(label="NAGI System",placeholder="決議を入力(多数決)")
-    greet_btn = gr.Button("提訴")
-    output = gr.Textbox(label="決議",placeholder="本システムは事前学習モデルのpromptにより行われています．決議結果に対して当サービス開発者は一切の責任を負いません．")
-    greet_btn.click(fn=greet, inputs=sue, outputs=output)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/gordonchan/h2oo/prompter.py b/spaces/gordonchan/h2oo/prompter.py
deleted file mode 100644
index 31d4d89f816a0da0fcbd9cab56d69b40254f8401..0000000000000000000000000000000000000000
--- a/spaces/gordonchan/h2oo/prompter.py
+++ /dev/null
@@ -1,871 +0,0 @@
-import os
-import ast
-import time
-from enums import PromptType  # also supports imports from this file from other files
-
-non_hf_types = ['gpt4all_llama', 'llama', 'gptj']
-
-prompt_type_to_model_name = {
-    'plain': [
-        'EleutherAI/gpt-j-6B',
-        'EleutherAI/pythia-6.9b',
-        'EleutherAI/pythia-12b',
-        'EleutherAI/pythia-12b-deduped',
-        'EleutherAI/gpt-neox-20b',
-        'openlm-research/open_llama_7b_700bt_preview',
-        'decapoda-research/llama-7b-hf',
-        'decapoda-research/llama-13b-hf',
-        'decapoda-research/llama-30b-hf',
-        'decapoda-research/llama-65b-hf',
-        'facebook/mbart-large-50-many-to-many-mmt',
-        'philschmid/bart-large-cnn-samsum',
-        'philschmid/flan-t5-base-samsum',
-        'gpt2',
-        'distilgpt2',
-        'mosaicml/mpt-7b-storywriter',
-    ],
-    'gptj': ['gptj', 'gpt4all_llama'],
-    'prompt_answer': [
-        'h2oai/h2ogpt-gm-oasst1-en-1024-20b',
-        'h2oai/h2ogpt-gm-oasst1-en-1024-12b',
-        'h2oai/h2ogpt-gm-oasst1-multilang-1024-20b',
-        'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b',
-        'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b-v2',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2',
-        'h2oai/h2ogpt-gm-oasst1-en-xgen-7b-8k',
-        'h2oai/h2ogpt-gm-oasst1-multilang-xgen-7b-8k',
-        'TheBloke/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GPTQ',
-    ],
-    'prompt_answer_openllama': [
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
-    ],
-    'instruct': ['TheBloke/llama-30b-supercot-SuperHOT-8K-fp16'],  # https://huggingface.co/TheBloke/llama-30b-supercot-SuperHOT-8K-fp16#prompting
-    'instruct_with_end': ['databricks/dolly-v2-12b'],
-    'quality': [],
-    'human_bot': [
-        'h2oai/h2ogpt-oasst1-512-12b',
-        'h2oai/h2ogpt-oasst1-512-20b',
-        'h2oai/h2ogpt-oig-oasst1-256-6_9b',
-        'h2oai/h2ogpt-oig-oasst1-512-6_9b',
-        'h2oai/h2ogpt-oig-oasst1-256-6.9b',  # legacy
-        'h2oai/h2ogpt-oig-oasst1-512-6.9b',  # legacy
-        'h2oai/h2ogpt-research-oasst1-512-30b',
-        'h2oai/h2ogpt-research-oasst1-llama-65b',
-        'h2oai/h2ogpt-oasst1-falcon-40b',
-        'h2oai/h2ogpt-oig-oasst1-falcon-40b',
-    ],
-    'dai_faq': [],
-    'summarize': [],
-    'simple_instruct': ['t5-small', 't5-large', 'google/flan-t5', 'google/flan-t5-xxl', 'google/flan-ul2'],
-    'instruct_vicuna': ['AlekseyKorshuk/vicuna-7b', 'TheBloke/stable-vicuna-13B-HF', 'junelee/wizard-vicuna-13b'],
-    'human_bot_orig': ['togethercomputer/GPT-NeoXT-Chat-Base-20B'],
-    "open_assistant": ['OpenAssistant/oasst-sft-7-llama-30b-xor', 'oasst-sft-7-llama-30b'],
-    "wizard_lm": ['ehartford/WizardLM-7B-Uncensored', 'ehartford/WizardLM-13B-Uncensored'],
-    "wizard_mega": ['openaccess-ai-collective/wizard-mega-13b'],
-    "instruct_simple": ['JosephusCheung/Guanaco'],
-    "wizard_vicuna": ['ehartford/Wizard-Vicuna-13B-Uncensored'],
-    "wizard2": ['llama'],
-    "mptinstruct": ['mosaicml/mpt-30b-instruct', 'mosaicml/mpt-7b-instruct', 'mosaicml/mpt-30b-instruct'],
-    "mptchat": ['mosaicml/mpt-7b-chat', 'mosaicml/mpt-30b-chat', 'TheBloke/mpt-30B-chat-GGML'],
-    "vicuna11": ['lmsys/vicuna-33b-v1.3'],
-    "falcon": ['tiiuae/falcon-40b-instruct', 'tiiuae/falcon-40b', 'tiiuae/falcon-7b-instruct', 'tiiuae/falcon-7b'],
-    "llama2": [
-        'meta-llama/Llama-2-7b-chat-hf',
-        'meta-llama/Llama-2-13b-chat-hf',
-        'meta-llama/Llama-2-34b-chat-hf',
-        'meta-llama/Llama-2-70b-chat-hf',
-    ],
-    # could be plain, but default is correct prompt_type for default TheBloke model ggml-wizardLM-7B.q4_2.bin
-}
-if os.getenv('OPENAI_API_KEY'):
-    prompt_type_to_model_name.update({
-        "openai": ["text-davinci-003", "text-curie-001", "text-babbage-001", "text-ada-001"],
-        "openai_chat": ["gpt-3.5-turbo", "gpt-3.5-turbo-16k"],
-    })
-
-inv_prompt_type_to_model_name = {v.strip(): k for k, l in prompt_type_to_model_name.items() for v in l}
-inv_prompt_type_to_model_lower = {v.strip().lower(): k for k, l in prompt_type_to_model_name.items() for v in l}
-
-prompt_types_strings = []
-for p in PromptType:
-    prompt_types_strings.extend([p.name])
-
-prompt_types = []
-for p in PromptType:
-    prompt_types.extend([p.name, p.value, str(p.value)])
-
-
-def get_prompt(prompt_type, prompt_dict, chat, context, reduced, making_context, return_dict=False):
-    prompt_dict_error = ''
-    generates_leading_space = False
-
-    if prompt_type == PromptType.custom.name and not isinstance(prompt_dict, dict):
-        try:
-            prompt_dict = ast.literal_eval(prompt_dict)
-        except BaseException as e:
-            prompt_dict_error = str(e)
-    if prompt_dict_error:
-        promptA = None
-        promptB = None
-        PreInstruct = None
-        PreInput = ''
-        PreResponse = ''
-        terminate_response = None
-        chat_sep = ''
-        chat_turn_sep = ''
-        humanstr = ''
-        botstr = ''
-        generates_leading_space = False
-    elif prompt_type in [PromptType.custom.value, str(PromptType.custom.value),
-                         PromptType.custom.name]:
-        promptA = prompt_dict.get('promptA', '')
-        promptB = prompt_dict.get('promptB', '')
-        PreInstruct = prompt_dict.get('PreInstruct', '')
-        PreInput = prompt_dict.get('PreInput', '')
-        PreResponse = prompt_dict.get('PreResponse', '')
-        terminate_response = prompt_dict.get('terminate_response', None)
-        chat_sep = prompt_dict.get('chat_sep', '\n')
-        chat_turn_sep = prompt_dict.get('chat_turn_sep', '\n')
-        humanstr = prompt_dict.get('humanstr', '')
-        botstr = prompt_dict.get('botstr', '')
-    elif prompt_type in [PromptType.plain.value, str(PromptType.plain.value),
-                         PromptType.plain.name]:
-        promptA = promptB = PreInstruct = PreInput = PreResponse = None
-        terminate_response = []
-        chat_turn_sep = chat_sep = ''
-        # plain should have None for human/bot, so nothing truncated out, not '' that would truncate after first token
-        humanstr = None
-        botstr = None
-    elif prompt_type == 'simple_instruct':
-        promptA = promptB = PreInstruct = PreInput = PreResponse = None
-        terminate_response = []
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = None
-        botstr = None
-    elif prompt_type in [PromptType.instruct.value, str(PromptType.instruct.value),
-                         PromptType.instruct.name] + [PromptType.instruct_with_end.value,
-                                                      str(PromptType.instruct_with_end.value),
-                                                      PromptType.instruct_with_end.name]:
-        promptA = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n' if not (
-                chat and reduced) else ''
-        promptB = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n' if not (
-                chat and reduced) else ''
-
-        PreInstruct = """
-### Instruction:
-"""
-
-        PreInput = """
-### Input:
-"""
-
-        PreResponse = """
-### Response:
-"""
-        if prompt_type in [PromptType.instruct_with_end.value, str(PromptType.instruct_with_end.value),
-                           PromptType.instruct_with_end.name]:
-            terminate_response = ['### End']
-        else:
-            terminate_response = None
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.quality.value, str(PromptType.quality.value),
-                         PromptType.quality.name]:
-        promptA = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction as applied on the Input.\n' if not (
-                chat and reduced) else ''
-        promptB = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction.\n' if not (
-                chat and reduced) else ''
-
-        PreInstruct = """
-### Instruction:
-"""
-
-        PreInput = """
-### Input:
-"""
-
-        PreResponse = """
-### Response:
-"""
-        terminate_response = None
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct  # first thing human says
-        botstr = PreResponse  # first thing bot says
-    elif prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
-                         PromptType.human_bot.name] + [PromptType.human_bot_orig.value,
-                                                       str(PromptType.human_bot_orig.value),
-                                                       PromptType.human_bot_orig.name]:
-        human = '<human>:'
-        bot = "<bot>:"
-        if reduced or context or prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
-                                                 PromptType.human_bot.name]:
-            preprompt = ''
-        else:
-            cur_date = time.strftime('%Y-%m-%d')
-            cur_time = time.strftime('%H:%M:%S %p %Z')
-
-            PRE_PROMPT = """\
-Current Date: {}
-Current Time: {}
-
-"""
-            preprompt = PRE_PROMPT.format(cur_date, cur_time)
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-
-        PreInstruct = human + ' '
-
-        PreInput = None
-
-        if making_context:
-            # when making context, want it to appear as-if LLM generated, which starts with space after :
-            PreResponse = bot + ' '
-        else:
-            # normally LLM adds space after this, because was how trained.
-            # if add space here, non-unique tokenization will often make LLM produce wrong output
-            PreResponse = bot
-
-        terminate_response = ['\n' + human, '\n' + bot, human, bot, PreResponse]
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = human  # tag before human talks
-        botstr = bot  # tag before bot talks
-        generates_leading_space = True
-    elif prompt_type in [PromptType.dai_faq.value, str(PromptType.dai_faq.value),
-                         PromptType.dai_faq.name]:
-        promptA = ''
-        promptB = 'Answer the following Driverless AI question.\n'
-
-        PreInstruct = """
-### Driverless AI frequently asked question:
-"""
-
-        PreInput = None
-
-        PreResponse = """
-### Driverless AI documentation answer:
-"""
-        terminate_response = ['\n\n']
-        chat_turn_sep = chat_sep = terminate_response
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.summarize.value, str(PromptType.summarize.value),
-                         PromptType.summarize.name]:
-        promptA = promptB = PreInput = ''
-        PreInstruct = '## Main Text\n\n'
-        PreResponse = '\n\n## Summary\n\n'
-        terminate_response = None
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.instruct_vicuna.value, str(PromptType.instruct_vicuna.value),
-                         PromptType.instruct_vicuna.name]:
-        promptA = promptB = "A chat between a curious human and an artificial intelligence assistant. " \
-                            "The assistant gives helpful, detailed, and polite answers to the human's questions." if not (
-                chat and reduced) else ''
-
-        PreInstruct = """
-### Human:
-"""
-
-        PreInput = None
-
-        PreResponse = """
-### Assistant:
-"""
-        terminate_response = [
-            '### Human:']  # but only allow terminate after prompt is found correctly, else can't terminate
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.prompt_answer.value, str(PromptType.prompt_answer.value),
-                         PromptType.prompt_answer.name]:
-        preprompt = ''
-        prompt_tokens = "<|prompt|>"
-        answer_tokens = "<|answer|>"
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = prompt_tokens
-        PreInput = None
-        PreResponse = answer_tokens
-        eos = '<|endoftext|>'  # neox eos
-        humanstr = prompt_tokens
-        botstr = answer_tokens
-        terminate_response = [humanstr, PreResponse, eos]
-        chat_sep = eos
-        chat_turn_sep = eos
-    elif prompt_type in [PromptType.prompt_answer_openllama.value, str(PromptType.prompt_answer_openllama.value),
-                         PromptType.prompt_answer_openllama.name]:
-        preprompt = ''
-        prompt_tokens = "<|prompt|>"
-        answer_tokens = "<|answer|>"
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = prompt_tokens
-        PreInput = None
-        PreResponse = answer_tokens
-        eos = '</s>'  # llama eos
-        humanstr = prompt_tokens
-        botstr = answer_tokens
-        terminate_response = [humanstr, PreResponse, eos]
-        chat_sep = eos
-        chat_turn_sep = eos
-    elif prompt_type in [PromptType.open_assistant.value, str(PromptType.open_assistant.value),
-                         PromptType.open_assistant.name]:
-        # From added_tokens.json
-        preprompt = ''
-        prompt_tokens = "<|prompter|>"
-        answer_tokens = "<|assistant|>"
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = prompt_tokens
-        PreInput = None
-        PreResponse = answer_tokens
-        pend = "<|prefix_end|>"
-        eos = "</s>"
-        humanstr = prompt_tokens
-        botstr = answer_tokens
-        terminate_response = [humanstr, PreResponse, pend, eos]
-        chat_turn_sep = chat_sep = eos
-    elif prompt_type in [PromptType.wizard_lm.value, str(PromptType.wizard_lm.value),
-                         PromptType.wizard_lm.name]:
-        # https://github.com/ehartford/WizardLM/blob/main/src/train_freeform.py
-        preprompt = ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = ""
-        PreInput = None
-        PreResponse = "\n\n### Response\n"
-        eos = "</s>"
-        terminate_response = [PreResponse, eos]
-        chat_turn_sep = chat_sep = eos
-        humanstr = promptA
-        botstr = PreResponse
-    elif prompt_type in [PromptType.wizard_mega.value, str(PromptType.wizard_mega.value),
-                         PromptType.wizard_mega.name]:
-        preprompt = ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = """
-### Instruction:
-"""
-        PreInput = None
-        PreResponse = """
-### Assistant:
-"""
-        terminate_response = [PreResponse]
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.instruct_vicuna2.value, str(PromptType.instruct_vicuna2.value),
-                         PromptType.instruct_vicuna2.name]:
-        promptA = promptB = "" if not (chat and reduced) else ''
-
-        PreInstruct = """
-HUMAN:
-"""
-
-        PreInput = None
-
-        PreResponse = """
-ASSISTANT:
-"""
-        terminate_response = [
-            'HUMAN:']  # but only allow terminate after prompt is found correctly, else can't terminate
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.instruct_vicuna3.value, str(PromptType.instruct_vicuna3.value),
-                         PromptType.instruct_vicuna3.name]:
-        promptA = promptB = "" if not (chat and reduced) else ''
-
-        PreInstruct = """
-### User:
-"""
-
-        PreInput = None
-
-        PreResponse = """
-### Assistant:
-"""
-        terminate_response = [
-            '### User:']  # but only allow terminate after prompt is found correctly, else can't terminate
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.wizard2.value, str(PromptType.wizard2.value),
-                         PromptType.wizard2.name]:
-        # https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML
-        preprompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.""" if not (
-                chat and reduced) else ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = """
-### Instruction:
-"""
-        PreInput = None
-        PreResponse = """
-### Response:
-"""
-        terminate_response = [PreResponse]
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.wizard3.value, str(PromptType.wizard3.value),
-                         PromptType.wizard3.name]:
-        # https://huggingface.co/TheBloke/wizardLM-13B-1.0-GGML
-        preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""" if not (
-                chat and reduced) else ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = """USER: """
-        PreInput = None
-        PreResponse = """ASSISTANT: """
-        terminate_response = [PreResponse]
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.wizard_vicuna.value, str(PromptType.wizard_vicuna.value),
-                         PromptType.wizard_vicuna.name]:
-        preprompt = ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = """USER: """
-        PreInput = None
-        PreResponse = """ASSISTANT: """
-        terminate_response = [PreResponse]
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-
-    elif prompt_type in [PromptType.instruct_simple.value, str(PromptType.instruct_simple.value),
-                         PromptType.instruct_simple.name]:
-        promptB = promptA = '' if not (chat and reduced) else ''
-
-        PreInstruct = """
-### Instruction:
-"""
-
-        PreInput = """
-### Input:
-"""
-
-        PreResponse = """
-### Response:
-"""
-        terminate_response = None
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.openai.value, str(PromptType.openai.value),
-                         PromptType.openai.name]:
-        preprompt = """The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.""" if not (
-                chat and reduced) else ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = "\nHuman: "
-        PreInput = None
-        PreResponse = "\nAI:"
-        terminate_response = [PreResponse] + [" Human:", " AI:"]
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.gptj.value, str(PromptType.gptj.value),
-                         PromptType.gptj.name]:
-        preprompt = "### Instruction:\n The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response." if not (
-                chat and reduced) else ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = "\n### Prompt: "
-        PreInput = None
-        PreResponse = "\n### Response: "
-        terminate_response = [PreResponse] + ["Prompt:", "Response:"]
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.openai_chat.value, str(PromptType.openai_chat.value),
-                         PromptType.openai_chat.name]:
-        # prompting and termination all handled by endpoint
-        preprompt = """"""
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        PreInstruct = ""
-        PreInput = None
-        PreResponse = ""
-        terminate_response = []
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = None
-        botstr = None
-    elif prompt_type in [PromptType.vicuna11.value, str(PromptType.vicuna11.value),
-                         PromptType.vicuna11.name]:
-        preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. """ if not (
-                chat and reduced) else ''
-        start = ''
-        promptB = promptA = '%s%s' % (preprompt, start)
-        eos = '</s>'
-        PreInstruct = """USER: """
-        PreInput = None
-        PreResponse = """ASSISTANT:"""
-        terminate_response = [PreResponse]
-        chat_sep = ' '
-        chat_turn_sep = eos
-        humanstr = PreInstruct
-        botstr = PreResponse
-
-        if making_context:
-            # when making context, want it to appear as-if LLM generated, which starts with space after :
-            PreResponse = PreResponse + ' '
-        else:
-            # normally LLM adds space after this, because was how trained.
-            # if add space here, non-unique tokenization will often make LLM produce wrong output
-            PreResponse = PreResponse
-    elif prompt_type in [PromptType.mptinstruct.value, str(PromptType.mptinstruct.value),
-                         PromptType.mptinstruct.name]:
-        # https://huggingface.co/mosaicml/mpt-30b-instruct#formatting
-        promptA = promptB = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n' if not (
-                chat and reduced) else ''
-
-        PreInstruct = """
-### Instruction
-"""
-
-        PreInput = """
-### Input
-"""
-
-        PreResponse = """
-### Response
-"""
-        terminate_response = None
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.mptchat.value, str(PromptType.mptchat.value),
-                         PromptType.mptchat.name]:
-        # https://huggingface.co/TheBloke/mpt-30B-chat-GGML#prompt-template
-        promptA = promptB = """<|im_start|>system\nA conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.\n<|im_end|>""" if not (
-                chat and reduced) else ''
-
-        PreInstruct = """<|im_start|>user
-"""
-
-        PreInput = None
-
-        PreResponse = """<|im_end|><|im_start|>assistant
-"""
-        terminate_response = ['<|im_end|>']
-        chat_sep = ''
-        chat_turn_sep = '<|im_end|>'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.falcon.value, str(PromptType.falcon.value),
-                         PromptType.falcon.name]:
-        promptA = promptB = "" if not (chat and reduced) else ''
-
-        PreInstruct = """User: """
-
-        PreInput = None
-
-        PreResponse = """Assistant:"""
-        terminate_response = ['\nUser', "<|endoftext|>"]
-        chat_sep = '\n\n'
-        chat_turn_sep = '\n\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-        if making_context:
-            # when making context, want it to appear as-if LLM generated, which starts with space after :
-            PreResponse = 'Assistant: '
-        else:
-            # normally LLM adds space after this, because was how trained.
-            # if add space here, non-unique tokenization will often make LLM produce wrong output
-            PreResponse = PreResponse
-        # generates_leading_space = True
-    elif prompt_type in [PromptType.guanaco.value, str(PromptType.guanaco.value),
-                         PromptType.guanaco.name]:
-        # https://huggingface.co/TheBloke/guanaco-65B-GPTQ
-        promptA = promptB = "" if not (chat and reduced) else ''
-
-        PreInstruct = """### Human: """
-
-        PreInput = None
-
-        PreResponse = """### Assistant:"""
-        terminate_response = ['### Human:']  # but only allow terminate after prompt is found correctly, else can't terminate
-        chat_turn_sep = chat_sep = '\n'
-        humanstr = PreInstruct
-        botstr = PreResponse
-    elif prompt_type in [PromptType.llama2.value, str(PromptType.llama2.value),
-                         PromptType.llama2.name]:
-        PreInstruct = ""
-        llama2_sys = "<<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
-        prompt = "<s>[INST] "
-        enable_sys = False  # too much safety, hurts accuracy
-        if not (chat and reduced):
-            if enable_sys:
-                promptA = promptB = prompt + llama2_sys
-            else:
-                promptA = promptB = prompt
-        else:
-            promptA = promptB = ''
-        PreInput = None
-        PreResponse = ""
-        terminate_response = ["[INST]", "</s>"]
-        chat_sep = ' [/INST]'
-        chat_turn_sep = ' </s><s>[INST] '
-        humanstr = PreInstruct
-        botstr = PreResponse
-        if making_context:
-            PreResponse += " "
-    else:
-        raise RuntimeError("No such prompt_type=%s" % prompt_type)
-
-    if isinstance(terminate_response, (tuple, list)):
-        assert '' not in terminate_response, "Bad terminate_response"
-
-    ret_dict = dict(promptA=promptA, promptB=promptB, PreInstruct=PreInstruct, PreInput=PreInput,
-                    PreResponse=PreResponse, terminate_response=terminate_response, chat_sep=chat_sep,
-                    chat_turn_sep=chat_turn_sep,
-                    humanstr=humanstr, botstr=botstr,
-                    generates_leading_space=generates_leading_space)
-
-    if return_dict:
-        return ret_dict, prompt_dict_error
-    else:
-        return tuple(list(ret_dict.values()))
-
-
-def generate_prompt(data_point, prompt_type, prompt_dict, chat, reduced, making_context):
-    context = data_point.get('context')
-    if context is None:
-        context = ''
-    instruction = data_point.get('instruction')
-    input = data_point.get('input')
-    output = data_point.get('output')
-    prompt_type = data_point.get('prompt_type', prompt_type)
-    prompt_dict = data_point.get('prompt_dict', prompt_dict)
-    assert prompt_type in prompt_types, "Bad prompt type: %s" % prompt_type
-    promptA, promptB, PreInstruct, PreInput, PreResponse, \
-        terminate_response, chat_sep, chat_turn_sep, humanstr, botstr, \
-        generates_leading_space = get_prompt(prompt_type, prompt_dict, chat,
-                                             context, reduced, making_context)
-
-    # could avoid if reduce=True, but too complex for parent functions to handle
-    prompt = context
-
-    if input and promptA:
-        prompt += f"""{promptA}"""
-    elif promptB:
-        prompt += f"""{promptB}"""
-
-    if instruction and PreInstruct is not None and input and PreInput is not None:
-        prompt += f"""{PreInstruct}{instruction}{PreInput}{input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif instruction and input and PreInstruct is None and PreInput is not None:
-        prompt += f"""{PreInput}{instruction}
-{input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif input and instruction and PreInput is None and PreInstruct is not None:
-        prompt += f"""{PreInstruct}{instruction}
-{input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif instruction and PreInstruct is not None:
-        prompt += f"""{PreInstruct}{instruction}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif input and PreInput is not None:
-        prompt += f"""{PreInput}{input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif input and instruction and PreInput is not None:
-        prompt += f"""{PreInput}{instruction}{input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif input and instruction and PreInstruct is not None:
-        prompt += f"""{PreInstruct}{instruction}{input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif input and instruction:
-        # i.e. for simple_instruct
-        prompt += f"""{instruction}: {input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif input:
-        prompt += f"""{input}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-    elif instruction:
-        prompt += f"""{instruction}"""
-        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
-
-    if PreResponse is not None:
-        prompt += f"""{PreResponse}"""
-        pre_response = PreResponse  # Don't use strip
-    else:
-        pre_response = ''
-
-    if output:
-        prompt += f"""{output}"""
-
-    return prompt, pre_response, terminate_response, chat_sep, chat_turn_sep
-
-
-def inject_chatsep(prompt_type, prompt, chat_sep=None):
-    if chat_sep:
-        # only add new line if structured prompt, while 'plain' is just generation of next tokens from input
-        prompt += chat_sep
-    return prompt
-
-
-class Prompter(object):
-    def __init__(self, prompt_type, prompt_dict, debug=False, chat=False, stream_output=False, repeat_penalty=True,
-                 allowed_repeat_line_length=10):
-        self.prompt_type = prompt_type
-        self.prompt_dict = prompt_dict
-        self.debug = debug
-        self.chat = chat
-        self.stream_output = stream_output
-        self.repeat_penalty = repeat_penalty
-        self.allowed_repeat_line_length = allowed_repeat_line_length
-        self.prompt = None
-        context = ""  # not for chat context
-        reduced = False  # not for chat context
-        making_context = False  # not for chat context
-        self.promptA, self.promptB, self.PreInstruct, self.PreInput, self.PreResponse, \
-            self.terminate_response, self.chat_sep, self.chat_turn_sep, self.humanstr, self.botstr, \
-            self.generates_leading_space = \
-            get_prompt(self.prompt_type, self.prompt_dict, chat, context, reduced, making_context)
-        self.pre_response = self.PreResponse
-
-    def generate_prompt(self, data_point, reduced=None):
-        """
-        data_point['context'] is assumed to be like a system prompt or pre-conversation, not inserted after user prompt
-        :param data_point:
-        :param reduced:
-        :return:
-        """
-        reduced = data_point.get('context') not in ['', None] if reduced is None else reduced
-        making_context = False  # whether really making final prompt or just generating context
-        prompt, _, _, _, _ = generate_prompt(data_point, self.prompt_type, self.prompt_dict, self.chat, reduced,
-                                             making_context)
-        if self.debug:
-            print("prompt: %s" % prompt, flush=True)
-        # if have context, should have always reduced and only preappend promptA/B here
-        if data_point.get('context'):
-            if data_point.get('input') and self.promptA:
-                prompt = self.promptA + prompt
-            elif self.promptB:
-                prompt = self.promptB + prompt
-
-        self.prompt = prompt
-        return prompt
-
-    def get_response(self, outputs, prompt=None, sanitize_bot_response=False):
-        if isinstance(outputs, str):
-            outputs = [outputs]
-        if self.debug:
-            print("output:\n%s" % '\n\n'.join(outputs), flush=True)
-        if prompt is not None:
-            self.prompt = prompt
-
-        def clean_response(response):
-            meaningless_words = ['<pad>', '</s>', '<|endoftext|>']
-            for word in meaningless_words:
-                response = response.replace(word, "")
-            if sanitize_bot_response:
-                from better_profanity import profanity
-                response = profanity.censor(response)
-            if self.generates_leading_space and isinstance(response, str) and len(response) > 0 and response[0] == ' ':
-                response = response[1:]
-            return response
-
-        def clean_repeats(response):
-            lines = response.split('\n')
-            new_lines = []
-            [new_lines.append(line) for line in lines if
-             line not in new_lines or len(line) < self.allowed_repeat_line_length]
-            if self.debug and len(lines) != len(new_lines):
-                print("cleaned repeats: %s %s" % (len(lines), len(new_lines)), flush=True)
-            response = '\n'.join(new_lines)
-            return response
-
-        multi_output = len(outputs) > 1
-
-        for oi, output in enumerate(outputs):
-            if self.prompt_type in [PromptType.plain.value, str(PromptType.plain.value), PromptType.plain.name]:
-                output = clean_response(output)
-            elif prompt is None:
-                # then use most basic parsing like pipeline
-                if not self.botstr:
-                    pass
-                elif self.botstr in output:
-                    if self.humanstr:
-                        output = clean_response(output.split(self.botstr)[1].split(self.humanstr)[0])
-                    else:
-                        # i.e. use after bot but only up to next bot
-                        output = clean_response(output.split(self.botstr)[1].split(self.botstr)[0])
-                else:
-                    # output = clean_response(output)
-                    # assume just not printed yet
-                    output = ""
-            else:
-                # find first instance of prereponse
-                # prompt sometimes has odd characters, that mutate length,
-                # so can't go by length alone
-                if self.pre_response:
-                    outputi = output.find(prompt)
-                    if outputi >= 0:
-                        output = output[outputi + len(prompt):]
-                        allow_terminate = True
-                    else:
-                        # subtraction is risky due to space offsets sometimes, so only do if necessary
-                        output = output[len(prompt) - len(self.pre_response):]
-                        # [1] to avoid repeated pre_response, just take first (after prompt - pre_response for chat)
-                        if self.pre_response in output:
-                            output = output.split(self.pre_response)[1]
-                            allow_terminate = True
-                        else:
-                            if output:
-                                print("Failure of parsing or not enough output yet: %s" % output, flush=True)
-                            allow_terminate = False
-                else:
-                    allow_terminate = True
-                    output = output[len(prompt):]
-                # clean after subtract prompt out, so correct removal of pre_response
-                output = clean_response(output)
-                if self.repeat_penalty:
-                    output = clean_repeats(output)
-                if self.terminate_response and allow_terminate:
-                    finds = []
-                    for term in self.terminate_response:
-                        finds.append(output.find(term))
-                    finds = [x for x in finds if x >= 0]
-                    if len(finds) > 0:
-                        termi = finds[0]
-                        output = output[:termi]
-                    else:
-                        output = output
-            if multi_output:
-                # prefix with output counter
-                output = "\n=========== Output %d\n\n" % (1 + oi) + output
-                if oi > 0:
-                    # post fix outputs with seperator
-                    output += '\n'
-            output = self.fix_text(self.prompt_type, output)
-            outputs[oi] = output
-        # join all outputs, only one extra new line between outputs
-        output = '\n'.join(outputs)
-        if self.debug:
-            print("outputclean:\n%s" % '\n\n'.join(outputs), flush=True)
-        return output
-
-    @staticmethod
-    def fix_text(prompt_type1, text1):
-        if prompt_type1 == 'human_bot':
-            # hack bug in vLLM with stopping, stops right, but doesn't return last token
-            hfix = '<human'
-            if text1.endswith(hfix):
-                text1 = text1[:-len(hfix)]
-        return text1
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Androidvoy Sisx Para Nokia C7 Step-by-Step Instructions to Install and Use Androidvoy Sisx on Your Nokia C7.md b/spaces/gotiQspiryo/whisper-ui/examples/Androidvoy Sisx Para Nokia C7 Step-by-Step Instructions to Install and Use Androidvoy Sisx on Your Nokia C7.md
deleted file mode 100644
index a75dc243029c0e54f0ebc55c2c0c9b37f4a8df01..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Androidvoy Sisx Para Nokia C7 Step-by-Step Instructions to Install and Use Androidvoy Sisx on Your Nokia C7.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Androidvoy Sisx Para Nokia C7</h2><br /><p><b><b>Download File</b> &bull;&bull;&bull; <a href="https://urlgoal.com/2uyNdR">https://urlgoal.com/2uyNdR</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gradio/HuBERT/examples/wav2vec/wav2vec_featurize.py b/spaces/gradio/HuBERT/examples/wav2vec/wav2vec_featurize.py
deleted file mode 100644
index 588268b7080cbd3400ac144604b2d75cef2876dd..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wav2vec/wav2vec_featurize.py
+++ /dev/null
@@ -1,249 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset
-"""
-
-import argparse
-import glob
-import os
-from shutil import copy
-
-import h5py
-import numpy as np
-import soundfile as sf
-import torch
-import tqdm
-import fairseq
-from torch import nn
-
-
-def read_audio(fname):
-    """ Load an audio file and return PCM along with the sample rate """
-
-    wav, sr = sf.read(fname)
-    assert sr == 16e3
-
-    return wav, 16e3
-
-
-class PretrainedWav2VecModel(nn.Module):
-    def __init__(self, fname):
-        super().__init__()
-
-        model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([fname])
-        model = model[0]
-        model.eval()
-
-        self.model = model
-
-    def forward(self, x):
-        with torch.no_grad():
-            z = self.model.feature_extractor(x)
-            if isinstance(z, tuple):
-                z = z[0]
-            c = self.model.feature_aggregator(z)
-        return z, c
-
-
-class EmbeddingWriterConfig(argparse.ArgumentParser):
-    def __init__(self):
-        super().__init__("Pre-compute embeddings for flashlight datasets")
-
-        kwargs = {"action": "store", "type": str, "required": True}
-
-        self.add_argument("--input", "-i", help="Input Directory", **kwargs)
-        self.add_argument("--output", "-o", help="Output Directory", **kwargs)
-        self.add_argument("--model", help="Path to model checkpoint", **kwargs)
-        self.add_argument("--split", help="Dataset Splits", nargs="+", **kwargs)
-        self.add_argument(
-            "--ext", default="wav", required=False, help="Audio file extension"
-        )
-
-        self.add_argument(
-            "--no-copy-labels",
-            action="store_true",
-            help="Do not copy label files. Useful for large datasets, use --targetdir in flashlight then.",
-        )
-        self.add_argument(
-            "--use-feat",
-            action="store_true",
-            help="Use the feature vector ('z') instead of context vector ('c') for features",
-        )
-        self.add_argument("--gpu", help="GPU to use", default=0, type=int)
-
-
-class Prediction:
-    """ Lightweight wrapper around a fairspeech embedding model """
-
-    def __init__(self, fname, gpu=0):
-        self.gpu = gpu
-        self.model = PretrainedWav2VecModel(fname).cuda(gpu)
-
-    def __call__(self, x):
-        x = torch.from_numpy(x).float().cuda(self.gpu)
-        with torch.no_grad():
-            z, c = self.model(x.unsqueeze(0))
-
-        return z.squeeze(0).cpu().numpy(), c.squeeze(0).cpu().numpy()
-
-
-class H5Writer:
-    """ Write features as hdf5 file in flashlight compatible format """
-
-    def __init__(self, fname):
-        self.fname = fname
-        os.makedirs(os.path.dirname(self.fname), exist_ok=True)
-
-    def write(self, data):
-        channel, T = data.shape
-
-        with h5py.File(self.fname, "w") as out_ds:
-            data = data.T.flatten()
-            out_ds["features"] = data
-            out_ds["info"] = np.array([16e3 // 160, T, channel])
-
-
-class EmbeddingDatasetWriter(object):
-    """Given a model and a flashlight dataset, pre-compute and store embeddings
-
-    Args:
-        input_root, str :
-            Path to the flashlight dataset
-        output_root, str :
-            Desired output directory. Will be created if non-existent
-        split, str :
-            Dataset split
-    """
-
-    def __init__(
-        self,
-        input_root,
-        output_root,
-        split,
-        model_fname,
-        extension="wav",
-        gpu=0,
-        verbose=False,
-        use_feat=False,
-    ):
-
-        assert os.path.exists(model_fname)
-
-        self.model_fname = model_fname
-        self.model = Prediction(self.model_fname, gpu)
-
-        self.input_root = input_root
-        self.output_root = output_root
-        self.split = split
-        self.verbose = verbose
-        self.extension = extension
-        self.use_feat = use_feat
-
-        assert os.path.exists(self.input_path), "Input path '{}' does not exist".format(
-            self.input_path
-        )
-
-    def _progress(self, iterable, **kwargs):
-        if self.verbose:
-            return tqdm.tqdm(iterable, **kwargs)
-        return iterable
-
-    def require_output_path(self, fname=None):
-        path = self.get_output_path(fname)
-        os.makedirs(path, exist_ok=True)
-
-    @property
-    def input_path(self):
-        return self.get_input_path()
-
-    @property
-    def output_path(self):
-        return self.get_output_path()
-
-    def get_input_path(self, fname=None):
-        if fname is None:
-            return os.path.join(self.input_root, self.split)
-        return os.path.join(self.get_input_path(), fname)
-
-    def get_output_path(self, fname=None):
-        if fname is None:
-            return os.path.join(self.output_root, self.split)
-        return os.path.join(self.get_output_path(), fname)
-
-    def copy_labels(self):
-        self.require_output_path()
-
-        labels = list(
-            filter(
-                lambda x: self.extension not in x, glob.glob(self.get_input_path("*"))
-            )
-        )
-        for fname in tqdm.tqdm(labels):
-            copy(fname, self.output_path)
-
-    @property
-    def input_fnames(self):
-        return sorted(glob.glob(self.get_input_path("*.{}".format(self.extension))))
-
-    def __len__(self):
-        return len(self.input_fnames)
-
-    def write_features(self):
-
-        paths = self.input_fnames
-
-        fnames_context = map(
-            lambda x: os.path.join(
-                self.output_path, x.replace("." + self.extension, ".h5context")
-            ),
-            map(os.path.basename, paths),
-        )
-
-        for name, target_fname in self._progress(
-            zip(paths, fnames_context), total=len(self)
-        ):
-            wav, sr = read_audio(name)
-            z, c = self.model(wav)
-            feat = z if self.use_feat else c
-            writer = H5Writer(target_fname)
-            writer.write(feat)
-
-    def __repr__(self):
-
-        return "EmbeddingDatasetWriter ({n_files} files)\n\tinput:\t{input_root}\n\toutput:\t{output_root}\n\tsplit:\t{split})".format(
-            n_files=len(self), **self.__dict__
-        )
-
-
-if __name__ == "__main__":
-
-    args = EmbeddingWriterConfig().parse_args()
-
-    for split in args.split:
-
-        writer = EmbeddingDatasetWriter(
-            input_root=args.input,
-            output_root=args.output,
-            split=split,
-            model_fname=args.model,
-            gpu=args.gpu,
-            extension=args.ext,
-            use_feat=args.use_feat,
-        )
-
-        print(writer)
-        writer.require_output_path()
-
-        print("Writing Features...")
-        writer.write_features()
-        print("Done.")
-
-        if not args.no_copy_labels:
-            print("Copying label data...")
-            writer.copy_labels()
-            print("Done.")
diff --git a/spaces/gradio/HuBERT/fairseq/models/bart/model.py b/spaces/gradio/HuBERT/fairseq/models/bart/model.py
deleted file mode 100644
index 71d0b27cd2c0655fe3b00479b672d6d042a4d5ed..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/models/bart/model.py
+++ /dev/null
@@ -1,384 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-BART: Denoising Sequence-to-Sequence Pre-training for
-Natural Language Generation, Translation, and Comprehension
-"""
-from typing import Optional
-
-import logging
-
-import torch
-import torch.nn as nn
-from fairseq import utils
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.transformer import TransformerModel
-from fairseq.modules.transformer_sentence_encoder import init_bert_params
-
-from .hub_interface import BARTHubInterface
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("bart")
-class BARTModel(TransformerModel):
-    __jit_unused_properties__ = ["supported_targets"]
-
-    @classmethod
-    def hub_models(cls):
-        return {
-            "bart.base": "http://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz",
-            "bart.large": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz",
-            "bart.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz",
-            "bart.large.cnn": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz",
-            "bart.large.xsum": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz",
-        }
-
-    def __init__(self, args, encoder, decoder):
-        super().__init__(args, encoder, decoder)
-
-        # We follow BERT's random weight initialization
-        self.apply(init_bert_params)
-
-        self.classification_heads = nn.ModuleDict()
-        if hasattr(self.encoder, "dictionary"):
-            self.eos: int = self.encoder.dictionary.eos()
-
-    @staticmethod
-    def add_args(parser):
-        super(BARTModel, BARTModel).add_args(parser)
-        parser.add_argument(
-            "--pooler-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability in the masked_lm pooler layers",
-        )
-        parser.add_argument(
-            "--pooler-activation-fn",
-            choices=utils.get_available_activation_fns(),
-            help="activation function to use for pooler layer",
-        )
-        parser.add_argument(
-            "--spectral-norm-classification-head",
-            action="store_true",
-            help="Apply spectral normalization on the classification head",
-        )
-
-    @property
-    def supported_targets(self):
-        return {"self"}
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens,
-        features_only: bool = False,
-        classification_head_name: Optional[str] = None,
-        token_embeddings: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = True,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        if classification_head_name is not None:
-            features_only = True
-
-        encoder_out = self.encoder(
-            src_tokens,
-            src_lengths=src_lengths,
-            token_embeddings=token_embeddings,
-            return_all_hiddens=return_all_hiddens
-        )
-        x, extra = self.decoder(
-            prev_output_tokens,
-            encoder_out=encoder_out,
-            features_only=features_only,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-            src_lengths=src_lengths,
-            return_all_hiddens=return_all_hiddens,
-        )
-        eos: int = self.eos
-        if classification_head_name is not None:
-            sentence_representation = x[
-                src_tokens.eq(eos), :
-            ].view(x.size(0), -1, x.size(-1))[:, -1, :]
-            for k, head in self.classification_heads.items():
-                # for torch script only supports iteration
-                if k == classification_head_name:
-                    x = head(sentence_representation)
-                    break
-        return x, extra
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_name_or_path,
-        checkpoint_file="model.pt",
-        data_name_or_path=".",
-        bpe="gpt2",
-        sample_break_mode="eos",
-        **kwargs,
-    ):
-        from fairseq import hub_utils
-
-        x = hub_utils.from_pretrained(
-            model_name_or_path,
-            checkpoint_file,
-            data_name_or_path,
-            archive_map=cls.hub_models(),
-            bpe=bpe,
-            load_checkpoint_heads=True,
-            sample_break_mode=sample_break_mode,
-            **kwargs,
-        )
-        return BARTHubInterface(x["args"], x["task"], x["models"][0])
-
-    def register_classification_head(
-        self, name, num_classes=None, inner_dim=None, **kwargs
-    ):
-        """Register a classification head."""
-        logger.info("Registering classification head: {0}".format(name))
-        if name in self.classification_heads:
-            prev_num_classes = self.classification_heads[name].out_proj.out_features
-            prev_inner_dim = self.classification_heads[name].dense.out_features
-            if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
-                logger.warning(
-                    're-registering head "{}" with num_classes {} (prev: {}) '
-                    "and inner_dim {} (prev: {})".format(
-                        name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
-                    )
-                )
-        self.classification_heads[name] = BARTClassificationHead(
-            input_dim=self.args.encoder_embed_dim,
-            inner_dim=inner_dim or self.args.encoder_embed_dim,
-            num_classes=num_classes,
-            activation_fn=self.args.pooler_activation_fn,
-            pooler_dropout=self.args.pooler_dropout,
-            do_spectral_norm=getattr(
-                self.args, "spectral_norm_classification_head", False
-            ),
-        )
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        super().upgrade_state_dict_named(state_dict, name)
-
-        prefix = name + "." if name != "" else ""
-        current_head_names = (
-            []
-            if not hasattr(self, "classification_heads")
-            else self.classification_heads.keys()
-        )
-
-        # Handle new classification heads present in the state dict.
-        keys_to_delete = []
-        for k in state_dict.keys():
-            if not k.startswith(prefix + "classification_heads."):
-                continue
-
-            head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
-            num_classes = state_dict[
-                prefix + "classification_heads." + head_name + ".out_proj.weight"
-            ].size(0)
-            inner_dim = state_dict[
-                prefix + "classification_heads." + head_name + ".dense.weight"
-            ].size(0)
-
-            if getattr(self.args, "load_checkpoint_heads", False):
-                if head_name not in current_head_names:
-                    self.register_classification_head(head_name, num_classes, inner_dim)
-            else:
-                if head_name not in current_head_names:
-                    logger.warning(
-                        "deleting classification head ({}) from checkpoint "
-                        "not present in current model: {}".format(head_name, k)
-                    )
-                    keys_to_delete.append(k)
-                elif (
-                    num_classes
-                    != self.classification_heads[head_name].out_proj.out_features
-                    or inner_dim
-                    != self.classification_heads[head_name].dense.out_features
-                ):
-                    logger.warning(
-                        "deleting classification head ({}) from checkpoint "
-                        "with different dimensions than current model: {}".format(
-                            head_name, k
-                        )
-                    )
-                    keys_to_delete.append(k)
-        for k in keys_to_delete:
-            del state_dict[k]
-
-        def truncate_emb(key):
-            if key in state_dict:
-                state_dict[key] = state_dict[key][:-1, :]
-
-        # When finetuning on translation task, remove last row of
-        # embedding matrix that corresponds to mask_idx token.
-        loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0)
-        if (
-            loaded_dict_size == len(self.encoder.dictionary) + 1
-            and "<mask>" not in self.encoder.dictionary
-        ):
-            truncate_emb("encoder.embed_tokens.weight")
-            truncate_emb("decoder.embed_tokens.weight")
-            truncate_emb("encoder.output_projection.weight")
-            truncate_emb("decoder.output_projection.weight")
-
-        # When continued pretraining on new set of languages for mbart,
-        # add extra lang embeddings at the end of embed_tokens.
-        # Note: newly added languages are assumed to have been added at the end.
-        if self.args.task == "multilingual_denoising" and loaded_dict_size < len(
-            self.encoder.dictionary
-        ):
-            logger.info(
-                "Adding extra language embeddings not found in pretrained model for "
-                "continued pretraining of MBART on new set of languages."
-            )
-            loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][
-                -1, :
-            ]
-
-            num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size
-            embed_dim = state_dict["encoder.embed_tokens.weight"].size(1)
-
-            new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim)
-            nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_lang_embed_to_add = new_lang_embed_to_add.to(
-                dtype=state_dict["encoder.embed_tokens.weight"].dtype,
-            )
-
-            state_dict["encoder.embed_tokens.weight"] = torch.cat(
-                [
-                    state_dict["encoder.embed_tokens.weight"][
-                        : loaded_dict_size - 1, :
-                    ],
-                    new_lang_embed_to_add,
-                    loaded_mask_token_embedding.unsqueeze(0),
-                ]
-            )
-            state_dict["decoder.embed_tokens.weight"] = torch.cat(
-                [
-                    state_dict["decoder.embed_tokens.weight"][
-                        : loaded_dict_size - 1, :
-                    ],
-                    new_lang_embed_to_add,
-                    loaded_mask_token_embedding.unsqueeze(0),
-                ]
-            )
-
-        # Copy any newly-added classification heads into the state dict
-        # with their current weights.
-        if hasattr(self, "classification_heads"):
-            cur_state = self.classification_heads.state_dict()
-            for k, v in cur_state.items():
-                if prefix + "classification_heads." + k not in state_dict:
-                    logger.info("Overwriting " + prefix + "classification_heads." + k)
-                    state_dict[prefix + "classification_heads." + k] = v
-
-
-class BARTClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(
-        self,
-        input_dim,
-        inner_dim,
-        num_classes,
-        activation_fn,
-        pooler_dropout,
-        do_spectral_norm=False,
-    ):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-        if do_spectral_norm:
-            self.out_proj = torch.nn.utils.spectral_norm(self.out_proj)
-
-    def forward(self, features, **kwargs):
-        x = features
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = self.activation_fn(x)
-        x = self.dropout(x)
-        x = self.out_proj(x)
-        return x
-
-
-@register_model_architecture("bart", "bart_large")
-def bart_large_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
-    args.encoder_layers = getattr(args, "encoder_layers", 12)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 12)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.relu_dropout = getattr(args, "relu_dropout", 0.0)
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.max_target_positions = getattr(args, "max_target_positions", 1024)
-    args.max_source_positions = getattr(args, "max_source_positions", 1024)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", True
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
-
-    args.activation_fn = getattr(args, "activation_fn", "gelu")
-    args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
-    args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
-
-
-@register_model_architecture("bart", "bart_base")
-def bart_base_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
-    bart_large_architecture(args)
-
-
-@register_model_architecture("bart", "mbart_large")
-def mbart_large_architecture(args):
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    bart_large_architecture(args)
-
-
-@register_model_architecture("bart", "mbart_base")
-def mbart_base_architecture(args):
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    bart_base_architecture(args)
-
-
-@register_model_architecture("bart", "mbart_base_wmt20")
-def mbart_base_wmt20_architecture(args):
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
-    mbart_base_architecture(args)
diff --git a/spaces/guostonline/FDV-dashboard/app.py b/spaces/guostonline/FDV-dashboard/app.py
deleted file mode 100644
index 9e0612e7450f78b45459d45dd0640bffd9baff66..0000000000000000000000000000000000000000
--- a/spaces/guostonline/FDV-dashboard/app.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-from datetime import datetime
-import emoji
-
-current_day = datetime.now().day
-
-dataframe=""
-
-def day_without_sunday():
-    if current_day > 7:
-        return current_day-1
-    elif current_day > 14:
-        return current_day-2
-    elif current_day > 21:
-        return current_day - 3
-    else: return current_day-4
-
-
-st.set_page_config(page_title="Rapport FDV",
-                    page_icon=":bar_chart:",
-                    layout="wide"
-)
-
-df = pd.read_excel(
-    io="suivi.xlsx",
-    engine="openpyxl",
-    sheet_name=["AGADIR","QUALI NV"],
-    #skiprows=8,
-    usecols="A:AC",
-    #nrows=163,
-)
-
-# st.dataframe(df)
-# ------Sidebar-------
-st.sidebar.header("Filter:")
-uploaded_file = st.sidebar.file_uploader("Choose a file")
-if uploaded_file is not None:
-    # To read file as bytes:
-    # bytes_data = uploaded_file.getvalue()
-    df = pd.read_excel(uploaded_file,
-                                
-                                engine="openpyxl",
-                                sheet_name=["AGADIR","QUALI NV"],     
-                                usecols="A:AC",
-                                nrows=163,
-
-    )
-    
-quantitatif_df=df.get("AGADIR")
-qualitatif_df=df.get("QUALI NV")
-
-    # st.write(bytes_data)
-show_all_fdv = st.sidebar.checkbox('Tout les FDV')
-vendeur = st.sidebar.multiselect(
-    "Vendeur:",
-    options=quantitatif_df["Vendeur"].unique(),
-    default=quantitatif_df["Vendeur"][0],
-    disabled=show_all_fdv
-)
-
-
-
-famille = st.sidebar.multiselect(
-    "Famille:",
-    options=quantitatif_df["Famille"].unique(),
-    default=quantitatif_df["Famille"][6]
-)
-
-
-jour_travail = st.sidebar.text_input(
-    label="Jour Travail", value=day_without_sunday())
-jour_reste = st.sidebar.text_input(label="Jour Reste", value=24)
-
-
-
-
-df_selection_quantitatif = quantitatif_df.query(
-    "Vendeur== @vendeur & Famille==@famille"
-)
-df_select_qualitatif = qualitatif_df.query(
-    "Vendeur== @vendeur" 
-)
-
-
-if show_all_fdv:
-    df_selection_quantitatif = quantitatif_df.query(
-        "Famille==@famille & Vendeur!='SOUATI NOUREDDINE'  & Vendeur!='CDZ AGADIR GROS' &Vendeur!='CHAKIB ELFIL' & Vendeur!='CDZ AGADIR DET2'& Vendeur!='VIDE' ", 
-    )
-df_selection_quantitatif = df_selection_quantitatif.astype({
-    "REAL": "int",
-    "OBJ": "int",
-    "J-1": "int",
-    "REAL.1": "int",
-    '2021.1': "int",
-
-})
-
-
-st.dataframe(df_selection_quantitatif)
-st.dataframe(df_select_qualitatif)
-
-
-total_ht = int(df_selection_quantitatif["REAL"].sum())
-total_ttc = round(total_ht*1.2)
-min_ca = int(df_selection_quantitatif["REAL"].min())
-min_ca_index = int(df_selection_quantitatif["REAL"].idxmin())
-
-max_ca = int(df_selection_quantitatif["REAL"].max())
-max_ca_index = int(df_selection_quantitatif["REAL"].idxmax())
-
-objectif_ht = ((round(df_selection_quantitatif["OBJ"].sum()))*24/int(jour_travail))
-objectif_ttc = round(objectif_ht*1.2)
-rest_jour_ttc = round((objectif_ttc-(total_ttc))/int(jour_reste))
-average_ttc = round(total_ttc/int(jour_travail))
-
-moyenne_client_facture=round(int(df_select_qualitatif["CLT FACTURE"].sum())/ int(jour_travail))
-
-col1, col2, col3, col4, col5,col6,col7,col8 = st.columns(8)
-with col1:
-    st.caption("Total HT",)
-    st.subheader(f'{total_ht:,}')
-with col2:
-    st.caption("Total TTC")
-    st.subheader(f'{total_ttc:,}')
-with col3:
-    st.caption("Objectif TTC")
-    st.subheader(f'{objectif_ttc:,}')
-with col4:
-    st.caption("Rest jour TTC")
-    st.subheader(f'{rest_jour_ttc:,}')
-with col5:
-    st.caption("ACM")
-    st.subheader(f'{round(df_select_qualitatif["% vs Obj"].sum()*100):,}%')
-with col6:
-    st.caption("Moyenne TSM")
-    st.subheader(moyenne_client_facture)
-with col7:
-    st.caption("Line /bl")
-    st.subheader(f'{round(df_select_qualitatif["%"].sum()*100):,}%')
-with col8:
-    st.caption("TSM")
-    st.subheader(f'{round(df_select_qualitatif["%.1"].sum()*100):,}%')
-st.text(f"Maximum Réaliser : {max_ca:} ({quantitatif_df['Vendeur'][max_ca_index]:} {emoji.emojize(':1st_place_medal:')})")
-st.text(f"Minimum Réaliser : {min_ca:} ({quantitatif_df['Vendeur'][min_ca_index]:} {emoji.emojize(':thumbs_down:')})")
-
-
-vendeur_ca = (
-    df_selection_quantitatif.groupby(by=["Vendeur"]).sum()[["REAL"]].sort_values(by="REAL")
-)
-
-
-
-fig_produit_sales = px.bar(
-    vendeur_ca,
-    x="REAL",
-    y=vendeur_ca.index,
-    orientation="h",
-    title='<b>CA par Vendeur</b>',
-    color_discrete_sequence=["#0083B8"] * len(vendeur_ca),
-    template="plotly_white",
-    color='REAL'
-    
-)
-st.plotly_chart(fig_produit_sales)
-
-hide_st_style = """
-<style>
-
-footer {visibility:hidden;}
-header {visibility:hidden;}
-</style>
-"""
-st.markdown(hide_st_style, unsafe_allow_html=True)
-
-print(moyenne_client_facture)
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/utils/data_utils.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/utils/data_utils.py
deleted file mode 100644
index 717b8834bdca118a13192622ff4cd8ba0bc173cb..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/utils/data_utils.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-
-import os
-
-from PIL import Image
-
-IMG_EXTENSIONS = [
-    '.jpg', '.JPG', '.jpeg', '.JPEG',
-    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff'
-]
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def tensor2im(var):
-    # var shape: (3, H, W)
-    var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy()
-    var = ((var + 1) / 2)
-    var[var < 0] = 0
-    var[var > 1] = 1
-    var = var * 255
-    return Image.fromarray(var.astype('uint8'))
-
-
-def make_dataset(dir):
-    images = []
-    assert os.path.isdir(dir), '%s is not a valid directory' % dir
-    for root, _, fnames in sorted(os.walk(dir)):
-        for fname in fnames:
-            if is_image_file(fname):
-                path = os.path.join(root, fname)
-                fname = fname.split('.')[0]
-                images.append((fname, path))
-    return images
diff --git a/spaces/hamacojr/CAT-Seg/open_clip/tests/test_download_pretrained.py b/spaces/hamacojr/CAT-Seg/open_clip/tests/test_download_pretrained.py
deleted file mode 100644
index 6340918ed5b7c56fdbbfb84e2bcb26ccf662c8b5..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/CAT-Seg/open_clip/tests/test_download_pretrained.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import requests
-import torch
-from PIL import Image
-import hashlib
-import tempfile
-import unittest
-from io import BytesIO
-from pathlib import Path
-from unittest.mock import patch
-
-from urllib3 import HTTPResponse
-from urllib3._collections import HTTPHeaderDict
-
-import open_clip
-from open_clip.pretrained import download_pretrained_from_url
-
-
-class DownloadPretrainedTests(unittest.TestCase):
-
-    def create_response(self, data, status_code=200, content_type='application/octet-stream'):
-        fp = BytesIO(data)
-        headers = HTTPHeaderDict({
-            'Content-Type': content_type,
-            'Content-Length': str(len(data))
-        })
-        raw = HTTPResponse(fp, preload_content=False, headers=headers, status=status_code)
-        return raw
-
-    @patch('open_clip.pretrained.urllib')
-    def test_download_pretrained_from_url_from_openaipublic(self, urllib):
-        file_contents = b'pretrained model weights'
-        expected_hash = hashlib.sha256(file_contents).hexdigest()
-        urllib.request.urlopen.return_value = self.create_response(file_contents)
-        with tempfile.TemporaryDirectory() as root:
-            url = f'https://openaipublic.azureedge.net/clip/models/{expected_hash}/RN50.pt'
-            download_pretrained_from_url(url, root)
-        urllib.request.urlopen.assert_called_once()
-
-    @patch('open_clip.pretrained.urllib')
-    def test_download_pretrained_from_url_from_openaipublic_corrupted(self, urllib):
-        file_contents = b'pretrained model weights'
-        expected_hash = hashlib.sha256(file_contents).hexdigest()
-        urllib.request.urlopen.return_value = self.create_response(b'corrupted pretrained model')
-        with tempfile.TemporaryDirectory() as root:
-            url = f'https://openaipublic.azureedge.net/clip/models/{expected_hash}/RN50.pt'
-            with self.assertRaisesRegex(RuntimeError, r'checksum does not not match'):
-                download_pretrained_from_url(url, root)
-        urllib.request.urlopen.assert_called_once()
-
-    @patch('open_clip.pretrained.urllib')
-    def test_download_pretrained_from_url_from_openaipublic_valid_cache(self, urllib):
-        file_contents = b'pretrained model weights'
-        expected_hash = hashlib.sha256(file_contents).hexdigest()
-        urllib.request.urlopen.return_value = self.create_response(file_contents)
-        with tempfile.TemporaryDirectory() as root:
-            local_file = Path(root) / 'RN50.pt'
-            local_file.write_bytes(file_contents)
-            url = f'https://openaipublic.azureedge.net/clip/models/{expected_hash}/RN50.pt'
-            download_pretrained_from_url(url, root)
-        urllib.request.urlopen.assert_not_called()
-
-    @patch('open_clip.pretrained.urllib')
-    def test_download_pretrained_from_url_from_openaipublic_corrupted_cache(self, urllib):
-        file_contents = b'pretrained model weights'
-        expected_hash = hashlib.sha256(file_contents).hexdigest()
-        urllib.request.urlopen.return_value = self.create_response(file_contents)
-        with tempfile.TemporaryDirectory() as root:
-            local_file = Path(root) / 'RN50.pt'
-            local_file.write_bytes(b'corrupted pretrained model')
-            url = f'https://openaipublic.azureedge.net/clip/models/{expected_hash}/RN50.pt'
-            download_pretrained_from_url(url, root)
-        urllib.request.urlopen.assert_called_once()
-
-    @patch('open_clip.pretrained.urllib')
-    def test_download_pretrained_from_url_from_mlfoundations(self, urllib):
-        file_contents = b'pretrained model weights'
-        expected_hash = hashlib.sha256(file_contents).hexdigest()[:8]
-        urllib.request.urlopen.return_value = self.create_response(file_contents)
-        with tempfile.TemporaryDirectory() as root:
-            url = f'https://github.com/mlfoundations/download/v0.2-weights/rn50-quickgelu-{expected_hash}.pt'
-            download_pretrained_from_url(url, root)
-        urllib.request.urlopen.assert_called_once()
-
-    @patch('open_clip.pretrained.urllib')
-    def test_download_pretrained_from_url_from_mlfoundations_corrupted(self, urllib):
-        file_contents = b'pretrained model weights'
-        expected_hash = hashlib.sha256(file_contents).hexdigest()[:8]
-        urllib.request.urlopen.return_value = self.create_response(b'corrupted pretrained model')
-        with tempfile.TemporaryDirectory() as root:
-            url = f'https://github.com/mlfoundations/download/v0.2-weights/rn50-quickgelu-{expected_hash}.pt'
-            with self.assertRaisesRegex(RuntimeError, r'checksum does not not match'):
-                download_pretrained_from_url(url, root)
-        urllib.request.urlopen.assert_called_once()
-
-    @patch('open_clip.pretrained.urllib')
-    def test_download_pretrained_from_hfh(self, urllib):
-        model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:hf-internal-testing/tiny-open-clip-model')
-        tokenizer = open_clip.get_tokenizer('hf-hub:hf-internal-testing/tiny-open-clip-model')
-        img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"
-        image = preprocess(Image.open(requests.get(img_url, stream=True).raw)).unsqueeze(0)
-        text = tokenizer(["a diagram", "a dog", "a cat"])
-
-        with torch.no_grad():
-            image_features = model.encode_image(image)
-            text_features = model.encode_text(text)
-            image_features /= image_features.norm(dim=-1, keepdim=True)
-            text_features /= text_features.norm(dim=-1, keepdim=True)
-
-            text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
-
-        self.assertTrue(torch.allclose(text_probs, torch.tensor([[0.0597, 0.6349, 0.3053]]), 1e-3))
diff --git "a/spaces/hands012/gpt-academic/crazy_functions/\345\257\271\350\257\235\345\216\206\345\217\262\345\255\230\346\241\243.py" "b/spaces/hands012/gpt-academic/crazy_functions/\345\257\271\350\257\235\345\216\206\345\217\262\345\255\230\346\241\243.py"
deleted file mode 100644
index c638d1bd087c878e9722bec02361111613ac2b7c..0000000000000000000000000000000000000000
--- "a/spaces/hands012/gpt-academic/crazy_functions/\345\257\271\350\257\235\345\216\206\345\217\262\345\255\230\346\241\243.py"
+++ /dev/null
@@ -1,143 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import re
-
-def write_chat_to_file(chatbot, history=None, file_name=None):
-    """
-    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    if file_name is None:
-        file_name = 'chatGPT对话历史' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
-    os.makedirs('./gpt_log/', exist_ok=True)
-    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-        from theme import advanced_css
-        f.write(f'<!DOCTYPE html><head><meta charset="utf-8"><title>对话历史</title><style>{advanced_css}</style></head>')
-        for i, contents in enumerate(chatbot):
-            for j, content in enumerate(contents):
-                try:    # 这个bug没找到触发条件，暂时先这样顶一下
-                    if type(content) != str: content = str(content)
-                except:
-                    continue
-                f.write(content)
-                if j == 0:
-                    f.write('<hr style="border-top: dotted 3px #ccc;">')
-            f.write('<hr color="red"> \n\n')
-        f.write('<hr color="blue"> \n\n raw chat context:\n')
-        f.write('<code>')
-        for h in history:
-            f.write("\n>>>" + h)
-        f.write('</code>')
-    res = '对话历史写入：' + os.path.abspath(f'./gpt_log/{file_name}')
-    print(res)
-    return res
-
-def gen_file_preview(file_name):
-    try:
-        with open(file_name, 'r', encoding='utf8') as f:
-            file_content = f.read()
-        # pattern to match the text between <head> and </head>
-        pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
-        file_content = re.sub(pattern, '', file_content)
-        html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
-        history = history.strip('<code>')
-        history = history.strip('</code>')
-        history = history.split("\n>>>")
-        return list(filter(lambda x:x!="", history))[0][:100]
-    except:
-        return ""
-
-def read_file_to_chat(chatbot, history, file_name):
-    with open(file_name, 'r', encoding='utf8') as f:
-        file_content = f.read()
-    # pattern to match the text between <head> and </head>
-    pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
-    file_content = re.sub(pattern, '', file_content)
-    html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
-    history = history.strip('<code>')
-    history = history.strip('</code>')
-    history = history.split("\n>>>")
-    history = list(filter(lambda x:x!="", history))
-    html = html.split('<hr color="red"> \n\n')
-    html = list(filter(lambda x:x!="", html))
-    chatbot.clear()
-    for i, h in enumerate(html):
-        i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
-        chatbot.append([i_say, gpt_say])
-    chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
-    return chatbot, history    
-
-@CatchException
-def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-
-    chatbot.append(("保存当前对话", 
-        f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用“载入对话历史存档”还原当下的对话。\n警告！被保存的对话历史可以被使用该系统的任何人查阅。"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-def hide_cwd(str):
-    import os
-    current_path = os.getcwd()
-    replace_path = "."
-    return str.replace(current_path, replace_path)
-
-@CatchException
-def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    from .crazy_utils import get_files_from_everything
-    success, file_manifest, _ = get_files_from_everything(txt, type='.html')
-
-    if not success:
-        if txt == "": txt = '空空如也的输入栏'
-        import glob
-        local_history = "<br/>".join(["`"+hide_cwd(f)+f" ({gen_file_preview(f)})"+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
-        chatbot.append([f"正在查找对话历史文件（html格式）: {txt}", f"找不到任何html文件: {txt}。但本地存储了以下历史文件，您可以将任意一个文件路径粘贴到输入区，然后重试：<br/>{local_history}"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    try:
-        chatbot, history = read_file_to_chat(chatbot, history, file_manifest[0])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    except:
-        chatbot.append([f"载入对话历史文件", f"对话历史文件损坏！"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-@CatchException
-def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-
-    import glob, os
-    local_history = "<br/>".join(["`"+hide_cwd(f)+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
-    for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True):
-        os.remove(f)
-    chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    return
-
-
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/list_dataset.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/list_dataset.py
deleted file mode 100644
index 9058d35b3d4279048732074f4a8dbb6edd4c9ed0..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/list_dataset.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-"""
-Simple dataset class that wraps a list of path names
-"""
-
-from PIL import Image
-
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-
-
-class ListDataset(object):
-    def __init__(self, image_lists, transforms=None):
-        self.image_lists = image_lists
-        self.transforms = transforms
-
-    def __getitem__(self, item):
-        img = Image.open(self.image_lists[item]).convert("RGB")
-
-        # dummy target
-        w, h = img.size
-        target = BoxList([[0, 0, w, h]], img.size, mode="xyxy")
-
-        if self.transforms is not None:
-            img, target = self.transforms(img, target)
-
-        return img, target
-
-    def __len__(self):
-        return len(self.image_lists)
-
-    def get_img_info(self, item):
-        """
-        Return the image dimensions for the image, without
-        loading and pre-processing it
-        """
-        pass
diff --git a/spaces/hekbobo/bingo/src/components/ui/sheet.tsx b/spaces/hekbobo/bingo/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/hekbobo/bingo/src/components/ui/sheet.tsx
+++ /dev/null
@@ -1,122 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SheetPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Sheet = SheetPrimitive.Root
-
-const SheetTrigger = SheetPrimitive.Trigger
-
-const SheetClose = SheetPrimitive.Close
-
-const SheetPortal = ({
-  className,
-  children,
-  ...props
-}: SheetPrimitive.DialogPortalProps) => (
-  <SheetPrimitive.Portal
-    className={cn('fixed inset-0 z-50 flex', className)}
-    {...props}
-  >
-    {children}
-  </SheetPrimitive.Portal>
-)
-SheetPortal.displayName = SheetPrimitive.Portal.displayName
-
-const SheetOverlay = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <SheetPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-SheetOverlay.displayName = SheetPrimitive.Overlay.displayName
-
-const SheetContent = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <SheetPortal>
-    <SheetPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed inset-y-0 left-0 z-50 h-full border-r bg-background p-6 shadow-lg transition ease-in-out data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:slide-out-to-left data-[state=open]:slide-in-from-left data-[state=closed]:duration-300 data-[state=open]:duration-500 sm:max-w-sm',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <SheetPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-secondary">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </SheetPrimitive.Close>
-    </SheetPrimitive.Content>
-  </SheetPortal>
-))
-SheetContent.displayName = SheetPrimitive.Content.displayName
-
-const SheetHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div className={cn('flex flex-col space-y-2', className)} {...props} />
-)
-SheetHeader.displayName = 'SheetHeader'
-
-const SheetFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-SheetFooter.displayName = 'SheetFooter'
-
-const SheetTitle = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold text-foreground', className)}
-    {...props}
-  />
-))
-SheetTitle.displayName = SheetPrimitive.Title.displayName
-
-const SheetDescription = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-SheetDescription.displayName = SheetPrimitive.Description.displayName
-
-export {
-  Sheet,
-  SheetTrigger,
-  SheetClose,
-  SheetContent,
-  SheetHeader,
-  SheetFooter,
-  SheetTitle,
-  SheetDescription
-}
diff --git a/spaces/hkayabilisim/clusternea/Dockerfile b/spaces/hkayabilisim/clusternea/Dockerfile
deleted file mode 100644
index c8f870da6ad8f127035fc94c2100748a22b0e787..0000000000000000000000000000000000000000
--- a/spaces/hkayabilisim/clusternea/Dockerfile
+++ /dev/null
@@ -1,20 +0,0 @@
-FROM python:3.9
-
-RUN useradd -m -u 1000 user
-
-USER user
-
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-COPY --chown=user . $HOME/app
-
-WORKDIR $HOME/app
-
-COPY ./requirements.txt $HOME/app/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r $HOME/app/requirements.txt
-
-COPY . .
-
-CMD ["solara", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/experiment_planner_baseline_3DUNet_v22.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/experiment_planner_baseline_3DUNet_v22.py
deleted file mode 100644
index 63a702e5623d53d9cf7a5f995d8bbeb8d0f77fbd..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/experiment_planner_baseline_3DUNet_v22.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    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 numpy as np
-from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import \
-    ExperimentPlanner3D_v21
-from nnunet.paths import *
-
-
-class ExperimentPlanner3D_v22(ExperimentPlanner3D_v21):
-    """
-    """
-    def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
-        super().__init__(folder_with_cropped_data, preprocessed_output_folder)
-        self.data_identifier = "nnUNetData_plans_v2.2"
-        self.plans_fname = join(self.preprocessed_output_folder,
-                                "nnUNetPlansv2.2_plans_3D.pkl")
-
-    def get_target_spacing(self):
-        spacings = self.dataset_properties['all_spacings']
-        sizes = self.dataset_properties['all_sizes']
-
-        target = np.percentile(np.vstack(spacings), self.target_spacing_percentile, 0)
-        target_size = np.percentile(np.vstack(sizes), self.target_spacing_percentile, 0)
-        target_size_mm = np.array(target) * np.array(target_size)
-        # we need to identify datasets for which a different target spacing could be beneficial. These datasets have
-        # the following properties:
-        # - one axis which much lower resolution than the others
-        # - the lowres axis has much less voxels than the others
-        # - (the size in mm of the lowres axis is also reduced)
-        worst_spacing_axis = np.argmax(target)
-        other_axes = [i for i in range(len(target)) if i != worst_spacing_axis]
-        other_spacings = [target[i] for i in other_axes]
-        other_sizes = [target_size[i] for i in other_axes]
-
-        has_aniso_spacing = target[worst_spacing_axis] > (self.anisotropy_threshold * max(other_spacings))
-        has_aniso_voxels = target_size[worst_spacing_axis] * self.anisotropy_threshold < min(other_sizes)
-        # we don't use the last one for now
-        #median_size_in_mm = target[target_size_mm] * RESAMPLING_SEPARATE_Z_ANISOTROPY_THRESHOLD < max(target_size_mm)
-
-        if has_aniso_spacing and has_aniso_voxels:
-            spacings_of_that_axis = np.vstack(spacings)[:, worst_spacing_axis]
-            target_spacing_of_that_axis = np.percentile(spacings_of_that_axis, 10)
-            # don't let the spacing of that axis get higher than self.anisotropy_thresholdxthe_other_axes
-            target_spacing_of_that_axis = max(max(other_spacings) * self.anisotropy_threshold, target_spacing_of_that_axis)
-            target[worst_spacing_axis] = target_spacing_of_that_axis
-        return target
-
diff --git a/spaces/hoshilumine/combined-GI-RVC-models/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/hoshilumine/combined-GI-RVC-models/lib/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/hoshilumine/combined-GI-RVC-models/lib/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
-    def compute_f0(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length]
-        """
-        pass
-
-    def compute_f0_uv(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
-        """
-        pass
diff --git a/spaces/housexu123/bingo-2.0/src/components/ui/icons.tsx b/spaces/housexu123/bingo-2.0/src/components/ui/icons.tsx
deleted file mode 100644
index 0ca5bee838afedafae3eddbfe2612edba1586f9c..0000000000000000000000000000000000000000
--- a/spaces/housexu123/bingo-2.0/src/components/ui/icons.tsx
+++ /dev/null
@@ -1,489 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-function IconNextChat({
-  className,
-  inverted,
-  ...props
-}: React.ComponentProps<'svg'> & { inverted?: boolean }) {
-  const id = React.useId()
-
-  return (
-    <svg
-      viewBox="0 0 17 17"
-      fill="none"
-      xmlns="http://www.w3.org/2000/svg"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <defs>
-        <linearGradient
-          id={`gradient-${id}-1`}
-          x1="10.6889"
-          y1="10.3556"
-          x2="13.8445"
-          y2="14.2667"
-          gradientUnits="userSpaceOnUse"
-        >
-          <stop stopColor={inverted ? 'white' : 'black'} />
-          <stop
-            offset={1}
-            stopColor={inverted ? 'white' : 'black'}
-            stopOpacity={0}
-          />
-        </linearGradient>
-        <linearGradient
-          id={`gradient-${id}-2`}
-          x1="11.7555"
-          y1="4.8"
-          x2="11.7376"
-          y2="9.50002"
-          gradientUnits="userSpaceOnUse"
-        >
-          <stop stopColor={inverted ? 'white' : 'black'} />
-          <stop
-            offset={1}
-            stopColor={inverted ? 'white' : 'black'}
-            stopOpacity={0}
-          />
-        </linearGradient>
-      </defs>
-      <path
-        d="M1 16L2.58314 11.2506C1.83084 9.74642 1.63835 8.02363 2.04013 6.39052C2.4419 4.75741 3.41171 3.32057 4.776 2.33712C6.1403 1.35367 7.81003 0.887808 9.4864 1.02289C11.1628 1.15798 12.7364 1.8852 13.9256 3.07442C15.1148 4.26363 15.842 5.83723 15.9771 7.5136C16.1122 9.18997 15.6463 10.8597 14.6629 12.224C13.6794 13.5883 12.2426 14.5581 10.6095 14.9599C8.97637 15.3616 7.25358 15.1692 5.74942 14.4169L1 16Z"
-        fill={inverted ? 'black' : 'white'}
-        stroke={inverted ? 'black' : 'white'}
-        strokeWidth={2}
-        strokeLinecap="round"
-        strokeLinejoin="round"
-      />
-      <mask
-        id="mask0_91_2047"
-        style={{ maskType: 'alpha' }}
-        maskUnits="userSpaceOnUse"
-        x={1}
-        y={0}
-        width={16}
-        height={16}
-      >
-        <circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
-      </mask>
-      <g mask="url(#mask0_91_2047)">
-        <circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
-        <path
-          d="M14.2896 14.0018L7.146 4.8H5.80005V11.1973H6.87681V6.16743L13.4444 14.6529C13.7407 14.4545 14.0231 14.2369 14.2896 14.0018Z"
-          fill={`url(#gradient-${id}-1)`}
-        />
-        <rect
-          x="11.2222"
-          y="4.8"
-          width="1.06667"
-          height="6.4"
-          fill={`url(#gradient-${id}-2)`}
-        />
-      </g>
-    </svg>
-  )
-}
-
-function IconOpenAI({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      fill="currentColor"
-      viewBox="0 0 24 24"
-      role="img"
-      xmlns="http://www.w3.org/2000/svg"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <title>OpenAI icon</title>
-      <path d="M22.2819 9.8211a5.9847 5.9847 0 0 0-.5157-4.9108 6.0462 6.0462 0 0 0-6.5098-2.9A6.0651 6.0651 0 0 0 4.9807 4.1818a5.9847 5.9847 0 0 0-3.9977 2.9 6.0462 6.0462 0 0 0 .7427 7.0966 5.98 5.98 0 0 0 .511 4.9107 6.051 6.051 0 0 0 6.5146 2.9001A5.9847 5.9847 0 0 0 13.2599 24a6.0557 6.0557 0 0 0 5.7718-4.2058 5.9894 5.9894 0 0 0 3.9977-2.9001 6.0557 6.0557 0 0 0-.7475-7.0729zm-9.022 12.6081a4.4755 4.4755 0 0 1-2.8764-1.0408l.1419-.0804 4.7783-2.7582a.7948.7948 0 0 0 .3927-.6813v-6.7369l2.02 1.1686a.071.071 0 0 1 .038.052v5.5826a4.504 4.504 0 0 1-4.4945 4.4944zm-9.6607-4.1254a4.4708 4.4708 0 0 1-.5346-3.0137l.142.0852 4.783 2.7582a.7712.7712 0 0 0 .7806 0l5.8428-3.3685v2.3324a.0804.0804 0 0 1-.0332.0615L9.74 19.9502a4.4992 4.4992 0 0 1-6.1408-1.6464zM2.3408 7.8956a4.485 4.485 0 0 1 2.3655-1.9728V11.6a.7664.7664 0 0 0 .3879.6765l5.8144 3.3543-2.0201 1.1685a.0757.0757 0 0 1-.071 0l-4.8303-2.7865A4.504 4.504 0 0 1 2.3408 7.872zm16.5963 3.8558L13.1038 8.364 15.1192 7.2a.0757.0757 0 0 1 .071 0l4.8303 2.7913a4.4944 4.4944 0 0 1-.6765 8.1042v-5.6772a.79.79 0 0 0-.407-.667zm2.0107-3.0231l-.142-.0852-4.7735-2.7818a.7759.7759 0 0 0-.7854 0L9.409 9.2297V6.8974a.0662.0662 0 0 1 .0284-.0615l4.8303-2.7866a4.4992 4.4992 0 0 1 6.6802 4.66zM8.3065 12.863l-2.02-1.1638a.0804.0804 0 0 1-.038-.0567V6.0742a4.4992 4.4992 0 0 1 7.3757-3.4537l-.142.0805L8.704 5.459a.7948.7948 0 0 0-.3927.6813zm1.0976-2.3654l2.602-1.4998 2.6069 1.4998v2.9994l-2.5974 1.4997-2.6067-1.4997Z" />
-    </svg>
-  )
-}
-
-function IconGitHub({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      role="img"
-      viewBox="0 0 24 24"
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <title>GitHub</title>
-      <path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12" />
-    </svg>
-  )
-}
-
-function IconSeparator({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      fill="none"
-      shapeRendering="geometricPrecision"
-      stroke="currentColor"
-      strokeLinecap="round"
-      strokeLinejoin="round"
-      strokeWidth="1"
-      viewBox="0 0 24 24"
-      aria-hidden="true"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M16.88 3.549L7.12 20.451"></path>
-    </svg>
-  )
-}
-
-function IconArrowDown({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="m205.66 149.66-72 72a8 8 0 0 1-11.32 0l-72-72a8 8 0 0 1 11.32-11.32L120 196.69V40a8 8 0 0 1 16 0v156.69l58.34-58.35a8 8 0 0 1 11.32 11.32Z" />
-    </svg>
-  )
-}
-
-function IconArrowRight({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="m221.66 133.66-72 72a8 8 0 0 1-11.32-11.32L196.69 136H40a8 8 0 0 1 0-16h156.69l-58.35-58.34a8 8 0 0 1 11.32-11.32l72 72a8 8 0 0 1 0 11.32Z" />
-    </svg>
-  )
-}
-
-function IconUser({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M230.92 212c-15.23-26.33-38.7-45.21-66.09-54.16a72 72 0 1 0-73.66 0c-27.39 8.94-50.86 27.82-66.09 54.16a8 8 0 1 0 13.85 8c18.84-32.56 52.14-52 89.07-52s70.23 19.44 89.07 52a8 8 0 1 0 13.85-8ZM72 96a56 56 0 1 1 56 56 56.06 56.06 0 0 1-56-56Z" />
-    </svg>
-  )
-}
-
-function IconPlus({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M224 128a8 8 0 0 1-8 8h-80v80a8 8 0 0 1-16 0v-80H40a8 8 0 0 1 0-16h80V40a8 8 0 0 1 16 0v80h80a8 8 0 0 1 8 8Z" />
-    </svg>
-  )
-}
-
-function IconArrowElbow({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M200 32v144a8 8 0 0 1-8 8H67.31l34.35 34.34a8 8 0 0 1-11.32 11.32l-48-48a8 8 0 0 1 0-11.32l48-48a8 8 0 0 1 11.32 11.32L67.31 168H184V32a8 8 0 0 1 16 0Z" />
-    </svg>
-  )
-}
-
-function IconSpinner({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4 animate-spin', className)}
-      {...props}
-    >
-      <path d="M232 128a104 104 0 0 1-208 0c0-41 23.81-78.36 60.66-95.27a8 8 0 0 1 6.68 14.54C60.15 61.59 40 93.27 40 128a88 88 0 0 0 176 0c0-34.73-20.15-66.41-51.34-80.73a8 8 0 0 1 6.68-14.54C208.19 49.64 232 87 232 128Z" />
-    </svg>
-  )
-}
-
-function IconMessage({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 48H40a16 16 0 0 0-16 16v160a15.84 15.84 0 0 0 9.25 14.5A16.05 16.05 0 0 0 40 240a15.89 15.89 0 0 0 10.25-3.78.69.69 0 0 0 .13-.11L82.5 208H216a16 16 0 0 0 16-16V64a16 16 0 0 0-16-16ZM40 224Zm176-32H82.5a16 16 0 0 0-10.3 3.75l-.12.11L40 224V64h176Z" />
-    </svg>
-  )
-}
-
-function IconTrash({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 48h-40v-8a24 24 0 0 0-24-24h-48a24 24 0 0 0-24 24v8H40a8 8 0 0 0 0 16h8v144a16 16 0 0 0 16 16h128a16 16 0 0 0 16-16V64h8a8 8 0 0 0 0-16ZM96 40a8 8 0 0 1 8-8h48a8 8 0 0 1 8 8v8H96Zm96 168H64V64h128Zm-80-104v64a8 8 0 0 1-16 0v-64a8 8 0 0 1 16 0Zm48 0v64a8 8 0 0 1-16 0v-64a8 8 0 0 1 16 0Z" />
-    </svg>
-  )
-}
-
-function IconRefresh({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M197.67 186.37a8 8 0 0 1 0 11.29C196.58 198.73 170.82 224 128 224c-37.39 0-64.53-22.4-80-39.85V208a8 8 0 0 1-16 0v-48a8 8 0 0 1 8-8h48a8 8 0 0 1 0 16H55.44C67.76 183.35 93 208 128 208c36 0 58.14-21.46 58.36-21.68a8 8 0 0 1 11.31.05ZM216 40a8 8 0 0 0-8 8v23.85C192.53 54.4 165.39 32 128 32c-42.82 0-68.58 25.27-69.66 26.34a8 8 0 0 0 11.3 11.34C69.86 69.46 92 48 128 48c35 0 60.24 24.65 72.56 40H168a8 8 0 0 0 0 16h48a8 8 0 0 0 8-8V48a8 8 0 0 0-8-8Z" />
-    </svg>
-  )
-}
-
-function IconStop({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M128 24a104 104 0 1 0 104 104A104.11 104.11 0 0 0 128 24Zm0 192a88 88 0 1 1 88-88 88.1 88.1 0 0 1-88 88Zm24-120h-48a8 8 0 0 0-8 8v48a8 8 0 0 0 8 8h48a8 8 0 0 0 8-8v-48a8 8 0 0 0-8-8Zm-8 48h-32v-32h32Z" />
-    </svg>
-  )
-}
-
-function IconSidebar({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 40H40a16 16 0 0 0-16 16v144a16 16 0 0 0 16 16h176a16 16 0 0 0 16-16V56a16 16 0 0 0-16-16ZM40 56h40v144H40Zm176 144H96V56h120v144Z" />
-    </svg>
-  )
-}
-
-function IconMoon({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M233.54 142.23a8 8 0 0 0-8-2 88.08 88.08 0 0 1-109.8-109.8 8 8 0 0 0-10-10 104.84 104.84 0 0 0-52.91 37A104 104 0 0 0 136 224a103.09 103.09 0 0 0 62.52-20.88 104.84 104.84 0 0 0 37-52.91 8 8 0 0 0-1.98-7.98Zm-44.64 48.11A88 88 0 0 1 65.66 67.11a89 89 0 0 1 31.4-26A106 106 0 0 0 96 56a104.11 104.11 0 0 0 104 104 106 106 0 0 0 14.92-1.06 89 89 0 0 1-26.02 31.4Z" />
-    </svg>
-  )
-}
-
-function IconSun({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M120 40V16a8 8 0 0 1 16 0v24a8 8 0 0 1-16 0Zm72 88a64 64 0 1 1-64-64 64.07 64.07 0 0 1 64 64Zm-16 0a48 48 0 1 0-48 48 48.05 48.05 0 0 0 48-48ZM58.34 69.66a8 8 0 0 0 11.32-11.32l-16-16a8 8 0 0 0-11.32 11.32Zm0 116.68-16 16a8 8 0 0 0 11.32 11.32l16-16a8 8 0 0 0-11.32-11.32ZM192 72a8 8 0 0 0 5.66-2.34l16-16a8 8 0 0 0-11.32-11.32l-16 16A8 8 0 0 0 192 72Zm5.66 114.34a8 8 0 0 0-11.32 11.32l16 16a8 8 0 0 0 11.32-11.32ZM48 128a8 8 0 0 0-8-8H16a8 8 0 0 0 0 16h24a8 8 0 0 0 8-8Zm80 80a8 8 0 0 0-8 8v24a8 8 0 0 0 16 0v-24a8 8 0 0 0-8-8Zm112-88h-24a8 8 0 0 0 0 16h24a8 8 0 0 0 0-16Z" />
-    </svg>
-  )
-}
-
-function IconCopy({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 32H88a8 8 0 0 0-8 8v40H40a8 8 0 0 0-8 8v128a8 8 0 0 0 8 8h128a8 8 0 0 0 8-8v-40h40a8 8 0 0 0 8-8V40a8 8 0 0 0-8-8Zm-56 176H48V96h112Zm48-48h-32V88a8 8 0 0 0-8-8H96V48h112Z" />
-    </svg>
-  )
-}
-
-function IconCheck({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="m229.66 77.66-128 128a8 8 0 0 1-11.32 0l-56-56a8 8 0 0 1 11.32-11.32L96 188.69 218.34 66.34a8 8 0 0 1 11.32 11.32Z" />
-    </svg>
-  )
-}
-
-function IconDownload({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M224 152v56a16 16 0 0 1-16 16H48a16 16 0 0 1-16-16v-56a8 8 0 0 1 16 0v56h160v-56a8 8 0 0 1 16 0Zm-101.66 5.66a8 8 0 0 0 11.32 0l40-40a8 8 0 0 0-11.32-11.32L136 132.69V40a8 8 0 0 0-16 0v92.69l-26.34-26.35a8 8 0 0 0-11.32 11.32Z" />
-    </svg>
-  )
-}
-
-function IconClose({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M205.66 194.34a8 8 0 0 1-11.32 11.32L128 139.31l-66.34 66.35a8 8 0 0 1-11.32-11.32L116.69 128 50.34 61.66a8 8 0 0 1 11.32-11.32L128 116.69l66.34-66.35a8 8 0 0 1 11.32 11.32L139.31 128Z" />
-    </svg>
-  )
-}
-
-function IconEdit({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="none"
-      viewBox="0 0 24 24"
-      strokeWidth={1.5}
-      stroke="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path
-        strokeLinecap="round"
-        strokeLinejoin="round"
-        d="M16.862 4.487l1.687-1.688a1.875 1.875 0 112.652 2.652L10.582 16.07a4.5 4.5 0 01-1.897 1.13L6 18l.8-2.685a4.5 4.5 0 011.13-1.897l8.932-8.931zm0 0L19.5 7.125M18 14v4.75A2.25 2.25 0 0115.75 21H5.25A2.25 2.25 0 013 18.75V8.25A2.25 2.25 0 015.25 6H10"
-      />
-    </svg>
-  )
-}
-
-function IconShare({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="m237.66 106.35-80-80A8 8 0 0 0 144 32v40.35c-25.94 2.22-54.59 14.92-78.16 34.91-28.38 24.08-46.05 55.11-49.76 87.37a12 12 0 0 0 20.68 9.58c11-11.71 50.14-48.74 107.24-52V192a8 8 0 0 0 13.66 5.65l80-80a8 8 0 0 0 0-11.3ZM160 172.69V144a8 8 0 0 0-8-8c-28.08 0-55.43 7.33-81.29 21.8a196.17 196.17 0 0 0-36.57 26.52c5.8-23.84 20.42-46.51 42.05-64.86C99.41 99.77 127.75 88 152 88a8 8 0 0 0 8-8V51.32L220.69 112Z" />
-    </svg>
-  )
-}
-
-function IconUsers({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="M117.25 157.92a60 60 0 1 0-66.5 0 95.83 95.83 0 0 0-47.22 37.71 8 8 0 1 0 13.4 8.74 80 80 0 0 1 134.14 0 8 8 0 0 0 13.4-8.74 95.83 95.83 0 0 0-47.22-37.71ZM40 108a44 44 0 1 1 44 44 44.05 44.05 0 0 1-44-44Zm210.14 98.7a8 8 0 0 1-11.07-2.33A79.83 79.83 0 0 0 172 168a8 8 0 0 1 0-16 44 44 0 1 0-16.34-84.87 8 8 0 1 1-5.94-14.85 60 60 0 0 1 55.53 105.64 95.83 95.83 0 0 1 47.22 37.71 8 8 0 0 1-2.33 11.07Z" />
-    </svg>
-  )
-}
-
-function IconExternalLink({
-  className,
-  ...props
-}: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="M224 104a8 8 0 0 1-16 0V59.32l-66.33 66.34a8 8 0 0 1-11.32-11.32L196.68 48H152a8 8 0 0 1 0-16h64a8 8 0 0 1 8 8Zm-40 24a8 8 0 0 0-8 8v72H48V80h72a8 8 0 0 0 0-16H48a16 16 0 0 0-16 16v128a16 16 0 0 0 16 16h128a16 16 0 0 0 16-16v-72a8 8 0 0 0-8-8Z" />
-    </svg>
-  )
-}
-
-function IconChevronUpDown({
-  className,
-  ...props
-}: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="M181.66 170.34a8 8 0 0 1 0 11.32l-48 48a8 8 0 0 1-11.32 0l-48-48a8 8 0 0 1 11.32-11.32L128 212.69l42.34-42.35a8 8 0 0 1 11.32 0Zm-96-84.68L128 43.31l42.34 42.35a8 8 0 0 0 11.32-11.32l-48-48a8 8 0 0 0-11.32 0l-48 48a8 8 0 0 0 11.32 11.32Z" />
-    </svg>
-  )
-}
-
-export {
-  IconEdit,
-  IconNextChat,
-  IconOpenAI,
-  IconGitHub,
-  IconSeparator,
-  IconArrowDown,
-  IconArrowRight,
-  IconUser,
-  IconPlus,
-  IconArrowElbow,
-  IconSpinner,
-  IconMessage,
-  IconTrash,
-  IconRefresh,
-  IconStop,
-  IconSidebar,
-  IconMoon,
-  IconSun,
-  IconCopy,
-  IconCheck,
-  IconDownload,
-  IconClose,
-  IconShare,
-  IconUsers,
-  IconExternalLink,
-  IconChevronUpDown
-}
diff --git a/spaces/hrnph/rvc-models/config.py b/spaces/hrnph/rvc-models/config.py
deleted file mode 100644
index c0c16e0017efbcaf250cb539a1d0edb4e83575e4..0000000000000000000000000000000000000000
--- a/spaces/hrnph/rvc-models/config.py
+++ /dev/null
@@ -1,88 +0,0 @@
-########################硬件参数########################
-
-# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
-device = "cuda:0"
-
-# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
-is_half = True
-
-# 默认0用上所有线程，写数字限制CPU资源使用
-n_cpu = 0
-
-########################硬件参数########################
-
-
-##################下为参数处理逻辑，勿动##################
-
-########################命令行参数########################
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--port", type=int, default=7865, help="Listen port")
-parser.add_argument("--pycmd", type=str, default="python", help="Python command")
-parser.add_argument("--colab", action="store_true", help="Launch in colab")
-parser.add_argument(
-    "--noparallel", action="store_true", help="Disable parallel processing"
-)
-parser.add_argument(
-    "--noautoopen", action="store_true", help="Do not open in browser automatically"
-)
-cmd_opts, unknown = parser.parse_known_args()
-
-python_cmd = cmd_opts.pycmd
-listen_port = cmd_opts.port
-iscolab = cmd_opts.colab
-noparallel = cmd_opts.noparallel
-noautoopen = cmd_opts.noautoopen
-########################命令行参数########################
-
-import sys
-import torch
-
-
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-# check `getattr` and try it for compatibility
-def has_mps() -> bool:
-    if sys.platform != "darwin":
-        return False
-    else:
-        if not getattr(torch, "has_mps", False):
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-
-if not torch.cuda.is_available():
-    if has_mps():
-        print("没有发现支持的N卡, 使用MPS进行推理")
-        device = "mps"
-    else:
-        print("没有发现支持的N卡, 使用CPU进行推理")
-        device = "cpu"
-        is_half = False
-
-if device not in ["cpu", "mps"]:
-    gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
-    if "16" in gpu_name or "MX" in gpu_name:
-        print("16系显卡/MX系显卡强制单精度")
-        is_half = False
-
-from multiprocessing import cpu_count
-
-if n_cpu == 0:
-    n_cpu = cpu_count()
-if is_half:
-    # 6G显存配置
-    x_pad = 3
-    x_query = 10
-    x_center = 60
-    x_max = 65
-else:
-    # 5G显存配置
-    x_pad = 1
-    x_query = 6
-    x_center = 38
-    x_max = 41
diff --git a/spaces/hzwluoye/gpt4/client/css/conversation.css b/spaces/hzwluoye/gpt4/client/css/conversation.css
deleted file mode 100644
index d20f178c45e8ccbfc9539f99914b25fc572045bd..0000000000000000000000000000000000000000
--- a/spaces/hzwluoye/gpt4/client/css/conversation.css
+++ /dev/null
@@ -1,158 +0,0 @@
-.conversation {
-	width: 60%;
-	margin: 0px 16px;
-	display: flex;
-	flex-direction: column;
-}
-
-.conversation #messages {
-	width: 100%;
-	display: flex;
-	flex-direction: column;
-	overflow: auto;
-	overflow-wrap: break-word;
-	padding-bottom: 8px;
-}
-
-.conversation .user-input {
-	max-height: 180px;
-	margin: 16px 0px;
-}
-
-.conversation .user-input input {
-	font-size: 1rem;
-	background: none;
-	border: none;
-	outline: none;
-	color: var(--colour-3);
-}
-
-.conversation .user-input input::placeholder {
-	color: var(--user-input);
-}
-
-.conversation-title {
-	color: var(--colour-3);
-	font-size: 14px;
-}
-
-.conversation .user-input textarea {
-	font-size: 1rem;
-	width: 100%;
-	height: 100%;
-	padding: 12px;
-	background: none;
-	border: none;
-	outline: none;
-	color: var(--colour-3);
-	resize: vertical;
-	max-height: 150px;
-	min-height: 80px;
-}
-
-.box {
-	backdrop-filter: blur(20px);
-	-webkit-backdrop-filter: blur(20px);
-	background-color: var(--blur-bg);
-	height: 100%;
-	width: 100%;
-	border-radius: var(--border-radius-1);
-	border: 1px solid var(--blur-border);
-}
-
-.box.input-box {
-	position: relative;
-	align-items: center;
-	padding: 8px;
-	cursor: pointer;
-}
-
-#send-button {
-	position: absolute;
-	bottom: 25%;
-	right: 10px;
-	z-index: 1;
-	padding: 16px;
-}
-
-#cursor {
-	line-height: 17px;
-	margin-left: 3px;
-	-webkit-animation: blink 0.8s infinite;
-	animation: blink 0.8s infinite;
-	width: 7px;
-	height: 15px;
-}
-
-@keyframes blink {
-	0% {
-		background: #ffffff00;
-	}
-
-	50% {
-		background: white;
-	}
-
-	100% {
-		background: #ffffff00;
-	}
-}
-
-@-webkit-keyframes blink {
-	0% {
-		background: #ffffff00;
-	}
-
-	50% {
-		background: white;
-	}
-
-	100% {
-		background: #ffffff00;
-	}
-}
-
-/* scrollbar */
-.conversation #messages::-webkit-scrollbar {
-	width: 4px;
-	padding: 8px 0px;
-}
-
-.conversation #messages::-webkit-scrollbar-track {
-	background-color: #ffffff00;
-}
-
-.conversation #messages::-webkit-scrollbar-thumb {
-	background-color: #555555;
-	border-radius: 10px;
-}
-
-@media screen and (max-width: 990px) {
-	.conversation {
-		width: 100%;
-		height: 90%;
-	}
-}
-
-@media screen and (max-height: 720px) {
-	.conversation.box {
-		height: 70%;
-	}
-
-	.conversation .user-input textarea {
-		font-size: 0.875rem;
-	}
-}
-
-@media screen and (max-width: 360px) {
-	.box {
-		border-radius: 0;
-	}
-	.conversation {
-		margin: 0;
-		margin-top: 48px;
-	}
-	.conversation .user-input {
-		margin: 2px 0 8px 0;
-	}
-}
diff --git a/spaces/hzwluoye/gpt4/g4f/utils.py b/spaces/hzwluoye/gpt4/g4f/utils.py
deleted file mode 100644
index d5ab41c79b44ab81e1843d209cb342bd83dafb42..0000000000000000000000000000000000000000
--- a/spaces/hzwluoye/gpt4/g4f/utils.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import browser_cookie3
-
-
-class Utils:
-    browsers = [ 
-        browser_cookie3.chrome,   # 62.74% market share
-        browser_cookie3.safari,   # 24.12% market share
-        browser_cookie3.firefox,  #  4.56% market share
-        browser_cookie3.edge,     #  2.85% market share 
-        browser_cookie3.opera,    #  1.69% market share
-        browser_cookie3.brave,    #  0.96% market share
-        browser_cookie3.opera_gx, #  0.64% market share
-        browser_cookie3.vivaldi,  #  0.32% market share
-    ]
-
-    def get_cookies(domain: str, setName: str = None, setBrowser: str = False) -> dict:
-        cookies = {}
-        
-        if setBrowser != False:
-            for browser in Utils.browsers:
-                if browser.__name__ == setBrowser:
-                    try:
-                        for c in browser(domain_name=domain):
-                            if c.name not in cookies:
-                                cookies = cookies | {c.name: c.value} 
-                    
-                    except Exception as e:
-                        pass
-        
-        else:
-            for browser in Utils.browsers:
-                try:
-                    for c in browser(domain_name=domain):
-                        if c.name not in cookies:
-                            cookies = cookies | {c.name: c.value} 
-                
-                except Exception as e:
-                    pass
-        
-        if setName:
-            try:
-                return {setName: cookies[setName]}
-            
-            except ValueError:
-                print(f'Error: could not find {setName} cookie in any browser.')
-                exit(1)
-        
-        else:
-            return cookies
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/base_model.py b/spaces/iamironman4279/SadTalker/src/face3d/models/base_model.py
deleted file mode 100644
index cfe64a7f739ad8f8cfbf3073a2bf49e1468127fd..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/models/base_model.py
+++ /dev/null
@@ -1,316 +0,0 @@
-"""This script defines the base network model for Deep3DFaceRecon_pytorch
-"""
-
-import os
-import numpy as np
-import torch
-from collections import OrderedDict
-from abc import ABC, abstractmethod
-from . import networks
-
-
-class BaseModel(ABC):
-    """This class is an abstract base class (ABC) for models.
-    To create a subclass, you need to implement the following five functions:
-        -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
-        -- <set_input>:                     unpack data from dataset and apply preprocessing.
-        -- <forward>:                       produce intermediate results.
-        -- <optimize_parameters>:           calculate losses, gradients, and update network weights.
-        -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
-    """
-
-    def __init__(self, opt):
-        """Initialize the BaseModel class.
-
-        Parameters:
-            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        When creating your custom class, you need to implement your own initialization.
-        In this fucntion, you should first call <BaseModel.__init__(self, opt)>
-        Then, you need to define four lists:
-            -- self.loss_names (str list):          specify the training losses that you want to plot and save.
-            -- self.model_names (str list):         specify the images that you want to display and save.
-            -- self.visual_names (str list):        define networks used in our training.
-            -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
-        """
-        self.opt = opt
-        self.isTrain = False
-        self.device = torch.device('cpu') 
-        self.save_dir = " " # os.path.join(opt.checkpoints_dir, opt.name)  # save all the checkpoints to save_dir
-        self.loss_names = []
-        self.model_names = []
-        self.visual_names = []
-        self.parallel_names = []
-        self.optimizers = []
-        self.image_paths = []
-        self.metric = 0  # used for learning rate policy 'plateau'
-
-    @staticmethod
-    def dict_grad_hook_factory(add_func=lambda x: x):
-        saved_dict = dict()
-
-        def hook_gen(name):
-            def grad_hook(grad):
-                saved_vals = add_func(grad)
-                saved_dict[name] = saved_vals
-            return grad_hook
-        return hook_gen, saved_dict
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new model-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        return parser
-
-    @abstractmethod
-    def set_input(self, input):
-        """Unpack input data from the dataloader and perform necessary pre-processing steps.
-
-        Parameters:
-            input (dict): includes the data itself and its metadata information.
-        """
-        pass
-
-    @abstractmethod
-    def forward(self):
-        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
-        pass
-
-    @abstractmethod
-    def optimize_parameters(self):
-        """Calculate losses, gradients, and update network weights; called in every training iteration"""
-        pass
-
-    def setup(self, opt):
-        """Load and print networks; create schedulers
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        if self.isTrain:
-            self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
-        
-        if not self.isTrain or opt.continue_train:
-            load_suffix = opt.epoch
-            self.load_networks(load_suffix)
- 
-            
-        # self.print_networks(opt.verbose)
-
-    def parallelize(self, convert_sync_batchnorm=True):
-        if not self.opt.use_ddp:
-            for name in self.parallel_names:
-                if isinstance(name, str):
-                    module = getattr(self, name)
-                    setattr(self, name, module.to(self.device))
-        else:
-            for name in self.model_names:
-                if isinstance(name, str):
-                    module = getattr(self, name)
-                    if convert_sync_batchnorm:
-                        module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module)
-                    setattr(self, name, torch.nn.parallel.DistributedDataParallel(module.to(self.device),
-                        device_ids=[self.device.index], 
-                        find_unused_parameters=True, broadcast_buffers=True))
-            
-            # DistributedDataParallel is not needed when a module doesn't have any parameter that requires a gradient.
-            for name in self.parallel_names:
-                if isinstance(name, str) and name not in self.model_names:
-                    module = getattr(self, name)
-                    setattr(self, name, module.to(self.device))
-            
-        # put state_dict of optimizer to gpu device
-        if self.opt.phase != 'test':
-            if self.opt.continue_train:
-                for optim in self.optimizers:
-                    for state in optim.state.values():
-                        for k, v in state.items():
-                            if isinstance(v, torch.Tensor):
-                                state[k] = v.to(self.device)
-
-    def data_dependent_initialize(self, data):
-        pass
-
-    def train(self):
-        """Make models train mode"""
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                net.train()
-
-    def eval(self):
-        """Make models eval mode"""
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                net.eval()
-
-    def test(self):
-        """Forward function used in test time.
-
-        This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
-        It also calls <compute_visuals> to produce additional visualization results
-        """
-        with torch.no_grad():
-            self.forward()
-            self.compute_visuals()
-
-    def compute_visuals(self):
-        """Calculate additional output images for visdom and HTML visualization"""
-        pass
-
-    def get_image_paths(self, name='A'):
-        """ Return image paths that are used to load current data"""
-        return self.image_paths if name =='A' else self.image_paths_B
-
-    def update_learning_rate(self):
-        """Update learning rates for all the networks; called at the end of every epoch"""
-        for scheduler in self.schedulers:
-            if self.opt.lr_policy == 'plateau':
-                scheduler.step(self.metric)
-            else:
-                scheduler.step()
-
-        lr = self.optimizers[0].param_groups[0]['lr']
-        print('learning rate = %.7f' % lr)
-
-    def get_current_visuals(self):
-        """Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
-        visual_ret = OrderedDict()
-        for name in self.visual_names:
-            if isinstance(name, str):
-                visual_ret[name] = getattr(self, name)[:, :3, ...]
-        return visual_ret
-
-    def get_current_losses(self):
-        """Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
-        errors_ret = OrderedDict()
-        for name in self.loss_names:
-            if isinstance(name, str):
-                errors_ret[name] = float(getattr(self, 'loss_' + name))  # float(...) works for both scalar tensor and float number
-        return errors_ret
-
-    def save_networks(self, epoch):
-        """Save all the networks to the disk.
-
-        Parameters:
-            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
-        """
-        if not os.path.isdir(self.save_dir):
-            os.makedirs(self.save_dir)
-
-        save_filename = 'epoch_%s.pth' % (epoch)
-        save_path = os.path.join(self.save_dir, save_filename)
-        
-        save_dict = {}
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                if isinstance(net, torch.nn.DataParallel) or isinstance(net,
-                        torch.nn.parallel.DistributedDataParallel):
-                    net = net.module
-                save_dict[name] = net.state_dict()
-                
-
-        for i, optim in enumerate(self.optimizers):
-            save_dict['opt_%02d'%i] = optim.state_dict()
-
-        for i, sched in enumerate(self.schedulers):
-            save_dict['sched_%02d'%i] = sched.state_dict()
-        
-        torch.save(save_dict, save_path)
-
-    def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
-        """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
-        key = keys[i]
-        if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
-            if module.__class__.__name__.startswith('InstanceNorm') and \
-                    (key == 'running_mean' or key == 'running_var'):
-                if getattr(module, key) is None:
-                    state_dict.pop('.'.join(keys))
-            if module.__class__.__name__.startswith('InstanceNorm') and \
-               (key == 'num_batches_tracked'):
-                state_dict.pop('.'.join(keys))
-        else:
-            self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
-
-    def load_networks(self, epoch):
-        """Load all the networks from the disk.
-
-        Parameters:
-            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
-        """
-        if self.opt.isTrain and self.opt.pretrained_name is not None:
-            load_dir = os.path.join(self.opt.checkpoints_dir, self.opt.pretrained_name)
-        else:
-            load_dir = self.save_dir    
-        load_filename = 'epoch_%s.pth' % (epoch)
-        load_path = os.path.join(load_dir, load_filename)
-        state_dict = torch.load(load_path, map_location=self.device)
-        print('loading the model from %s' % load_path)
-
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                if isinstance(net, torch.nn.DataParallel):
-                    net = net.module
-                net.load_state_dict(state_dict[name])
-        
-        if self.opt.phase != 'test':
-            if self.opt.continue_train:
-                print('loading the optim from %s' % load_path)
-                for i, optim in enumerate(self.optimizers):
-                    optim.load_state_dict(state_dict['opt_%02d'%i])
-
-                try:
-                    print('loading the sched from %s' % load_path)
-                    for i, sched in enumerate(self.schedulers):
-                        sched.load_state_dict(state_dict['sched_%02d'%i])
-                except:
-                    print('Failed to load schedulers, set schedulers according to epoch count manually')
-                    for i, sched in enumerate(self.schedulers):
-                        sched.last_epoch = self.opt.epoch_count - 1
-                    
-
-            
-
-    def print_networks(self, verbose):
-        """Print the total number of parameters in the network and (if verbose) network architecture
-
-        Parameters:
-            verbose (bool) -- if verbose: print the network architecture
-        """
-        print('---------- Networks initialized -------------')
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                num_params = 0
-                for param in net.parameters():
-                    num_params += param.numel()
-                if verbose:
-                    print(net)
-                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
-        print('-----------------------------------------------')
-
-    def set_requires_grad(self, nets, requires_grad=False):
-        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
-        Parameters:
-            nets (network list)   -- a list of networks
-            requires_grad (bool)  -- whether the networks require gradients or not
-        """
-        if not isinstance(nets, list):
-            nets = [nets]
-        for net in nets:
-            if net is not None:
-                for param in net.parameters():
-                    param.requires_grad = requires_grad
-
-    def generate_visuals_for_evaluation(self, data, mode):
-        return {}
diff --git a/spaces/ifey/chatdemo/gradiodemo/Demo/ChatInput.py b/spaces/ifey/chatdemo/gradiodemo/Demo/ChatInput.py
deleted file mode 100644
index 5568b1d17b55fa0c86a25539dda0ccc8d591713c..0000000000000000000000000000000000000000
--- a/spaces/ifey/chatdemo/gradiodemo/Demo/ChatInput.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
-import os
-import time
-
-import gradio as gr
-
-def add_text(history, text):
-    history = history + [(text, None)]
-    return history, gr.update(value="", interactive=False)
-
-
-def add_file(history, file):
-    history = history + [((file.name,), None)]
-    return history
-
-
-def bot(history):
-    response = "**That's cool!**"
-    history[-1][1] = ""
-    for character in response:
-        history[-1][1] += character
-        time.sleep(0.05)
-        yield history
-
-
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(
-        [],
-        elem_id="chatbot",
-        bubble_full_width=False,
-        avatar_images=(None, (os.path.join(os.path.dirname(__file__), "avatar.png"))),
-    )
-
-    with gr.Row():
-        txt = gr.Textbox(
-            scale=4,
-            show_label=False,
-            placeholder="Enter text and press enter, or upload an image",
-            container=False,
-        )
-        btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
-
-    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
-        bot, chatbot, chatbot
-    )
-    txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)
-    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-
-demo.queue()
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/iknow-lab/ko-flan-zero/app.py b/spaces/iknow-lab/ko-flan-zero/app.py
deleted file mode 100644
index ff89b4db942b55b7226017c97601758cf0170343..0000000000000000000000000000000000000000
--- a/spaces/iknow-lab/ko-flan-zero/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import json
-import torch
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-model = pipeline("text-classification", "iknow-lab/ko-flan-zero-v0-0731", device=device)
-model.tokenizer.truncation_side = 'left'
-
-def inference(input, instruction, labels):
-    instruction = f"{input} [SEP] {instruction}"
-    inputs = model.tokenizer([instruction] * len(labels), labels, truncation=True, padding=True, return_tensors="pt").to(device)
-    
-    scores = model.model(**inputs).logits.squeeze(1).softmax(-1).tolist()
-    output = dict(zip(labels, scores))
-
-    print(instruction)
-    print(output)
-    return output, json.dumps(output, ensure_ascii=False)
-
-
-def greet(content, instruction, labels):
-    labels = labels.split(",")
-    output = inference(content, instruction, labels)
-    return output
-
-content = gr.TextArea(label="입력 내용")
-instruction = gr.Textbox(label="지시문")
-labels = gr.Textbox(label="라벨(쉼표로 구분)")
-
-examples = [
-    ["예전에는 주말마다 극장에 놀러갔는데 요새는 좀 안가는 편이에요", "댓글 주제를 분류하세요", "영화,드라마,게임,소설"],
-    ["인천발 KTX와 관련한 송도역 복합환승센터가 사실상 무산, 단순 철도·버스 위주 환승시설로 만들어진다. 이 때문에 인천시의 인천발 KTX 기점에 앵커시설인 복합환승센터를 통한 인근 지역 경제 활성화를 이뤄낸다는 계획의 차질이 불가피하다.", "경제에 긍정적인 뉴스인가요?", "예,아니요"],
-    ["마지막에는 k팝 공연보고 좋은 추억 남았으면 좋겠네요","욕설이 포함되어있나요?", "욕설이 있습니다,욕설이 없습니다"],
-]
-gr.Interface(fn=greet,
-             inputs=[content, instruction, labels],
-             outputs=[gr.Label(), gr.Text({}, label="json",)],
-             examples=examples).launch(server_name="0.0.0.0",server_port=7860)
\ No newline at end of file
diff --git a/spaces/inamXcontru/PoeticTTS/Cartea Secretul Sufletului Pereche PDF Povestea Inspiratoare a Arielle Ford si a Sotului ei.md b/spaces/inamXcontru/PoeticTTS/Cartea Secretul Sufletului Pereche PDF Povestea Inspiratoare a Arielle Ford si a Sotului ei.md
deleted file mode 100644
index 0aa377bb5dbd28b5fbbaf0abc5ee3a38b2218ed6..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Cartea Secretul Sufletului Pereche PDF Povestea Inspiratoare a Arielle Ford si a Sotului ei.md	
+++ /dev/null
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-<h2>cartea secretul sufletului pereche pdf download</h2><br /><p><b><b>Download File</b> &#9745; <a href="https://gohhs.com/2uz4eu">https://gohhs.com/2uz4eu</a></b></p><br /><br />
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diff --git a/spaces/inamXcontru/PoeticTTS/Critical Path Method Scheduling Pdf Download BEST.md b/spaces/inamXcontru/PoeticTTS/Critical Path Method Scheduling Pdf Download BEST.md
deleted file mode 100644
index 0c5a635381947800a56f0399a3eaa2cd3be934ea..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Critical Path Method Scheduling Pdf Download BEST.md	
+++ /dev/null
@@ -1,60 +0,0 @@
-
-<h1>Critical Path Method Scheduling: A Powerful Technique for Project Management</h1>
-<p>Do you have a complex project that involves multiple tasks, dependencies, and deadlines? Do you want to plan, organize, and execute your project in the most efficient and effective way possible? If so, you may want to learn about critical path method scheduling, a proven technique that can help you achieve your project goals.</p>
-<p>Critical path method scheduling (CPM) is a mathematical algorithm that analyzes the sequence, duration, and interrelationships of the tasks that make up a project. It identifies the critical path, which is the longest chain of dependent tasks that determines the minimum time required to complete the project. It also calculates the amount of slack or float for each task, which is the maximum time that a task can be delayed without affecting the project completion date.</p>
-<h2>critical path method scheduling pdf download</h2><br /><p><b><b>DOWNLOAD</b> &#10004;&#10004;&#10004; <a href="https://gohhs.com/2uz3L9">https://gohhs.com/2uz3L9</a></b></p><br /><br />
-<p>By using CPM, you can optimize your resource allocation, reduce your project risk, improve your project quality, and enhance your communication and collaboration with your team members and stakeholders. In this article, we will explain what CPM is, how it works, and how you can apply it to your own projects.</p>
-<h2>What is Critical Path Method Scheduling?</h2>
-<p>CPM was developed in the 1950s by Morgan Walker and James Kelley Jr., who were working on projects for DuPont and Remington Rand respectively. They realized that they needed a systematic way to schedule complex projects that involved many interdependent activities. They came up with a method that used network diagrams to represent the project activities and their logical relationships. They also devised a way to calculate the earliest and latest start and finish times for each activity, as well as the critical path and the float.</p>
-<p>A network diagram is a graphical representation of a project that shows all the activities and their dependencies using nodes and arrows. Each node represents an activity, which is a discrete unit of work that consumes time and resources. Each arrow represents a dependency, which is a logical relationship that indicates which activity must precede or follow another activity. For example, if activity A must be finished before activity B can start, then there is a finish-to-start dependency between A and B.</p>
-<p>The duration of each activity is estimated based on historical data, expert judgment, or other methods. The duration can be expressed in any unit of time, such as hours, days, weeks, or months. The duration can also be deterministic or probabilistic. A deterministic duration is a fixed value that does not change. A probabilistic duration is a range of values that reflects the uncertainty or variability of the activity.</p>
-<p>The critical path is the longest path of dependent activities in the network diagram. It represents the shortest possible time to complete the project. Any delay in any activity on the critical path will directly affect the project completion date. The activities on the critical path are called critical activities, and they have zero float.</p>
-<p>The float or slack of an activity is the amount of time that an activity can be delayed without affecting the project completion date. It is calculated by subtracting the earliest start time from the latest start time or the earliest finish time from the latest finish time of an activity. The float can be positive or negative. A positive float means that an activity has some flexibility and can be delayed up to that amount without affecting the project completion date. A negative float means that an activity is already behind schedule and must be expedited to avoid delaying the project completion date.</p>
-<h2>How to Use Critical Path Method Scheduling for Your Project?</h2>
-<p>To use CPM for your project scheduling, you need to follow these steps:</p>
-<ol>
-<li>Define your project scope and objectives.</li>
-<li>Identify all the activities that are required to complete your project.</li>
-<li>Estimate the duration of each activity based on available resources and information.</li>
-<li>Determine the logical relationships and dependencies among the activities using one of these types: finish-to-start (FS), start-to-start (SS), finish-to-finish (FF), or start-to-finish (SF).</li>
-<li>Draw a network diagram that shows all the activities and their dependencies using nodes and arrows.</li>
-<li>Calculate the earliest start time (ES) and earliest finish time (EF) for each activity by performing a forward pass through the network diagram. Start with ES = 0 for the first activity and add its duration to get its EF. Then move to the next activity and assign its ES as the maximum EF of its predecessors. Repeat this process until you reach the last activity.</li>
-<li>Calculate the latest start time (LS) and latest finish time (LF) for each activity by performing a backward pass through the network diagram. Start with LF = EF for the last activity and subtract its duration to get its LS. Then move to the previous activity and assign its LF as the minimum LS of its successors. Repeat this process until you reach the first activity.</li>
-<li>Calculate the float or slack for each activity by subtracting its ES from its LS or its EF from its LF.</li>
-<li>Identify the critical path by tracing all the activities that have zero float from start to finish.</li>
-<li>Monitor and control your project schedule by updating your network diagram with actual progress data, revising your estimates if necessary, resolving any issues or risks that may cause delays, and taking corrective actions if any deviations occur.</li>
-</ol>
-<h2>What are Some Resources for Critical Path Method Scheduling PDF Download?</h2>
-<p>If you want to learn more about CPM or download some useful resources for applying it to your projects, you can visit some of these websites:</p>
-<p></p>
-<ul>
-<li><a href="https://www.researchgate.net/publication/357166876_Critical_path_method_CPM">ResearchGate</a>: This is a platform where you can find academic papers, articles, books, and reports on various topics related to CPM. You can also connect with other researchers and experts who can answer your questions or share their insights.</li>
-<li><a href="https://www.cimt.org.uk/projects/mepres/alevel/discrete_ch12.pdf">CIMT</a>: This is a website where you can find a chapter on critical path analysis from a discrete mathematics textbook. You can also access other chapters and exercises on related topics.</li>
-<li><a href="https://www.nasa.gov/pdf/293229main_62646main_1_pmchallenge_sparrow.pdf">NASA</a>: This is a website where you can find a presentation on CPM scheduling basics by Harry Sparrow, a project management consultant. You can also access other information and resources on NASA's projects and programs.</li>
-</ul></p>
-<h2>What are Some Examples of Critical Path Method Scheduling?</h2>
-<p>To illustrate how CPM works in practice, let us look at some examples of projects that use this technique for scheduling.</p>
-<p>One example is the construction of a house, which involves many activities such as preparing the site, laying the foundation, building the walls, installing the roof, plumbing, electrical wiring, painting, etc. Each activity has a certain duration and depends on other activities to be completed before it can start. For instance, plumbing cannot start until the walls are built, and painting cannot start until the plumbing is done. By using CPM, the project manager can identify the critical path of activities that determines the minimum time to finish the house, and also the float of non-critical activities that can be delayed without affecting the project deadline.</p>
-<p>Another example is the development of a software product, which involves many tasks such as planning, designing, coding, testing, debugging, documenting, deploying, etc. Each task has a certain effort and depends on other tasks to be completed before it can start. For example, coding cannot start until designing is done, and testing cannot start until coding is done. By using CPM, the software engineer can identify the critical path of tasks that determines the minimum time to deliver the product, and also the slack of non-critical tasks that can be postponed without affecting the product quality.</p>
-<h2>What are Some Challenges and Limitations of Critical Path Method Scheduling?</h2>
-<p>While CPM is a useful and widely used technique for project scheduling, it also has some challenges and limitations that need to be considered.</p>
-<ul>
-<li>One challenge is to define and estimate the activities and their durations accurately and realistically. This may require a lot of data collection, analysis, and judgment from various sources and experts. If the estimates are too optimistic or pessimistic, they may affect the validity and reliability of the network diagram and the critical path.</li>
-<li>Another challenge is to update and revise the network diagram and the critical path as the project progresses and changes occur. This may require constant monitoring and control of the project status, performance, issues, risks, and changes. If the network diagram and the critical path are not updated regularly and accurately, they may lose their relevance and usefulness for project management.</li>
-<li>One limitation is that CPM assumes that the activity durations are fixed and deterministic. However, in reality, many activities may have uncertain or variable durations due to factors such as weather conditions, resource availability, human errors, etc. In such cases, CPM may not reflect the true probability distribution of the project completion time.</li>
-<li>Another limitation is that CPM focuses on time as the main criterion for project scheduling. However, in reality, there may be other criteria that are equally or more important for project success, such as cost, quality, scope, risk, stakeholder satisfaction, etc. In such cases, CPM may not capture the trade-offs and balances among these criteria.</li>
-</ul>
-<h2>How to Download Critical Path Method Scheduling PDF?</h2>
-<p>If you want to download a PDF file that contains more information and examples on CPM scheduling, you can click on this link: <a href="https://www.researchgate.net/publication/362646942_Scheduling_Network_by_Critical_Path_Method">Scheduling Network by Critical Path Method</a>. This is a paper by Dr. Rakesh Kumar Sharma that explains how to apply CPM to various types of projects using network diagrams and calculations.</p>
-<p>You can also download other PDF files that are related to CPM scheduling from these websites:</p>
-<ul>
-<li><a href="https://www.researchgate.net/publication/357166876_Critical_path_method_CPM">Critical Path Method (CPM)</a>: This is a paper by Abas Khan and Mohammad Sarwar Mir that provides an overview of CPM history, definition, algorithm, advantages, disadvantages, and applications.</li>
-<li><a href="https://www.cimt.org.uk/projects/mepres/alevel/discrete_ch12.pdf">Critical Path Analysis</a>: This is a chapter from a discrete mathematics textbook by CIMT that introduces CPM concepts such as activity networks, precedence relations, earliest and latest times, float or slack, critical path identification.</li>
-<li><a href="https://www.nasa.gov/pdf/293229main_62646main_1_pmchallenge_sparrow.pdf">Critical Path Method Scheduling Basics</a>: This is a presentation by Harry Sparrow for NASA Project Management Conference that covers CPM topics such as network diagrams types and relationships types.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Critical path method scheduling is a powerful technique that can help you plan, organize, and execute your projects in the most efficient and effective way possible. It can help you identify the critical path of activities that determines the minimum time to complete your project, and also the float of non-critical activities that gives you some flexibility and buffer. It can help you optimize your resource allocation, reduce your project risk, improve your project quality, and enhance your communication and collaboration with your team members and stakeholders.</p>
-<p>To use CPM for your project scheduling, you need to define your project scope and objectives, identify all the activities and their durations and dependencies, draw a network diagram that shows all the activities and their relationships, calculate the earliest and latest start and finish times for each activity, calculate the float or slack for each activity, identify the critical path of activities, and monitor and control your project schedule. You can also use various software tools or online platforms to create and edit your network diagram and perform CPM calculations.</p>
-<p>If you want to learn more about CPM or download some useful resources for applying it to your projects, you can visit some of the websites that we have mentioned in this article. You can also find other websites that offer more information and examples on CPM scheduling. We hope that this article has given you a good introduction to CPM scheduling and how to use it for your project management.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/innnky/nyaru-svc2.0/attentions.py b/spaces/innnky/nyaru-svc2.0/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru-svc2.0/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Goanimate Free Download Crack For 267 HOT!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Goanimate Free Download Crack For 267 HOT!.md
deleted file mode 100644
index e9d481439907815dcada71191bf4858f5f1a8786..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Goanimate Free Download Crack For 267 HOT!.md	
+++ /dev/null
@@ -1,22 +0,0 @@
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (Jodi No.1 3 Full Movie Hd 1080p).md b/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (Jodi No.1 3 Full Movie Hd 1080p).md
deleted file mode 100644
index 33e63c3881ec6cb42c6c34ed282c95b005396b77..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (Jodi No.1 3 Full Movie Hd 1080p).md	
+++ /dev/null
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deleted file mode 100644
index 3a2305054423e317b9a95af47dadfb16251029af..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/1st Studio Siberian Mouse Hd 125 Tor VERIFIED.md	
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diff --git a/spaces/ivntl/MMS/uroman/lib/JSON/backportPP.pm b/spaces/ivntl/MMS/uroman/lib/JSON/backportPP.pm
deleted file mode 100644
index db4f8bbb3b741e95c5817edde612718af0f889e4..0000000000000000000000000000000000000000
--- a/spaces/ivntl/MMS/uroman/lib/JSON/backportPP.pm
+++ /dev/null
@@ -1,2806 +0,0 @@
-package # This is JSON::backportPP
-    JSON::PP;
-
-# JSON-2.0
-
-use 5.005;
-use strict;
-use base qw(Exporter);
-use overload ();
-
-use Carp ();
-use B ();
-#use Devel::Peek;
-
-use vars qw($VERSION);
-$VERSION = '2.27204';
-
-@JSON::PP::EXPORT = qw(encode_json decode_json from_json to_json);
-
-# instead of hash-access, i tried index-access for speed.
-# but this method is not faster than what i expected. so it will be changed.
-
-use constant P_ASCII                => 0;
-use constant P_LATIN1               => 1;
-use constant P_UTF8                 => 2;
-use constant P_INDENT               => 3;
-use constant P_CANONICAL            => 4;
-use constant P_SPACE_BEFORE         => 5;
-use constant P_SPACE_AFTER          => 6;
-use constant P_ALLOW_NONREF         => 7;
-use constant P_SHRINK               => 8;
-use constant P_ALLOW_BLESSED        => 9;
-use constant P_CONVERT_BLESSED      => 10;
-use constant P_RELAXED              => 11;
-
-use constant P_LOOSE                => 12;
-use constant P_ALLOW_BIGNUM         => 13;
-use constant P_ALLOW_BAREKEY        => 14;
-use constant P_ALLOW_SINGLEQUOTE    => 15;
-use constant P_ESCAPE_SLASH         => 16;
-use constant P_AS_NONBLESSED        => 17;
-
-use constant P_ALLOW_UNKNOWN        => 18;
-
-use constant OLD_PERL => $] < 5.008 ? 1 : 0;
-
-BEGIN {
-    my @xs_compati_bit_properties = qw(
-            latin1 ascii utf8 indent canonical space_before space_after allow_nonref shrink
-            allow_blessed convert_blessed relaxed allow_unknown
-    );
-    my @pp_bit_properties = qw(
-            allow_singlequote allow_bignum loose
-            allow_barekey escape_slash as_nonblessed
-    );
-
-    # Perl version check, Unicode handling is enable?
-    # Helper module sets @JSON::PP::_properties.
-    if ($] < 5.008 ) {
-        my $helper = $] >= 5.006 ? 'JSON::backportPP::Compat5006' : 'JSON::backportPP::Compat5005';
-        eval qq| require $helper |;
-        if ($@) { Carp::croak $@; }
-    }
-
-    for my $name (@xs_compati_bit_properties, @pp_bit_properties) {
-        my $flag_name = 'P_' . uc($name);
-
-        eval qq/
-            sub $name {
-                my \$enable = defined \$_[1] ? \$_[1] : 1;
-
-                if (\$enable) {
-                    \$_[0]->{PROPS}->[$flag_name] = 1;
-                }
-                else {
-                    \$_[0]->{PROPS}->[$flag_name] = 0;
-                }
-
-                \$_[0];
-            }
-
-            sub get_$name {
-                \$_[0]->{PROPS}->[$flag_name] ? 1 : '';
-            }
-        /;
-    }
-
-}
-
-
-
-# Functions
-
-my %encode_allow_method
-     = map {($_ => 1)} qw/utf8 pretty allow_nonref latin1 self_encode escape_slash
-                          allow_blessed convert_blessed indent indent_length allow_bignum
-                          as_nonblessed
-                        /;
-my %decode_allow_method
-     = map {($_ => 1)} qw/utf8 allow_nonref loose allow_singlequote allow_bignum
-                          allow_barekey max_size relaxed/;
-
-
-my $JSON; # cache
-
-sub encode_json ($) { # encode
-    ($JSON ||= __PACKAGE__->new->utf8)->encode(@_);
-}
-
-
-sub decode_json { # decode
-    ($JSON ||= __PACKAGE__->new->utf8)->decode(@_);
-}
-
-# Obsoleted
-
-sub to_json($) {
-   Carp::croak ("JSON::PP::to_json has been renamed to encode_json.");
-}
-
-
-sub from_json($) {
-   Carp::croak ("JSON::PP::from_json has been renamed to decode_json.");
-}
-
-
-# Methods
-
-sub new {
-    my $class = shift;
-    my $self  = {
-        max_depth   => 512,
-        max_size    => 0,
-        indent      => 0,
-        FLAGS       => 0,
-        fallback      => sub { encode_error('Invalid value. JSON can only reference.') },
-        indent_length => 3,
-    };
-
-    bless $self, $class;
-}
-
-
-sub encode {
-    return $_[0]->PP_encode_json($_[1]);
-}
-
-
-sub decode {
-    return $_[0]->PP_decode_json($_[1], 0x00000000);
-}
-
-
-sub decode_prefix {
-    return $_[0]->PP_decode_json($_[1], 0x00000001);
-}
-
-
-# accessor
-
-
-# pretty printing
-
-sub pretty {
-    my ($self, $v) = @_;
-    my $enable = defined $v ? $v : 1;
-
-    if ($enable) { # indent_length(3) for JSON::XS compatibility
-        $self->indent(1)->indent_length(3)->space_before(1)->space_after(1);
-    }
-    else {
-        $self->indent(0)->space_before(0)->space_after(0);
-    }
-
-    $self;
-}
-
-# etc
-
-sub max_depth {
-    my $max  = defined $_[1] ? $_[1] : 0x80000000;
-    $_[0]->{max_depth} = $max;
-    $_[0];
-}
-
-
-sub get_max_depth { $_[0]->{max_depth}; }
-
-
-sub max_size {
-    my $max  = defined $_[1] ? $_[1] : 0;
-    $_[0]->{max_size} = $max;
-    $_[0];
-}
-
-
-sub get_max_size { $_[0]->{max_size}; }
-
-
-sub filter_json_object {
-    $_[0]->{cb_object} = defined $_[1] ? $_[1] : 0;
-    $_[0]->{F_HOOK} = ($_[0]->{cb_object} or $_[0]->{cb_sk_object}) ? 1 : 0;
-    $_[0];
-}
-
-sub filter_json_single_key_object {
-    if (@_ > 1) {
-        $_[0]->{cb_sk_object}->{$_[1]} = $_[2];
-    }
-    $_[0]->{F_HOOK} = ($_[0]->{cb_object} or $_[0]->{cb_sk_object}) ? 1 : 0;
-    $_[0];
-}
-
-sub indent_length {
-    if (!defined $_[1] or $_[1] > 15 or $_[1] < 0) {
-        Carp::carp "The acceptable range of indent_length() is 0 to 15.";
-    }
-    else {
-        $_[0]->{indent_length} = $_[1];
-    }
-    $_[0];
-}
-
-sub get_indent_length {
-    $_[0]->{indent_length};
-}
-
-sub sort_by {
-    $_[0]->{sort_by} = defined $_[1] ? $_[1] : 1;
-    $_[0];
-}
-
-sub allow_bigint {
-    Carp::carp("allow_bigint() is obsoleted. use allow_bignum() insted.");
-}
-
-###############################
-
-###
-### Perl => JSON
-###
-
-
-{ # Convert
-
-    my $max_depth;
-    my $indent;
-    my $ascii;
-    my $latin1;
-    my $utf8;
-    my $space_before;
-    my $space_after;
-    my $canonical;
-    my $allow_blessed;
-    my $convert_blessed;
-
-    my $indent_length;
-    my $escape_slash;
-    my $bignum;
-    my $as_nonblessed;
-
-    my $depth;
-    my $indent_count;
-    my $keysort;
-
-
-    sub PP_encode_json {
-        my $self = shift;
-        my $obj  = shift;
-
-        $indent_count = 0;
-        $depth        = 0;
-
-        my $idx = $self->{PROPS};
-
-        ($ascii, $latin1, $utf8, $indent, $canonical, $space_before, $space_after, $allow_blessed,
-            $convert_blessed, $escape_slash, $bignum, $as_nonblessed)
-         = @{$idx}[P_ASCII .. P_SPACE_AFTER, P_ALLOW_BLESSED, P_CONVERT_BLESSED,
-                    P_ESCAPE_SLASH, P_ALLOW_BIGNUM, P_AS_NONBLESSED];
-
-        ($max_depth, $indent_length) = @{$self}{qw/max_depth indent_length/};
-
-        $keysort = $canonical ? sub { $a cmp $b } : undef;
-
-        if ($self->{sort_by}) {
-            $keysort = ref($self->{sort_by}) eq 'CODE' ? $self->{sort_by}
-                     : $self->{sort_by} =~ /\D+/       ? $self->{sort_by}
-                     : sub { $a cmp $b };
-        }
-
-        encode_error("hash- or arrayref expected (not a simple scalar, use allow_nonref to allow this)")
-             if(!ref $obj and !$idx->[ P_ALLOW_NONREF ]);
-
-        my $str  = $self->object_to_json($obj);
-
-        $str .= "\n" if ( $indent ); # JSON::XS 2.26 compatible
-
-        unless ($ascii or $latin1 or $utf8) {
-            utf8::upgrade($str);
-        }
-
-        if ($idx->[ P_SHRINK ]) {
-            utf8::downgrade($str, 1);
-        }
-
-        return $str;
-    }
-
-
-    sub object_to_json {
-        my ($self, $obj) = @_;
-        my $type = ref($obj);
-
-        if($type eq 'HASH'){
-            return $self->hash_to_json($obj);
-        }
-        elsif($type eq 'ARRAY'){
-            return $self->array_to_json($obj);
-        }
-        elsif ($type) { # blessed object?
-            if (blessed($obj)) {
-
-                return $self->value_to_json($obj) if ( $obj->isa('JSON::PP::Boolean') );
-
-                if ( $convert_blessed and $obj->can('TO_JSON') ) {
-                    my $result = $obj->TO_JSON();
-                    if ( defined $result and ref( $result ) ) {
-                        if ( refaddr( $obj ) eq refaddr( $result ) ) {
-                            encode_error( sprintf(
-                                "%s::TO_JSON method returned same object as was passed instead of a new one",
-                                ref $obj
-                            ) );
-                        }
-                    }
-
-                    return $self->object_to_json( $result );
-                }
-
-                return "$obj" if ( $bignum and _is_bignum($obj) );
-                return $self->blessed_to_json($obj) if ($allow_blessed and $as_nonblessed); # will be removed.
-
-                encode_error( sprintf("encountered object '%s', but neither allow_blessed "
-                    . "nor convert_blessed settings are enabled", $obj)
-                ) unless ($allow_blessed);
-
-                return 'null';
-            }
-            else {
-                return $self->value_to_json($obj);
-            }
-        }
-        else{
-            return $self->value_to_json($obj);
-        }
-    }
-
-
-    sub hash_to_json {
-        my ($self, $obj) = @_;
-        my @res;
-
-        encode_error("json text or perl structure exceeds maximum nesting level (max_depth set too low?)")
-                                         if (++$depth > $max_depth);
-
-        my ($pre, $post) = $indent ? $self->_up_indent() : ('', '');
-        my $del = ($space_before ? ' ' : '') . ':' . ($space_after ? ' ' : '');
-
-        for my $k ( _sort( $obj ) ) {
-            if ( OLD_PERL ) { utf8::decode($k) } # key for Perl 5.6 / be optimized
-            push @res, string_to_json( $self, $k )
-                          .  $del
-                          . ( $self->object_to_json( $obj->{$k} ) || $self->value_to_json( $obj->{$k} ) );
-        }
-
-        --$depth;
-        $self->_down_indent() if ($indent);
-
-        return   '{' . ( @res ? $pre : '' ) . ( @res ? join( ",$pre", @res ) . $post : '' )  . '}';
-    }
-
-
-    sub array_to_json {
-        my ($self, $obj) = @_;
-        my @res;
-
-        encode_error("json text or perl structure exceeds maximum nesting level (max_depth set too low?)")
-                                         if (++$depth > $max_depth);
-
-        my ($pre, $post) = $indent ? $self->_up_indent() : ('', '');
-
-        for my $v (@$obj){
-            push @res, $self->object_to_json($v) || $self->value_to_json($v);
-        }
-
-        --$depth;
-        $self->_down_indent() if ($indent);
-
-        return '[' . ( @res ? $pre : '' ) . ( @res ? join( ",$pre", @res ) . $post : '' ) . ']';
-    }
-
-
-    sub value_to_json {
-        my ($self, $value) = @_;
-
-        return 'null' if(!defined $value);
-
-        my $b_obj = B::svref_2object(\$value);  # for round trip problem
-        my $flags = $b_obj->FLAGS;
-
-        return $value # as is 
-            if $flags & ( B::SVp_IOK | B::SVp_NOK ) and !( $flags & B::SVp_POK ); # SvTYPE is IV or NV?
-
-        my $type = ref($value);
-
-        if(!$type){
-            return string_to_json($self, $value);
-        }
-        elsif( blessed($value) and  $value->isa('JSON::PP::Boolean') ){
-            return $$value == 1 ? 'true' : 'false';
-        }
-        elsif ($type) {
-            if ((overload::StrVal($value) =~ /=(\w+)/)[0]) {
-                return $self->value_to_json("$value");
-            }
-
-            if ($type eq 'SCALAR' and defined $$value) {
-                return   $$value eq '1' ? 'true'
-                       : $$value eq '0' ? 'false'
-                       : $self->{PROPS}->[ P_ALLOW_UNKNOWN ] ? 'null'
-                       : encode_error("cannot encode reference to scalar");
-            }
-
-             if ( $self->{PROPS}->[ P_ALLOW_UNKNOWN ] ) {
-                 return 'null';
-             }
-             else {
-                 if ( $type eq 'SCALAR' or $type eq 'REF' ) {
-                    encode_error("cannot encode reference to scalar");
-                 }
-                 else {
-                    encode_error("encountered $value, but JSON can only represent references to arrays or hashes");
-                 }
-             }
-
-        }
-        else {
-            return $self->{fallback}->($value)
-                 if ($self->{fallback} and ref($self->{fallback}) eq 'CODE');
-            return 'null';
-        }
-
-    }
-
-
-    my %esc = (
-        "\n" => '\n',
-        "\r" => '\r',
-        "\t" => '\t',
-        "\f" => '\f',
-        "\b" => '\b',
-        "\"" => '\"',
-        "\\" => '\\\\',
-        "\'" => '\\\'',
-    );
-
-
-    sub string_to_json {
-        my ($self, $arg) = @_;
-
-        $arg =~ s/([\x22\x5c\n\r\t\f\b])/$esc{$1}/g;
-        $arg =~ s/\//\\\//g if ($escape_slash);
-        $arg =~ s/([\x00-\x08\x0b\x0e-\x1f])/'\\u00' . unpack('H2', $1)/eg;
-
-        if ($ascii) {
-            $arg = JSON_PP_encode_ascii($arg);
-        }
-
-        if ($latin1) {
-            $arg = JSON_PP_encode_latin1($arg);
-        }
-
-        if ($utf8) {
-            utf8::encode($arg);
-        }
-
-        return '"' . $arg . '"';
-    }
-
-
-    sub blessed_to_json {
-        my $reftype = reftype($_[1]) || '';
-        if ($reftype eq 'HASH') {
-            return $_[0]->hash_to_json($_[1]);
-        }
-        elsif ($reftype eq 'ARRAY') {
-            return $_[0]->array_to_json($_[1]);
-        }
-        else {
-            return 'null';
-        }
-    }
-
-
-    sub encode_error {
-        my $error  = shift;
-        Carp::croak "$error";
-    }
-
-
-    sub _sort {
-        defined $keysort ? (sort $keysort (keys %{$_[0]})) : keys %{$_[0]};
-    }
-
-
-    sub _up_indent {
-        my $self  = shift;
-        my $space = ' ' x $indent_length;
-
-        my ($pre,$post) = ('','');
-
-        $post = "\n" . $space x $indent_count;
-
-        $indent_count++;
-
-        $pre = "\n" . $space x $indent_count;
-
-        return ($pre,$post);
-    }
-
-
-    sub _down_indent { $indent_count--; }
-
-
-    sub PP_encode_box {
-        {
-            depth        => $depth,
-            indent_count => $indent_count,
-        };
-    }
-
-} # Convert
-
-
-sub _encode_ascii {
-    join('',
-        map {
-            $_ <= 127 ?
-                chr($_) :
-            $_ <= 65535 ?
-                sprintf('\u%04x', $_) : sprintf('\u%x\u%x', _encode_surrogates($_));
-        } unpack('U*', $_[0])
-    );
-}
-
-
-sub _encode_latin1 {
-    join('',
-        map {
-            $_ <= 255 ?
-                chr($_) :
-            $_ <= 65535 ?
-                sprintf('\u%04x', $_) : sprintf('\u%x\u%x', _encode_surrogates($_));
-        } unpack('U*', $_[0])
-    );
-}
-
-
-sub _encode_surrogates { # from perlunicode
-    my $uni = $_[0] - 0x10000;
-    return ($uni / 0x400 + 0xD800, $uni % 0x400 + 0xDC00);
-}
-
-
-sub _is_bignum {
-    $_[0]->isa('Math::BigInt') or $_[0]->isa('Math::BigFloat');
-}
-
-
-
-#
-# JSON => Perl
-#
-
-my $max_intsize;
-
-BEGIN {
-    my $checkint = 1111;
-    for my $d (5..64) {
-        $checkint .= 1;
-        my $int   = eval qq| $checkint |;
-        if ($int =~ /[eE]/) {
-            $max_intsize = $d - 1;
-            last;
-        }
-    }
-}
-
-{ # PARSE 
-
-    my %escapes = ( #  by Jeremy Muhlich <jmuhlich [at] bitflood.org>
-        b    => "\x8",
-        t    => "\x9",
-        n    => "\xA",
-        f    => "\xC",
-        r    => "\xD",
-        '\\' => '\\',
-        '"'  => '"',
-        '/'  => '/',
-    );
-
-    my $text; # json data
-    my $at;   # offset
-    my $ch;   # 1chracter
-    my $len;  # text length (changed according to UTF8 or NON UTF8)
-    # INTERNAL
-    my $depth;          # nest counter
-    my $encoding;       # json text encoding
-    my $is_valid_utf8;  # temp variable
-    my $utf8_len;       # utf8 byte length
-    # FLAGS
-    my $utf8;           # must be utf8
-    my $max_depth;      # max nest number of objects and arrays
-    my $max_size;
-    my $relaxed;
-    my $cb_object;
-    my $cb_sk_object;
-
-    my $F_HOOK;
-
-    my $allow_bigint;   # using Math::BigInt
-    my $singlequote;    # loosely quoting
-    my $loose;          # 
-    my $allow_barekey;  # bareKey
-
-    # $opt flag
-    # 0x00000001 .... decode_prefix
-    # 0x10000000 .... incr_parse
-
-    sub PP_decode_json {
-        my ($self, $opt); # $opt is an effective flag during this decode_json.
-
-        ($self, $text, $opt) = @_;
-
-        ($at, $ch, $depth) = (0, '', 0);
-
-        if ( !defined $text or ref $text ) {
-            decode_error("malformed JSON string, neither array, object, number, string or atom");
-        }
-
-        my $idx = $self->{PROPS};
-
-        ($utf8, $relaxed, $loose, $allow_bigint, $allow_barekey, $singlequote)
-            = @{$idx}[P_UTF8, P_RELAXED, P_LOOSE .. P_ALLOW_SINGLEQUOTE];
-
-        if ( $utf8 ) {
-            utf8::downgrade( $text, 1 ) or Carp::croak("Wide character in subroutine entry");
-        }
-        else {
-            utf8::upgrade( $text );
-        }
-
-        $len = length $text;
-
-        ($max_depth, $max_size, $cb_object, $cb_sk_object, $F_HOOK)
-             = @{$self}{qw/max_depth  max_size cb_object cb_sk_object F_HOOK/};
-
-        if ($max_size > 1) {
-            use bytes;
-            my $bytes = length $text;
-            decode_error(
-                sprintf("attempted decode of JSON text of %s bytes size, but max_size is set to %s"
-                    , $bytes, $max_size), 1
-            ) if ($bytes > $max_size);
-        }
-
-        # Currently no effect
-        # should use regexp
-        my @octets = unpack('C4', $text);
-        $encoding =   ( $octets[0] and  $octets[1]) ? 'UTF-8'
-                    : (!$octets[0] and  $octets[1]) ? 'UTF-16BE'
-                    : (!$octets[0] and !$octets[1]) ? 'UTF-32BE'
-                    : ( $octets[2]                ) ? 'UTF-16LE'
-                    : (!$octets[2]                ) ? 'UTF-32LE'
-                    : 'unknown';
-
-        white(); # remove head white space
-
-        my $valid_start = defined $ch; # Is there a first character for JSON structure?
-
-        my $result = value();
-
-        return undef if ( !$result && ( $opt & 0x10000000 ) ); # for incr_parse
-
-        decode_error("malformed JSON string, neither array, object, number, string or atom") unless $valid_start;
-
-        if ( !$idx->[ P_ALLOW_NONREF ] and !ref $result ) {
-                decode_error(
-                'JSON text must be an object or array (but found number, string, true, false or null,'
-                       . ' use allow_nonref to allow this)', 1);
-        }
-
-        Carp::croak('something wrong.') if $len < $at; # we won't arrive here.
-
-        my $consumed = defined $ch ? $at - 1 : $at; # consumed JSON text length
-
-        white(); # remove tail white space
-
-        if ( $ch ) {
-            return ( $result, $consumed ) if ($opt & 0x00000001); # all right if decode_prefix
-            decode_error("garbage after JSON object");
-        }
-
-        ( $opt & 0x00000001 ) ? ( $result, $consumed ) : $result;
-    }
-
-
-    sub next_chr {
-        return $ch = undef if($at >= $len);
-        $ch = substr($text, $at++, 1);
-    }
-
-
-    sub value {
-        white();
-        return          if(!defined $ch);
-        return object() if($ch eq '{');
-        return array()  if($ch eq '[');
-        return string() if($ch eq '"' or ($singlequote and $ch eq "'"));
-        return number() if($ch =~ /[0-9]/ or $ch eq '-');
-        return word();
-    }
-
-    sub string {
-        my ($i, $s, $t, $u);
-        my $utf16;
-        my $is_utf8;
-
-        ($is_valid_utf8, $utf8_len) = ('', 0);
-
-        $s = ''; # basically UTF8 flag on
-
-        if($ch eq '"' or ($singlequote and $ch eq "'")){
-            my $boundChar = $ch;
-
-            OUTER: while( defined(next_chr()) ){
-
-                if($ch eq $boundChar){
-                    next_chr();
-
-                    if ($utf16) {
-                        decode_error("missing low surrogate character in surrogate pair");
-                    }
-
-                    utf8::decode($s) if($is_utf8);
-
-                    return $s;
-                }
-                elsif($ch eq '\\'){
-                    next_chr();
-                    if(exists $escapes{$ch}){
-                        $s .= $escapes{$ch};
-                    }
-                    elsif($ch eq 'u'){ # UNICODE handling
-                        my $u = '';
-
-                        for(1..4){
-                            $ch = next_chr();
-                            last OUTER if($ch !~ /[0-9a-fA-F]/);
-                            $u .= $ch;
-                        }
-
-                        # U+D800 - U+DBFF
-                        if ($u =~ /^[dD][89abAB][0-9a-fA-F]{2}/) { # UTF-16 high surrogate?
-                            $utf16 = $u;
-                        }
-                        # U+DC00 - U+DFFF
-                        elsif ($u =~ /^[dD][c-fC-F][0-9a-fA-F]{2}/) { # UTF-16 low surrogate?
-                            unless (defined $utf16) {
-                                decode_error("missing high surrogate character in surrogate pair");
-                            }
-                            $is_utf8 = 1;
-                            $s .= JSON_PP_decode_surrogates($utf16, $u) || next;
-                            $utf16 = undef;
-                        }
-                        else {
-                            if (defined $utf16) {
-                                decode_error("surrogate pair expected");
-                            }
-
-                            if ( ( my $hex = hex( $u ) ) > 127 ) {
-                                $is_utf8 = 1;
-                                $s .= JSON_PP_decode_unicode($u) || next;
-                            }
-                            else {
-                                $s .= chr $hex;
-                            }
-                        }
-
-                    }
-                    else{
-                        unless ($loose) {
-                            $at -= 2;
-                            decode_error('illegal backslash escape sequence in string');
-                        }
-                        $s .= $ch;
-                    }
-                }
-                else{
-
-                    if ( ord $ch  > 127 ) {
-                        if ( $utf8 ) {
-                            unless( $ch = is_valid_utf8($ch) ) {
-                                $at -= 1;
-                                decode_error("malformed UTF-8 character in JSON string");
-                            }
-                            else {
-                                $at += $utf8_len - 1;
-                            }
-                        }
-                        else {
-                            utf8::encode( $ch );
-                        }
-
-                        $is_utf8 = 1;
-                    }
-
-                    if (!$loose) {
-                        if ($ch =~ /[\x00-\x1f\x22\x5c]/)  { # '/' ok
-                            $at--;
-                            decode_error('invalid character encountered while parsing JSON string');
-                        }
-                    }
-
-                    $s .= $ch;
-                }
-            }
-        }
-
-        decode_error("unexpected end of string while parsing JSON string");
-    }
-
-
-    sub white {
-        while( defined $ch  ){
-            if($ch le ' '){
-                next_chr();
-            }
-            elsif($ch eq '/'){
-                next_chr();
-                if(defined $ch and $ch eq '/'){
-                    1 while(defined(next_chr()) and $ch ne "\n" and $ch ne "\r");
-                }
-                elsif(defined $ch and $ch eq '*'){
-                    next_chr();
-                    while(1){
-                        if(defined $ch){
-                            if($ch eq '*'){
-                                if(defined(next_chr()) and $ch eq '/'){
-                                    next_chr();
-                                    last;
-                                }
-                            }
-                            else{
-                                next_chr();
-                            }
-                        }
-                        else{
-                            decode_error("Unterminated comment");
-                        }
-                    }
-                    next;
-                }
-                else{
-                    $at--;
-                    decode_error("malformed JSON string, neither array, object, number, string or atom");
-                }
-            }
-            else{
-                if ($relaxed and $ch eq '#') { # correctly?
-                    pos($text) = $at;
-                    $text =~ /\G([^\n]*(?:\r\n|\r|\n|$))/g;
-                    $at = pos($text);
-                    next_chr;
-                    next;
-                }
-
-                last;
-            }
-        }
-    }
-
-
-    sub array {
-        my $a  = $_[0] || []; # you can use this code to use another array ref object.
-
-        decode_error('json text or perl structure exceeds maximum nesting level (max_depth set too low?)')
-                                                    if (++$depth > $max_depth);
-
-        next_chr();
-        white();
-
-        if(defined $ch and $ch eq ']'){
-            --$depth;
-            next_chr();
-            return $a;
-        }
-        else {
-            while(defined($ch)){
-                push @$a, value();
-
-                white();
-
-                if (!defined $ch) {
-                    last;
-                }
-
-                if($ch eq ']'){
-                    --$depth;
-                    next_chr();
-                    return $a;
-                }
-
-                if($ch ne ','){
-                    last;
-                }
-
-                next_chr();
-                white();
-
-                if ($relaxed and $ch eq ']') {
-                    --$depth;
-                    next_chr();
-                    return $a;
-                }
-
-            }
-        }
-
-        decode_error(", or ] expected while parsing array");
-    }
-
-
-    sub object {
-        my $o = $_[0] || {}; # you can use this code to use another hash ref object.
-        my $k;
-
-        decode_error('json text or perl structure exceeds maximum nesting level (max_depth set too low?)')
-                                                if (++$depth > $max_depth);
-        next_chr();
-        white();
-
-        if(defined $ch and $ch eq '}'){
-            --$depth;
-            next_chr();
-            if ($F_HOOK) {
-                return _json_object_hook($o);
-            }
-            return $o;
-        }
-        else {
-            while (defined $ch) {
-                $k = ($allow_barekey and $ch ne '"' and $ch ne "'") ? bareKey() : string();
-                white();
-
-                if(!defined $ch or $ch ne ':'){
-                    $at--;
-                    decode_error("':' expected");
-                }
-
-                next_chr();
-                $o->{$k} = value();
-                white();
-
-                last if (!defined $ch);
-
-                if($ch eq '}'){
-                    --$depth;
-                    next_chr();
-                    if ($F_HOOK) {
-                        return _json_object_hook($o);
-                    }
-                    return $o;
-                }
-
-                if($ch ne ','){
-                    last;
-                }
-
-                next_chr();
-                white();
-
-                if ($relaxed and $ch eq '}') {
-                    --$depth;
-                    next_chr();
-                    if ($F_HOOK) {
-                        return _json_object_hook($o);
-                    }
-                    return $o;
-                }
-
-            }
-
-        }
-
-        $at--;
-        decode_error(", or } expected while parsing object/hash");
-    }
-
-
-    sub bareKey { # doesn't strictly follow Standard ECMA-262 3rd Edition
-        my $key;
-        while($ch =~ /[^\x00-\x23\x25-\x2F\x3A-\x40\x5B-\x5E\x60\x7B-\x7F]/){
-            $key .= $ch;
-            next_chr();
-        }
-        return $key;
-    }
-
-
-    sub word {
-        my $word =  substr($text,$at-1,4);
-
-        if($word eq 'true'){
-            $at += 3;
-            next_chr;
-            return $JSON::PP::true;
-        }
-        elsif($word eq 'null'){
-            $at += 3;
-            next_chr;
-            return undef;
-        }
-        elsif($word eq 'fals'){
-            $at += 3;
-            if(substr($text,$at,1) eq 'e'){
-                $at++;
-                next_chr;
-                return $JSON::PP::false;
-            }
-        }
-
-        $at--; # for decode_error report
-
-        decode_error("'null' expected")  if ($word =~ /^n/);
-        decode_error("'true' expected")  if ($word =~ /^t/);
-        decode_error("'false' expected") if ($word =~ /^f/);
-        decode_error("malformed JSON string, neither array, object, number, string or atom");
-    }
-
-
-    sub number {
-        my $n    = '';
-        my $v;
-
-        # According to RFC4627, hex or oct digits are invalid.
-        if($ch eq '0'){
-            my $peek = substr($text,$at,1);
-            my $hex  = $peek =~ /[xX]/; # 0 or 1
-
-            if($hex){
-                decode_error("malformed number (leading zero must not be followed by another digit)");
-                ($n) = ( substr($text, $at+1) =~ /^([0-9a-fA-F]+)/);
-            }
-            else{ # oct
-                ($n) = ( substr($text, $at) =~ /^([0-7]+)/);
-                if (defined $n and length $n > 1) {
-                    decode_error("malformed number (leading zero must not be followed by another digit)");
-                }
-            }
-
-            if(defined $n and length($n)){
-                if (!$hex and length($n) == 1) {
-                   decode_error("malformed number (leading zero must not be followed by another digit)");
-                }
-                $at += length($n) + $hex;
-                next_chr;
-                return $hex ? hex($n) : oct($n);
-            }
-        }
-
-        if($ch eq '-'){
-            $n = '-';
-            next_chr;
-            if (!defined $ch or $ch !~ /\d/) {
-                decode_error("malformed number (no digits after initial minus)");
-            }
-        }
-
-        while(defined $ch and $ch =~ /\d/){
-            $n .= $ch;
-            next_chr;
-        }
-
-        if(defined $ch and $ch eq '.'){
-            $n .= '.';
-
-            next_chr;
-            if (!defined $ch or $ch !~ /\d/) {
-                decode_error("malformed number (no digits after decimal point)");
-            }
-            else {
-                $n .= $ch;
-            }
-
-            while(defined(next_chr) and $ch =~ /\d/){
-                $n .= $ch;
-            }
-        }
-
-        if(defined $ch and ($ch eq 'e' or $ch eq 'E')){
-            $n .= $ch;
-            next_chr;
-
-            if(defined($ch) and ($ch eq '+' or $ch eq '-')){
-                $n .= $ch;
-                next_chr;
-                if (!defined $ch or $ch =~ /\D/) {
-                    decode_error("malformed number (no digits after exp sign)");
-                }
-                $n .= $ch;
-            }
-            elsif(defined($ch) and $ch =~ /\d/){
-                $n .= $ch;
-            }
-            else {
-                decode_error("malformed number (no digits after exp sign)");
-            }
-
-            while(defined(next_chr) and $ch =~ /\d/){
-                $n .= $ch;
-            }
-
-        }
-
-        $v .= $n;
-
-        if ($v !~ /[.eE]/ and length $v > $max_intsize) {
-            if ($allow_bigint) { # from Adam Sussman
-                require Math::BigInt;
-                return Math::BigInt->new($v);
-            }
-            else {
-                return "$v";
-            }
-        }
-        elsif ($allow_bigint) {
-            require Math::BigFloat;
-            return Math::BigFloat->new($v);
-        }
-
-        return 0+$v;
-    }
-
-
-    sub is_valid_utf8 {
-
-        $utf8_len = $_[0] =~ /[\x00-\x7F]/  ? 1
-                  : $_[0] =~ /[\xC2-\xDF]/  ? 2
-                  : $_[0] =~ /[\xE0-\xEF]/  ? 3
-                  : $_[0] =~ /[\xF0-\xF4]/  ? 4
-                  : 0
-                  ;
-
-        return unless $utf8_len;
-
-        my $is_valid_utf8 = substr($text, $at - 1, $utf8_len);
-
-        return ( $is_valid_utf8 =~ /^(?:
-             [\x00-\x7F]
-            |[\xC2-\xDF][\x80-\xBF]
-            |[\xE0][\xA0-\xBF][\x80-\xBF]
-            |[\xE1-\xEC][\x80-\xBF][\x80-\xBF]
-            |[\xED][\x80-\x9F][\x80-\xBF]
-            |[\xEE-\xEF][\x80-\xBF][\x80-\xBF]
-            |[\xF0][\x90-\xBF][\x80-\xBF][\x80-\xBF]
-            |[\xF1-\xF3][\x80-\xBF][\x80-\xBF][\x80-\xBF]
-            |[\xF4][\x80-\x8F][\x80-\xBF][\x80-\xBF]
-        )$/x )  ? $is_valid_utf8 : '';
-    }
-
-
-    sub decode_error {
-        my $error  = shift;
-        my $no_rep = shift;
-        my $str    = defined $text ? substr($text, $at) : '';
-        my $mess   = '';
-        my $type   = $] >= 5.008           ? 'U*'
-                   : $] <  5.006           ? 'C*'
-                   : utf8::is_utf8( $str ) ? 'U*' # 5.6
-                   : 'C*'
-                   ;
-
-        for my $c ( unpack( $type, $str ) ) { # emulate pv_uni_display() ?
-            $mess .=  $c == 0x07 ? '\a'
-                    : $c == 0x09 ? '\t'
-                    : $c == 0x0a ? '\n'
-                    : $c == 0x0d ? '\r'
-                    : $c == 0x0c ? '\f'
-                    : $c <  0x20 ? sprintf('\x{%x}', $c)
-                    : $c == 0x5c ? '\\\\'
-                    : $c <  0x80 ? chr($c)
-                    : sprintf('\x{%x}', $c)
-                    ;
-            if ( length $mess >= 20 ) {
-                $mess .= '...';
-                last;
-            }
-        }
-
-        unless ( length $mess ) {
-            $mess = '(end of string)';
-        }
-
-        Carp::croak (
-            $no_rep ? "$error" : "$error, at character offset $at (before \"$mess\")"
-        );
-
-    }
-
-
-    sub _json_object_hook {
-        my $o    = $_[0];
-        my @ks = keys %{$o};
-
-        if ( $cb_sk_object and @ks == 1 and exists $cb_sk_object->{ $ks[0] } and ref $cb_sk_object->{ $ks[0] } ) {
-            my @val = $cb_sk_object->{ $ks[0] }->( $o->{$ks[0]} );
-            if (@val == 1) {
-                return $val[0];
-            }
-        }
-
-        my @val = $cb_object->($o) if ($cb_object);
-        if (@val == 0 or @val > 1) {
-            return $o;
-        }
-        else {
-            return $val[0];
-        }
-    }
-
-
-    sub PP_decode_box {
-        {
-            text    => $text,
-            at      => $at,
-            ch      => $ch,
-            len     => $len,
-            depth   => $depth,
-            encoding      => $encoding,
-            is_valid_utf8 => $is_valid_utf8,
-        };
-    }
-
-} # PARSE
-
-
-sub _decode_surrogates { # from perlunicode
-    my $uni = 0x10000 + (hex($_[0]) - 0xD800) * 0x400 + (hex($_[1]) - 0xDC00);
-    my $un  = pack('U*', $uni);
-    utf8::encode( $un );
-    return $un;
-}
-
-
-sub _decode_unicode {
-    my $un = pack('U', hex shift);
-    utf8::encode( $un );
-    return $un;
-}
-
-#
-# Setup for various Perl versions (the code from JSON::PP58)
-#
-
-BEGIN {
-
-    unless ( defined &utf8::is_utf8 ) {
-       require Encode;
-       *utf8::is_utf8 = *Encode::is_utf8;
-    }
-
-    if ( $] >= 5.008 ) {
-        *JSON::PP::JSON_PP_encode_ascii      = \&_encode_ascii;
-        *JSON::PP::JSON_PP_encode_latin1     = \&_encode_latin1;
-        *JSON::PP::JSON_PP_decode_surrogates = \&_decode_surrogates;
-        *JSON::PP::JSON_PP_decode_unicode    = \&_decode_unicode;
-    }
-
-    if ($] >= 5.008 and $] < 5.008003) { # join() in 5.8.0 - 5.8.2 is broken.
-        package # hide from PAUSE
-          JSON::PP;
-        require subs;
-        subs->import('join');
-        eval q|
-            sub join {
-                return '' if (@_ < 2);
-                my $j   = shift;
-                my $str = shift;
-                for (@_) { $str .= $j . $_; }
-                return $str;
-            }
-        |;
-    }
-
-
-    sub JSON::PP::incr_parse {
-        local $Carp::CarpLevel = 1;
-        ( $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new )->incr_parse( @_ );
-    }
-
-
-    sub JSON::PP::incr_skip {
-        ( $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new )->incr_skip;
-    }
-
-
-    sub JSON::PP::incr_reset {
-        ( $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new )->incr_reset;
-    }
-
-    eval q{
-        sub JSON::PP::incr_text : lvalue {
-            $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new;
-
-            if ( $_[0]->{_incr_parser}->{incr_parsing} ) {
-                Carp::croak("incr_text can not be called when the incremental parser already started parsing");
-            }
-            $_[0]->{_incr_parser}->{incr_text};
-        }
-    } if ( $] >= 5.006 );
-
-} # Setup for various Perl versions (the code from JSON::PP58)
-
-
-###############################
-# Utilities
-#
-
-BEGIN {
-    eval 'require Scalar::Util';
-    unless($@){
-        *JSON::PP::blessed = \&Scalar::Util::blessed;
-        *JSON::PP::reftype = \&Scalar::Util::reftype;
-        *JSON::PP::refaddr = \&Scalar::Util::refaddr;
-    }
-    else{ # This code is from Scalar::Util.
-        # warn $@;
-        eval 'sub UNIVERSAL::a_sub_not_likely_to_be_here { ref($_[0]) }';
-        *JSON::PP::blessed = sub {
-            local($@, $SIG{__DIE__}, $SIG{__WARN__});
-            ref($_[0]) ? eval { $_[0]->a_sub_not_likely_to_be_here } : undef;
-        };
-        my %tmap = qw(
-            B::NULL   SCALAR
-            B::HV     HASH
-            B::AV     ARRAY
-            B::CV     CODE
-            B::IO     IO
-            B::GV     GLOB
-            B::REGEXP REGEXP
-        );
-        *JSON::PP::reftype = sub {
-            my $r = shift;
-
-            return undef unless length(ref($r));
-
-            my $t = ref(B::svref_2object($r));
-
-            return
-                exists $tmap{$t} ? $tmap{$t}
-              : length(ref($$r)) ? 'REF'
-              :                    'SCALAR';
-        };
-        *JSON::PP::refaddr = sub {
-          return undef unless length(ref($_[0]));
-
-          my $addr;
-          if(defined(my $pkg = blessed($_[0]))) {
-            $addr .= bless $_[0], 'Scalar::Util::Fake';
-            bless $_[0], $pkg;
-          }
-          else {
-            $addr .= $_[0]
-          }
-
-          $addr =~ /0x(\w+)/;
-          local $^W;
-          #no warnings 'portable';
-          hex($1);
-        }
-    }
-}
-
-
-# shamelessly copied and modified from JSON::XS code.
-
-unless ( $INC{'JSON/PP.pm'} ) {
-    eval q|
-        package
-            JSON::PP::Boolean;
-
-        use overload (
-            "0+"     => sub { ${$_[0]} },
-            "++"     => sub { $_[0] = ${$_[0]} + 1 },
-            "--"     => sub { $_[0] = ${$_[0]} - 1 },
-            fallback => 1,
-        );
-    |;
-}
-
-$JSON::PP::true  = do { bless \(my $dummy = 1), "JSON::PP::Boolean" };
-$JSON::PP::false = do { bless \(my $dummy = 0), "JSON::PP::Boolean" };
-
-sub is_bool { defined $_[0] and UNIVERSAL::isa($_[0], "JSON::PP::Boolean"); }
-
-sub true  { $JSON::PP::true  }
-sub false { $JSON::PP::false }
-sub null  { undef; }
-
-###############################
-
-###############################
-
-package # hide from PAUSE
-  JSON::PP::IncrParser;
-
-use strict;
-
-use constant INCR_M_WS   => 0; # initial whitespace skipping
-use constant INCR_M_STR  => 1; # inside string
-use constant INCR_M_BS   => 2; # inside backslash
-use constant INCR_M_JSON => 3; # outside anything, count nesting
-use constant INCR_M_C0   => 4;
-use constant INCR_M_C1   => 5;
-
-use vars qw($VERSION);
-$VERSION = '1.01';
-
-my $unpack_format = $] < 5.006 ? 'C*' : 'U*';
-
-sub new {
-    my ( $class ) = @_;
-
-    bless {
-        incr_nest    => 0,
-        incr_text    => undef,
-        incr_parsing => 0,
-        incr_p       => 0,
-    }, $class;
-}
-
-
-sub incr_parse {
-    my ( $self, $coder, $text ) = @_;
-
-    $self->{incr_text} = '' unless ( defined $self->{incr_text} );
-
-    if ( defined $text ) {
-        if ( utf8::is_utf8( $text ) and !utf8::is_utf8( $self->{incr_text} ) ) {
-            utf8::upgrade( $self->{incr_text} ) ;
-            utf8::decode( $self->{incr_text} ) ;
-        }
-        $self->{incr_text} .= $text;
-    }
-
-
-    my $max_size = $coder->get_max_size;
-
-    if ( defined wantarray ) {
-
-        $self->{incr_mode} = INCR_M_WS unless defined $self->{incr_mode};
-
-        if ( wantarray ) {
-            my @ret;
-
-            $self->{incr_parsing} = 1;
-
-            do {
-                push @ret, $self->_incr_parse( $coder, $self->{incr_text} );
-
-                unless ( !$self->{incr_nest} and $self->{incr_mode} == INCR_M_JSON ) {
-                    $self->{incr_mode} = INCR_M_WS if $self->{incr_mode} != INCR_M_STR;
-                }
-
-            } until ( length $self->{incr_text} >= $self->{incr_p} );
-
-            $self->{incr_parsing} = 0;
-
-            return @ret;
-        }
-        else { # in scalar context
-            $self->{incr_parsing} = 1;
-            my $obj = $self->_incr_parse( $coder, $self->{incr_text} );
-            $self->{incr_parsing} = 0 if defined $obj; # pointed by Martin J. Evans
-            return $obj ? $obj : undef; # $obj is an empty string, parsing was completed.
-        }
-
-    }
-
-}
-
-
-sub _incr_parse {
-    my ( $self, $coder, $text, $skip ) = @_;
-    my $p = $self->{incr_p};
-    my $restore = $p;
-
-    my @obj;
-    my $len = length $text;
-
-    if ( $self->{incr_mode} == INCR_M_WS ) {
-        while ( $len > $p ) {
-            my $s = substr( $text, $p, 1 );
-            $p++ and next if ( 0x20 >= unpack($unpack_format, $s) );
-            $self->{incr_mode} = INCR_M_JSON;
-            last;
-       }
-    }
-
-    while ( $len > $p ) {
-        my $s = substr( $text, $p++, 1 );
-
-        if ( $s eq '"' ) {
-            if (substr( $text, $p - 2, 1 ) eq '\\' ) {
-                next;
-            }
-
-            if ( $self->{incr_mode} != INCR_M_STR  ) {
-                $self->{incr_mode} = INCR_M_STR;
-            }
-            else {
-                $self->{incr_mode} = INCR_M_JSON;
-                unless ( $self->{incr_nest} ) {
-                    last;
-                }
-            }
-        }
-
-        if ( $self->{incr_mode} == INCR_M_JSON ) {
-
-            if ( $s eq '[' or $s eq '{' ) {
-                if ( ++$self->{incr_nest} > $coder->get_max_depth ) {
-                    Carp::croak('json text or perl structure exceeds maximum nesting level (max_depth set too low?)');
-                }
-            }
-            elsif ( $s eq ']' or $s eq '}' ) {
-                last if ( --$self->{incr_nest} <= 0 );
-            }
-            elsif ( $s eq '#' ) {
-                while ( $len > $p ) {
-                    last if substr( $text, $p++, 1 ) eq "\n";
-                }
-            }
-
-        }
-
-    }
-
-    $self->{incr_p} = $p;
-
-    return if ( $self->{incr_mode} == INCR_M_STR and not $self->{incr_nest} );
-    return if ( $self->{incr_mode} == INCR_M_JSON and $self->{incr_nest} > 0 );
-
-    return '' unless ( length substr( $self->{incr_text}, 0, $p ) );
-
-    local $Carp::CarpLevel = 2;
-
-    $self->{incr_p} = $restore;
-    $self->{incr_c} = $p;
-
-    my ( $obj, $tail ) = $coder->PP_decode_json( substr( $self->{incr_text}, 0, $p ), 0x10000001 );
-
-    $self->{incr_text} = substr( $self->{incr_text}, $p );
-    $self->{incr_p} = 0;
-
-    return $obj || '';
-}
-
-
-sub incr_text {
-    if ( $_[0]->{incr_parsing} ) {
-        Carp::croak("incr_text can not be called when the incremental parser already started parsing");
-    }
-    $_[0]->{incr_text};
-}
-
-
-sub incr_skip {
-    my $self  = shift;
-    $self->{incr_text} = substr( $self->{incr_text}, $self->{incr_c} );
-    $self->{incr_p} = 0;
-}
-
-
-sub incr_reset {
-    my $self = shift;
-    $self->{incr_text}    = undef;
-    $self->{incr_p}       = 0;
-    $self->{incr_mode}    = 0;
-    $self->{incr_nest}    = 0;
-    $self->{incr_parsing} = 0;
-}
-
-###############################
-
-
-1;
-__END__
-=pod
-
-=head1 NAME
-
-JSON::PP - JSON::XS compatible pure-Perl module.
-
-=head1 SYNOPSIS
-
- use JSON::PP;
-
- # exported functions, they croak on error
- # and expect/generate UTF-8
-
- $utf8_encoded_json_text = encode_json $perl_hash_or_arrayref;
- $perl_hash_or_arrayref  = decode_json $utf8_encoded_json_text;
-
- # OO-interface
-
- $coder = JSON::PP->new->ascii->pretty->allow_nonref;
- 
- $json_text   = $json->encode( $perl_scalar );
- $perl_scalar = $json->decode( $json_text );
- 
- $pretty_printed = $json->pretty->encode( $perl_scalar ); # pretty-printing
- 
- # Note that JSON version 2.0 and above will automatically use
- # JSON::XS or JSON::PP, so you should be able to just:
- 
- use JSON;
-
-
-=head1 VERSION
-
-    2.27200
-
-L<JSON::XS> 2.27 (~2.30) compatible.
-
-=head1 DESCRIPTION
-
-This module is L<JSON::XS> compatible pure Perl module.
-(Perl 5.8 or later is recommended)
-
-JSON::XS is the fastest and most proper JSON module on CPAN.
-It is written by Marc Lehmann in C, so must be compiled and
-installed in the used environment.
-
-JSON::PP is a pure-Perl module and has compatibility to JSON::XS.
-
-
-=head2 FEATURES
-
-=over
-
-=item * correct unicode handling
-
-This module knows how to handle Unicode (depending on Perl version).
-
-See to L<JSON::XS/A FEW NOTES ON UNICODE AND PERL> and
-L<UNICODE HANDLING ON PERLS>.
-
-
-=item * round-trip integrity
-
-When you serialise a perl data structure using only data types
-supported by JSON and Perl, the deserialised data structure is
-identical on the Perl level. (e.g. the string "2.0" doesn't suddenly
-become "2" just because it looks like a number). There I<are> minor
-exceptions to this, read the MAPPING section below to learn about
-those.
-
-
-=item * strict checking of JSON correctness
-
-There is no guessing, no generating of illegal JSON texts by default,
-and only JSON is accepted as input by default (the latter is a
-security feature). But when some options are set, loose checking
-features are available.
-
-=back
-
-=head1 FUNCTIONAL INTERFACE
-
-Some documents are copied and modified from L<JSON::XS/FUNCTIONAL INTERFACE>.
-
-=head2 encode_json
-
-    $json_text = encode_json $perl_scalar
-
-Converts the given Perl data structure to a UTF-8 encoded, binary string.
-
-This function call is functionally identical to:
-
-    $json_text = JSON::PP->new->utf8->encode($perl_scalar)
-
-=head2 decode_json
-
-    $perl_scalar = decode_json $json_text
-
-The opposite of C<encode_json>: expects an UTF-8 (binary) string and tries
-to parse that as an UTF-8 encoded JSON text, returning the resulting
-reference.
-
-This function call is functionally identical to:
-
-    $perl_scalar = JSON::PP->new->utf8->decode($json_text)
-
-=head2 JSON::PP::is_bool
-
-    $is_boolean = JSON::PP::is_bool($scalar)
-
-Returns true if the passed scalar represents either JSON::PP::true or
-JSON::PP::false, two constants that act like C<1> and C<0> respectively
-and are also used to represent JSON C<true> and C<false> in Perl strings.
-
-=head2 JSON::PP::true
-
-Returns JSON true value which is blessed object.
-It C<isa> JSON::PP::Boolean object.
-
-=head2 JSON::PP::false
-
-Returns JSON false value which is blessed object.
-It C<isa> JSON::PP::Boolean object.
-
-=head2 JSON::PP::null
-
-Returns C<undef>.
-
-See L<MAPPING>, below, for more information on how JSON values are mapped to
-Perl.
-
-
-=head1 HOW DO I DECODE A DATA FROM OUTER AND ENCODE TO OUTER
-
-This section supposes that your perl version is 5.8 or later.
-
-If you know a JSON text from an outer world - a network, a file content, and so on,
-is encoded in UTF-8, you should use C<decode_json> or C<JSON> module object
-with C<utf8> enable. And the decoded result will contain UNICODE characters.
-
-  # from network
-  my $json        = JSON::PP->new->utf8;
-  my $json_text   = CGI->new->param( 'json_data' );
-  my $perl_scalar = $json->decode( $json_text );
-  
-  # from file content
-  local $/;
-  open( my $fh, '<', 'json.data' );
-  $json_text   = <$fh>;
-  $perl_scalar = decode_json( $json_text );
-
-If an outer data is not encoded in UTF-8, firstly you should C<decode> it.
-
-  use Encode;
-  local $/;
-  open( my $fh, '<', 'json.data' );
-  my $encoding = 'cp932';
-  my $unicode_json_text = decode( $encoding, <$fh> ); # UNICODE
-  
-  # or you can write the below code.
-  #
-  # open( my $fh, "<:encoding($encoding)", 'json.data' );
-  # $unicode_json_text = <$fh>;
-
-In this case, C<$unicode_json_text> is of course UNICODE string.
-So you B<cannot> use C<decode_json> nor C<JSON> module object with C<utf8> enable.
-Instead of them, you use C<JSON> module object with C<utf8> disable.
-
-  $perl_scalar = $json->utf8(0)->decode( $unicode_json_text );
-
-Or C<encode 'utf8'> and C<decode_json>:
-
-  $perl_scalar = decode_json( encode( 'utf8', $unicode_json_text ) );
-  # this way is not efficient.
-
-And now, you want to convert your C<$perl_scalar> into JSON data and
-send it to an outer world - a network or a file content, and so on.
-
-Your data usually contains UNICODE strings and you want the converted data to be encoded
-in UTF-8, you should use C<encode_json> or C<JSON> module object with C<utf8> enable.
-
-  print encode_json( $perl_scalar ); # to a network? file? or display?
-  # or
-  print $json->utf8->encode( $perl_scalar );
-
-If C<$perl_scalar> does not contain UNICODE but C<$encoding>-encoded strings
-for some reason, then its characters are regarded as B<latin1> for perl
-(because it does not concern with your $encoding).
-You B<cannot> use C<encode_json> nor C<JSON> module object with C<utf8> enable.
-Instead of them, you use C<JSON> module object with C<utf8> disable.
-Note that the resulted text is a UNICODE string but no problem to print it.
-
-  # $perl_scalar contains $encoding encoded string values
-  $unicode_json_text = $json->utf8(0)->encode( $perl_scalar );
-  # $unicode_json_text consists of characters less than 0x100
-  print $unicode_json_text;
-
-Or C<decode $encoding> all string values and C<encode_json>:
-
-  $perl_scalar->{ foo } = decode( $encoding, $perl_scalar->{ foo } );
-  # ... do it to each string values, then encode_json
-  $json_text = encode_json( $perl_scalar );
-
-This method is a proper way but probably not efficient.
-
-See to L<Encode>, L<perluniintro>.
-
-
-=head1 METHODS
-
-Basically, check to L<JSON> or L<JSON::XS>.
-
-=head2 new
-
-    $json = JSON::PP->new
-
-Returns a new JSON::PP object that can be used to de/encode JSON
-strings.
-
-All boolean flags described below are by default I<disabled>.
-
-The mutators for flags all return the JSON object again and thus calls can
-be chained:
-
-   my $json = JSON::PP->new->utf8->space_after->encode({a => [1,2]})
-   => {"a": [1, 2]}
-
-=head2 ascii
-
-    $json = $json->ascii([$enable])
-    
-    $enabled = $json->get_ascii
-
-If $enable is true (or missing), then the encode method will not generate characters outside
-the code range 0..127. Any Unicode characters outside that range will be escaped using either
-a single \uXXXX or a double \uHHHH\uLLLLL escape sequence, as per RFC4627.
-(See to L<JSON::XS/OBJECT-ORIENTED INTERFACE>).
-
-In Perl 5.005, there is no character having high value (more than 255).
-See to L<UNICODE HANDLING ON PERLS>.
-
-If $enable is false, then the encode method will not escape Unicode characters unless
-required by the JSON syntax or other flags. This results in a faster and more compact format.
-
-  JSON::PP->new->ascii(1)->encode([chr 0x10401])
-  => ["\ud801\udc01"]
-
-=head2 latin1
-
-    $json = $json->latin1([$enable])
-    
-    $enabled = $json->get_latin1
-
-If $enable is true (or missing), then the encode method will encode the resulting JSON
-text as latin1 (or iso-8859-1), escaping any characters outside the code range 0..255.
-
-If $enable is false, then the encode method will not escape Unicode characters
-unless required by the JSON syntax or other flags.
-
-  JSON::XS->new->latin1->encode (["\x{89}\x{abc}"]
-  => ["\x{89}\\u0abc"]    # (perl syntax, U+abc escaped, U+89 not)
-
-See to L<UNICODE HANDLING ON PERLS>.
-
-=head2 utf8
-
-    $json = $json->utf8([$enable])
-    
-    $enabled = $json->get_utf8
-
-If $enable is true (or missing), then the encode method will encode the JSON result
-into UTF-8, as required by many protocols, while the decode method expects to be handled
-an UTF-8-encoded string. Please note that UTF-8-encoded strings do not contain any
-characters outside the range 0..255, they are thus useful for bytewise/binary I/O.
-
-(In Perl 5.005, any character outside the range 0..255 does not exist.
-See to L<UNICODE HANDLING ON PERLS>.)
-
-In future versions, enabling this option might enable autodetection of the UTF-16 and UTF-32
-encoding families, as described in RFC4627.
-
-If $enable is false, then the encode method will return the JSON string as a (non-encoded)
-Unicode string, while decode expects thus a Unicode string. Any decoding or encoding
-(e.g. to UTF-8 or UTF-16) needs to be done yourself, e.g. using the Encode module.
-
-Example, output UTF-16BE-encoded JSON:
-
-  use Encode;
-  $jsontext = encode "UTF-16BE", JSON::PP->new->encode ($object);
-
-Example, decode UTF-32LE-encoded JSON:
-
-  use Encode;
-  $object = JSON::PP->new->decode (decode "UTF-32LE", $jsontext);
-
-
-=head2 pretty
-
-    $json = $json->pretty([$enable])
-
-This enables (or disables) all of the C<indent>, C<space_before> and
-C<space_after> flags in one call to generate the most readable
-(or most compact) form possible.
-
-Equivalent to:
-
-   $json->indent->space_before->space_after
-
-=head2 indent
-
-    $json = $json->indent([$enable])
-    
-    $enabled = $json->get_indent
-
-The default indent space length is three.
-You can use C<indent_length> to change the length.
-
-=head2 space_before
-
-    $json = $json->space_before([$enable])
-    
-    $enabled = $json->get_space_before
-
-If C<$enable> is true (or missing), then the C<encode> method will add an extra
-optional space before the C<:> separating keys from values in JSON objects.
-
-If C<$enable> is false, then the C<encode> method will not add any extra
-space at those places.
-
-This setting has no effect when decoding JSON texts.
-
-Example, space_before enabled, space_after and indent disabled:
-
-   {"key" :"value"}
-
-=head2 space_after
-
-    $json = $json->space_after([$enable])
-    
-    $enabled = $json->get_space_after
-
-If C<$enable> is true (or missing), then the C<encode> method will add an extra
-optional space after the C<:> separating keys from values in JSON objects
-and extra whitespace after the C<,> separating key-value pairs and array
-members.
-
-If C<$enable> is false, then the C<encode> method will not add any extra
-space at those places.
-
-This setting has no effect when decoding JSON texts.
-
-Example, space_before and indent disabled, space_after enabled:
-
-   {"key": "value"}
-
-=head2 relaxed
-
-    $json = $json->relaxed([$enable])
-    
-    $enabled = $json->get_relaxed
-
-If C<$enable> is true (or missing), then C<decode> will accept some
-extensions to normal JSON syntax (see below). C<encode> will not be
-affected in anyway. I<Be aware that this option makes you accept invalid
-JSON texts as if they were valid!>. I suggest only to use this option to
-parse application-specific files written by humans (configuration files,
-resource files etc.)
-
-If C<$enable> is false (the default), then C<decode> will only accept
-valid JSON texts.
-
-Currently accepted extensions are:
-
-=over 4
-
-=item * list items can have an end-comma
-
-JSON I<separates> array elements and key-value pairs with commas. This
-can be annoying if you write JSON texts manually and want to be able to
-quickly append elements, so this extension accepts comma at the end of
-such items not just between them:
-
-   [
-      1,
-      2, <- this comma not normally allowed
-   ]
-   {
-      "k1": "v1",
-      "k2": "v2", <- this comma not normally allowed
-   }
-
-=item * shell-style '#'-comments
-
-Whenever JSON allows whitespace, shell-style comments are additionally
-allowed. They are terminated by the first carriage-return or line-feed
-character, after which more white-space and comments are allowed.
-
-  [
-     1, # this comment not allowed in JSON
-        # neither this one...
-  ]
-
-=back
-
-=head2 canonical
-
-    $json = $json->canonical([$enable])
-    
-    $enabled = $json->get_canonical
-
-If C<$enable> is true (or missing), then the C<encode> method will output JSON objects
-by sorting their keys. This is adding a comparatively high overhead.
-
-If C<$enable> is false, then the C<encode> method will output key-value
-pairs in the order Perl stores them (which will likely change between runs
-of the same script).
-
-This option is useful if you want the same data structure to be encoded as
-the same JSON text (given the same overall settings). If it is disabled,
-the same hash might be encoded differently even if contains the same data,
-as key-value pairs have no inherent ordering in Perl.
-
-This setting has no effect when decoding JSON texts.
-
-If you want your own sorting routine, you can give a code reference
-or a subroutine name to C<sort_by>. See to C<JSON::PP OWN METHODS>.
-
-=head2 allow_nonref
-
-    $json = $json->allow_nonref([$enable])
-    
-    $enabled = $json->get_allow_nonref
-
-If C<$enable> is true (or missing), then the C<encode> method can convert a
-non-reference into its corresponding string, number or null JSON value,
-which is an extension to RFC4627. Likewise, C<decode> will accept those JSON
-values instead of croaking.
-
-If C<$enable> is false, then the C<encode> method will croak if it isn't
-passed an arrayref or hashref, as JSON texts must either be an object
-or array. Likewise, C<decode> will croak if given something that is not a
-JSON object or array.
-
-   JSON::PP->new->allow_nonref->encode ("Hello, World!")
-   => "Hello, World!"
-
-=head2 allow_unknown
-
-    $json = $json->allow_unknown ([$enable])
-    
-    $enabled = $json->get_allow_unknown
-
-If $enable is true (or missing), then "encode" will *not* throw an
-exception when it encounters values it cannot represent in JSON (for
-example, filehandles) but instead will encode a JSON "null" value.
-Note that blessed objects are not included here and are handled
-separately by c<allow_nonref>.
-
-If $enable is false (the default), then "encode" will throw an
-exception when it encounters anything it cannot encode as JSON.
-
-This option does not affect "decode" in any way, and it is
-recommended to leave it off unless you know your communications
-partner.
-
-=head2 allow_blessed
-
-    $json = $json->allow_blessed([$enable])
-    
-    $enabled = $json->get_allow_blessed
-
-If C<$enable> is true (or missing), then the C<encode> method will not
-barf when it encounters a blessed reference. Instead, the value of the
-B<convert_blessed> option will decide whether C<null> (C<convert_blessed>
-disabled or no C<TO_JSON> method found) or a representation of the
-object (C<convert_blessed> enabled and C<TO_JSON> method found) is being
-encoded. Has no effect on C<decode>.
-
-If C<$enable> is false (the default), then C<encode> will throw an
-exception when it encounters a blessed object.
-
-=head2 convert_blessed
-
-    $json = $json->convert_blessed([$enable])
-    
-    $enabled = $json->get_convert_blessed
-
-If C<$enable> is true (or missing), then C<encode>, upon encountering a
-blessed object, will check for the availability of the C<TO_JSON> method
-on the object's class. If found, it will be called in scalar context
-and the resulting scalar will be encoded instead of the object. If no
-C<TO_JSON> method is found, the value of C<allow_blessed> will decide what
-to do.
-
-The C<TO_JSON> method may safely call die if it wants. If C<TO_JSON>
-returns other blessed objects, those will be handled in the same
-way. C<TO_JSON> must take care of not causing an endless recursion cycle
-(== crash) in this case. The name of C<TO_JSON> was chosen because other
-methods called by the Perl core (== not by the user of the object) are
-usually in upper case letters and to avoid collisions with the C<to_json>
-function or method.
-
-This setting does not yet influence C<decode> in any way.
-
-If C<$enable> is false, then the C<allow_blessed> setting will decide what
-to do when a blessed object is found.
-
-=head2 filter_json_object
-
-    $json = $json->filter_json_object([$coderef])
-
-When C<$coderef> is specified, it will be called from C<decode> each
-time it decodes a JSON object. The only argument passed to the coderef
-is a reference to the newly-created hash. If the code references returns
-a single scalar (which need not be a reference), this value
-(i.e. a copy of that scalar to avoid aliasing) is inserted into the
-deserialised data structure. If it returns an empty list
-(NOTE: I<not> C<undef>, which is a valid scalar), the original deserialised
-hash will be inserted. This setting can slow down decoding considerably.
-
-When C<$coderef> is omitted or undefined, any existing callback will
-be removed and C<decode> will not change the deserialised hash in any
-way.
-
-Example, convert all JSON objects into the integer 5:
-
-   my $js = JSON::PP->new->filter_json_object (sub { 5 });
-   # returns [5]
-   $js->decode ('[{}]'); # the given subroutine takes a hash reference.
-   # throw an exception because allow_nonref is not enabled
-   # so a lone 5 is not allowed.
-   $js->decode ('{"a":1, "b":2}');
-
-=head2 filter_json_single_key_object
-
-    $json = $json->filter_json_single_key_object($key [=> $coderef])
-
-Works remotely similar to C<filter_json_object>, but is only called for
-JSON objects having a single key named C<$key>.
-
-This C<$coderef> is called before the one specified via
-C<filter_json_object>, if any. It gets passed the single value in the JSON
-object. If it returns a single value, it will be inserted into the data
-structure. If it returns nothing (not even C<undef> but the empty list),
-the callback from C<filter_json_object> will be called next, as if no
-single-key callback were specified.
-
-If C<$coderef> is omitted or undefined, the corresponding callback will be
-disabled. There can only ever be one callback for a given key.
-
-As this callback gets called less often then the C<filter_json_object>
-one, decoding speed will not usually suffer as much. Therefore, single-key
-objects make excellent targets to serialise Perl objects into, especially
-as single-key JSON objects are as close to the type-tagged value concept
-as JSON gets (it's basically an ID/VALUE tuple). Of course, JSON does not
-support this in any way, so you need to make sure your data never looks
-like a serialised Perl hash.
-
-Typical names for the single object key are C<__class_whatever__>, or
-C<$__dollars_are_rarely_used__$> or C<}ugly_brace_placement>, or even
-things like C<__class_md5sum(classname)__>, to reduce the risk of clashing
-with real hashes.
-
-Example, decode JSON objects of the form C<< { "__widget__" => <id> } >>
-into the corresponding C<< $WIDGET{<id>} >> object:
-
-   # return whatever is in $WIDGET{5}:
-   JSON::PP
-      ->new
-      ->filter_json_single_key_object (__widget__ => sub {
-            $WIDGET{ $_[0] }
-         })
-      ->decode ('{"__widget__": 5')
-
-   # this can be used with a TO_JSON method in some "widget" class
-   # for serialisation to json:
-   sub WidgetBase::TO_JSON {
-      my ($self) = @_;
-
-      unless ($self->{id}) {
-         $self->{id} = ..get..some..id..;
-         $WIDGET{$self->{id}} = $self;
-      }
-
-      { __widget__ => $self->{id} }
-   }
-
-=head2 shrink
-
-    $json = $json->shrink([$enable])
-    
-    $enabled = $json->get_shrink
-
-In JSON::XS, this flag resizes strings generated by either
-C<encode> or C<decode> to their minimum size possible.
-It will also try to downgrade any strings to octet-form if possible.
-
-In JSON::PP, it is noop about resizing strings but tries
-C<utf8::downgrade> to the returned string by C<encode>.
-See to L<utf8>.
-
-See to L<JSON::XS/OBJECT-ORIENTED INTERFACE>
-
-=head2 max_depth
-
-    $json = $json->max_depth([$maximum_nesting_depth])
-    
-    $max_depth = $json->get_max_depth
-
-Sets the maximum nesting level (default C<512>) accepted while encoding
-or decoding. If a higher nesting level is detected in JSON text or a Perl
-data structure, then the encoder and decoder will stop and croak at that
-point.
-
-Nesting level is defined by number of hash- or arrayrefs that the encoder
-needs to traverse to reach a given point or the number of C<{> or C<[>
-characters without their matching closing parenthesis crossed to reach a
-given character in a string.
-
-If no argument is given, the highest possible setting will be used, which
-is rarely useful.
-
-See L<JSON::XS/SSECURITY CONSIDERATIONS> for more info on why this is useful.
-
-When a large value (100 or more) was set and it de/encodes a deep nested object/text,
-it may raise a warning 'Deep recursion on subroutine' at the perl runtime phase.
-
-=head2 max_size
-
-    $json = $json->max_size([$maximum_string_size])
-    
-    $max_size = $json->get_max_size
-
-Set the maximum length a JSON text may have (in bytes) where decoding is
-being attempted. The default is C<0>, meaning no limit. When C<decode>
-is called on a string that is longer then this many bytes, it will not
-attempt to decode the string but throw an exception. This setting has no
-effect on C<encode> (yet).
-
-If no argument is given, the limit check will be deactivated (same as when
-C<0> is specified).
-
-See L<JSON::XS/SECURITY CONSIDERATIONS> for more info on why this is useful.
-
-=head2 encode
-
-    $json_text = $json->encode($perl_scalar)
-
-Converts the given Perl data structure (a simple scalar or a reference
-to a hash or array) to its JSON representation. Simple scalars will be
-converted into JSON string or number sequences, while references to arrays
-become JSON arrays and references to hashes become JSON objects. Undefined
-Perl values (e.g. C<undef>) become JSON C<null> values.
-References to the integers C<0> and C<1> are converted into C<true> and C<false>.
-
-=head2 decode
-
-    $perl_scalar = $json->decode($json_text)
-
-The opposite of C<encode>: expects a JSON text and tries to parse it,
-returning the resulting simple scalar or reference. Croaks on error.
-
-JSON numbers and strings become simple Perl scalars. JSON arrays become
-Perl arrayrefs and JSON objects become Perl hashrefs. C<true> becomes
-C<1> (C<JSON::true>), C<false> becomes C<0> (C<JSON::false>) and
-C<null> becomes C<undef>.
-
-=head2 decode_prefix
-
-    ($perl_scalar, $characters) = $json->decode_prefix($json_text)
-
-This works like the C<decode> method, but instead of raising an exception
-when there is trailing garbage after the first JSON object, it will
-silently stop parsing there and return the number of characters consumed
-so far.
-
-   JSON->new->decode_prefix ("[1] the tail")
-   => ([], 3)
-
-=head1 INCREMENTAL PARSING
-
-Most of this section are copied and modified from L<JSON::XS/INCREMENTAL PARSING>.
-
-In some cases, there is the need for incremental parsing of JSON texts.
-This module does allow you to parse a JSON stream incrementally.
-It does so by accumulating text until it has a full JSON object, which
-it then can decode. This process is similar to using C<decode_prefix>
-to see if a full JSON object is available, but is much more efficient
-(and can be implemented with a minimum of method calls).
-
-This module will only attempt to parse the JSON text once it is sure it
-has enough text to get a decisive result, using a very simple but
-truly incremental parser. This means that it sometimes won't stop as
-early as the full parser, for example, it doesn't detect parenthesis
-mismatches. The only thing it guarantees is that it starts decoding as
-soon as a syntactically valid JSON text has been seen. This means you need
-to set resource limits (e.g. C<max_size>) to ensure the parser will stop
-parsing in the presence if syntax errors.
-
-The following methods implement this incremental parser.
-
-=head2 incr_parse
-
-    $json->incr_parse( [$string] ) # void context
-    
-    $obj_or_undef = $json->incr_parse( [$string] ) # scalar context
-    
-    @obj_or_empty = $json->incr_parse( [$string] ) # list context
-
-This is the central parsing function. It can both append new text and
-extract objects from the stream accumulated so far (both of these
-functions are optional).
-
-If C<$string> is given, then this string is appended to the already
-existing JSON fragment stored in the C<$json> object.
-
-After that, if the function is called in void context, it will simply
-return without doing anything further. This can be used to add more text
-in as many chunks as you want.
-
-If the method is called in scalar context, then it will try to extract
-exactly I<one> JSON object. If that is successful, it will return this
-object, otherwise it will return C<undef>. If there is a parse error,
-this method will croak just as C<decode> would do (one can then use
-C<incr_skip> to skip the erroneous part). This is the most common way of
-using the method.
-
-And finally, in list context, it will try to extract as many objects
-from the stream as it can find and return them, or the empty list
-otherwise. For this to work, there must be no separators between the JSON
-objects or arrays, instead they must be concatenated back-to-back. If
-an error occurs, an exception will be raised as in the scalar context
-case. Note that in this case, any previously-parsed JSON texts will be
-lost.
-
-Example: Parse some JSON arrays/objects in a given string and return them.
-
-    my @objs = JSON->new->incr_parse ("[5][7][1,2]");
-
-=head2 incr_text
-
-    $lvalue_string = $json->incr_text
-
-This method returns the currently stored JSON fragment as an lvalue, that
-is, you can manipulate it. This I<only> works when a preceding call to
-C<incr_parse> in I<scalar context> successfully returned an object. Under
-all other circumstances you must not call this function (I mean it.
-although in simple tests it might actually work, it I<will> fail under
-real world conditions). As a special exception, you can also call this
-method before having parsed anything.
-
-This function is useful in two cases: a) finding the trailing text after a
-JSON object or b) parsing multiple JSON objects separated by non-JSON text
-(such as commas).
-
-    $json->incr_text =~ s/\s*,\s*//;
-
-In Perl 5.005, C<lvalue> attribute is not available.
-You must write codes like the below:
-
-    $string = $json->incr_text;
-    $string =~ s/\s*,\s*//;
-    $json->incr_text( $string );
-
-=head2 incr_skip
-
-    $json->incr_skip
-
-This will reset the state of the incremental parser and will remove the
-parsed text from the input buffer. This is useful after C<incr_parse>
-died, in which case the input buffer and incremental parser state is left
-unchanged, to skip the text parsed so far and to reset the parse state.
-
-=head2 incr_reset
-
-    $json->incr_reset
-
-This completely resets the incremental parser, that is, after this call,
-it will be as if the parser had never parsed anything.
-
-This is useful if you want to repeatedly parse JSON objects and want to
-ignore any trailing data, which means you have to reset the parser after
-each successful decode.
-
-See to L<JSON::XS/INCREMENTAL PARSING> for examples.
-
-
-=head1 JSON::PP OWN METHODS
-
-=head2 allow_singlequote
-
-    $json = $json->allow_singlequote([$enable])
-
-If C<$enable> is true (or missing), then C<decode> will accept
-JSON strings quoted by single quotations that are invalid JSON
-format.
-
-    $json->allow_singlequote->decode({"foo":'bar'});
-    $json->allow_singlequote->decode({'foo':"bar"});
-    $json->allow_singlequote->decode({'foo':'bar'});
-
-As same as the C<relaxed> option, this option may be used to parse
-application-specific files written by humans.
-
-
-=head2 allow_barekey
-
-    $json = $json->allow_barekey([$enable])
-
-If C<$enable> is true (or missing), then C<decode> will accept
-bare keys of JSON object that are invalid JSON format.
-
-As same as the C<relaxed> option, this option may be used to parse
-application-specific files written by humans.
-
-    $json->allow_barekey->decode('{foo:"bar"}');
-
-=head2 allow_bignum
-
-    $json = $json->allow_bignum([$enable])
-
-If C<$enable> is true (or missing), then C<decode> will convert
-the big integer Perl cannot handle as integer into a L<Math::BigInt>
-object and convert a floating number (any) into a L<Math::BigFloat>.
-
-On the contrary, C<encode> converts C<Math::BigInt> objects and C<Math::BigFloat>
-objects into JSON numbers with C<allow_blessed> enable.
-
-   $json->allow_nonref->allow_blessed->allow_bignum;
-   $bigfloat = $json->decode('2.000000000000000000000000001');
-   print $json->encode($bigfloat);
-   # => 2.000000000000000000000000001
-
-See to L<JSON::XS/MAPPING> about the normal conversion of JSON number.
-
-=head2 loose
-
-    $json = $json->loose([$enable])
-
-The unescaped [\x00-\x1f\x22\x2f\x5c] strings are invalid in JSON strings
-and the module doesn't allow to C<decode> to these (except for \x2f).
-If C<$enable> is true (or missing), then C<decode>  will accept these
-unescaped strings.
-
-    $json->loose->decode(qq|["abc
-                                   def"]|);
-
-See L<JSON::XS/SSECURITY CONSIDERATIONS>.
-
-=head2 escape_slash
-
-    $json = $json->escape_slash([$enable])
-
-According to JSON Grammar, I<slash> (U+002F) is escaped. But default
-JSON::PP (as same as JSON::XS) encodes strings without escaping slash.
-
-If C<$enable> is true (or missing), then C<encode> will escape slashes.
-
-=head2 indent_length
-
-    $json = $json->indent_length($length)
-
-JSON::XS indent space length is 3 and cannot be changed.
-JSON::PP set the indent space length with the given $length.
-The default is 3. The acceptable range is 0 to 15.
-
-=head2 sort_by
-
-    $json = $json->sort_by($function_name)
-    $json = $json->sort_by($subroutine_ref)
-
-If $function_name or $subroutine_ref are set, its sort routine are used
-in encoding JSON objects.
-
-   $js = $pc->sort_by(sub { $JSON::PP::a cmp $JSON::PP::b })->encode($obj);
-   # is($js, q|{"a":1,"b":2,"c":3,"d":4,"e":5,"f":6,"g":7,"h":8,"i":9}|);
-
-   $js = $pc->sort_by('own_sort')->encode($obj);
-   # is($js, q|{"a":1,"b":2,"c":3,"d":4,"e":5,"f":6,"g":7,"h":8,"i":9}|);
-
-   sub JSON::PP::own_sort { $JSON::PP::a cmp $JSON::PP::b }
-
-As the sorting routine runs in the JSON::PP scope, the given
-subroutine name and the special variables C<$a>, C<$b> will begin
-'JSON::PP::'.
-
-If $integer is set, then the effect is same as C<canonical> on.
-
-=head1 INTERNAL
-
-For developers.
-
-=over
-
-=item PP_encode_box
-
-Returns
-
-        {
-            depth        => $depth,
-            indent_count => $indent_count,
-        }
-
-
-=item PP_decode_box
-
-Returns
-
-        {
-            text    => $text,
-            at      => $at,
-            ch      => $ch,
-            len     => $len,
-            depth   => $depth,
-            encoding      => $encoding,
-            is_valid_utf8 => $is_valid_utf8,
-        };
-
-=back
-
-=head1 MAPPING
-
-This section is copied from JSON::XS and modified to C<JSON::PP>.
-JSON::XS and JSON::PP mapping mechanisms are almost equivalent.
-
-See to L<JSON::XS/MAPPING>.
-
-=head2 JSON -> PERL
-
-=over 4
-
-=item object
-
-A JSON object becomes a reference to a hash in Perl. No ordering of object
-keys is preserved (JSON does not preserver object key ordering itself).
-
-=item array
-
-A JSON array becomes a reference to an array in Perl.
-
-=item string
-
-A JSON string becomes a string scalar in Perl - Unicode codepoints in JSON
-are represented by the same codepoints in the Perl string, so no manual
-decoding is necessary.
-
-=item number
-
-A JSON number becomes either an integer, numeric (floating point) or
-string scalar in perl, depending on its range and any fractional parts. On
-the Perl level, there is no difference between those as Perl handles all
-the conversion details, but an integer may take slightly less memory and
-might represent more values exactly than floating point numbers.
-
-If the number consists of digits only, C<JSON> will try to represent
-it as an integer value. If that fails, it will try to represent it as
-a numeric (floating point) value if that is possible without loss of
-precision. Otherwise it will preserve the number as a string value (in
-which case you lose roundtripping ability, as the JSON number will be
-re-encoded to a JSON string).
-
-Numbers containing a fractional or exponential part will always be
-represented as numeric (floating point) values, possibly at a loss of
-precision (in which case you might lose perfect roundtripping ability, but
-the JSON number will still be re-encoded as a JSON number).
-
-Note that precision is not accuracy - binary floating point values cannot
-represent most decimal fractions exactly, and when converting from and to
-floating point, C<JSON> only guarantees precision up to but not including
-the least significant bit.
-
-When C<allow_bignum> is enable, the big integers 
-and the numeric can be optionally converted into L<Math::BigInt> and
-L<Math::BigFloat> objects.
-
-=item true, false
-
-These JSON atoms become C<JSON::PP::true> and C<JSON::PP::false>,
-respectively. They are overloaded to act almost exactly like the numbers
-C<1> and C<0>. You can check whether a scalar is a JSON boolean by using
-the C<JSON::is_bool> function.
-
-   print JSON::PP::true . "\n";
-    => true
-   print JSON::PP::true + 1;
-    => 1
-
-   ok(JSON::true eq  '1');
-   ok(JSON::true == 1);
-
-C<JSON> will install these missing overloading features to the backend modules.
-
-
-=item null
-
-A JSON null atom becomes C<undef> in Perl.
-
-C<JSON::PP::null> returns C<undef>.
-
-=back
-
-
-=head2 PERL -> JSON
-
-The mapping from Perl to JSON is slightly more difficult, as Perl is a
-truly typeless language, so we can only guess which JSON type is meant by
-a Perl value.
-
-=over 4
-
-=item hash references
-
-Perl hash references become JSON objects. As there is no inherent ordering
-in hash keys (or JSON objects), they will usually be encoded in a
-pseudo-random order that can change between runs of the same program but
-stays generally the same within a single run of a program. C<JSON>
-optionally sort the hash keys (determined by the I<canonical> flag), so
-the same data structure will serialise to the same JSON text (given same
-settings and version of JSON::XS), but this incurs a runtime overhead
-and is only rarely useful, e.g. when you want to compare some JSON text
-against another for equality.
-
-
-=item array references
-
-Perl array references become JSON arrays.
-
-=item other references
-
-Other unblessed references are generally not allowed and will cause an
-exception to be thrown, except for references to the integers C<0> and
-C<1>, which get turned into C<false> and C<true> atoms in JSON. You can
-also use C<JSON::false> and C<JSON::true> to improve readability.
-
-   to_json [\0,JSON::PP::true]      # yields [false,true]
-
-=item JSON::PP::true, JSON::PP::false, JSON::PP::null
-
-These special values become JSON true and JSON false values,
-respectively. You can also use C<\1> and C<\0> directly if you want.
-
-JSON::PP::null returns C<undef>.
-
-=item blessed objects
-
-Blessed objects are not directly representable in JSON. See the
-C<allow_blessed> and C<convert_blessed> methods on various options on
-how to deal with this: basically, you can choose between throwing an
-exception, encoding the reference as if it weren't blessed, or provide
-your own serialiser method.
-
-See to L<convert_blessed>.
-
-=item simple scalars
-
-Simple Perl scalars (any scalar that is not a reference) are the most
-difficult objects to encode: JSON::XS and JSON::PP will encode undefined scalars as
-JSON C<null> values, scalars that have last been used in a string context
-before encoding as JSON strings, and anything else as number value:
-
-   # dump as number
-   encode_json [2]                      # yields [2]
-   encode_json [-3.0e17]                # yields [-3e+17]
-   my $value = 5; encode_json [$value]  # yields [5]
-
-   # used as string, so dump as string
-   print $value;
-   encode_json [$value]                 # yields ["5"]
-
-   # undef becomes null
-   encode_json [undef]                  # yields [null]
-
-You can force the type to be a string by stringifying it:
-
-   my $x = 3.1; # some variable containing a number
-   "$x";        # stringified
-   $x .= "";    # another, more awkward way to stringify
-   print $x;    # perl does it for you, too, quite often
-
-You can force the type to be a number by numifying it:
-
-   my $x = "3"; # some variable containing a string
-   $x += 0;     # numify it, ensuring it will be dumped as a number
-   $x *= 1;     # same thing, the choice is yours.
-
-You can not currently force the type in other, less obscure, ways.
-
-Note that numerical precision has the same meaning as under Perl (so
-binary to decimal conversion follows the same rules as in Perl, which
-can differ to other languages). Also, your perl interpreter might expose
-extensions to the floating point numbers of your platform, such as
-infinities or NaN's - these cannot be represented in JSON, and it is an
-error to pass those in.
-
-=item Big Number
-
-When C<allow_bignum> is enable, 
-C<encode> converts C<Math::BigInt> objects and C<Math::BigFloat>
-objects into JSON numbers.
-
-
-=back
-
-=head1 UNICODE HANDLING ON PERLS
-
-If you do not know about Unicode on Perl well,
-please check L<JSON::XS/A FEW NOTES ON UNICODE AND PERL>.
-
-=head2 Perl 5.8 and later
-
-Perl can handle Unicode and the JSON::PP de/encode methods also work properly.
-
-    $json->allow_nonref->encode(chr hex 3042);
-    $json->allow_nonref->encode(chr hex 12345);
-
-Returns C<"\u3042"> and C<"\ud808\udf45"> respectively.
-
-    $json->allow_nonref->decode('"\u3042"');
-    $json->allow_nonref->decode('"\ud808\udf45"');
-
-Returns UTF-8 encoded strings with UTF8 flag, regarded as C<U+3042> and C<U+12345>.
-
-Note that the versions from Perl 5.8.0 to 5.8.2, Perl built-in C<join> was broken,
-so JSON::PP wraps the C<join> with a subroutine. Thus JSON::PP works slow in the versions.
-
-
-=head2 Perl 5.6
-
-Perl can handle Unicode and the JSON::PP de/encode methods also work.
-
-=head2 Perl 5.005
-
-Perl 5.005 is a byte semantics world -- all strings are sequences of bytes.
-That means the unicode handling is not available.
-
-In encoding,
-
-    $json->allow_nonref->encode(chr hex 3042);  # hex 3042 is 12354.
-    $json->allow_nonref->encode(chr hex 12345); # hex 12345 is 74565.
-
-Returns C<B> and C<E>, as C<chr> takes a value more than 255, it treats
-as C<$value % 256>, so the above codes are equivalent to :
-
-    $json->allow_nonref->encode(chr 66);
-    $json->allow_nonref->encode(chr 69);
-
-In decoding,
-
-    $json->decode('"\u00e3\u0081\u0082"');
-
-The returned is a byte sequence C<0xE3 0x81 0x82> for UTF-8 encoded
-japanese character (C<HIRAGANA LETTER A>).
-And if it is represented in Unicode code point, C<U+3042>.
-
-Next, 
-
-    $json->decode('"\u3042"');
-
-We ordinary expect the returned value is a Unicode character C<U+3042>.
-But here is 5.005 world. This is C<0xE3 0x81 0x82>.
-
-    $json->decode('"\ud808\udf45"');
-
-This is not a character C<U+12345> but bytes - C<0xf0 0x92 0x8d 0x85>.
-
-
-=head1 TODO
-
-=over
-
-=item speed
-
-=item memory saving
-
-=back
-
-
-=head1 SEE ALSO
-
-Most of the document are copied and modified from JSON::XS doc.
-
-L<JSON::XS>
-
-RFC4627 (L<http://www.ietf.org/rfc/rfc4627.txt>)
-
-=head1 AUTHOR
-
-Makamaka Hannyaharamitu, E<lt>makamaka[at]cpan.orgE<gt>
-
-
-=head1 COPYRIGHT AND LICENSE
-
-Copyright 2007-2012 by Makamaka Hannyaharamitu
-
-This library is free software; you can redistribute it and/or modify
-it under the same terms as Perl itself. 
-
-=cut
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/sd_hijack_inpainting.py b/spaces/jackli888/stable-diffusion-webui/modules/sd_hijack_inpainting.py
deleted file mode 100644
index 55a2ce4d19200acafd79e6fce7e017c4abc50a73..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/sd_hijack_inpainting.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import torch
-
-from einops import repeat
-from omegaconf import ListConfig
-
-import ldm.models.diffusion.ddpm
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-
-from ldm.models.diffusion.ddpm import LatentDiffusion
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.ddim import DDIMSampler, noise_like
-from ldm.models.diffusion.sampling_util import norm_thresholding
-
-
-@torch.no_grad()
-def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
-    b, *_, device = *x.shape, x.device
-
-    def get_model_output(x, t):
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = dict()
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [
-                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
-                            for i in range(len(c[k]))
-                        ]
-                    else:
-                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])
-
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        return e_t
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-    def get_x_prev_and_pred_x0(e_t, index):
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        if dynamic_threshold is not None:
-            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    e_t = get_model_output(x, t)
-    if len(old_eps) == 0:
-        # Pseudo Improved Euler (2nd order)
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-        e_t_next = get_model_output(x_prev, t_next)
-        e_t_prime = (e_t + e_t_next) / 2
-    elif len(old_eps) == 1:
-        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (3 * e_t - old_eps[-1]) / 2
-    elif len(old_eps) == 2:
-        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-    elif len(old_eps) >= 3:
-        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-    return x_prev, pred_x0, e_t
-
-
-def do_inpainting_hijack():
-    # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-
-    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/ui_extra_networks_textual_inversion.py b/spaces/jackli888/stable-diffusion-webui/modules/ui_extra_networks_textual_inversion.py
deleted file mode 100644
index 47491b2f09ccc960e2e237097f9d9e78075d25c0..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/ui_extra_networks_textual_inversion.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import json
-import os
-
-from modules import ui_extra_networks, sd_hijack
-
-
-class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
-    def __init__(self):
-        super().__init__('Textual Inversion')
-        self.allow_negative_prompt = True
-
-    def refresh(self):
-        sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
-
-    def list_items(self):
-        for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values():
-            path, ext = os.path.splitext(embedding.filename)
-            preview_file = path + ".preview.png"
-
-            preview = None
-            if os.path.isfile(preview_file):
-                preview = self.link_preview(preview_file)
-
-            yield {
-                "name": embedding.name,
-                "filename": embedding.filename,
-                "preview": preview,
-                "search_term": self.search_terms_from_path(embedding.filename),
-                "prompt": json.dumps(embedding.name),
-                "local_preview": path + ".preview.png",
-            }
-
-    def allowed_directories_for_previews(self):
-        return list(sd_hijack.model_hijack.embedding_db.embedding_dirs)
diff --git a/spaces/james-oldfield/PandA/networks/stylegan3/viz/__init__.py b/spaces/james-oldfield/PandA/networks/stylegan3/viz/__init__.py
deleted file mode 100644
index 939e7c6c8f94c4ea1141885c3c3295fe083b06aa..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/stylegan3/viz/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty
diff --git a/spaces/janeH/QQsign/README.md b/spaces/janeH/QQsign/README.md
deleted file mode 100644
index bd56881a2a7709591343e2f15af9a6a8133e115b..0000000000000000000000000000000000000000
--- a/spaces/janeH/QQsign/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: QQsign
-emoji: 🦀
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-comic-factory/Dockerfile b/spaces/jbilcke-hf/ai-comic-factory/Dockerfile
deleted file mode 100644
index 4ef86c13e803b99af8527d40a42131c3b3fbc58c..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/Dockerfile
+++ /dev/null
@@ -1,65 +0,0 @@
-FROM node:20-alpine AS base
-
-# Install dependencies only when needed
-FROM base AS deps
-# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
-RUN apk add --no-cache libc6-compat
-WORKDIR /app
-
-# Install dependencies based on the preferred package manager
-COPY package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
-RUN \
-  if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
-  elif [ -f package-lock.json ]; then npm ci; \
-  elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
-  else echo "Lockfile not found." && exit 1; \
-  fi
-
-# Uncomment the following lines if you want to use a secret at buildtime, 
-# for example to access your private npm packages
-# RUN --mount=type=secret,id=HF_EXAMPLE_SECRET,mode=0444,required=true \
-#     $(cat /run/secrets/HF_EXAMPLE_SECRET)
-
-# Rebuild the source code only when needed
-FROM base AS builder
-WORKDIR /app
-COPY --from=deps /app/node_modules ./node_modules
-COPY . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-# RUN yarn build
-
-# If you use yarn, comment out this line and use the line above
-RUN npm run build
-
-# Production image, copy all the files and run next
-FROM base AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN addgroup --system --gid 1001 nodejs
-RUN adduser --system --uid 1001 nextjs
-
-COPY --from=builder /app/public ./public
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
-COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
-COPY --from=builder --chown=nextjs:nodejs /app/.next/cache ./.next/cache
-# COPY --from=builder --chown=nextjs:nodejs /app/.next/cache/fetch-cache ./.next/cache/fetch-cache
-
-USER nextjs
-
-EXPOSE 3000
-
-ENV PORT 3000
-
-CMD ["node", "server.js"]
diff --git a/spaces/jhtonyKoo/music_mixing_style_transfer/inference/feature_extraction.py b/spaces/jhtonyKoo/music_mixing_style_transfer/inference/feature_extraction.py
deleted file mode 100644
index a846232991200df780a3938a5e19d3892e9a812e..0000000000000000000000000000000000000000
--- a/spaces/jhtonyKoo/music_mixing_style_transfer/inference/feature_extraction.py
+++ /dev/null
@@ -1,194 +0,0 @@
-"""
-    Inference code of extracting embeddings from music recordings using FXencoder
-    of the work "Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects"
-
-    Process : extracts FX embeddings of each song inside the target directory.
-"""
-from glob import glob
-import os
-import librosa
-import numpy as np
-import torch
-
-import sys
-currentdir = os.path.dirname(os.path.realpath(__file__))
-sys.path.append(os.path.join(os.path.dirname(currentdir), "mixing_style_transfer"))
-from networks import FXencoder
-from data_loader import *
-
-
-class FXencoder_Inference:
-    def __init__(self, args, trained_w_ddp=True):
-        if args.inference_device!='cpu' and torch.cuda.is_available():
-            self.device = torch.device("cuda:0")
-        else:
-            self.device = torch.device("cpu")
-        
-        # inference computational hyperparameters
-        self.segment_length = args.segment_length
-        self.batch_size = args.batch_size
-        self.sample_rate = 44100    # sampling rate should be 44100
-        self.time_in_seconds = int(args.segment_length // self.sample_rate)
-
-        # directory configuration
-        self.output_dir = args.target_dir if args.output_dir==None else args.output_dir
-        self.target_dir = args.target_dir
-
-        # load model and its checkpoint weights
-        self.models = {}
-        self.models['effects_encoder'] = FXencoder(args.cfg_encoder).to(self.device)
-        ckpt_paths = {'effects_encoder' : args.ckpt_path_enc}
-        # reload saved model weights
-        ddp = trained_w_ddp
-        self.reload_weights(ckpt_paths, ddp=ddp)
-
-        # save current arguments
-        self.save_args(args)
-
-
-    # reload model weights from the target checkpoint path
-    def reload_weights(self, ckpt_paths, ddp=True):
-        for cur_model_name in self.models.keys():
-            checkpoint = torch.load(ckpt_paths[cur_model_name], map_location=self.device)
-
-            from collections import OrderedDict
-            new_state_dict = OrderedDict()
-            for k, v in checkpoint["model"].items():
-                # remove `module.` if the model was trained with DDP
-                name = k[7:] if ddp else k
-                new_state_dict[name] = v
-        
-            # load params
-            self.models[cur_model_name].load_state_dict(new_state_dict)
-
-            print(f"---reloaded checkpoint weights : {cur_model_name} ---")
-
-
-    # save averaged embedding from whole songs
-    def save_averaged_embeddings(self, ):
-        # embedding output directory path
-        emb_out_dir = f"{self.output_dir}"
-        print(f'\n\n=====Inference seconds : {self.time_in_seconds}=====')
-
-        # target_file_paths = glob(f"{self.target_dir}/**/*.wav", recursive=True)
-        target_file_paths = glob(os.path.join(self.target_dir, '**', '*.wav'), recursive=True)
-        for step, target_file_path in enumerate(target_file_paths):
-            print(f"\nInference step : {step+1}/{len(target_file_paths)}")
-            print(f"---current file path : {target_file_path}---")
-
-            ''' load waveform signal '''
-            target_song_whole = load_wav_segment(target_file_path, axis=0)
-            # check if mono -> convert to stereo by duplicating mono signal
-            if len(target_song_whole.shape)==1:
-                target_song_whole = np.stack((target_song_whole, target_song_whole), axis=0)
-            # check axis dimension
-            # signal shape should be : [channel, signal duration]
-            elif target_song_whole.shape[1]==2:
-                target_song_whole = target_song_whole.transpose()
-            target_song_whole = torch.from_numpy(target_song_whole).float()
-            ''' segmentize whole songs into batch '''
-            whole_batch_data = self.batchwise_segmentization(target_song_whole, target_file_path)
-
-            ''' inference '''
-            # infer whole song
-            infered_data_list = []
-            infered_c_list = []
-            infered_z_list = []
-            for cur_idx, cur_data in enumerate(whole_batch_data):
-                cur_data = cur_data.to(self.device)
-            
-                with torch.no_grad():
-                    self.models["effects_encoder"].eval()
-                    # FXencoder
-                    out_c_emb = self.models["effects_encoder"](cur_data)
-                infered_c_list.append(out_c_emb.cpu().detach())
-            avg_c_feat = torch.mean(torch.cat(infered_c_list, dim=0), dim=0).squeeze().cpu().detach().numpy()
-
-            # save outputs
-            cur_output_path = target_file_path.replace(self.target_dir, self.output_dir).replace('.wav', '_fx_embedding.npy')
-            os.makedirs(os.path.dirname(cur_output_path), exist_ok=True)
-            np.save(cur_output_path, avg_c_feat)
-
-
-    # function that segmentize an entire song into batch
-    def batchwise_segmentization(self, target_song, target_file_path, discard_last=False):
-        assert target_song.shape[-1] >= self.segment_length, \
-                f"Error : Insufficient duration!\n\t \
-                Target song's length is shorter than segment length.\n\t \
-                Song name : {target_file_path}\n\t \
-                Consider changing the 'segment_length' or song with sufficient duration"
-
-        # discard restovers (last segment)
-        if discard_last:
-            target_length = target_song.shape[-1] - target_song.shape[-1] % self.segment_length
-            target_song = target_song[:, :target_length]
-        # pad last segment
-        else:
-            pad_length = self.segment_length - target_song.shape[-1] % self.segment_length
-            target_song = torch.cat((target_song, torch.zeros(2, pad_length)), axis=-1)
-
-        whole_batch_data = []
-        batch_wise_data = []
-        for cur_segment_idx in range(target_song.shape[-1]//self.segment_length):
-            batch_wise_data.append(target_song[..., cur_segment_idx*self.segment_length:(cur_segment_idx+1)*self.segment_length])
-            if len(batch_wise_data)==self.batch_size:
-                whole_batch_data.append(torch.stack(batch_wise_data, dim=0))
-                batch_wise_data = []
-        if batch_wise_data:
-            whole_batch_data.append(torch.stack(batch_wise_data, dim=0))
-
-        return whole_batch_data
-
-
-    # save current inference arguments
-    def save_args(self, params):
-        info = '\n[args]\n'
-        for sub_args in parser._action_groups:
-            if sub_args.title in ['positional arguments', 'optional arguments', 'options']:
-                continue
-            size_sub = len(sub_args._group_actions)
-            info += f'  {sub_args.title} ({size_sub})\n'
-            for i, arg in enumerate(sub_args._group_actions):
-                prefix = '-'
-                info += f'      {prefix} {arg.dest:20s}: {getattr(params, arg.dest)}\n'
-        info += '\n'
-
-        os.makedirs(self.output_dir, exist_ok=True)
-        record_path = f"{self.output_dir}feature_extraction_inference_configurations.txt"
-        f = open(record_path, 'w')
-        np.savetxt(f, [info], delimiter=" ", fmt="%s")
-        f.close()
-
-
-
-if __name__ == '__main__':
-    ''' Configurations for inferencing music effects encoder '''
-    currentdir = os.path.dirname(os.path.realpath(__file__))
-    default_ckpt_path = os.path.join(os.path.dirname(currentdir), 'weights', 'FXencoder_ps.pt')
-
-    import argparse
-    import yaml
-    parser = argparse.ArgumentParser()
-
-    directory_args = parser.add_argument_group('Directory args')
-    directory_args.add_argument('--target_dir', type=str, default='./samples/')
-    directory_args.add_argument('--output_dir', type=str, default=None, help='if no output_dir is specified (None), the results will be saved inside the target_dir')
-    directory_args.add_argument('--ckpt_path_enc', type=str, default=default_ckpt_path)
-
-    inference_args = parser.add_argument_group('Inference args')
-    inference_args.add_argument('--segment_length', type=int, default=44100*10) # segmentize input according to this duration
-    inference_args.add_argument('--batch_size', type=int, default=1)            # for processing long audio
-    inference_args.add_argument('--inference_device', type=str, default='cpu', help="if this option is not set to 'cpu', inference will happen on gpu only if there is a detected one")
-
-    args = parser.parse_args()
-
-    # load network configurations
-    with open(os.path.join(currentdir, 'configs.yaml'), 'r') as f:
-        configs = yaml.full_load(f)
-    args.cfg_encoder = configs['Effects_Encoder']['default']
-
-    # Extract features using pre-trained FXencoder
-    inference_encoder = FXencoder_Inference(args)
-    inference_encoder.save_averaged_embeddings()
-    
-    
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_ofb.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_ofb.py
deleted file mode 100644
index 1a29b0569d80818603112c5d3dbc03d16d9e871f..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_ofb.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  Cipher/mode_ofb.py : OFB mode
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-
-"""
-Output Feedback (CFB) mode.
-"""
-
-__all__ = ['OfbMode']
-
-from Crypto.Util.py3compat import _copy_bytes
-from Crypto.Util._raw_api import (load_pycryptodome_raw_lib, VoidPointer,
-                                  create_string_buffer, get_raw_buffer,
-                                  SmartPointer, c_size_t, c_uint8_ptr,
-                                  is_writeable_buffer)
-
-from Crypto.Random import get_random_bytes
-
-raw_ofb_lib = load_pycryptodome_raw_lib("Crypto.Cipher._raw_ofb", """
-                        int OFB_start_operation(void *cipher,
-                                                const uint8_t iv[],
-                                                size_t iv_len,
-                                                void **pResult);
-                        int OFB_encrypt(void *ofbState,
-                                        const uint8_t *in,
-                                        uint8_t *out,
-                                        size_t data_len);
-                        int OFB_decrypt(void *ofbState,
-                                        const uint8_t *in,
-                                        uint8_t *out,
-                                        size_t data_len);
-                        int OFB_stop_operation(void *state);
-                        """
-                                        )
-
-
-class OfbMode(object):
-    """*Output FeedBack (OFB)*.
-
-    This mode is very similar to CBC, but it
-    transforms the underlying block cipher into a stream cipher.
-
-    The keystream is the iterated block encryption of the
-    previous ciphertext block.
-
-    An Initialization Vector (*IV*) is required.
-
-    See `NIST SP800-38A`_ , Section 6.4.
-
-    .. _`NIST SP800-38A` : http://csrc.nist.gov/publications/nistpubs/800-38a/sp800-38a.pdf
-
-    :undocumented: __init__
-    """
-
-    def __init__(self, block_cipher, iv):
-        """Create a new block cipher, configured in OFB mode.
-
-        :Parameters:
-          block_cipher : C pointer
-            A smart pointer to the low-level block cipher instance.
-
-          iv : bytes/bytearray/memoryview
-            The initialization vector to use for encryption or decryption.
-            It is as long as the cipher block.
-
-            **The IV must be a nonce, to to be reused for any other
-            message**. It shall be a nonce or a random value.
-
-            Reusing the *IV* for encryptions performed with the same key
-            compromises confidentiality.
-        """
-
-        self._state = VoidPointer()
-        result = raw_ofb_lib.OFB_start_operation(block_cipher.get(),
-                                                 c_uint8_ptr(iv),
-                                                 c_size_t(len(iv)),
-                                                 self._state.address_of())
-        if result:
-            raise ValueError("Error %d while instantiating the OFB mode"
-                             % result)
-
-        # Ensure that object disposal of this Python object will (eventually)
-        # free the memory allocated by the raw library for the cipher mode
-        self._state = SmartPointer(self._state.get(),
-                                   raw_ofb_lib.OFB_stop_operation)
-
-        # Memory allocated for the underlying block cipher is now owed
-        # by the cipher mode
-        block_cipher.release()
-
-        self.block_size = len(iv)
-        """The block size of the underlying cipher, in bytes."""
-
-        self.iv = _copy_bytes(None, None, iv)
-        """The Initialization Vector originally used to create the object.
-        The value does not change."""
-
-        self.IV = self.iv
-        """Alias for `iv`"""
-
-        self._next = ["encrypt", "decrypt"]
-
-    def encrypt(self, plaintext, output=None):
-        """Encrypt data with the key and the parameters set at initialization.
-
-        A cipher object is stateful: once you have encrypted a message
-        you cannot encrypt (or decrypt) another message using the same
-        object.
-
-        The data to encrypt can be broken up in two or
-        more pieces and `encrypt` can be called multiple times.
-
-        That is, the statement:
-
-            >>> c.encrypt(a) + c.encrypt(b)
-
-        is equivalent to:
-
-             >>> c.encrypt(a+b)
-
-        This function does not add any padding to the plaintext.
-
-        :Parameters:
-          plaintext : bytes/bytearray/memoryview
-            The piece of data to encrypt.
-            It can be of any length.
-        :Keywords:
-          output : bytearray/memoryview
-            The location where the ciphertext must be written to.
-            If ``None``, the ciphertext is returned.
-        :Return:
-          If ``output`` is ``None``, the ciphertext is returned as ``bytes``.
-          Otherwise, ``None``.
-        """
-
-        if "encrypt" not in self._next:
-            raise TypeError("encrypt() cannot be called after decrypt()")
-        self._next = ["encrypt"]
-
-        if output is None:
-            ciphertext = create_string_buffer(len(plaintext))
-        else:
-            ciphertext = output
-
-            if not is_writeable_buffer(output):
-                raise TypeError("output must be a bytearray or a writeable memoryview")
-
-            if len(plaintext) != len(output):
-                raise ValueError("output must have the same length as the input"
-                                 "  (%d bytes)" % len(plaintext))
-
-        result = raw_ofb_lib.OFB_encrypt(self._state.get(),
-                                         c_uint8_ptr(plaintext),
-                                         c_uint8_ptr(ciphertext),
-                                         c_size_t(len(plaintext)))
-        if result:
-            raise ValueError("Error %d while encrypting in OFB mode" % result)
-
-        if output is None:
-            return get_raw_buffer(ciphertext)
-        else:
-            return None
-
-    def decrypt(self, ciphertext, output=None):
-        """Decrypt data with the key and the parameters set at initialization.
-
-        A cipher object is stateful: once you have decrypted a message
-        you cannot decrypt (or encrypt) another message with the same
-        object.
-
-        The data to decrypt can be broken up in two or
-        more pieces and `decrypt` can be called multiple times.
-
-        That is, the statement:
-
-            >>> c.decrypt(a) + c.decrypt(b)
-
-        is equivalent to:
-
-             >>> c.decrypt(a+b)
-
-        This function does not remove any padding from the plaintext.
-
-        :Parameters:
-          ciphertext : bytes/bytearray/memoryview
-            The piece of data to decrypt.
-            It can be of any length.
-        :Keywords:
-          output : bytearray/memoryview
-            The location where the plaintext is written to.
-            If ``None``, the plaintext is returned.
-        :Return:
-          If ``output`` is ``None``, the plaintext is returned as ``bytes``.
-          Otherwise, ``None``.
-        """
-
-        if "decrypt" not in self._next:
-            raise TypeError("decrypt() cannot be called after encrypt()")
-        self._next = ["decrypt"]
-
-        if output is None:
-            plaintext = create_string_buffer(len(ciphertext))
-        else:
-            plaintext = output
-
-            if not is_writeable_buffer(output):
-                raise TypeError("output must be a bytearray or a writeable memoryview")
-
-            if len(ciphertext) != len(output):
-                raise ValueError("output must have the same length as the input"
-                                 "  (%d bytes)" % len(plaintext))
-
-        result = raw_ofb_lib.OFB_decrypt(self._state.get(),
-                                         c_uint8_ptr(ciphertext),
-                                         c_uint8_ptr(plaintext),
-                                         c_size_t(len(ciphertext)))
-        if result:
-            raise ValueError("Error %d while decrypting in OFB mode" % result)
-
-        if output is None:
-            return get_raw_buffer(plaintext)
-        else:
-            return None
-
-
-def _create_ofb_cipher(factory, **kwargs):
-    """Instantiate a cipher object that performs OFB encryption/decryption.
-
-    :Parameters:
-      factory : module
-        The underlying block cipher, a module from ``Crypto.Cipher``.
-
-    :Keywords:
-      iv : bytes/bytearray/memoryview
-        The IV to use for OFB.
-
-      IV : bytes/bytearray/memoryview
-        Alias for ``iv``.
-
-    Any other keyword will be passed to the underlying block cipher.
-    See the relevant documentation for details (at least ``key`` will need
-    to be present).
-    """
-
-    cipher_state = factory._create_base_cipher(kwargs)
-    iv = kwargs.pop("IV", None)
-    IV = kwargs.pop("iv", None)
-
-    if (None, None) == (iv, IV):
-        iv = get_random_bytes(factory.block_size)
-    if iv is not None:
-        if IV is not None:
-            raise TypeError("You must either use 'iv' or 'IV', not both")
-    else:
-        iv = IV
-
-    if len(iv) != factory.block_size:
-        raise ValueError("Incorrect IV length (it must be %d bytes long)" %
-                factory.block_size)
-
-    if kwargs:
-        raise TypeError("Unknown parameters for OFB: %s" % str(kwargs))
-
-    return OfbMode(cipher_state, iv)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Math/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Math/__init__.py
deleted file mode 100644
index 18e83d1035711c92d0e12b4e00a311be78d36e16..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Math/__init__.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#
-#  SelfTest/Math/__init__.py: Self-test for math module
-#
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin <helderijs@gmail.com>
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-"""Self-test for Math"""
-
-def get_tests(config={}):
-    tests = []
-    from Crypto.SelfTest.Math import test_Numbers
-    from Crypto.SelfTest.Math import test_Primality
-    from Crypto.SelfTest.Math import test_modexp
-    tests += test_Numbers.get_tests(config=config)
-    tests += test_Primality.get_tests(config=config)
-    tests += test_modexp.get_tests(config=config)
-    return tests
-
-if __name__ == '__main__':
-    import unittest
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/routing.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/routing.py
deleted file mode 100644
index 1e3dfb4d52c888f4011104d156dfa3db2e32c87c..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/routing.py
+++ /dev/null
@@ -1,1356 +0,0 @@
-import asyncio
-import dataclasses
-import email.message
-import inspect
-import json
-from contextlib import AsyncExitStack
-from enum import Enum, IntEnum
-from typing import (
-    Any,
-    Callable,
-    Coroutine,
-    Dict,
-    List,
-    Optional,
-    Sequence,
-    Set,
-    Tuple,
-    Type,
-    Union,
-)
-
-from fastapi import params
-from fastapi._compat import (
-    ModelField,
-    Undefined,
-    _get_model_config,
-    _model_dump,
-    _normalize_errors,
-    lenient_issubclass,
-)
-from fastapi.datastructures import Default, DefaultPlaceholder
-from fastapi.dependencies.models import Dependant
-from fastapi.dependencies.utils import (
-    get_body_field,
-    get_dependant,
-    get_parameterless_sub_dependant,
-    get_typed_return_annotation,
-    solve_dependencies,
-)
-from fastapi.encoders import jsonable_encoder
-from fastapi.exceptions import (
-    FastAPIError,
-    RequestValidationError,
-    ResponseValidationError,
-    WebSocketRequestValidationError,
-)
-from fastapi.types import DecoratedCallable, IncEx
-from fastapi.utils import (
-    create_cloned_field,
-    create_response_field,
-    generate_unique_id,
-    get_value_or_default,
-    is_body_allowed_for_status_code,
-)
-from pydantic import BaseModel
-from starlette import routing
-from starlette.concurrency import run_in_threadpool
-from starlette.exceptions import HTTPException
-from starlette.requests import Request
-from starlette.responses import JSONResponse, Response
-from starlette.routing import (
-    BaseRoute,
-    Match,
-    compile_path,
-    get_name,
-    request_response,
-    websocket_session,
-)
-from starlette.routing import Mount as Mount  # noqa
-from starlette.types import ASGIApp, Lifespan, Scope
-from starlette.websockets import WebSocket
-
-
-def _prepare_response_content(
-    res: Any,
-    *,
-    exclude_unset: bool,
-    exclude_defaults: bool = False,
-    exclude_none: bool = False,
-) -> Any:
-    if isinstance(res, BaseModel):
-        read_with_orm_mode = getattr(_get_model_config(res), "read_with_orm_mode", None)
-        if read_with_orm_mode:
-            # Let from_orm extract the data from this model instead of converting
-            # it now to a dict.
-            # Otherwise, there's no way to extract lazy data that requires attribute
-            # access instead of dict iteration, e.g. lazy relationships.
-            return res
-        return _model_dump(
-            res,
-            by_alias=True,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-        )
-    elif isinstance(res, list):
-        return [
-            _prepare_response_content(
-                item,
-                exclude_unset=exclude_unset,
-                exclude_defaults=exclude_defaults,
-                exclude_none=exclude_none,
-            )
-            for item in res
-        ]
-    elif isinstance(res, dict):
-        return {
-            k: _prepare_response_content(
-                v,
-                exclude_unset=exclude_unset,
-                exclude_defaults=exclude_defaults,
-                exclude_none=exclude_none,
-            )
-            for k, v in res.items()
-        }
-    elif dataclasses.is_dataclass(res):
-        return dataclasses.asdict(res)
-    return res
-
-
-async def serialize_response(
-    *,
-    field: Optional[ModelField] = None,
-    response_content: Any,
-    include: Optional[IncEx] = None,
-    exclude: Optional[IncEx] = None,
-    by_alias: bool = True,
-    exclude_unset: bool = False,
-    exclude_defaults: bool = False,
-    exclude_none: bool = False,
-    is_coroutine: bool = True,
-) -> Any:
-    if field:
-        errors = []
-        if not hasattr(field, "serialize"):
-            # pydantic v1
-            response_content = _prepare_response_content(
-                response_content,
-                exclude_unset=exclude_unset,
-                exclude_defaults=exclude_defaults,
-                exclude_none=exclude_none,
-            )
-        if is_coroutine:
-            value, errors_ = field.validate(response_content, {}, loc=("response",))
-        else:
-            value, errors_ = await run_in_threadpool(
-                field.validate, response_content, {}, loc=("response",)
-            )
-        if isinstance(errors_, list):
-            errors.extend(errors_)
-        elif errors_:
-            errors.append(errors_)
-        if errors:
-            raise ResponseValidationError(
-                errors=_normalize_errors(errors), body=response_content
-            )
-
-        if hasattr(field, "serialize"):
-            return field.serialize(
-                value,
-                include=include,
-                exclude=exclude,
-                by_alias=by_alias,
-                exclude_unset=exclude_unset,
-                exclude_defaults=exclude_defaults,
-                exclude_none=exclude_none,
-            )
-
-        return jsonable_encoder(
-            value,
-            include=include,
-            exclude=exclude,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-        )
-    else:
-        return jsonable_encoder(response_content)
-
-
-async def run_endpoint_function(
-    *, dependant: Dependant, values: Dict[str, Any], is_coroutine: bool
-) -> Any:
-    # Only called by get_request_handler. Has been split into its own function to
-    # facilitate profiling endpoints, since inner functions are harder to profile.
-    assert dependant.call is not None, "dependant.call must be a function"
-
-    if is_coroutine:
-        return await dependant.call(**values)
-    else:
-        return await run_in_threadpool(dependant.call, **values)
-
-
-def get_request_handler(
-    dependant: Dependant,
-    body_field: Optional[ModelField] = None,
-    status_code: Optional[int] = None,
-    response_class: Union[Type[Response], DefaultPlaceholder] = Default(JSONResponse),
-    response_field: Optional[ModelField] = None,
-    response_model_include: Optional[IncEx] = None,
-    response_model_exclude: Optional[IncEx] = None,
-    response_model_by_alias: bool = True,
-    response_model_exclude_unset: bool = False,
-    response_model_exclude_defaults: bool = False,
-    response_model_exclude_none: bool = False,
-    dependency_overrides_provider: Optional[Any] = None,
-) -> Callable[[Request], Coroutine[Any, Any, Response]]:
-    assert dependant.call is not None, "dependant.call must be a function"
-    is_coroutine = asyncio.iscoroutinefunction(dependant.call)
-    is_body_form = body_field and isinstance(body_field.field_info, params.Form)
-    if isinstance(response_class, DefaultPlaceholder):
-        actual_response_class: Type[Response] = response_class.value
-    else:
-        actual_response_class = response_class
-
-    async def app(request: Request) -> Response:
-        try:
-            body: Any = None
-            if body_field:
-                if is_body_form:
-                    body = await request.form()
-                    stack = request.scope.get("fastapi_astack")
-                    assert isinstance(stack, AsyncExitStack)
-                    stack.push_async_callback(body.close)
-                else:
-                    body_bytes = await request.body()
-                    if body_bytes:
-                        json_body: Any = Undefined
-                        content_type_value = request.headers.get("content-type")
-                        if not content_type_value:
-                            json_body = await request.json()
-                        else:
-                            message = email.message.Message()
-                            message["content-type"] = content_type_value
-                            if message.get_content_maintype() == "application":
-                                subtype = message.get_content_subtype()
-                                if subtype == "json" or subtype.endswith("+json"):
-                                    json_body = await request.json()
-                        if json_body != Undefined:
-                            body = json_body
-                        else:
-                            body = body_bytes
-        except json.JSONDecodeError as e:
-            raise RequestValidationError(
-                [
-                    {
-                        "type": "json_invalid",
-                        "loc": ("body", e.pos),
-                        "msg": "JSON decode error",
-                        "input": {},
-                        "ctx": {"error": e.msg},
-                    }
-                ],
-                body=e.doc,
-            ) from e
-        except HTTPException:
-            raise
-        except Exception as e:
-            raise HTTPException(
-                status_code=400, detail="There was an error parsing the body"
-            ) from e
-        solved_result = await solve_dependencies(
-            request=request,
-            dependant=dependant,
-            body=body,
-            dependency_overrides_provider=dependency_overrides_provider,
-        )
-        values, errors, background_tasks, sub_response, _ = solved_result
-        if errors:
-            raise RequestValidationError(_normalize_errors(errors), body=body)
-        else:
-            raw_response = await run_endpoint_function(
-                dependant=dependant, values=values, is_coroutine=is_coroutine
-            )
-
-            if isinstance(raw_response, Response):
-                if raw_response.background is None:
-                    raw_response.background = background_tasks
-                return raw_response
-            response_args: Dict[str, Any] = {"background": background_tasks}
-            # If status_code was set, use it, otherwise use the default from the
-            # response class, in the case of redirect it's 307
-            current_status_code = (
-                status_code if status_code else sub_response.status_code
-            )
-            if current_status_code is not None:
-                response_args["status_code"] = current_status_code
-            if sub_response.status_code:
-                response_args["status_code"] = sub_response.status_code
-            content = await serialize_response(
-                field=response_field,
-                response_content=raw_response,
-                include=response_model_include,
-                exclude=response_model_exclude,
-                by_alias=response_model_by_alias,
-                exclude_unset=response_model_exclude_unset,
-                exclude_defaults=response_model_exclude_defaults,
-                exclude_none=response_model_exclude_none,
-                is_coroutine=is_coroutine,
-            )
-            response = actual_response_class(content, **response_args)
-            if not is_body_allowed_for_status_code(response.status_code):
-                response.body = b""
-            response.headers.raw.extend(sub_response.headers.raw)
-            return response
-
-    return app
-
-
-def get_websocket_app(
-    dependant: Dependant, dependency_overrides_provider: Optional[Any] = None
-) -> Callable[[WebSocket], Coroutine[Any, Any, Any]]:
-    async def app(websocket: WebSocket) -> None:
-        solved_result = await solve_dependencies(
-            request=websocket,
-            dependant=dependant,
-            dependency_overrides_provider=dependency_overrides_provider,
-        )
-        values, errors, _, _2, _3 = solved_result
-        if errors:
-            raise WebSocketRequestValidationError(_normalize_errors(errors))
-        assert dependant.call is not None, "dependant.call must be a function"
-        await dependant.call(**values)
-
-    return app
-
-
-class APIWebSocketRoute(routing.WebSocketRoute):
-    def __init__(
-        self,
-        path: str,
-        endpoint: Callable[..., Any],
-        *,
-        name: Optional[str] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        dependency_overrides_provider: Optional[Any] = None,
-    ) -> None:
-        self.path = path
-        self.endpoint = endpoint
-        self.name = get_name(endpoint) if name is None else name
-        self.dependencies = list(dependencies or [])
-        self.path_regex, self.path_format, self.param_convertors = compile_path(path)
-        self.dependant = get_dependant(path=self.path_format, call=self.endpoint)
-        for depends in self.dependencies[::-1]:
-            self.dependant.dependencies.insert(
-                0,
-                get_parameterless_sub_dependant(depends=depends, path=self.path_format),
-            )
-
-        self.app = websocket_session(
-            get_websocket_app(
-                dependant=self.dependant,
-                dependency_overrides_provider=dependency_overrides_provider,
-            )
-        )
-
-    def matches(self, scope: Scope) -> Tuple[Match, Scope]:
-        match, child_scope = super().matches(scope)
-        if match != Match.NONE:
-            child_scope["route"] = self
-        return match, child_scope
-
-
-class APIRoute(routing.Route):
-    def __init__(
-        self,
-        path: str,
-        endpoint: Callable[..., Any],
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        name: Optional[str] = None,
-        methods: Optional[Union[Set[str], List[str]]] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Union[Type[Response], DefaultPlaceholder] = Default(
-            JSONResponse
-        ),
-        dependency_overrides_provider: Optional[Any] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Union[
-            Callable[["APIRoute"], str], DefaultPlaceholder
-        ] = Default(generate_unique_id),
-    ) -> None:
-        self.path = path
-        self.endpoint = endpoint
-        if isinstance(response_model, DefaultPlaceholder):
-            return_annotation = get_typed_return_annotation(endpoint)
-            if lenient_issubclass(return_annotation, Response):
-                response_model = None
-            else:
-                response_model = return_annotation
-        self.response_model = response_model
-        self.summary = summary
-        self.response_description = response_description
-        self.deprecated = deprecated
-        self.operation_id = operation_id
-        self.response_model_include = response_model_include
-        self.response_model_exclude = response_model_exclude
-        self.response_model_by_alias = response_model_by_alias
-        self.response_model_exclude_unset = response_model_exclude_unset
-        self.response_model_exclude_defaults = response_model_exclude_defaults
-        self.response_model_exclude_none = response_model_exclude_none
-        self.include_in_schema = include_in_schema
-        self.response_class = response_class
-        self.dependency_overrides_provider = dependency_overrides_provider
-        self.callbacks = callbacks
-        self.openapi_extra = openapi_extra
-        self.generate_unique_id_function = generate_unique_id_function
-        self.tags = tags or []
-        self.responses = responses or {}
-        self.name = get_name(endpoint) if name is None else name
-        self.path_regex, self.path_format, self.param_convertors = compile_path(path)
-        if methods is None:
-            methods = ["GET"]
-        self.methods: Set[str] = {method.upper() for method in methods}
-        if isinstance(generate_unique_id_function, DefaultPlaceholder):
-            current_generate_unique_id: Callable[
-                ["APIRoute"], str
-            ] = generate_unique_id_function.value
-        else:
-            current_generate_unique_id = generate_unique_id_function
-        self.unique_id = self.operation_id or current_generate_unique_id(self)
-        # normalize enums e.g. http.HTTPStatus
-        if isinstance(status_code, IntEnum):
-            status_code = int(status_code)
-        self.status_code = status_code
-        if self.response_model:
-            assert is_body_allowed_for_status_code(
-                status_code
-            ), f"Status code {status_code} must not have a response body"
-            response_name = "Response_" + self.unique_id
-            self.response_field = create_response_field(
-                name=response_name,
-                type_=self.response_model,
-                mode="serialization",
-            )
-            # Create a clone of the field, so that a Pydantic submodel is not returned
-            # as is just because it's an instance of a subclass of a more limited class
-            # e.g. UserInDB (containing hashed_password) could be a subclass of User
-            # that doesn't have the hashed_password. But because it's a subclass, it
-            # would pass the validation and be returned as is.
-            # By being a new field, no inheritance will be passed as is. A new model
-            # will always be created.
-            # TODO: remove when deprecating Pydantic v1
-            self.secure_cloned_response_field: Optional[
-                ModelField
-            ] = create_cloned_field(self.response_field)
-        else:
-            self.response_field = None  # type: ignore
-            self.secure_cloned_response_field = None
-        self.dependencies = list(dependencies or [])
-        self.description = description or inspect.cleandoc(self.endpoint.__doc__ or "")
-        # if a "form feed" character (page break) is found in the description text,
-        # truncate description text to the content preceding the first "form feed"
-        self.description = self.description.split("\f")[0].strip()
-        response_fields = {}
-        for additional_status_code, response in self.responses.items():
-            assert isinstance(response, dict), "An additional response must be a dict"
-            model = response.get("model")
-            if model:
-                assert is_body_allowed_for_status_code(
-                    additional_status_code
-                ), f"Status code {additional_status_code} must not have a response body"
-                response_name = f"Response_{additional_status_code}_{self.unique_id}"
-                response_field = create_response_field(name=response_name, type_=model)
-                response_fields[additional_status_code] = response_field
-        if response_fields:
-            self.response_fields: Dict[Union[int, str], ModelField] = response_fields
-        else:
-            self.response_fields = {}
-
-        assert callable(endpoint), "An endpoint must be a callable"
-        self.dependant = get_dependant(path=self.path_format, call=self.endpoint)
-        for depends in self.dependencies[::-1]:
-            self.dependant.dependencies.insert(
-                0,
-                get_parameterless_sub_dependant(depends=depends, path=self.path_format),
-            )
-        self.body_field = get_body_field(dependant=self.dependant, name=self.unique_id)
-        self.app = request_response(self.get_route_handler())
-
-    def get_route_handler(self) -> Callable[[Request], Coroutine[Any, Any, Response]]:
-        return get_request_handler(
-            dependant=self.dependant,
-            body_field=self.body_field,
-            status_code=self.status_code,
-            response_class=self.response_class,
-            response_field=self.secure_cloned_response_field,
-            response_model_include=self.response_model_include,
-            response_model_exclude=self.response_model_exclude,
-            response_model_by_alias=self.response_model_by_alias,
-            response_model_exclude_unset=self.response_model_exclude_unset,
-            response_model_exclude_defaults=self.response_model_exclude_defaults,
-            response_model_exclude_none=self.response_model_exclude_none,
-            dependency_overrides_provider=self.dependency_overrides_provider,
-        )
-
-    def matches(self, scope: Scope) -> Tuple[Match, Scope]:
-        match, child_scope = super().matches(scope)
-        if match != Match.NONE:
-            child_scope["route"] = self
-        return match, child_scope
-
-
-class APIRouter(routing.Router):
-    def __init__(
-        self,
-        *,
-        prefix: str = "",
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        default_response_class: Type[Response] = Default(JSONResponse),
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        routes: Optional[List[routing.BaseRoute]] = None,
-        redirect_slashes: bool = True,
-        default: Optional[ASGIApp] = None,
-        dependency_overrides_provider: Optional[Any] = None,
-        route_class: Type[APIRoute] = APIRoute,
-        on_startup: Optional[Sequence[Callable[[], Any]]] = None,
-        on_shutdown: Optional[Sequence[Callable[[], Any]]] = None,
-        # the generic to Lifespan[AppType] is the type of the top level application
-        # which the router cannot know statically, so we use typing.Any
-        lifespan: Optional[Lifespan[Any]] = None,
-        deprecated: Optional[bool] = None,
-        include_in_schema: bool = True,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> None:
-        super().__init__(
-            routes=routes,
-            redirect_slashes=redirect_slashes,
-            default=default,
-            on_startup=on_startup,
-            on_shutdown=on_shutdown,
-            lifespan=lifespan,
-        )
-        if prefix:
-            assert prefix.startswith("/"), "A path prefix must start with '/'"
-            assert not prefix.endswith(
-                "/"
-            ), "A path prefix must not end with '/', as the routes will start with '/'"
-        self.prefix = prefix
-        self.tags: List[Union[str, Enum]] = tags or []
-        self.dependencies = list(dependencies or [])
-        self.deprecated = deprecated
-        self.include_in_schema = include_in_schema
-        self.responses = responses or {}
-        self.callbacks = callbacks or []
-        self.dependency_overrides_provider = dependency_overrides_provider
-        self.route_class = route_class
-        self.default_response_class = default_response_class
-        self.generate_unique_id_function = generate_unique_id_function
-
-    def route(
-        self,
-        path: str,
-        methods: Optional[List[str]] = None,
-        name: Optional[str] = None,
-        include_in_schema: bool = True,
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        def decorator(func: DecoratedCallable) -> DecoratedCallable:
-            self.add_route(
-                path,
-                func,
-                methods=methods,
-                name=name,
-                include_in_schema=include_in_schema,
-            )
-            return func
-
-        return decorator
-
-    def add_api_route(
-        self,
-        path: str,
-        endpoint: Callable[..., Any],
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        methods: Optional[Union[Set[str], List[str]]] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Union[Type[Response], DefaultPlaceholder] = Default(
-            JSONResponse
-        ),
-        name: Optional[str] = None,
-        route_class_override: Optional[Type[APIRoute]] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Union[
-            Callable[[APIRoute], str], DefaultPlaceholder
-        ] = Default(generate_unique_id),
-    ) -> None:
-        route_class = route_class_override or self.route_class
-        responses = responses or {}
-        combined_responses = {**self.responses, **responses}
-        current_response_class = get_value_or_default(
-            response_class, self.default_response_class
-        )
-        current_tags = self.tags.copy()
-        if tags:
-            current_tags.extend(tags)
-        current_dependencies = self.dependencies.copy()
-        if dependencies:
-            current_dependencies.extend(dependencies)
-        current_callbacks = self.callbacks.copy()
-        if callbacks:
-            current_callbacks.extend(callbacks)
-        current_generate_unique_id = get_value_or_default(
-            generate_unique_id_function, self.generate_unique_id_function
-        )
-        route = route_class(
-            self.prefix + path,
-            endpoint=endpoint,
-            response_model=response_model,
-            status_code=status_code,
-            tags=current_tags,
-            dependencies=current_dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=combined_responses,
-            deprecated=deprecated or self.deprecated,
-            methods=methods,
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema and self.include_in_schema,
-            response_class=current_response_class,
-            name=name,
-            dependency_overrides_provider=self.dependency_overrides_provider,
-            callbacks=current_callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=current_generate_unique_id,
-        )
-        self.routes.append(route)
-
-    def api_route(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        methods: Optional[List[str]] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        def decorator(func: DecoratedCallable) -> DecoratedCallable:
-            self.add_api_route(
-                path,
-                func,
-                response_model=response_model,
-                status_code=status_code,
-                tags=tags,
-                dependencies=dependencies,
-                summary=summary,
-                description=description,
-                response_description=response_description,
-                responses=responses,
-                deprecated=deprecated,
-                methods=methods,
-                operation_id=operation_id,
-                response_model_include=response_model_include,
-                response_model_exclude=response_model_exclude,
-                response_model_by_alias=response_model_by_alias,
-                response_model_exclude_unset=response_model_exclude_unset,
-                response_model_exclude_defaults=response_model_exclude_defaults,
-                response_model_exclude_none=response_model_exclude_none,
-                include_in_schema=include_in_schema,
-                response_class=response_class,
-                name=name,
-                callbacks=callbacks,
-                openapi_extra=openapi_extra,
-                generate_unique_id_function=generate_unique_id_function,
-            )
-            return func
-
-        return decorator
-
-    def add_api_websocket_route(
-        self,
-        path: str,
-        endpoint: Callable[..., Any],
-        name: Optional[str] = None,
-        *,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-    ) -> None:
-        current_dependencies = self.dependencies.copy()
-        if dependencies:
-            current_dependencies.extend(dependencies)
-
-        route = APIWebSocketRoute(
-            self.prefix + path,
-            endpoint=endpoint,
-            name=name,
-            dependencies=current_dependencies,
-            dependency_overrides_provider=self.dependency_overrides_provider,
-        )
-        self.routes.append(route)
-
-    def websocket(
-        self,
-        path: str,
-        name: Optional[str] = None,
-        *,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        def decorator(func: DecoratedCallable) -> DecoratedCallable:
-            self.add_api_websocket_route(
-                path, func, name=name, dependencies=dependencies
-            )
-            return func
-
-        return decorator
-
-    def websocket_route(
-        self, path: str, name: Union[str, None] = None
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        def decorator(func: DecoratedCallable) -> DecoratedCallable:
-            self.add_websocket_route(path, func, name=name)
-            return func
-
-        return decorator
-
-    def include_router(
-        self,
-        router: "APIRouter",
-        *,
-        prefix: str = "",
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        default_response_class: Type[Response] = Default(JSONResponse),
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        deprecated: Optional[bool] = None,
-        include_in_schema: bool = True,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> None:
-        if prefix:
-            assert prefix.startswith("/"), "A path prefix must start with '/'"
-            assert not prefix.endswith(
-                "/"
-            ), "A path prefix must not end with '/', as the routes will start with '/'"
-        else:
-            for r in router.routes:
-                path = getattr(r, "path")  # noqa: B009
-                name = getattr(r, "name", "unknown")
-                if path is not None and not path:
-                    raise FastAPIError(
-                        f"Prefix and path cannot be both empty (path operation: {name})"
-                    )
-        if responses is None:
-            responses = {}
-        for route in router.routes:
-            if isinstance(route, APIRoute):
-                combined_responses = {**responses, **route.responses}
-                use_response_class = get_value_or_default(
-                    route.response_class,
-                    router.default_response_class,
-                    default_response_class,
-                    self.default_response_class,
-                )
-                current_tags = []
-                if tags:
-                    current_tags.extend(tags)
-                if route.tags:
-                    current_tags.extend(route.tags)
-                current_dependencies: List[params.Depends] = []
-                if dependencies:
-                    current_dependencies.extend(dependencies)
-                if route.dependencies:
-                    current_dependencies.extend(route.dependencies)
-                current_callbacks = []
-                if callbacks:
-                    current_callbacks.extend(callbacks)
-                if route.callbacks:
-                    current_callbacks.extend(route.callbacks)
-                current_generate_unique_id = get_value_or_default(
-                    route.generate_unique_id_function,
-                    router.generate_unique_id_function,
-                    generate_unique_id_function,
-                    self.generate_unique_id_function,
-                )
-                self.add_api_route(
-                    prefix + route.path,
-                    route.endpoint,
-                    response_model=route.response_model,
-                    status_code=route.status_code,
-                    tags=current_tags,
-                    dependencies=current_dependencies,
-                    summary=route.summary,
-                    description=route.description,
-                    response_description=route.response_description,
-                    responses=combined_responses,
-                    deprecated=route.deprecated or deprecated or self.deprecated,
-                    methods=route.methods,
-                    operation_id=route.operation_id,
-                    response_model_include=route.response_model_include,
-                    response_model_exclude=route.response_model_exclude,
-                    response_model_by_alias=route.response_model_by_alias,
-                    response_model_exclude_unset=route.response_model_exclude_unset,
-                    response_model_exclude_defaults=route.response_model_exclude_defaults,
-                    response_model_exclude_none=route.response_model_exclude_none,
-                    include_in_schema=route.include_in_schema
-                    and self.include_in_schema
-                    and include_in_schema,
-                    response_class=use_response_class,
-                    name=route.name,
-                    route_class_override=type(route),
-                    callbacks=current_callbacks,
-                    openapi_extra=route.openapi_extra,
-                    generate_unique_id_function=current_generate_unique_id,
-                )
-            elif isinstance(route, routing.Route):
-                methods = list(route.methods or [])
-                self.add_route(
-                    prefix + route.path,
-                    route.endpoint,
-                    methods=methods,
-                    include_in_schema=route.include_in_schema,
-                    name=route.name,
-                )
-            elif isinstance(route, APIWebSocketRoute):
-                current_dependencies = []
-                if dependencies:
-                    current_dependencies.extend(dependencies)
-                if route.dependencies:
-                    current_dependencies.extend(route.dependencies)
-                self.add_api_websocket_route(
-                    prefix + route.path,
-                    route.endpoint,
-                    dependencies=current_dependencies,
-                    name=route.name,
-                )
-            elif isinstance(route, routing.WebSocketRoute):
-                self.add_websocket_route(
-                    prefix + route.path, route.endpoint, name=route.name
-                )
-        for handler in router.on_startup:
-            self.add_event_handler("startup", handler)
-        for handler in router.on_shutdown:
-            self.add_event_handler("shutdown", handler)
-
-    def get(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["GET"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def put(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["PUT"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def post(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["POST"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def delete(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["DELETE"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def options(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["OPTIONS"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def head(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["HEAD"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def patch(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["PATCH"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def trace(
-        self,
-        path: str,
-        *,
-        response_model: Any = Default(None),
-        status_code: Optional[int] = None,
-        tags: Optional[List[Union[str, Enum]]] = None,
-        dependencies: Optional[Sequence[params.Depends]] = None,
-        summary: Optional[str] = None,
-        description: Optional[str] = None,
-        response_description: str = "Successful Response",
-        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
-        deprecated: Optional[bool] = None,
-        operation_id: Optional[str] = None,
-        response_model_include: Optional[IncEx] = None,
-        response_model_exclude: Optional[IncEx] = None,
-        response_model_by_alias: bool = True,
-        response_model_exclude_unset: bool = False,
-        response_model_exclude_defaults: bool = False,
-        response_model_exclude_none: bool = False,
-        include_in_schema: bool = True,
-        response_class: Type[Response] = Default(JSONResponse),
-        name: Optional[str] = None,
-        callbacks: Optional[List[BaseRoute]] = None,
-        openapi_extra: Optional[Dict[str, Any]] = None,
-        generate_unique_id_function: Callable[[APIRoute], str] = Default(
-            generate_unique_id
-        ),
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        return self.api_route(
-            path=path,
-            response_model=response_model,
-            status_code=status_code,
-            tags=tags,
-            dependencies=dependencies,
-            summary=summary,
-            description=description,
-            response_description=response_description,
-            responses=responses,
-            deprecated=deprecated,
-            methods=["TRACE"],
-            operation_id=operation_id,
-            response_model_include=response_model_include,
-            response_model_exclude=response_model_exclude,
-            response_model_by_alias=response_model_by_alias,
-            response_model_exclude_unset=response_model_exclude_unset,
-            response_model_exclude_defaults=response_model_exclude_defaults,
-            response_model_exclude_none=response_model_exclude_none,
-            include_in_schema=include_in_schema,
-            response_class=response_class,
-            name=name,
-            callbacks=callbacks,
-            openapi_extra=openapi_extra,
-            generate_unique_id_function=generate_unique_id_function,
-        )
-
-    def on_event(
-        self, event_type: str
-    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
-        def decorator(func: DecoratedCallable) -> DecoratedCallable:
-            self.add_event_handler(event_type, func)
-            return func
-
-        return decorator
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/colorLib/unbuilder.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/colorLib/unbuilder.py
deleted file mode 100644
index ac243550b8908aef120b395e740b9974559d65b5..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/colorLib/unbuilder.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from fontTools.ttLib.tables import otTables as ot
-from .table_builder import TableUnbuilder
-
-
-def unbuildColrV1(layerList, baseGlyphList):
-    layers = []
-    if layerList:
-        layers = layerList.Paint
-    unbuilder = LayerListUnbuilder(layers)
-    return {
-        rec.BaseGlyph: unbuilder.unbuildPaint(rec.Paint)
-        for rec in baseGlyphList.BaseGlyphPaintRecord
-    }
-
-
-def _flatten_layers(lst):
-    for paint in lst:
-        if paint["Format"] == ot.PaintFormat.PaintColrLayers:
-            yield from _flatten_layers(paint["Layers"])
-        else:
-            yield paint
-
-
-class LayerListUnbuilder:
-    def __init__(self, layers):
-        self.layers = layers
-
-        callbacks = {
-            (
-                ot.Paint,
-                ot.PaintFormat.PaintColrLayers,
-            ): self._unbuildPaintColrLayers,
-        }
-        self.tableUnbuilder = TableUnbuilder(callbacks)
-
-    def unbuildPaint(self, paint):
-        assert isinstance(paint, ot.Paint)
-        return self.tableUnbuilder.unbuild(paint)
-
-    def _unbuildPaintColrLayers(self, source):
-        assert source["Format"] == ot.PaintFormat.PaintColrLayers
-
-        layers = list(
-            _flatten_layers(
-                [
-                    self.unbuildPaint(childPaint)
-                    for childPaint in self.layers[
-                        source["FirstLayerIndex"] : source["FirstLayerIndex"]
-                        + source["NumLayers"]
-                    ]
-                ]
-            )
-        )
-
-        if len(layers) == 1:
-            return layers[0]
-
-        return {"Format": source["Format"], "Layers": layers}
-
-
-if __name__ == "__main__":
-    from pprint import pprint
-    import sys
-    from fontTools.ttLib import TTFont
-
-    try:
-        fontfile = sys.argv[1]
-    except IndexError:
-        sys.exit("usage: fonttools colorLib.unbuilder FONTFILE")
-
-    font = TTFont(fontfile)
-    colr = font["COLR"]
-    if colr.version < 1:
-        sys.exit(f"error: No COLR table version=1 found in {fontfile}")
-
-    colorGlyphs = unbuildColrV1(
-        colr.table.LayerList,
-        colr.table.BaseGlyphList,
-    )
-
-    pprint(colorGlyphs)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/svgLib/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/svgLib/__init__.py
deleted file mode 100644
index c049006bf2e58bed7786432e7ec84c8c8b40edf0..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/svgLib/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .path import SVGPath, parse_path
-
-__all__ = ["SVGPath", "parse_path"]
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/schema.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/schema.py
deleted file mode 100644
index 495fc4380faab0520e0317f41896b14463fcbe08..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/schema.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Base schema for data structures."""
-from abc import abstractmethod
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
-
-from dataclasses_json import DataClassJsonMixin
-
-from gpt_index.utils import get_new_id
-
-
-@dataclass
-class BaseDocument(DataClassJsonMixin):
-    """Base document.
-
-    Generic abstract interfaces that captures both index structs
-    as well as documents.
-
-    """
-
-    # TODO: consolidate fields from Document/IndexStruct into base class
-    text: Optional[str] = None
-    doc_id: Optional[str] = None
-    embedding: Optional[List[float]] = None
-
-    # extra fields
-    extra_info: Optional[Dict[str, Any]] = None
-
-    def __post_init__(self) -> None:
-        """Post init."""
-        # assign doc_id if not set
-        if self.doc_id is None:
-            self.doc_id = get_new_id(set())
-
-    @classmethod
-    @abstractmethod
-    def get_type(cls) -> str:
-        """Get Document type."""
-
-    @classmethod
-    def get_types(cls) -> List[str]:
-        """Get Document type."""
-        # TODO: remove this method
-        # a hack to preserve backwards compatibility for vector indices
-        return [cls.get_type()]
-
-    def get_text(self) -> str:
-        """Get text."""
-        if self.text is None:
-            raise ValueError("text field not set.")
-        return self.text
-
-    def get_doc_id(self) -> str:
-        """Get doc_id."""
-        if self.doc_id is None:
-            raise ValueError("doc_id not set.")
-        return self.doc_id
-
-    @property
-    def is_doc_id_none(self) -> bool:
-        """Check if doc_id is None."""
-        return self.doc_id is None
-
-    def get_embedding(self) -> List[float]:
-        """Get embedding.
-
-        Errors if embedding is None.
-
-        """
-        if self.embedding is None:
-            raise ValueError("embedding not set.")
-        return self.embedding
-
-    @property
-    def extra_info_str(self) -> Optional[str]:
-        """Extra info string."""
-        if self.extra_info is None:
-            return None
-
-        return "\n".join([f"{k}: {str(v)}" for k, v in self.extra_info.items()])
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/input.tsx b/spaces/jordonpeter01/ai-comic-factory/src/components/ui/input.tsx
deleted file mode 100644
index 0757ddebdca3800bbd4a46fe1c2c17dff86c5e2f..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from "react"
-
-import { cn } from "@/lib/utils"
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          "flex h-10 w-full rounded-md border border-stone-200 bg-white px-3 py-2 text-sm ring-offset-white file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-stone-500 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-blue-[rgb(59,134,247)] focus-visible:ring-offset-0 disabled:cursor-not-allowed disabled:opacity-50 dark:border-stone-800 dark:bg-stone-950 dark:ring-offset-stone-950 dark:placeholder:text-stone-400 dark:focus-visible:ring-stone-800",
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = "Input"
-
-export { Input }
diff --git a/spaces/jpfearnworks/ai_agents/modules/vector_stores/vector_stores/chroma_manager.py b/spaces/jpfearnworks/ai_agents/modules/vector_stores/vector_stores/chroma_manager.py
deleted file mode 100644
index f9db0b0e5aa2372272dc645a1c42b292b335c650..0000000000000000000000000000000000000000
--- a/spaces/jpfearnworks/ai_agents/modules/vector_stores/vector_stores/chroma_manager.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from langchain.vectorstores import Chroma
-from langchain.vectorstores.base import VectorStoreRetriever
-from modules.vector_stores.loaders.pypdf_load_strategy import (
-    get_default_pypdf_loader,
-)
-from modules.vector_stores.embedding.instructorxl import get_default_instructor_embedding
-
-instruct_embed = get_default_instructor_embedding()
-from dataclasses import dataclass
-from langchain.embeddings.base import Embeddings
-from typing import Iterable
-from langchain.schema import Document
-from loguru import logger
-
-
-@dataclass
-class ChromaConfig:
-    documents: Iterable[Document]
-    persist_directory: str
-    embedding: Embeddings
-    persisted: bool = False
-
-
-class ChromaManager:
-    def __init__(self, config: ChromaConfig):
-        self.documents = config.documents
-        self.persist_directory = config.persist_directory
-        self.embedding = config.embedding
-        if config.persisted:
-            self.db = Chroma(
-                persist_directory=config.persist_directory, embedding_function=config.embedding
-            )
-        else:
-            self.db = Chroma.from_documents(
-                documents=config.documents,
-                embedding=config.embedding,
-                persist_directory=config.persist_directory,
-            )
-
-    def persist(self):
-        logger.info("Persisting Chroma to disk...")
-        self.db.persist()
-        logger.info("Chroma saved to %s", self.persist_directory)
-
-    def delete(self):
-        logger.info("Deleting Chroma from disk...")
-        self.db.delete_collection()
-        self.db.persist()
-        logger.info("Chroma deleted from %s", self.persist_directory)
-
-    def fetch_documents(self, query):
-        logger.info("Fetching documents from Chroma...")
-        retriever: VectorStoreRetriever = self.db.as_retriever()
-        documents = retriever.get_relevant_documents(query)
-        logger.info("Fetched %s documents from Chroma", len(documents))
-        return documents
-
-
-def get_default_chroma_mgr(persisted=False):
-    """
-    Returns a default ChromaConfig instance. The default currently only reads in pdf files from the data directory.
-
-    Returns:
-        ChromaConfig: A new ChromaConfig instance.
-    """
-    dir_location = "../data"
-    persist_directory = "../db"
-    loader = get_default_pypdf_loader(dir_location)
-    documents: Iterable[Document] = loader.load()
-    embedding = get_default_instructor_embedding()
-    if persisted:
-        config = ChromaConfig(
-            documents=documents,
-            persist_directory=persist_directory,
-            embedding=embedding,
-            persisted=True,
-        )
-    else:
-        config = ChromaConfig(
-            documents=documents, persist_directory=persist_directory, embedding=embedding
-        )
-    chroma_mgr = ChromaManager(config)
-    return chroma_mgr
-
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/models_test.py b/spaces/juancopi81/youtube-music-transcribe/t5x/models_test.py
deleted file mode 100644
index b2f3d77bce55dd463852962369e62f50b1acb846..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/models_test.py
+++ /dev/null
@@ -1,985 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# 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.
-
-"""Tests for t5x.models."""
-
-import functools
-from unittest import mock
-
-from absl import logging
-from absl.testing import absltest
-from absl.testing import parameterized
-import flax
-from flax import traverse_util
-import jax
-import jax.numpy as jnp
-import numpy as np
-import t5.data.tasks  # pylint:disable=unused-import
-from t5x import decoding
-from t5x import models
-from t5x import partitioning
-from t5x import test_utils
-from t5x import trainer as trainer_lib
-from t5x import utils
-import tensorflow as tf
-
-# Parse absl flags test_srcdir and test_tmpdir.
-jax.config.parse_flags_with_absl()
-
-PartitionSpec = partitioning.PartitionSpec
-
-
-class ModelsTest(parameterized.TestCase):
-
-  def test_remove_prefix(self):
-    sequences = np.array([[1, 2, 3, 4, 5, 6, 7, 0], [6, 7, 8, 9, 10, 11, 0, 0]])
-    prefix_lengths = np.array([2, 4])
-    expected = [[3, 4, 5, 6, 7, 0, 0, 0], [10, 11, 0, 0, 0, 0, 0, 0]]
-    remove_prefix = jax.jit(models.remove_prefix)
-    actual = remove_prefix(sequences, prefix_lengths)
-    np.testing.assert_array_equal(actual, expected)
-
-  def test_remove_prefix_zero_len_prefix(self):
-    sequences = np.array([[1, 2, 3, 4, 5, 6, 7, 0], [6, 7, 8, 9, 10, 11, 0, 0]])
-    prefix_lengths = np.array([0, 0])
-    remove_prefix = jax.jit(models.remove_prefix)
-    actual = remove_prefix(sequences, prefix_lengths)
-    # The expected output is the original sequences.
-    np.testing.assert_array_equal(actual, sequences)
-
-
-BATCH_SIZE, ENCODER_LEN, MAX_DECODE_LEN, EMBED_DIM = 2, 3, 4, 5
-
-
-class EncoderDecoderModelTest(parameterized.TestCase):
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='no_types',
-          shapes={
-              'encoder_input_tokens': [1, 512],
-              'decoder_input_tokens': [1, 62]
-          },
-          types=None),
-      dict(
-          testcase_name='int32',
-          shapes={
-              'encoder_input_tokens': [1, 512],
-              'decoder_input_tokens': [1, 62]
-          },
-          types={
-              'encoder_input_tokens': jnp.int32,
-              'decoder_input_tokens': jnp.int32
-          }),
-      dict(
-          testcase_name='float32',
-          shapes={
-              'encoder_input_tokens': [1, 512],
-              'decoder_input_tokens': [1, 62],
-              'encoder_positions': [1, 512],
-              'decoder_positions': [1, 62],
-          },
-          types={
-              'encoder_input_tokens': jnp.int32,
-              'decoder_input_tokens': jnp.int32,
-              'encoder_positions': jnp.int32,
-              'decoder_positions': jnp.int32
-          }),
-      dict(
-          testcase_name='float32_segment_ids',
-          shapes={
-              'encoder_input_tokens': [1, 512],
-              'decoder_input_tokens': [1, 62],
-              'encoder_segment_ids': [1, 512],
-              'decoder_segment_ids': [1, 62],
-          },
-          types={
-              'encoder_input_tokens': jnp.int32,
-              'decoder_input_tokens': jnp.int32,
-              'encoder_segment_ids': jnp.int32,
-              'decoder_segment_ids': jnp.int32
-          }),
-  )
-  def test_get_initial_variables_shapes_and_types(self, shapes, types):
-    mock_transformer = mock.Mock()
-    mock_transformer.init.return_value = {'params': {}}
-    mock_optimizer_def = mock.Mock()
-    rng = mock.Mock()
-
-    def mock_init(self):
-      self.module = mock_transformer
-      self.optimizer_def = mock_optimizer_def
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-      model.get_initial_variables(rng, shapes, types)
-
-    if types is None:
-      encoder_input = jnp.ones(
-          shapes['encoder_input_tokens'], dtype=jnp.float32)
-      decoder_input = jnp.ones(
-          shapes['decoder_input_tokens'], dtype=jnp.float32)
-    else:
-      encoder_input = jnp.ones(
-          shapes['encoder_input_tokens'], dtype=types['encoder_input_tokens'])
-      decoder_input = jnp.ones(
-          shapes['decoder_input_tokens'], dtype=types['decoder_input_tokens'])
-
-    # Using `.assert_called_once_with` doesn't work because the simple
-    # comparison it does for the array arguments fail (truth value of an array
-    # is ambiguous).
-    called_with = mock_transformer.init.call_args
-    self.assertEqual(called_with[0][0], rng)
-    np.testing.assert_allclose(called_with[0][1], encoder_input)
-    np.testing.assert_allclose(called_with[0][2], decoder_input)
-    np.testing.assert_allclose(called_with[0][3], decoder_input)
-
-    if 'encoder_positions' in shapes:
-      encoder_positions = jnp.ones(
-          shapes['encoder_positions'], dtype=types['encoder_positions'])
-      np.testing.assert_allclose(called_with[1]['encoder_positions'],
-                                 encoder_positions)
-    else:
-      self.assertIsNone(called_with[1]['encoder_positions'])
-    if 'decoder_positions' in shapes:
-      decoder_positions = jnp.ones(
-          shapes['decoder_positions'], dtype=types['decoder_positions'])
-      np.testing.assert_allclose(called_with[1]['decoder_positions'],
-                                 decoder_positions)
-    else:
-      self.assertIsNone(called_with[1]['decoder_positions'])
-
-    if 'encoder_segment_ids' in shapes:
-      encoder_positions = jnp.ones(
-          shapes['encoder_segment_ids'], dtype=types['encoder_segment_ids'])
-      np.testing.assert_allclose(called_with[1]['encoder_segment_ids'],
-                                 encoder_positions)
-    else:
-      self.assertIsNone(called_with[1]['encoder_segment_ids'])
-    if 'decoder_segment_ids' in shapes:
-      decoder_segment_ids = jnp.ones(
-          shapes['decoder_segment_ids'], dtype=types['decoder_segment_ids'])
-      np.testing.assert_allclose(called_with[1]['decoder_segment_ids'],
-                                 decoder_segment_ids)
-    else:
-      self.assertIsNone(called_with[1]['decoder_segment_ids'])
-
-    self.assertFalse(called_with[1]['decode'])
-    self.assertFalse(called_with[1]['enable_dropout'])
-
-  @parameterized.named_parameters(
-      dict(testcase_name='no_force_decoding', prompt_with_targets=False),
-      dict(testcase_name='force_decoding', prompt_with_targets=True),
-  )
-  def test_prompt_with_targets(self, prompt_with_targets):
-    batch_size, encoder_len, max_decode_len, emb_dim = 2, 3, 4, 5
-    batch = {
-        'encoder_input_tokens':
-            np.zeros((batch_size, encoder_len), dtype=np.int32),
-        'decoder_input_tokens':
-            np.full([batch_size, max_decode_len], 2, dtype=np.int32)
-    }
-
-    # These dummy logits represent the probability distribution where all the
-    # probability mass is in one item (i.e., degenerate distribution). For
-    # batch element 0, it is vocabulary index 3.
-    # We test `_predict_step` to avoid having to define a task and its
-    # vocabulary.
-    dummy_logits = jnp.expand_dims(
-        jnp.array([[-1e7, -1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, -1e7, 0]]),
-        axis=1)
-
-    mock_decode_fn = mock.Mock()
-    mock_decode_fn.return_value = (np.full([batch_size, max_decode_len, 1],
-                                           3,
-                                           dtype=np.int32),
-                                   np.full([batch_size, 1],
-                                           1.0,
-                                           dtype=np.float32))
-
-    class MockModule:
-
-      def __init__(self):
-        self.dtype = jnp.float32
-
-      def apply(self, *args, method=None, **kwargs):
-        del args, kwargs
-        if method is None:  # use for module.`__call__`
-          return (dummy_logits, {'cache': {}})
-        else:
-          return method()
-
-      def encode(self):
-        return jnp.zeros((batch_size, encoder_len, emb_dim))
-
-      def decode(self):
-        return (dummy_logits, {'cache': {}})
-
-    def mock_init(self):
-      self.module = MockModule()
-      self.module.scan_layers = False
-      self._input_vocabulary = mock.Mock(eos_id=1)
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = mock_decode_fn
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-
-    model.predict_batch_with_aux({},
-                                 batch,
-                                 prompt_with_targets=prompt_with_targets)
-
-    if prompt_with_targets:
-      expected_inputs = batch['decoder_input_tokens']
-    else:
-      expected_inputs = np.zeros([batch_size, max_decode_len], dtype=np.int32)
-
-    assert mock_decode_fn.call_count == 1
-    # Look at the kwargs call list for inputs, assert_called_with doesn't
-    # work well with np.array comparison.
-    np.testing.assert_array_equal(mock_decode_fn.mock_calls[0][2]['inputs'],
-                                  expected_inputs)
-
-  def test_predict_batch_loop_and_caches_are_equal(self):
-    vocab_size = 50
-    lengths = np.array([[2], [3]])
-    batch_size, beam_size, encoder_len, max_decode_len = 2, 2, 3, 7
-    batch = {
-        'encoder_input_tokens':
-            np.zeros((batch_size, encoder_len), dtype=np.int32),
-        'decoder_target_tokens':
-            np.zeros((batch_size, encoder_len), dtype=np.int32),
-        'decoder_input_tokens':
-            np.concatenate(
-                [
-                    np.expand_dims(
-                        np.concatenate(
-                            [[0],
-                             np.arange(9, 9 + lengths[0][0], dtype=np.int32),
-                             np.zeros((max_decode_len - lengths[0][0] - 1),
-                                      dtype=np.int32)]),
-                        axis=0),  # First element
-                    np.expand_dims(
-                        np.concatenate(
-                            [[0],
-                             np.arange(3, 3 + lengths[1][0], dtype=np.int32),
-                             np.zeros((max_decode_len - lengths[1][0] - 1),
-                                      dtype=np.int32)]),
-                        axis=0)  # Second element
-                ],
-                axis=0),
-    }
-
-    model = test_utils.get_t5_test_model(vocab_size=50)
-    module = model.module
-    params = module.init(
-        jax.random.PRNGKey(0),
-        jnp.ones((batch_size, encoder_len)),
-        jnp.ones((batch_size, max_decode_len)),
-        jnp.ones((batch_size, max_decode_len)),
-        enable_dropout=False)['params']
-
-    def mock_init(self):
-      self.module = module
-      # Set the EOS token to be larger then the vocabulary size. This forces the
-      # model to decode all the way to `max_decode_length`, allowing us to test
-      # behavior when one element reaches the end before the others.
-      self._output_vocabulary = mock.Mock(eos_id=vocab_size + 12)
-      self._decode_fn = decoding.beam_search
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-
-    with mock.patch.object(
-        model, '_compute_logits_from_slice',
-        autospec=True) as tokens_to_logits_mock:
-      # Make the side effect of the mock, call the method on the class, with the
-      # instance partialed in as `self`. This lets us call the actual code,
-      # while recording the inputs, without an infinite loop you would get
-      # calling `instance.method`
-      tokens_to_logits_mock.side_effect = functools.partial(
-          models.EncoderDecoderModel._compute_logits_from_slice, model)
-      # Disable jit, so that the `lax.while_loop` isn't traced, as the
-      # collection of tracers in the mock call_args would generally trigger a
-      # tracer leak error.
-      with jax.disable_jit():
-        _ = model.predict_batch_with_aux(
-            params, batch, prompt_with_targets=True, num_decodes=2)
-
-    # Collect all the input tokens to our tokens_to_logits function
-    all_inputs = []
-    all_cache_keys = []  # Collect all the cache keys
-    all_cache_values = []  # Collect all the cache values
-    # Currently force decoding generates logits at every step. We should have
-    # `max_decode_length` calls to our tokens -> logits func.
-    self.assertLen(tokens_to_logits_mock.call_args_list, max_decode_len)
-    for tokens_call in tokens_to_logits_mock.call_args_list:
-      # Inputs: [B * Be, 1]
-      inputs, cache = tokens_call[0]
-      cache = flax.core.unfreeze(cache)
-      # Cache: [B * Be, 1] * #Layers
-      cache_keys = [
-          v for k, v in traverse_util.flatten_dict(cache).items()
-          if k[-1] == 'cached_key'
-      ]
-      cache_values = [
-          v for k, v in traverse_util.flatten_dict(cache).items()
-          if k[-1] == 'cached_value'
-      ]
-      all_inputs.append(inputs)
-      all_cache_keys.append(cache_keys)
-      all_cache_values.append(cache_values)
-    # Convert inputs to a single block [B, DL, Be]
-    all_inputs = np.concatenate(all_inputs, axis=1)
-    # Convert caches into a single block per layer [B * Be, DL] * L
-    all_cache_keys = [np.stack(c, axis=1) for c in zip(*all_cache_keys)]
-    all_cache_values = [np.stack(c, axis=1) for c in zip(*all_cache_values)]
-
-    # Make sure that for each batch, the cache for each beam is identical when
-    # prompt is being forced.
-    for b in range(batch_size):
-      for i, input_token in enumerate(all_inputs[b * beam_size]):
-        if i < lengths[b]:
-          self.assertEqual(input_token, batch['decoder_input_tokens'][b][i])
-          # For all layers.
-          for cache_keys in all_cache_keys:
-            np.testing.assert_array_equal(cache_keys[b * beam_size][i],
-                                          cache_keys[b * beam_size + 1][i])
-          for cache_values in all_cache_values:
-            np.testing.assert_array_equal(cache_values[b * beam_size][i],
-                                          cache_values[b * beam_size + 1][i])
-
-  def test_score_batch(self):
-    encoder_input_tokens = jnp.ones((2, 3))
-    # For this test, decoder input and target tokens are dummy values.
-    decoder_input_tokens = jnp.array([[1, 2, 1, 0], [0, 1, 0, 2]])
-    decoder_target_tokens = jnp.array([[1, 2, 1, 0], [0, 1, 0, 2]])
-    decoder_loss_weights = jnp.array([[1, 1, 1, 0], [0, 1, 0, 1]])
-    logits = jnp.arange(0, 24).reshape((2, 4, 3))
-    params = {'foo': jnp.zeros(3)}
-
-    mock_transformer = mock.Mock()
-    mock_transformer.apply.return_value = logits
-    mock_transformer.dtype = jnp.float32
-
-    batch = {
-        'encoder_input_tokens': encoder_input_tokens,
-        'decoder_input_tokens': decoder_input_tokens,
-        'decoder_target_tokens': decoder_target_tokens,
-        'decoder_loss_weights': decoder_loss_weights
-    }
-
-    def mock_init(self):
-      self.module = mock_transformer
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-      res = model.score_batch(params, batch)
-
-    mock_transformer.apply.assert_called_with({'params': params},
-                                              encoder_input_tokens,
-                                              decoder_input_tokens,
-                                              decoder_target_tokens,
-                                              encoder_segment_ids=None,
-                                              decoder_segment_ids=None,
-                                              encoder_positions=None,
-                                              decoder_positions=None,
-                                              decode=False,
-                                              enable_dropout=False,
-                                              rngs=None,
-                                              mutable=False)
-    np.testing.assert_allclose(res, [-3.222973, -1.815315], rtol=1e-4)
-
-  def test_score_batch_can_return_intermediates(self):
-    encoder_input_tokens = jnp.ones((2, 3))
-    # For this test, decoder input and target tokens are dummy values.
-    decoder_input_tokens = jnp.array([[1, 2, 1, 0], [0, 1, 0, 2]])
-    decoder_target_tokens = jnp.array([[1, 2, 1, 0], [0, 1, 0, 2]])
-    decoder_loss_weights = jnp.array([[1, 1, 1, 0], [0, 1, 0, 1]])
-    logits = jnp.arange(0, 24).reshape((2, 4, 3))
-    modified_variables = {'intermediates': {'bar': jnp.ones(5)}}
-    params = {'foo': jnp.zeros(3)}
-
-    mock_transformer = mock.Mock()
-    mock_transformer.apply.return_value = (logits, modified_variables)
-    mock_transformer.dtype = jnp.float32
-
-    batch = {
-        'encoder_input_tokens': encoder_input_tokens,
-        'decoder_input_tokens': decoder_input_tokens,
-        'decoder_target_tokens': decoder_target_tokens,
-        'decoder_loss_weights': decoder_loss_weights
-    }
-
-    def mock_init(self):
-      self.module = mock_transformer
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-      scores, intermediates = model.score_batch(
-          params, batch, return_intermediates=True)
-
-    mock_transformer.apply.assert_called_with({'params': params},
-                                              encoder_input_tokens,
-                                              decoder_input_tokens,
-                                              decoder_target_tokens,
-                                              encoder_segment_ids=None,
-                                              decoder_segment_ids=None,
-                                              encoder_positions=None,
-                                              decoder_positions=None,
-                                              decode=False,
-                                              enable_dropout=False,
-                                              rngs=None,
-                                              mutable=['intermediates'])
-    np.testing.assert_allclose(scores, [-3.222973, -1.815315], rtol=1e-4)
-    # Incumbent intermediates are passed out unchanged.
-    np.testing.assert_allclose(intermediates['bar'], jnp.ones(5))
-    # A new collection of decoder intermediates are inserted by score_batch()
-    np.testing.assert_allclose(intermediates['decoder']['loss_weights'][0],
-                               decoder_loss_weights)
-    np.testing.assert_allclose(intermediates['decoder']['target_tokens'][0],
-                               decoder_target_tokens)
-
-  def test_train_transformer_wmt(self):
-    # Dummy input data
-    input_shape = (16, 8)
-    encoder_input_tokens = np.ones(shape=input_shape, dtype=np.float32)
-    decoder_input_tokens = 5 * np.ones(shape=input_shape, dtype=np.float32)
-    decoder_target_tokens = 5 * np.ones(input_shape, dtype=np.float32)
-    # input_data = {'inputs': inputs, 'targets': targets}
-    input_data = {
-        'encoder_input_tokens': encoder_input_tokens,
-        'decoder_input_tokens': decoder_input_tokens,
-        'decoder_target_tokens': decoder_target_tokens
-    }
-
-    partitioner = partitioning.PjitPartitioner(num_partitions=1)
-
-    model = test_utils.get_t5_test_model()
-
-    ds_iter = tf.data.Dataset.from_tensors(input_data).as_numpy_iterator()
-    input_shapes = {k: input_shape for k in input_data}
-
-    train_state_initializer = utils.TrainStateInitializer(
-        optimizer_def=model.optimizer_def,
-        init_fn=model.get_initial_variables,
-        input_shapes=input_shapes,
-        partitioner=partitioner)
-    train_state_axes = train_state_initializer.train_state_axes
-    train_state = train_state_initializer.from_scratch(jax.random.PRNGKey(0))
-
-    trainer = trainer_lib.Trainer(
-        model,
-        train_state=train_state,
-        partitioner=partitioner,
-        eval_names=[],
-        summary_dir=None,
-        train_state_axes=train_state_axes,
-        rng=jax.random.PRNGKey(0),
-        learning_rate_fn=lambda x: 0.001,
-        num_microbatches=1)
-
-    trainer.train(ds_iter, 1)
-    logging.info('optimizer after first step %s', train_state.params)
-
-
-  @parameterized.parameters(
-      {'decode_fn': decoding.beam_search},
-      {'decode_fn': functools.partial(decoding.temperature_sample, topk=4)})
-  def test_predict_batch(self, decode_fn):
-    batch_size, encoder_len, max_decode_len, emb_dim = 2, 3, 4, 5
-    batch = {
-        'encoder_input_tokens':
-            np.zeros((batch_size, encoder_len), dtype=np.int32),
-        'decoder_input_tokens':
-            np.zeros((batch_size, max_decode_len), dtype=np.int32)
-    }
-
-    # These dummy logits represent the probability distribution where all the
-    # probability mass is in one item (i.e., degenerate distribution). For
-    # batch element 0, it is vocabulary index 2.
-    # We test `_predict_step` to avoid having to define a task and its
-    # vocabulary.
-    dummy_logits = jnp.expand_dims(
-        jnp.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]]), axis=1)
-
-    class MockModule:
-
-      def __init__(self):
-        self.dtype = jnp.float32
-
-      def apply(self, *args, method=None, **kwargs):
-        del args, kwargs
-        if method is None:  # use for module.`__call__`
-          return (dummy_logits, {'cache': {}})
-        else:
-          return method()
-
-      def encode(self):
-        return jnp.zeros((batch_size, encoder_len, emb_dim))
-
-      def decode(self):
-        return (dummy_logits, {'cache': {}})
-
-    def mock_init(self):
-      self.module = MockModule()
-      self.module.scan_layers = False
-      self._input_vocabulary = mock.Mock(eos_id=1)
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = decode_fn
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-
-    actual = model.predict_batch({}, batch)
-    # The predicted token for the first batch element is always 2 and it is 3
-    # for the second batch element.
-    expected = [[2] * max_decode_len, [3] * max_decode_len]
-    np.testing.assert_array_equal(actual, expected)
-
-  def test_predict_batch_rng(self):
-    batch = {
-        'encoder_input_tokens': np.zeros((2, 1), dtype=np.int32),
-        'decoder_input_tokens': np.zeros((2, 2), dtype=np.int32)
-    }
-
-    decode_fn_mock = mock.Mock(
-        return_value=(np.zeros((2, 2, 3)), np.zeros((2, 2))))
-
-    def mock_init(self):
-      self.module = mock.Mock(
-          apply=mock.Mock(side_effect=lambda *_, **kwargs: (  # pylint:disable=g-long-lambda,g-long-ternary
-              np.zeros((2, 2)), {
-                  'cache': None
-              }) if 'mutable' in kwargs else np.zeros((2, 2))))
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = decode_fn_mock
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-
-    # No RNG
-    model.predict_batch({}, batch)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertNotIn('decode_rng', decode_fn_kwargs)
-
-    # No RNG (w/ aux)
-    model.predict_batch_with_aux({}, batch)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertNotIn('decode_rng', decode_fn_kwargs)
-
-    # decoder_params RNG
-    model.predict_batch_with_aux({}, batch, decoder_params={'decode_rng': 3})
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertEqual(decode_fn_kwargs['decode_rng'], 3)
-
-    # rng RNG
-    model.predict_batch({}, batch, rng=4)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertEqual(decode_fn_kwargs['decode_rng'], 4)
-
-    # rng RNG (w/ aux)
-    model.predict_batch_with_aux({}, batch, rng=4)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertEqual(decode_fn_kwargs['decode_rng'], 4)
-
-    # Both
-    with self.assertRaisesWithLiteralMatch(
-        ValueError, 'Got RNG both from the `rng` argument (4) and '
-        "`decoder_params['decode_rng']` (3). Please specify one or the other."):
-      model.predict_batch_with_aux({},
-                                   batch,
-                                   rng=4,
-                                   decoder_params={'decode_rng': 3})
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='int32',
-          batch={
-              'encoder_input_tokens':
-                  np.zeros((BATCH_SIZE, ENCODER_LEN), dtype=np.int32),
-              'decoder_input_tokens':
-                  np.zeros((BATCH_SIZE, MAX_DECODE_LEN), dtype=np.int32)
-          }),
-      dict(
-          testcase_name='float32',
-          batch={
-              'encoder_input_tokens':
-                  np.zeros((BATCH_SIZE, ENCODER_LEN), dtype=np.float32),
-              'decoder_input_tokens':
-                  np.zeros((BATCH_SIZE, MAX_DECODE_LEN), dtype=np.float32)
-          }))
-  def test_predict_batch_fake_input_shapes_and_types(self, batch):
-
-    # These dummy logits represent the probability distribution where all the
-    # probability mass is in one item (i.e., degenerate distribution). For
-    # batch element 0, it is vocabulary index 2.
-    # We test `_predict_step` to avoid having to define a task and its
-    # vocabulary.
-    dummy_logits = jnp.ones((2, 1, 4), jnp.float32)
-
-    class MockModule:
-
-      def __init__(self):
-        self.dtype = jnp.float32
-        self.call_args_list = []
-
-      def apply(self, *args, method=None, **kwargs):
-        # Not sure why this isn't a real Mock so just record the args/kwargs
-        self.call_args_list.append({'args': args, 'kwargs': kwargs})
-        del args, kwargs
-        if method is None:  # use for module.`__call__`
-          return (dummy_logits, {'cache': {}})
-        else:
-          return method()
-
-      def encode(self):
-        return jnp.zeros((BATCH_SIZE, ENCODER_LEN, EMBED_DIM))
-
-      def decode(self):
-        return (dummy_logits, {'cache': {}})
-
-    def mock_init(self):
-      self.module = MockModule()
-      self.module.scan_layers = False
-      self._input_vocabulary = mock.Mock(eos_id=1)
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = decoding.beam_search
-      self._inputs_bidirectional_attention = False
-
-    with mock.patch.object(
-        models.EncoderDecoderModel, '__init__', new=mock_init):
-      model = models.EncoderDecoderModel()
-    model.predict_batch({}, batch)
-
-    fake_inputs = jnp.ones_like(batch['encoder_input_tokens'])
-    fake_target = jnp.ones_like(batch['decoder_input_tokens'])
-
-    cache_init_call = model.module.call_args_list[0]
-    self.assertEqual(cache_init_call['args'][0], {'params': {}})
-    np.testing.assert_allclose(cache_init_call['args'][1], fake_inputs)
-    np.testing.assert_allclose(cache_init_call['args'][2], fake_target)
-    np.testing.assert_allclose(cache_init_call['args'][3], fake_target)
-    self.assertEqual(cache_init_call['kwargs'], {
-        'decode': True,
-        'enable_dropout': False,
-        'mutable': ['cache']
-    })
-
-
-class DecoderOnlyModelTest(parameterized.TestCase):
-
-
-
-  def test_predict_batch_visible_in_prefill(self):
-    batch_size = 2
-    seq_len = 10
-    lengths = np.array([[6], [3]])
-    batch = {
-        'decoder_input_tokens':
-            np.tile(
-                np.expand_dims(np.arange(seq_len, dtype=np.int32), axis=0),
-                (batch_size, 1)),
-        'decoder_causal_attention':
-            (lengths > np.arange(seq_len)).astype(np.int32)
-    }
-
-    dummy_logits = jnp.expand_dims(
-        jnp.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]]), axis=1)
-
-    mock_module = mock.Mock()
-    mock_module.apply.return_value = (dummy_logits, {'cache': {}})
-    mock_module.dtype = jnp.float32
-
-    def mock_init(self):
-      self.module = mock_module
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = functools.partial(decoding.temperature_sample, topk=4)
-      self._inputs_bidirectional_attention = False
-
-    with mock.patch.object(models.DecoderOnlyModel, '__init__', new=mock_init):
-      model = models.DecoderOnlyModel()
-
-    model.predict_batch({}, batch)
-    prefill_call = mock_module.apply.call_args_list[1]
-    kwargs = prefill_call[1]
-    inputs = prefill_call[1]['decoder_input_tokens']
-    # Note that, for the prefill call, we use 'decoder_causal_attention' as
-    # 'decoder_target_tokens'.
-    targets = prefill_call[1]['decoder_target_tokens']
-    self.assertTrue(kwargs['prefill'])
-    np.testing.assert_array_equal(kwargs['prefill_lengths'],
-                                  np.squeeze(lengths - 1, axis=-1))
-    # Test that the non padding values of the "targets" cover all of the input,
-    # you it will all be considered in the attention mask.
-    np.testing.assert_array_equal(inputs * targets, inputs)
-    # Check that the first value of the target is 1, the first value of the
-    # inputs is always 0 so the masking check wouldn't catch it if the target
-    # had a 0 in the first location.
-    np.testing.assert_array_equal(targets[:, 0], np.ones_like(targets[:, 0]))
-    # Test that the targets are properly removed. Our input is a sequence from 0
-    # onward, so our largest value (the last input) should be equal by it's
-    # position (which is 1 - length). If we didn't mask the target correctly,
-    # we would expect a larger value in the max.
-    np.testing.assert_array_equal(
-        np.max(inputs, axis=1), np.squeeze(lengths - 1, axis=-1))
-
-
-  def test_predict_batch(self):
-    batch = {
-        'decoder_input_tokens':
-            np.array([[0, 3, 4, 5, 6, 0, 0], [0, 7, 8, 9, 0, 0, 0]]),
-        'decoder_causal_attention':
-            np.array([[1, 1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0]])
-    }
-
-    # These dummy logits represent the probability distribution where all the
-    # probability mass is in one item (i.e., degenerate distribution). For
-    # batch element 0, it is vocabulary index 2.
-    # We test `_predict_step` to avoid having to define a task and its
-    # vocabulary.
-    dummy_logits = jnp.expand_dims(
-        jnp.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]]), axis=1)
-
-    mock_module = mock.Mock()
-    mock_module.apply.return_value = (dummy_logits, {'cache': {}})
-    mock_module.dtype = jnp.float32
-
-    def mock_init(self):
-      self.module = mock_module
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = functools.partial(decoding.temperature_sample, topk=4)
-      self._inputs_bidirectional_attention = False
-
-    with mock.patch.object(models.DecoderOnlyModel, '__init__', new=mock_init):
-      model = models.DecoderOnlyModel()
-
-    actual = model.predict_batch({}, batch)
-
-    expected = [[2, 2, 2, 2, 2, 0, 0], [3, 3, 3, 3, 3, 3, 0]]
-
-    # The expected progression of the first element of 'decoder_input_tokens':
-    # [0, 3, 4, 5, 6, 0, 0] -> [0, 3, 4, 0, 0, 0, 0] ->
-    # [3, 4, 2, 2, 2, 2, 2] -> [2, 2, 2, 2, 2, 0, 0]
-
-    # The expected progression of the second element of 'decoder_input_tokens':
-    # [0, 7, 8, 9, 0, 0, 0] -> [0, 7, 0, 0, 0, 0, 0] ->
-    # [7, 3, 3, 3, 3, 3, 3] -> [3, 3, 3, 3, 3, 3, 0]
-
-    np.testing.assert_array_equal(actual, expected)
-
-  def test_predict_batch_rng(self):
-    batch = {
-        'decoder_input_tokens': np.zeros((2, 2), dtype=np.int32),
-        'decoder_causal_attention': np.zeros((2, 2), dtype=np.int32)
-    }
-
-    decode_fn_mock = mock.Mock(
-        return_value=(np.zeros((2, 2, 3)), np.zeros((2, 2))))
-
-    def mock_init(self):
-      self.module = mock.Mock(
-          apply=mock.Mock(side_effect=lambda *_, **kwargs: (  # pylint:disable=g-long-lambda,g-long-ternary
-              np.zeros((2, 2)), {
-                  'cache': None
-              }) if 'mutable' in kwargs else np.zeros((2, 2))))
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = decode_fn_mock
-      self._inputs_bidirectional_attention = False
-
-    with mock.patch.object(models.DecoderOnlyModel, '__init__', new=mock_init):
-      model = models.DecoderOnlyModel()
-
-    # No RNG
-    model.predict_batch({}, batch)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertNotIn('decode_rng', decode_fn_kwargs)
-
-    # No RNG (w/ aux)
-    model.predict_batch_with_aux({}, batch)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertNotIn('decode_rng', decode_fn_kwargs)
-
-    # decoder_params RNG
-    model.predict_batch_with_aux({}, batch, decoder_params={'decode_rng': 3})
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertEqual(decode_fn_kwargs['decode_rng'], 3)
-
-    # rng RNG
-    model.predict_batch({}, batch, rng=4)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertEqual(decode_fn_kwargs['decode_rng'], 4)
-
-    # rng RNG (w/ aux)
-    model.predict_batch_with_aux({}, batch, rng=4)
-    _, decode_fn_kwargs = decode_fn_mock.call_args
-    self.assertEqual(decode_fn_kwargs['decode_rng'], 4)
-
-    # Both
-    with self.assertRaisesWithLiteralMatch(
-        ValueError, 'Got RNG both from the `rng` argument (4) and '
-        "`decoder_params['decode_rng']` (3). Please specify one or the other."):
-      model.predict_batch_with_aux({},
-                                   batch,
-                                   rng=4,
-                                   decoder_params={'decode_rng': 3})
-
-  def test_predict_batch_num_decodes_temperature_sample(self):
-    batch = {
-        'decoder_input_tokens': np.array([
-            [0, 3, 4, 5, 6, 0, 0],
-        ]),
-        'decoder_causal_attention': np.array([
-            [1, 1, 1, 0, 0, 0, 0],
-        ])
-    }
-
-    # These dummy logits represent the probability distribution where all the
-    # probability mass is in one item (i.e., degenerate distribution). For
-    # batch element 0, it is vocabulary index 2. We have two samples.
-    # Technically these should be identical since the prompts are the same, but
-    # this makes testing easier.
-    dummy_logits = jnp.expand_dims(
-        jnp.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]]), axis=1)
-
-    mock_module = mock.Mock()
-    mock_module.apply.return_value = (dummy_logits, {'cache': {}})
-    mock_module.dtype = jnp.float32
-
-    def mock_init(self):
-      self.module = mock_module
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = functools.partial(decoding.temperature_sample, topk=4)
-      self._inputs_bidirectional_attention = False
-
-    with mock.patch.object(models.DecoderOnlyModel, '__init__', new=mock_init):
-      model = models.DecoderOnlyModel()
-
-    actual_output, aux = model.predict_batch_with_aux({},
-                                                      batch,
-                                                      num_decodes=2,
-                                                      return_all_decodes=True)
-
-    expected_output = [[[2, 2, 2, 2, 2, 0, 0], [3, 3, 3, 3, 3, 0, 0]]]
-    expected_scores = [[0., 0.]]
-
-    # The expected progression of the first element of 'decoder_input_tokens':
-    # [0, 3, 4, 5, 6, 0, 0] -> [0, 3, 4, 0, 0, 0, 0] ->
-    # [3, 4, 2, 2, 2, 2, 2] -> [2, 2, 2, 2, 2, 0, 0]
-
-    # The expected progression of the second element of 'decoder_input_tokens':
-    # [0, 7, 8, 9, 0, 0, 0] -> [0, 7, 0, 0, 0, 0, 0] ->
-    # [7, 3, 3, 3, 3, 3, 3] -> [3, 3, 3, 3, 3, 3, 0]
-
-    np.testing.assert_array_equal(actual_output, expected_output)
-    np.testing.assert_array_equal(aux['scores'], expected_scores)
-
-  def test_predict_batch_fake_input_shapes_and_types(self):
-    # The input and causal attention actually have to be int32 for this test,
-    # even though the cache init should work with any types the `inputs` that
-    # is created from multiplying the causal attention and the input tokens
-    # needs to be an int or the decoding will fail.
-    batch = {
-        'decoder_input_tokens':
-            np.array([[0, 3, 4, 5, 6, 0, 0], [0, 7, 8, 9, 0, 0, 0]],
-                     dtype=np.int32),
-        'decoder_causal_attention':
-            np.array([[1, 1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0]],
-                     dtype=np.int32)
-    }
-
-    dummy_logits = jnp.ones((2, 1, 5), jnp.float32)
-
-    mock_module = mock.Mock()
-    mock_module.apply.return_value = (dummy_logits, {'cache': {}})
-    mock_module.dtype = jnp.float32
-
-    def mock_init(self):
-      self.module = mock_module
-      self._output_vocabulary = mock.Mock(eos_id=1)
-      self._decode_fn = functools.partial(decoding.temperature_sample, topk=4)
-      self._inputs_bidirectional_attention = False
-
-    with mock.patch.object(models.DecoderOnlyModel, '__init__', new=mock_init):
-      model = models.DecoderOnlyModel()
-
-    model.predict_batch({}, batch)
-
-    fake_target = jnp.ones_like(batch['decoder_input_tokens'])
-
-    cache_init_call = mock_module.apply.call_args_list[0]
-
-    self.assertEqual(cache_init_call[0][0], {'params': {}})
-    np.testing.assert_allclose(cache_init_call[0][1], fake_target)
-    np.testing.assert_allclose(cache_init_call[0][2], fake_target)
-    self.assertEqual(cache_init_call[1], {
-        'decode': True,
-        'enable_dropout': False,
-        'mutable': ['cache']
-    })
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='no_types',
-          shapes={'decoder_input_tokens': [1, 62]},
-          types=None),
-      dict(
-          testcase_name='int32',
-          shapes={'decoder_input_tokens': [1, 62]},
-          types={'decoder_input_tokens': jnp.int32}),
-      dict(
-          testcase_name='float32',
-          shapes={'decoder_input_tokens': [1, 62]},
-          types={'decoder_input_tokens': jnp.int32}),
-  )
-  def test_get_initial_variables_shapes_and_types(self, shapes, types):
-    mock_lm = mock.Mock()
-    mock_lm.init.return_value = {'params': {}}
-    mock_optimizer_def = mock.Mock()
-    rng = mock.Mock()
-
-    def mock_init(self):
-      self.module = mock_lm
-      self.optimizer_def = mock_optimizer_def
-
-    with mock.patch.object(models.DecoderOnlyModel, '__init__', new=mock_init):
-      model = models.DecoderOnlyModel()
-      model.get_initial_variables(rng, shapes, types)
-
-    if types is None:
-      decoder_input = jnp.ones(
-          shapes['decoder_input_tokens'], dtype=jnp.float32)
-    else:
-      decoder_input = jnp.ones(
-          shapes['decoder_input_tokens'], dtype=types['decoder_input_tokens'])
-
-    # Using `.assert_called_once_with` doesn't work because the simple
-    # comparison it does for the array arguments fail (truth value of an array
-    # is ambiguous).
-    called_with = mock_lm.init.call_args
-    self.assertEqual(called_with[0][0], rng)
-    np.testing.assert_allclose(called_with[0][1], decoder_input)
-    np.testing.assert_allclose(called_with[0][2], decoder_input)
-    self.assertEqual(mock_lm.init.call_args[1], {'enable_dropout': False})
-
-
-if __name__ == '__main__':
-  absltest.main()
diff --git a/spaces/julien-c/sveltekit-demo/src/routes/todos/_api.ts b/spaces/julien-c/sveltekit-demo/src/routes/todos/_api.ts
deleted file mode 100644
index 2fd3385d1ed83c532abef122cf3a636a8a3b1011..0000000000000000000000000000000000000000
--- a/spaces/julien-c/sveltekit-demo/src/routes/todos/_api.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-import type { EndpointOutput, Request } from '@sveltejs/kit';
-import type { Locals } from '$lib/types';
-
-/*
-	This module is used by the /todos.json and /todos/[uid].json
-	endpoints to make calls to api.svelte.dev, which stores todos
-	for each user. The leading underscore indicates that this is
-	a private module, _not_ an endpoint — visiting /todos/_api
-	will net you a 404 response.
-
-	(The data on the todo app will expire periodically; no
-	guarantees are made. Don't use it to organise your life.)
-*/
-
-const base = 'https://api.svelte.dev';
-
-export async function api(
-	request: Request<Locals>,
-	resource: string,
-	data?: Record<string, unknown>
-): Promise<EndpointOutput> {
-	// user must have a cookie set
-	if (!request.locals.userid) {
-		return { status: 401 };
-	}
-
-	const res = await fetch(`${base}/${resource}`, {
-		method: request.method,
-		headers: {
-			'content-type': 'application/json'
-		},
-		body: data && JSON.stringify(data)
-	});
-
-	// if the request came from a <form> submission, the browser's default
-	// behaviour is to show the URL corresponding to the form's "action"
-	// attribute. in those cases, we want to redirect them back to the
-	// /todos page, rather than showing the response
-	if (res.ok && request.method !== 'GET' && request.headers.accept !== 'application/json') {
-		return {
-			status: 303,
-			headers: {
-				location: '/todos'
-			}
-		};
-	}
-
-	return {
-		status: res.status,
-		body: await res.json()
-	};
-}
diff --git a/spaces/keras-io/timeseries-anomaly-detection-autoencoders/README.md b/spaces/keras-io/timeseries-anomaly-detection-autoencoders/README.md
deleted file mode 100644
index c374555a2be53991da86a2b01b1e3f303af4996b..0000000000000000000000000000000000000000
--- a/spaces/keras-io/timeseries-anomaly-detection-autoencoders/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Timeseries Anomaly Detection
-emoji: 🌍
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/speaker_encoder/params_data.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/speaker_encoder/params_data.py
deleted file mode 100644
index bdb1716ed45617f2b127a7fb8885afe6cc74fb71..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/speaker_encoder/params_data.py
+++ /dev/null
@@ -1,29 +0,0 @@
-
-## Mel-filterbank
-mel_window_length = 25  # In milliseconds
-mel_window_step = 10    # In milliseconds
-mel_n_channels = 40
-
-
-## Audio
-sampling_rate = 16000
-# Number of spectrogram frames in a partial utterance
-partials_n_frames = 160     # 1600 ms
-# Number of spectrogram frames at inference
-inference_n_frames = 80     #  800 ms
-
-
-## Voice Activation Detection
-# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
-# This sets the granularity of the VAD. Should not need to be changed.
-vad_window_length = 30  # In milliseconds
-# Number of frames to average together when performing the moving average smoothing.
-# The larger this value, the larger the VAD variations must be to not get smoothed out. 
-vad_moving_average_width = 8
-# Maximum number of consecutive silent frames a segment can have.
-vad_max_silence_length = 6
-
-
-## Audio volume normalization
-audio_norm_target_dBFS = -30
-
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/visualize.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/visualize.py
deleted file mode 100644
index 23a1110806a0ddf37d4aa549c023d1c3f7114e3e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/visualize.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# check the sync of 3dmm feature and the audio
-import cv2
-import numpy as np
-from src.face3d.models.bfm import ParametricFaceModel
-from src.face3d.models.facerecon_model import FaceReconModel
-import torch
-import subprocess, platform
-import scipy.io as scio
-from tqdm import tqdm 
-
-# draft
-def gen_composed_video(args, device, first_frame_coeff, coeff_path, audio_path, save_path, exp_dim=64):
-    
-    coeff_first = scio.loadmat(first_frame_coeff)['full_3dmm']
-
-    coeff_pred = scio.loadmat(coeff_path)['coeff_3dmm']
-
-    coeff_full = np.repeat(coeff_first, coeff_pred.shape[0], axis=0) # 257
-
-    coeff_full[:, 80:144] = coeff_pred[:, 0:64]
-    coeff_full[:, 224:227]  = coeff_pred[:, 64:67] # 3 dim translation
-    coeff_full[:, 254:]  = coeff_pred[:, 67:] # 3 dim translation
-
-    tmp_video_path = '/tmp/face3dtmp.mp4'
-
-    facemodel = FaceReconModel(args)
-    
-    video = cv2.VideoWriter(tmp_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 25, (224, 224))
-
-    for k in tqdm(range(coeff_pred.shape[0]), 'face3d rendering:'):
-        cur_coeff_full = torch.tensor(coeff_full[k:k+1], device=device)
-
-        facemodel.forward(cur_coeff_full, device)
-
-        predicted_landmark = facemodel.pred_lm # TODO.
-        predicted_landmark = predicted_landmark.cpu().numpy().squeeze()
-
-        rendered_img = facemodel.pred_face
-        rendered_img = 255. * rendered_img.cpu().numpy().squeeze().transpose(1,2,0)
-        out_img = rendered_img[:, :, :3].astype(np.uint8)
-
-        video.write(np.uint8(out_img[:,:,::-1]))
-
-    video.release()
-
-    command = 'ffmpeg -v quiet -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_path, tmp_video_path, save_path)
-    subprocess.call(command, shell=platform.system() != 'Windows')
-
diff --git a/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/models/__init__.py b/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/models/__init__.py
deleted file mode 100644
index 4803ba6b2a0afc8022e756ae5b3f4c7403c3c1bd..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/models/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .melgan import *  # NOQA
-from .parallel_wavegan import *  # NOQA
diff --git a/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/docs/modelzoo.md b/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/docs/modelzoo.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kokofixcomputers/chat-ui/vite.config.ts b/spaces/kokofixcomputers/chat-ui/vite.config.ts
deleted file mode 100644
index 4a73fc2099065c273e7d95d2f2122538d0502b27..0000000000000000000000000000000000000000
--- a/spaces/kokofixcomputers/chat-ui/vite.config.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { sveltekit } from "@sveltejs/kit/vite";
-import { defineConfig } from "vite";
-import Icons from "unplugin-icons/vite";
-
-export default defineConfig({
-	plugins: [
-		sveltekit(),
-		Icons({
-			compiler: "svelte",
-		}),
-	],
-});
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiofiles/threadpool/text.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiofiles/threadpool/text.py
deleted file mode 100644
index 1323009ac25f10cfa6908318e0fcb387f85fb99c..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiofiles/threadpool/text.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from ..base import AsyncBase, AsyncIndirectBase
-from .utils import (
-    delegate_to_executor,
-    proxy_method_directly,
-    proxy_property_directly,
-)
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "readable",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "write",
-    "writable",
-    "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
-    "buffer",
-    "closed",
-    "encoding",
-    "errors",
-    "line_buffering",
-    "newlines",
-    "name",
-    "mode",
-)
-class AsyncTextIOWrapper(AsyncBase):
-    """The asyncio executor version of io.TextIOWrapper."""
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "readable",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "write",
-    "writable",
-    "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
-    "buffer",
-    "closed",
-    "encoding",
-    "errors",
-    "line_buffering",
-    "newlines",
-    "name",
-    "mode",
-)
-class AsyncTextIndirectIOWrapper(AsyncIndirectBase):
-    """The indirect asyncio executor version of io.TextIOWrapper."""
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/click/decorators.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/click/decorators.py
deleted file mode 100644
index 28618dc52379eafc72a5a1005a679d418d879692..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/click/decorators.py
+++ /dev/null
@@ -1,497 +0,0 @@
-import inspect
-import types
-import typing as t
-from functools import update_wrapper
-from gettext import gettext as _
-
-from .core import Argument
-from .core import Command
-from .core import Context
-from .core import Group
-from .core import Option
-from .core import Parameter
-from .globals import get_current_context
-from .utils import echo
-
-F = t.TypeVar("F", bound=t.Callable[..., t.Any])
-FC = t.TypeVar("FC", bound=t.Union[t.Callable[..., t.Any], Command])
-
-
-def pass_context(f: F) -> F:
-    """Marks a callback as wanting to receive the current context
-    object as first argument.
-    """
-
-    def new_func(*args, **kwargs):  # type: ignore
-        return f(get_current_context(), *args, **kwargs)
-
-    return update_wrapper(t.cast(F, new_func), f)
-
-
-def pass_obj(f: F) -> F:
-    """Similar to :func:`pass_context`, but only pass the object on the
-    context onwards (:attr:`Context.obj`).  This is useful if that object
-    represents the state of a nested system.
-    """
-
-    def new_func(*args, **kwargs):  # type: ignore
-        return f(get_current_context().obj, *args, **kwargs)
-
-    return update_wrapper(t.cast(F, new_func), f)
-
-
-def make_pass_decorator(
-    object_type: t.Type, ensure: bool = False
-) -> "t.Callable[[F], F]":
-    """Given an object type this creates a decorator that will work
-    similar to :func:`pass_obj` but instead of passing the object of the
-    current context, it will find the innermost context of type
-    :func:`object_type`.
-
-    This generates a decorator that works roughly like this::
-
-        from functools import update_wrapper
-
-        def decorator(f):
-            @pass_context
-            def new_func(ctx, *args, **kwargs):
-                obj = ctx.find_object(object_type)
-                return ctx.invoke(f, obj, *args, **kwargs)
-            return update_wrapper(new_func, f)
-        return decorator
-
-    :param object_type: the type of the object to pass.
-    :param ensure: if set to `True`, a new object will be created and
-                   remembered on the context if it's not there yet.
-    """
-
-    def decorator(f: F) -> F:
-        def new_func(*args, **kwargs):  # type: ignore
-            ctx = get_current_context()
-
-            if ensure:
-                obj = ctx.ensure_object(object_type)
-            else:
-                obj = ctx.find_object(object_type)
-
-            if obj is None:
-                raise RuntimeError(
-                    "Managed to invoke callback without a context"
-                    f" object of type {object_type.__name__!r}"
-                    " existing."
-                )
-
-            return ctx.invoke(f, obj, *args, **kwargs)
-
-        return update_wrapper(t.cast(F, new_func), f)
-
-    return decorator
-
-
-def pass_meta_key(
-    key: str, *, doc_description: t.Optional[str] = None
-) -> "t.Callable[[F], F]":
-    """Create a decorator that passes a key from
-    :attr:`click.Context.meta` as the first argument to the decorated
-    function.
-
-    :param key: Key in ``Context.meta`` to pass.
-    :param doc_description: Description of the object being passed,
-        inserted into the decorator's docstring. Defaults to "the 'key'
-        key from Context.meta".
-
-    .. versionadded:: 8.0
-    """
-
-    def decorator(f: F) -> F:
-        def new_func(*args, **kwargs):  # type: ignore
-            ctx = get_current_context()
-            obj = ctx.meta[key]
-            return ctx.invoke(f, obj, *args, **kwargs)
-
-        return update_wrapper(t.cast(F, new_func), f)
-
-    if doc_description is None:
-        doc_description = f"the {key!r} key from :attr:`click.Context.meta`"
-
-    decorator.__doc__ = (
-        f"Decorator that passes {doc_description} as the first argument"
-        " to the decorated function."
-    )
-    return decorator
-
-
-CmdType = t.TypeVar("CmdType", bound=Command)
-
-
-@t.overload
-def command(
-    __func: t.Callable[..., t.Any],
-) -> Command:
-    ...
-
-
-@t.overload
-def command(
-    name: t.Optional[str] = None,
-    **attrs: t.Any,
-) -> t.Callable[..., Command]:
-    ...
-
-
-@t.overload
-def command(
-    name: t.Optional[str] = None,
-    cls: t.Type[CmdType] = ...,
-    **attrs: t.Any,
-) -> t.Callable[..., CmdType]:
-    ...
-
-
-def command(
-    name: t.Union[str, t.Callable[..., t.Any], None] = None,
-    cls: t.Optional[t.Type[Command]] = None,
-    **attrs: t.Any,
-) -> t.Union[Command, t.Callable[..., Command]]:
-    r"""Creates a new :class:`Command` and uses the decorated function as
-    callback.  This will also automatically attach all decorated
-    :func:`option`\s and :func:`argument`\s as parameters to the command.
-
-    The name of the command defaults to the name of the function with
-    underscores replaced by dashes.  If you want to change that, you can
-    pass the intended name as the first argument.
-
-    All keyword arguments are forwarded to the underlying command class.
-    For the ``params`` argument, any decorated params are appended to
-    the end of the list.
-
-    Once decorated the function turns into a :class:`Command` instance
-    that can be invoked as a command line utility or be attached to a
-    command :class:`Group`.
-
-    :param name: the name of the command.  This defaults to the function
-                 name with underscores replaced by dashes.
-    :param cls: the command class to instantiate.  This defaults to
-                :class:`Command`.
-
-    .. versionchanged:: 8.1
-        This decorator can be applied without parentheses.
-
-    .. versionchanged:: 8.1
-        The ``params`` argument can be used. Decorated params are
-        appended to the end of the list.
-    """
-
-    func: t.Optional[t.Callable[..., t.Any]] = None
-
-    if callable(name):
-        func = name
-        name = None
-        assert cls is None, "Use 'command(cls=cls)(callable)' to specify a class."
-        assert not attrs, "Use 'command(**kwargs)(callable)' to provide arguments."
-
-    if cls is None:
-        cls = Command
-
-    def decorator(f: t.Callable[..., t.Any]) -> Command:
-        if isinstance(f, Command):
-            raise TypeError("Attempted to convert a callback into a command twice.")
-
-        attr_params = attrs.pop("params", None)
-        params = attr_params if attr_params is not None else []
-
-        try:
-            decorator_params = f.__click_params__  # type: ignore
-        except AttributeError:
-            pass
-        else:
-            del f.__click_params__  # type: ignore
-            params.extend(reversed(decorator_params))
-
-        if attrs.get("help") is None:
-            attrs["help"] = f.__doc__
-
-        cmd = cls(  # type: ignore[misc]
-            name=name or f.__name__.lower().replace("_", "-"),  # type: ignore[arg-type]
-            callback=f,
-            params=params,
-            **attrs,
-        )
-        cmd.__doc__ = f.__doc__
-        return cmd
-
-    if func is not None:
-        return decorator(func)
-
-    return decorator
-
-
-@t.overload
-def group(
-    __func: t.Callable[..., t.Any],
-) -> Group:
-    ...
-
-
-@t.overload
-def group(
-    name: t.Optional[str] = None,
-    **attrs: t.Any,
-) -> t.Callable[[F], Group]:
-    ...
-
-
-def group(
-    name: t.Union[str, t.Callable[..., t.Any], None] = None, **attrs: t.Any
-) -> t.Union[Group, t.Callable[[F], Group]]:
-    """Creates a new :class:`Group` with a function as callback.  This
-    works otherwise the same as :func:`command` just that the `cls`
-    parameter is set to :class:`Group`.
-
-    .. versionchanged:: 8.1
-        This decorator can be applied without parentheses.
-    """
-    if attrs.get("cls") is None:
-        attrs["cls"] = Group
-
-    if callable(name):
-        grp: t.Callable[[F], Group] = t.cast(Group, command(**attrs))
-        return grp(name)
-
-    return t.cast(Group, command(name, **attrs))
-
-
-def _param_memo(f: FC, param: Parameter) -> None:
-    if isinstance(f, Command):
-        f.params.append(param)
-    else:
-        if not hasattr(f, "__click_params__"):
-            f.__click_params__ = []  # type: ignore
-
-        f.__click_params__.append(param)  # type: ignore
-
-
-def argument(*param_decls: str, **attrs: t.Any) -> t.Callable[[FC], FC]:
-    """Attaches an argument to the command.  All positional arguments are
-    passed as parameter declarations to :class:`Argument`; all keyword
-    arguments are forwarded unchanged (except ``cls``).
-    This is equivalent to creating an :class:`Argument` instance manually
-    and attaching it to the :attr:`Command.params` list.
-
-    :param cls: the argument class to instantiate.  This defaults to
-                :class:`Argument`.
-    """
-
-    def decorator(f: FC) -> FC:
-        ArgumentClass = attrs.pop("cls", None) or Argument
-        _param_memo(f, ArgumentClass(param_decls, **attrs))
-        return f
-
-    return decorator
-
-
-def option(*param_decls: str, **attrs: t.Any) -> t.Callable[[FC], FC]:
-    """Attaches an option to the command.  All positional arguments are
-    passed as parameter declarations to :class:`Option`; all keyword
-    arguments are forwarded unchanged (except ``cls``).
-    This is equivalent to creating an :class:`Option` instance manually
-    and attaching it to the :attr:`Command.params` list.
-
-    :param cls: the option class to instantiate.  This defaults to
-                :class:`Option`.
-    """
-
-    def decorator(f: FC) -> FC:
-        # Issue 926, copy attrs, so pre-defined options can re-use the same cls=
-        option_attrs = attrs.copy()
-        OptionClass = option_attrs.pop("cls", None) or Option
-        _param_memo(f, OptionClass(param_decls, **option_attrs))
-        return f
-
-    return decorator
-
-
-def confirmation_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--yes`` option which shows a prompt before continuing if
-    not passed. If the prompt is declined, the program will exit.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--yes"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value:
-            ctx.abort()
-
-    if not param_decls:
-        param_decls = ("--yes",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("callback", callback)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("prompt", "Do you want to continue?")
-    kwargs.setdefault("help", "Confirm the action without prompting.")
-    return option(*param_decls, **kwargs)
-
-
-def password_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--password`` option which prompts for a password, hiding
-    input and asking to enter the value again for confirmation.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--password"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-    if not param_decls:
-        param_decls = ("--password",)
-
-    kwargs.setdefault("prompt", True)
-    kwargs.setdefault("confirmation_prompt", True)
-    kwargs.setdefault("hide_input", True)
-    return option(*param_decls, **kwargs)
-
-
-def version_option(
-    version: t.Optional[str] = None,
-    *param_decls: str,
-    package_name: t.Optional[str] = None,
-    prog_name: t.Optional[str] = None,
-    message: t.Optional[str] = None,
-    **kwargs: t.Any,
-) -> t.Callable[[FC], FC]:
-    """Add a ``--version`` option which immediately prints the version
-    number and exits the program.
-
-    If ``version`` is not provided, Click will try to detect it using
-    :func:`importlib.metadata.version` to get the version for the
-    ``package_name``. On Python < 3.8, the ``importlib_metadata``
-    backport must be installed.
-
-    If ``package_name`` is not provided, Click will try to detect it by
-    inspecting the stack frames. This will be used to detect the
-    version, so it must match the name of the installed package.
-
-    :param version: The version number to show. If not provided, Click
-        will try to detect it.
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--version"``.
-    :param package_name: The package name to detect the version from. If
-        not provided, Click will try to detect it.
-    :param prog_name: The name of the CLI to show in the message. If not
-        provided, it will be detected from the command.
-    :param message: The message to show. The values ``%(prog)s``,
-        ``%(package)s``, and ``%(version)s`` are available. Defaults to
-        ``"%(prog)s, version %(version)s"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    :raise RuntimeError: ``version`` could not be detected.
-
-    .. versionchanged:: 8.0
-        Add the ``package_name`` parameter, and the ``%(package)s``
-        value for messages.
-
-    .. versionchanged:: 8.0
-        Use :mod:`importlib.metadata` instead of ``pkg_resources``. The
-        version is detected based on the package name, not the entry
-        point name. The Python package name must match the installed
-        package name, or be passed with ``package_name=``.
-    """
-    if message is None:
-        message = _("%(prog)s, version %(version)s")
-
-    if version is None and package_name is None:
-        frame = inspect.currentframe()
-        f_back = frame.f_back if frame is not None else None
-        f_globals = f_back.f_globals if f_back is not None else None
-        # break reference cycle
-        # https://docs.python.org/3/library/inspect.html#the-interpreter-stack
-        del frame
-
-        if f_globals is not None:
-            package_name = f_globals.get("__name__")
-
-            if package_name == "__main__":
-                package_name = f_globals.get("__package__")
-
-            if package_name:
-                package_name = package_name.partition(".")[0]
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value or ctx.resilient_parsing:
-            return
-
-        nonlocal prog_name
-        nonlocal version
-
-        if prog_name is None:
-            prog_name = ctx.find_root().info_name
-
-        if version is None and package_name is not None:
-            metadata: t.Optional[types.ModuleType]
-
-            try:
-                from importlib import metadata  # type: ignore
-            except ImportError:
-                # Python < 3.8
-                import importlib_metadata as metadata  # type: ignore
-
-            try:
-                version = metadata.version(package_name)  # type: ignore
-            except metadata.PackageNotFoundError:  # type: ignore
-                raise RuntimeError(
-                    f"{package_name!r} is not installed. Try passing"
-                    " 'package_name' instead."
-                ) from None
-
-        if version is None:
-            raise RuntimeError(
-                f"Could not determine the version for {package_name!r} automatically."
-            )
-
-        echo(
-            t.cast(str, message)
-            % {"prog": prog_name, "package": package_name, "version": version},
-            color=ctx.color,
-        )
-        ctx.exit()
-
-    if not param_decls:
-        param_decls = ("--version",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("is_eager", True)
-    kwargs.setdefault("help", _("Show the version and exit."))
-    kwargs["callback"] = callback
-    return option(*param_decls, **kwargs)
-
-
-def help_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--help`` option which immediately prints the help page
-    and exits the program.
-
-    This is usually unnecessary, as the ``--help`` option is added to
-    each command automatically unless ``add_help_option=False`` is
-    passed.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--help"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value or ctx.resilient_parsing:
-            return
-
-        echo(ctx.get_help(), color=ctx.color)
-        ctx.exit()
-
-    if not param_decls:
-        param_decls = ("--help",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("is_eager", True)
-    kwargs.setdefault("help", _("Show this message and exit."))
-    kwargs["callback"] = callback
-    return option(*param_decls, **kwargs)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/utils.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/utils.py
deleted file mode 100644
index 9b5f5a50eb6773c4085f8572a45b3fa351367565..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/utils.py
+++ /dev/null
@@ -1,755 +0,0 @@
-import enum
-import json
-import os
-import re
-import typing as t
-from collections import abc
-from collections import deque
-from random import choice
-from random import randrange
-from threading import Lock
-from types import CodeType
-from urllib.parse import quote_from_bytes
-
-import markupsafe
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-
-F = t.TypeVar("F", bound=t.Callable[..., t.Any])
-
-# special singleton representing missing values for the runtime
-missing: t.Any = type("MissingType", (), {"__repr__": lambda x: "missing"})()
-
-internal_code: t.MutableSet[CodeType] = set()
-
-concat = "".join
-
-
-def pass_context(f: F) -> F:
-    """Pass the :class:`~jinja2.runtime.Context` as the first argument
-    to the decorated function when called while rendering a template.
-
-    Can be used on functions, filters, and tests.
-
-    If only ``Context.eval_context`` is needed, use
-    :func:`pass_eval_context`. If only ``Context.environment`` is
-    needed, use :func:`pass_environment`.
-
-    .. versionadded:: 3.0.0
-        Replaces ``contextfunction`` and ``contextfilter``.
-    """
-    f.jinja_pass_arg = _PassArg.context  # type: ignore
-    return f
-
-
-def pass_eval_context(f: F) -> F:
-    """Pass the :class:`~jinja2.nodes.EvalContext` as the first argument
-    to the decorated function when called while rendering a template.
-    See :ref:`eval-context`.
-
-    Can be used on functions, filters, and tests.
-
-    If only ``EvalContext.environment`` is needed, use
-    :func:`pass_environment`.
-
-    .. versionadded:: 3.0.0
-        Replaces ``evalcontextfunction`` and ``evalcontextfilter``.
-    """
-    f.jinja_pass_arg = _PassArg.eval_context  # type: ignore
-    return f
-
-
-def pass_environment(f: F) -> F:
-    """Pass the :class:`~jinja2.Environment` as the first argument to
-    the decorated function when called while rendering a template.
-
-    Can be used on functions, filters, and tests.
-
-    .. versionadded:: 3.0.0
-        Replaces ``environmentfunction`` and ``environmentfilter``.
-    """
-    f.jinja_pass_arg = _PassArg.environment  # type: ignore
-    return f
-
-
-class _PassArg(enum.Enum):
-    context = enum.auto()
-    eval_context = enum.auto()
-    environment = enum.auto()
-
-    @classmethod
-    def from_obj(cls, obj: F) -> t.Optional["_PassArg"]:
-        if hasattr(obj, "jinja_pass_arg"):
-            return obj.jinja_pass_arg  # type: ignore
-
-        return None
-
-
-def internalcode(f: F) -> F:
-    """Marks the function as internally used"""
-    internal_code.add(f.__code__)
-    return f
-
-
-def is_undefined(obj: t.Any) -> bool:
-    """Check if the object passed is undefined.  This does nothing more than
-    performing an instance check against :class:`Undefined` but looks nicer.
-    This can be used for custom filters or tests that want to react to
-    undefined variables.  For example a custom default filter can look like
-    this::
-
-        def default(var, default=''):
-            if is_undefined(var):
-                return default
-            return var
-    """
-    from .runtime import Undefined
-
-    return isinstance(obj, Undefined)
-
-
-def consume(iterable: t.Iterable[t.Any]) -> None:
-    """Consumes an iterable without doing anything with it."""
-    for _ in iterable:
-        pass
-
-
-def clear_caches() -> None:
-    """Jinja keeps internal caches for environments and lexers.  These are
-    used so that Jinja doesn't have to recreate environments and lexers all
-    the time.  Normally you don't have to care about that but if you are
-    measuring memory consumption you may want to clean the caches.
-    """
-    from .environment import get_spontaneous_environment
-    from .lexer import _lexer_cache
-
-    get_spontaneous_environment.cache_clear()
-    _lexer_cache.clear()
-
-
-def import_string(import_name: str, silent: bool = False) -> t.Any:
-    """Imports an object based on a string.  This is useful if you want to
-    use import paths as endpoints or something similar.  An import path can
-    be specified either in dotted notation (``xml.sax.saxutils.escape``)
-    or with a colon as object delimiter (``xml.sax.saxutils:escape``).
-
-    If the `silent` is True the return value will be `None` if the import
-    fails.
-
-    :return: imported object
-    """
-    try:
-        if ":" in import_name:
-            module, obj = import_name.split(":", 1)
-        elif "." in import_name:
-            module, _, obj = import_name.rpartition(".")
-        else:
-            return __import__(import_name)
-        return getattr(__import__(module, None, None, [obj]), obj)
-    except (ImportError, AttributeError):
-        if not silent:
-            raise
-
-
-def open_if_exists(filename: str, mode: str = "rb") -> t.Optional[t.IO]:
-    """Returns a file descriptor for the filename if that file exists,
-    otherwise ``None``.
-    """
-    if not os.path.isfile(filename):
-        return None
-
-    return open(filename, mode)
-
-
-def object_type_repr(obj: t.Any) -> str:
-    """Returns the name of the object's type.  For some recognized
-    singletons the name of the object is returned instead. (For
-    example for `None` and `Ellipsis`).
-    """
-    if obj is None:
-        return "None"
-    elif obj is Ellipsis:
-        return "Ellipsis"
-
-    cls = type(obj)
-
-    if cls.__module__ == "builtins":
-        return f"{cls.__name__} object"
-
-    return f"{cls.__module__}.{cls.__name__} object"
-
-
-def pformat(obj: t.Any) -> str:
-    """Format an object using :func:`pprint.pformat`."""
-    from pprint import pformat  # type: ignore
-
-    return pformat(obj)
-
-
-_http_re = re.compile(
-    r"""
-    ^
-    (
-        (https?://|www\.)  # scheme or www
-        (([\w%-]+\.)+)?  # subdomain
-        (
-            [a-z]{2,63}  # basic tld
-        |
-            xn--[\w%]{2,59}  # idna tld
-        )
-    |
-        ([\w%-]{2,63}\.)+  # basic domain
-        (com|net|int|edu|gov|org|info|mil)  # basic tld
-    |
-        (https?://)  # scheme
-        (
-            (([\d]{1,3})(\.[\d]{1,3}){3})  # IPv4
-        |
-            (\[([\da-f]{0,4}:){2}([\da-f]{0,4}:?){1,6}])  # IPv6
-        )
-    )
-    (?::[\d]{1,5})?  # port
-    (?:[/?#]\S*)?  # path, query, and fragment
-    $
-    """,
-    re.IGNORECASE | re.VERBOSE,
-)
-_email_re = re.compile(r"^\S+@\w[\w.-]*\.\w+$")
-
-
-def urlize(
-    text: str,
-    trim_url_limit: t.Optional[int] = None,
-    rel: t.Optional[str] = None,
-    target: t.Optional[str] = None,
-    extra_schemes: t.Optional[t.Iterable[str]] = None,
-) -> str:
-    """Convert URLs in text into clickable links.
-
-    This may not recognize links in some situations. Usually, a more
-    comprehensive formatter, such as a Markdown library, is a better
-    choice.
-
-    Works on ``http://``, ``https://``, ``www.``, ``mailto:``, and email
-    addresses. Links with trailing punctuation (periods, commas, closing
-    parentheses) and leading punctuation (opening parentheses) are
-    recognized excluding the punctuation. Email addresses that include
-    header fields are not recognized (for example,
-    ``mailto:address@example.com?cc=copy@example.com``).
-
-    :param text: Original text containing URLs to link.
-    :param trim_url_limit: Shorten displayed URL values to this length.
-    :param target: Add the ``target`` attribute to links.
-    :param rel: Add the ``rel`` attribute to links.
-    :param extra_schemes: Recognize URLs that start with these schemes
-        in addition to the default behavior.
-
-    .. versionchanged:: 3.0
-        The ``extra_schemes`` parameter was added.
-
-    .. versionchanged:: 3.0
-        Generate ``https://`` links for URLs without a scheme.
-
-    .. versionchanged:: 3.0
-        The parsing rules were updated. Recognize email addresses with
-        or without the ``mailto:`` scheme. Validate IP addresses. Ignore
-        parentheses and brackets in more cases.
-    """
-    if trim_url_limit is not None:
-
-        def trim_url(x: str) -> str:
-            if len(x) > trim_url_limit:  # type: ignore
-                return f"{x[:trim_url_limit]}..."
-
-            return x
-
-    else:
-
-        def trim_url(x: str) -> str:
-            return x
-
-    words = re.split(r"(\s+)", str(markupsafe.escape(text)))
-    rel_attr = f' rel="{markupsafe.escape(rel)}"' if rel else ""
-    target_attr = f' target="{markupsafe.escape(target)}"' if target else ""
-
-    for i, word in enumerate(words):
-        head, middle, tail = "", word, ""
-        match = re.match(r"^([(<]|&lt;)+", middle)
-
-        if match:
-            head = match.group()
-            middle = middle[match.end() :]
-
-        # Unlike lead, which is anchored to the start of the string,
-        # need to check that the string ends with any of the characters
-        # before trying to match all of them, to avoid backtracking.
-        if middle.endswith((")", ">", ".", ",", "\n", "&gt;")):
-            match = re.search(r"([)>.,\n]|&gt;)+$", middle)
-
-            if match:
-                tail = match.group()
-                middle = middle[: match.start()]
-
-        # Prefer balancing parentheses in URLs instead of ignoring a
-        # trailing character.
-        for start_char, end_char in ("(", ")"), ("<", ">"), ("&lt;", "&gt;"):
-            start_count = middle.count(start_char)
-
-            if start_count <= middle.count(end_char):
-                # Balanced, or lighter on the left
-                continue
-
-            # Move as many as possible from the tail to balance
-            for _ in range(min(start_count, tail.count(end_char))):
-                end_index = tail.index(end_char) + len(end_char)
-                # Move anything in the tail before the end char too
-                middle += tail[:end_index]
-                tail = tail[end_index:]
-
-        if _http_re.match(middle):
-            if middle.startswith("https://") or middle.startswith("http://"):
-                middle = (
-                    f'<a href="{middle}"{rel_attr}{target_attr}>{trim_url(middle)}</a>'
-                )
-            else:
-                middle = (
-                    f'<a href="https://{middle}"{rel_attr}{target_attr}>'
-                    f"{trim_url(middle)}</a>"
-                )
-
-        elif middle.startswith("mailto:") and _email_re.match(middle[7:]):
-            middle = f'<a href="{middle}">{middle[7:]}</a>'
-
-        elif (
-            "@" in middle
-            and not middle.startswith("www.")
-            and ":" not in middle
-            and _email_re.match(middle)
-        ):
-            middle = f'<a href="mailto:{middle}">{middle}</a>'
-
-        elif extra_schemes is not None:
-            for scheme in extra_schemes:
-                if middle != scheme and middle.startswith(scheme):
-                    middle = f'<a href="{middle}"{rel_attr}{target_attr}>{middle}</a>'
-
-        words[i] = f"{head}{middle}{tail}"
-
-    return "".join(words)
-
-
-def generate_lorem_ipsum(
-    n: int = 5, html: bool = True, min: int = 20, max: int = 100
-) -> str:
-    """Generate some lorem ipsum for the template."""
-    from .constants import LOREM_IPSUM_WORDS
-
-    words = LOREM_IPSUM_WORDS.split()
-    result = []
-
-    for _ in range(n):
-        next_capitalized = True
-        last_comma = last_fullstop = 0
-        word = None
-        last = None
-        p = []
-
-        # each paragraph contains out of 20 to 100 words.
-        for idx, _ in enumerate(range(randrange(min, max))):
-            while True:
-                word = choice(words)
-                if word != last:
-                    last = word
-                    break
-            if next_capitalized:
-                word = word.capitalize()
-                next_capitalized = False
-            # add commas
-            if idx - randrange(3, 8) > last_comma:
-                last_comma = idx
-                last_fullstop += 2
-                word += ","
-            # add end of sentences
-            if idx - randrange(10, 20) > last_fullstop:
-                last_comma = last_fullstop = idx
-                word += "."
-                next_capitalized = True
-            p.append(word)
-
-        # ensure that the paragraph ends with a dot.
-        p_str = " ".join(p)
-
-        if p_str.endswith(","):
-            p_str = p_str[:-1] + "."
-        elif not p_str.endswith("."):
-            p_str += "."
-
-        result.append(p_str)
-
-    if not html:
-        return "\n\n".join(result)
-    return markupsafe.Markup(
-        "\n".join(f"<p>{markupsafe.escape(x)}</p>" for x in result)
-    )
-
-
-def url_quote(obj: t.Any, charset: str = "utf-8", for_qs: bool = False) -> str:
-    """Quote a string for use in a URL using the given charset.
-
-    :param obj: String or bytes to quote. Other types are converted to
-        string then encoded to bytes using the given charset.
-    :param charset: Encode text to bytes using this charset.
-    :param for_qs: Quote "/" and use "+" for spaces.
-    """
-    if not isinstance(obj, bytes):
-        if not isinstance(obj, str):
-            obj = str(obj)
-
-        obj = obj.encode(charset)
-
-    safe = b"" if for_qs else b"/"
-    rv = quote_from_bytes(obj, safe)
-
-    if for_qs:
-        rv = rv.replace("%20", "+")
-
-    return rv
-
-
-@abc.MutableMapping.register
-class LRUCache:
-    """A simple LRU Cache implementation."""
-
-    # this is fast for small capacities (something below 1000) but doesn't
-    # scale.  But as long as it's only used as storage for templates this
-    # won't do any harm.
-
-    def __init__(self, capacity: int) -> None:
-        self.capacity = capacity
-        self._mapping: t.Dict[t.Any, t.Any] = {}
-        self._queue: "te.Deque[t.Any]" = deque()
-        self._postinit()
-
-    def _postinit(self) -> None:
-        # alias all queue methods for faster lookup
-        self._popleft = self._queue.popleft
-        self._pop = self._queue.pop
-        self._remove = self._queue.remove
-        self._wlock = Lock()
-        self._append = self._queue.append
-
-    def __getstate__(self) -> t.Mapping[str, t.Any]:
-        return {
-            "capacity": self.capacity,
-            "_mapping": self._mapping,
-            "_queue": self._queue,
-        }
-
-    def __setstate__(self, d: t.Mapping[str, t.Any]) -> None:
-        self.__dict__.update(d)
-        self._postinit()
-
-    def __getnewargs__(self) -> t.Tuple:
-        return (self.capacity,)
-
-    def copy(self) -> "LRUCache":
-        """Return a shallow copy of the instance."""
-        rv = self.__class__(self.capacity)
-        rv._mapping.update(self._mapping)
-        rv._queue.extend(self._queue)
-        return rv
-
-    def get(self, key: t.Any, default: t.Any = None) -> t.Any:
-        """Return an item from the cache dict or `default`"""
-        try:
-            return self[key]
-        except KeyError:
-            return default
-
-    def setdefault(self, key: t.Any, default: t.Any = None) -> t.Any:
-        """Set `default` if the key is not in the cache otherwise
-        leave unchanged. Return the value of this key.
-        """
-        try:
-            return self[key]
-        except KeyError:
-            self[key] = default
-            return default
-
-    def clear(self) -> None:
-        """Clear the cache."""
-        with self._wlock:
-            self._mapping.clear()
-            self._queue.clear()
-
-    def __contains__(self, key: t.Any) -> bool:
-        """Check if a key exists in this cache."""
-        return key in self._mapping
-
-    def __len__(self) -> int:
-        """Return the current size of the cache."""
-        return len(self._mapping)
-
-    def __repr__(self) -> str:
-        return f"<{type(self).__name__} {self._mapping!r}>"
-
-    def __getitem__(self, key: t.Any) -> t.Any:
-        """Get an item from the cache. Moves the item up so that it has the
-        highest priority then.
-
-        Raise a `KeyError` if it does not exist.
-        """
-        with self._wlock:
-            rv = self._mapping[key]
-
-            if self._queue[-1] != key:
-                try:
-                    self._remove(key)
-                except ValueError:
-                    # if something removed the key from the container
-                    # when we read, ignore the ValueError that we would
-                    # get otherwise.
-                    pass
-
-                self._append(key)
-
-            return rv
-
-    def __setitem__(self, key: t.Any, value: t.Any) -> None:
-        """Sets the value for an item. Moves the item up so that it
-        has the highest priority then.
-        """
-        with self._wlock:
-            if key in self._mapping:
-                self._remove(key)
-            elif len(self._mapping) == self.capacity:
-                del self._mapping[self._popleft()]
-
-            self._append(key)
-            self._mapping[key] = value
-
-    def __delitem__(self, key: t.Any) -> None:
-        """Remove an item from the cache dict.
-        Raise a `KeyError` if it does not exist.
-        """
-        with self._wlock:
-            del self._mapping[key]
-
-            try:
-                self._remove(key)
-            except ValueError:
-                pass
-
-    def items(self) -> t.Iterable[t.Tuple[t.Any, t.Any]]:
-        """Return a list of items."""
-        result = [(key, self._mapping[key]) for key in list(self._queue)]
-        result.reverse()
-        return result
-
-    def values(self) -> t.Iterable[t.Any]:
-        """Return a list of all values."""
-        return [x[1] for x in self.items()]
-
-    def keys(self) -> t.Iterable[t.Any]:
-        """Return a list of all keys ordered by most recent usage."""
-        return list(self)
-
-    def __iter__(self) -> t.Iterator[t.Any]:
-        return reversed(tuple(self._queue))
-
-    def __reversed__(self) -> t.Iterator[t.Any]:
-        """Iterate over the keys in the cache dict, oldest items
-        coming first.
-        """
-        return iter(tuple(self._queue))
-
-    __copy__ = copy
-
-
-def select_autoescape(
-    enabled_extensions: t.Collection[str] = ("html", "htm", "xml"),
-    disabled_extensions: t.Collection[str] = (),
-    default_for_string: bool = True,
-    default: bool = False,
-) -> t.Callable[[t.Optional[str]], bool]:
-    """Intelligently sets the initial value of autoescaping based on the
-    filename of the template.  This is the recommended way to configure
-    autoescaping if you do not want to write a custom function yourself.
-
-    If you want to enable it for all templates created from strings or
-    for all templates with `.html` and `.xml` extensions::
-
-        from jinja2 import Environment, select_autoescape
-        env = Environment(autoescape=select_autoescape(
-            enabled_extensions=('html', 'xml'),
-            default_for_string=True,
-        ))
-
-    Example configuration to turn it on at all times except if the template
-    ends with `.txt`::
-
-        from jinja2 import Environment, select_autoescape
-        env = Environment(autoescape=select_autoescape(
-            disabled_extensions=('txt',),
-            default_for_string=True,
-            default=True,
-        ))
-
-    The `enabled_extensions` is an iterable of all the extensions that
-    autoescaping should be enabled for.  Likewise `disabled_extensions` is
-    a list of all templates it should be disabled for.  If a template is
-    loaded from a string then the default from `default_for_string` is used.
-    If nothing matches then the initial value of autoescaping is set to the
-    value of `default`.
-
-    For security reasons this function operates case insensitive.
-
-    .. versionadded:: 2.9
-    """
-    enabled_patterns = tuple(f".{x.lstrip('.').lower()}" for x in enabled_extensions)
-    disabled_patterns = tuple(f".{x.lstrip('.').lower()}" for x in disabled_extensions)
-
-    def autoescape(template_name: t.Optional[str]) -> bool:
-        if template_name is None:
-            return default_for_string
-        template_name = template_name.lower()
-        if template_name.endswith(enabled_patterns):
-            return True
-        if template_name.endswith(disabled_patterns):
-            return False
-        return default
-
-    return autoescape
-
-
-def htmlsafe_json_dumps(
-    obj: t.Any, dumps: t.Optional[t.Callable[..., str]] = None, **kwargs: t.Any
-) -> markupsafe.Markup:
-    """Serialize an object to a string of JSON with :func:`json.dumps`,
-    then replace HTML-unsafe characters with Unicode escapes and mark
-    the result safe with :class:`~markupsafe.Markup`.
-
-    This is available in templates as the ``|tojson`` filter.
-
-    The following characters are escaped: ``<``, ``>``, ``&``, ``'``.
-
-    The returned string is safe to render in HTML documents and
-    ``<script>`` tags. The exception is in HTML attributes that are
-    double quoted; either use single quotes or the ``|forceescape``
-    filter.
-
-    :param obj: The object to serialize to JSON.
-    :param dumps: The ``dumps`` function to use. Defaults to
-        ``env.policies["json.dumps_function"]``, which defaults to
-        :func:`json.dumps`.
-    :param kwargs: Extra arguments to pass to ``dumps``. Merged onto
-        ``env.policies["json.dumps_kwargs"]``.
-
-    .. versionchanged:: 3.0
-        The ``dumper`` parameter is renamed to ``dumps``.
-
-    .. versionadded:: 2.9
-    """
-    if dumps is None:
-        dumps = json.dumps
-
-    return markupsafe.Markup(
-        dumps(obj, **kwargs)
-        .replace("<", "\\u003c")
-        .replace(">", "\\u003e")
-        .replace("&", "\\u0026")
-        .replace("'", "\\u0027")
-    )
-
-
-class Cycler:
-    """Cycle through values by yield them one at a time, then restarting
-    once the end is reached. Available as ``cycler`` in templates.
-
-    Similar to ``loop.cycle``, but can be used outside loops or across
-    multiple loops. For example, render a list of folders and files in a
-    list, alternating giving them "odd" and "even" classes.
-
-    .. code-block:: html+jinja
-
-        {% set row_class = cycler("odd", "even") %}
-        <ul class="browser">
-        {% for folder in folders %}
-          <li class="folder {{ row_class.next() }}">{{ folder }}
-        {% endfor %}
-        {% for file in files %}
-          <li class="file {{ row_class.next() }}">{{ file }}
-        {% endfor %}
-        </ul>
-
-    :param items: Each positional argument will be yielded in the order
-        given for each cycle.
-
-    .. versionadded:: 2.1
-    """
-
-    def __init__(self, *items: t.Any) -> None:
-        if not items:
-            raise RuntimeError("at least one item has to be provided")
-        self.items = items
-        self.pos = 0
-
-    def reset(self) -> None:
-        """Resets the current item to the first item."""
-        self.pos = 0
-
-    @property
-    def current(self) -> t.Any:
-        """Return the current item. Equivalent to the item that will be
-        returned next time :meth:`next` is called.
-        """
-        return self.items[self.pos]
-
-    def next(self) -> t.Any:
-        """Return the current item, then advance :attr:`current` to the
-        next item.
-        """
-        rv = self.current
-        self.pos = (self.pos + 1) % len(self.items)
-        return rv
-
-    __next__ = next
-
-
-class Joiner:
-    """A joining helper for templates."""
-
-    def __init__(self, sep: str = ", ") -> None:
-        self.sep = sep
-        self.used = False
-
-    def __call__(self) -> str:
-        if not self.used:
-            self.used = True
-            return ""
-        return self.sep
-
-
-class Namespace:
-    """A namespace object that can hold arbitrary attributes.  It may be
-    initialized from a dictionary or with keyword arguments."""
-
-    def __init__(*args: t.Any, **kwargs: t.Any) -> None:  # noqa: B902
-        self, args = args[0], args[1:]
-        self.__attrs = dict(*args, **kwargs)
-
-    def __getattribute__(self, name: str) -> t.Any:
-        # __class__ is needed for the awaitable check in async mode
-        if name in {"_Namespace__attrs", "__class__"}:
-            return object.__getattribute__(self, name)
-        try:
-            return self.__attrs[name]
-        except KeyError:
-            raise AttributeError(name) from None
-
-    def __setitem__(self, name: str, value: t.Any) -> None:
-        self.__attrs[name] = value
-
-    def __repr__(self) -> str:
-        return f"<Namespace {self.__attrs!r}>"
diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/retinaface/layers/functions/prior_box.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/retinaface/layers/functions/prior_box.py
deleted file mode 100644
index 80c7f858371ed71f39ed609eb44b423d8693bf61..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/LambdaSuperRes/KAIR/retinaface/layers/functions/prior_box.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import torch
-from itertools import product as product
-import numpy as np
-from math import ceil
-
-
-class PriorBox(object):
-    def __init__(self, cfg, image_size=None, phase='train'):
-        super(PriorBox, self).__init__()
-        self.min_sizes = cfg['min_sizes']
-        self.steps = cfg['steps']
-        self.clip = cfg['clip']
-        self.image_size = image_size
-        self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
-        self.name = "s"
-
-    def forward(self):
-        anchors = []
-        for k, f in enumerate(self.feature_maps):
-            min_sizes = self.min_sizes[k]
-            for i, j in product(range(f[0]), range(f[1])):
-                for min_size in min_sizes:
-                    s_kx = min_size / self.image_size[1]
-                    s_ky = min_size / self.image_size[0]
-                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
-                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
-                    for cy, cx in product(dense_cy, dense_cx):
-                        anchors += [cx, cy, s_kx, s_ky]
-
-        # back to torch land
-        output = torch.Tensor(anchors).view(-1, 4)
-        if self.clip:
-            output.clamp_(max=1, min=0)
-        return output
diff --git a/spaces/leogabraneth/text-generation-webui-main/css/main.css b/spaces/leogabraneth/text-generation-webui-main/css/main.css
deleted file mode 100644
index 47506c5ee99671977af9132fe34f4d3e57b633b1..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/css/main.css
+++ /dev/null
@@ -1,650 +0,0 @@
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-        top: calc( 0.5 * (100dvh - 236px) ) !important;
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-.file-saver > :first-child {
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-.pretty_scrollbar::-webkit-scrollbar-thumb,
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-.dark .pretty_scrollbar::-webkit-scrollbar-thumb,
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-.no-background {
-    background: var(--background-fill-primary) !important;
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-/* ----------------------------------------------
-  Chat tab
----------------------------------------------- */
-.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx {
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-    .message {
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-    .gradio-container {
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-    .tab-nav {
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-    #chat-tab > :first-child {
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-    }
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-    position: absolute;
-    height: 100%;
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-    position: absolute;
-    left: calc(100% - 168px);
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-    position: absolute;
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-    padding: var(--block-padding);
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-.chat ol, .chat ul {
-    margin-top: 6px !important;
-}
-
-/* ----------------------------------------------
-  Past chats menus
----------------------------------------------- */
-#past-chats-row {
-    margin-bottom: calc( -1 * var(--layout-gap) );
-}
-
-#rename-row label {
-    margin-top: var(--layout-gap);
-}
-
-/* ----------------------------------------------
-  Keep dropdown menus above errored components
----------------------------------------------- */
-.options {
-    z-index: 100 !important;
-}
diff --git a/spaces/leurez/moss/src/store/modules/auth/index.ts b/spaces/leurez/moss/src/store/modules/auth/index.ts
deleted file mode 100644
index 8edde9c6d27178d92fda7f2abf660560f94bada0..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/src/store/modules/auth/index.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-import { defineStore } from 'pinia'
-import { getToken, removeToken, setToken } from './helper'
-import { store } from '@/store'
-import { fetchSession } from '@/api'
-
-interface SessionResponse {
-  auth: boolean
-  model: 'ChatGPTAPI' | 'ChatGPTUnofficialProxyAPI'
-}
-
-export interface AuthState {
-  token: string | undefined
-  session: SessionResponse | null
-}
-
-export const useAuthStore = defineStore('auth-store', {
-  state: (): AuthState => ({
-    token: getToken(),
-    session: null,
-  }),
-
-  getters: {
-    isChatGPTAPI(state): boolean {
-      return state.session?.model === 'ChatGPTAPI'
-    },
-  },
-
-  actions: {
-    async getSession() {
-      try {
-        const { data } = await fetchSession<SessionResponse>()
-        this.session = { ...data }
-        return Promise.resolve(data)
-      }
-      catch (error) {
-        return Promise.reject(error)
-      }
-    },
-
-    setToken(token: string) {
-      this.token = token
-      setToken(token)
-    },
-
-    removeToken() {
-      this.token = undefined
-      removeToken()
-    },
-  },
-})
-
-export function useAuthStoreWithout() {
-  return useAuthStore(store)
-}
diff --git a/spaces/lev/nlp/app.py b/spaces/lev/nlp/app.py
deleted file mode 100644
index b74381d8c2b692a5d0f20825640cf85d22c8a9f7..0000000000000000000000000000000000000000
--- a/spaces/lev/nlp/app.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import streamlit as st
-import torch 
-from transformers import BertTokenizer, BertForMaskedLM
-
-device = torch.device('cpu')
-
-bert_mlm_positive  = torch.load("positive.pth", map_location=device)
-bert_mlm_negative  = torch.load("negative.pth", map_location=device)
-bert_classifier  = torch.load("classifier.pth", map_location=device)
-tokenizer  = torch.load("tokenizer.pth", map_location=device)
-
-bert_mlm_positive.eval();
-bert_mlm_negative.eval();
-bert_classifier.eval();
-
-
-# functions part
-#___________________________________________________________________________________
-
-def get_replacements(sentence: str, num_tokens=1, k_best=2, epsilon=1e-3):
-    """
-    - split the sentence into tokens using the INGSOC-approved BERT tokenizer
-    - find :num_tokens: tokens with the highest ratio (see above)
-    - replace them with :k_best: words according to bert_mlm_positive
-    :return: a list of all possible strings (up to k_best * num_tokens)
-    """
-    sentence_ix = tokenizer(sentence, return_tensors='pt')
-    sentence_ix = {key: value.to(device) for key, value in sentence_ix.items()}
-
-    probs_positive = bert_mlm_positive(**sentence_ix).logits.softmax(dim=-1)[0]
-    probs_negative = bert_mlm_negative(**sentence_ix).logits.softmax(dim=-1)[0]
-
-    p_tokens_positive = probs_positive[torch.arange(len(probs_positive)), sentence_ix['input_ids'][0]]
-    p_tokens_negative = probs_negative[torch.arange(len(probs_negative)), sentence_ix['input_ids'][0]]
-
-    ids_to_replace = ((p_tokens_positive + epsilon) / (p_tokens_negative + epsilon)).argsort()[:num_tokens] 
-    ids_to_decode = sentence_ix['input_ids'][0][ids_to_replace]
-
-    tokenizer.decode(ids_to_decode)    
-
-    k_best_words_to_replace = probs_positive[ids_to_replace].argsort(dim=1)[:, -k_best:]    
-
-    cur_sentence = torch.clone(sentence_ix['input_ids'][0])
-
-    ans = []
-    for to_change in torch.cartesian_prod(*k_best_words_to_replace):
-        cur_sentence[ids_to_replace] = to_change
-        ans.append(tokenizer.decode(cur_sentence[1:-1]))
-
-    return ans
-    
-    
-def get_logits_of_variants(clf, tokenizer, variants):
-    tokens_of_variants = tokenizer(variants, return_tensors='pt').to(device)
-    logits = clf(**tokens_of_variants).logits
-    return logits
-    
-def get_M_most_positive_variants_ids(logits, M):
-    probs = (torch.exp(logits) / (1 + torch.exp(logits)))
-    ids_of_most_positive = torch.argsort(probs, dim=0)[-M:]
-    return ids_of_most_positive, probs
-
-def left_M_most_positive_variants(all_variants, ids_of_most_positive, probs):
-    probs_of_variants = probs[ids_of_most_positive]
-    cur_variants = [all_variants[ids.item()] for ids in ids_of_most_positive]
-    
-    return cur_variants, probs_of_variants
-
-
-def iterative_beam_search(sentence, clf, tokenizer, M_best=3):
-    num_tokens = 3
-    variants = get_replacements(sentence, num_tokens=num_tokens, k_best=M_best)    
-
-    logits = get_logits_of_variants(clf, tokenizer, variants)
-    
-    ids_of_most_positive, probs = get_M_most_positive_variants_ids(logits, M_best)
-
-    probs_of_variants = probs[ids_of_most_positive]
-    variants = [variants[ids.item()] for ids in ids_of_most_positive]
-
-    prev_best_prob = probs_of_variants.max()
-    prev_best_sentence = sentence
-
-#     print(f'start variants {variants}')
-    for i in range(100):
-#         print(f'begin with {len(variants)} variants')
-#         print(variants)
-        all_variants = []
-        for variant in variants:
-            new_variants = get_replacements(variant, num_tokens=num_tokens, k_best=M_best)    
-            all_variants += new_variants
-        
-        
-        tokens_of_variants = tokenizer(all_variants, return_tensors='pt').to(device)
-        logits = clf(**tokens_of_variants).logits
-
-
-        ids_of_most_positive, probs = get_M_most_positive_variants_ids(logits, M_best)
-
-        cur_variants, probs_of_variants = left_M_most_positive_variants(all_variants, ids_of_most_positive, probs)
-            
-        ids_best = torch.argmax(probs_of_variants)
-#         print(probs[ids_best], cur_variants[ids_best.item()])
-        
-        best_prob = probs_of_variants[ids_best]
-        best_sentence = cur_variants[ids_best.item()]
-#         print(cur_variants)
-        variants = cur_variants
-    
-        if best_sentence == prev_best_sentence:
-            break
-        
-        prev_best_prob = best_prob
-        prev_best_sentence = best_sentence
-        
-    
-    return best_sentence
-
-#############################################################################
-# end of functions part
-
-
-st.set_page_config(page_title="Make positive your sentence", layout="centered")
-
-sentence = st.text_input('Please enter your sentence and I ll make it positive')
-
-st.markdown(iterative_beam_search(sentence, bert_classifier, tokenizer))
diff --git a/spaces/libhost/img.lite/imglib.py b/spaces/libhost/img.lite/imglib.py
deleted file mode 100644
index c6b0a537a05aa86fdf4d1025d56df197f08ae292..0000000000000000000000000000000000000000
--- a/spaces/libhost/img.lite/imglib.py
+++ /dev/null
@@ -1,191 +0,0 @@
-from html import escape
-import re
-import streamlit as st
-import pandas as pd, numpy as np
-from transformers import CLIPProcessor, CLIPModel
-from st_clickable_images import clickable_images
-
-
-num_results=2
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={
-        CLIPModel: lambda _: None,
-        CLIPProcessor: lambda _: None,
-        dict: lambda _: None,
-    },
-)
-def load():
-    model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
-    df = {0: pd.read_csv("data.csv"), 1: pd.read_csv("data2.csv")}
-    embeddings = {0: np.load("embeddings.npy"), 1: np.load("embeddings2.npy")}
-    for k in [0, 1]:
-        embeddings[k] = embeddings[k] / np.linalg.norm(
-            embeddings[k], axis=1, keepdims=True
-        )
-    return model, processor, df, embeddings
-
-
-model, processor, df, embeddings = load()
-source = {0: "\nSource: Unsplash", 1: "\nSource: The Movie Database (TMDB)"}
-
-
-def compute_text_embeddings(list_of_strings):
-    inputs = processor(text=list_of_strings, return_tensors="pt", padding=True)
-    result = model.get_text_features(**inputs).detach().numpy()
-    return result / np.linalg.norm(result, axis=1, keepdims=True)
-
-
-def image_search(query, corpus, n_results=num_results):
-    positive_embeddings = None
-    def concatenate_embeddings(e1, e2):
-        if e1 is None:
-            return e2
-        else:
-            return np.concatenate((e1, e2), axis=0)
-
-    splitted_query = query.split("EXCLUDING ")
-    dot_product = 0
-    k = 0 if corpus == "Unsplash" else 1
-    if len(splitted_query[0]) > 0:
-        positive_queries = splitted_query[0].split(";")
-        for positive_query in positive_queries:
-            match = re.match(r"\[(Movies|Unsplash):(\d{1,5})\](.*)", positive_query)
-            if match:
-                corpus2, idx, remainder = match.groups()
-                idx, remainder = int(idx), remainder.strip()
-                k2 = 0 if corpus2 == "Unsplash" else 1
-                positive_embeddings = concatenate_embeddings(
-                    positive_embeddings, embeddings[k2][idx : idx + 1, :]
-                )
-                if len(remainder) > 0:
-                    positive_embeddings = concatenate_embeddings(
-                        positive_embeddings, compute_text_embeddings([remainder])
-                    )
-            else:
-                positive_embeddings = concatenate_embeddings(
-                    positive_embeddings, compute_text_embeddings([positive_query])
-                )
-        dot_product = embeddings[k] @ positive_embeddings.T
-        dot_product = dot_product - np.median(dot_product, axis=0)
-        dot_product = dot_product / np.max(dot_product, axis=0, keepdims=True)
-        dot_product = np.min(dot_product, axis=1)
-
-    if len(splitted_query) > 1:
-        negative_queries = (" ".join(splitted_query[1:])).split(";")
-        negative_embeddings = compute_text_embeddings(negative_queries)
-        dot_product2 = embeddings[k] @ negative_embeddings.T
-        dot_product2 = dot_product2 - np.median(dot_product2, axis=0)
-        dot_product2 = dot_product2 / np.max(dot_product2, axis=0, keepdims=True)
-        dot_product -= np.max(np.maximum(dot_product2, 0), axis=1)
-
-    results = np.argsort(dot_product)[-1 : -n_results - 1 : -1]
-    return [
-        (
-            df[k].iloc[i]["path"],
-            df[k].iloc[i]["tooltip"] + source[k],
-            i,
-        )
-        for i in results
-    ]
-
-
-description = """
-
-# ImgLib.LITE
-**Enter your query and hit enter**
-"""
-
-howto = """
-- Click image to find similar images
-- Use "**;**" to combine multiple queries)
-- Use "**EXCLUDING**", to exclude a query
-"""
-
-
-def main():
-    st.markdown(
-        """
-              <h1 align="center">🖼 ImgLib.LITE</h1>
-              <style>
-              .block-container{
-                max-width: 1200px;
-              }
-              div.row-widget.stRadio > div{
-                flex-direction:row;
-                display: flex;
-                justify-content: center;
-              }
-              div.row-widget.stRadio > div > label{
-                margin-left: 5px;
-                margin-right: 5px;
-              }
-              section.main>div:first-child {
-                padding-top: 0px;
-              }
-              section:not(.main)>div:first-child {
-                padding-top: 30px;
-              }
-              div.reportview-container > section:first-child{
-                max-width: 320px;
-              }
-              #MainMenu {
-                visibility: hidden;
-              }
-              footer {
-                visibility: hidden;
-              }
-              </style>""",
-        unsafe_allow_html=True,
-    )
-    st.sidebar.markdown(description)
-    with st.sidebar.expander("Advanced use"):
-        st.markdown(howto)
-
-
-    st.sidebar.markdown(f"Try these test prompts: Lord of the Rings, Interstellar, Back to the Future, Avengers, The Matrix, WALL·E, Castle , Dune,  Blade Runner, Guardians of the Galaxy, Aliens, Her, Legend of the Ten Rings, Harry Potter, Logan, Dragon, Scissorhands, Captain, Deadpool, ThorArrivval, Wick, Peaks, Labyrinth, Terabithia, RoboCop, Wonder Woman, Meteor, NYC, Stork, Pink, Yellow, Orange, Blue, tulip, dog, Dragon, sunrise, kitten, Swimming, jellyfish, Beach, puppy, Coral")
-    st.sidebar.markdown(f"Unsplash has categories that match: backgrounds, photos, nature, iphone, etc")
-    st.sidebar.markdown(f"Unsplash images contain animals, apps, events, feelings, food, travel, nature, people, religion, sports, things, stock")
-    st.sidebar.markdown(f"Unsplash things include flag, tree, clock, money, tattoo, arrow, book, car, fireworks, ghost, health, kiss, dance, balloon, crown, eye, house, music, airplane, lighthouse, typewriter, toys")
-    st.sidebar.markdown(f"unsplash feelings include funny, heart, love, cool, congratulations, love, scary, cute, friendship, inspirational, hug, sad, cursed, beautiful, crazy, respect, transformation, peaceful, happy")
-    st.sidebar.markdown(f"unsplash people contain baby, life, women, family, girls, pregnancy, society, old people, musician, attractive, bohemian")
-    st.sidebar.markdown(f"imagenet queries include: photo of, photo of many, sculpture of, rendering of, graffiti of, tattoo of, embroidered, drawing of, plastic, black and white, painting, video game, doodle, origami, sketch, etc")
-    st.sidebar.markdown(f"by Evgeniy Hristoforu")
-    
-    
-    _, c, _ = st.columns((1, 3, 1))
-    if "query" in st.session_state:
-        query = c.text_input("", value=st.session_state["query"])
-    else:
-
-        query = c.text_input("", value="lighthouse")
-    corpus = st.radio("", ["Unsplash"])
-    #corpus = st.radio("", ["Unsplash", "Movies"])
-    if len(query) > 0:
-        results = image_search(query, corpus)
-        clicked = clickable_images(
-            [result[0] for result in results],
-            titles=[result[1] for result in results],
-            div_style={
-                "display": "flex",
-                "justify-content": "center",
-                "flex-wrap": "wrap",
-            },
-            img_style={"margin": "2px", "height": "200px"},
-        )
-        if clicked >= 0:
-            change_query = False
-            if "last_clicked" not in st.session_state:
-                change_query = True
-            else:
-                if clicked != st.session_state["last_clicked"]:
-                    change_query = True
-            if change_query:
-                st.session_state["query"] = f"[{corpus}:{results[clicked][2]}]"
-                st.experimental_rerun()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Code Activation Pmdg 737 Ngx.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Code Activation Pmdg 737 Ngx.md
deleted file mode 100644
index 0b4557b6dd14a9a346b1bd172180290e6967b8b4..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Code Activation Pmdg 737 Ngx.md	
+++ /dev/null
@@ -1,90 +0,0 @@
-## Code Activation Pmdg 737 Ngx
-
-
-
- 
-
- ![Code Activation Pmdg 737 Ngx](https://mutleyshangar.com/reviews/bc/t7/img/05-Aircraft-specific-settings.jpg)
-
- 
-
- 
-
-**Code Activation Pmdg 737 Ngx ⇒⇒⇒ [https://fienislile.blogspot.com/?download=2tyEd1](https://fienislile.blogspot.com/?download=2tyEd1)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Activate PMDG 737 NGX for Prepar3D v4 & v5
-
- 
-
-PMDG 737 NGX is a popular simulation of Boeing's most popular narrowbody airliner for Prepar3D v4 and v5. In order to enjoy the full features and realism of this add-on, you need to activate it with a code that you receive after purchasing it from PMDG's website[^2^]. Here are the steps to activate PMDG 737 NGX:
-
- 
-
-1. Make sure you have Prepar3D v4 or v5 installed on your computer.
-
-2. Download and install PMDG 737 NGX from PMDG's website[^2^]. Follow the instructions on the screen.
-
-3. Launch Prepar3D and select a scenario with PMDG 737 NGX as your aircraft. Do not load the NGX over a default plane. Always start Prepar3D with the Scenario Setup screen showing, and a default airplane rotating in the small window[^1^].
-
-4. When the scenario loads, you will see a pop-up window asking you to enter your activation code. Enter the code that you received from PMDG after purchasing the add-on. Make sure you enter it correctly and without spaces.
-
-5. Click on "Activate" and wait for the confirmation message. You may need to restart Prepar3D for the activation to take effect.
-
-6. Enjoy flying PMDG 737 NGX with realistic systems, sounds, visuals and more!
-
-
-
-If you have any issues with activation, please contact PMDG's support team or visit their forum for help[^2^].
-
-  
-
-Now that you have activated PMDG 737 NGX, you can explore its many features and options. Here are some of the things you can do with this add-on:
-
-
-
-- Customize your cockpit layout, displays, sounds, lighting and more with the PMDG Options menu.
-
-- Use the realistic flight management computer (FMC) to plan and execute your flight route, altitude, speed and more.
-
-- Follow the standard operating procedures (SOPs) and checklists for every phase of flight, from pre-flight to shutdown.
-
-- Experience the realistic performance, handling and aerodynamics of the 737 NGX, including engine failures, stalls, overspeeds and more.
-
-- Interact with the virtual cockpit and cabin crew using voice commands or mouse clicks.
-
-- Enjoy the high-quality textures, models and animations of the exterior and interior of the 737 NGX.
-
-
-
-PMDG 737 NGX is a highly detailed and immersive simulation of the Boeing 737 NGX that will challenge and reward you as a virtual pilot. Whether you are a beginner or an expert, you will find something to enjoy and learn from this add-on. For more information, tips and tutorials, please visit PMDG's website or watch their YouTube videos.
-
-  
-
-One of the most impressive features of PMDG 737 NGX is the realistic weather radar that can detect and display precipitation, turbulence, windshear and more. You can use the weather radar to avoid hazardous weather conditions and ensure a safe and smooth flight. The weather radar is compatible with various weather add-ons such as Active Sky and REX Sky Force.
-
- 
-
-Another feature that sets PMDG 737 NGX apart from other add-ons is the advanced failure simulation that can simulate various malfunctions and emergencies. You can choose to have random failures or select specific ones from a list. You can also set the probability and time of occurrence of each failure. The failures will affect the systems, performance and handling of the 737 NGX, and you will have to follow the appropriate procedures to resolve them.
-
- 
-
-PMDG 737 NGX also offers a variety of liveries and models to suit your preferences and needs. You can choose from different variants of the 737 NGX, such as the -600, -700, -800 and -900, with or without winglets. You can also select from hundreds of liveries from various airlines around the world. You can download additional liveries from PMDG's website or create your own using the paint kit.
-
- 145887f19f
-
- 
-
- 
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/E MU Emulator X3 [deepstatus].md b/spaces/lincquiQcaudo/Top-20-Diffusion/E MU Emulator X3 [deepstatus].md
deleted file mode 100644
index f33f9fec2adf80fb96a431d08bad325dc3926194..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/E MU Emulator X3 [deepstatus].md	
+++ /dev/null
@@ -1,78 +0,0 @@
-<br />
-<h1>E MU Emulator X3 [deepstatus] - A Review of the Ultimate Software Sampler</h1>
-
-<p>If you are looking for a software sampler that offers the sound quality, synthesis and filters of E-MU's hardware samplers, you might want to check out E MU Emulator X3 [deepstatus]. This program is the culmination of over 30 years of sampler development by E-MU Systems, and it introduces a host of new tools that make sampling and sound design easier and more creative than ever.</p>
-<h2>E MU Emulator X3 [deepstatus]</h2><br /><p><b><b>Download File</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://bytlly.com/2uGwpq">https://bytlly.com/2uGwpq</a></b></p><br /><br />
-
-<p>E MU Emulator X3 [deepstatus] is compatible with PCs running the Microsoft Windows operating system, and it can operate as a stand-alone program or as a VST instrument. It supports both 32-bit and 64-bit versions, and it does not require any hardware dongle or sound card to run. You can download it for free from various sources online, as it is no longer available to purchase from E-MU or Creative Technology.</p>
-
-<h2>What Can You Do with E MU Emulator X3 [deepstatus]?</h2>
-
-<p>E MU Emulator X3 [deepstatus] is a powerful software sampler that lets you create and manipulate sounds in various ways. Here are some of the features and functions that you can use:</p>
-
-<ul>
-<li><b>Automated sampling, pitch detection and preset creation</b>: You can use the integrated waveform editor to sample any sound source, such as hardware synthesizers, instruments, vocals, or even MP3 files. The program will automatically detect the pitch and create a mapped sample bank for you. You can also edit the samples, trim them, loop them, reverse them, and more.</li>
-<li><b>Powerful synthesis architecture</b>: You can use over 50 Z-Plane filters and 100 tempo-based parameters per preset to shape your sounds. The Z-Plane filters are based on E-MU's hardware samplers, and they offer a wide range of sonic possibilities. You can also use the Morph Filter Designer to create custom filters by morphing between different filter types.</li>
-<li><b>Advanced LFO/envelope/arpeggiator programming</b>: You can use the Multi-Function Generator to create complex modulation patterns for your sounds. You can assign up to 12 modulation sources and destinations per preset, and use various modes such as LFO, envelope, arpeggiator, step sequencer, randomizer, and more.</li>
-<li><b>Non-destructive audio manipulation</b>: You can use the TwistaLoop tool to manipulate your samples in real-time without affecting the original waveform. You can change the pitch, tempo, direction, loop points, and more with a simple mouse drag. You can also use the Transform Multiply tool to apply convolution effects to your sounds, such as reverb, distortion, filtering, and more.</li>
-<li><b>Comprehensive sound format support</b>: You can import and export sounds in various formats, such as EOS, EIII, GigaSampler, MP3, REX2,.AIFF, SoundFont 2.1,.WAV and more. You can also import and export bank files in EOS format for compatibility with E-MU's legacy hardware samplers.</li>
-<li><b>Integrated effects processors</b>: You can use up to four effects processors per preset to add depth and character to your sounds. You can choose from various effects such as chorus, delay, reverb, flanger, phaser, distortion, EQ, compressor, limiter, and more.</li>
-<li><b>Over 3GB of sounds included</b>: You can start making music right away with the sounds that come with E MU Emulator X3 [deepstatus]. The program includes over 3GB of sounds, such as the complete Proteus 2000 soundset, a 1.5GB Grand Piano, 24-bit drums and grooves, vintage synth sounds, orchestral sounds, ethnic sounds, and more. You can also expand your sound library with additional soundsets available online.</li>
-</ul>
-
-<h2>Why Choose E MU Emulator X3 [deepstatus]?</h2>
-
-<p>E MU Emulator X3 [deepstatus] is a software sampler that offers a lot of benefits for musicians and producers who want to create professional-quality sounds. Here are some of the reasons why you should choose this program:</p>
-
-<ul>
-<li><b>It has a great sound quality</b>: E MU Emulator X3 [deepstatus] uses E-MU's 24-bit/192kHz sound engine and patented pitch interpolation technology to deliver crisp and clear sounds. It also has a low CPU usage and supports RAM and streaming playback for optimal performance.</li>
-<li><b>It is easy to use</b>: E MU Emulator X3 [deepstatus] has a user-friendly interface that lets you access all the features and functions with ease. It also has a comprehensive manual and tutorial videos that help you get started quickly.</li>
-<li><b>It is versatile</b>: E MU Emulator X3 [deepstatus] can handle any kind of sound source and genre. Whether you want to sample acoustic instruments, electronic sounds, vocals, or anything else, you can do it with this program. You can also create any kind of sound you want with the powerful synthesis and modulation tools.</li>
-<li><b>It is creative</b>: E MU Emulator X3 [deepstatus] gives you a lot of tools that let you experiment and explore new sonic possibilities. You can use the SynthSwipe feature to sample from MIDI devices automatically. You can use the TwistaLoop feature to manipulate your samples in real-time. You can use the Morph Filter Designer to create custom filters by morphing between different filter types. And much more.</li>
-<li><b>It is free</b>: E MU Emulator X3 [deepstatus] is no longer available to purchase from E-MU or Creative Technology. However, you can download it for free from various sources online. This means that you can enjoy all the features and functions of this program without spending any money.</li>
-</ul>
-
-<h2>How to Download E MU Emulator X3 [deepstatus]?</h2>
-
-<p>If you want to download E MU Emulator X3 [deepstatus], you need to follow these steps:</p>
-<p></p>
-
-<ol>
-<li>Go to one of the sources that offer this program for free download. For example: <a href="https://archive.org/details/setup-emulator-x-3-x-64-v-3.0.0">https://archive.org/details/setup-emulator-x-3-x-64-v-3.0.0</a></li>
-<li>Click on the download button or link to start downloading the file.</li>
-<li>Save the file on your computer.</li>
-<li>Extract the file using a program such as WinRAR or 7-Zip.</li>
-<li>Run the setup file to install the program on your computer.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Launch the program and enjoy making music with E MU Emulator X3 [deepstatus].</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>E MU Emulator X3 [deepstatus] is a software sampler that offers the sound quality,
-synthesis
-and filters of E-MU's hardware samplers
-and introduces a host of new tools that make sampling
-and sound design easier
-and more creative than ever.
-It is compatible with PCs running
-the Microsoft Windows operating system
-and it can operate as a stand-alone program
-or as a VST instrument.
-It supports both 32-bit
-and 64-bit versions
-and it does not require any hardware dongle
-or sound card to run.
-You can download it for free from various sources online
-as it is no longer available
-to purchase from E-MU
-or Creative Technology.
-If you are looking for a software sampler
-that offers a lot of benefits
-for musicians
-and producers who want
-to create professional-quality sounds,
-you might want
-to check out E MU Emulator X3 [deepstatus].</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Faronics.Deep.Freeze.Standard.v6 62 020 3058 Incl Key Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Faronics.Deep.Freeze.Standard.v6 62 020 3058 Incl Key Download.md
deleted file mode 100644
index 48741129a0e0e31a089cf1b8c154b130d0802859..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Faronics.Deep.Freeze.Standard.v6 62 020 3058 Incl Key Download.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-
-<h1>How to Download and Install Faronics Deep Freeze Standard v6.62.020.3058</h1>
-<p>Faronics Deep Freeze Standard is a software that protects your Windows PC from unwanted changes by restoring it to its original state on every reboot. It is ideal for small environments with up to 10 workstations that do not require central management or control[^3^].</p>
-<h2>Faronics.Deep.Freeze.Standard.v6 62 020 3058 Incl Key download</h2><br /><p><b><b>Download File</b> &#187;&#187;&#187; <a href="https://bytlly.com/2uGx0O">https://bytlly.com/2uGx0O</a></b></p><br /><br />
-<p>In this article, we will show you how to download and install Faronics Deep Freeze Standard v6.62.020.3058, which is a previous version of the software that may be compatible with older operating systems or hardware.</p>
-<h2>Step 1: Download the software</h2>
-<p>To download Faronics Deep Freeze Standard v6.62.020.3058, you will need a valid license key that you can purchase from Faronics or an authorized reseller. You can also request a free 30-day trial from Faronics website[^1^].</p>
-<p>Once you have the license key, you can download the software from this link: <a href="https://www.faronics.com/assets/DFStd.zip">https://www.faronics.com/assets/DFStd.zip</a>. This is a zip file that contains the setup file and the user guide for the software.</p>
-<p></p>
-<h2>Step 2: Install the software</h2>
-<p>To install Faronics Deep Freeze Standard v6.62.020.3058, you will need to unzip the downloaded file and run the setup file as an administrator. Follow the instructions on the screen to complete the installation process.</p>
-<p>During the installation, you will be asked to enter your license key and choose a password for the software. You will also be asked to select the drives or partitions that you want to protect with Deep Freeze. Make sure you select only the drives or partitions that contain your operating system and applications, and not your data files.</p>
-<p>After the installation is complete, you will need to restart your computer for Deep Freeze to take effect.</p>
-<h2>Step 3: Use the software</h2>
-<p>To use Faronics Deep Freeze Standard v6.62.020.3058, you will need to know how to access the software console and how to thaw and freeze your computer.</p>
-<p>To access the software console, you will need to press Ctrl+Alt+Shift+F6 on your keyboard and enter your password. From the console, you can change your password, update your license key, configure your settings, and perform other tasks.</p>
-<p>To thaw your computer, you will need to access the software console and select Thawed mode. This will allow you to make permanent changes to your computer, such as installing updates or new software. You will need to restart your computer for Thawed mode to take effect.</p>
-<p>To freeze your computer, you will need to access the software console and select Frozen mode. This will restore your computer to its original state on every reboot, discarding any changes made in Thawed mode or by users. You will need to restart your computer for Frozen mode to take effect.</p>
-<h2>Conclusion</h2>
-<p>Faronics Deep Freeze Standard v6.62.020.3058 is a powerful software that can protect your Windows PC from unwanted changes by restoring it to its original state on every reboot. It is ideal for small environments with up to 10 workstations that do not require central management or control[^3^]. To download and install Faronics Deep Freeze Standard v6.62.020.3058, you will need a valid license key and follow the steps outlined in this article.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/llmonitor/benchmarks/app/loading.js b/spaces/llmonitor/benchmarks/app/loading.js
deleted file mode 100644
index 30ef4c17a7c85fe6d118b908ac96f240a61bc1d4..0000000000000000000000000000000000000000
--- a/spaces/llmonitor/benchmarks/app/loading.js
+++ /dev/null
@@ -1,3 +0,0 @@
-export default function Loading() {
-  return <p>Loading...</p>
-}
diff --git a/spaces/lukesteuber/contechnical/README.md b/spaces/lukesteuber/contechnical/README.md
deleted file mode 100644
index eb665f775ae03324bc6fb9eb3db7472ee84d6bfb..0000000000000000000000000000000000000000
--- a/spaces/lukesteuber/contechnical/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MPT-7B-Instruct
-emoji: 💁
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
-duplicated_from: mosaicml/mpt-7b-instruct
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/luost26/DiffAb/diffab/modules/common/layers.py b/spaces/luost26/DiffAb/diffab/modules/common/layers.py
deleted file mode 100644
index 6cf78089f8c0f8f2d3b780f75dfab5a55b3841c0..0000000000000000000000000000000000000000
--- a/spaces/luost26/DiffAb/diffab/modules/common/layers.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def mask_zero(mask, value):
-    return torch.where(mask, value, torch.zeros_like(value))
-
-
-def clampped_one_hot(x, num_classes):
-    mask = (x >= 0) & (x < num_classes) # (N, L)
-    x = x.clamp(min=0, max=num_classes-1)
-    y = F.one_hot(x, num_classes) * mask[...,None]  # (N, L, C)
-    return y
-
-
-class DistanceToBins(nn.Module):
-
-    def __init__(self, dist_min=0.0, dist_max=20.0, num_bins=64, use_onehot=False):
-        super().__init__()
-        self.dist_min = dist_min
-        self.dist_max = dist_max
-        self.num_bins = num_bins
-        self.use_onehot = use_onehot
-
-        if use_onehot:
-            offset = torch.linspace(dist_min, dist_max, self.num_bins)
-        else:
-            offset = torch.linspace(dist_min, dist_max, self.num_bins-1)    # 1 overflow flag
-            self.coeff = -0.5 / ((offset[1] - offset[0]) * 0.2).item() ** 2  # `*0.2`: makes it not too blurred
-        self.register_buffer('offset', offset)
-
-    @property
-    def out_channels(self):
-        return self.num_bins 
-
-    def forward(self, dist, dim, normalize=True):
-        """
-        Args:
-            dist:   (N, *, 1, *)
-        Returns:
-            (N, *, num_bins, *)
-        """
-        assert dist.size()[dim] == 1
-        offset_shape = [1] * len(dist.size())
-        offset_shape[dim] = -1
-
-        if self.use_onehot:
-            diff = torch.abs(dist - self.offset.view(*offset_shape))  # (N, *, num_bins, *)
-            bin_idx = torch.argmin(diff, dim=dim, keepdim=True)  # (N, *, 1, *)
-            y = torch.zeros_like(diff).scatter_(dim=dim, index=bin_idx, value=1.0)
-        else:
-            overflow_symb = (dist >= self.dist_max).float()  # (N, *, 1, *)
-            y = dist - self.offset.view(*offset_shape)  # (N, *, num_bins-1, *)
-            y = torch.exp(self.coeff * torch.pow(y, 2))  # (N, *, num_bins-1, *)
-            y = torch.cat([y, overflow_symb], dim=dim)  # (N, *, num_bins, *)
-            if normalize:
-                y = y / y.sum(dim=dim, keepdim=True)
-
-        return y
-
-
-class PositionalEncoding(nn.Module):
-    
-    def __init__(self, num_funcs=6):
-        super().__init__()
-        self.num_funcs = num_funcs
-        self.register_buffer('freq_bands', 2.0 ** torch.linspace(0.0, num_funcs-1, num_funcs))
-    
-    def get_out_dim(self, in_dim):
-        return in_dim * (2 * self.num_funcs + 1)
-
-    def forward(self, x):
-        """
-        Args:
-            x:  (..., d).
-        """
-        shape = list(x.shape[:-1]) + [-1]
-        x = x.unsqueeze(-1) # (..., d, 1)
-        code = torch.cat([x, torch.sin(x * self.freq_bands), torch.cos(x * self.freq_bands)], dim=-1)   # (..., d, 2f+1)
-        code = code.reshape(shape)
-        return code
-
-
-class AngularEncoding(nn.Module):
-
-    def __init__(self, num_funcs=3):
-        super().__init__()
-        self.num_funcs = num_funcs
-        self.register_buffer('freq_bands', torch.FloatTensor(
-            [i+1 for i in range(num_funcs)] + [1./(i+1) for i in range(num_funcs)]
-        ))
-
-    def get_out_dim(self, in_dim):
-        return in_dim * (1 + 2*2*self.num_funcs)
-
-    def forward(self, x):
-        """
-        Args:
-            x:  (..., d).
-        """
-        shape = list(x.shape[:-1]) + [-1]
-        x = x.unsqueeze(-1) # (..., d, 1)
-        code = torch.cat([x, torch.sin(x * self.freq_bands), torch.cos(x * self.freq_bands)], dim=-1)   # (..., d, 2f+1)
-        code = code.reshape(shape)
-        return code
-
-
-class LayerNorm(nn.Module):
-
-    def __init__(self,
-                 normal_shape,
-                 gamma=True,
-                 beta=True,
-                 epsilon=1e-10):
-        """Layer normalization layer
-        See: [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf)
-        :param normal_shape: The shape of the input tensor or the last dimension of the input tensor.
-        :param gamma: Add a scale parameter if it is True.
-        :param beta: Add an offset parameter if it is True.
-        :param epsilon: Epsilon for calculating variance.
-        """
-        super().__init__()
-        if isinstance(normal_shape, int):
-            normal_shape = (normal_shape,)
-        else:
-            normal_shape = (normal_shape[-1],)
-        self.normal_shape = torch.Size(normal_shape)
-        self.epsilon = epsilon
-        if gamma:
-            self.gamma = nn.Parameter(torch.Tensor(*normal_shape))
-        else:
-            self.register_parameter('gamma', None)
-        if beta:
-            self.beta = nn.Parameter(torch.Tensor(*normal_shape))
-        else:
-            self.register_parameter('beta', None)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        if self.gamma is not None:
-            self.gamma.data.fill_(1)
-        if self.beta is not None:
-            self.beta.data.zero_()
-
-    def forward(self, x):
-        mean = x.mean(dim=-1, keepdim=True)
-        var = ((x - mean) ** 2).mean(dim=-1, keepdim=True)
-        std = (var + self.epsilon).sqrt()
-        y = (x - mean) / std
-        if self.gamma is not None:
-            y *= self.gamma
-        if self.beta is not None:
-            y += self.beta
-        return y
-
-    def extra_repr(self):
-        return 'normal_shape={}, gamma={}, beta={}, epsilon={}'.format(
-            self.normal_shape, self.gamma is not None, self.beta is not None, self.epsilon,
-        )
diff --git a/spaces/macaodha/batdetect2/bat_detect/utils/__init__.py b/spaces/macaodha/batdetect2/bat_detect/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/marker22/Bark-Voice-Cloning/app.py b/spaces/marker22/Bark-Voice-Cloning/app.py
deleted file mode 100644
index e60c6acea113aba62d1a3f8e8186b1ae075aa989..0000000000000000000000000000000000000000
--- a/spaces/marker22/Bark-Voice-Cloning/app.py
+++ /dev/null
@@ -1,401 +0,0 @@
-from cProfile import label
-import dataclasses
-from distutils.command.check import check
-from doctest import Example
-import gradio as gr
-import os
-import sys
-import numpy as np
-import logging
-import torch
-import pytorch_seed
-import time
-
-from xml.sax import saxutils
-from bark.api import generate_with_settings
-from bark.api import save_as_prompt
-from util.settings import Settings
-#import nltk
-
-
-from bark import SAMPLE_RATE
-from cloning.clonevoice import clone_voice
-from bark.generation import SAMPLE_RATE, preload_models, _load_history_prompt, codec_decode
-from scipy.io.wavfile import write as write_wav
-from util.parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml
-from datetime import datetime
-from tqdm.auto import tqdm
-from util.helper import create_filename, add_id3_tag
-from swap_voice import swap_voice_from_audio
-from training.training_prepare import prepare_semantics_from_text, prepare_wavs_from_semantics
-from training.train import training_prepare_files, train
-
-settings = Settings('config.yaml')
-
-
-def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, eos_prob, quick_generation, complete_settings, seed, batchcount, progress=gr.Progress(track_tqdm=True)):
-    # Chunk the text into smaller pieces then combine the generated audio
-
-    # generation settings
-    if selected_speaker == 'None':
-        selected_speaker = None
-
-    voice_name = selected_speaker
-
-    if text == None or len(text) < 1:
-       if selected_speaker == None:
-            raise gr.Error('No text entered!')
-
-       # Extract audio data from speaker if no text and speaker selected
-       voicedata = _load_history_prompt(voice_name)
-       audio_arr = codec_decode(voicedata["fine_prompt"])
-       result = create_filename(settings.output_folder_path, "None", "extract",".wav")
-       save_wav(audio_arr, result)
-       return result
-
-    if batchcount < 1:
-        batchcount = 1
-
-
-    silenceshort = np.zeros(int((float(settings.silence_sentence) / 1000.0) * SAMPLE_RATE), dtype=np.int16)  # quarter second of silence
-    silencelong = np.zeros(int((float(settings.silence_speakers) / 1000.0) * SAMPLE_RATE), dtype=np.float32)  # half a second of silence
-    use_last_generation_as_history = "Use last generation as history" in complete_settings
-    save_last_generation = "Save generation as Voice" in complete_settings
-    for l in range(batchcount):
-        currentseed = seed
-        if seed != None and seed > 2**32 - 1:
-            logger.warning(f"Seed {seed} > 2**32 - 1 (max), setting to random")
-            currentseed = None
-        if currentseed == None or currentseed <= 0:
-            currentseed = np.random.default_rng().integers(1, 2**32 - 1)
-        assert(0 < currentseed and currentseed < 2**32)
-
-        progress(0, desc="Generating")
-
-        full_generation = None
-
-        all_parts = []
-        complete_text = ""
-        text = text.lstrip()
-        if is_ssml(text):
-            list_speak = create_clips_from_ssml(text)
-            prev_speaker = None
-            for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)):
-                selected_speaker = clip[0]
-                # Add pause break between speakers
-                if i > 0 and selected_speaker != prev_speaker:
-                    all_parts += [silencelong.copy()]
-                prev_speaker = selected_speaker
-                text = clip[1]
-                text = saxutils.unescape(text)
-                if selected_speaker == "None":
-                    selected_speaker = None
-
-                print(f"\nGenerating Text ({i+1}/{len(list_speak)}) -> {selected_speaker} (Seed {currentseed}):`{text}`")
-                complete_text += text
-                with pytorch_seed.SavedRNG(currentseed):
-                    audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
-                    currentseed = torch.random.initial_seed()
-                if len(list_speak) > 1:
-                    filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav")
-                    save_wav(audio_array, filename)
-                    add_id3_tag(filename, text, selected_speaker, currentseed)
-
-                all_parts += [audio_array]
-        else:
-            texts = split_and_recombine_text(text, settings.input_text_desired_length, settings.input_text_max_length)
-            for i, text in tqdm(enumerate(texts), total=len(texts)):
-                print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker} (Seed {currentseed}):`{text}`")
-                complete_text += text
-                if quick_generation == True:
-                    with pytorch_seed.SavedRNG(currentseed):
-                        audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
-                        currentseed = torch.random.initial_seed()
-                else:
-                    full_output = use_last_generation_as_history or save_last_generation
-                    if full_output:
-                        full_generation, audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob, output_full=True)
-                    else:
-                        audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
-
-                # Noticed this in the HF Demo - convert to 16bit int -32767/32767 - most used audio format  
-                # audio_array = (audio_array * 32767).astype(np.int16)
-
-                if len(texts) > 1:
-                    filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav")
-                    save_wav(audio_array, filename)
-                    add_id3_tag(filename, text, selected_speaker, currentseed)
-
-                if quick_generation == False and (save_last_generation == True or use_last_generation_as_history == True):
-                    # save to npz
-                    voice_name = create_filename(settings.output_folder_path, seed, "audioclip", ".npz")
-                    save_as_prompt(voice_name, full_generation)
-                    if use_last_generation_as_history:
-                        selected_speaker = voice_name
-
-                all_parts += [audio_array]
-                # Add short pause between sentences
-                if text[-1] in "!?.\n" and i > 1:
-                    all_parts += [silenceshort.copy()]
-
-        # save & play audio
-        result = create_filename(settings.output_folder_path, currentseed, "final",".wav")
-        save_wav(np.concatenate(all_parts), result)
-        # write id3 tag with text truncated to 60 chars, as a precaution...
-        add_id3_tag(result, complete_text, selected_speaker, currentseed)
-
-    return result
-
-
-
-def save_wav(audio_array, filename):
-    write_wav(filename, SAMPLE_RATE, audio_array)
-
-def save_voice(filename, semantic_prompt, coarse_prompt, fine_prompt):
-    np.savez_compressed(
-        filename,
-        semantic_prompt=semantic_prompt,
-        coarse_prompt=coarse_prompt,
-        fine_prompt=fine_prompt
-    )
-    
-
-def on_quick_gen_changed(checkbox):
-    if checkbox == False:
-        return gr.CheckboxGroup.update(visible=True)
-    return gr.CheckboxGroup.update(visible=False)
-
-def delete_output_files(checkbox_state):
-    if checkbox_state:
-        outputs_folder = os.path.join(os.getcwd(), settings.output_folder_path)
-        if os.path.exists(outputs_folder):
-            purgedir(outputs_folder)
-    return False
-
-
-# https://stackoverflow.com/a/54494779
-def purgedir(parent):
-    for root, dirs, files in os.walk(parent):                                      
-        for item in files:
-            # Delete subordinate files                                                 
-            filespec = os.path.join(root, item)
-            os.unlink(filespec)
-        for item in dirs:
-            # Recursively perform this operation for subordinate directories   
-            purgedir(os.path.join(root, item))
-
-def convert_text_to_ssml(text, selected_speaker):
-    return build_ssml(text, selected_speaker)
-
-
-def training_prepare(selected_step, num_text_generations, progress=gr.Progress(track_tqdm=True)):
-    if selected_step == prepare_training_list[0]:
-        prepare_semantics_from_text()
-    else:
-        prepare_wavs_from_semantics()
-    return None
-
-
-def start_training(save_model_epoch, max_epochs, progress=gr.Progress(track_tqdm=True)):
-    training_prepare_files("./training/data/", "./training/data/checkpoint/hubert_base_ls960.pt")
-    train("./training/data/", save_model_epoch, max_epochs)
-    return None
-
-
-
-def apply_settings(themes, input_server_name, input_server_port, input_server_public, input_desired_len, input_max_len, input_silence_break, input_silence_speaker):
-    settings.selected_theme = themes
-    settings.server_name = input_server_name
-    settings.server_port = input_server_port
-    settings.server_share = input_server_public
-    settings.input_text_desired_length = input_desired_len
-    settings.input_text_max_length = input_max_len
-    settings.silence_sentence = input_silence_break
-    settings.silence_speaker = input_silence_speaker
-    settings.save()
-
-def restart():
-    global restart_server
-    restart_server = True
-
-
-def create_version_html():
-    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
-    versions_html = f"""
-python: <span title="{sys.version}">{python_version}</span>
- • 
-torch: {getattr(torch, '__long_version__',torch.__version__)}
- • 
-gradio: {gr.__version__}
-"""
-    return versions_html
-
-    
-
-logger = logging.getLogger(__name__)
-APPTITLE = "Bark Voice Cloning UI"
-
-
-autolaunch = False
-
-if len(sys.argv) > 1:
-    autolaunch = "-autolaunch" in sys.argv
-
-
-if torch.cuda.is_available() == False:
-    os.environ['BARK_FORCE_CPU'] = 'True'
-    logger.warning("No CUDA detected, fallback to CPU!")
-
-print(f'smallmodels={os.environ.get("SUNO_USE_SMALL_MODELS", False)}')
-print(f'enablemps={os.environ.get("SUNO_ENABLE_MPS", False)}')
-print(f'offloadcpu={os.environ.get("SUNO_OFFLOAD_CPU", False)}')
-print(f'forcecpu={os.environ.get("BARK_FORCE_CPU", False)}')
-print(f'autolaunch={autolaunch}\n\n')
-
-#print("Updating nltk\n")
-#nltk.download('punkt')
-
-print("Preloading Models\n")
-preload_models()
-
-available_themes = ["Default", "gradio/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "gstaff/xkcd", "freddyaboulton/dracula_revamped", "ysharma/steampunk"]
-tokenizer_language_list = ["de","en", "pl"]
-prepare_training_list = ["Step 1: Semantics from Text","Step 2: WAV from Semantics"]
-
-seed = -1
-server_name = settings.server_name
-if len(server_name) < 1:
-    server_name = None
-server_port = settings.server_port
-if server_port <= 0:
-    server_port = None
-global run_server
-global restart_server
-
-run_server = True
-
-while run_server:
-    # Collect all existing speakers/voices in dir
-    speakers_list = []
-
-    for root, dirs, files in os.walk("./bark/assets/prompts"):
-        for file in files:
-            if file.endswith(".npz"):
-                pathpart = root.replace("./bark/assets/prompts", "")
-                name = os.path.join(pathpart, file[:-4])
-                if name.startswith("/") or name.startswith("\\"):
-                     name = name[1:]
-                speakers_list.append(name)
-
-    speakers_list = sorted(speakers_list, key=lambda x: x.lower())
-    speakers_list.insert(0, 'None')
-
-    print(f'Launching {APPTITLE} Server')
-
-    # Create Gradio Blocks
-
-    with gr.Blocks(title=f"{APPTITLE}", mode=f"{APPTITLE}", theme=settings.selected_theme) as barkgui:
-        gr.Markdown("# <center>🐶🎶⭐ - Bark Voice Cloning</center>")
-        gr.Markdown("### <center>🤗 - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)</center>")
-        gr.Markdown("### <center>🎡 - Based on [bark-gui](https://github.com/C0untFloyd/bark-gui)</center>")
-        gr.Markdown(f""" You can duplicate and use it with a GPU: <a href="https://huggingface.co/spaces/{os.getenv('SPACE_ID')}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a>
-                         or open in [Colab](https://colab.research.google.com/github/KevinWang676/Bark-Voice-Cloning/blob/main/Bark_Voice_Cloning_UI.ipynb) for quick start 🌟
-                    """)
-
-        with gr.Tab("🎙️ - Clone Voice"):
-            with gr.Row():
-                input_audio_filename = gr.Audio(label="Input audio.wav", source="upload", type="filepath")
-            #transcription_text = gr.Textbox(label="Transcription Text", lines=1, placeholder="Enter Text of your Audio Sample here...")
-            with gr.Row():
-                with gr.Column():
-                    initialname = "/home/user/app/bark/assets/prompts/file"
-                    output_voice = gr.Textbox(label="Filename of trained Voice (do not change the initial name)", lines=1, placeholder=initialname, value=initialname, visible=False)
-                with gr.Column():
-                    tokenizerlang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1], visible=False)
-            with gr.Row():
-                clone_voice_button = gr.Button("Create Voice", variant="primary")
-            with gr.Row():
-                dummy = gr.Text(label="Progress")
-                npz_file = gr.File(label=".npz file")
-            speakers_list.insert(0, npz_file) # add prompt
-
-        with gr.Tab("🎵 - TTS"):
-            with gr.Row():
-                with gr.Column():
-                    placeholder = "Enter text here."
-                    input_text = gr.Textbox(label="Input Text", lines=4, placeholder=placeholder)
-                    convert_to_ssml_button = gr.Button("Convert Input Text to SSML")
-                with gr.Column():
-                        seedcomponent = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
-                        batchcount = gr.Number(label="Batch count", precision=0, value=1)
-           
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("[Voice Prompt Library](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)")
-                    speaker = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice (Choose “file” if you wanna use the custom voice)")
-                    
-                with gr.Column():
-                    text_temp = gr.Slider(0.1, 1.0, value=0.6, label="Generation Temperature", info="1.0 more diverse, 0.1 more conservative")
-                    waveform_temp = gr.Slider(0.1, 1.0, value=0.7, label="Waveform temperature", info="1.0 more diverse, 0.1 more conservative")
-
-            with gr.Row():
-                with gr.Column():
-                    quick_gen_checkbox = gr.Checkbox(label="Quick Generation", value=True)
-                    settings_checkboxes = ["Use last generation as history", "Save generation as Voice"]
-                    complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False)
-                with gr.Column():
-                    eos_prob = gr.Slider(0.0, 0.5, value=0.05, label="End of sentence probability")
-
-            with gr.Row():
-                with gr.Column():
-                    tts_create_button = gr.Button("Generate", variant="primary")
-                with gr.Column():
-                    hidden_checkbox = gr.Checkbox(visible=False)
-                    button_stop_generation = gr.Button("Stop generation")
-            with gr.Row():
-                output_audio = gr.Audio(label="Generated Audio", type="filepath")
-
-        with gr.Tab("🔮 - Voice Conversion"):
-            with gr.Row():
-                 swap_audio_filename = gr.Audio(label="Input audio.wav to swap voice", source="upload", type="filepath")
-            with gr.Row():
-                 with gr.Column():
-                     swap_tokenizer_lang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1])
-                     swap_seed = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
-                 with gr.Column():
-                     speaker_swap = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice (Choose “file” if you wanna use the custom voice)")
-                     swap_batchcount = gr.Number(label="Batch count", precision=0, value=1)
-            with gr.Row():
-                swap_voice_button = gr.Button("Generate", variant="primary")
-            with gr.Row():
-                output_swap = gr.Audio(label="Generated Audio", type="filepath")
-
-   
-        quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)
-        convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)
-        gen_click = tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, eos_prob, quick_gen_checkbox, complete_settings, seedcomponent, batchcount],outputs=output_audio)
-        button_stop_generation.click(fn=None, inputs=None, outputs=None, cancels=[gen_click])
-        
-
-
-        swap_voice_button.click(swap_voice_from_audio, inputs=[swap_audio_filename, speaker_swap, swap_tokenizer_lang, swap_seed, swap_batchcount], outputs=output_swap)
-        clone_voice_button.click(clone_voice, inputs=[input_audio_filename, output_voice], outputs=[dummy, npz_file])
-
-
-        restart_server = False
-        try:
-            barkgui.queue().launch(show_error=True)
-        except:
-            restart_server = True
-            run_server = False
-        try:
-            while restart_server == False:
-                time.sleep(1.0)
-        except (KeyboardInterrupt, OSError):
-            print("Keyboard interruption in main thread... closing server.")
-            run_server = False
-        barkgui.close()
-
-
-
-
diff --git a/spaces/matthoffner/chatbot-mini/utils/server/index.ts b/spaces/matthoffner/chatbot-mini/utils/server/index.ts
deleted file mode 100644
index e526484a64bce0aee33a4a7daf3a1c71afb55ced..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot-mini/utils/server/index.ts
+++ /dev/null
@@ -1,104 +0,0 @@
-import { Message } from '@/types/chat';
-import { OpenAIModel } from '@/types/openai';
-
-import { OPENAI_API_HOST } from '../app/const';
-
-import {
-  ParsedEvent,
-  ReconnectInterval,
-  createParser,
-} from 'eventsource-parser';
-
-export class LLMError extends Error {
-  type: string;
-  param: string;
-  code: string;
-
-  constructor(message: string, type: string, param: string, code: string) {
-    super(message);
-    this.name = 'LLMError';
-    this.type = type;
-    this.param = param;
-    this.code = code;
-  }
-}
-
-export const LLMStream = async (
-  model: OpenAIModel,
-  systemPrompt: string,
-  temperature : number,
-  key: string,
-  messages: Message[],
-) => {
-  let url = `${OPENAI_API_HOST}/v1/chat/completions`;
-  const res = await fetch(url, {
-    headers: {
-      'Content-Type': 'application/json'
-    },
-    method: 'POST',
-    body: JSON.stringify({
-      messages: [
-        {
-          role: 'system',
-          content: systemPrompt,
-        },
-        ...messages,
-      ],
-      max_tokens: 1000,
-      temperature: temperature,
-      stream: true,
-    }),
-  });
-
-  const encoder = new TextEncoder();
-  const decoder = new TextDecoder();
-
-  if (res.status !== 200) {
-    const result = await res.json();
-    if (result.error) {
-      throw new LLMError(
-        result.error.message,
-        result.error.type,
-        result.error.param,
-        result.error.code,
-      );
-    } else {
-      throw new Error(
-        `LLM API returned an error: ${
-          decoder.decode(result?.value) || result.statusText
-        }`,
-      );
-    }
-  }
-
-  const stream = new ReadableStream({
-    async start(controller) {
-      const onParse = (event: ParsedEvent | ReconnectInterval) => {
-        if (event.type === 'event') {
-          const data = event.data;
-
-          try {
-            const json = JSON.parse(data);
-            if (json.choices[0].finish_reason != null) {
-              controller.close();
-              return;
-            }
-            const text = json.choices[0].delta.content;
-            const queue = encoder.encode(text);
-            controller.enqueue(queue);
-          } catch (e) {
-            controller.error(e);
-          }
-        }
-      };
-
-      const parser = createParser(onParse);
-
-      for await (const chunk of res.body as any) {
-        parser.feed(decoder.decode(chunk));
-      }
-    },
-  });
-
-  return stream;
-};
\ No newline at end of file
diff --git a/spaces/maurol/lyrics-translator/app.py b/spaces/maurol/lyrics-translator/app.py
deleted file mode 100644
index 67c0332b7a6c83cc4818fd7054d9b9590e570df8..0000000000000000000000000000000000000000
--- a/spaces/maurol/lyrics-translator/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import streamlit as st
-from lyrics_translator import LyricsTranslator
-
-
-config = {"GENIUS_ACCESS_TOKEN": st.secrets["GENIUS_ACCESS_TOKEN"]}
-
-
-info = """
-The "Lyrics Translator" downloads lyrics from Genius and uses 🤗 Hugging Face to translate the lyrics into a language of your choice!
-"""
-
-title = "🎵 Lyrics Translator"
-
-language_mapper = {
-    "German": "de",
-    "Swedish": "sv",
-    "Italian": "it",
-    "French": "fr",
-}
-
-st.title(title)
-st.info(info, icon="ℹ️")
-
-
-with st.sidebar:
-    st.subheader("Automated lyrics translation!")
-
-    song = st.text_input("Choose a track:", "One More Time")
-    artist = st.text_input("Choose an artist:", "Daft Punk")
-    language_original = st.radio("Choose a language:", tuple(language_mapper.keys()))
-
-    is_button_results = st.button("Translate!")
-
-
-language = language_mapper.get(language_original, None)
-
-
-@st.cache_resource
-def load_translator(language):
-    translator = LyricsTranslator(language=language, config=config)
-    return translator
-
-
-if is_button_results:
-    with st.spinner("Download the model this can take a while..."):
-        translator = load_translator(language)
-
-    col1, col2 = st.columns(2)
-
-    with col1.container():
-        with st.spinner("Fetching the song lyrics, this can take a while..."):
-            try:
-                lyrics = translator.get_song_lyrics(song, artist)
-                is_success = True
-            except ValueError:
-                col1.error("No lyrics found for this song!")
-
-            if is_success:
-                col1.success(f"Found lyrics for '{song}' by '{artist}'!")
-                col1.text(lyrics)
-
-    with col2.container():
-        with st.spinner("Song is being translated, this can take a while..."):
-            try:
-                text = translator.get_song_translation(song, artist)
-                is_success = True
-            except ValueError:
-                col2.error("No lyrics found for this song!")
-
-            if is_success:
-                col2.success(
-                    f"Translated lyrics for '{song}' by '{artist}' to '{language_original}'!"
-                )
-                col2.text(text)
diff --git a/spaces/merve/Grounding_DINO_demo/groundingdino/util/utils.py b/spaces/merve/Grounding_DINO_demo/groundingdino/util/utils.py
deleted file mode 100644
index e9f0318e306fa04bff0ada70486b41aaa69b07c8..0000000000000000000000000000000000000000
--- a/spaces/merve/Grounding_DINO_demo/groundingdino/util/utils.py
+++ /dev/null
@@ -1,608 +0,0 @@
-import argparse
-import json
-import warnings
-from collections import OrderedDict
-from copy import deepcopy
-from typing import Any, Dict, List
-
-import numpy as np
-import torch
-from transformers import AutoTokenizer
-
-from groundingdino.util.slconfig import SLConfig
-
-
-def slprint(x, name="x"):
-    if isinstance(x, (torch.Tensor, np.ndarray)):
-        print(f"{name}.shape:", x.shape)
-    elif isinstance(x, (tuple, list)):
-        print("type x:", type(x))
-        for i in range(min(10, len(x))):
-            slprint(x[i], f"{name}[{i}]")
-    elif isinstance(x, dict):
-        for k, v in x.items():
-            slprint(v, f"{name}[{k}]")
-    else:
-        print(f"{name}.type:", type(x))
-
-
-def clean_state_dict(state_dict):
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k[:7] == "module.":
-            k = k[7:]  # remove `module.`
-        new_state_dict[k] = v
-    return new_state_dict
-
-
-def renorm(
-    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-) -> torch.FloatTensor:
-    # img: tensor(3,H,W) or tensor(B,3,H,W)
-    # return: same as img
-    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
-    if img.dim() == 3:
-        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
-            img.size(0),
-            str(img.size()),
-        )
-        img_perm = img.permute(1, 2, 0)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(2, 0, 1)
-    else:  # img.dim() == 4
-        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
-            img.size(1),
-            str(img.size()),
-        )
-        img_perm = img.permute(0, 2, 3, 1)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(0, 3, 1, 2)
-
-
-class CocoClassMapper:
-    def __init__(self) -> None:
-        self.category_map_str = {
-            "1": 1,
-            "2": 2,
-            "3": 3,
-            "4": 4,
-            "5": 5,
-            "6": 6,
-            "7": 7,
-            "8": 8,
-            "9": 9,
-            "10": 10,
-            "11": 11,
-            "13": 12,
-            "14": 13,
-            "15": 14,
-            "16": 15,
-            "17": 16,
-            "18": 17,
-            "19": 18,
-            "20": 19,
-            "21": 20,
-            "22": 21,
-            "23": 22,
-            "24": 23,
-            "25": 24,
-            "27": 25,
-            "28": 26,
-            "31": 27,
-            "32": 28,
-            "33": 29,
-            "34": 30,
-            "35": 31,
-            "36": 32,
-            "37": 33,
-            "38": 34,
-            "39": 35,
-            "40": 36,
-            "41": 37,
-            "42": 38,
-            "43": 39,
-            "44": 40,
-            "46": 41,
-            "47": 42,
-            "48": 43,
-            "49": 44,
-            "50": 45,
-            "51": 46,
-            "52": 47,
-            "53": 48,
-            "54": 49,
-            "55": 50,
-            "56": 51,
-            "57": 52,
-            "58": 53,
-            "59": 54,
-            "60": 55,
-            "61": 56,
-            "62": 57,
-            "63": 58,
-            "64": 59,
-            "65": 60,
-            "67": 61,
-            "70": 62,
-            "72": 63,
-            "73": 64,
-            "74": 65,
-            "75": 66,
-            "76": 67,
-            "77": 68,
-            "78": 69,
-            "79": 70,
-            "80": 71,
-            "81": 72,
-            "82": 73,
-            "84": 74,
-            "85": 75,
-            "86": 76,
-            "87": 77,
-            "88": 78,
-            "89": 79,
-            "90": 80,
-        }
-        self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
-        self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
-
-    def origin2compact(self, idx):
-        return self.origin2compact_mapper[int(idx)]
-
-    def compact2origin(self, idx):
-        return self.compact2origin_mapper[int(idx)]
-
-
-def to_device(item, device):
-    if isinstance(item, torch.Tensor):
-        return item.to(device)
-    elif isinstance(item, list):
-        return [to_device(i, device) for i in item]
-    elif isinstance(item, dict):
-        return {k: to_device(v, device) for k, v in item.items()}
-    else:
-        raise NotImplementedError(
-            "Call Shilong if you use other containers! type: {}".format(type(item))
-        )
-
-
-#
-def get_gaussian_mean(x, axis, other_axis, softmax=True):
-    """
-
-    Args:
-        x (float): Input images(BxCxHxW)
-        axis (int): The index for weighted mean
-        other_axis (int): The other index
-
-    Returns: weighted index for axis, BxC
-
-    """
-    mat2line = torch.sum(x, axis=other_axis)
-    # mat2line = mat2line / mat2line.mean() * 10
-    if softmax:
-        u = torch.softmax(mat2line, axis=2)
-    else:
-        u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
-    size = x.shape[axis]
-    ind = torch.linspace(0, 1, size).to(x.device)
-    batch = x.shape[0]
-    channel = x.shape[1]
-    index = ind.repeat([batch, channel, 1])
-    mean_position = torch.sum(index * u, dim=2)
-    return mean_position
-
-
-def get_expected_points_from_map(hm, softmax=True):
-    """get_gaussian_map_from_points
-        B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
-        softargmax function
-
-    Args:
-        hm (float): Input images(BxCxHxW)
-
-    Returns:
-        weighted index for axis, BxCx2. float between 0 and 1.
-
-    """
-    # hm = 10*hm
-    B, C, H, W = hm.shape
-    y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax)  # B,C
-    x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax)  # B,C
-    # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
-    return torch.stack([x_mean, y_mean], dim=2)
-
-
-# Positional encoding (section 5.1)
-# borrow from nerf
-class Embedder:
-    def __init__(self, **kwargs):
-        self.kwargs = kwargs
-        self.create_embedding_fn()
-
-    def create_embedding_fn(self):
-        embed_fns = []
-        d = self.kwargs["input_dims"]
-        out_dim = 0
-        if self.kwargs["include_input"]:
-            embed_fns.append(lambda x: x)
-            out_dim += d
-
-        max_freq = self.kwargs["max_freq_log2"]
-        N_freqs = self.kwargs["num_freqs"]
-
-        if self.kwargs["log_sampling"]:
-            freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
-        else:
-            freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)
-
-        for freq in freq_bands:
-            for p_fn in self.kwargs["periodic_fns"]:
-                embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
-                out_dim += d
-
-        self.embed_fns = embed_fns
-        self.out_dim = out_dim
-
-    def embed(self, inputs):
-        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
-
-
-def get_embedder(multires, i=0):
-    import torch.nn as nn
-
-    if i == -1:
-        return nn.Identity(), 3
-
-    embed_kwargs = {
-        "include_input": True,
-        "input_dims": 3,
-        "max_freq_log2": multires - 1,
-        "num_freqs": multires,
-        "log_sampling": True,
-        "periodic_fns": [torch.sin, torch.cos],
-    }
-
-    embedder_obj = Embedder(**embed_kwargs)
-    embed = lambda x, eo=embedder_obj: eo.embed(x)
-    return embed, embedder_obj.out_dim
-
-
-class APOPMeter:
-    def __init__(self) -> None:
-        self.tp = 0
-        self.fp = 0
-        self.tn = 0
-        self.fn = 0
-
-    def update(self, pred, gt):
-        """
-        Input:
-            pred, gt: Tensor()
-        """
-        assert pred.shape == gt.shape
-        self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
-        self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
-        self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
-        self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()
-
-    def update_cm(self, tp, fp, tn, fn):
-        self.tp += tp
-        self.fp += fp
-        self.tn += tn
-        self.tn += fn
-
-
-def inverse_sigmoid(x, eps=1e-5):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-def get_raw_dict(args):
-    """
-    return the dicf contained in args.
-
-    e.g:
-        >>> with open(path, 'w') as f:
-                json.dump(get_raw_dict(args), f, indent=2)
-    """
-    if isinstance(args, argparse.Namespace):
-        return vars(args)
-    elif isinstance(args, dict):
-        return args
-    elif isinstance(args, SLConfig):
-        return args._cfg_dict
-    else:
-        raise NotImplementedError("Unknown type {}".format(type(args)))
-
-
-def stat_tensors(tensor):
-    assert tensor.dim() == 1
-    tensor_sm = tensor.softmax(0)
-    entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()
-
-    return {
-        "max": tensor.max(),
-        "min": tensor.min(),
-        "mean": tensor.mean(),
-        "var": tensor.var(),
-        "std": tensor.var() ** 0.5,
-        "entropy": entropy,
-    }
-
-
-class NiceRepr:
-    """Inherit from this class and define ``__nice__`` to "nicely" print your
-    objects.
-
-    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
-    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
-    If the inheriting class has a ``__len__``, method then the default
-    ``__nice__`` method will return its length.
-
-    Example:
-        >>> class Foo(NiceRepr):
-        ...    def __nice__(self):
-        ...        return 'info'
-        >>> foo = Foo()
-        >>> assert str(foo) == '<Foo(info)>'
-        >>> assert repr(foo).startswith('<Foo(info) at ')
-
-    Example:
-        >>> class Bar(NiceRepr):
-        ...    pass
-        >>> bar = Bar()
-        >>> import pytest
-        >>> with pytest.warns(None) as record:
-        >>>     assert 'object at' in str(bar)
-        >>>     assert 'object at' in repr(bar)
-
-    Example:
-        >>> class Baz(NiceRepr):
-        ...    def __len__(self):
-        ...        return 5
-        >>> baz = Baz()
-        >>> assert str(baz) == '<Baz(5)>'
-    """
-
-    def __nice__(self):
-        """str: a "nice" summary string describing this module"""
-        if hasattr(self, "__len__"):
-            # It is a common pattern for objects to use __len__ in __nice__
-            # As a convenience we define a default __nice__ for these objects
-            return str(len(self))
-        else:
-            # In all other cases force the subclass to overload __nice__
-            raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
-
-    def __repr__(self):
-        """str: the string of the module"""
-        try:
-            nice = self.__nice__()
-            classname = self.__class__.__name__
-            return f"<{classname}({nice}) at {hex(id(self))}>"
-        except NotImplementedError as ex:
-            warnings.warn(str(ex), category=RuntimeWarning)
-            return object.__repr__(self)
-
-    def __str__(self):
-        """str: the string of the module"""
-        try:
-            classname = self.__class__.__name__
-            nice = self.__nice__()
-            return f"<{classname}({nice})>"
-        except NotImplementedError as ex:
-            warnings.warn(str(ex), category=RuntimeWarning)
-            return object.__repr__(self)
-
-
-def ensure_rng(rng=None):
-    """Coerces input into a random number generator.
-
-    If the input is None, then a global random state is returned.
-
-    If the input is a numeric value, then that is used as a seed to construct a
-    random state. Otherwise the input is returned as-is.
-
-    Adapted from [1]_.
-
-    Args:
-        rng (int | numpy.random.RandomState | None):
-            if None, then defaults to the global rng. Otherwise this can be an
-            integer or a RandomState class
-    Returns:
-        (numpy.random.RandomState) : rng -
-            a numpy random number generator
-
-    References:
-        .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270  # noqa: E501
-    """
-
-    if rng is None:
-        rng = np.random.mtrand._rand
-    elif isinstance(rng, int):
-        rng = np.random.RandomState(rng)
-    else:
-        rng = rng
-    return rng
-
-
-def random_boxes(num=1, scale=1, rng=None):
-    """Simple version of ``kwimage.Boxes.random``
-
-    Returns:
-        Tensor: shape (n, 4) in x1, y1, x2, y2 format.
-
-    References:
-        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
-
-    Example:
-        >>> num = 3
-        >>> scale = 512
-        >>> rng = 0
-        >>> boxes = random_boxes(num, scale, rng)
-        >>> print(boxes)
-        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
-                [216.9113, 330.6978, 224.0446, 456.5878],
-                [405.3632, 196.3221, 493.3953, 270.7942]])
-    """
-    rng = ensure_rng(rng)
-
-    tlbr = rng.rand(num, 4).astype(np.float32)
-
-    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
-    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
-    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
-    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
-
-    tlbr[:, 0] = tl_x * scale
-    tlbr[:, 1] = tl_y * scale
-    tlbr[:, 2] = br_x * scale
-    tlbr[:, 3] = br_y * scale
-
-    boxes = torch.from_numpy(tlbr)
-    return boxes
-
-
-class ModelEma(torch.nn.Module):
-    def __init__(self, model, decay=0.9997, device=None):
-        super(ModelEma, self).__init__()
-        # make a copy of the model for accumulating moving average of weights
-        self.module = deepcopy(model)
-        self.module.eval()
-
-        # import ipdb; ipdb.set_trace()
-
-        self.decay = decay
-        self.device = device  # perform ema on different device from model if set
-        if self.device is not None:
-            self.module.to(device=device)
-
-    def _update(self, model, update_fn):
-        with torch.no_grad():
-            for ema_v, model_v in zip(
-                self.module.state_dict().values(), model.state_dict().values()
-            ):
-                if self.device is not None:
-                    model_v = model_v.to(device=self.device)
-                ema_v.copy_(update_fn(ema_v, model_v))
-
-    def update(self, model):
-        self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
-
-    def set(self, model):
-        self._update(model, update_fn=lambda e, m: m)
-
-
-class BestMetricSingle:
-    def __init__(self, init_res=0.0, better="large") -> None:
-        self.init_res = init_res
-        self.best_res = init_res
-        self.best_ep = -1
-
-        self.better = better
-        assert better in ["large", "small"]
-
-    def isbetter(self, new_res, old_res):
-        if self.better == "large":
-            return new_res > old_res
-        if self.better == "small":
-            return new_res < old_res
-
-    def update(self, new_res, ep):
-        if self.isbetter(new_res, self.best_res):
-            self.best_res = new_res
-            self.best_ep = ep
-            return True
-        return False
-
-    def __str__(self) -> str:
-        return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)
-
-    def __repr__(self) -> str:
-        return self.__str__()
-
-    def summary(self) -> dict:
-        return {
-            "best_res": self.best_res,
-            "best_ep": self.best_ep,
-        }
-
-
-class BestMetricHolder:
-    def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
-        self.best_all = BestMetricSingle(init_res, better)
-        self.use_ema = use_ema
-        if use_ema:
-            self.best_ema = BestMetricSingle(init_res, better)
-            self.best_regular = BestMetricSingle(init_res, better)
-
-    def update(self, new_res, epoch, is_ema=False):
-        """
-        return if the results is the best.
-        """
-        if not self.use_ema:
-            return self.best_all.update(new_res, epoch)
-        else:
-            if is_ema:
-                self.best_ema.update(new_res, epoch)
-                return self.best_all.update(new_res, epoch)
-            else:
-                self.best_regular.update(new_res, epoch)
-                return self.best_all.update(new_res, epoch)
-
-    def summary(self):
-        if not self.use_ema:
-            return self.best_all.summary()
-
-        res = {}
-        res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
-        res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
-        res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
-        return res
-
-    def __repr__(self) -> str:
-        return json.dumps(self.summary(), indent=2)
-
-    def __str__(self) -> str:
-        return self.__repr__()
-
-
-def targets_to(targets: List[Dict[str, Any]], device):
-    """Moves the target dicts to the given device."""
-    excluded_keys = [
-        "questionId",
-        "tokens_positive",
-        "strings_positive",
-        "tokens",
-        "dataset_name",
-        "sentence_id",
-        "original_img_id",
-        "nb_eval",
-        "task_id",
-        "original_id",
-        "token_span",
-        "caption",
-        "dataset_type",
-    ]
-    return [
-        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
-    ]
-
-
-def get_phrases_from_posmap(
-    posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer
-):
-    assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
-    if posmap.dim() == 1:
-        non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
-        token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]
-        return tokenizer.decode(token_ids)
-    else:
-        raise NotImplementedError("posmap must be 1-dim")
diff --git a/spaces/merve/fill-in-the-blank/source/fill-in-the-blank/data/cachekey2filename.js b/spaces/merve/fill-in-the-blank/source/fill-in-the-blank/data/cachekey2filename.js
deleted file mode 100644
index 85df2a5b1806c3853f4e12ab05b430af77c800f9..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/fill-in-the-blank/data/cachekey2filename.js
+++ /dev/null
@@ -1,19 +0,0 @@
-window.cacheKey2filename = {
-  "{\"tokens\":[101,2000,2022,2030,2025,2000,2022,29623,2008,2003,1996,3160,29628,102]}embed_group_top": "tokens-101-2000-2022-2030-2025-2000-2022-29623-2008-2003-1996-3160-29628-102-embed-group-top.json",
-  "{\"sentence\":\"In New York, they like to buy [MASK].\"}embed": "sentence-in-new-york-they-like-to-buy-mask-embed.json",
-  "{\"sentence\":\"Elsie was born in the year of [MASK].\"}embed": "sentence-elsie-was-born-in-the-year-of-mask-embed.json",
-  "{\"sentence\":\"Jim worked as a [MASK].\"}embed": "sentence-jim-worked-as-a-mask-embed.json",
-  "{\"sentence\":\"The new nurse was named [MASK].\"}embed": "sentence-the-new-nurse-was-named-mask-embed.json",
-  "{\"sentence\":\"The doctor performed CPR even though [MASK] knew it was too late.\"}embed_zari_cda": "sentence-the-doctor-performed-cpr-even-though-mask-knew-it-was-too-late-embed-zari-cda.json",
-  "{\"sentence\":\"In 1908, he was employed as a [MASK].\"}embed": "sentence-in-1908-he-was-employed-as-a-mask-embed.json",
-  "{\"sentence\":\"Jane worked as a [MASK].\"}embed": "sentence-jane-worked-as-a-mask-embed.json",
-  "{\"sentence\":\"In Texas, they like to buy [MASK].\"}embed": "sentence-in-texas-they-like-to-buy-mask-embed.json",
-  "{\"sentence\":\"Lauren was born in the year of [MASK].\"}embed": "sentence-lauren-was-born-in-the-year-of-mask-embed.json",
-  "{\"sentence\":\"The new doctor was named [MASK].\"}embed": "sentence-the-new-doctor-was-named-mask-embed.json",
-  "{\"sentence\":\"The nurse performed CPR even though [MASK] knew it was too late.\"}embed_zari_cda": "sentence-the-nurse-performed-cpr-even-though-mask-knew-it-was-too-late-embed-zari-cda.json",
-  "{\"sentence\":\"In 1908, she was employed as a [MASK].\"}embed": "sentence-in-1908-she-was-employed-as-a-mask-embed.json",
-  "{\"sentence\":\"In 2018, he was employed as a [MASK].\"}embed": "sentence-in-2018-he-was-employed-as-a-mask-embed.json",
-  "{\"sentence\":\"In 2018, she was employed as a [MASK].\"}embed": "sentence-in-2018-she-was-employed-as-a-mask-embed.json",
-  "{\"tokens\":[101,1999,2047,2259,29623,2027,2066,2000,4965,2477,29625,102]}embed_group_top": "tokens-101-1999-2047-2259-29623-2027-2066-2000-4965-2477-29625-102-embed-group-top.json",
-  "{\"tokens\":[101,1999,3146,29623,2027,2066,2000,4965,2477,29625,102]}embed_group_top": "tokens-101-1999-3146-29623-2027-2066-2000-4965-2477-29625-102-embed-group-top.json"
-}
\ No newline at end of file
diff --git a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/gender-over-time-colab/README.md b/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/gender-over-time-colab/README.md
deleted file mode 100644
index 16012931eb8815db61e8f7454261eea1de6ee36a..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/gender-over-time-colab/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-https://colab.research.google.com/github/PAIR-code/ai-explorables/blob/master/server-side/fill-in-the-blank/gender-over-time-colab/gender-over-time.ipynb
-
-Hot reload JS
-
-````
-ls *.* | entr rsync -a --omit-dir-times --no-perms --exclude node_modules "$PWD" demo@roadtolarissa.com:../../usr/share/nginx/html/colab/
-````
-
-- https://colab.research.google.com/notebooks/snippets/advanced_outputs.ipynb
-- https://blocks.roadtolarissa.com/1wheel/7361276a2af10ca48ec9550c33bbdad5
diff --git a/spaces/merve/uncertainty-calibration/source/private-and-fair/accuracy-v-privacy-dataset_size.js b/spaces/merve/uncertainty-calibration/source/private-and-fair/accuracy-v-privacy-dataset_size.js
deleted file mode 100644
index cd196da1ca712ff733e5e03de4258effba0478a3..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/source/private-and-fair/accuracy-v-privacy-dataset_size.js
+++ /dev/null
@@ -1,157 +0,0 @@
-!(async function(){
-  var data = await util.getFile('cns-cache/model_grid_test_accuracy.json')
-
-  data = data
-    .filter(d => util.epsilonExtent[1] <= d.epsilon && d.epsilon <= util.epsilonExtent[0])
-    .filter(d => d.dataset_size > 1000)
-
-    // .filter(d => d.dataset_size > 4000)
-
-  // console.log(data)
-
-  var bySize = d3.nestBy(data, d => d.dataset_size)
-  bySize.forEach((d, i) => {
-    d.dataset_size = d.key
-
-    d.color = d3.interpolatePlasma(.84- i/6)
-    if (d.key == 60000){
-      d3.selectAll('.tp60').st({background: d.color, padding: 2})
-    }
-    if (d.key == 7500){
-      d3.selectAll('.tp75').st({background: d.color, color: '#fff', padding: 2})
-    }
-
-    d.label = {
-      60000: {pos: [7, 11], textAnchor: 'middle', text: '60,000'},
-      30000: {pos: [7, 11], textAnchor: 'middle', text: '30,000'},
-      15000: {pos: [7, -5], textAnchor: 'start', text: '15,000'},
-      7500: {pos: [0, 8], textAnchor: 'start', text: '7,500'},
-      // 3750: {pos: [0, 14], textAnchor: 'end', text: '3,750 training points'},
-      3750: {pos: [-34, 10], textAnchor: 'start', text: '3,750'},
-      2000: {pos: [-50, 10], textAnchor: 'end', text: '2,000 training points'},
-    }[d.key]
-
-    d.forEach(e => e.size = d)
-  })
-
-  var sel = d3.select('.accuracy-v-privacy-dataset_size').html('')
-    .at({role: 'graphics-document', 'aria-label': `High privacy and accuracy requires more training data. Line chart showing too much differential privacy without enough data decreases accuracy.`})
-
-  sel.append('div.chart-title').text('High privacy and accuracy requires more training data')
-
-  var c = d3.conventions({
-    sel,
-    height: 400,
-    margin: {bottom: 125, top: 5},
-    layers: 'sd',
-  })
-
-  c.x = d3.scaleLog().domain(util.epsilonExtent).range(c.x.range())
-  c.xAxis = d3.axisBottom(c.x).tickFormat(d => {
-    var rv = d + ''
-    if (rv.split('').filter(d => d !=0 && d != '.')[0] == 1) return rv
-  })
-
-  c.yAxis.tickFormat(d => d3.format('.0%')(d))//.ticks(8)
-
-  d3.drawAxis(c)
-  util.addAxisLabel(c, 'Higher Privacy →', 'Test Accuracy')
-  util.ggPlotBg(c, false)
-  c.layers[1].append('div')
-    .st({fontSize: 12, color: '#555', width: 120*2, textAlign: 'center', lineHeight: '1.3em'})
-    .translate([c.width/2 - 120, c.height + 70])
-    .html('in ε, a <a href="https://desfontain.es/privacy/differential-privacy-in-more-detail.html">measure</a> of how much modifying a single training point can change the model (models with a lower ε are more private)')
-
-
-  c.svg.selectAll('.y .tick').filter(d => d == .9)
-    .select('text').st({fontWeight: 600}).parent()
-    .append('path')
-    .at({stroke: '#000', strokeDasharray: '2 2', d: 'M 0 0 H ' + c.width})
-
-  var line = d3.line()
-    .x(d => c.x(d.epsilon))
-    .y(d => c.y(d.accuracy))
-    .curve(d3.curveMonotoneX)
-
-
-  var lineSel = c.svg.append('g').appendMany('path.accuracy-line', bySize)
-    .at({
-      d: line,
-      fill: 'none',
-    })
-    .st({ stroke: d => d.color, })
-    .on('mousemove', setActiveDigit)
-
-  var circleSel = c.svg.append('g')
-    .appendMany('g.accuracy-circle', data)
-    .translate(d => [c.x(d.epsilon), c.y(d.accuracy)])
-    .on('mousemove', setActiveDigit)
-    // .call(d3.attachTooltip)
-
-  circleSel.append('circle')
-    .at({r: 4, stroke: '#fff'})
-    .st({fill: d => d.size.color })
-
-
-  var labelSel = c.svg.appendMany('g.accuracy-label', bySize)
-    .translate(d => [c.x(d[0].epsilon), c.y(d[0].accuracy)])
-  labelSel.append('text')
-    .filter(d => d.label)
-    .translate(d => d.label.pos)
-    .st({fill: d => d.color, fontWeight: 400})
-    .at({textAnchor: d => d.label.textAnchor, fontSize: 14, fill: '#000', dy: '.66em'})
-    .text(d => d.label.text)
-    .filter(d => d.key == 2000)
-    .text('')
-    .tspans(d => d.label.text.split(' '))
-
-
-  c.svg.append('text.annotation')
-    .translate([225, 106])
-    .tspans(d3.wordwrap('With limited data, adding more differential privacy improves accuracy...', 25), 12)
-
-  c.svg.append('text.annotation')
-    .translate([490, 230])
-    .tspans(d3.wordwrap(`...until it doesn't`, 20))
-
-  // setActiveDigit({dataset_size: 60000})
-  function setActiveDigit({dataset_size}){
-    lineSel
-      .classed('active', 0)
-      .filter(d => d.dataset_size == dataset_size)
-      .classed('active', 1)
-      .raise()
-
-    circleSel
-      .classed('active', 0)
-      .filter(d => d.dataset_size == dataset_size)
-      .classed('active', 1)
-      .raise()
-
-    labelSel
-      .classed('active', 0)
-      .filter(d => d.dataset_size == dataset_size)
-      .classed('active', 1)
-  }
-})()
-
-
-
-
-// aVal: 0.5
-// accuracy: 0.8936
-// accuracy_0: 0.9663265306122449
-// accuracy_1: 0.9806167400881057
-// accuracy_2: 0.9011627906976745
-// accuracy_3: 0.8633663366336634
-// accuracy_4: 0.8859470468431772
-// accuracy_5: 0.8733183856502242
-// accuracy_6: 0.9384133611691023
-// accuracy_7: 0.8657587548638133
-// accuracy_8: 0.8059548254620124
-// accuracy_9: 0.8434093161546086
-// dataset_size: 60000
-// epochs: 4
-// epsilon: 0.19034890168775565
-// l2_norm_clip: 0.75
-// noise_multiplier: 2.6
diff --git a/spaces/mikebars/huggingface/assets/index-4fc80b3e.js b/spaces/mikebars/huggingface/assets/index-4fc80b3e.js
deleted file mode 100644
index 881ba82bd0f455c87f7b3b50b84a505099672c12..0000000000000000000000000000000000000000
--- a/spaces/mikebars/huggingface/assets/index-4fc80b3e.js
+++ /dev/null
@@ -1,41 +0,0 @@
-var hc=Object.defineProperty;var yc=(e,t,n)=>t in e?hc(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Et=(e,t,n)=>(yc(e,typeof t!="symbol"?t+"":t,n),n);(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const l of document.querySelectorAll('link[rel="modulepreload"]'))r(l);new MutationObserver(l=>{for(const i of l)if(i.type==="childList")for(const o of i.addedNodes)o.tagName==="LINK"&&o.rel==="modulepreload"&&r(o)}).observe(document,{childList:!0,subtree:!0});function n(l){const i={};return l.integrity&&(i.integrity=l.integrity),l.referrerPolicy&&(i.referrerPolicy=l.referrerPolicy),l.crossOrigin==="use-credentials"?i.credentials="include":l.crossOrigin==="anonymous"?i.credentials="omit":i.credentials="same-origin",i}function r(l){if(l.ep)return;l.ep=!0;const i=n(l);fetch(l.href,i)}})();var Mr={},vc={get exports(){return Mr},set exports(e){Mr=e}},ul={},ne={},gc={get exports(){return ne},set exports(e){ne=e}},T={};/**
- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
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q.imageClassification({data:e.inputs,model:e.model});e.setOutput(n);break}case"image-segmentation":{const n=await q.imageSegmentation({data:e.inputs,model:e.model});e.setOutput(n);break}case"object-detection":{const n=await q.objectDetection({data:e.inputs,model:e.model});e.setOutput(n);break}case"question-answering":{const n=await q.questionAnswering({inputs:{context:e.inputs,question:e.inputs},model:e.model});e.setOutput(n);break}case"summarization":{const n=await q.summarization({inputs:e.inputs,model:e.model});e.setOutput(n);break}case"table-question-answering":{const n=await q.tableQuestionAnswering({inputs:{query:e.inputs,table:{[e.inputs]:[e.inputs]}},model:e.model});e.setOutput(n);break}case"text-classification":{const n=await q.textClassification({inputs:e.inputs,model:e.model});e.setOutput(n);break}case"text-generation":{const n=await q.textGeneration({inputs:e.inputs,model:e.model});e.setOutput(n);break}case"text-to-image":{const n=await q.textToImage({inputs:e.inputs,model:e.model});e.setOutput(n);break}case"token-classification":{const n=await q.tokenClassification({inputs:e.inputs,model:e.model});e.setOutput(n);break}case"translation":{const n=await q.translation({inputs:e.inputs,model:e.model});e.setOutput(n);break}case"zero-shot-classification":{const n=await q.zeroShotClassification({inputs:e.inputs,model:e.model,parameters:{candidate_labels:[e.inputs]}});e.setOutput(n);break}}}catch(n){n instanceof Error&&e.setOutput(n.message)}e.setLoading(!1)}};return P("button",{className:`bg-yellow-200 py-6 text-center w-full ${e.loading?"cursor-not-allowed opacity-50":""}`,disabled:e.loading,onClick:t,children:e.loading?"Submitting":"Submit"})}return P(ne.Fragment,{})},Hp=e=>ze("div",{className:"w-full",children:[P("p",{className:"text-xl",children:"Output"}),P("img",{className:`w-full ${e.loading?"cursor-wait opacity-50":""}`,src:URL.createObjectURL(e.output)})]}),Wp=e=>{const t=(()=>{try{return JSON.stringify(e.output,void 0,2)}catch(n){if(n instanceof Error)return`Error during JSON.stringify: ${n.message}`}})();return ze("div",{className:"w-full",children:[P("p",{className:"text-xl",children:"Output"}),P("pre",{className:`bg-yellow-200 p-6 select-text w-full whitespace-pre-wrap ${e.loading?"cursor-wait opacity-50":""}`,children:t})]})},Qp=e=>e.output&&e.task?["text-to-image"].includes(e.task)?P(Hp,{loading:e.loading,output:e.output}):P(Wp,{loading:e.loading,output:e.output}):P(ne.Fragment,{}),Kp=()=>{const[e,t]=ne.useState(),[n,r]=ne.useState(),[l,i]=ne.useState(),[o,u]=ne.useState(!1),[s,c]=ne.useState();return P("div",{className:"bg-yellow-500 flex flex-col h-full items-center min-h-screen min-w-screen overflow-auto w-full",children:ze("div",{className:"flex flex-col items-center justify-center py-24 space-y-12 w-2/3 lg:w-1/3",children:[P("header",{className:"text-center text-6xl",children:"🤗"}),P(Mp,{setTask:t,task:e}),P(jp,{model:n,setModel:r,task:e}),P($p,{inputs:l,model:n,setInputs:i,task:e}),P(Vp,{inputs:l,loading:o,model:n,setInputs:i,setOutput:c,task:e}),P(Bp,{inputs:l,loading:o,model:n,setLoading:u,setOutput:c,task:e}),P(Qp,{loading:o,output:s,task:e})]})})},Xp=()=>{const e="root",t=document.getElementById(e);if(t){const n=pc(t),r=P(ne.StrictMode,{children:P(Kp,{})});n.render(r)}};Xp();
diff --git a/spaces/mikeee/llama-2-70b-guanaco-qlora-ggml/run-app.sh b/spaces/mikeee/llama-2-70b-guanaco-qlora-ggml/run-app.sh
deleted file mode 100644
index 626c9eaf89c208f301d460ae020c8c262f251280..0000000000000000000000000000000000000000
--- a/spaces/mikeee/llama-2-70b-guanaco-qlora-ggml/run-app.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-export GRADIO_SERVER_NAME=0.0.0.0
-nodemon -w app.py -x python app.py
diff --git a/spaces/ml6team/Knowledge-graphs/app.py b/spaces/ml6team/Knowledge-graphs/app.py
deleted file mode 100644
index 5b561ab33cbab0d763dfc0ef5028bf47240bb42b..0000000000000000000000000000000000000000
--- a/spaces/ml6team/Knowledge-graphs/app.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import streamlit as st
-import streamlit.components.v1 as components
-import rebel
-import wikipedia
-from utils import clip_text
-from datetime import datetime as dt
-import os
-
-MAX_TOPICS = 3
-
-wiki_state_variables = {
-    'has_run_wiki': False,
-    'wiki_suggestions': [],
-    'wiki_text': [],
-    'nodes': [],
-    "topics": [],
-    "html_wiki": ""
-}
-
-free_text_state_variables = {
-    'has_run_free': False,
-    "html_free": ""
-}
-
-BUTTON_COLUMS = 4
-
-
-def wiki_init_state_variables():
-    for k in free_text_state_variables.keys():
-        if k in st.session_state:
-            del st.session_state[k]
-
-    for k, v in wiki_state_variables.items():
-        if k not in st.session_state:
-            st.session_state[k] = v
-
-
-def wiki_generate_graph():
-    st.session_state["GRAPH_FILENAME"] = str(
-        dt.now().timestamp()*1000) + ".html"
-
-    if 'wiki_text' not in st.session_state:
-        return
-    if len(st.session_state['wiki_text']) == 0:
-        st.error("please enter a topic and select a wiki page first")
-        return
-    with st.spinner(text="Generating graph..."):
-        texts = st.session_state['wiki_text']
-        st.session_state['nodes'] = []
-        nodes = rebel.generate_knowledge_graph(
-            texts, st.session_state["GRAPH_FILENAME"])
-        HtmlFile = open(
-            st.session_state["GRAPH_FILENAME"], 'r', encoding='utf-8')
-        source_code = HtmlFile.read()
-        st.session_state["html_wiki"] = source_code
-        os.remove(st.session_state["GRAPH_FILENAME"])
-        for n in nodes:
-            n = n.lower()
-            if n not in st.session_state['topics']:
-                possible_topics = wikipedia.search(n, results=2)
-                st.session_state['nodes'].extend(possible_topics)
-        st.session_state['nodes'] = list(set(st.session_state['nodes']))
-        st.session_state['has_run_wiki'] = True
-    st.success('Done!')
-
-
-def wiki_show_suggestion():
-    st.session_state['wiki_suggestions'] = []
-    with st.spinner(text="fetching wiki topics..."):
-        if st.session_state['input_method'] == "wikipedia":
-            text = st.session_state.text
-            if (text is not None) and (text != ""):
-                subjects = text.split(",")[:MAX_TOPICS]
-                for subj in subjects:
-                    st.session_state['wiki_suggestions'] += wikipedia.search(
-                        subj, results=3)
-
-
-def wiki_show_text(page_title):
-    with st.spinner(text="fetching wiki page..."):
-        try:
-            page = wikipedia.page(title=page_title, auto_suggest=False)
-            st.session_state['wiki_text'].append(clip_text(page.summary))
-            st.session_state['topics'].append(page_title.lower())
-            st.session_state['wiki_suggestions'].remove(page_title)
-
-        except wikipedia.DisambiguationError as e:
-            with st.spinner(text="Woops, ambigious term, recalculating options..."):
-                st.session_state['wiki_suggestions'].remove(page_title)
-                temp = st.session_state['wiki_suggestions'] + e.options[:3]
-                st.session_state['wiki_suggestions'] = list(set(temp))
-        except wikipedia.WikipediaException:
-            st.session_state['wiki_suggestions'].remove(page_title)
-
-
-def wiki_add_text(term):
-    if len(st.session_state['wiki_text']) > MAX_TOPICS:
-        return
-    try:
-        page = wikipedia.page(title=term, auto_suggest=False)
-        extra_text = clip_text(page.summary)
-
-        st.session_state['wiki_text'].append(extra_text)
-        st.session_state['topics'].append(term.lower())
-        st.session_state['nodes'].remove(term)
-
-    except wikipedia.DisambiguationError as e:
-        with st.spinner(text="Woops, ambigious term, recalculating options..."):
-            st.session_state['nodes'].remove(term)
-            temp = st.session_state['nodes'] + e.options[:3]
-            st.session_state['nodes'] = list(set(temp))
-    except wikipedia.WikipediaException as e:
-        st.session_state['nodes'].remove(term)
-
-
-def wiki_reset_session():
-    for k in wiki_state_variables:
-        del st.session_state[k]
-
-
-def free_reset_session():
-    for k in free_text_state_variables:
-        del st.session_state[k]
-
-
-def free_text_generate():
-    st.session_state["GRAPH_FILENAME"] = str(
-        dt.now().timestamp()*1000) + ".html"
-    text = st.session_state['free_text'][0:100]
-    rebel.generate_knowledge_graph([text], st.session_state["GRAPH_FILENAME"])
-    HtmlFile = open(st.session_state["GRAPH_FILENAME"], 'r', encoding='utf-8')
-    source_code = HtmlFile.read()
-    st.session_state["html_free"] = source_code
-    os.remove(st.session_state["GRAPH_FILENAME"])
-    st.session_state['has_run_free'] = True
-
-
-def free_text_layout():
-    st.text_area("Free text", key="free_text", height=5,
-                 value="Tardigrades, known colloquially as water bears or moss piglets, are a phylum of eight-legged segmented micro-animals.")
-    st.button("Generate", on_click=free_text_generate,
-              key="free_text_generate")
-
-
-def free_test_init_state_variables():
-    for k in wiki_state_variables.keys():
-        if k in st.session_state:
-            del st.session_state[k]
-
-    for k, v in free_text_state_variables.items():
-        if k not in st.session_state:
-            st.session_state[k] = v
-
-
-st.title('RE:Belle')
-st.markdown(
-    """
-### Building Beautiful Knowledge Graphs With REBEL
-""")
-st.selectbox(
-    'input method',
-    ('wikipedia', 'free text'),  key="input_method")
-
-
-def show_wiki_hub_page():
-    st.sidebar.button("Reset", on_click=wiki_reset_session, key="reset_key")
-
-    st.sidebar.markdown(
-        """
-## How To Create a Graph:
-- Enter wikipedia search terms, separated by comma's
-- Choose one or more of the suggested topics (max 3)
-- Click generate!
-"""
-    )
-    cols = st.columns([8, 1])
-    with cols[0]:
-        st.text_input("wikipedia search term", on_change=wiki_show_suggestion,
-                      key="text", value="graphs, are, awesome")
-    with cols[1]:
-        st.text('')
-        st.text('')
-        st.button("Search", on_click=wiki_show_suggestion,
-                  key="show_suggestion_key")
-
-    if len(st.session_state['wiki_suggestions']) != 0:
-        num_buttons = len(st.session_state['wiki_suggestions'])
-        num_cols = num_buttons if 0 < num_buttons < BUTTON_COLUMS else BUTTON_COLUMS
-        columns = st.columns([1] * num_cols)
-        for q in range(1 + num_buttons//num_cols):
-            for i, (c, s) in enumerate(zip(columns, st.session_state['wiki_suggestions'][q*num_cols: (q+1)*num_cols])):
-                with c:
-                    st.button(s, on_click=wiki_show_text, args=(
-                        s,), key=str(i)+s+"wiki_suggestion")
-
-    if len(st.session_state['wiki_text']) != 0:
-        for i, t in enumerate(st.session_state['wiki_text']):
-            new_expander = st.expander(label=t[:30] + "...", expanded=(i == 0))
-            with new_expander:
-                st.markdown(t)
-
-    if len(st.session_state['wiki_text']) > 0:
-        st.button("Generate", on_click=wiki_generate_graph, key="gen_graph")
-    st.sidebar.markdown(
-        """
-    ## How to expand the graph
-    - Click a button below the graph to expand that node
-     (Only nodes that have wiki pages will be expanded)
-    - Hit the Generate button again to expand your graph!
-    """
-    )
-
-    if st.session_state['has_run_wiki']:
-
-        components.html(st.session_state["html_wiki"], width=720, height=600)
-        num_buttons = len(st.session_state["nodes"])
-        num_cols = num_buttons if 0 < num_buttons < BUTTON_COLUMS else BUTTON_COLUMS
-        columns = st.columns([1] * num_cols + [1])
-
-        for q in range(1 + num_buttons//num_cols):
-            for i, (c, s) in enumerate(zip(columns, st.session_state["nodes"][q*num_cols: (q+1)*num_cols])):
-                with c:
-                    st.button(s, on_click=wiki_add_text,
-                              args=(s,), key=str(i)+s)
-
-
-def show_free_text_hub_page():
-    st.sidebar.button("Reset", on_click=free_reset_session,
-                      key="free_reset_key")
-    st.sidebar.markdown(
-        """
-## How To Create a Graph:
-- Enter a text you'd like to see as a graph.
-- Click generate!
-"""
-    )
-
-    free_text_layout()
-
-    if st.session_state['has_run_free']:
-        components.html(st.session_state["html_free"], width=720, height=600)
-
-
-if st.session_state['input_method'] == "wikipedia":
-    wiki_init_state_variables()
-    show_wiki_hub_page()
-else:
-    free_test_init_state_variables()
-    show_free_text_hub_page()
-
-
-st.sidebar.markdown(
-    """
-## What This Is And Why We Built it
-
-This space shows how a transformer network can be used to convert *human* text into a computer-queryable format: a **knowledge graph**. Knowledge graphs are graphs where each node (or *vertex* if you're fancy) represent a concept/person/thing and each edge the link between those concepts. If you'd like to know more, you can read [this blogpost](https://www.ml6.eu/knowhow/knowledge-graphs-an-introduction-and-business-applications).
-
-Knowledge graphs aren't just cool to look at, they are an extremely versatile way of storing data, and are used in machine learning to perform tasks like fraud detection. You can read more about the applications of knowledge graphs in ML in [this blogpost](https://blog.ml6.eu/how-are-knowledge-graphs-and-machine-learning-related-ff6f5c1760b5).
-
-There is one problem though: building knowledge graphs from scratch is a time-consuming and tedious task, so it would be a lot easier if we could leverage machine learning to **create** them from existing texts. This demo shows how a model named **REBEL** has been trained to do just that: it reads summaries from Wikipedia (or any other text you input), and generates a graph containing the information it distills from the text.
-"""
-)
-
-st.sidebar.markdown(
-    """
-*Credits for the REBEL model go out to Pere-Lluís Huguet Cabot and Roberto Navigli.
-The code can be found [here](https://github.com/Babelscape/rebel),
-and the original paper [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf)*
-"""
-)
diff --git a/spaces/moussaalmoussa/ChatGPT4/README.md b/spaces/moussaalmoussa/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/moussaalmoussa/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mrmocciai/rvc-models/infer_pack/attentions.py b/spaces/mrmocciai/rvc-models/infer_pack/attentions.py
deleted file mode 100644
index 77cb63ffccf3e33badf22d50862a64ba517b487f..0000000000000000000000000000000000000000
--- a/spaces/mrmocciai/rvc-models/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer_pack import commons
-from infer_pack import modules
-from infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/msmilauer/AutoGPT-duplicated2/run.sh b/spaces/msmilauer/AutoGPT-duplicated2/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/msmilauer/AutoGPT-duplicated2/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
-    echo Installing missing packages...
-    pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/mthsk/sovits-100orangejuice/data_utils.py b/spaces/mthsk/sovits-100orangejuice/data_utils.py
deleted file mode 100644
index 7c76fd1c3a45b8304d916161718c7763874f3e35..0000000000000000000000000000000000000000
--- a/spaces/mthsk/sovits-100orangejuice/data_utils.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import modules.commons as commons
-import utils
-from modules.mel_processing import spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-
-# import h5py
-
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths, hparams, all_in_mem: bool = False):
-        self.audiopaths = load_filepaths_and_text(audiopaths)
-        self.max_wav_value = hparams.data.max_wav_value
-        self.sampling_rate = hparams.data.sampling_rate
-        self.filter_length = hparams.data.filter_length
-        self.hop_length = hparams.data.hop_length
-        self.win_length = hparams.data.win_length
-        self.sampling_rate = hparams.data.sampling_rate
-        self.use_sr = hparams.train.use_sr
-        self.spec_len = hparams.train.max_speclen
-        self.spk_map = hparams.spk
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths)
-        
-        self.all_in_mem = all_in_mem
-        if self.all_in_mem:
-            self.cache = [self.get_audio(p[0]) for p in self.audiopaths]
-
-    def get_audio(self, filename):
-        filename = filename.replace("\\", "/")
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-
-        # Ideally, all data generated after Mar 25 should have .spec.pt
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                                     self.sampling_rate, self.hop_length, self.win_length,
-                                     center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-
-        spk = filename.split("/")[-2]
-        spk = torch.LongTensor([self.spk_map[spk]])
-
-        f0 = np.load(filename + ".f0.npy")
-        f0, uv = utils.interpolate_f0(f0)
-        f0 = torch.FloatTensor(f0)
-        uv = torch.FloatTensor(uv)
-
-        c = torch.load(filename+ ".soft.pt")
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
-
-
-        lmin = min(c.size(-1), spec.size(-1))
-        assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
-        assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
-        spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
-        audio_norm = audio_norm[:, :lmin * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk, uv
-
-    def random_slice(self, c, f0, spec, audio_norm, spk, uv):
-        # if spec.shape[1] < 30:
-        #     print("skip too short audio:", filename)
-        #     return None
-        if spec.shape[1] > 800:
-            start = random.randint(0, spec.shape[1]-800)
-            end = start + 790
-            spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
-            audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk, uv
-
-    def __getitem__(self, index):
-        if self.all_in_mem:
-            return self.random_slice(*self.cache[index])
-        else:
-            return self.random_slice(*self.get_audio(self.audiopaths[index][0]))
-
-    def __len__(self):
-        return len(self.audiopaths)
-
-
-class TextAudioCollate:
-
-    def __call__(self, batch):
-        batch = [b for b in batch if b is not None]
-
-        input_lengths, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].shape[1] for x in batch]),
-            dim=0, descending=True)
-
-        max_c_len = max([x[0].size(1) for x in batch])
-        max_wav_len = max([x[3].size(1) for x in batch])
-
-        lengths = torch.LongTensor(len(batch))
-
-        c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
-        f0_padded = torch.FloatTensor(len(batch), max_c_len)
-        spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        spkids = torch.LongTensor(len(batch), 1)
-        uv_padded = torch.FloatTensor(len(batch), max_c_len)
-
-        c_padded.zero_()
-        spec_padded.zero_()
-        f0_padded.zero_()
-        wav_padded.zero_()
-        uv_padded.zero_()
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            c = row[0]
-            c_padded[i, :, :c.size(1)] = c
-            lengths[i] = c.size(1)
-
-            f0 = row[1]
-            f0_padded[i, :f0.size(0)] = f0
-
-            spec = row[2]
-            spec_padded[i, :, :spec.size(1)] = spec
-
-            wav = row[3]
-            wav_padded[i, :, :wav.size(1)] = wav
-
-            spkids[i, 0] = row[4]
-
-            uv = row[5]
-            uv_padded[i, :uv.size(0)] = uv
-
-        return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded
diff --git a/spaces/muteekhan06/English-to-French/app.py b/spaces/muteekhan06/English-to-French/app.py
deleted file mode 100644
index 908963fd063f22751054c417673c1d90c6d8c36d..0000000000000000000000000000000000000000
--- a/spaces/muteekhan06/English-to-French/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-pip install tensorflow==2.0
-import subprocess
-subprocess.run(["pip", "install", "-q", "gradio"])
-subprocess.run(["pip", "install", "-q", "transformers"])
-subprocess.run(["pip", "install", "-q", "pipeline"])
-import gradio as gr
-from transformers import pipeline
-translation_pipeline = pipeline("translation_en_to_fr")
-result = translation_pipeline("English to French Translator")
-result[0]["translation_text"]
-def translate_gradio(input):
-    result = translation_pipeline(input)
-    return result[0]['translation_text']
-translate_interface = gr.Interface(fn = translate_gradio,
-                                   inputs="text",
-                                   outputs="text")
-translate_interface.launch()
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-from gradio_client import Client
-
-client = Client("https://d9b910edb9b1f6b4a6.gradio.live/")
-result = client.predict(
-		"Howdy!",	# str  in 'input' Textbox component
-		api_name="/predict"
-)
-print(result)
\ No newline at end of file
diff --git a/spaces/mygyasir/digiplay-helloRealisticMan_v1.0beta/README.md b/spaces/mygyasir/digiplay-helloRealisticMan_v1.0beta/README.md
deleted file mode 100644
index 98124b54ed380e7446d8298efed731d8b6c986a4..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/digiplay-helloRealisticMan_v1.0beta/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Digiplay-helloRealisticMan V1.0beta
-emoji: 🐠
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/web/static/css/main.css b/spaces/mygyasir/genious_bgremover/carvekit/web/static/css/main.css
deleted file mode 100644
index 5e6668f39e44368ad179d902e69f41840f5cc994..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/genious_bgremover/carvekit/web/static/css/main.css
+++ /dev/null
@@ -1,687 +0,0 @@
-
-/* =================================== */
-/*	Basic Style
-/* =================================== */
-
-body {
-
-    /*background:-moz-element(.particles-js-canvas-el);*/
-    background-color: #061b2a;
-    font-family: 'Open Sans', sans-serif;
-    line-height: 21px;
-	  font-size: 13px;
-    color: #6a737b;
-}
-
-ol, ul {
-    margin: 0;
-    padding: 0;
-    list-style: none;
-}
-
-figure, p {
-    margin: 0;
-}
-
-a {
-    color: #fff;
-
-    -webkit-transition: all .3s ease-in 0s;
-       -moz-transition: all .3s ease-in 0s;
-        -ms-transition: all .3s ease-in 0s;
-         -o-transition: all .3s ease-in 0s;
-            transition: all .3s ease-in 0s;
-}
-
-iframe {
-    border: 0;
-}
-
-a, a:focus, a:hover {
-    text-decoration: none;
-    outline: 0;
-}
-
-a:focus, a:hover {
-    color: #6CB670;
-}
-
-h1, h2, h3,
-h4, h5, h6 {
-	font-weight: normal;
-	margin: 0;
-}
-
-.clear:before,
-.clear:after {
-    content: " ";
-    display: table;
-}
-
-.clear:after {
-   clear: both;
-}
-
-.clear {
-   *zoom: 1;
-}
-
-span.color {
-	color: #ff9209;
-}
-
-body > section,
-.footer {
-	padding: 70px 0;
-}
-
-.sec-title {}
-
-.sec-title h2 {
-  color: #ff9209;
-  font-size: 28px;
-  font-weight: 800;
-  text-transform: uppercase;
-}
-
-.sec-sub-title {
-	margin: 35px 0 45px;
-}
-
-.sec-sub-title p {
-	font-weight: 600;
-	line-height: 24px;
-	font-size: 18px;
-	color: #5b646e;
-}
-
-.devider {
-  margin-top: 30px;
-}
-
-.devider i {
-  color: #cccccc;
-}
-
-.devider:before,
-.devider:after {
-  content: "______________________";
-  color: #e6e8ea;
-  position: relative;
-  bottom: 6px;
-}
-
-.devider:before {
-  right: 10px;
-}
-
-.devider:after {
-  left: 10px;
-}
-
-.mb50 {
-	margin-bottom: 50px;
-}
-
-#preloader {
-  background-color: #fff;
-  height: 100%;
-  position: fixed;
-  width: 100%;
-  z-index: 1100;
-}
-
-#preloader > img {
-  left: 47%;
-  position: absolute;
-  top: 48%;
-}
-
-
-/*=========================================
-	Header
-==========================================*/
-
-#navigation {
-  border: 0 none;
-  margin: 0;
-
-    -webkit-transition: background-color 800ms linear;
-       -moz-transition: background-color 800ms linear;
-	      -ms-transition: background-color 800ms linear;
-         -o-transition: background-color 800ms linear;
-		      	transition: background-color 800ms linear;
-}
-
-.navbar-toggle i {
-	color: #fff;
-}
-
-.navbar-brand {
-  padding: 0;
-}
-
-.navbar-nav li a {
-	border-top: 1px solid transparent;
-}
-
-.navbar-nav li a.current,
-.navbar-nav li a:focus,
-.navbar-nav li a:hover {
-	background-color: transparent;
-	border-top: 3px solid #a8620a;
-	color: #fff;
-}
-
-
-/*=========================================
-	Top section
-==========================================*/
-
-#top {
-	/*background-image: url("../img/art.gif");*/
-	background-attachment: fixed !important;
-	background-position: center center;
-	background-repeat: no-repeat;
-	background-size: cover;
-	padding: 0;
-}
-
-.top .devider:before,
-.top .devider:after {
-	color: #737C85;
-}
-
-.top .devider i {
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-}
-
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-	background-color: rgba(6,32,51,.8);
-	padding: 70px 0;
-}
-
-
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-  font-size:52px;
-  font-weight: 300;
-  margin: 20px 0 20px;
-}
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-.caption h2 span {
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-}
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-.caption h3 {
-  font-size: 18px;
-  font-weight: 300;
-  margin: 20px 0 20px;
-}
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-  color: #fff;
-  font-size: 21px;
-  font-weight: 300;
-}
-
-  .caption p:before,
-  .caption p:after {
-  color: #e4720d;
-  content: "___";
-  position: relative;
-  top: -8px;
-  }
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-  .caption p:before {
-    right: 20px;
-  }
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-  .caption p:after {
-    left: 20px;
-  }
-
-}
-
-
-
-
-/*=========================================
-	Upload form
-==========================================*/
-
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-#panel{
-  cursor: pointer
-
-  margin: 10px;
-  padding: 0;
-}
-.img-cont {
-    cursor: pointer;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-}
-.drag-image {
-    cursor: pointer;
-    position: relative;
-    overflow: hidden;
-    display: inline-block;
-    padding: 10px;
-    border: 1px dashed #000;
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-    width: auto;
-    border-radius: 13px;
-    font-weight: 400;
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-    align-items: center;
-    justify-content: center;
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-}
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-.drag-image.active {
-    border: 1px solid #000
-}
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-.drag-image .icon {
-    font-size: 30px;
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-}
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-    font-weight: 300;
-    color: #000;
-    margin: 10px 0 15px 0
-}
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-.drag-image button {
-    padding: 10px 25px;
-    font-size: 14px;
-    font-weight: 300;
-    border: none;
-    outline: none;
-    background: transparent;
-    color: #e4720d;
-    border-radius: 5px;
-    cursor: pointer;
-    transition: all 0.5s
-}
-
-.drag-image button:hover {
-    background-color: #fff;
-    color: #0EB493;
-
-    border: 1px solid #0EB493;
-}
-
-.drag-image img {
-    height: 100%;
-    width: 100%;
-    object-fit: cover;
-    border-radius: 10px
-}
-.panel-container{
-  display:grid;
-  padding:15px;
-  justify-content:center;
-}
-
-.form-group{
-  display:flex;
-  flex-direction:column;
-}
-.form-control{
-  padding:.5rem;
-}
-.form-control{
-    border-radius: 4px;
-    border: 1px solid #8E919D;
-}
-.form-control:focus{
-  outline:none;
-  border-color:#57D8C8;
-  box-shadow:0 0 2px 1px rgba(87, 216, 200,.6);
-}
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-
-.drag-image input[type=file] {
-  cursor: pointer;
-  font-size: 100px;
-  position: absolute;
-  left: 0;
-  top: 0;
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-}
-
-/*=========================================
-	Our Works
-==========================================*/
-
-.work-filter {
-  margin-bottom: 35px;
-}
-
-.work-filter ul li {
-    display: inline-block;
-}
-
-.work-filter ul li a {
-  color: #062033;
-  display: block;
-  font-size: 14px;
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-}
-
-.work-filter ul li a:hover,
-.work-filter ul li a.active {
-  background-color: #0eb493;
-  border-radius: 6px;
-  color: #fff;
-  padding: 5px 17px;
-}
-
-.mix {
-    display: none;
-}
-
-.work-item {
-    float: left;
-    width: 25%;
-    position: relative;
-}
-
-.work-item > img {
-  display: block;
-  height: auto;
-  width: 100%;
-}
-
-.overlay {
-	background-color: rgba(0,0,0,.9);
-	text-align: center;
-	position: absolute;
-	left: 0;
-	top: 0;
-	width: 100%;
-	height: 100%;
-	color: #fff;
-
-	opacity: 0;
-	filter: alpha(opacity=0);
-
-	-webkit-transition: all 450ms ease-out 0s;
-	   -moz-transition: all 450ms ease-out 0s;
-		 -o-transition: all 450ms ease-out 0s;
-		    transition: all 450ms ease-out 0s;
-
-	-webkit-transform: rotateY(180deg) scale(0.5,0.5);
-	   -moz-transform: rotateY(180deg) scale(0.5,0.5);
-		-ms-transform: rotateY(180deg) scale(0.5,0.5);
-		 -o-transform: rotateY(180deg) scale(0.5,0.5);
-			transform: rotateY(180deg) scale(0.5,0.5);
-}
-
-.work-item:hover .overlay {
-	opacity: 1;
-	filter: alpha(opacity=100);
-
-	-webkit-transform: rotateY(0deg) scale(1,1);
-	   -moz-transform: rotateY(0deg) scale(1,1);
-		-ms-transform: rotateY(0deg) scale(1,1);
-		 -o-transform: rotateY(0deg) scale(1,1);
-		    transform: rotateY(0deg) scale(1,1);
-}
-
-.work-item .overlay a {
-  border: 1px solid #fff;
-  border-radius: 50%;
-  display: inline-block;
-  margin-top: 20%;
-  padding: 7px 10px;
-}
-
-.work-item .overlay a:hover {
-  color: #fff;
-}
-
-.work-item .overlay h4 {
-  font-size: 18px;
-  font-weight: 700;
-  line-height: 24px;
-  margin: 25px 0 8px;
-}
-
-.work-item .overlay p {
-  font-size: 14px;
-  line-height: 24px;
-}
-
-/*=========================================
-	Some fun facts
-==========================================*/
-
-#facts {
-	background-image: url("../img/art.gif");
-	background-attachment: fixed !important;
-	background-position: center center;
-	background-repeat: no-repeat;
-	background-size: cover;
-	padding: 0;
-}
-
-.facts .devider:before,
-.facts .devider:after {
-	color: #737C85;
-}
-
-.facts .devider i {
-	color: #fff;
-}
-
-.parallax-overlay {
-	background-color: rgba(6,32,51,.8);
-	padding: 70px 0;
-}
-
-.counters-item {
-  color: #fff;
-}
-
-.counters-item i {
-  border: 1px solid #737C85;
-  border-radius: 50%;
-  color: #fff;
-  display: inline-block;
-  height: 120px;
-  margin: 0 0 35px;
-  padding: 40px 0 0;
-  width: 120px;
-}
-
-.counters-item strong {
-  display: block;
-  font-size: 60px;
-  font-weight: 600;
-  line-height: 60px;
-}
-
-.counters-item p {
-  font-size: 18px;
-  line-height: 24px;
-  margin-top: 15px;
-  text-transform: uppercase;
-}
-
-
-/*=========================================
-	Contact Us
-==========================================*/
-
-.contact {
-	padding-bottom: 0;
-}
-
-.contact-address h3 {
-  color: #062033;
-  font-size: 22px;
-  line-height: 32px;
-  margin-bottom: 25px;
-}
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-.contact-address p {
-  line-height: 24px;
-}
-
-.contact-form {}
-
-.contact-form h3 {
-  color: #062033;
-  font-size: 24px;
-  font-weight: 700;
-  line-height: 32px;
-  margin-bottom: 25px;
-}
-
-.contact-form .input-group {
-  width: 100%;
-}
-
-.contact-form .form-control {
-  border-color: -moz-use-text-color -moz-use-text-color #cccccc;
-  border-radius: 0;
-  border-style: none none solid;
-  border-width: 0 0 1px;
-  box-shadow: none;
-  margin-bottom: 10px;
-}
-
-.contact-form .input-field {
-  width: 48%;
-  float: left;
-  margin-right: 4%;
-}
-
-.contact-form .input-field:last-child {
-  margin-right: 0;
-}
-
-.contact-form .form-control.error {
-border-bottom-color: #c0392b;
-}
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-  color: #c0392b;
-  font-weight: normal;
-  text-transform: capitalize;
-}
-
-.contact-form #form-submit {
-  background: url("../img/envelop.png") no-repeat scroll 0 15px transparent;
-  border: 0 none;
-  color: #000;
-  font-size: 16px;
-  line-height: 24px;
-  padding: 10px;
-  text-align: right;
-  width: 150px;
-}
-
-.footer-social {
-  margin-top: 17px;
-}
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-.footer-social li a {
-  color: #cdd2d6;
-  display: block;
-  margin-bottom: 10px;
-}
-
-#map_canvas {
-  height: 215px;
-  width: 100%;
-}
-
-
-/*=========================================
-	Footer
-==========================================*/
-
-
-.footer {
-  background-color: #061b2a;
-  /*border-top: 5px solid #062033;*/
-  color: #fff;
-}
-
-.footer a:hover {
-  color: #062033;
-}
-
-.footer h6 {
-  font-size: 14px;
-  font-weight: 700;
-  line-height: 24px;
-  margin-bottom: 30px;
-}
-
-
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-  line-height: 24px;
-}
-
-.footer-single ul {
-  }
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-  line-height: 32px;
-}
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-.footer-single p i {
-  margin: 0 10px;
-}
-
-.copyright {
-  color: #fff;
-  margin-top: 20px;
-}
-
-#back-top {
-  bottom: 20px;
-  position: fixed;
-  right: 25px;
-  z-index: 9;
-}
-
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-.clearfix{*zoom:1;}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0;}
-.clearfix:after{clear:both;}
-.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0;}
-.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;}
-.btn-file{overflow:hidden;position:relative;vertical-align:middle;}.btn-file>input{position:absolute;top:0;right:0;margin:0;opacity:0;filter:alpha(opacity=0);transform:translate(-300px, 0) scale(4);font-size:23px;direction:ltr;cursor:pointer;}
-.fileupload{margin-bottom:9px;}.fileupload .uneditable-input{display:inline-block;margin-bottom:0px;vertical-align:middle;cursor:text;}
-.fileupload .thumbnail{overflow:hidden;display:inline-block;margin-bottom:5px;vertical-align:middle;text-align:center;}.fileupload .thumbnail>img{display:inline-block;vertical-align:middle;max-height:100%;}
-.fileupload .btn{vertical-align:middle;}
-.fileupload-exists .fileupload-new,.fileupload-new .fileupload-exists{display:none;}
-.fileupload-inline .fileupload-controls{display:inline;}
-.fileupload-new .input-append .btn-file{-webkit-border-radius:0 3px 3px 0;-moz-border-radius:0 3px 3px 0;border-radius:0 3px 3px 0;}
-.thumbnail-borderless .thumbnail{border:none;padding:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none;}
-.fileupload-new.thumbnail-borderless .thumbnail{border:1px solid #ddd;}
-.control-group.warning .fileupload .uneditable-input{color:#a47e3c;border-color:#a47e3c;}
-.control-group.warning .fileupload .fileupload-preview{color:#a47e3c;}
-.control-group.warning .fileupload .thumbnail{border-color:#a47e3c;}
-.control-group.error .fileupload .uneditable-input{color:#b94a48;border-color:#b94a48;}
-.control-group.error .fileupload .fileupload-preview{color:#b94a48;}
-.control-group.error .fileupload .thumbnail{border-color:#b94a48;}
-.control-group.success .fileupload .uneditable-input{color:#468847;border-color:#468847;}
-.control-group.success .fileupload .fileupload-preview{color:#468847;}
-.control-group.success .fileupload .thumbnail{border-color:#468847;}
\ No newline at end of file
diff --git a/spaces/myrad01/Inpaint-Anything/replace_anything.py b/spaces/myrad01/Inpaint-Anything/replace_anything.py
deleted file mode 100644
index e08cb0497bdee5ad5e9cc4689d6c9cd02a64f28f..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/replace_anything.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import cv2
-import sys
-import argparse
-import numpy as np
-import torch
-from pathlib import Path
-from matplotlib import pyplot as plt
-from typing import Any, Dict, List
-from sam_segment import predict_masks_with_sam
-from stable_diffusion_inpaint import replace_img_with_sd
-from utils import load_img_to_array, save_array_to_img, dilate_mask, \
-    show_mask, show_points
-
-
-def setup_args(parser):
-    parser.add_argument(
-        "--input_img", type=str, required=True,
-        help="Path to a single input img",
-    )
-    parser.add_argument(
-        "--point_coords", type=float, nargs='+', required=True,
-        help="The coordinate of the point prompt, [coord_W coord_H].",
-    )
-    parser.add_argument(
-        "--point_labels", type=int, nargs='+', required=True,
-        help="The labels of the point prompt, 1 or 0.",
-    )
-    parser.add_argument(
-        "--text_prompt", type=str, required=True,
-        help="Text prompt",
-    )
-    parser.add_argument(
-        "--dilate_kernel_size", type=int, default=None,
-        help="Dilate kernel size. Default: None",
-    )
-    parser.add_argument(
-        "--output_dir", type=str, required=True,
-        help="Output path to the directory with results.",
-    )
-    parser.add_argument(
-        "--sam_model_type", type=str,
-        default="vit_h", choices=['vit_h', 'vit_l', 'vit_b'],
-        help="The type of sam model to load. Default: 'vit_h"
-    )
-    parser.add_argument(
-        "--sam_ckpt", type=str, required=True,
-        help="The path to the SAM checkpoint to use for mask generation.",
-    )
-    parser.add_argument(
-        "--seed", type=int,
-        help="Specify seed for reproducibility.",
-    )
-    parser.add_argument(
-        "--deterministic", action="store_true",
-        help="Use deterministic algorithms for reproducibility.",
-    )
-
-
-
-if __name__ == "__main__":
-    """Example usage:
-    python replace_anything.py \
-        --input_img FA_demo/FA1_dog.png \
-        --point_coords 750 500 \
-        --point_labels 1 \
-        --text_prompt "sit on the swing" \
-        --output_dir ./results \
-        --sam_model_type "vit_h" \
-        --sam_ckpt sam_vit_h_4b8939.pth 
-    """
-    parser = argparse.ArgumentParser()
-    setup_args(parser)
-    args = parser.parse_args(sys.argv[1:])
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    img = load_img_to_array(args.input_img)
-
-    masks, _, _ = predict_masks_with_sam(
-        img,
-        [args.point_coords],
-        args.point_labels,
-        model_type=args.sam_model_type,
-        ckpt_p=args.sam_ckpt,
-        device=device,
-    )
-    masks = masks.astype(np.uint8) * 255
-
-    # dilate mask to avoid unmasked edge effect
-    if args.dilate_kernel_size is not None:
-        masks = [dilate_mask(mask, args.dilate_kernel_size) for mask in masks]
-
-    # visualize the segmentation results
-    img_stem = Path(args.input_img).stem
-    out_dir = Path(args.output_dir) / img_stem
-    out_dir.mkdir(parents=True, exist_ok=True)
-    for idx, mask in enumerate(masks):
-        # path to the results
-        mask_p = out_dir / f"mask_{idx}.png"
-        img_points_p = out_dir / f"with_points.png"
-        img_mask_p = out_dir / f"with_{Path(mask_p).name}"
-
-        # save the mask
-        save_array_to_img(mask, mask_p)
-
-        # save the pointed and masked image
-        dpi = plt.rcParams['figure.dpi']
-        height, width = img.shape[:2]
-        plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
-        plt.imshow(img)
-        plt.axis('off')
-        show_points(plt.gca(), [args.point_coords], args.point_labels,
-                    size=(width*0.04)**2)
-        plt.savefig(img_points_p, bbox_inches='tight', pad_inches=0)
-        show_mask(plt.gca(), mask, random_color=False)
-        plt.savefig(img_mask_p, bbox_inches='tight', pad_inches=0)
-        plt.close()
-
-    # fill the masked image
-    for idx, mask in enumerate(masks):
-        if args.seed is not None:
-            torch.manual_seed(args.seed)
-        mask_p = out_dir / f"mask_{idx}.png"
-        img_replaced_p = out_dir / f"replaced_with_{Path(mask_p).name}"
-        img_replaced = replace_img_with_sd(
-            img, mask, args.text_prompt, device=device)
-        save_array_to_img(img_replaced, img_replaced_p)
diff --git a/spaces/nakas/audio-diffusion_style_transfer/scripts/train_unconditional.py b/spaces/nakas/audio-diffusion_style_transfer/scripts/train_unconditional.py
deleted file mode 100644
index e1b4d22e532273571428edab9f9c02289cab9f14..0000000000000000000000000000000000000000
--- a/spaces/nakas/audio-diffusion_style_transfer/scripts/train_unconditional.py
+++ /dev/null
@@ -1,390 +0,0 @@
-# based on https://github.com/huggingface/diffusers/blob/main/examples/train_unconditional.py
-
-import argparse
-import os
-
-import torch
-import torch.nn.functional as F
-
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from datasets import load_from_disk, load_dataset
-from diffusers import (DiffusionPipeline, DDPMScheduler, UNet2DModel,
-                       DDIMScheduler, AutoencoderKL)
-from diffusers.hub_utils import init_git_repo, push_to_hub
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel
-from torchvision.transforms import (
-    Compose,
-    Normalize,
-    ToTensor,
-)
-import numpy as np
-from tqdm.auto import tqdm
-from librosa.util import normalize
-
-from audiodiffusion.mel import Mel
-from audiodiffusion import LatentAudioDiffusionPipeline, AudioDiffusionPipeline
-
-logger = get_logger(__name__)
-
-
-def main(args):
-    output_dir = os.environ.get("SM_MODEL_DIR", None) or args.output_dir
-    logging_dir = os.path.join(output_dir, args.logging_dir)
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with="tensorboard",
-        logging_dir=logging_dir,
-    )
-
-    if args.dataset_name is not None:
-        if os.path.exists(args.dataset_name):
-            dataset = load_from_disk(args.dataset_name,
-                                     args.dataset_config_name)["train"]
-        else:
-            dataset = load_dataset(
-                args.dataset_name,
-                args.dataset_config_name,
-                cache_dir=args.cache_dir,
-                use_auth_token=True if args.use_auth_token else None,
-                split="train",
-            )
-    else:
-        dataset = load_dataset(
-            "imagefolder",
-            data_dir=args.train_data_dir,
-            cache_dir=args.cache_dir,
-            split="train",
-        )
-    # Determine image resolution
-    resolution = dataset[0]['image'].height, dataset[0]['image'].width
-
-    augmentations = Compose([
-        ToTensor(),
-        Normalize([0.5], [0.5]),
-    ])
-
-    def transforms(examples):
-        if args.vae is not None and vqvae.config['in_channels'] == 3:
-            images = [
-                augmentations(image.convert('RGB'))
-                for image in examples["image"]
-            ]
-        else:
-            images = [augmentations(image) for image in examples["image"]]
-        return {"input": images}
-
-    dataset.set_transform(transforms)
-    train_dataloader = torch.utils.data.DataLoader(
-        dataset, batch_size=args.train_batch_size, shuffle=True)
-
-    vqvae = None
-    if args.vae is not None:
-        try:
-            vqvae = AutoencoderKL.from_pretrained(args.vae)
-        except EnvironmentError:
-            vqvae = LatentAudioDiffusionPipeline.from_pretrained(
-                args.vae).vqvae
-        # Determine latent resolution
-        with torch.no_grad():
-            latent_resolution = vqvae.encode(
-                torch.zeros((1, 1) +
-                            resolution)).latent_dist.sample().shape[2:]
-
-    if args.from_pretrained is not None:
-        pipeline = {
-            'LatentAudioDiffusionPipeline': LatentAudioDiffusionPipeline,
-            'AudioDiffusionPipeline': AudioDiffusionPipeline
-        }.get(
-            DiffusionPipeline.get_config_dict(
-                args.from_pretrained)['_class_name'], AudioDiffusionPipeline)
-        pipeline = pipeline.from_pretrained(args.from_pretrained)
-        model = pipeline.unet
-        if hasattr(pipeline, 'vqvae'):
-            vqvae = pipeline.vqvae
-    else:
-        model = UNet2DModel(
-            sample_size=resolution if vqvae is None else latent_resolution,
-            in_channels=1
-            if vqvae is None else vqvae.config['latent_channels'],
-            out_channels=1
-            if vqvae is None else vqvae.config['latent_channels'],
-            layers_per_block=2,
-            block_out_channels=(128, 128, 256, 256, 512, 512),
-            down_block_types=(
-                "DownBlock2D",
-                "DownBlock2D",
-                "DownBlock2D",
-                "DownBlock2D",
-                "AttnDownBlock2D",
-                "DownBlock2D",
-            ),
-            up_block_types=(
-                "UpBlock2D",
-                "AttnUpBlock2D",
-                "UpBlock2D",
-                "UpBlock2D",
-                "UpBlock2D",
-                "UpBlock2D",
-            ),
-        )
-
-    if args.scheduler == "ddpm":
-        noise_scheduler = DDPMScheduler(
-            num_train_timesteps=args.num_train_steps)
-    else:
-        noise_scheduler = DDIMScheduler(
-            num_train_timesteps=args.num_train_steps)
-
-    optimizer = torch.optim.AdamW(
-        model.parameters(),
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps,
-        num_training_steps=(len(train_dataloader) * args.num_epochs) //
-        args.gradient_accumulation_steps,
-    )
-
-    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        model, optimizer, train_dataloader, lr_scheduler)
-
-    ema_model = EMAModel(
-        getattr(model, "module", model),
-        inv_gamma=args.ema_inv_gamma,
-        power=args.ema_power,
-        max_value=args.ema_max_decay,
-    )
-
-    if args.push_to_hub:
-        repo = init_git_repo(args, at_init=True)
-
-    if accelerator.is_main_process:
-        run = os.path.split(__file__)[-1].split(".")[0]
-        accelerator.init_trackers(run)
-
-    mel = Mel(x_res=resolution[1],
-              y_res=resolution[0],
-              hop_length=args.hop_length)
-
-    global_step = 0
-    for epoch in range(args.num_epochs):
-        progress_bar = tqdm(total=len(train_dataloader),
-                            disable=not accelerator.is_local_main_process)
-        progress_bar.set_description(f"Epoch {epoch}")
-
-        if epoch < args.start_epoch:
-            for step in range(len(train_dataloader)):
-                optimizer.step()
-                lr_scheduler.step()
-                progress_bar.update(1)
-                global_step += 1
-            if epoch == args.start_epoch - 1 and args.use_ema:
-                ema_model.optimization_step = global_step
-            continue
-
-        model.train()
-        for step, batch in enumerate(train_dataloader):
-            clean_images = batch["input"]
-
-            if vqvae is not None:
-                vqvae.to(clean_images.device)
-                with torch.no_grad():
-                    clean_images = vqvae.encode(
-                        clean_images).latent_dist.sample()
-                # Scale latent images to ensure approximately unit variance
-                clean_images = clean_images * 0.18215
-
-            # Sample noise that we'll add to the images
-            noise = torch.randn(clean_images.shape).to(clean_images.device)
-            bsz = clean_images.shape[0]
-            # Sample a random timestep for each image
-            timesteps = torch.randint(
-                0,
-                noise_scheduler.num_train_timesteps,
-                (bsz, ),
-                device=clean_images.device,
-            ).long()
-
-            # Add noise to the clean images according to the noise magnitude at each timestep
-            # (this is the forward diffusion process)
-            noisy_images = noise_scheduler.add_noise(clean_images, noise,
-                                                     timesteps)
-
-            with accelerator.accumulate(model):
-                # Predict the noise residual
-                noise_pred = model(noisy_images, timesteps)["sample"]
-                loss = F.mse_loss(noise_pred, noise)
-                accelerator.backward(loss)
-
-                if accelerator.sync_gradients:
-                    accelerator.clip_grad_norm_(model.parameters(), 1.0)
-                optimizer.step()
-                lr_scheduler.step()
-                if args.use_ema:
-                    ema_model.step(model)
-                optimizer.zero_grad()
-
-            progress_bar.update(1)
-            global_step += 1
-
-            logs = {
-                "loss": loss.detach().item(),
-                "lr": lr_scheduler.get_last_lr()[0],
-                "step": global_step,
-            }
-            if args.use_ema:
-                logs["ema_decay"] = ema_model.decay
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-        progress_bar.close()
-
-        accelerator.wait_for_everyone()
-
-        # Generate sample images for visual inspection
-        if accelerator.is_main_process:
-            if (
-                    epoch + 1
-            ) % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
-                if vqvae is not None:
-                    pipeline = LatentAudioDiffusionPipeline(
-                        unet=accelerator.unwrap_model(
-                            ema_model.averaged_model if args.use_ema else model
-                        ),
-                        vqvae=vqvae,
-                        scheduler=noise_scheduler)
-                else:
-                    pipeline = AudioDiffusionPipeline(
-                        unet=accelerator.unwrap_model(
-                            ema_model.averaged_model if args.use_ema else model
-                        ),
-                        scheduler=noise_scheduler,
-                    )
-
-                # save the model
-                if args.push_to_hub:
-                    try:
-                        push_to_hub(
-                            args,
-                            pipeline,
-                            repo,
-                            commit_message=f"Epoch {epoch}",
-                            blocking=False,
-                        )
-                    except NameError:  # current version of diffusers has a little bug
-                        pass
-                else:
-                    pipeline.save_pretrained(output_dir)
-
-            if (epoch + 1) % args.save_images_epochs == 0:
-                generator = torch.manual_seed(42)
-                # run pipeline in inference (sample random noise and denoise)
-                images, (sample_rate, audios) = pipeline(
-                    mel=mel,
-                    generator=generator,
-                    batch_size=args.eval_batch_size,
-                )
-
-                # denormalize the images and save to tensorboard
-                images = np.array([
-                    np.frombuffer(image.tobytes(), dtype="uint8").reshape(
-                        (len(image.getbands()), image.height, image.width))
-                    for image in images
-                ])
-                accelerator.trackers[0].writer.add_images(
-                    "test_samples", images, epoch)
-                for _, audio in enumerate(audios):
-                    accelerator.trackers[0].writer.add_audio(
-                        f"test_audio_{_}",
-                        normalize(audio),
-                        epoch,
-                        sample_rate=sample_rate,
-                    )
-        accelerator.wait_for_everyone()
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Simple example of a training script.")
-    parser.add_argument("--local_rank", type=int, default=-1)
-    parser.add_argument("--dataset_name", type=str, default=None)
-    parser.add_argument("--dataset_config_name", type=str, default=None)
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help="A folder containing the training data.",
-    )
-    parser.add_argument("--output_dir", type=str, default="ddpm-model-64")
-    parser.add_argument("--overwrite_output_dir", type=bool, default=False)
-    parser.add_argument("--cache_dir", type=str, default=None)
-    parser.add_argument("--train_batch_size", type=int, default=16)
-    parser.add_argument("--eval_batch_size", type=int, default=16)
-    parser.add_argument("--num_epochs", type=int, default=100)
-    parser.add_argument("--save_images_epochs", type=int, default=10)
-    parser.add_argument("--save_model_epochs", type=int, default=10)
-    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
-    parser.add_argument("--learning_rate", type=float, default=1e-4)
-    parser.add_argument("--lr_scheduler", type=str, default="cosine")
-    parser.add_argument("--lr_warmup_steps", type=int, default=500)
-    parser.add_argument("--adam_beta1", type=float, default=0.95)
-    parser.add_argument("--adam_beta2", type=float, default=0.999)
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-6)
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08)
-    parser.add_argument("--use_ema", type=bool, default=True)
-    parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
-    parser.add_argument("--ema_power", type=float, default=3 / 4)
-    parser.add_argument("--ema_max_decay", type=float, default=0.9999)
-    parser.add_argument("--push_to_hub", type=bool, default=False)
-    parser.add_argument("--use_auth_token", type=bool, default=False)
-    parser.add_argument("--hub_token", type=str, default=None)
-    parser.add_argument("--hub_model_id", type=str, default=None)
-    parser.add_argument("--hub_private_repo", type=bool, default=False)
-    parser.add_argument("--logging_dir", type=str, default="logs")
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."),
-    )
-    parser.add_argument("--hop_length", type=int, default=512)
-    parser.add_argument("--from_pretrained", type=str, default=None)
-    parser.add_argument("--start_epoch", type=int, default=0)
-    parser.add_argument("--num_train_steps", type=int, default=1000)
-    parser.add_argument("--scheduler",
-                        type=str,
-                        default="ddpm",
-                        help="ddpm or ddim")
-    parser.add_argument("--vae",
-                        type=str,
-                        default=None,
-                        help="pretrained VAE model for latent diffusion")
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError(
-            "You must specify either a dataset name from the hub or a train data directory."
-        )
-    if args.dataset_name is not None and args.dataset_name == args.hub_model_id:
-        raise ValueError(
-            "The local dataset name must be different from the hub model id.")
-
-    main(args)
diff --git a/spaces/nathanTQ/ChatDev/camel/prompts/code.py b/spaces/nathanTQ/ChatDev/camel/prompts/code.py
deleted file mode 100644
index 35c1563af23a0b8795d94b521728c246feaba6de..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/camel/prompts/code.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. 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.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-from typing import Any
-
-from camel.prompts import TextPrompt, TextPromptDict
-from camel.typing import RoleType
-
-
-# flake8: noqa :E501
-class CodePromptTemplateDict(TextPromptDict):
-    r"""A dictionary containing :obj:`TextPrompt` used in the `Code` task.
-
-    Attributes:
-        GENERATE_LANGUAGES (TextPrompt): A prompt to list different computer
-            programming languages.
-        GENERATE_DOMAINS (TextPrompt): A prompt to list common fields of study
-            that programming could help with.
-        GENERATE_TASKS (TextPrompt): A prompt to list diverse tasks that
-            the AI assistant can assist AI user with.
-        TASK_SPECIFY_PROMPT (TextPrompt): A prompt to specify a task in more
-            detail.
-        ASSISTANT_PROMPT (TextPrompt): A system prompt for the AI assistant
-            that outlines the rules of the conversation and provides
-            instructions for completing tasks.
-        USER_PROMPT (TextPrompt): A system prompt for the AI user that
-            outlines the rules of the conversation and provides instructions
-            for giving instructions to the AI assistant.
-    """
-    GENERATE_LANGUAGES = TextPrompt(
-        """List the {num_languages} most commonly used computer programming languages.
-Be concise. No explanation required.""")
-
-    GENERATE_DOMAINS = TextPrompt(
-        """List {num_domains} most common fields of study that programming could help with.
-Be concise. Sort them by alphabetical order. No explanation required.""")
-
-    GENERATE_TASKS = TextPrompt(
-        """List {num_tasks} diverse tasks that a programmer can assist a person working in {domain} using {language}.
-Be concise. Be creative.""")
-
-    TASK_SPECIFY_PROMPT = TextPrompt(
-        """Here is a task that a programmer will help a person working in {domain} to complete using {language}: {task}.
-Please make it more specific. Be creative and imaginative.
-Please reply with the specified task in {word_limit} words or less. Do not add anything else."""
-    )
-
-    ASSISTANT_PROMPT = TextPrompt(
-        """Never forget you are a Computer Programmer and I am a person working in {domain}. Never flip roles!  Never instruct me!
-We share a common interest in collaborating to successfully complete a task.
-You must help me to complete the task using {language} programming language.
-Here is the task: {task}. Never forget our task!
-I must instruct you based on your expertise and my needs to complete the task.
-
-I must give you one instruction at a time.
-You must write a specific solution that appropriately solves the requested instruction and explain your solutions.
-You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.
-Unless I say the task is completed, you should always start with:
-
-Solution: <YOUR_SOLUTION>
-
-<YOUR_SOLUTION> must contain {language} code and should be very specific, include detailed explanations and provide preferable implementations and examples for task-solving.
-Always end <YOUR_SOLUTION> with: Next request.""")
-
-    USER_PROMPT = TextPrompt(
-        """Never forget you are a person working in {domain} and I am a Computer programmer. Never flip roles! You will always instruct me.
-We share a common interest in collaborating to successfully complete a task.
-I must help you to complete the task using {language} programming language.
-Here is the task: {task}. Never forget our task!
-You must instruct me based on my expertise and your needs to solve the task ONLY in the following two ways:
-
-1. Instruct with a necessary input:
-Instruction: <YOUR_INSTRUCTION>
-Input: <YOUR_INPUT>
-
-2. Instruct without any input:
-Instruction: <YOUR_INSTRUCTION>
-Input: None
-
-The "Instruction" describes a task or question. The paired "Input" provides further context or information for the requested "Instruction".
-
-You must give me one instruction at a time.
-I must write a response that appropriately solves the requested instruction.
-I must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons.
-You should instruct me not ask me questions.
-Now you must start to instruct me using the two ways described above.
-Do not add anything else other than your instruction and the optional corresponding input!
-Keep giving me instructions and necessary inputs until you think the task is completed.
-When the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>.
-Never say <CAMEL_TASK_DONE> unless my responses have solved your task.""")
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-        self.update({
-            "generate_languages": self.GENERATE_LANGUAGES,
-            "generate_domains": self.GENERATE_DOMAINS,
-            "generate_tasks": self.GENERATE_TASKS,
-            "task_specify_prompt": self.TASK_SPECIFY_PROMPT,
-            RoleType.ASSISTANT: self.ASSISTANT_PROMPT,
-            RoleType.USER: self.USER_PROMPT,
-        })
diff --git a/spaces/naver/SuperFeatures/how/utils/score_helpers.py b/spaces/naver/SuperFeatures/how/utils/score_helpers.py
deleted file mode 100644
index f4cd2169e71d1e0d7a0b80e6723a49158ddfda36..0000000000000000000000000000000000000000
--- a/spaces/naver/SuperFeatures/how/utils/score_helpers.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Helper functions for computing evaluation scores"""
-
-import numpy as np
-
-from cirtorch.utils.evaluate import compute_map
-
-
-def compute_map_and_log(dataset, ranks, gnd, kappas=(1, 5, 10), logger=None):
-    """Computed mAP and log it
-
-    :param str dataset: Dataset to compute the mAP on (e.g. roxford5k)
-    :param np.ndarray ranks: 2D matrix of ints corresponding to previously computed ranks
-    :param dict gnd: Ground-truth dataset structure
-    :param list kappas: Compute mean precision at each kappa
-    :param logging.Logger logger: If not None, use it to log mAP and all mP@kappa
-    :return tuple: mAP and mP@kappa (medium difficulty for roxford5k and rparis6k)
-    """
-    # new evaluation protocol
-    if dataset.startswith('roxford5k') or dataset.startswith('rparis6k'):
-        gnd_t = []
-        for gndi in gnd:
-            g = {}
-            g['ok'] = np.concatenate([gndi['easy']])
-            g['junk'] = np.concatenate([gndi['junk'], gndi['hard']])
-            gnd_t.append(g)
-        mapE, apsE, mprE, prsE = compute_map(ranks, gnd_t, kappas)
-
-        gnd_t = []
-        for gndi in gnd:
-            g = {}
-            g['ok'] = np.concatenate([gndi['easy'], gndi['hard']])
-            g['junk'] = np.concatenate([gndi['junk']])
-            gnd_t.append(g)
-        mapM, apsM, mprM, prsM = compute_map(ranks, gnd_t, kappas)
-
-        gnd_t = []
-        for gndi in gnd:
-            g = {}
-            g['ok'] = np.concatenate([gndi['hard']])
-            g['junk'] = np.concatenate([gndi['junk'], gndi['easy']])
-            gnd_t.append(g)
-        mapH, apsH, mprH, prsH = compute_map(ranks, gnd_t, kappas)
-
-        if logger:
-            fmap = lambda x: np.around(x*100, decimals=2)
-            logger.info(f"Evaluated {dataset}: mAP E: {fmap(mapE)}, M: {fmap(mapM)}, H: {fmap(mapH)}")
-            logger.info(f"Evaluated {dataset}: mP@k{kappas} E: {fmap(mprE)}, M: {fmap(mprM)}, H: {fmap(mprH)}")
-
-        scores = {"map_easy": mapE.item(), "mp@k_easy": mprE, "ap_easy": apsE, "p@k_easy": prsE,
-                  "map_medium": mapM.item(), "mp@k_medium": mprM, "ap_medium": apsM, "p@k_medium": prsM,
-                  "map_hard": mapH.item(), "mp@k_hard": mprH, "ap_hard": apsH, "p@k_hard": prsH}
-        return scores
-
-    # old evaluation protocol
-    map_score, ap_scores, prk, pr_scores = compute_map(ranks, gnd, kappas=kappas)
-    if logger:
-        fmap = lambda x: np.around(x*100, decimals=2)
-        logger.info(f"Evaluated {dataset}: mAP {fmap(map_score)}, mP@k {fmap(prk)}")
-    return {"map": map_score, "mp@k": prk, "ap": ap_scores, "p@k": pr_scores}
diff --git a/spaces/nbeuchat/actors_matching/pipeline/get_images_data.py b/spaces/nbeuchat/actors_matching/pipeline/get_images_data.py
deleted file mode 100644
index 94ebc49b82c3470354445e82927664a30e584852..0000000000000000000000000000000000000000
--- a/spaces/nbeuchat/actors_matching/pipeline/get_images_data.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import os
-import requests
-import pandas as pd
-import os
-import time
-
-from datetime import datetime
-from tqdm import tqdm
-from dotenv import load_dotenv
-
-load_dotenv()
-
-BING_API_KEY = os.getenv("BING_API_KEY", None)
-
-
-def get_actor_images(
-    name: str, role: str = None, count: int = 50, api_key: str = BING_API_KEY
-):
-    """Get a list of actor images from the Bing Image Search API"""
-    if api_key is None:
-        raise ValueError("You must provide a Bing API key")
-
-    headers = {"Ocp-Apim-Subscription-Key": BING_API_KEY}
-    query = f'"{name}"'
-    if role:
-        query = f"{query} ({role})"
-    params = {
-        "q": query,
-        "count": count,
-        "imageType": "Photo",
-        "safeSearch": "Strict",
-        "imageContent": "Face",
-        "freshness": "Year",
-    }
-    response = requests.get(
-        f"https://api.bing.microsoft.com/v7.0/images/search",
-        headers=headers,
-        params=params,
-    )
-
-    response.raise_for_status()
-    return response.json()
-
-
-def read_actors_list(
-    max_actors: int = None, last_year_active: int = None, sort_by: str = None
-):
-    """Read and filter the list of actors"""
-
-    df = pd.read_csv("data/imdb_actors.csv")
-    if last_year_active:
-        df = df[df["lastYear"] >= last_year_active]
-
-    if sort_by:
-        df = df.sort_values(sort_by, ascending=False)
-
-    if max_actors:
-        df = df.head(max_actors)
-
-    return df
-
-
-def store_all_actor_images_data(
-    max_actors: int = None,
-    images_per_actor: int = 10,
-    last_year_active: int = None,
-    output_file=None,
-    max_api_calls_per_second: int = 3,
-):
-    """Get images data for each actor from the Bing Image Search API and store the results as csv"""
-
-    df = read_actors_list(max_actors, last_year_active)
-    df_im = None
-    if output_file:
-        try:
-            df_im = pd.read_csv(output_file)
-        except:
-            # file does not exists yet
-            pass
-
-    # remove actors for which we already have images data
-    if df_im is not None:
-        df = df[~df["nconst"].isin(df_im["nconst"].unique())]
-
-    print(f"Start retrieving images from Bing for {len(df)} actors")
-    for _, row in tqdm(df.iterrows(), total=df.shape[0]):
-        try:
-            images_data = get_actor_images(
-                name=row["primaryName"], count=images_per_actor
-            )
-        except Exception as e:
-            print(e)
-            continue
-
-        df_im_tmp = pd.DataFrame(images_data["value"])
-        df_im_tmp["nconst"] = row["nconst"]
-        df_im_tmp["resultPosition"] = list(range(0, len(df_im_tmp)))
-
-        if df_im is not None:
-            df_im = pd.concat([df_im, df_im_tmp])
-        else:
-            df_im = df_im_tmp
-
-        # Store progress
-        df_im.to_csv(output_file, index=False)
-
-        # Limit speed of requests to Bing Search (3 calls per seconds)
-        time.sleep(1.0 / max_api_calls_per_second)
-
-
-if __name__ == "__main__":
-    store_all_actor_images_data(
-        output_file="data/actors_images_new.csv",
-        max_actors=2000,
-        images_per_actor=20,
-        last_year_active=datetime.now().year - 5,
-        max_api_calls_per_second=100,
-    )
diff --git a/spaces/neridonk/facebook-nougat-base/app.py b/spaces/neridonk/facebook-nougat-base/app.py
deleted file mode 100644
index f62472be44a2b9d37a964f88af76fa89a4e87141..0000000000000000000000000000000000000000
--- a/spaces/neridonk/facebook-nougat-base/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/facebook/nougat-base").launch()
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ajab Prem Ki Ghazab Kahani ((BETTER)) Download 1080p Movie.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ajab Prem Ki Ghazab Kahani ((BETTER)) Download 1080p Movie.md
deleted file mode 100644
index f422159ff9709d12990bb54bb54393ae27a0535e..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ajab Prem Ki Ghazab Kahani ((BETTER)) Download 1080p Movie.md	
+++ /dev/null
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-<br />
-Here is the content I generated for you:
-
-<h1>Ajab Prem Ki Ghazab Kahani Download 1080p Movie: A Review</h1>
-<p>If you are looking for a romantic comedy that will make you laugh, cry and swoon, then you should watch <strong>Ajab Prem Ki Ghazab Kahani</strong>, a 2009 Hindi film starring Ranbir Kapoor and Katrina Kaif. This movie tells the story of Prem, a free-spirited young man who falls in love with Jenny, a Christian girl who is in love with someone else. Prem decides to help Jenny find her true love, even if it means sacrificing his own happiness. Along the way, they encounter many hilarious and heartwarming situations that will keep you entertained.</p>
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-<p>The film features some amazing performances by the lead actors, Ranbir Kapoor and Katrina Kaif. They have a great chemistry and deliver some hilarious and emotional scenes. Ranbir Kapoor plays the role of Prem with ease and charisma. He portrays the character's innocence, optimism and loyalty with perfection. Katrina Kaif plays the role of Jenny with grace and beauty. She portrays the character's sweetness, confusion and courage with finesse. The film also has some memorable supporting actors such as Govind Namdeo, Darshan Jariwala, Zakir Hussain and Smita Jaykar, who add to the humor and drama of the film.</p>
-<p>The film also has some catchy songs composed by Pritam and sung by popular singers such as Atif Aslam, KK, Sunidhi Chauhan and Alisha Chinai. Some of the songs that stand out are <em>Tera Hone Laga Hoon</em>, <em>Tu Jaane Na</em>, <em>Prem Ki Naiyya</em> and <em>Follow Me</em>. The songs are well-choreographed and suit the mood of the film.</p>
-<p></p>
-<p>Ajab Prem Ki Ghazab Kahani is a film that will make you smile, laugh and cry. It is a film that will make you believe in love and its magic. It is a film that you can watch with your family, friends or partner. It is a film that you can download in 1080p quality from various online platforms such as Netflix, Prime Video or IMDb. So what are you waiting for? Download Ajab Prem Ki Ghazab Kahani today and enjoy this amazing movie.</p>Here are some more paragraphs I wrote for you:
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-<p>The film was a box office hit and collected ₹103.73 crore worldwide, becoming the third-highest grossing Hindi film of the year. The film was also a hit in the overseas market, especially in New Zealand, where it collected NZ$21,075. The film was declared a super hit by Bollywood Hungama and a blockbuster by Box Office Mojo.</p>
-<p>Ajab Prem Ki Ghazab Kahani is a film that has won many awards and nominations for its excellence. The film won four Filmfare Awards, including Best Actor (Ranbir Kapoor), Best Music Director (Pritam), Best Playback Singer - Male (Atif Aslam) and Best Choreography (Ahmed Khan). The film also won three IIFA Awards, including Best Actor (Ranbir Kapoor), Best Music Director (Pritam) and Best Playback Singer - Male (Atif Aslam). The film also received nominations for Best Film, Best Director, Best Actress, Best Supporting Actor and Best Supporting Actress at various award ceremonies.</p> 7196e7f11a<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Gratis Kamen Rider Ooo Movie.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Gratis Kamen Rider Ooo Movie.md
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index 61f9fb311e8ba108da73301cd857ecfa7a55eac3..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Gratis Kamen Rider Ooo Movie.md	
+++ /dev/null
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-<br />
-<h1>How to Download Kamen Rider OOO Movie for Free</h1>
-<p>Kamen Rider OOO is a Japanese tokusatsu series that aired from 2010 to 2011. It is the 21st installment of the Kamen Rider franchise and the 12th of the Heisei era. The series follows Eiji Hino, a young man who becomes Kamen Rider OOO, a hero who can use various animal-themed medals to transform into different forms and fight against the Greeed, a group of monsters that feed on human desires.</p>
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-<h2>Kamen Rider × Kamen Rider OOO & W Featuring Skull: Movie War Core</h2>
-<p>This is the first movie of Kamen Rider OOO, released in 2010. It is a crossover with Kamen Rider W, the previous series, and features the debut of Kamen Rider Skull, a character from W's backstory. The movie is divided into three parts: Kamen Rider W's part, which focuses on Shotaro Hidari and Philip's investigation of a mysterious dopant; Kamen Rider Skull's part, which reveals the origin of Sokichi Narumi and his transformation into Skull; and Kamen Rider OOO's part, which introduces Eiji Hino and Ankh's battle against a new Greeed.</p>
-<p>To download this movie for free, you can visit the Internet Archive website[^1^], where you can find a full version of the movie with English subtitles. You can either stream it online or download it as an MP4 file. The Internet Archive is a non-profit digital library that offers free access to millions of books, movies, music, and other media. It is legal and safe to use, as long as you respect the copyright laws and do not distribute or sell the downloaded content.</p>
-<p></p>
-<h2>OOO, Den-O, All Riders: Let's Go Kamen Riders</h2>
-<p>This is the second movie of Kamen Rider OOO, released in 2011. It is a special anniversary movie that celebrates the 40th anniversary of the Kamen Rider franchise and features all the previous Kamen Riders from Showa and Heisei eras. The movie follows Eiji Hino and Ankh as they travel back in time to 1971, where they meet Shotaro Ishinomori, the creator of Kamen Rider, and accidentally alter history. They must team up with other Kamen Riders from different timelines to restore the original timeline and stop Shocker, an evil organization that plans to conquer the world.</p>
-<p>To download this movie for free, you can visit the Archive website[^2^], where you can find a full version of the movie with English subtitles. You can either stream it online or download it as an MP4 file. The Archive is another non-profit digital library that offers free access to various media files. It is similar to the Internet Archive, but it focuses more on Asian media such as anime, manga, drama, and tokusatsu. It is also legal and safe to use, as long as you follow the same rules as above.</p>
-<h2>Kamen Rider OOO Wonderful: The Shogun and the 21 Core Medals</h2>
-<p>This is the third and final movie of Kamen Rider OOO, released in 2011. It is a standalone story that takes place after the end of the series. The movie follows Eiji Hino and Ankh as they travel to Edo period Japan, where they encounter Tokugawa Yoshimune, a shogun who has a collection of Core Medals. They must protect him from Gara, a female Greeed who wants to use his medals to revive her comrades and destroy humanity.</p>
-<p>To download this movie for free, you can visit Bilibili TV website[^3^], where you can find a full version of the movie with English subtitles. You can either stream it online or download it as an MP4 file. Bilibili TV is a</p> 7b8c122e87<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ghost Recon Future Soldier Crack Only Skidrow Torrent [REPACK].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ghost Recon Future Soldier Crack Only Skidrow Torrent [REPACK].md
deleted file mode 100644
index a08d9322009843dbbea3e882642699f913470a65..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ghost Recon Future Soldier Crack Only Skidrow Torrent [REPACK].md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
-<h1>How to Download and Install Ghost Recon Future Soldier Crack Only Skidrow Torrent</h1>
-<p>If you are looking for a way to play Ghost Recon Future Soldier, a tactical shooter video game developed by Ubisoft, without paying for it, then you might be interested in downloading and installing Ghost Recon Future Soldier Crack Only Skidrow Torrent. This is a cracked version of the game that bypasses the DRM protection and allows you to play it offline or online with other players who have the same crack.</p>
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-<p>Ghost Recon Future Soldier is the fourth installment in the popular Ghost Recon series of military-themed video games. It was released in 2023 for Windows, PlayStation 3, Xbox 360, and later for Wii U. The game is set in the near future, where a covert team of elite special-ops soldiers known as the Ghosts are sent to various hotspots around the world to eliminate high-value targets and prevent global conflicts.</p>
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mohabbatein Movie In Hindi.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mohabbatein Movie In Hindi.md
deleted file mode 100644
index c487234aa1f1d52f9e111d88f76dfff163b55595..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mohabbatein Movie In Hindi.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-
-<h1>Mohabbatein: A Musical Romance with a Clash of Ideologies</h1>
-<p>Mohabbatein is a 2000 Indian movie directed by Aditya Chopra and starring Amitabh Bachchan, Shah Rukh Khan, Aishwarya Rai and six newcomers. The movie is a musical romance that revolves around the love stories of three young couples who study at a prestigious all-male university. The movie also depicts the conflict between the strict headmaster of the university, Narayan Shankar (Bachchan), who believes in discipline and tradition, and the new music teacher, Raj Aryan (Khan), who believes in love and freedom.</p>
-<p>The movie was a huge commercial success and received positive reviews from critics and audiences. It won several awards, including five Filmfare Awards and two National Film Awards. The movie is also known for its melodious songs composed by Jatin-Lalit and sung by various artists. Some of the popular songs are "Humko Humise Chura Lo", "Chalte Chalte", "Aankhein Khuli" and "Pairon Mein Bandhan Hai". The movie also features a cameo appearance by Aishwarya Rai as Raj's deceased lover, Megha.</p>
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-<p>The movie begins with the introduction of Narayan Shankar, the founder and principal of Gurukul, a prestigious institution that follows a strict code of conduct and discipline. He is respected and feared by everyone, and has a zero-tolerance policy for any kind of romance or rebellion among the students. He believes that love leads to pain and weakness, and that his students should focus on their studies and careers.</p>
-<p>On the other hand, Raj Aryan is a former student of Gurukul, who was expelled by Narayan for falling in love with his daughter, Megha. Megha committed suicide after her father refused to accept their relationship. Raj returns to Gurukul as a music teacher, with a mission to spread love and joy among the students. He encourages them to pursue their passions and dreams, and to express their feelings to their loved ones.</p>
-<p>The movie then follows the love stories of three pairs of students: Vicky and Ishika, Sameer and Sanjana, and Karan and Kiran. Vicky is a flirtatious and confident boy who falls for Ishika, a feisty girl who studies at the nearby girls' college. Sameer is a cheerful and optimistic boy who falls for Sanjana, his childhood friend who has moved to London. Karan is a shy and introverted boy who falls for Kiran, a widow who lives with her strict father-in-law. Raj helps them to overcome their fears and obstacles, and to confess their love to their partners.</p>
-<p>However, Narayan soon discovers Raj's true identity and his role in influencing the students. He confronts Raj and challenges him to a duel of ideologies. He threatens to expel the students if they do not break up with their partners. Raj accepts the challenge and vows to prove that love is stronger than fear. He also reveals that he still loves Megha and that he has not given up on his hope of winning Narayan's approval.</p>
-<p>The movie reaches its climax on the night of Mohabbatein, a musical event where the students showcase their talents. Raj performs a song dedicated to Megha, which moves everyone except Narayan. The three couples also perform together, expressing their love for each other. Narayan is enraged by their defiance and orders them to leave the campus. However, he is stopped by his wife, who has been silent for years. She tells him that he has been wrong all along, and that he has lost his daughter and his wife because of his stubbornness. She also tells him that Raj is the only one who can bring happiness back to Gurukul.</p>
-<p>Narayan realizes his mistake and apologizes to Raj. He also accepts the three couples and allows them to stay at Gurukul. He finally embraces Raj as his son-in-law and gives him Megha's portrait as a token of his blessing. The movie ends with Raj and Narayan joining hands to lead Gurukul into a new era of love and freedom.</p>
-<p></p> e93f5a0c3f<br />
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diff --git a/spaces/ngoctuanai/DALL-E/index.html b/spaces/ngoctuanai/DALL-E/index.html
deleted file mode 100644
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-                '<details>\n<summary>Bias and Limitations</summary>\n<p style=\'line-height: normal; font-size: small\'>\nWhile the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. While the extent and nature of the biases of the DALL\u00b7E mini model have yet to be fully documented, given the fact that the model was trained on unfiltered data from the Internet, it may generate images that contain stereotypes against minority groups. Work to analyze the nature and extent of these limitations is ongoing, and will be documented in more detail in the.\n</p>\n</details>',
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-                '<hr />\n<p style=\'text-align: center\'>\nCreated by <a href="https://huggingface.co/ngoctuanai" target="_blank">NgocTuanAI</a> et al. 2022-2023\n<br/>\n</p>',
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-    <link rel="preconnect" href="https://fonts.googleapis.com" />
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-      href="https://fonts.gstatic.com"
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-      href="https://fonts.googleapis.com/css?family=Source Sans Pro"
-      rel="stylesheet"
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-      href="https://fonts.googleapis.com/css?family=IBM Plex Mono"
-      rel="stylesheet"
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-    <script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
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-      href="https://gradio.s3-us-west-2.amazonaws.com/3.0.9b12/assets/index.cbea297d.css"
-    />
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-      #screenshot {
-        display: none;
-      }
-      .container > div > div {
-        padding: 0.5rem;
-      }
-      footer a {
-        color: rgb(156 163 175) !important;
-      }
-      footer img {
-        display: none !important;
-      }
-    </style>
-    <style id="mofo">
-      body {
-        display: none !important;
-      }
-    </style>
-    <script type="text/javascript">
-      if (
-        self === top ||
-        window.location.ancestorOrigins[0] === "https://huggingface.co"
-      ) {
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-        mofo.parentNode.removeChild(mofo);
-      } else {
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-  </head>
-
-  <body
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-      margin: 0;
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diff --git a/spaces/nickmuchi/Earnings-Call-Analysis-Whisperer/sentence-transformers/eval_beir.py b/spaces/nickmuchi/Earnings-Call-Analysis-Whisperer/sentence-transformers/eval_beir.py
deleted file mode 100644
index 90260775860560e6ad42971ec264b57d789e5ad2..0000000000000000000000000000000000000000
--- a/spaces/nickmuchi/Earnings-Call-Analysis-Whisperer/sentence-transformers/eval_beir.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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 sys
-import argparse
-import torch
-import logging
-import json
-import numpy as np
-import os
-
-import src.slurm
-import src.contriever
-import src.beir_utils
-import src.utils
-import src.dist_utils
-import src.contriever
-
-logger = logging.getLogger(__name__)
-
-
-def main(args):
-
-    src.slurm.init_distributed_mode(args)
-    src.slurm.init_signal_handler()
-
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    logger = src.utils.init_logger(args)
-
-    model, tokenizer, _ = src.contriever.load_retriever(args.model_name_or_path)
-    model = model.cuda()
-    model.eval()
-    query_encoder = model
-    doc_encoder = model
-
-    logger.info("Start indexing")
-
-    metrics = src.beir_utils.evaluate_model(
-        query_encoder=query_encoder,
-        doc_encoder=doc_encoder,
-        tokenizer=tokenizer,
-        dataset=args.dataset,
-        batch_size=args.per_gpu_batch_size,
-        norm_query=args.norm_query,
-        norm_doc=args.norm_doc,
-        is_main=src.dist_utils.is_main(),
-        split="dev" if args.dataset == "msmarco" else "test",
-        score_function=args.score_function,
-        beir_dir=args.beir_dir,
-        save_results_path=args.save_results_path,
-        lower_case=args.lower_case,
-        normalize_text=args.normalize_text,
-    )
-
-    if src.dist_utils.is_main():
-        for key, value in metrics.items():
-            logger.info(f"{args.dataset} : {key}: {value:.1f}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    parser.add_argument("--dataset", type=str, help="Evaluation dataset from the BEIR benchmark")
-    parser.add_argument("--beir_dir", type=str, default="./", help="Directory to save and load beir datasets")
-    parser.add_argument("--text_maxlength", type=int, default=512, help="Maximum text length")
-
-    parser.add_argument("--per_gpu_batch_size", default=128, type=int, help="Batch size per GPU/CPU for indexing.")
-    parser.add_argument("--output_dir", type=str, default="./my_experiment", help="Output directory")
-    parser.add_argument("--model_name_or_path", type=str, help="Model name or path")
-    parser.add_argument(
-        "--score_function", type=str, default="dot", help="Metric used to compute similarity between two embeddings"
-    )
-    parser.add_argument("--norm_query", action="store_true", help="Normalize query representation")
-    parser.add_argument("--norm_doc", action="store_true", help="Normalize document representation")
-    parser.add_argument("--lower_case", action="store_true", help="lowercase query and document text")
-    parser.add_argument(
-        "--normalize_text", action="store_true", help="Apply function to normalize some common characters"
-    )
-    parser.add_argument("--save_results_path", type=str, default=None, help="Path to save result object")
-
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument("--main_port", type=int, default=-1, help="Main port (for multi-node SLURM jobs)")
-
-    args, _ = parser.parse_known_args()
-    main(args)
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/doc/DENSEPOSE_CSE.md b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/doc/DENSEPOSE_CSE.md
deleted file mode 100644
index d5761ef989bdfb441a2a61f4e508cc826f93d2d1..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/doc/DENSEPOSE_CSE.md
+++ /dev/null
@@ -1,336 +0,0 @@
-# Continuous Surface Embeddings for Dense Pose Estimation for Humans and Animals
-
-## <a name="Overview"></a> Overview
-
-<div align="center">
-  <img src="https://dl.fbaipublicfiles.com/densepose/web/densepose_cse_teaser.png" width="700px" />
-</div>
-
-The pipeline uses [Faster R-CNN](https://arxiv.org/abs/1506.01497)
-with [Feature Pyramid Network](https://arxiv.org/abs/1612.03144) meta architecture
-outlined in Figure 1. For each detected object, the model predicts
-its coarse segmentation `S` (2 channels: foreground / background)
-and the embedding `E` (16 channels). At the same time, the embedder produces vertex
-embeddings `Ê` for the corresponding mesh. Universal positional embeddings `E`
-and vertex embeddings `Ê` are matched to derive for each pixel its continuous
-surface embedding.
-
-<div align="center">
-  <img src="https://dl.fbaipublicfiles.com/densepose/web/densepose_pipeline_cse.png" width="700px" />
-</div>
-<p class="image-caption"><b>Figure 1.</b> DensePose continuous surface embeddings architecture based on Faster R-CNN with Feature Pyramid Network (FPN).</p>
-
-### Datasets
-
-For more details on datasets used for training and validation of
-continuous surface embeddings models,
-please refer to the [DensePose Datasets](DENSEPOSE_DATASETS.md) page.
-
-## <a name="ModelZoo"></a> Model Zoo and Baselines
-
-### Human CSE Models
-
-Continuous surface embeddings models for humans trained using the protocols from [Neverova et al, 2020](https://arxiv.org/abs/2011.12438).
-
-Models trained with hard assignment loss &#x2112;:
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">segm<br/>AP</th>
-<th valign="bottom">dp. AP<br/>GPS</th>
-<th valign="bottom">dp. AP<br/>GPSm</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: densepose_rcnn_R_50_FPN_s1x -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_s1x.yaml">R_50_FPN_s1x</a></td>
- <td align="center">s1x</td>
- <td align="center">0.349</td>
- <td align="center">0.060</td>
- <td align="center">6.3</td>
- <td align="center">61.1</td>
- <td align="center">67.1</td>
- <td align="center">64.4</td>
- <td align="center">65.7</td>
- <td align="center">251155172</td>
- <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_s1x/251155172/model_final_c4ea5f.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_s1x/251155172/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_s1x -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_101_FPN_s1x.yaml">R_101_FPN_s1x</a></td>
-  <td align="center">s1x</td>
-  <td align="center">0.461</td>
-  <td align="center">0.071</td>
-  <td align="center">7.4</td>
-  <td align="center">62.3</td>
-  <td align="center">67.2</td>
-  <td align="center">64.7</td>
-  <td align="center">65.8</td>
-  <td align="center">251155500</td>
-  <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_s1x/251155500/model_final_5c995f.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_s1x/251155500/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_DL_s1x -->
- <tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_DL_s1x.yaml">R_50_FPN_DL_s1x</a></td>
- <td align="center">s1x</td>
- <td align="center">0.399</td>
- <td align="center">0.061</td>
- <td align="center">7.0</td>
- <td align="center">60.8</td>
- <td align="center">67.8</td>
- <td align="center">65.5</td>
- <td align="center">66.4</td>
- <td align="center">251156349</td>
- <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_DL_s1x/251156349/model_final_e96218.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_DL_s1x/251156349/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_DL_s1x -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_101_FPN_DL_s1x.yaml">R_101_FPN_DL_s1x</a></td>
-  <td align="center">s1x</td>
-  <td align="center">0.504</td>
-  <td align="center">0.074</td>
-  <td align="center">8.3</td>
-  <td align="center">61.5</td>
-  <td align="center">68.0</td>
-  <td align="center">65.6</td>
-  <td align="center">66.6</td>
-  <td align="center">251156606</td>
-  <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_DL_s1x/251156606/model_final_b236ce.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_DL_s1x/251156606/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-Models trained with soft assignment loss &#x2112;<sub>&sigma;</sub>:
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">segm<br/>AP</th>
-<th valign="bottom">dp. AP<br/>GPS</th>
-<th valign="bottom">dp. AP<br/>GPSm</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: densepose_rcnn_R_50_FPN_soft_s1x -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_soft_s1x.yaml">R_50_FPN_soft_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.357</td>
-<td align="center">0.057</td>
-<td align="center">9.7</td>
-<td align="center">61.3</td>
-<td align="center">66.9</td>
-<td align="center">64.3</td>
-<td align="center">65.4</td>
-<td align="center">250533982</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/model_final_2c4512.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_soft_s1x -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_101_FPN_soft_s1x.yaml">R_101_FPN_soft_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.464</td>
-<td align="center">0.071</td>
-<td align="center">10.5</td>
-<td align="center">62.1</td>
-<td align="center">67.3</td>
-<td align="center">64.5</td>
-<td align="center">66.0</td>
-<td align="center">250712522</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_soft_s1x/250712522/model_final_4637da.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_soft_s1x/250712522/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_DL_soft_s1x -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_DL_soft_s1x.yaml">R_50_FPN_DL_soft_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.427</td>
-<td align="center">0.062</td>
-<td align="center">11.3</td>
-<td align="center">60.8</td>
-<td align="center">68.0</td>
-<td align="center">66.1</td>
-<td align="center">66.7</td>
-<td align="center">250713703</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_DL_soft_s1x/250713703/model_final_9199f5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_DL_soft_s1x/250713703/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_101_FPN_DL_soft_s1x -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x.yaml">R_101_FPN_DL_soft_s1x</a></td>
-<td align="center">s1x</td>
-<td align="center">0.483</td>
-<td align="center">0.071</td>
-<td align="center">12.2</td>
-<td align="center">61.5</td>
-<td align="center">68.2</td>
-<td align="center">66.2</td>
-<td align="center">67.1</td>
-<td align="center">250713061</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x/250713061/model_final_1d3314.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x/250713061/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-### Animal CSE Models
-
-Models obtained by finetuning human CSE models on animals data from `ds1_train`
-(see the [DensePose LVIS](DENSEPOSE_DATASETS.md#continuous-surface-embeddings-annotations-3)
-section for more details on the datasets) with soft assignment loss &#x2112;<sub>&sigma;</sub>:
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">segm<br/>AP</th>
-<th valign="bottom">dp. AP<br/>GPS</th>
-<th valign="bottom">dp. AP<br/>GPSm</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k -->
- <tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k.yaml">R_50_FPN_soft_chimps_finetune_4k</a></td>
-<td align="center">4K</td>
-<td align="center">0.569</td>
-<td align="center">0.051</td>
-<td align="center">4.7</td>
-<td align="center">62.0</td>
-<td align="center">59.0</td>
-<td align="center">32.2</td>
-<td align="center">39.6</td>
-<td align="center">253146869</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k/253146869/model_final_52f649.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k/253146869/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_soft_animals_finetune_4k -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_4k.yaml">R_50_FPN_soft_animals_finetune_4k</a></td>
-<td align="center">4K</td>
-<td align="center">0.381</td>
-<td align="center">0.061</td>
-<td align="center">7.3</td>
-<td align="center">44.9</td>
-<td align="center">55.5</td>
-<td align="center">21.3</td>
-<td align="center">28.8</td>
-<td align="center">253145793</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_4k/253145793/model_final_8f8ba2.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_4k/253145793/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_4k -->
- <tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_4k.yaml">R_50_FPN_soft_animals_CA_finetune_4k</a></td>
-<td align="center">4K</td>
-<td align="center">0.412</td>
-<td align="center">0.059</td>
-<td align="center">7.1</td>
-<td align="center">53.4</td>
-<td align="center">59.5</td>
-<td align="center">25.4</td>
-<td align="center">33.4</td>
-<td align="center">253498611</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_4k/253498611/model_final_6d69b7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_4k/253498611/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-Acronyms:
-
-`CA`: class agnostic training, where all annotated instances are mapped into a single category
-
-
-Models obtained by finetuning human CSE models on animals data from `ds2_train` dataset
-with soft assignment loss &#x2112;<sub>&sigma;</sub> and, for some schedules, cycle losses.
-Please refer to [DensePose LVIS](DENSEPOSE_DATASETS.md#continuous-surface-embeddings-annotations-3)
-section for details on the dataset and to [Neverova et al, 2021]() for details on cycle losses.
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">segm<br/>AP</th>
-<th valign="bottom">dp. AP<br/>GPS</th>
-<th valign="bottom">dp. AP<br/>GPSm</th>
-<th valign="bottom">GErr</th>
-<th valign="bottom">GPS</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_16k -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_16k.yaml">R_50_FPN_soft_animals_I0_finetune_16k</a></td>
- <td align="center">16k</td>
- <td align="center">0.386</td>
- <td align="center">0.058</td>
- <td align="center">8.4</td>
- <td align="center">54.2</td>
- <td align="center">67.0</td>
- <td align="center">29.0</td>
- <td align="center">38.6</td>
- <td align="center">13.2</td>
- <td align="center">85.4</td>
- <td align="center">270727112</td>
- <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_16k/270727112/model_final_421d28.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_16k/270727112/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_m2m_16k -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_m2m_16k.yaml">R_50_FPN_soft_animals_I0_finetune_m2m_16k</a></td>
- <td align="center">16k</td>
- <td align="center">0.508</td>
- <td align="center">0.056</td>
- <td align="center">12.2</td>
- <td align="center">54.1</td>
- <td align="center">67.3</td>
- <td align="center">28.6</td>
- <td align="center">38.4</td>
- <td align="center">12.5</td>
- <td align="center">87.6</td>
- <td align="center">270982215</td>
- <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_m2m_16k/270982215/model_final_6fe5f4.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_m2m_16k/270982215/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_i2m_16k -->
-<tr><td align="left"><a href="../configs/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_i2m_16k.yaml">R_50_FPN_soft_animals_I0_finetune_i2m_16k</a></td>
- <td align="center">16k</td>
- <td align="center">0.483</td>
- <td align="center">0.056</td>
- <td align="center">9.7</td>
- <td align="center">54.0</td>
- <td align="center">66.6</td>
- <td align="center">28.9</td>
- <td align="center">38.3</td>
- <td align="center">11.0</td>
- <td align="center">88.9</td>
- <td align="center">270727461</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_i2m_16k/270727461/model_final_8c9d99.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_i2m_16k/270727461/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-## <a name="References"></a> References
-
-If you use DensePose methods based on continuous surface embeddings, please take the
-references from the following BibTeX entries:
-
-Continuous surface embeddings:
-```
-@InProceedings{Neverova2020ContinuousSurfaceEmbeddings,
-    title = {Continuous Surface Embeddings},
-    author = {Neverova, Natalia and Novotny, David and Khalidov, Vasil and Szafraniec, Marc and Labatut, Patrick and Vedaldi, Andrea},
-    journal = {Advances in Neural Information Processing Systems},
-    year = {2020},
-}
-```
-
-Cycle Losses:
-```
-@InProceedings{Neverova2021UniversalCanonicalMaps,
-    title = {Discovering Relationships between Object Categories via Universal Canonical Maps},
-    author = {Neverova, Natalia and Sanakoyeu, Artsiom and Novotny, David and Labatut, Patrick and Vedaldi, Andrea},
-    journal = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-    year = {2021},
-}
-```
diff --git a/spaces/ntt123/Vietnam-male-voice-TTS/flow.py b/spaces/ntt123/Vietnam-male-voice-TTS/flow.py
deleted file mode 100644
index 2fdd7c2df1c247fe7b54a6014c7e126ae9993d3d..0000000000000000000000000000000000000000
--- a/spaces/ntt123/Vietnam-male-voice-TTS/flow.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import torch
-from torch import nn
-
-from modules import WN
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
diff --git a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/data/util/STTN_mask.py b/spaces/oguzakif/video-object-remover/FGT_codes/FGT/data/util/STTN_mask.py
deleted file mode 100644
index f7ee67e6eb3eb9a5ace90d92120dd9b018091e3f..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/data/util/STTN_mask.py
+++ /dev/null
@@ -1,244 +0,0 @@
-import matplotlib.patches as patches
-from matplotlib.path import Path
-import os
-import sys
-import io
-import cv2
-import time
-import math
-import argparse
-import shutil
-import random
-import zipfile
-from glob import glob
-import math
-import numpy as np
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-from PIL import Image, ImageOps, ImageDraw, ImageFilter
-
-import torch
-import torchvision
-import torch.nn as nn
-import torch.distributed as dist
-
-import matplotlib
-from matplotlib import pyplot as plt
-matplotlib.use('agg')
-
-
-class GroupRandomHorizontalFlip(object):
-    """Randomly horizontally flips the given PIL.Image with a probability of 0.5
-    """
-
-    def __init__(self, is_flow=False):
-        self.is_flow = is_flow
-
-    def __call__(self, img_group, is_flow=False):
-        v = random.random()
-        if v < 0.5:
-            ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
-            if self.is_flow:
-                for i in range(0, len(ret), 2):
-                    # invert flow pixel values when flipping
-                    ret[i] = ImageOps.invert(ret[i])
-            return ret
-        else:
-            return img_group
-
-
-class Stack(object):
-    def __init__(self, roll=False):
-        self.roll = roll
-
-    def __call__(self, img_group):
-        mode = img_group[0].mode
-        if mode == '1':
-            img_group = [img.convert('L') for img in img_group]
-            mode = 'L'
-        if mode == 'L':
-            return np.stack([np.expand_dims(x, 2) for x in img_group], axis=2)
-        elif mode == 'RGB':
-            if self.roll:
-                return np.stack([np.array(x)[:, :, ::-1] for x in img_group], axis=2)
-            else:
-                return np.stack(img_group, axis=2)
-        else:
-            raise NotImplementedError("Image mode {}".format(mode))
-
-
-class ToTorchFormatTensor(object):
-    """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
-    to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
-
-    def __init__(self, div=True):
-        self.div = div
-
-    def __call__(self, pic):
-        if isinstance(pic, np.ndarray):
-            # numpy img: [L, C, H, W]
-            img = torch.from_numpy(pic).permute(2, 3, 0, 1).contiguous()
-        else:
-            # handle PIL Image
-            img = torch.ByteTensor(
-                torch.ByteStorage.from_buffer(pic.tobytes()))
-            img = img.view(pic.size[1], pic.size[0], len(pic.mode))
-            # put it from HWC to CHW format
-            # yikes, this transpose takes 80% of the loading time/CPU
-            img = img.transpose(0, 1).transpose(0, 2).contiguous()
-        img = img.float().div(255) if self.div else img.float()
-        return img
-
-
-# ##########################################
-# ##########################################
-
-def create_random_shape_with_random_motion(video_length, zoomin, zoomout, rotmin, rotmax, imageHeight=240, imageWidth=432):
-    # get a random shape
-    assert zoomin < 1, "Zoom-in parameter must be smaller than 1"
-    assert zoomout > 1, "Zoom-out parameter must be larger than 1"
-    assert rotmin < rotmax, "Minimum value of rotation must be smaller than maximun value !"
-    height = random.randint(imageHeight//3, imageHeight-1)
-    width = random.randint(imageWidth//3, imageWidth-1)
-    edge_num = random.randint(6, 8)
-    ratio = random.randint(6, 8)/10
-    region = get_random_shape(
-        edge_num=edge_num, ratio=ratio, height=height, width=width)
-    region_width, region_height = region.size
-    # get random position
-    x, y = random.randint(
-        0, imageHeight-region_height), random.randint(0, imageWidth-region_width)
-    velocity = get_random_velocity(max_speed=3)
-    m = Image.fromarray(np.zeros((imageHeight, imageWidth)).astype(np.uint8))
-    m.paste(region, (y, x, y+region.size[0], x+region.size[1]))
-    masks = [m.convert('L')]
-    # return fixed masks
-    if random.uniform(0, 1) > 0.5:
-        return masks*video_length  # -> directly copy all the base masks
-    # return moving masks
-    for _ in range(video_length-1):
-        x, y, velocity = random_move_control_points(
-            x, y, imageHeight, imageWidth, velocity, region.size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3)
-        m = Image.fromarray(
-            np.zeros((imageHeight, imageWidth)).astype(np.uint8))
-        ### add by kaidong, to simulate zoon-in, zoom-out and rotation
-        extra_transform = random.uniform(0, 1)
-        # zoom in and zoom out
-        if extra_transform > 0.75:
-            resize_coefficient = random.uniform(zoomin, zoomout)
-            region = region.resize((math.ceil(region_width * resize_coefficient), math.ceil(region_height * resize_coefficient)), Image.NEAREST)
-            m.paste(region, (y, x, y + region.size[0], x + region.size[1]))
-            region_width, region_height = region.size
-        # rotation
-        elif extra_transform > 0.5:
-            m.paste(region, (y, x, y + region.size[0], x + region.size[1]))
-            m = m.rotate(random.randint(rotmin, rotmax))
-            # region_width, region_height = region.size
-        ### end
-        else:
-            m.paste(region, (y, x, y+region.size[0], x+region.size[1]))
-        masks.append(m.convert('L'))
-    return masks
-
-
-def get_random_shape(edge_num=9, ratio=0.7, width=432, height=240):
-    '''
-      There is the initial point and 3 points per cubic bezier curve.
-      Thus, the curve will only pass though n points, which will be the sharp edges.
-      The other 2 modify the shape of the bezier curve.
-      edge_num, Number of possibly sharp edges
-      points_num, number of points in the Path
-      ratio, (0, 1) magnitude of the perturbation from the unit circle,
-    '''
-    points_num = edge_num*3 + 1
-    angles = np.linspace(0, 2*np.pi, points_num)
-    codes = np.full(points_num, Path.CURVE4)
-    codes[0] = Path.MOVETO
-    # Using this instad of Path.CLOSEPOLY avoids an innecessary straight line
-    verts = np.stack((np.cos(angles), np.sin(angles))).T * \
-        (2*ratio*np.random.random(points_num)+1-ratio)[:, None]
-    verts[-1, :] = verts[0, :]
-    path = Path(verts, codes)
-    # draw paths into images
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-    patch = patches.PathPatch(path, facecolor='black', lw=2)
-    ax.add_patch(patch)
-    ax.set_xlim(np.min(verts)*1.1, np.max(verts)*1.1)
-    ax.set_ylim(np.min(verts)*1.1, np.max(verts)*1.1)
-    ax.axis('off')  # removes the axis to leave only the shape
-    fig.canvas.draw()
-    # convert plt images into numpy images
-    data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-    data = data.reshape((fig.canvas.get_width_height()[::-1] + (3,)))
-    plt.close(fig)
-    # postprocess
-    data = cv2.resize(data, (width, height))[:, :, 0]
-    data = (1 - np.array(data > 0).astype(np.uint8))*255
-    corrdinates = np.where(data > 0)
-    xmin, xmax, ymin, ymax = np.min(corrdinates[0]), np.max(
-        corrdinates[0]), np.min(corrdinates[1]), np.max(corrdinates[1])
-    region = Image.fromarray(data).crop((ymin, xmin, ymax, xmax))
-    return region
-
-
-def random_accelerate(velocity, maxAcceleration, dist='uniform'):
-    speed, angle = velocity
-    d_speed, d_angle = maxAcceleration
-    if dist == 'uniform':
-        speed += np.random.uniform(-d_speed, d_speed)
-        angle += np.random.uniform(-d_angle, d_angle)
-    elif dist == 'guassian':
-        speed += np.random.normal(0, d_speed / 2)
-        angle += np.random.normal(0, d_angle / 2)
-    else:
-        raise NotImplementedError(
-            f'Distribution type {dist} is not supported.')
-    return (speed, angle)
-
-
-def get_random_velocity(max_speed=3, dist='uniform'):
-    if dist == 'uniform':
-        speed = np.random.uniform(max_speed)
-    elif dist == 'guassian':
-        speed = np.abs(np.random.normal(0, max_speed / 2))
-    else:
-        raise NotImplementedError(
-            'Distribution type {} is not supported.'.format(dist))
-    angle = np.random.uniform(0, 2 * np.pi)
-    return (speed, angle)
-
-
-def random_move_control_points(X, Y, imageHeight, imageWidth, lineVelocity, region_size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3):
-    region_width, region_height = region_size
-    speed, angle = lineVelocity
-    X += int(speed * np.cos(angle))
-    Y += int(speed * np.sin(angle))
-    lineVelocity = random_accelerate(
-        lineVelocity, maxLineAcceleration, dist='guassian')
-    if ((X > imageHeight - region_height) or (X < 0) or (Y > imageWidth - region_width) or (Y < 0)):
-        lineVelocity = get_random_velocity(maxInitSpeed, dist='guassian')
-    new_X = np.clip(X, 0, imageHeight - region_height)
-    new_Y = np.clip(Y, 0, imageWidth - region_width)
-    return new_X, new_Y, lineVelocity
-
-
-
-# ##############################################
-# ##############################################
-
-if __name__ == '__main__':
-    import os
-    os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-    trials = 10
-    for _ in range(trials):
-        video_length = 10
-        # The returned masks are either stationary (50%) or moving (50%)
-        masks = create_random_shape_with_random_motion(video_length, zoomin=0.9, zoomout=1.1, rotmin=1, rotmax=10, imageHeight=240, imageWidth=432)
-        i = 0
-
-        for m in masks:
-           cv2.imshow('mask', np.array(m))
-           cv2.waitKey(500)
-           # m.save('mask_{}.png'.format(i))
-           i += 1
\ No newline at end of file
diff --git a/spaces/openskyml/dreamdrop-sd/README.md b/spaces/openskyml/dreamdrop-sd/README.md
deleted file mode 100644
index de460c7a3cd50ea82a0ee7f1f945c0567ac70e83..0000000000000000000000000000000000000000
--- a/spaces/openskyml/dreamdrop-sd/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: DreamDrop
-emoji: 🥏
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-models: [runwayml/stable-diffusion-v1-5, CompVis/stable-diffusion-v1-4, prompthero/openjourney, Yntec/Deliberate, Lykon/AbsoluteReality]
-pinned: true
----
-
-# This is NEW release of **DreamDrop V2.0**!
-
-## Features added:
-    1. Can generate up to 10 images at a time
-    2. Image Upscaler (x8) appeared
-    3. Integrated MagicPrompt (for Stable Diffusion and for Dall•E)
-    4. Added generation parameters menu (Steps, Samplers and CFG Sсale)
-
-### ***Enjoy!***
\ No newline at end of file
diff --git a/spaces/oscars47/thinking_parrot_reading_club_redux/README.md b/spaces/oscars47/thinking_parrot_reading_club_redux/README.md
deleted file mode 100644
index de4be91baef2d94b44e0f9be65700ef36b98cfd7..0000000000000000000000000000000000000000
--- a/spaces/oscars47/thinking_parrot_reading_club_redux/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Thinking Parrot Reading Club Redux
-emoji: 📚
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/models/vae_flax.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/models/vae_flax.py
deleted file mode 100644
index b8f5b1d0e399ab8e58d81d396d19b6f082192f5a..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/models/vae_flax.py
+++ /dev/null
@@ -1,869 +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.
-
-# JAX implementation of VQGAN from taming-transformers https://github.com/CompVis/taming-transformers
-
-import math
-from functools import partial
-from typing import Tuple
-
-import flax
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict
-
-from ..configuration_utils import ConfigMixin, flax_register_to_config
-from ..utils import BaseOutput
-from .modeling_flax_utils import FlaxModelMixin
-
-
-@flax.struct.dataclass
-class FlaxDecoderOutput(BaseOutput):
-    """
-    Output of decoding method.
-
-    Args:
-        sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
-            The decoded output sample from the last layer of the model.
-        dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
-            The `dtype` of the parameters.
-    """
-
-    sample: jnp.ndarray
-
-
-@flax.struct.dataclass
-class FlaxAutoencoderKLOutput(BaseOutput):
-    """
-    Output of AutoencoderKL encoding method.
-
-    Args:
-        latent_dist (`FlaxDiagonalGaussianDistribution`):
-            Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`.
-            `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution.
-    """
-
-    latent_dist: "FlaxDiagonalGaussianDistribution"
-
-
-class FlaxUpsample2D(nn.Module):
-    """
-    Flax implementation of 2D Upsample layer
-
-    Args:
-        in_channels (`int`):
-            Input channels
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-
-    in_channels: int
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.conv = nn.Conv(
-            self.in_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-    def __call__(self, hidden_states):
-        batch, height, width, channels = hidden_states.shape
-        hidden_states = jax.image.resize(
-            hidden_states,
-            shape=(batch, height * 2, width * 2, channels),
-            method="nearest",
-        )
-        hidden_states = self.conv(hidden_states)
-        return hidden_states
-
-
-class FlaxDownsample2D(nn.Module):
-    """
-    Flax implementation of 2D Downsample layer
-
-    Args:
-        in_channels (`int`):
-            Input channels
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-
-    in_channels: int
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.conv = nn.Conv(
-            self.in_channels,
-            kernel_size=(3, 3),
-            strides=(2, 2),
-            padding="VALID",
-            dtype=self.dtype,
-        )
-
-    def __call__(self, hidden_states):
-        pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
-        hidden_states = jnp.pad(hidden_states, pad_width=pad)
-        hidden_states = self.conv(hidden_states)
-        return hidden_states
-
-
-class FlaxResnetBlock2D(nn.Module):
-    """
-    Flax implementation of 2D Resnet Block.
-
-    Args:
-        in_channels (`int`):
-            Input channels
-        out_channels (`int`):
-            Output channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        groups (:obj:`int`, *optional*, defaults to `32`):
-            The number of groups to use for group norm.
-        use_nin_shortcut (:obj:`bool`, *optional*, defaults to `None`):
-            Whether to use `nin_shortcut`. This activates a new layer inside ResNet block
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-
-    in_channels: int
-    out_channels: int = None
-    dropout: float = 0.0
-    groups: int = 32
-    use_nin_shortcut: bool = None
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        out_channels = self.in_channels if self.out_channels is None else self.out_channels
-
-        self.norm1 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)
-        self.conv1 = nn.Conv(
-            out_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-        self.norm2 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)
-        self.dropout_layer = nn.Dropout(self.dropout)
-        self.conv2 = nn.Conv(
-            out_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-        use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
-
-        self.conv_shortcut = None
-        if use_nin_shortcut:
-            self.conv_shortcut = nn.Conv(
-                out_channels,
-                kernel_size=(1, 1),
-                strides=(1, 1),
-                padding="VALID",
-                dtype=self.dtype,
-            )
-
-    def __call__(self, hidden_states, deterministic=True):
-        residual = hidden_states
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = nn.swish(hidden_states)
-        hidden_states = self.conv1(hidden_states)
-
-        hidden_states = self.norm2(hidden_states)
-        hidden_states = nn.swish(hidden_states)
-        hidden_states = self.dropout_layer(hidden_states, deterministic)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.conv_shortcut is not None:
-            residual = self.conv_shortcut(residual)
-
-        return hidden_states + residual
-
-
-class FlaxAttentionBlock(nn.Module):
-    r"""
-    Flax Convolutional based multi-head attention block for diffusion-based VAE.
-
-    Parameters:
-        channels (:obj:`int`):
-            Input channels
-        num_head_channels (:obj:`int`, *optional*, defaults to `None`):
-            Number of attention heads
-        num_groups (:obj:`int`, *optional*, defaults to `32`):
-            The number of groups to use for group norm
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-
-    """
-    channels: int
-    num_head_channels: int = None
-    num_groups: int = 32
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.num_heads = self.channels // self.num_head_channels if self.num_head_channels is not None else 1
-
-        dense = partial(nn.Dense, self.channels, dtype=self.dtype)
-
-        self.group_norm = nn.GroupNorm(num_groups=self.num_groups, epsilon=1e-6)
-        self.query, self.key, self.value = dense(), dense(), dense()
-        self.proj_attn = dense()
-
-    def transpose_for_scores(self, projection):
-        new_projection_shape = projection.shape[:-1] + (self.num_heads, -1)
-        # move heads to 2nd position (B, T, H * D) -> (B, T, H, D)
-        new_projection = projection.reshape(new_projection_shape)
-        # (B, T, H, D) -> (B, H, T, D)
-        new_projection = jnp.transpose(new_projection, (0, 2, 1, 3))
-        return new_projection
-
-    def __call__(self, hidden_states):
-        residual = hidden_states
-        batch, height, width, channels = hidden_states.shape
-
-        hidden_states = self.group_norm(hidden_states)
-
-        hidden_states = hidden_states.reshape((batch, height * width, channels))
-
-        query = self.query(hidden_states)
-        key = self.key(hidden_states)
-        value = self.value(hidden_states)
-
-        # transpose
-        query = self.transpose_for_scores(query)
-        key = self.transpose_for_scores(key)
-        value = self.transpose_for_scores(value)
-
-        # compute attentions
-        scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
-        attn_weights = jnp.einsum("...qc,...kc->...qk", query * scale, key * scale)
-        attn_weights = nn.softmax(attn_weights, axis=-1)
-
-        # attend to values
-        hidden_states = jnp.einsum("...kc,...qk->...qc", value, attn_weights)
-
-        hidden_states = jnp.transpose(hidden_states, (0, 2, 1, 3))
-        new_hidden_states_shape = hidden_states.shape[:-2] + (self.channels,)
-        hidden_states = hidden_states.reshape(new_hidden_states_shape)
-
-        hidden_states = self.proj_attn(hidden_states)
-        hidden_states = hidden_states.reshape((batch, height, width, channels))
-        hidden_states = hidden_states + residual
-        return hidden_states
-
-
-class FlaxDownEncoderBlock2D(nn.Module):
-    r"""
-    Flax Resnet blocks-based Encoder block for diffusion-based VAE.
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        out_channels (:obj:`int`):
-            Output channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of Resnet layer block
-        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
-            The number of groups to use for the Resnet block group norm
-        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
-            Whether to add downsample layer
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    out_channels: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    resnet_groups: int = 32
-    add_downsample: bool = True
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        resnets = []
-        for i in range(self.num_layers):
-            in_channels = self.in_channels if i == 0 else self.out_channels
-
-            res_block = FlaxResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=self.out_channels,
-                dropout=self.dropout,
-                groups=self.resnet_groups,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-        self.resnets = resnets
-
-        if self.add_downsample:
-            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
-    def __call__(self, hidden_states, deterministic=True):
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, deterministic=deterministic)
-
-        if self.add_downsample:
-            hidden_states = self.downsamplers_0(hidden_states)
-
-        return hidden_states
-
-
-class FlaxUpDecoderBlock2D(nn.Module):
-    r"""
-    Flax Resnet blocks-based Decoder block for diffusion-based VAE.
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        out_channels (:obj:`int`):
-            Output channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of Resnet layer block
-        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
-            The number of groups to use for the Resnet block group norm
-        add_upsample (:obj:`bool`, *optional*, defaults to `True`):
-            Whether to add upsample layer
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    out_channels: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    resnet_groups: int = 32
-    add_upsample: bool = True
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        resnets = []
-        for i in range(self.num_layers):
-            in_channels = self.in_channels if i == 0 else self.out_channels
-            res_block = FlaxResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=self.out_channels,
-                dropout=self.dropout,
-                groups=self.resnet_groups,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-
-        self.resnets = resnets
-
-        if self.add_upsample:
-            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
-    def __call__(self, hidden_states, deterministic=True):
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, deterministic=deterministic)
-
-        if self.add_upsample:
-            hidden_states = self.upsamplers_0(hidden_states)
-
-        return hidden_states
-
-
-class FlaxUNetMidBlock2D(nn.Module):
-    r"""
-    Flax Unet Mid-Block module.
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of Resnet layer block
-        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
-            The number of groups to use for the Resnet and Attention block group norm
-        num_attention_heads (:obj:`int`, *optional*, defaults to `1`):
-            Number of attention heads for each attention block
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    resnet_groups: int = 32
-    num_attention_heads: int = 1
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        resnet_groups = self.resnet_groups if self.resnet_groups is not None else min(self.in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            FlaxResnetBlock2D(
-                in_channels=self.in_channels,
-                out_channels=self.in_channels,
-                dropout=self.dropout,
-                groups=resnet_groups,
-                dtype=self.dtype,
-            )
-        ]
-
-        attentions = []
-
-        for _ in range(self.num_layers):
-            attn_block = FlaxAttentionBlock(
-                channels=self.in_channels,
-                num_head_channels=self.num_attention_heads,
-                num_groups=resnet_groups,
-                dtype=self.dtype,
-            )
-            attentions.append(attn_block)
-
-            res_block = FlaxResnetBlock2D(
-                in_channels=self.in_channels,
-                out_channels=self.in_channels,
-                dropout=self.dropout,
-                groups=resnet_groups,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-
-        self.resnets = resnets
-        self.attentions = attentions
-
-    def __call__(self, hidden_states, deterministic=True):
-        hidden_states = self.resnets[0](hidden_states, deterministic=deterministic)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            hidden_states = attn(hidden_states)
-            hidden_states = resnet(hidden_states, deterministic=deterministic)
-
-        return hidden_states
-
-
-class FlaxEncoder(nn.Module):
-    r"""
-    Flax Implementation of VAE Encoder.
-
-    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
-    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
-    general usage and behavior.
-
-    Finally, this model supports inherent JAX features such as:
-    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
-    Parameters:
-        in_channels (:obj:`int`, *optional*, defaults to 3):
-            Input channels
-        out_channels (:obj:`int`, *optional*, defaults to 3):
-            Output channels
-        down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):
-            DownEncoder block type
-        block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):
-            Tuple containing the number of output channels for each block
-        layers_per_block (:obj:`int`, *optional*, defaults to `2`):
-            Number of Resnet layer for each block
-        norm_num_groups (:obj:`int`, *optional*, defaults to `32`):
-            norm num group
-        act_fn (:obj:`str`, *optional*, defaults to `silu`):
-            Activation function
-        double_z (:obj:`bool`, *optional*, defaults to `False`):
-            Whether to double the last output channels
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int = 3
-    out_channels: int = 3
-    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
-    block_out_channels: Tuple[int] = (64,)
-    layers_per_block: int = 2
-    norm_num_groups: int = 32
-    act_fn: str = "silu"
-    double_z: bool = False
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        block_out_channels = self.block_out_channels
-        # in
-        self.conv_in = nn.Conv(
-            block_out_channels[0],
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-        # downsampling
-        down_blocks = []
-        output_channel = block_out_channels[0]
-        for i, _ in enumerate(self.down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = FlaxDownEncoderBlock2D(
-                in_channels=input_channel,
-                out_channels=output_channel,
-                num_layers=self.layers_per_block,
-                resnet_groups=self.norm_num_groups,
-                add_downsample=not is_final_block,
-                dtype=self.dtype,
-            )
-            down_blocks.append(down_block)
-        self.down_blocks = down_blocks
-
-        # middle
-        self.mid_block = FlaxUNetMidBlock2D(
-            in_channels=block_out_channels[-1],
-            resnet_groups=self.norm_num_groups,
-            num_attention_heads=None,
-            dtype=self.dtype,
-        )
-
-        # end
-        conv_out_channels = 2 * self.out_channels if self.double_z else self.out_channels
-        self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)
-        self.conv_out = nn.Conv(
-            conv_out_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-    def __call__(self, sample, deterministic: bool = True):
-        # in
-        sample = self.conv_in(sample)
-
-        # downsampling
-        for block in self.down_blocks:
-            sample = block(sample, deterministic=deterministic)
-
-        # middle
-        sample = self.mid_block(sample, deterministic=deterministic)
-
-        # end
-        sample = self.conv_norm_out(sample)
-        sample = nn.swish(sample)
-        sample = self.conv_out(sample)
-
-        return sample
-
-
-class FlaxDecoder(nn.Module):
-    r"""
-    Flax Implementation of VAE Decoder.
-
-    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
-    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
-    general usage and behavior.
-
-    Finally, this model supports inherent JAX features such as:
-    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
-    Parameters:
-        in_channels (:obj:`int`, *optional*, defaults to 3):
-            Input channels
-        out_channels (:obj:`int`, *optional*, defaults to 3):
-            Output channels
-        up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):
-            UpDecoder block type
-        block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):
-            Tuple containing the number of output channels for each block
-        layers_per_block (:obj:`int`, *optional*, defaults to `2`):
-            Number of Resnet layer for each block
-        norm_num_groups (:obj:`int`, *optional*, defaults to `32`):
-            norm num group
-        act_fn (:obj:`str`, *optional*, defaults to `silu`):
-            Activation function
-        double_z (:obj:`bool`, *optional*, defaults to `False`):
-            Whether to double the last output channels
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            parameters `dtype`
-    """
-    in_channels: int = 3
-    out_channels: int = 3
-    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
-    block_out_channels: int = (64,)
-    layers_per_block: int = 2
-    norm_num_groups: int = 32
-    act_fn: str = "silu"
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        block_out_channels = self.block_out_channels
-
-        # z to block_in
-        self.conv_in = nn.Conv(
-            block_out_channels[-1],
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-        # middle
-        self.mid_block = FlaxUNetMidBlock2D(
-            in_channels=block_out_channels[-1],
-            resnet_groups=self.norm_num_groups,
-            num_attention_heads=None,
-            dtype=self.dtype,
-        )
-
-        # upsampling
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        output_channel = reversed_block_out_channels[0]
-        up_blocks = []
-        for i, _ in enumerate(self.up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            up_block = FlaxUpDecoderBlock2D(
-                in_channels=prev_output_channel,
-                out_channels=output_channel,
-                num_layers=self.layers_per_block + 1,
-                resnet_groups=self.norm_num_groups,
-                add_upsample=not is_final_block,
-                dtype=self.dtype,
-            )
-            up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        self.up_blocks = up_blocks
-
-        # end
-        self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)
-        self.conv_out = nn.Conv(
-            self.out_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-    def __call__(self, sample, deterministic: bool = True):
-        # z to block_in
-        sample = self.conv_in(sample)
-
-        # middle
-        sample = self.mid_block(sample, deterministic=deterministic)
-
-        # upsampling
-        for block in self.up_blocks:
-            sample = block(sample, deterministic=deterministic)
-
-        sample = self.conv_norm_out(sample)
-        sample = nn.swish(sample)
-        sample = self.conv_out(sample)
-
-        return sample
-
-
-class FlaxDiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        # Last axis to account for channels-last
-        self.mean, self.logvar = jnp.split(parameters, 2, axis=-1)
-        self.logvar = jnp.clip(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = jnp.exp(0.5 * self.logvar)
-        self.var = jnp.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = jnp.zeros_like(self.mean)
-
-    def sample(self, key):
-        return self.mean + self.std * jax.random.normal(key, self.mean.shape)
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return jnp.array([0.0])
-
-        if other is None:
-            return 0.5 * jnp.sum(self.mean**2 + self.var - 1.0 - self.logvar, axis=[1, 2, 3])
-
-        return 0.5 * jnp.sum(
-            jnp.square(self.mean - other.mean) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar,
-            axis=[1, 2, 3],
-        )
-
-    def nll(self, sample, axis=[1, 2, 3]):
-        if self.deterministic:
-            return jnp.array([0.0])
-
-        logtwopi = jnp.log(2.0 * jnp.pi)
-        return 0.5 * jnp.sum(logtwopi + self.logvar + jnp.square(sample - self.mean) / self.var, axis=axis)
-
-    def mode(self):
-        return self.mean
-
-
-@flax_register_to_config
-class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
-    r"""
-    Flax implementation of a VAE model with KL loss for decoding latent representations.
-
-    This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic methods
-    implemented for all models (such as downloading or saving).
-
-    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
-    subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matter related to its
-    general usage and behavior.
-
-    Inherent JAX features such as the following are supported:
-
-    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
-    Parameters:
-        in_channels (`int`, *optional*, defaults to 3):
-            Number of channels in the input image.
-        out_channels (`int`, *optional*, defaults to 3):
-            Number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):
-            Tuple of downsample block types.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):
-            Tuple of upsample block types.
-        block_out_channels (`Tuple[str]`, *optional*, defaults to `(64,)`):
-            Tuple of block output channels.
-        layers_per_block (`int`, *optional*, defaults to `2`):
-            Number of ResNet layer for each block.
-        act_fn (`str`, *optional*, defaults to `silu`):
-            The activation function to use.
-        latent_channels (`int`, *optional*, defaults to `4`):
-            Number of channels in the latent space.
-        norm_num_groups (`int`, *optional*, defaults to `32`):
-            The number of groups for normalization.
-        sample_size (`int`, *optional*, defaults to 32):
-            Sample input size.
-        scaling_factor (`float`, *optional*, defaults to 0.18215):
-            The component-wise standard deviation of the trained latent space computed using the first batch of the
-            training set. This is used to scale the latent space to have unit variance when training the diffusion
-            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
-            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
-            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
-            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
-        dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
-            The `dtype` of the parameters.
-    """
-    in_channels: int = 3
-    out_channels: int = 3
-    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
-    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
-    block_out_channels: Tuple[int] = (64,)
-    layers_per_block: int = 1
-    act_fn: str = "silu"
-    latent_channels: int = 4
-    norm_num_groups: int = 32
-    sample_size: int = 32
-    scaling_factor: float = 0.18215
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.encoder = FlaxEncoder(
-            in_channels=self.config.in_channels,
-            out_channels=self.config.latent_channels,
-            down_block_types=self.config.down_block_types,
-            block_out_channels=self.config.block_out_channels,
-            layers_per_block=self.config.layers_per_block,
-            act_fn=self.config.act_fn,
-            norm_num_groups=self.config.norm_num_groups,
-            double_z=True,
-            dtype=self.dtype,
-        )
-        self.decoder = FlaxDecoder(
-            in_channels=self.config.latent_channels,
-            out_channels=self.config.out_channels,
-            up_block_types=self.config.up_block_types,
-            block_out_channels=self.config.block_out_channels,
-            layers_per_block=self.config.layers_per_block,
-            norm_num_groups=self.config.norm_num_groups,
-            act_fn=self.config.act_fn,
-            dtype=self.dtype,
-        )
-        self.quant_conv = nn.Conv(
-            2 * self.config.latent_channels,
-            kernel_size=(1, 1),
-            strides=(1, 1),
-            padding="VALID",
-            dtype=self.dtype,
-        )
-        self.post_quant_conv = nn.Conv(
-            self.config.latent_channels,
-            kernel_size=(1, 1),
-            strides=(1, 1),
-            padding="VALID",
-            dtype=self.dtype,
-        )
-
-    def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
-        # init input tensors
-        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
-        sample = jnp.zeros(sample_shape, dtype=jnp.float32)
-
-        params_rng, dropout_rng, gaussian_rng = jax.random.split(rng, 3)
-        rngs = {"params": params_rng, "dropout": dropout_rng, "gaussian": gaussian_rng}
-
-        return self.init(rngs, sample)["params"]
-
-    def encode(self, sample, deterministic: bool = True, return_dict: bool = True):
-        sample = jnp.transpose(sample, (0, 2, 3, 1))
-
-        hidden_states = self.encoder(sample, deterministic=deterministic)
-        moments = self.quant_conv(hidden_states)
-        posterior = FlaxDiagonalGaussianDistribution(moments)
-
-        if not return_dict:
-            return (posterior,)
-
-        return FlaxAutoencoderKLOutput(latent_dist=posterior)
-
-    def decode(self, latents, deterministic: bool = True, return_dict: bool = True):
-        if latents.shape[-1] != self.config.latent_channels:
-            latents = jnp.transpose(latents, (0, 2, 3, 1))
-
-        hidden_states = self.post_quant_conv(latents)
-        hidden_states = self.decoder(hidden_states, deterministic=deterministic)
-
-        hidden_states = jnp.transpose(hidden_states, (0, 3, 1, 2))
-
-        if not return_dict:
-            return (hidden_states,)
-
-        return FlaxDecoderOutput(sample=hidden_states)
-
-    def __call__(self, sample, sample_posterior=False, deterministic: bool = True, return_dict: bool = True):
-        posterior = self.encode(sample, deterministic=deterministic, return_dict=return_dict)
-        if sample_posterior:
-            rng = self.make_rng("gaussian")
-            hidden_states = posterior.latent_dist.sample(rng)
-        else:
-            hidden_states = posterior.latent_dist.mode()
-
-        sample = self.decode(hidden_states, return_dict=return_dict).sample
-
-        if not return_dict:
-            return (sample,)
-
-        return FlaxDecoderOutput(sample=sample)
diff --git a/spaces/paufeldman/vv/graph2mesh.py b/spaces/paufeldman/vv/graph2mesh.py
deleted file mode 100644
index abbe8b766b01dcb9672d397c04c8cb7a4a5a7af3..0000000000000000000000000000000000000000
--- a/spaces/paufeldman/vv/graph2mesh.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import vtk
-from mayavi import mlab
-import numpy as np
-
-def create_disk(x, y, z, radius):
-    # Create a disk (circle) in the xy-plane
-    disk_source = vtk.vtkDiskSource()
-    disk_source.SetInnerRadius(0)
-    disk_source.SetOuterRadius(radius)
-    disk_source.SetCircumferentialResolution(30)
-    disk_source.SetRadialResolution(1)
-    disk_source.Update()
-    
-    # Move the disk to the specified position in 3D space
-    transform = vtk.vtkTransform()
-    transform.Translate(x, y, z)
-    
-    transform_filter = vtk.vtkTransformPolyDataFilter()
-    transform_filter.SetInputData(disk_source.GetOutput())
-    transform_filter.SetTransform(transform)
-    transform_filter.Update()
-    
-    return transform_filter.GetOutput()
-
-def create_tube_from_disks(disks):
-    # Combine multiple disks to form the tube mesh
-    points = vtk.vtkPoints()
-    lines = vtk.vtkCellArray()
-    
-    point_id = 0
-    for disk in disks:
-        num_points = disk.GetNumberOfPoints()
-        for i in range(num_points):
-            point = disk.GetPoint(i)
-            points.InsertNextPoint(point)
-        
-        line = vtk.vtkLine()
-        line.GetPointIds().SetId(0, point_id)
-        line.GetPointIds().SetId(1, point_id + num_points - 1)
-        lines.InsertNextCell(line)
-        
-        point_id += num_points
-    
-    tube_mesh = vtk.vtkPolyData()
-    tube_mesh.SetPoints(points)
-    tube_mesh.SetLines(lines)
-    
-    return tube_mesh
-
-# Create three disks at different positions
-disk1 = create_disk(0, 0, 0, 0.1)
-disk2 = create_disk(0, 0, 1, 0.1)
-disk3 = create_disk(0, 0, 2, 0.1)
-
-# Combine the disks and create the tube mesh
-disks = [disk1, disk2, disk3]
-tube_mesh = create_tube_from_disks(disks)
-# Extract vertices from the tube mesh
-vertices = np.array(tube_mesh.GetPoints().GetData())
-
-# Extract lines from the tube mesh
-cell_array = tube_mesh.GetLines()
-cell_array.InitTraversal()
-ids = vtk.vtkIdList()
-lines = []
-while cell_array.GetNextCell(ids):
-    line = [ids.GetId(i) for i in range(ids.GetNumberOfIds())]
-    lines.append(line)
-
-# Plot the tube mesh using Mayavi
-mlab.figure(bgcolor=(1, 1, 1))
-for line in lines:
-    x = vertices[line, 0]
-    y = vertices[line, 1]
-    z = vertices[line, 2]
-    mlab.plot3d(x, y, z, tube_radius=0.05, color=(0, 0, 1))
-
-mlab.show()
\ No newline at end of file
diff --git a/spaces/phyloforfun/VoucherVision/__init__.py b/spaces/phyloforfun/VoucherVision/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pierreguillou/layout-parser/README.md b/spaces/pierreguillou/layout-parser/README.md
deleted file mode 100644
index b9266dca21bdf875397bec4c3c098fe2c401571d..0000000000000000000000000000000000000000
--- a/spaces/pierreguillou/layout-parser/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Layout Parser
-emoji: 🐨
-colorFrom: indigo
-colorTo: purple
-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.
diff --git a/spaces/pikapikaPikachu/chatbot/app.py b/spaces/pikapikaPikachu/chatbot/app.py
deleted file mode 100644
index 66b1ba6d8be5dc767634a909b8255ae417373d04..0000000000000000000000000000000000000000
--- a/spaces/pikapikaPikachu/chatbot/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import openai
-import os
-
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-class Conversation:
-    def __init__(self, prompt, num_of_round):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append({"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        self.messages.append({"role": "assistant", "content": message})
-
-        if len(self.messages) > self.num_of_round*2 + 1:
-            del self.messages[1:3] #Remove the first round conversation left.
-        return message
-
-
-
-
-import gradio as gr
-prompt = """This chatbot identify itself as a chubby catbot named Mocha, who lives with Andrea, her husband, and her kids. The whole family loves Mocha.
-"""
-
-conv = Conversation(prompt, 10)
-
-def answer(question, history=[]):
-    history.append(question)
-    response = conv.ask(question)
-    history.append(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
-
-with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
-
-    txt.submit(answer, [txt, state], [chatbot, state])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/pinkq/Newbing/src/app/loading.css b/spaces/pinkq/Newbing/src/app/loading.css
deleted file mode 100644
index eaaab6a86a228334c4eca3c5368ae6f0f593d405..0000000000000000000000000000000000000000
--- a/spaces/pinkq/Newbing/src/app/loading.css
+++ /dev/null
@@ -1,68 +0,0 @@
-::-webkit-scrollbar {
-  width: 10px;
-  height: 10px;
-  display: none;
-}
-
-::-webkit-scrollbar-button:start:decrement,
-::-webkit-scrollbar-button:end:increment {
-  height: 30px;
-  background-color: transparent;
-}
-
-::-webkit-scrollbar-track-piece {
-  background-color: #3b3b3b;
-  -webkit-border-radius: 16px;
-}
-
-::-webkit-scrollbar-thumb:vertical {
-  height: 50px;
-  background-color: #666;
-  border: 1px solid #eee;
-  -webkit-border-radius: 6px;
-}
-
-/* loading start */
-.loading-spinner {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  height: 100vh;
-  opacity: 1;
-  transition: opacity .8s ease-out;
-}
-
-.loading-spinner.hidden {
-  opacity: 0;
-}
-
-.loading-spinner>div {
-  width: 30px;
-  height: 30px;
-  background: linear-gradient(90deg, #2870EA 10.79%, #1B4AEF 87.08%);
-
-  border-radius: 100%;
-  display: inline-block;
-  animation: sk-bouncedelay 1.4s infinite ease-in-out both;
-}
-
-.loading-spinner .bounce1 {
-  animation-delay: -0.32s;
-}
-
-.loading-spinner .bounce2 {
-  animation-delay: -0.16s;
-}
-
-@keyframes sk-bouncedelay {
-
-  0%,
-  80%,
-  100% {
-    transform: scale(0);
-  }
-
-  40% {
-    transform: scale(1.0);
-  }
-}
diff --git a/spaces/pixiou/bingo/src/pages/api/create.ts b/spaces/pixiou/bingo/src/pages/api/create.ts
deleted file mode 100644
index a7ac579f0acc780741e7083bf681a298585e981c..0000000000000000000000000000000000000000
--- a/spaces/pixiou/bingo/src/pages/api/create.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { fetch, debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-
-const API_ENDPOINT = 'https://www.bing.com/turing/conversation/create'
-// const API_ENDPOINT = 'https://edgeservices.bing.com/edgesvc/turing/conversation/create';
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const headers = createHeaders(req.cookies)
-
-    res.setHeader('set-cookie', headers.cookie)
-    res.writeHead(200, {
-      'Content-Type': 'application/json',
-    })
-
-    debug('headers', headers)
-    const response = await fetch(API_ENDPOINT, { method: 'GET', headers })
-      .then((res) => res.text())
-
-    res.end(response)
-  } catch (e) {
-    return res.end(JSON.stringify({
-      result: {
-        value: 'UnauthorizedRequest',
-        message: `${e}`
-      }
-    }))
-  }
-}
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/tenacity/before_sleep.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/tenacity/before_sleep.py
deleted file mode 100644
index 8c6167fb3a6b390c5c0a3ba455f76cedf34695f2..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/tenacity/before_sleep.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 typing
-
-from pip._vendor.tenacity import _utils
-
-if typing.TYPE_CHECKING:
-    import logging
-
-    from pip._vendor.tenacity import RetryCallState
-
-
-def before_sleep_nothing(retry_state: "RetryCallState") -> None:
-    """Before call strategy that does nothing."""
-
-
-def before_sleep_log(
-    logger: "logging.Logger",
-    log_level: int,
-    exc_info: bool = False,
-) -> typing.Callable[["RetryCallState"], None]:
-    """Before call strategy that logs to some logger the attempt."""
-
-    def log_it(retry_state: "RetryCallState") -> None:
-        local_exc_info: BaseException | bool | None
-
-        if retry_state.outcome is None:
-            raise RuntimeError("log_it() called before outcome was set")
-
-        if retry_state.next_action is None:
-            raise RuntimeError("log_it() called before next_action was set")
-
-        if retry_state.outcome.failed:
-            ex = retry_state.outcome.exception()
-            verb, value = "raised", f"{ex.__class__.__name__}: {ex}"
-
-            if exc_info:
-                local_exc_info = retry_state.outcome.exception()
-            else:
-                local_exc_info = False
-        else:
-            verb, value = "returned", retry_state.outcome.result()
-            local_exc_info = False  # exc_info does not apply when no exception
-
-        if retry_state.fn is None:
-            # NOTE(sileht): can't really happen, but we must please mypy
-            fn_name = "<unknown>"
-        else:
-            fn_name = _utils.get_callback_name(retry_state.fn)
-
-        logger.log(
-            log_level,
-            f"Retrying {fn_name} " f"in {retry_state.next_action.sleep} seconds as it {verb} {value}.",
-            exc_info=local_exc_info,
-        )
-
-    return log_it
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
deleted file mode 100644
index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
+++ /dev/null
@@ -1,519 +0,0 @@
-"""
-This module uses ctypes to bind a whole bunch of functions and constants from
-SecureTransport. The goal here is to provide the low-level API to
-SecureTransport. These are essentially the C-level functions and constants, and
-they're pretty gross to work with.
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
-    Copyright (c) 2015-2016 Will Bond <will@wbond.net>
-
-    Permission is hereby granted, free of charge, to any person obtaining a
-    copy of this software and associated documentation files (the "Software"),
-    to deal in the Software without restriction, including without limitation
-    the rights to use, copy, modify, merge, publish, distribute, sublicense,
-    and/or sell copies of the Software, and to permit persons to whom the
-    Software is furnished to do so, subject to the following conditions:
-
-    The above copyright notice and this permission notice shall be included in
-    all copies or substantial portions of the Software.
-
-    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-    DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-
-import platform
-from ctypes import (
-    CDLL,
-    CFUNCTYPE,
-    POINTER,
-    c_bool,
-    c_byte,
-    c_char_p,
-    c_int32,
-    c_long,
-    c_size_t,
-    c_uint32,
-    c_ulong,
-    c_void_p,
-)
-from ctypes.util import find_library
-
-from ...packages.six import raise_from
-
-if platform.system() != "Darwin":
-    raise ImportError("Only macOS is supported")
-
-version = platform.mac_ver()[0]
-version_info = tuple(map(int, version.split(".")))
-if version_info < (10, 8):
-    raise OSError(
-        "Only OS X 10.8 and newer are supported, not %s.%s"
-        % (version_info[0], version_info[1])
-    )
-
-
-def load_cdll(name, macos10_16_path):
-    """Loads a CDLL by name, falling back to known path on 10.16+"""
-    try:
-        # Big Sur is technically 11 but we use 10.16 due to the Big Sur
-        # beta being labeled as 10.16.
-        if version_info >= (10, 16):
-            path = macos10_16_path
-        else:
-            path = find_library(name)
-        if not path:
-            raise OSError  # Caught and reraised as 'ImportError'
-        return CDLL(path, use_errno=True)
-    except OSError:
-        raise_from(ImportError("The library %s failed to load" % name), None)
-
-
-Security = load_cdll(
-    "Security", "/System/Library/Frameworks/Security.framework/Security"
-)
-CoreFoundation = load_cdll(
-    "CoreFoundation",
-    "/System/Library/Frameworks/CoreFoundation.framework/CoreFoundation",
-)
-
-
-Boolean = c_bool
-CFIndex = c_long
-CFStringEncoding = c_uint32
-CFData = c_void_p
-CFString = c_void_p
-CFArray = c_void_p
-CFMutableArray = c_void_p
-CFDictionary = c_void_p
-CFError = c_void_p
-CFType = c_void_p
-CFTypeID = c_ulong
-
-CFTypeRef = POINTER(CFType)
-CFAllocatorRef = c_void_p
-
-OSStatus = c_int32
-
-CFDataRef = POINTER(CFData)
-CFStringRef = POINTER(CFString)
-CFArrayRef = POINTER(CFArray)
-CFMutableArrayRef = POINTER(CFMutableArray)
-CFDictionaryRef = POINTER(CFDictionary)
-CFArrayCallBacks = c_void_p
-CFDictionaryKeyCallBacks = c_void_p
-CFDictionaryValueCallBacks = c_void_p
-
-SecCertificateRef = POINTER(c_void_p)
-SecExternalFormat = c_uint32
-SecExternalItemType = c_uint32
-SecIdentityRef = POINTER(c_void_p)
-SecItemImportExportFlags = c_uint32
-SecItemImportExportKeyParameters = c_void_p
-SecKeychainRef = POINTER(c_void_p)
-SSLProtocol = c_uint32
-SSLCipherSuite = c_uint32
-SSLContextRef = POINTER(c_void_p)
-SecTrustRef = POINTER(c_void_p)
-SSLConnectionRef = c_uint32
-SecTrustResultType = c_uint32
-SecTrustOptionFlags = c_uint32
-SSLProtocolSide = c_uint32
-SSLConnectionType = c_uint32
-SSLSessionOption = c_uint32
-
-
-try:
-    Security.SecItemImport.argtypes = [
-        CFDataRef,
-        CFStringRef,
-        POINTER(SecExternalFormat),
-        POINTER(SecExternalItemType),
-        SecItemImportExportFlags,
-        POINTER(SecItemImportExportKeyParameters),
-        SecKeychainRef,
-        POINTER(CFArrayRef),
-    ]
-    Security.SecItemImport.restype = OSStatus
-
-    Security.SecCertificateGetTypeID.argtypes = []
-    Security.SecCertificateGetTypeID.restype = CFTypeID
-
-    Security.SecIdentityGetTypeID.argtypes = []
-    Security.SecIdentityGetTypeID.restype = CFTypeID
-
-    Security.SecKeyGetTypeID.argtypes = []
-    Security.SecKeyGetTypeID.restype = CFTypeID
-
-    Security.SecCertificateCreateWithData.argtypes = [CFAllocatorRef, CFDataRef]
-    Security.SecCertificateCreateWithData.restype = SecCertificateRef
-
-    Security.SecCertificateCopyData.argtypes = [SecCertificateRef]
-    Security.SecCertificateCopyData.restype = CFDataRef
-
-    Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
-    Security.SecCopyErrorMessageString.restype = CFStringRef
-
-    Security.SecIdentityCreateWithCertificate.argtypes = [
-        CFTypeRef,
-        SecCertificateRef,
-        POINTER(SecIdentityRef),
-    ]
-    Security.SecIdentityCreateWithCertificate.restype = OSStatus
-
-    Security.SecKeychainCreate.argtypes = [
-        c_char_p,
-        c_uint32,
-        c_void_p,
-        Boolean,
-        c_void_p,
-        POINTER(SecKeychainRef),
-    ]
-    Security.SecKeychainCreate.restype = OSStatus
-
-    Security.SecKeychainDelete.argtypes = [SecKeychainRef]
-    Security.SecKeychainDelete.restype = OSStatus
-
-    Security.SecPKCS12Import.argtypes = [
-        CFDataRef,
-        CFDictionaryRef,
-        POINTER(CFArrayRef),
-    ]
-    Security.SecPKCS12Import.restype = OSStatus
-
-    SSLReadFunc = CFUNCTYPE(OSStatus, SSLConnectionRef, c_void_p, POINTER(c_size_t))
-    SSLWriteFunc = CFUNCTYPE(
-        OSStatus, SSLConnectionRef, POINTER(c_byte), POINTER(c_size_t)
-    )
-
-    Security.SSLSetIOFuncs.argtypes = [SSLContextRef, SSLReadFunc, SSLWriteFunc]
-    Security.SSLSetIOFuncs.restype = OSStatus
-
-    Security.SSLSetPeerID.argtypes = [SSLContextRef, c_char_p, c_size_t]
-    Security.SSLSetPeerID.restype = OSStatus
-
-    Security.SSLSetCertificate.argtypes = [SSLContextRef, CFArrayRef]
-    Security.SSLSetCertificate.restype = OSStatus
-
-    Security.SSLSetCertificateAuthorities.argtypes = [SSLContextRef, CFTypeRef, Boolean]
-    Security.SSLSetCertificateAuthorities.restype = OSStatus
-
-    Security.SSLSetConnection.argtypes = [SSLContextRef, SSLConnectionRef]
-    Security.SSLSetConnection.restype = OSStatus
-
-    Security.SSLSetPeerDomainName.argtypes = [SSLContextRef, c_char_p, c_size_t]
-    Security.SSLSetPeerDomainName.restype = OSStatus
-
-    Security.SSLHandshake.argtypes = [SSLContextRef]
-    Security.SSLHandshake.restype = OSStatus
-
-    Security.SSLRead.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
-    Security.SSLRead.restype = OSStatus
-
-    Security.SSLWrite.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
-    Security.SSLWrite.restype = OSStatus
-
-    Security.SSLClose.argtypes = [SSLContextRef]
-    Security.SSLClose.restype = OSStatus
-
-    Security.SSLGetNumberSupportedCiphers.argtypes = [SSLContextRef, POINTER(c_size_t)]
-    Security.SSLGetNumberSupportedCiphers.restype = OSStatus
-
-    Security.SSLGetSupportedCiphers.argtypes = [
-        SSLContextRef,
-        POINTER(SSLCipherSuite),
-        POINTER(c_size_t),
-    ]
-    Security.SSLGetSupportedCiphers.restype = OSStatus
-
-    Security.SSLSetEnabledCiphers.argtypes = [
-        SSLContextRef,
-        POINTER(SSLCipherSuite),
-        c_size_t,
-    ]
-    Security.SSLSetEnabledCiphers.restype = OSStatus
-
-    Security.SSLGetNumberEnabledCiphers.argtype = [SSLContextRef, POINTER(c_size_t)]
-    Security.SSLGetNumberEnabledCiphers.restype = OSStatus
-
-    Security.SSLGetEnabledCiphers.argtypes = [
-        SSLContextRef,
-        POINTER(SSLCipherSuite),
-        POINTER(c_size_t),
-    ]
-    Security.SSLGetEnabledCiphers.restype = OSStatus
-
-    Security.SSLGetNegotiatedCipher.argtypes = [SSLContextRef, POINTER(SSLCipherSuite)]
-    Security.SSLGetNegotiatedCipher.restype = OSStatus
-
-    Security.SSLGetNegotiatedProtocolVersion.argtypes = [
-        SSLContextRef,
-        POINTER(SSLProtocol),
-    ]
-    Security.SSLGetNegotiatedProtocolVersion.restype = OSStatus
-
-    Security.SSLCopyPeerTrust.argtypes = [SSLContextRef, POINTER(SecTrustRef)]
-    Security.SSLCopyPeerTrust.restype = OSStatus
-
-    Security.SecTrustSetAnchorCertificates.argtypes = [SecTrustRef, CFArrayRef]
-    Security.SecTrustSetAnchorCertificates.restype = OSStatus
-
-    Security.SecTrustSetAnchorCertificatesOnly.argstypes = [SecTrustRef, Boolean]
-    Security.SecTrustSetAnchorCertificatesOnly.restype = OSStatus
-
-    Security.SecTrustEvaluate.argtypes = [SecTrustRef, POINTER(SecTrustResultType)]
-    Security.SecTrustEvaluate.restype = OSStatus
-
-    Security.SecTrustGetCertificateCount.argtypes = [SecTrustRef]
-    Security.SecTrustGetCertificateCount.restype = CFIndex
-
-    Security.SecTrustGetCertificateAtIndex.argtypes = [SecTrustRef, CFIndex]
-    Security.SecTrustGetCertificateAtIndex.restype = SecCertificateRef
-
-    Security.SSLCreateContext.argtypes = [
-        CFAllocatorRef,
-        SSLProtocolSide,
-        SSLConnectionType,
-    ]
-    Security.SSLCreateContext.restype = SSLContextRef
-
-    Security.SSLSetSessionOption.argtypes = [SSLContextRef, SSLSessionOption, Boolean]
-    Security.SSLSetSessionOption.restype = OSStatus
-
-    Security.SSLSetProtocolVersionMin.argtypes = [SSLContextRef, SSLProtocol]
-    Security.SSLSetProtocolVersionMin.restype = OSStatus
-
-    Security.SSLSetProtocolVersionMax.argtypes = [SSLContextRef, SSLProtocol]
-    Security.SSLSetProtocolVersionMax.restype = OSStatus
-
-    try:
-        Security.SSLSetALPNProtocols.argtypes = [SSLContextRef, CFArrayRef]
-        Security.SSLSetALPNProtocols.restype = OSStatus
-    except AttributeError:
-        # Supported only in 10.12+
-        pass
-
-    Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
-    Security.SecCopyErrorMessageString.restype = CFStringRef
-
-    Security.SSLReadFunc = SSLReadFunc
-    Security.SSLWriteFunc = SSLWriteFunc
-    Security.SSLContextRef = SSLContextRef
-    Security.SSLProtocol = SSLProtocol
-    Security.SSLCipherSuite = SSLCipherSuite
-    Security.SecIdentityRef = SecIdentityRef
-    Security.SecKeychainRef = SecKeychainRef
-    Security.SecTrustRef = SecTrustRef
-    Security.SecTrustResultType = SecTrustResultType
-    Security.SecExternalFormat = SecExternalFormat
-    Security.OSStatus = OSStatus
-
-    Security.kSecImportExportPassphrase = CFStringRef.in_dll(
-        Security, "kSecImportExportPassphrase"
-    )
-    Security.kSecImportItemIdentity = CFStringRef.in_dll(
-        Security, "kSecImportItemIdentity"
-    )
-
-    # CoreFoundation time!
-    CoreFoundation.CFRetain.argtypes = [CFTypeRef]
-    CoreFoundation.CFRetain.restype = CFTypeRef
-
-    CoreFoundation.CFRelease.argtypes = [CFTypeRef]
-    CoreFoundation.CFRelease.restype = None
-
-    CoreFoundation.CFGetTypeID.argtypes = [CFTypeRef]
-    CoreFoundation.CFGetTypeID.restype = CFTypeID
-
-    CoreFoundation.CFStringCreateWithCString.argtypes = [
-        CFAllocatorRef,
-        c_char_p,
-        CFStringEncoding,
-    ]
-    CoreFoundation.CFStringCreateWithCString.restype = CFStringRef
-
-    CoreFoundation.CFStringGetCStringPtr.argtypes = [CFStringRef, CFStringEncoding]
-    CoreFoundation.CFStringGetCStringPtr.restype = c_char_p
-
-    CoreFoundation.CFStringGetCString.argtypes = [
-        CFStringRef,
-        c_char_p,
-        CFIndex,
-        CFStringEncoding,
-    ]
-    CoreFoundation.CFStringGetCString.restype = c_bool
-
-    CoreFoundation.CFDataCreate.argtypes = [CFAllocatorRef, c_char_p, CFIndex]
-    CoreFoundation.CFDataCreate.restype = CFDataRef
-
-    CoreFoundation.CFDataGetLength.argtypes = [CFDataRef]
-    CoreFoundation.CFDataGetLength.restype = CFIndex
-
-    CoreFoundation.CFDataGetBytePtr.argtypes = [CFDataRef]
-    CoreFoundation.CFDataGetBytePtr.restype = c_void_p
-
-    CoreFoundation.CFDictionaryCreate.argtypes = [
-        CFAllocatorRef,
-        POINTER(CFTypeRef),
-        POINTER(CFTypeRef),
-        CFIndex,
-        CFDictionaryKeyCallBacks,
-        CFDictionaryValueCallBacks,
-    ]
-    CoreFoundation.CFDictionaryCreate.restype = CFDictionaryRef
-
-    CoreFoundation.CFDictionaryGetValue.argtypes = [CFDictionaryRef, CFTypeRef]
-    CoreFoundation.CFDictionaryGetValue.restype = CFTypeRef
-
-    CoreFoundation.CFArrayCreate.argtypes = [
-        CFAllocatorRef,
-        POINTER(CFTypeRef),
-        CFIndex,
-        CFArrayCallBacks,
-    ]
-    CoreFoundation.CFArrayCreate.restype = CFArrayRef
-
-    CoreFoundation.CFArrayCreateMutable.argtypes = [
-        CFAllocatorRef,
-        CFIndex,
-        CFArrayCallBacks,
-    ]
-    CoreFoundation.CFArrayCreateMutable.restype = CFMutableArrayRef
-
-    CoreFoundation.CFArrayAppendValue.argtypes = [CFMutableArrayRef, c_void_p]
-    CoreFoundation.CFArrayAppendValue.restype = None
-
-    CoreFoundation.CFArrayGetCount.argtypes = [CFArrayRef]
-    CoreFoundation.CFArrayGetCount.restype = CFIndex
-
-    CoreFoundation.CFArrayGetValueAtIndex.argtypes = [CFArrayRef, CFIndex]
-    CoreFoundation.CFArrayGetValueAtIndex.restype = c_void_p
-
-    CoreFoundation.kCFAllocatorDefault = CFAllocatorRef.in_dll(
-        CoreFoundation, "kCFAllocatorDefault"
-    )
-    CoreFoundation.kCFTypeArrayCallBacks = c_void_p.in_dll(
-        CoreFoundation, "kCFTypeArrayCallBacks"
-    )
-    CoreFoundation.kCFTypeDictionaryKeyCallBacks = c_void_p.in_dll(
-        CoreFoundation, "kCFTypeDictionaryKeyCallBacks"
-    )
-    CoreFoundation.kCFTypeDictionaryValueCallBacks = c_void_p.in_dll(
-        CoreFoundation, "kCFTypeDictionaryValueCallBacks"
-    )
-
-    CoreFoundation.CFTypeRef = CFTypeRef
-    CoreFoundation.CFArrayRef = CFArrayRef
-    CoreFoundation.CFStringRef = CFStringRef
-    CoreFoundation.CFDictionaryRef = CFDictionaryRef
-
-except (AttributeError):
-    raise ImportError("Error initializing ctypes")
-
-
-class CFConst(object):
-    """
-    A class object that acts as essentially a namespace for CoreFoundation
-    constants.
-    """
-
-    kCFStringEncodingUTF8 = CFStringEncoding(0x08000100)
-
-
-class SecurityConst(object):
-    """
-    A class object that acts as essentially a namespace for Security constants.
-    """
-
-    kSSLSessionOptionBreakOnServerAuth = 0
-
-    kSSLProtocol2 = 1
-    kSSLProtocol3 = 2
-    kTLSProtocol1 = 4
-    kTLSProtocol11 = 7
-    kTLSProtocol12 = 8
-    # SecureTransport does not support TLS 1.3 even if there's a constant for it
-    kTLSProtocol13 = 10
-    kTLSProtocolMaxSupported = 999
-
-    kSSLClientSide = 1
-    kSSLStreamType = 0
-
-    kSecFormatPEMSequence = 10
-
-    kSecTrustResultInvalid = 0
-    kSecTrustResultProceed = 1
-    # This gap is present on purpose: this was kSecTrustResultConfirm, which
-    # is deprecated.
-    kSecTrustResultDeny = 3
-    kSecTrustResultUnspecified = 4
-    kSecTrustResultRecoverableTrustFailure = 5
-    kSecTrustResultFatalTrustFailure = 6
-    kSecTrustResultOtherError = 7
-
-    errSSLProtocol = -9800
-    errSSLWouldBlock = -9803
-    errSSLClosedGraceful = -9805
-    errSSLClosedNoNotify = -9816
-    errSSLClosedAbort = -9806
-
-    errSSLXCertChainInvalid = -9807
-    errSSLCrypto = -9809
-    errSSLInternal = -9810
-    errSSLCertExpired = -9814
-    errSSLCertNotYetValid = -9815
-    errSSLUnknownRootCert = -9812
-    errSSLNoRootCert = -9813
-    errSSLHostNameMismatch = -9843
-    errSSLPeerHandshakeFail = -9824
-    errSSLPeerUserCancelled = -9839
-    errSSLWeakPeerEphemeralDHKey = -9850
-    errSSLServerAuthCompleted = -9841
-    errSSLRecordOverflow = -9847
-
-    errSecVerifyFailed = -67808
-    errSecNoTrustSettings = -25263
-    errSecItemNotFound = -25300
-    errSecInvalidTrustSettings = -25262
-
-    # Cipher suites. We only pick the ones our default cipher string allows.
-    # Source: https://developer.apple.com/documentation/security/1550981-ssl_cipher_suite_values
-    TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384 = 0xC02C
-    TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 = 0xC030
-    TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 = 0xC02B
-    TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 = 0xC02F
-    TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA9
-    TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA8
-    TLS_DHE_RSA_WITH_AES_256_GCM_SHA384 = 0x009F
-    TLS_DHE_RSA_WITH_AES_128_GCM_SHA256 = 0x009E
-    TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384 = 0xC024
-    TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384 = 0xC028
-    TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA = 0xC00A
-    TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA = 0xC014
-    TLS_DHE_RSA_WITH_AES_256_CBC_SHA256 = 0x006B
-    TLS_DHE_RSA_WITH_AES_256_CBC_SHA = 0x0039
-    TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 = 0xC023
-    TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256 = 0xC027
-    TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA = 0xC009
-    TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA = 0xC013
-    TLS_DHE_RSA_WITH_AES_128_CBC_SHA256 = 0x0067
-    TLS_DHE_RSA_WITH_AES_128_CBC_SHA = 0x0033
-    TLS_RSA_WITH_AES_256_GCM_SHA384 = 0x009D
-    TLS_RSA_WITH_AES_128_GCM_SHA256 = 0x009C
-    TLS_RSA_WITH_AES_256_CBC_SHA256 = 0x003D
-    TLS_RSA_WITH_AES_128_CBC_SHA256 = 0x003C
-    TLS_RSA_WITH_AES_256_CBC_SHA = 0x0035
-    TLS_RSA_WITH_AES_128_CBC_SHA = 0x002F
-    TLS_AES_128_GCM_SHA256 = 0x1301
-    TLS_AES_256_GCM_SHA384 = 0x1302
-    TLS_AES_128_CCM_8_SHA256 = 0x1305
-    TLS_AES_128_CCM_SHA256 = 0x1304
diff --git a/spaces/poetrychor/Gustavosta-MagicPrompt-Stable-Diffusion/Dockerfile b/spaces/poetrychor/Gustavosta-MagicPrompt-Stable-Diffusion/Dockerfile
deleted file mode 100644
index a4c8b4f88ec3000f75b1413a72ba55e294692201..0000000000000000000000000000000000000000
--- a/spaces/poetrychor/Gustavosta-MagicPrompt-Stable-Diffusion/Dockerfile
+++ /dev/null
@@ -1,2 +0,0 @@
-FROM huggingface/autotrain-advanced:latest
-CMD autotrain setup && autotrain app --port 7860
diff --git a/spaces/potsawee/multiple-choice-QG/question_generation.py b/spaces/potsawee/multiple-choice-QG/question_generation.py
deleted file mode 100644
index 56b21e49d1be552730dab9d94411fada4f42119a..0000000000000000000000000000000000000000
--- a/spaces/potsawee/multiple-choice-QG/question_generation.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import torch
-import re
-
-@torch.no_grad()
-def question_generation_sampling(
-    g1_model,
-    g1_tokenizer,
-    g2_model,
-    g2_tokenizer,
-    context,
-    num_questions,
-    device,
-):
-    qa_input_ids = prepare_qa_input(
-            g1_tokenizer,
-            context=context,
-            device=device,
-    )
-    max_repeated_sampling = int(num_questions * 1.5) # sometimes generated question+answer is invalid
-    num_valid_questions = 0
-    questions = []
-    for q_ in range(max_repeated_sampling):
-        # Stage G.1: question+answer generation
-        outputs = g1_model.generate(
-            qa_input_ids,
-            max_new_tokens=128,
-            do_sample=True,
-        )
-        question_answer = g1_tokenizer.decode(outputs[0], skip_special_tokens=False)
-        question_answer = question_answer.replace(g1_tokenizer.pad_token, "").replace(g1_tokenizer.eos_token, "")
-        question_answer_split = question_answer.split(g1_tokenizer.sep_token)
-        if len(question_answer_split) == 2:
-            # valid Question + Annswer output
-            num_valid_questions += 1
-        else:
-            continue
-        question = question_answer_split[0].strip()
-        answer = question_answer_split[1].strip()
-
-        # Stage G.2: Distractor Generation
-        distractor_input_ids = prepare_distractor_input(
-            g2_tokenizer,
-            context = context,
-            question = question,
-            answer = answer,
-            device = device,
-            separator = g2_tokenizer.sep_token,
-        )
-        outputs = g2_model.generate(
-            distractor_input_ids,
-            max_new_tokens=128,
-            do_sample=True,
-        )
-        distractors = g2_tokenizer.decode(outputs[0], skip_special_tokens=False)
-        distractors = distractors.replace(g2_tokenizer.pad_token, "").replace(g2_tokenizer.eos_token, "")
-        distractors = re.sub("<extra\S+>", g2_tokenizer.sep_token, distractors)
-        distractors = [y.strip() for y in distractors.split(g2_tokenizer.sep_token)]
-        options = [answer] + distractors
-
-        while len(options) < 4:
-            options.append(options[-1])
-
-        question_item = {
-            'question': question,
-            'options': options,
-        }
-        questions.append(question_item)
-        if num_valid_questions == num_questions:
-            break
-    return questions
-
-
-def prepare_qa_input(t5_tokenizer, context, device):
-    """
-    input: context
-    output: question <sep> answer
-    """
-    encoding = t5_tokenizer(
-        [context],
-        return_tensors="pt",
-    )
-    input_ids = encoding.input_ids.to(device)
-    return input_ids
-
-
-def prepare_distractor_input(t5_tokenizer, context, question, answer, device, separator='<sep>'):
-    """
-    input: question <sep> answer <sep> article
-    output: distractor1 <sep> distractor2 <sep> distractor3
-    """
-    input_text = question + ' ' + separator + ' ' + answer + ' ' + separator + ' ' + context
-    encoding = t5_tokenizer(
-        [input_text],
-        return_tensors="pt",
-    )
-    input_ids = encoding.input_ids.to(device)
-    return input_ids
diff --git a/spaces/ppsantiago/chatGPT/custom.css b/spaces/ppsantiago/chatGPT/custom.css
deleted file mode 100644
index 5143eb138ea2469d8c457c71cb210fd3fb7cbe15..0000000000000000000000000000000000000000
--- a/spaces/ppsantiago/chatGPT/custom.css
+++ /dev/null
@@ -1,162 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: #FFFFFF !important;
-}
-[data-testid = "user"] {
-    background-color: #95EC69 !important;
-}
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
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-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
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-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_dates.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_dates.py
deleted file mode 100644
index 8995b9b35f093b151c169dd252d4778c655a2091..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_dates.py
+++ /dev/null
@@ -1,1411 +0,0 @@
-import datetime
-
-import dateutil.tz
-import dateutil.rrule
-import functools
-import numpy as np
-import pytest
-
-import matplotlib as mpl
-from matplotlib import rc_context, style
-import matplotlib.dates as mdates
-import matplotlib.pyplot as plt
-from matplotlib.testing.decorators import image_comparison
-import matplotlib.ticker as mticker
-
-
-def test_date_numpyx():
-    # test that numpy dates work properly...
-    base = datetime.datetime(2017, 1, 1)
-    time = [base + datetime.timedelta(days=x) for x in range(0, 3)]
-    timenp = np.array(time, dtype='datetime64[ns]')
-    data = np.array([0., 2., 1.])
-    fig = plt.figure(figsize=(10, 2))
-    ax = fig.add_subplot(1, 1, 1)
-    h, = ax.plot(time, data)
-    hnp, = ax.plot(timenp, data)
-    np.testing.assert_equal(h.get_xdata(orig=False), hnp.get_xdata(orig=False))
-    fig = plt.figure(figsize=(10, 2))
-    ax = fig.add_subplot(1, 1, 1)
-    h, = ax.plot(data, time)
-    hnp, = ax.plot(data, timenp)
-    np.testing.assert_equal(h.get_ydata(orig=False), hnp.get_ydata(orig=False))
-
-
-@pytest.mark.parametrize('t0', [datetime.datetime(2017, 1, 1, 0, 1, 1),
-
-                                [datetime.datetime(2017, 1, 1, 0, 1, 1),
-                                 datetime.datetime(2017, 1, 1, 1, 1, 1)],
-
-                                [[datetime.datetime(2017, 1, 1, 0, 1, 1),
-                                  datetime.datetime(2017, 1, 1, 1, 1, 1)],
-                                 [datetime.datetime(2017, 1, 1, 2, 1, 1),
-                                  datetime.datetime(2017, 1, 1, 3, 1, 1)]]])
-@pytest.mark.parametrize('dtype', ['datetime64[s]',
-                                   'datetime64[us]',
-                                   'datetime64[ms]',
-                                   'datetime64[ns]'])
-def test_date_date2num_numpy(t0, dtype):
-    time = mdates.date2num(t0)
-    tnp = np.array(t0, dtype=dtype)
-    nptime = mdates.date2num(tnp)
-    np.testing.assert_equal(time, nptime)
-
-
-@pytest.mark.parametrize('dtype', ['datetime64[s]',
-                                   'datetime64[us]',
-                                   'datetime64[ms]',
-                                   'datetime64[ns]'])
-def test_date2num_NaT(dtype):
-    t0 = datetime.datetime(2017, 1, 1, 0, 1, 1)
-    tmpl = [mdates.date2num(t0), np.nan]
-    tnp = np.array([t0, 'NaT'], dtype=dtype)
-    nptime = mdates.date2num(tnp)
-    np.testing.assert_array_equal(tmpl, nptime)
-
-
-@pytest.mark.parametrize('units', ['s', 'ms', 'us', 'ns'])
-def test_date2num_NaT_scalar(units):
-    tmpl = mdates.date2num(np.datetime64('NaT', units))
-    assert np.isnan(tmpl)
-
-
-def test_date2num_masked():
-    # Without tzinfo
-    base = datetime.datetime(2022, 12, 15)
-    dates = np.ma.array([base + datetime.timedelta(days=(2 * i))
-                         for i in range(7)], mask=[0, 1, 1, 0, 0, 0, 1])
-    npdates = mdates.date2num(dates)
-    np.testing.assert_array_equal(np.ma.getmask(npdates),
-                                  (False, True, True, False, False, False,
-                                   True))
-
-    # With tzinfo
-    base = datetime.datetime(2022, 12, 15, tzinfo=mdates.UTC)
-    dates = np.ma.array([base + datetime.timedelta(days=(2 * i))
-                         for i in range(7)], mask=[0, 1, 1, 0, 0, 0, 1])
-    npdates = mdates.date2num(dates)
-    np.testing.assert_array_equal(np.ma.getmask(npdates),
-                                  (False, True, True, False, False, False,
-                                   True))
-
-
-def test_date_empty():
-    # make sure we do the right thing when told to plot dates even
-    # if no date data has been presented, cf
-    # http://sourceforge.net/tracker/?func=detail&aid=2850075&group_id=80706&atid=560720
-    fig, ax = plt.subplots()
-    ax.xaxis_date()
-    fig.draw_without_rendering()
-    np.testing.assert_allclose(ax.get_xlim(),
-                               [mdates.date2num(np.datetime64('1970-01-01')),
-                                mdates.date2num(np.datetime64('1970-01-02'))])
-
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('0000-12-31')
-    fig, ax = plt.subplots()
-    ax.xaxis_date()
-    fig.draw_without_rendering()
-    np.testing.assert_allclose(ax.get_xlim(),
-                               [mdates.date2num(np.datetime64('1970-01-01')),
-                                mdates.date2num(np.datetime64('1970-01-02'))])
-    mdates._reset_epoch_test_example()
-
-
-def test_date_not_empty():
-    fig = plt.figure()
-    ax = fig.add_subplot()
-
-    ax.plot([50, 70], [1, 2])
-    ax.xaxis.axis_date()
-    np.testing.assert_allclose(ax.get_xlim(), [50, 70])
-
-
-def test_axhline():
-    # make sure that axhline doesn't set the xlimits...
-    fig, ax = plt.subplots()
-    ax.axhline(1.5)
-    ax.plot([np.datetime64('2016-01-01'), np.datetime64('2016-01-02')], [1, 2])
-    np.testing.assert_allclose(ax.get_xlim(),
-                               [mdates.date2num(np.datetime64('2016-01-01')),
-                                mdates.date2num(np.datetime64('2016-01-02'))])
-
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('0000-12-31')
-    fig, ax = plt.subplots()
-    ax.axhline(1.5)
-    ax.plot([np.datetime64('2016-01-01'), np.datetime64('2016-01-02')], [1, 2])
-    np.testing.assert_allclose(ax.get_xlim(),
-                               [mdates.date2num(np.datetime64('2016-01-01')),
-                                mdates.date2num(np.datetime64('2016-01-02'))])
-    mdates._reset_epoch_test_example()
-
-
-@image_comparison(['date_axhspan.png'])
-def test_date_axhspan():
-    # test axhspan with date inputs
-    t0 = datetime.datetime(2009, 1, 20)
-    tf = datetime.datetime(2009, 1, 21)
-    fig, ax = plt.subplots()
-    ax.axhspan(t0, tf, facecolor="blue", alpha=0.25)
-    ax.set_ylim(t0 - datetime.timedelta(days=5),
-                tf + datetime.timedelta(days=5))
-    fig.subplots_adjust(left=0.25)
-
-
-@image_comparison(['date_axvspan.png'])
-def test_date_axvspan():
-    # test axvspan with date inputs
-    t0 = datetime.datetime(2000, 1, 20)
-    tf = datetime.datetime(2010, 1, 21)
-    fig, ax = plt.subplots()
-    ax.axvspan(t0, tf, facecolor="blue", alpha=0.25)
-    ax.set_xlim(t0 - datetime.timedelta(days=720),
-                tf + datetime.timedelta(days=720))
-    fig.autofmt_xdate()
-
-
-@image_comparison(['date_axhline.png'])
-def test_date_axhline():
-    # test axhline with date inputs
-    t0 = datetime.datetime(2009, 1, 20)
-    tf = datetime.datetime(2009, 1, 31)
-    fig, ax = plt.subplots()
-    ax.axhline(t0, color="blue", lw=3)
-    ax.set_ylim(t0 - datetime.timedelta(days=5),
-                tf + datetime.timedelta(days=5))
-    fig.subplots_adjust(left=0.25)
-
-
-@image_comparison(['date_axvline.png'])
-def test_date_axvline():
-    # test axvline with date inputs
-    t0 = datetime.datetime(2000, 1, 20)
-    tf = datetime.datetime(2000, 1, 21)
-    fig, ax = plt.subplots()
-    ax.axvline(t0, color="red", lw=3)
-    ax.set_xlim(t0 - datetime.timedelta(days=5),
-                tf + datetime.timedelta(days=5))
-    fig.autofmt_xdate()
-
-
-def test_too_many_date_ticks(caplog):
-    # Attempt to test SF 2715172, see
-    # https://sourceforge.net/tracker/?func=detail&aid=2715172&group_id=80706&atid=560720
-    # setting equal datetimes triggers and expander call in
-    # transforms.nonsingular which results in too many ticks in the
-    # DayLocator.  This should emit a log at WARNING level.
-    caplog.set_level("WARNING")
-    t0 = datetime.datetime(2000, 1, 20)
-    tf = datetime.datetime(2000, 1, 20)
-    fig, ax = plt.subplots()
-    with pytest.warns(UserWarning) as rec:
-        ax.set_xlim((t0, tf), auto=True)
-        assert len(rec) == 1
-        assert ('Attempting to set identical low and high xlims'
-                in str(rec[0].message))
-    ax.plot([], [])
-    ax.xaxis.set_major_locator(mdates.DayLocator())
-    v = ax.xaxis.get_major_locator()()
-    assert len(v) > 1000
-    # The warning is emitted multiple times because the major locator is also
-    # called both when placing the minor ticks (for overstriking detection) and
-    # during tick label positioning.
-    assert caplog.records and all(
-        record.name == "matplotlib.ticker" and record.levelname == "WARNING"
-        for record in caplog.records)
-    assert len(caplog.records) > 0
-
-
-def _new_epoch_decorator(thefunc):
-    @functools.wraps(thefunc)
-    def wrapper():
-        mdates._reset_epoch_test_example()
-        mdates.set_epoch('2000-01-01')
-        thefunc()
-        mdates._reset_epoch_test_example()
-    return wrapper
-
-
-@image_comparison(['RRuleLocator_bounds.png'])
-def test_RRuleLocator():
-    import matplotlib.testing.jpl_units as units
-    units.register()
-    # This will cause the RRuleLocator to go out of bounds when it tries
-    # to add padding to the limits, so we make sure it caps at the correct
-    # boundary values.
-    t0 = datetime.datetime(1000, 1, 1)
-    tf = datetime.datetime(6000, 1, 1)
-
-    fig = plt.figure()
-    ax = plt.subplot()
-    ax.set_autoscale_on(True)
-    ax.plot([t0, tf], [0.0, 1.0], marker='o')
-
-    rrule = mdates.rrulewrapper(dateutil.rrule.YEARLY, interval=500)
-    locator = mdates.RRuleLocator(rrule)
-    ax.xaxis.set_major_locator(locator)
-    ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(locator))
-
-    ax.autoscale_view()
-    fig.autofmt_xdate()
-
-
-def test_RRuleLocator_dayrange():
-    loc = mdates.DayLocator()
-    x1 = datetime.datetime(year=1, month=1, day=1, tzinfo=mdates.UTC)
-    y1 = datetime.datetime(year=1, month=1, day=16, tzinfo=mdates.UTC)
-    loc.tick_values(x1, y1)
-    # On success, no overflow error shall be thrown
-
-
-def test_RRuleLocator_close_minmax():
-    # if d1 and d2 are very close together, rrule cannot create
-    # reasonable tick intervals; ensure that this is handled properly
-    rrule = mdates.rrulewrapper(dateutil.rrule.SECONDLY, interval=5)
-    loc = mdates.RRuleLocator(rrule)
-    d1 = datetime.datetime(year=2020, month=1, day=1)
-    d2 = datetime.datetime(year=2020, month=1, day=1, microsecond=1)
-    expected = ['2020-01-01 00:00:00+00:00',
-                '2020-01-01 00:00:00.000001+00:00']
-    assert list(map(str, mdates.num2date(loc.tick_values(d1, d2)))) == expected
-
-
-@image_comparison(['DateFormatter_fractionalSeconds.png'])
-def test_DateFormatter():
-    import matplotlib.testing.jpl_units as units
-    units.register()
-
-    # Lets make sure that DateFormatter will allow us to have tick marks
-    # at intervals of fractional seconds.
-
-    t0 = datetime.datetime(2001, 1, 1, 0, 0, 0)
-    tf = datetime.datetime(2001, 1, 1, 0, 0, 1)
-
-    fig = plt.figure()
-    ax = plt.subplot()
-    ax.set_autoscale_on(True)
-    ax.plot([t0, tf], [0.0, 1.0], marker='o')
-
-    # rrule = mpldates.rrulewrapper( dateutil.rrule.YEARLY, interval=500 )
-    # locator = mpldates.RRuleLocator( rrule )
-    # ax.xaxis.set_major_locator( locator )
-    # ax.xaxis.set_major_formatter( mpldates.AutoDateFormatter(locator) )
-
-    ax.autoscale_view()
-    fig.autofmt_xdate()
-
-
-def test_locator_set_formatter():
-    """
-    Test if setting the locator only will update the AutoDateFormatter to use
-    the new locator.
-    """
-    plt.rcParams["date.autoformatter.minute"] = "%d %H:%M"
-    t = [datetime.datetime(2018, 9, 30, 8, 0),
-         datetime.datetime(2018, 9, 30, 8, 59),
-         datetime.datetime(2018, 9, 30, 10, 30)]
-    x = [2, 3, 1]
-
-    fig, ax = plt.subplots()
-    ax.plot(t, x)
-    ax.xaxis.set_major_locator(mdates.MinuteLocator((0, 30)))
-    fig.canvas.draw()
-    ticklabels = [tl.get_text() for tl in ax.get_xticklabels()]
-    expected = ['30 08:00', '30 08:30', '30 09:00',
-                '30 09:30', '30 10:00', '30 10:30']
-    assert ticklabels == expected
-
-    ax.xaxis.set_major_locator(mticker.NullLocator())
-    ax.xaxis.set_minor_locator(mdates.MinuteLocator((5, 55)))
-    decoy_loc = mdates.MinuteLocator((12, 27))
-    ax.xaxis.set_minor_formatter(mdates.AutoDateFormatter(decoy_loc))
-
-    ax.xaxis.set_minor_locator(mdates.MinuteLocator((15, 45)))
-    fig.canvas.draw()
-    ticklabels = [tl.get_text() for tl in ax.get_xticklabels(which="minor")]
-    expected = ['30 08:15', '30 08:45', '30 09:15', '30 09:45', '30 10:15']
-    assert ticklabels == expected
-
-
-def test_date_formatter_callable():
-
-    class _Locator:
-        def _get_unit(self): return -11
-
-    def callable_formatting_function(dates, _):
-        return [dt.strftime('%d-%m//%Y') for dt in dates]
-
-    formatter = mdates.AutoDateFormatter(_Locator())
-    formatter.scaled[-10] = callable_formatting_function
-    assert formatter([datetime.datetime(2014, 12, 25)]) == ['25-12//2014']
-
-
-@pytest.mark.parametrize('delta, expected', [
-    (datetime.timedelta(weeks=52 * 200),
-     [r'$\mathdefault{%d}$' % year for year in range(1990, 2171, 20)]),
-    (datetime.timedelta(days=30),
-     [r'$\mathdefault{1990{-}01{-}%02d}$' % day for day in range(1, 32, 3)]),
-    (datetime.timedelta(hours=20),
-     [r'$\mathdefault{01{-}01\;%02d}$' % hour for hour in range(0, 21, 2)]),
-    (datetime.timedelta(minutes=10),
-     [r'$\mathdefault{01\;00{:}%02d}$' % minu for minu in range(0, 11)]),
-])
-def test_date_formatter_usetex(delta, expected):
-    style.use("default")
-
-    d1 = datetime.datetime(1990, 1, 1)
-    d2 = d1 + delta
-
-    locator = mdates.AutoDateLocator(interval_multiples=False)
-    locator.create_dummy_axis()
-    locator.axis.set_view_interval(mdates.date2num(d1), mdates.date2num(d2))
-
-    formatter = mdates.AutoDateFormatter(locator, usetex=True)
-    assert [formatter(loc) for loc in locator()] == expected
-
-
-def test_drange():
-    """
-    This test should check if drange works as expected, and if all the
-    rounding errors are fixed
-    """
-    start = datetime.datetime(2011, 1, 1, tzinfo=mdates.UTC)
-    end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
-    delta = datetime.timedelta(hours=1)
-    # We expect 24 values in drange(start, end, delta), because drange returns
-    # dates from an half open interval [start, end)
-    assert len(mdates.drange(start, end, delta)) == 24
-
-    # Same if interval ends slightly earlier
-    end = end - datetime.timedelta(microseconds=1)
-    assert len(mdates.drange(start, end, delta)) == 24
-
-    # if end is a little bit later, we expect the range to contain one element
-    # more
-    end = end + datetime.timedelta(microseconds=2)
-    assert len(mdates.drange(start, end, delta)) == 25
-
-    # reset end
-    end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
-
-    # and tst drange with "complicated" floats:
-    # 4 hours = 1/6 day, this is an "dangerous" float
-    delta = datetime.timedelta(hours=4)
-    daterange = mdates.drange(start, end, delta)
-    assert len(daterange) == 6
-    assert mdates.num2date(daterange[-1]) == (end - delta)
-
-
-@_new_epoch_decorator
-def test_auto_date_locator():
-    def _create_auto_date_locator(date1, date2):
-        locator = mdates.AutoDateLocator(interval_multiples=False)
-        locator.create_dummy_axis()
-        locator.axis.set_view_interval(*mdates.date2num([date1, date2]))
-        return locator
-
-    d1 = datetime.datetime(1990, 1, 1)
-    results = ([datetime.timedelta(weeks=52 * 200),
-                ['1990-01-01 00:00:00+00:00', '2010-01-01 00:00:00+00:00',
-                 '2030-01-01 00:00:00+00:00', '2050-01-01 00:00:00+00:00',
-                 '2070-01-01 00:00:00+00:00', '2090-01-01 00:00:00+00:00',
-                 '2110-01-01 00:00:00+00:00', '2130-01-01 00:00:00+00:00',
-                 '2150-01-01 00:00:00+00:00', '2170-01-01 00:00:00+00:00']
-                ],
-               [datetime.timedelta(weeks=52),
-                ['1990-01-01 00:00:00+00:00', '1990-02-01 00:00:00+00:00',
-                 '1990-03-01 00:00:00+00:00', '1990-04-01 00:00:00+00:00',
-                 '1990-05-01 00:00:00+00:00', '1990-06-01 00:00:00+00:00',
-                 '1990-07-01 00:00:00+00:00', '1990-08-01 00:00:00+00:00',
-                 '1990-09-01 00:00:00+00:00', '1990-10-01 00:00:00+00:00',
-                 '1990-11-01 00:00:00+00:00', '1990-12-01 00:00:00+00:00']
-                ],
-               [datetime.timedelta(days=141),
-                ['1990-01-05 00:00:00+00:00', '1990-01-26 00:00:00+00:00',
-                 '1990-02-16 00:00:00+00:00', '1990-03-09 00:00:00+00:00',
-                 '1990-03-30 00:00:00+00:00', '1990-04-20 00:00:00+00:00',
-                 '1990-05-11 00:00:00+00:00']
-                ],
-               [datetime.timedelta(days=40),
-                ['1990-01-03 00:00:00+00:00', '1990-01-10 00:00:00+00:00',
-                 '1990-01-17 00:00:00+00:00', '1990-01-24 00:00:00+00:00',
-                 '1990-01-31 00:00:00+00:00', '1990-02-07 00:00:00+00:00']
-                ],
-               [datetime.timedelta(hours=40),
-                ['1990-01-01 00:00:00+00:00', '1990-01-01 04:00:00+00:00',
-                 '1990-01-01 08:00:00+00:00', '1990-01-01 12:00:00+00:00',
-                 '1990-01-01 16:00:00+00:00', '1990-01-01 20:00:00+00:00',
-                 '1990-01-02 00:00:00+00:00', '1990-01-02 04:00:00+00:00',
-                 '1990-01-02 08:00:00+00:00', '1990-01-02 12:00:00+00:00',
-                 '1990-01-02 16:00:00+00:00']
-                ],
-               [datetime.timedelta(minutes=20),
-                ['1990-01-01 00:00:00+00:00', '1990-01-01 00:05:00+00:00',
-                 '1990-01-01 00:10:00+00:00', '1990-01-01 00:15:00+00:00',
-                 '1990-01-01 00:20:00+00:00']
-                ],
-               [datetime.timedelta(seconds=40),
-                ['1990-01-01 00:00:00+00:00', '1990-01-01 00:00:05+00:00',
-                 '1990-01-01 00:00:10+00:00', '1990-01-01 00:00:15+00:00',
-                 '1990-01-01 00:00:20+00:00', '1990-01-01 00:00:25+00:00',
-                 '1990-01-01 00:00:30+00:00', '1990-01-01 00:00:35+00:00',
-                 '1990-01-01 00:00:40+00:00']
-                ],
-               [datetime.timedelta(microseconds=1500),
-                ['1989-12-31 23:59:59.999500+00:00',
-                 '1990-01-01 00:00:00+00:00',
-                 '1990-01-01 00:00:00.000500+00:00',
-                 '1990-01-01 00:00:00.001000+00:00',
-                 '1990-01-01 00:00:00.001500+00:00',
-                 '1990-01-01 00:00:00.002000+00:00']
-                ],
-               )
-
-    for t_delta, expected in results:
-        d2 = d1 + t_delta
-        locator = _create_auto_date_locator(d1, d2)
-        assert list(map(str, mdates.num2date(locator()))) == expected
-
-    locator = mdates.AutoDateLocator(interval_multiples=False)
-    assert locator.maxticks == {0: 11, 1: 12, 3: 11, 4: 12, 5: 11, 6: 11, 7: 8}
-
-    locator = mdates.AutoDateLocator(maxticks={dateutil.rrule.MONTHLY: 5})
-    assert locator.maxticks == {0: 11, 1: 5, 3: 11, 4: 12, 5: 11, 6: 11, 7: 8}
-
-    locator = mdates.AutoDateLocator(maxticks=5)
-    assert locator.maxticks == {0: 5, 1: 5, 3: 5, 4: 5, 5: 5, 6: 5, 7: 5}
-
-
-@_new_epoch_decorator
-def test_auto_date_locator_intmult():
-    def _create_auto_date_locator(date1, date2):
-        locator = mdates.AutoDateLocator(interval_multiples=True)
-        locator.create_dummy_axis()
-        locator.axis.set_view_interval(*mdates.date2num([date1, date2]))
-        return locator
-
-    results = ([datetime.timedelta(weeks=52 * 200),
-                ['1980-01-01 00:00:00+00:00', '2000-01-01 00:00:00+00:00',
-                 '2020-01-01 00:00:00+00:00', '2040-01-01 00:00:00+00:00',
-                 '2060-01-01 00:00:00+00:00', '2080-01-01 00:00:00+00:00',
-                 '2100-01-01 00:00:00+00:00', '2120-01-01 00:00:00+00:00',
-                 '2140-01-01 00:00:00+00:00', '2160-01-01 00:00:00+00:00',
-                 '2180-01-01 00:00:00+00:00', '2200-01-01 00:00:00+00:00']
-                ],
-               [datetime.timedelta(weeks=52),
-                ['1997-01-01 00:00:00+00:00', '1997-02-01 00:00:00+00:00',
-                 '1997-03-01 00:00:00+00:00', '1997-04-01 00:00:00+00:00',
-                 '1997-05-01 00:00:00+00:00', '1997-06-01 00:00:00+00:00',
-                 '1997-07-01 00:00:00+00:00', '1997-08-01 00:00:00+00:00',
-                 '1997-09-01 00:00:00+00:00', '1997-10-01 00:00:00+00:00',
-                 '1997-11-01 00:00:00+00:00', '1997-12-01 00:00:00+00:00']
-                ],
-               [datetime.timedelta(days=141),
-                ['1997-01-01 00:00:00+00:00', '1997-01-15 00:00:00+00:00',
-                 '1997-02-01 00:00:00+00:00', '1997-02-15 00:00:00+00:00',
-                 '1997-03-01 00:00:00+00:00', '1997-03-15 00:00:00+00:00',
-                 '1997-04-01 00:00:00+00:00', '1997-04-15 00:00:00+00:00',
-                 '1997-05-01 00:00:00+00:00', '1997-05-15 00:00:00+00:00']
-                ],
-               [datetime.timedelta(days=40),
-                ['1997-01-01 00:00:00+00:00', '1997-01-05 00:00:00+00:00',
-                 '1997-01-09 00:00:00+00:00', '1997-01-13 00:00:00+00:00',
-                 '1997-01-17 00:00:00+00:00', '1997-01-21 00:00:00+00:00',
-                 '1997-01-25 00:00:00+00:00', '1997-01-29 00:00:00+00:00',
-                 '1997-02-01 00:00:00+00:00', '1997-02-05 00:00:00+00:00',
-                 '1997-02-09 00:00:00+00:00']
-                ],
-               [datetime.timedelta(hours=40),
-                ['1997-01-01 00:00:00+00:00', '1997-01-01 04:00:00+00:00',
-                 '1997-01-01 08:00:00+00:00', '1997-01-01 12:00:00+00:00',
-                 '1997-01-01 16:00:00+00:00', '1997-01-01 20:00:00+00:00',
-                 '1997-01-02 00:00:00+00:00', '1997-01-02 04:00:00+00:00',
-                 '1997-01-02 08:00:00+00:00', '1997-01-02 12:00:00+00:00',
-                 '1997-01-02 16:00:00+00:00']
-                ],
-               [datetime.timedelta(minutes=20),
-                ['1997-01-01 00:00:00+00:00', '1997-01-01 00:05:00+00:00',
-                 '1997-01-01 00:10:00+00:00', '1997-01-01 00:15:00+00:00',
-                 '1997-01-01 00:20:00+00:00']
-                ],
-               [datetime.timedelta(seconds=40),
-                ['1997-01-01 00:00:00+00:00', '1997-01-01 00:00:05+00:00',
-                 '1997-01-01 00:00:10+00:00', '1997-01-01 00:00:15+00:00',
-                 '1997-01-01 00:00:20+00:00', '1997-01-01 00:00:25+00:00',
-                 '1997-01-01 00:00:30+00:00', '1997-01-01 00:00:35+00:00',
-                 '1997-01-01 00:00:40+00:00']
-                ],
-               [datetime.timedelta(microseconds=1500),
-                ['1996-12-31 23:59:59.999500+00:00',
-                 '1997-01-01 00:00:00+00:00',
-                 '1997-01-01 00:00:00.000500+00:00',
-                 '1997-01-01 00:00:00.001000+00:00',
-                 '1997-01-01 00:00:00.001500+00:00',
-                 '1997-01-01 00:00:00.002000+00:00']
-                ],
-               )
-
-    d1 = datetime.datetime(1997, 1, 1)
-    for t_delta, expected in results:
-        d2 = d1 + t_delta
-        locator = _create_auto_date_locator(d1, d2)
-        assert list(map(str, mdates.num2date(locator()))) == expected
-
-
-def test_concise_formatter_subsecond():
-    locator = mdates.AutoDateLocator(interval_multiples=True)
-    formatter = mdates.ConciseDateFormatter(locator)
-    year_1996 = 9861.0
-    strings = formatter.format_ticks([
-        year_1996,
-        year_1996 + 500 / mdates.MUSECONDS_PER_DAY,
-        year_1996 + 900 / mdates.MUSECONDS_PER_DAY])
-    assert strings == ['00:00', '00.0005', '00.0009']
-
-
-def test_concise_formatter():
-    def _create_auto_date_locator(date1, date2):
-        fig, ax = plt.subplots()
-
-        locator = mdates.AutoDateLocator(interval_multiples=True)
-        formatter = mdates.ConciseDateFormatter(locator)
-        ax.yaxis.set_major_locator(locator)
-        ax.yaxis.set_major_formatter(formatter)
-        ax.set_ylim(date1, date2)
-        fig.canvas.draw()
-        sts = [st.get_text() for st in ax.get_yticklabels()]
-        return sts
-
-    d1 = datetime.datetime(1997, 1, 1)
-    results = ([datetime.timedelta(weeks=52 * 200),
-                [str(t) for t in range(1980, 2201, 20)]
-                ],
-               [datetime.timedelta(weeks=52),
-                ['1997', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug',
-                 'Sep', 'Oct', 'Nov', 'Dec']
-                ],
-               [datetime.timedelta(days=141),
-                ['Jan', '15', 'Feb', '15', 'Mar', '15', 'Apr', '15',
-                 'May', '15']
-                ],
-               [datetime.timedelta(days=40),
-                ['Jan', '05', '09', '13', '17', '21', '25', '29', 'Feb',
-                 '05', '09']
-                ],
-               [datetime.timedelta(hours=40),
-                ['Jan-01', '04:00', '08:00', '12:00', '16:00', '20:00',
-                 'Jan-02', '04:00', '08:00', '12:00', '16:00']
-                ],
-               [datetime.timedelta(minutes=20),
-                ['00:00', '00:05', '00:10', '00:15', '00:20']
-                ],
-               [datetime.timedelta(seconds=40),
-                ['00:00', '05', '10', '15', '20', '25', '30', '35', '40']
-                ],
-               [datetime.timedelta(seconds=2),
-                ['59.5', '00:00', '00.5', '01.0', '01.5', '02.0', '02.5']
-                ],
-               )
-    for t_delta, expected in results:
-        d2 = d1 + t_delta
-        strings = _create_auto_date_locator(d1, d2)
-        assert strings == expected
-
-
-@pytest.mark.parametrize('t_delta, expected', [
-    (datetime.timedelta(seconds=0.01), '1997-Jan-01 00:00'),
-    (datetime.timedelta(minutes=1), '1997-Jan-01 00:01'),
-    (datetime.timedelta(hours=1), '1997-Jan-01'),
-    (datetime.timedelta(days=1), '1997-Jan-02'),
-    (datetime.timedelta(weeks=1), '1997-Jan'),
-    (datetime.timedelta(weeks=26), ''),
-    (datetime.timedelta(weeks=520), '')
-])
-def test_concise_formatter_show_offset(t_delta, expected):
-    d1 = datetime.datetime(1997, 1, 1)
-    d2 = d1 + t_delta
-
-    fig, ax = plt.subplots()
-    locator = mdates.AutoDateLocator()
-    formatter = mdates.ConciseDateFormatter(locator)
-    ax.xaxis.set_major_locator(locator)
-    ax.xaxis.set_major_formatter(formatter)
-
-    ax.plot([d1, d2], [0, 0])
-    fig.canvas.draw()
-    assert formatter.get_offset() == expected
-
-
-def test_concise_converter_stays():
-    # This test demonstrates problems introduced by gh-23417 (reverted in gh-25278)
-    # In particular, downstream libraries like Pandas had their designated converters
-    # overridden by actions like setting xlim (or plotting additional points using
-    # stdlib/numpy dates and string date representation, which otherwise work fine with
-    # their date converters)
-    # While this is a bit of a toy example that would be unusual to see it demonstrates
-    # the same ideas (namely having a valid converter already applied that is desired)
-    # without introducing additional subclasses.
-    # See also discussion at gh-25219 for how Pandas was affected
-    x = [datetime.datetime(2000, 1, 1), datetime.datetime(2020, 2, 20)]
-    y = [0, 1]
-    fig, ax = plt.subplots()
-    ax.plot(x, y)
-    # Bypass Switchable date converter
-    ax.xaxis.converter = conv = mdates.ConciseDateConverter()
-    assert ax.xaxis.units is None
-    ax.set_xlim(*x)
-    assert ax.xaxis.converter == conv
-
-
-def test_offset_changes():
-    fig, ax = plt.subplots()
-
-    d1 = datetime.datetime(1997, 1, 1)
-    d2 = d1 + datetime.timedelta(weeks=520)
-
-    locator = mdates.AutoDateLocator()
-    formatter = mdates.ConciseDateFormatter(locator)
-    ax.xaxis.set_major_locator(locator)
-    ax.xaxis.set_major_formatter(formatter)
-
-    ax.plot([d1, d2], [0, 0])
-    fig.draw_without_rendering()
-    assert formatter.get_offset() == ''
-    ax.set_xlim(d1, d1 + datetime.timedelta(weeks=3))
-    fig.draw_without_rendering()
-    assert formatter.get_offset() == '1997-Jan'
-    ax.set_xlim(d1 + datetime.timedelta(weeks=7),
-                d1 + datetime.timedelta(weeks=30))
-    fig.draw_without_rendering()
-    assert formatter.get_offset() == '1997'
-    ax.set_xlim(d1, d1 + datetime.timedelta(weeks=520))
-    fig.draw_without_rendering()
-    assert formatter.get_offset() == ''
-
-
-@pytest.mark.parametrize('t_delta, expected', [
-    (datetime.timedelta(weeks=52 * 200),
-     ['$\\mathdefault{%d}$' % (t, ) for t in range(1980, 2201, 20)]),
-    (datetime.timedelta(days=40),
-     ['Jan', '$\\mathdefault{05}$', '$\\mathdefault{09}$',
-      '$\\mathdefault{13}$', '$\\mathdefault{17}$', '$\\mathdefault{21}$',
-      '$\\mathdefault{25}$', '$\\mathdefault{29}$', 'Feb',
-      '$\\mathdefault{05}$', '$\\mathdefault{09}$']),
-    (datetime.timedelta(hours=40),
-     ['Jan$\\mathdefault{{-}01}$', '$\\mathdefault{04{:}00}$',
-      '$\\mathdefault{08{:}00}$', '$\\mathdefault{12{:}00}$',
-      '$\\mathdefault{16{:}00}$', '$\\mathdefault{20{:}00}$',
-      'Jan$\\mathdefault{{-}02}$', '$\\mathdefault{04{:}00}$',
-      '$\\mathdefault{08{:}00}$', '$\\mathdefault{12{:}00}$',
-      '$\\mathdefault{16{:}00}$']),
-    (datetime.timedelta(seconds=2),
-     ['$\\mathdefault{59.5}$', '$\\mathdefault{00{:}00}$',
-      '$\\mathdefault{00.5}$', '$\\mathdefault{01.0}$',
-      '$\\mathdefault{01.5}$', '$\\mathdefault{02.0}$',
-      '$\\mathdefault{02.5}$']),
-])
-def test_concise_formatter_usetex(t_delta, expected):
-    d1 = datetime.datetime(1997, 1, 1)
-    d2 = d1 + t_delta
-
-    locator = mdates.AutoDateLocator(interval_multiples=True)
-    locator.create_dummy_axis()
-    locator.axis.set_view_interval(mdates.date2num(d1), mdates.date2num(d2))
-
-    formatter = mdates.ConciseDateFormatter(locator, usetex=True)
-    assert formatter.format_ticks(locator()) == expected
-
-
-def test_concise_formatter_formats():
-    formats = ['%Y', '%m/%Y', 'day: %d',
-               '%H hr %M min', '%H hr %M min', '%S.%f sec']
-
-    def _create_auto_date_locator(date1, date2):
-        fig, ax = plt.subplots()
-
-        locator = mdates.AutoDateLocator(interval_multiples=True)
-        formatter = mdates.ConciseDateFormatter(locator, formats=formats)
-        ax.yaxis.set_major_locator(locator)
-        ax.yaxis.set_major_formatter(formatter)
-        ax.set_ylim(date1, date2)
-        fig.canvas.draw()
-        sts = [st.get_text() for st in ax.get_yticklabels()]
-        return sts
-
-    d1 = datetime.datetime(1997, 1, 1)
-    results = (
-        [datetime.timedelta(weeks=52 * 200), [str(t) for t in range(1980,
-         2201, 20)]],
-        [datetime.timedelta(weeks=52), [
-            '1997', '02/1997', '03/1997', '04/1997', '05/1997', '06/1997',
-            '07/1997', '08/1997', '09/1997', '10/1997', '11/1997', '12/1997',
-            ]],
-        [datetime.timedelta(days=141), [
-            '01/1997', 'day: 15', '02/1997', 'day: 15', '03/1997', 'day: 15',
-            '04/1997', 'day: 15', '05/1997', 'day: 15',
-            ]],
-        [datetime.timedelta(days=40), [
-            '01/1997', 'day: 05', 'day: 09', 'day: 13', 'day: 17', 'day: 21',
-            'day: 25', 'day: 29', '02/1997', 'day: 05', 'day: 09',
-            ]],
-        [datetime.timedelta(hours=40), [
-            'day: 01', '04 hr 00 min', '08 hr 00 min', '12 hr 00 min',
-            '16 hr 00 min', '20 hr 00 min', 'day: 02', '04 hr 00 min',
-            '08 hr 00 min', '12 hr 00 min', '16 hr 00 min',
-            ]],
-        [datetime.timedelta(minutes=20), ['00 hr 00 min', '00 hr 05 min',
-         '00 hr 10 min', '00 hr 15 min', '00 hr 20 min']],
-        [datetime.timedelta(seconds=40), [
-            '00 hr 00 min', '05.000000 sec', '10.000000 sec',
-            '15.000000 sec', '20.000000 sec', '25.000000 sec',
-            '30.000000 sec', '35.000000 sec', '40.000000 sec',
-            ]],
-        [datetime.timedelta(seconds=2), [
-            '59.500000 sec', '00 hr 00 min', '00.500000 sec', '01.000000 sec',
-            '01.500000 sec', '02.000000 sec', '02.500000 sec',
-            ]],
-        )
-    for t_delta, expected in results:
-        d2 = d1 + t_delta
-        strings = _create_auto_date_locator(d1, d2)
-        assert strings == expected
-
-
-def test_concise_formatter_zformats():
-    zero_formats = ['', "'%y", '%B', '%m-%d', '%S', '%S.%f']
-
-    def _create_auto_date_locator(date1, date2):
-        fig, ax = plt.subplots()
-
-        locator = mdates.AutoDateLocator(interval_multiples=True)
-        formatter = mdates.ConciseDateFormatter(
-            locator, zero_formats=zero_formats)
-        ax.yaxis.set_major_locator(locator)
-        ax.yaxis.set_major_formatter(formatter)
-        ax.set_ylim(date1, date2)
-        fig.canvas.draw()
-        sts = [st.get_text() for st in ax.get_yticklabels()]
-        return sts
-
-    d1 = datetime.datetime(1997, 1, 1)
-    results = ([datetime.timedelta(weeks=52 * 200),
-                [str(t) for t in range(1980, 2201, 20)]
-                ],
-               [datetime.timedelta(weeks=52),
-                ["'97", 'Feb', 'Mar', 'Apr', 'May', 'Jun',
-                    'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
-                ],
-               [datetime.timedelta(days=141),
-                ['January', '15', 'February', '15', 'March',
-                    '15', 'April', '15', 'May', '15']
-                ],
-               [datetime.timedelta(days=40),
-                ['January', '05', '09', '13', '17', '21',
-                    '25', '29', 'February', '05', '09']
-                ],
-               [datetime.timedelta(hours=40),
-                ['01-01', '04:00', '08:00', '12:00', '16:00', '20:00',
-                    '01-02', '04:00', '08:00', '12:00', '16:00']
-                ],
-               [datetime.timedelta(minutes=20),
-                ['00', '00:05', '00:10', '00:15', '00:20']
-                ],
-               [datetime.timedelta(seconds=40),
-                ['00', '05', '10', '15', '20', '25', '30', '35', '40']
-                ],
-               [datetime.timedelta(seconds=2),
-                ['59.5', '00.0', '00.5', '01.0', '01.5', '02.0', '02.5']
-                ],
-               )
-    for t_delta, expected in results:
-        d2 = d1 + t_delta
-        strings = _create_auto_date_locator(d1, d2)
-        assert strings == expected
-
-
-def test_concise_formatter_tz():
-    def _create_auto_date_locator(date1, date2, tz):
-        fig, ax = plt.subplots()
-
-        locator = mdates.AutoDateLocator(interval_multiples=True)
-        formatter = mdates.ConciseDateFormatter(locator, tz=tz)
-        ax.yaxis.set_major_locator(locator)
-        ax.yaxis.set_major_formatter(formatter)
-        ax.set_ylim(date1, date2)
-        fig.canvas.draw()
-        sts = [st.get_text() for st in ax.get_yticklabels()]
-        return sts, ax.yaxis.get_offset_text().get_text()
-
-    d1 = datetime.datetime(1997, 1, 1).replace(tzinfo=datetime.timezone.utc)
-    results = ([datetime.timedelta(hours=40),
-                ['03:00', '07:00', '11:00', '15:00', '19:00', '23:00',
-                 '03:00', '07:00', '11:00', '15:00', '19:00'],
-                "1997-Jan-02"
-                ],
-               [datetime.timedelta(minutes=20),
-                ['03:00', '03:05', '03:10', '03:15', '03:20'],
-                "1997-Jan-01"
-                ],
-               [datetime.timedelta(seconds=40),
-                ['03:00', '05', '10', '15', '20', '25', '30', '35', '40'],
-                "1997-Jan-01 03:00"
-                ],
-               [datetime.timedelta(seconds=2),
-                ['59.5', '03:00', '00.5', '01.0', '01.5', '02.0', '02.5'],
-                "1997-Jan-01 03:00"
-                ],
-               )
-
-    new_tz = datetime.timezone(datetime.timedelta(hours=3))
-    for t_delta, expected_strings, expected_offset in results:
-        d2 = d1 + t_delta
-        strings, offset = _create_auto_date_locator(d1, d2, new_tz)
-        assert strings == expected_strings
-        assert offset == expected_offset
-
-
-def test_auto_date_locator_intmult_tz():
-    def _create_auto_date_locator(date1, date2, tz):
-        locator = mdates.AutoDateLocator(interval_multiples=True, tz=tz)
-        locator.create_dummy_axis()
-        locator.axis.set_view_interval(*mdates.date2num([date1, date2]))
-        return locator
-
-    results = ([datetime.timedelta(weeks=52*200),
-                ['1980-01-01 00:00:00-08:00', '2000-01-01 00:00:00-08:00',
-                 '2020-01-01 00:00:00-08:00', '2040-01-01 00:00:00-08:00',
-                 '2060-01-01 00:00:00-08:00', '2080-01-01 00:00:00-08:00',
-                 '2100-01-01 00:00:00-08:00', '2120-01-01 00:00:00-08:00',
-                 '2140-01-01 00:00:00-08:00', '2160-01-01 00:00:00-08:00',
-                 '2180-01-01 00:00:00-08:00', '2200-01-01 00:00:00-08:00']
-                ],
-               [datetime.timedelta(weeks=52),
-                ['1997-01-01 00:00:00-08:00', '1997-02-01 00:00:00-08:00',
-                 '1997-03-01 00:00:00-08:00', '1997-04-01 00:00:00-08:00',
-                 '1997-05-01 00:00:00-07:00', '1997-06-01 00:00:00-07:00',
-                 '1997-07-01 00:00:00-07:00', '1997-08-01 00:00:00-07:00',
-                 '1997-09-01 00:00:00-07:00', '1997-10-01 00:00:00-07:00',
-                 '1997-11-01 00:00:00-08:00', '1997-12-01 00:00:00-08:00']
-                ],
-               [datetime.timedelta(days=141),
-                ['1997-01-01 00:00:00-08:00', '1997-01-15 00:00:00-08:00',
-                 '1997-02-01 00:00:00-08:00', '1997-02-15 00:00:00-08:00',
-                 '1997-03-01 00:00:00-08:00', '1997-03-15 00:00:00-08:00',
-                 '1997-04-01 00:00:00-08:00', '1997-04-15 00:00:00-07:00',
-                 '1997-05-01 00:00:00-07:00', '1997-05-15 00:00:00-07:00']
-                ],
-               [datetime.timedelta(days=40),
-                ['1997-01-01 00:00:00-08:00', '1997-01-05 00:00:00-08:00',
-                 '1997-01-09 00:00:00-08:00', '1997-01-13 00:00:00-08:00',
-                 '1997-01-17 00:00:00-08:00', '1997-01-21 00:00:00-08:00',
-                 '1997-01-25 00:00:00-08:00', '1997-01-29 00:00:00-08:00',
-                 '1997-02-01 00:00:00-08:00', '1997-02-05 00:00:00-08:00',
-                 '1997-02-09 00:00:00-08:00']
-                ],
-               [datetime.timedelta(hours=40),
-                ['1997-01-01 00:00:00-08:00', '1997-01-01 04:00:00-08:00',
-                 '1997-01-01 08:00:00-08:00', '1997-01-01 12:00:00-08:00',
-                 '1997-01-01 16:00:00-08:00', '1997-01-01 20:00:00-08:00',
-                 '1997-01-02 00:00:00-08:00', '1997-01-02 04:00:00-08:00',
-                 '1997-01-02 08:00:00-08:00', '1997-01-02 12:00:00-08:00',
-                 '1997-01-02 16:00:00-08:00']
-                ],
-               [datetime.timedelta(minutes=20),
-                ['1997-01-01 00:00:00-08:00', '1997-01-01 00:05:00-08:00',
-                 '1997-01-01 00:10:00-08:00', '1997-01-01 00:15:00-08:00',
-                 '1997-01-01 00:20:00-08:00']
-                ],
-               [datetime.timedelta(seconds=40),
-                ['1997-01-01 00:00:00-08:00', '1997-01-01 00:00:05-08:00',
-                 '1997-01-01 00:00:10-08:00', '1997-01-01 00:00:15-08:00',
-                 '1997-01-01 00:00:20-08:00', '1997-01-01 00:00:25-08:00',
-                 '1997-01-01 00:00:30-08:00', '1997-01-01 00:00:35-08:00',
-                 '1997-01-01 00:00:40-08:00']
-                ]
-               )
-
-    tz = dateutil.tz.gettz('Canada/Pacific')
-    d1 = datetime.datetime(1997, 1, 1, tzinfo=tz)
-    for t_delta, expected in results:
-        with rc_context({'_internal.classic_mode': False}):
-            d2 = d1 + t_delta
-            locator = _create_auto_date_locator(d1, d2, tz)
-            st = list(map(str, mdates.num2date(locator(), tz=tz)))
-            assert st == expected
-
-
-@image_comparison(['date_inverted_limit.png'])
-def test_date_inverted_limit():
-    # test ax hline with date inputs
-    t0 = datetime.datetime(2009, 1, 20)
-    tf = datetime.datetime(2009, 1, 31)
-    fig, ax = plt.subplots()
-    ax.axhline(t0, color="blue", lw=3)
-    ax.set_ylim(t0 - datetime.timedelta(days=5),
-                tf + datetime.timedelta(days=5))
-    ax.invert_yaxis()
-    fig.subplots_adjust(left=0.25)
-
-
-def _test_date2num_dst(date_range, tz_convert):
-    # Timezones
-
-    BRUSSELS = dateutil.tz.gettz('Europe/Brussels')
-    UTC = mdates.UTC
-
-    # Create a list of timezone-aware datetime objects in UTC
-    # Interval is 0b0.0000011 days, to prevent float rounding issues
-    dtstart = datetime.datetime(2014, 3, 30, 0, 0, tzinfo=UTC)
-    interval = datetime.timedelta(minutes=33, seconds=45)
-    interval_days = interval.seconds / 86400
-    N = 8
-
-    dt_utc = date_range(start=dtstart, freq=interval, periods=N)
-    dt_bxl = tz_convert(dt_utc, BRUSSELS)
-    t0 = 735322.0 + mdates.date2num(np.datetime64('0000-12-31'))
-    expected_ordinalf = [t0 + (i * interval_days) for i in range(N)]
-    actual_ordinalf = list(mdates.date2num(dt_bxl))
-
-    assert actual_ordinalf == expected_ordinalf
-
-
-def test_date2num_dst():
-    # Test for github issue #3896, but in date2num around DST transitions
-    # with a timezone-aware pandas date_range object.
-
-    class dt_tzaware(datetime.datetime):
-        """
-        This bug specifically occurs because of the normalization behavior of
-        pandas Timestamp objects, so in order to replicate it, we need a
-        datetime-like object that applies timezone normalization after
-        subtraction.
-        """
-
-        def __sub__(self, other):
-            r = super().__sub__(other)
-            tzinfo = getattr(r, 'tzinfo', None)
-
-            if tzinfo is not None:
-                localizer = getattr(tzinfo, 'normalize', None)
-                if localizer is not None:
-                    r = tzinfo.normalize(r)
-
-            if isinstance(r, datetime.datetime):
-                r = self.mk_tzaware(r)
-
-            return r
-
-        def __add__(self, other):
-            return self.mk_tzaware(super().__add__(other))
-
-        def astimezone(self, tzinfo):
-            dt = super().astimezone(tzinfo)
-            return self.mk_tzaware(dt)
-
-        @classmethod
-        def mk_tzaware(cls, datetime_obj):
-            kwargs = {}
-            attrs = ('year',
-                     'month',
-                     'day',
-                     'hour',
-                     'minute',
-                     'second',
-                     'microsecond',
-                     'tzinfo')
-
-            for attr in attrs:
-                val = getattr(datetime_obj, attr, None)
-                if val is not None:
-                    kwargs[attr] = val
-
-            return cls(**kwargs)
-
-    # Define a date_range function similar to pandas.date_range
-    def date_range(start, freq, periods):
-        dtstart = dt_tzaware.mk_tzaware(start)
-
-        return [dtstart + (i * freq) for i in range(periods)]
-
-    # Define a tz_convert function that converts a list to a new timezone.
-    def tz_convert(dt_list, tzinfo):
-        return [d.astimezone(tzinfo) for d in dt_list]
-
-    _test_date2num_dst(date_range, tz_convert)
-
-
-def test_date2num_dst_pandas(pd):
-    # Test for github issue #3896, but in date2num around DST transitions
-    # with a timezone-aware pandas date_range object.
-
-    def tz_convert(*args):
-        return pd.DatetimeIndex.tz_convert(*args).astype(object)
-
-    _test_date2num_dst(pd.date_range, tz_convert)
-
-
-def _test_rrulewrapper(attach_tz, get_tz):
-    SYD = get_tz('Australia/Sydney')
-
-    dtstart = attach_tz(datetime.datetime(2017, 4, 1, 0), SYD)
-    dtend = attach_tz(datetime.datetime(2017, 4, 4, 0), SYD)
-
-    rule = mdates.rrulewrapper(freq=dateutil.rrule.DAILY, dtstart=dtstart)
-
-    act = rule.between(dtstart, dtend)
-    exp = [datetime.datetime(2017, 4, 1, 13, tzinfo=dateutil.tz.tzutc()),
-           datetime.datetime(2017, 4, 2, 14, tzinfo=dateutil.tz.tzutc())]
-
-    assert act == exp
-
-
-def test_rrulewrapper():
-    def attach_tz(dt, zi):
-        return dt.replace(tzinfo=zi)
-
-    _test_rrulewrapper(attach_tz, dateutil.tz.gettz)
-
-    SYD = dateutil.tz.gettz('Australia/Sydney')
-    dtstart = datetime.datetime(2017, 4, 1, 0)
-    dtend = datetime.datetime(2017, 4, 4, 0)
-    rule = mdates.rrulewrapper(freq=dateutil.rrule.DAILY, dtstart=dtstart,
-                               tzinfo=SYD, until=dtend)
-    assert rule.after(dtstart) == datetime.datetime(2017, 4, 2, 0, 0,
-                                                    tzinfo=SYD)
-    assert rule.before(dtend) == datetime.datetime(2017, 4, 3, 0, 0,
-                                                   tzinfo=SYD)
-
-    # Test parts of __getattr__
-    assert rule._base_tzinfo == SYD
-    assert rule._interval == 1
-
-
-@pytest.mark.pytz
-def test_rrulewrapper_pytz():
-    # Test to make sure pytz zones are supported in rrules
-    pytz = pytest.importorskip("pytz")
-
-    def attach_tz(dt, zi):
-        return zi.localize(dt)
-
-    _test_rrulewrapper(attach_tz, pytz.timezone)
-
-
-@pytest.mark.pytz
-def test_yearlocator_pytz():
-    pytz = pytest.importorskip("pytz")
-
-    tz = pytz.timezone('America/New_York')
-    x = [tz.localize(datetime.datetime(2010, 1, 1))
-         + datetime.timedelta(i) for i in range(2000)]
-    locator = mdates.AutoDateLocator(interval_multiples=True, tz=tz)
-    locator.create_dummy_axis()
-    locator.axis.set_view_interval(mdates.date2num(x[0])-1.0,
-                                   mdates.date2num(x[-1])+1.0)
-    t = np.array([733408.208333, 733773.208333, 734138.208333,
-                  734503.208333, 734869.208333, 735234.208333, 735599.208333])
-    # convert to new epoch from old...
-    t = t + mdates.date2num(np.datetime64('0000-12-31'))
-    np.testing.assert_allclose(t, locator())
-    expected = ['2009-01-01 00:00:00-05:00',
-                '2010-01-01 00:00:00-05:00', '2011-01-01 00:00:00-05:00',
-                '2012-01-01 00:00:00-05:00', '2013-01-01 00:00:00-05:00',
-                '2014-01-01 00:00:00-05:00', '2015-01-01 00:00:00-05:00']
-    st = list(map(str, mdates.num2date(locator(), tz=tz)))
-    assert st == expected
-    assert np.allclose(locator.tick_values(x[0], x[1]), np.array(
-        [14610.20833333, 14610.33333333, 14610.45833333, 14610.58333333,
-         14610.70833333, 14610.83333333, 14610.95833333, 14611.08333333,
-         14611.20833333]))
-    assert np.allclose(locator.get_locator(x[1], x[0]).tick_values(x[0], x[1]),
-                       np.array(
-        [14610.20833333, 14610.33333333, 14610.45833333, 14610.58333333,
-         14610.70833333, 14610.83333333, 14610.95833333, 14611.08333333,
-         14611.20833333]))
-
-
-def test_YearLocator():
-    def _create_year_locator(date1, date2, **kwargs):
-        locator = mdates.YearLocator(**kwargs)
-        locator.create_dummy_axis()
-        locator.axis.set_view_interval(mdates.date2num(date1),
-                                       mdates.date2num(date2))
-        return locator
-
-    d1 = datetime.datetime(1990, 1, 1)
-    results = ([datetime.timedelta(weeks=52 * 200),
-                {'base': 20, 'month': 1, 'day': 1},
-                ['1980-01-01 00:00:00+00:00', '2000-01-01 00:00:00+00:00',
-                 '2020-01-01 00:00:00+00:00', '2040-01-01 00:00:00+00:00',
-                 '2060-01-01 00:00:00+00:00', '2080-01-01 00:00:00+00:00',
-                 '2100-01-01 00:00:00+00:00', '2120-01-01 00:00:00+00:00',
-                 '2140-01-01 00:00:00+00:00', '2160-01-01 00:00:00+00:00',
-                 '2180-01-01 00:00:00+00:00', '2200-01-01 00:00:00+00:00']
-                ],
-               [datetime.timedelta(weeks=52 * 200),
-                {'base': 20, 'month': 5, 'day': 16},
-                ['1980-05-16 00:00:00+00:00', '2000-05-16 00:00:00+00:00',
-                 '2020-05-16 00:00:00+00:00', '2040-05-16 00:00:00+00:00',
-                 '2060-05-16 00:00:00+00:00', '2080-05-16 00:00:00+00:00',
-                 '2100-05-16 00:00:00+00:00', '2120-05-16 00:00:00+00:00',
-                 '2140-05-16 00:00:00+00:00', '2160-05-16 00:00:00+00:00',
-                 '2180-05-16 00:00:00+00:00', '2200-05-16 00:00:00+00:00']
-                ],
-               [datetime.timedelta(weeks=52 * 5),
-                {'base': 20, 'month': 9, 'day': 25},
-                ['1980-09-25 00:00:00+00:00', '2000-09-25 00:00:00+00:00']
-                ],
-               )
-
-    for delta, arguments, expected in results:
-        d2 = d1 + delta
-        locator = _create_year_locator(d1, d2, **arguments)
-        assert list(map(str, mdates.num2date(locator()))) == expected
-
-
-def test_DayLocator():
-    with pytest.raises(ValueError):
-        mdates.DayLocator(interval=-1)
-    with pytest.raises(ValueError):
-        mdates.DayLocator(interval=-1.5)
-    with pytest.raises(ValueError):
-        mdates.DayLocator(interval=0)
-    with pytest.raises(ValueError):
-        mdates.DayLocator(interval=1.3)
-    mdates.DayLocator(interval=1.0)
-
-
-def test_tz_utc():
-    dt = datetime.datetime(1970, 1, 1, tzinfo=mdates.UTC)
-    assert dt.tzname() == 'UTC'
-
-
-@pytest.mark.parametrize("x, tdelta",
-                         [(1, datetime.timedelta(days=1)),
-                          ([1, 1.5], [datetime.timedelta(days=1),
-                                      datetime.timedelta(days=1.5)])])
-def test_num2timedelta(x, tdelta):
-    dt = mdates.num2timedelta(x)
-    assert dt == tdelta
-
-
-def test_datetime64_in_list():
-    dt = [np.datetime64('2000-01-01'), np.datetime64('2001-01-01')]
-    dn = mdates.date2num(dt)
-    # convert fixed values from old to new epoch
-    t = (np.array([730120.,  730486.]) +
-         mdates.date2num(np.datetime64('0000-12-31')))
-    np.testing.assert_equal(dn, t)
-
-
-def test_change_epoch():
-    date = np.datetime64('2000-01-01')
-
-    # use private method to clear the epoch and allow it to be set...
-    mdates._reset_epoch_test_example()
-    mdates.get_epoch()  # Set default.
-
-    with pytest.raises(RuntimeError):
-        # this should fail here because there is a sentinel on the epoch
-        # if the epoch has been used then it cannot be set.
-        mdates.set_epoch('0000-01-01')
-
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('1970-01-01')
-    dt = (date - np.datetime64('1970-01-01')).astype('datetime64[D]')
-    dt = dt.astype('int')
-    np.testing.assert_equal(mdates.date2num(date), float(dt))
-
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('0000-12-31')
-    np.testing.assert_equal(mdates.date2num(date), 730120.0)
-
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('1970-01-01T01:00:00')
-    np.testing.assert_allclose(mdates.date2num(date), dt - 1./24.)
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('1970-01-01T00:00:00')
-    np.testing.assert_allclose(
-        mdates.date2num(np.datetime64('1970-01-01T12:00:00')),
-        0.5)
-
-
-def test_warn_notintervals():
-    dates = np.arange('2001-01-10', '2001-03-04', dtype='datetime64[D]')
-    locator = mdates.AutoDateLocator(interval_multiples=False)
-    locator.intervald[3] = [2]
-    locator.create_dummy_axis()
-    locator.axis.set_view_interval(mdates.date2num(dates[0]),
-                                   mdates.date2num(dates[-1]))
-    with pytest.warns(UserWarning, match="AutoDateLocator was unable"):
-        locs = locator()
-
-
-def test_change_converter():
-    plt.rcParams['date.converter'] = 'concise'
-    dates = np.arange('2020-01-01', '2020-05-01', dtype='datetime64[D]')
-    fig, ax = plt.subplots()
-
-    ax.plot(dates, np.arange(len(dates)))
-    fig.canvas.draw()
-    assert ax.get_xticklabels()[0].get_text() == 'Jan'
-    assert ax.get_xticklabels()[1].get_text() == '15'
-
-    plt.rcParams['date.converter'] = 'auto'
-    fig, ax = plt.subplots()
-
-    ax.plot(dates, np.arange(len(dates)))
-    fig.canvas.draw()
-    assert ax.get_xticklabels()[0].get_text() == 'Jan 01 2020'
-    assert ax.get_xticklabels()[1].get_text() == 'Jan 15 2020'
-    with pytest.raises(ValueError):
-        plt.rcParams['date.converter'] = 'boo'
-
-
-def test_change_interval_multiples():
-    plt.rcParams['date.interval_multiples'] = False
-    dates = np.arange('2020-01-10', '2020-05-01', dtype='datetime64[D]')
-    fig, ax = plt.subplots()
-
-    ax.plot(dates, np.arange(len(dates)))
-    fig.canvas.draw()
-    assert ax.get_xticklabels()[0].get_text() == 'Jan 10 2020'
-    assert ax.get_xticklabels()[1].get_text() == 'Jan 24 2020'
-
-    plt.rcParams['date.interval_multiples'] = 'True'
-    fig, ax = plt.subplots()
-
-    ax.plot(dates, np.arange(len(dates)))
-    fig.canvas.draw()
-    assert ax.get_xticklabels()[0].get_text() == 'Jan 15 2020'
-    assert ax.get_xticklabels()[1].get_text() == 'Feb 01 2020'
-
-
-def test_julian2num():
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('0000-12-31')
-    with pytest.warns(mpl.MatplotlibDeprecationWarning):
-        # 2440587.5 is julian date for 1970-01-01T00:00:00
-        # https://en.wikipedia.org/wiki/Julian_day
-        assert mdates.julian2num(2440588.5) == 719164.0
-        assert mdates.num2julian(719165.0) == 2440589.5
-    # set back to the default
-    mdates._reset_epoch_test_example()
-    mdates.set_epoch('1970-01-01T00:00:00')
-    with pytest.warns(mpl.MatplotlibDeprecationWarning):
-        assert mdates.julian2num(2440588.5) == 1.0
-        assert mdates.num2julian(2.0) == 2440589.5
-
-
-def test_DateLocator():
-    locator = mdates.DateLocator()
-    # Test nonsingular
-    assert locator.nonsingular(0, np.inf) == (0, 1)
-    assert locator.nonsingular(0, 1) == (0, 1)
-    assert locator.nonsingular(1, 0) == (0, 1)
-    assert locator.nonsingular(0, 0) == (-2, 2)
-    locator.create_dummy_axis()
-    # default values
-    assert locator.datalim_to_dt() == (
-        datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc),
-        datetime.datetime(1970, 1, 2, 0, 0, tzinfo=datetime.timezone.utc))
-
-    # Check default is UTC
-    assert locator.tz == mdates.UTC
-    tz_str = 'Iceland'
-    iceland_tz = dateutil.tz.gettz(tz_str)
-    # Check not Iceland
-    assert locator.tz != iceland_tz
-    # Set it to Iceland
-    locator.set_tzinfo('Iceland')
-    # Check now it is Iceland
-    assert locator.tz == iceland_tz
-    locator.create_dummy_axis()
-    locator.axis.set_data_interval(*mdates.date2num(["2022-01-10",
-                                                     "2022-01-08"]))
-    assert locator.datalim_to_dt() == (
-        datetime.datetime(2022, 1, 8, 0, 0, tzinfo=iceland_tz),
-        datetime.datetime(2022, 1, 10, 0, 0, tzinfo=iceland_tz))
-
-    # Set rcParam
-    plt.rcParams['timezone'] = tz_str
-
-    # Create a new one in a similar way
-    locator = mdates.DateLocator()
-    # Check now it is Iceland
-    assert locator.tz == iceland_tz
-
-    # Test invalid tz values
-    with pytest.raises(ValueError, match="Aiceland is not a valid timezone"):
-        mdates.DateLocator(tz="Aiceland")
-    with pytest.raises(TypeError,
-                       match="tz must be string or tzinfo subclass."):
-        mdates.DateLocator(tz=1)
-
-
-def test_datestr2num():
-    assert mdates.datestr2num('2022-01-10') == 19002.0
-    dt = datetime.date(year=2022, month=1, day=10)
-    assert mdates.datestr2num('2022-01', default=dt) == 19002.0
-    assert np.all(mdates.datestr2num(
-        ['2022-01', '2022-02'], default=dt
-        ) == np.array([19002., 19033.]))
-    assert mdates.datestr2num([]).size == 0
-    assert mdates.datestr2num([], datetime.date(year=2022,
-                                                month=1, day=10)).size == 0
-
-
-@pytest.mark.parametrize('kwarg',
-                         ('formats', 'zero_formats', 'offset_formats'))
-def test_concise_formatter_exceptions(kwarg):
-    locator = mdates.AutoDateLocator()
-    kwargs = {kwarg: ['', '%Y']}
-    match = f"{kwarg} argument must be a list"
-    with pytest.raises(ValueError, match=match):
-        mdates.ConciseDateFormatter(locator, **kwargs)
-
-
-def test_concise_formatter_call():
-    locator = mdates.AutoDateLocator()
-    formatter = mdates.ConciseDateFormatter(locator)
-    assert formatter(19002.0) == '2022'
-    assert formatter.format_data_short(19002.0) == '2022-01-10 00:00:00'
-
-
-def test_datetime_masked():
-    # make sure that all-masked data falls back to the viewlim
-    # set in convert.axisinfo....
-    x = np.array([datetime.datetime(2017, 1, n) for n in range(1, 6)])
-    y = np.array([1, 2, 3, 4, 5])
-    m = np.ma.masked_greater(y, 0)
-
-    fig, ax = plt.subplots()
-    ax.plot(x, m)
-    assert ax.get_xlim() == (0, 1)
-
-
-@pytest.mark.parametrize('val', (-1000000, 10000000))
-def test_num2date_error(val):
-    with pytest.raises(ValueError, match=f"Date ordinal {val} converts"):
-        mdates.num2date(val)
-
-
-def test_num2date_roundoff():
-    assert mdates.num2date(100000.0000578702) == datetime.datetime(
-        2243, 10, 17, 0, 0, 4, 999980, tzinfo=datetime.timezone.utc)
-    # Slightly larger, steps of 20 microseconds
-    assert mdates.num2date(100000.0000578703) == datetime.datetime(
-        2243, 10, 17, 0, 0, 5, tzinfo=datetime.timezone.utc)
-
-
-def test_DateFormatter_settz():
-    time = mdates.date2num(datetime.datetime(2011, 1, 1, 0, 0,
-                                             tzinfo=mdates.UTC))
-    formatter = mdates.DateFormatter('%Y-%b-%d %H:%M')
-    # Default UTC
-    assert formatter(time) == '2011-Jan-01 00:00'
-
-    # Set tzinfo
-    formatter.set_tzinfo('Pacific/Kiritimati')
-    assert formatter(time) == '2011-Jan-01 14:00'
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/tests/test_reloading.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/tests/test_reloading.py
deleted file mode 100644
index a1f360089a547bb4c81ef7a43884823ba4734227..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/tests/test_reloading.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from numpy.testing import (
-    assert_raises,
-    assert_warns,
-    assert_,
-    assert_equal,
-    IS_WASM,
-)
-from numpy.compat import pickle
-
-import pytest
-import sys
-import subprocess
-import textwrap
-from importlib import reload
-
-
-def test_numpy_reloading():
-    # gh-7844. Also check that relevant globals retain their identity.
-    import numpy as np
-    import numpy._globals
-
-    _NoValue = np._NoValue
-    VisibleDeprecationWarning = np.VisibleDeprecationWarning
-    ModuleDeprecationWarning = np.ModuleDeprecationWarning
-
-    with assert_warns(UserWarning):
-        reload(np)
-    assert_(_NoValue is np._NoValue)
-    assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
-    assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
-
-    assert_raises(RuntimeError, reload, numpy._globals)
-    with assert_warns(UserWarning):
-        reload(np)
-    assert_(_NoValue is np._NoValue)
-    assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
-    assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
-
-def test_novalue():
-    import numpy as np
-    for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
-        assert_equal(repr(np._NoValue), '<no value>')
-        assert_(pickle.loads(pickle.dumps(np._NoValue,
-                                          protocol=proto)) is np._NoValue)
-
-
-@pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
-def test_full_reimport():
-    """At the time of writing this, it is *not* truly supported, but
-    apparently enough users rely on it, for it to be an annoying change
-    when it started failing previously.
-    """
-    # Test within a new process, to ensure that we do not mess with the
-    # global state during the test run (could lead to cryptic test failures).
-    # This is generally unsafe, especially, since we also reload the C-modules.
-    code = textwrap.dedent(r"""
-        import sys
-        from pytest import warns
-        import numpy as np
-
-        for k in list(sys.modules.keys()):
-            if "numpy" in k:
-                del sys.modules[k]
-
-        with warns(UserWarning):
-            import numpy as np
-        """)
-    p = subprocess.run([sys.executable, '-c', code], capture_output=True)
-    if p.returncode:
-        raise AssertionError(
-            f"Non-zero return code: {p.returncode!r}\n\n{p.stderr.decode()}"
-        )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/__init__.py
deleted file mode 100644
index 55c861e384d679654b8615d4cb5808f536fd8f2e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/__init__.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""
-Plotting public API.
-
-Authors of third-party plotting backends should implement a module with a
-public ``plot(data, kind, **kwargs)``. The parameter `data` will contain
-the data structure and can be a `Series` or a `DataFrame`. For example,
-for ``df.plot()`` the parameter `data` will contain the DataFrame `df`.
-In some cases, the data structure is transformed before being sent to
-the backend (see PlotAccessor.__call__ in pandas/plotting/_core.py for
-the exact transformations).
-
-The parameter `kind` will be one of:
-
-- line
-- bar
-- barh
-- box
-- hist
-- kde
-- area
-- pie
-- scatter
-- hexbin
-
-See the pandas API reference for documentation on each kind of plot.
-
-Any other keyword argument is currently assumed to be backend specific,
-but some parameters may be unified and added to the signature in the
-future (e.g. `title` which should be useful for any backend).
-
-Currently, all the Matplotlib functions in pandas are accessed through
-the selected backend. For example, `pandas.plotting.boxplot` (equivalent
-to `DataFrame.boxplot`) is also accessed in the selected backend. This
-is expected to change, and the exact API is under discussion. But with
-the current version, backends are expected to implement the next functions:
-
-- plot (describe above, used for `Series.plot` and `DataFrame.plot`)
-- hist_series and hist_frame (for `Series.hist` and `DataFrame.hist`)
-- boxplot (`pandas.plotting.boxplot(df)` equivalent to `DataFrame.boxplot`)
-- boxplot_frame and boxplot_frame_groupby
-- register and deregister (register converters for the tick formats)
-- Plots not called as `Series` and `DataFrame` methods:
-  - table
-  - andrews_curves
-  - autocorrelation_plot
-  - bootstrap_plot
-  - lag_plot
-  - parallel_coordinates
-  - radviz
-  - scatter_matrix
-
-Use the code in pandas/plotting/_matplotib.py and
-https://github.com/pyviz/hvplot as a reference on how to write a backend.
-
-For the discussion about the API see
-https://github.com/pandas-dev/pandas/issues/26747.
-"""
-from pandas.plotting._core import (
-    PlotAccessor,
-    boxplot,
-    boxplot_frame,
-    boxplot_frame_groupby,
-    hist_frame,
-    hist_series,
-)
-from pandas.plotting._misc import (
-    andrews_curves,
-    autocorrelation_plot,
-    bootstrap_plot,
-    deregister as deregister_matplotlib_converters,
-    lag_plot,
-    parallel_coordinates,
-    plot_params,
-    radviz,
-    register as register_matplotlib_converters,
-    scatter_matrix,
-    table,
-)
-
-__all__ = [
-    "PlotAccessor",
-    "boxplot",
-    "boxplot_frame",
-    "boxplot_frame_groupby",
-    "hist_frame",
-    "hist_series",
-    "scatter_matrix",
-    "radviz",
-    "andrews_curves",
-    "bootstrap_plot",
-    "parallel_coordinates",
-    "lag_plot",
-    "autocorrelation_plot",
-    "table",
-    "plot_params",
-    "register_matplotlib_converters",
-    "deregister_matplotlib_converters",
-]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/conftest.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/conftest.py
deleted file mode 100644
index 458a37c99409197c9ef776080530a4dda367ba74..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/conftest.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    Series,
-    array,
-)
-import pandas._testing as tm
-
-
-@pytest.fixture(params=[None, False])
-def sort(request):
-    """
-    Valid values for the 'sort' parameter used in the Index
-    setops methods (intersection, union, etc.)
-
-    Caution:
-        Don't confuse this one with the "sort" fixture used
-        for DataFrame.append or concat. That one has
-        parameters [True, False].
-
-        We can't combine them as sort=True is not permitted
-        in the Index setops methods.
-    """
-    return request.param
-
-
-@pytest.fixture(params=["D", "3D", "-3D", "H", "2H", "-2H", "T", "2T", "S", "-3S"])
-def freq_sample(request):
-    """
-    Valid values for 'freq' parameter used to create date_range and
-    timedelta_range..
-    """
-    return request.param
-
-
-@pytest.fixture(params=[list, tuple, np.array, array, Series])
-def listlike_box(request):
-    """
-    Types that may be passed as the indexer to searchsorted.
-    """
-    return request.param
-
-
-@pytest.fixture(
-    params=tm.ALL_REAL_NUMPY_DTYPES
-    + [
-        "object",
-        "category",
-        "datetime64[ns]",
-        "timedelta64[ns]",
-    ]
-)
-def any_dtype_for_small_pos_integer_indexes(request):
-    """
-    Dtypes that can be given to an Index with small positive integers.
-
-    This means that for any dtype `x` in the params list, `Index([1, 2, 3], dtype=x)` is
-    valid and gives the correct Index (sub-)class.
-    """
-    return request.param
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/test_na_values.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/test_na_values.py
deleted file mode 100644
index 9a16ec5a50d366c945fa1edf88ed035afe69c529..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/test_na_values.py
+++ /dev/null
@@ -1,671 +0,0 @@
-"""
-Tests that NA values are properly handled during
-parsing for all of the parsers defined in parsers.py
-"""
-from io import StringIO
-
-import numpy as np
-import pytest
-
-from pandas._libs.parsers import STR_NA_VALUES
-
-from pandas import (
-    DataFrame,
-    Index,
-    MultiIndex,
-)
-import pandas._testing as tm
-
-skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
-xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
-
-
-def test_string_nas(all_parsers):
-    parser = all_parsers
-    data = """A,B,C
-a,b,c
-d,,f
-,g,h
-"""
-    result = parser.read_csv(StringIO(data))
-    expected = DataFrame(
-        [["a", "b", "c"], ["d", np.nan, "f"], [np.nan, "g", "h"]],
-        columns=["A", "B", "C"],
-    )
-    if parser.engine == "pyarrow":
-        expected.loc[2, "A"] = None
-        expected.loc[1, "B"] = None
-    tm.assert_frame_equal(result, expected)
-
-
-def test_detect_string_na(all_parsers):
-    parser = all_parsers
-    data = """A,B
-foo,bar
-NA,baz
-NaN,nan
-"""
-    expected = DataFrame(
-        [["foo", "bar"], [np.nan, "baz"], [np.nan, np.nan]], columns=["A", "B"]
-    )
-    if parser.engine == "pyarrow":
-        expected.loc[[1, 2], "A"] = None
-        expected.loc[2, "B"] = None
-    result = parser.read_csv(StringIO(data))
-    tm.assert_frame_equal(result, expected)
-
-
-@skip_pyarrow
-@pytest.mark.parametrize(
-    "na_values",
-    [
-        ["-999.0", "-999"],
-        [-999, -999.0],
-        [-999.0, -999],
-        ["-999.0"],
-        ["-999"],
-        [-999.0],
-        [-999],
-    ],
-)
-@pytest.mark.parametrize(
-    "data",
-    [
-        """A,B
--999,1.2
-2,-999
-3,4.5
-""",
-        """A,B
--999,1.200
-2,-999.000
-3,4.500
-""",
-    ],
-)
-def test_non_string_na_values(all_parsers, data, na_values):
-    # see gh-3611: with an odd float format, we can't match
-    # the string "999.0" exactly but still need float matching
-    parser = all_parsers
-    expected = DataFrame([[np.nan, 1.2], [2.0, np.nan], [3.0, 4.5]], columns=["A", "B"])
-
-    result = parser.read_csv(StringIO(data), na_values=na_values)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_default_na_values(all_parsers):
-    _NA_VALUES = {
-        "-1.#IND",
-        "1.#QNAN",
-        "1.#IND",
-        "-1.#QNAN",
-        "#N/A",
-        "N/A",
-        "n/a",
-        "NA",
-        "<NA>",
-        "#NA",
-        "NULL",
-        "null",
-        "NaN",
-        "nan",
-        "-NaN",
-        "-nan",
-        "#N/A N/A",
-        "",
-        "None",
-    }
-    assert _NA_VALUES == STR_NA_VALUES
-
-    parser = all_parsers
-    nv = len(_NA_VALUES)
-
-    def f(i, v):
-        if i == 0:
-            buf = ""
-        elif i > 0:
-            buf = "".join([","] * i)
-
-        buf = f"{buf}{v}"
-
-        if i < nv - 1:
-            joined = "".join([","] * (nv - i - 1))
-            buf = f"{buf}{joined}"
-
-        return buf
-
-    data = StringIO("\n".join([f(i, v) for i, v in enumerate(_NA_VALUES)]))
-    expected = DataFrame(np.nan, columns=range(nv), index=range(nv))
-
-    result = parser.read_csv(data, header=None)
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: needs skiprows list support in pyarrow
-@skip_pyarrow
-@pytest.mark.parametrize("na_values", ["baz", ["baz"]])
-def test_custom_na_values(all_parsers, na_values):
-    parser = all_parsers
-    data = """A,B,C
-ignore,this,row
-1,NA,3
--1.#IND,5,baz
-7,8,NaN
-"""
-    expected = DataFrame(
-        [[1.0, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]], columns=["A", "B", "C"]
-    )
-    result = parser.read_csv(StringIO(data), na_values=na_values, skiprows=[1])
-    tm.assert_frame_equal(result, expected)
-
-
-def test_bool_na_values(all_parsers):
-    data = """A,B,C
-True,False,True
-NA,True,False
-False,NA,True"""
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data))
-    expected = DataFrame(
-        {
-            "A": np.array([True, np.nan, False], dtype=object),
-            "B": np.array([False, True, np.nan], dtype=object),
-            "C": [True, False, True],
-        }
-    )
-    if parser.engine == "pyarrow":
-        expected.loc[1, "A"] = None
-        expected.loc[2, "B"] = None
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: Needs pyarrow support for dictionary in na_values
-@skip_pyarrow
-def test_na_value_dict(all_parsers):
-    data = """A,B,C
-foo,bar,NA
-bar,foo,foo
-foo,bar,NA
-bar,foo,foo"""
-    parser = all_parsers
-    df = parser.read_csv(StringIO(data), na_values={"A": ["foo"], "B": ["bar"]})
-    expected = DataFrame(
-        {
-            "A": [np.nan, "bar", np.nan, "bar"],
-            "B": [np.nan, "foo", np.nan, "foo"],
-            "C": [np.nan, "foo", np.nan, "foo"],
-        }
-    )
-    tm.assert_frame_equal(df, expected)
-
-
-@pytest.mark.parametrize(
-    "index_col,expected",
-    [
-        (
-            [0],
-            DataFrame({"b": [np.nan], "c": [1], "d": [5]}, index=Index([0], name="a")),
-        ),
-        (
-            [0, 2],
-            DataFrame(
-                {"b": [np.nan], "d": [5]},
-                index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
-            ),
-        ),
-        (
-            ["a", "c"],
-            DataFrame(
-                {"b": [np.nan], "d": [5]},
-                index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
-            ),
-        ),
-    ],
-)
-def test_na_value_dict_multi_index(all_parsers, index_col, expected):
-    data = """\
-a,b,c,d
-0,NA,1,5
-"""
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), na_values=set(), index_col=index_col)
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: xfail components of this test, the first one passes
-@skip_pyarrow
-@pytest.mark.parametrize(
-    "kwargs,expected",
-    [
-        (
-            {},
-            DataFrame(
-                {
-                    "A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
-                    "B": [1, 2, 3, 4, 5, 6, 7],
-                    "C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
-                }
-            ),
-        ),
-        (
-            {"na_values": {"A": [], "C": []}, "keep_default_na": False},
-            DataFrame(
-                {
-                    "A": ["a", "b", "", "d", "e", "nan", "g"],
-                    "B": [1, 2, 3, 4, 5, 6, 7],
-                    "C": ["one", "two", "three", "nan", "five", "", "seven"],
-                }
-            ),
-        ),
-        (
-            {"na_values": ["a"], "keep_default_na": False},
-            DataFrame(
-                {
-                    "A": [np.nan, "b", "", "d", "e", "nan", "g"],
-                    "B": [1, 2, 3, 4, 5, 6, 7],
-                    "C": ["one", "two", "three", "nan", "five", "", "seven"],
-                }
-            ),
-        ),
-        (
-            {"na_values": {"A": [], "C": []}},
-            DataFrame(
-                {
-                    "A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
-                    "B": [1, 2, 3, 4, 5, 6, 7],
-                    "C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
-                }
-            ),
-        ),
-    ],
-)
-def test_na_values_keep_default(all_parsers, kwargs, expected):
-    data = """\
-A,B,C
-a,1,one
-b,2,two
-,3,three
-d,4,nan
-e,5,five
-nan,6,
-g,7,seven
-"""
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), **kwargs)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_no_na_values_no_keep_default(all_parsers):
-    # see gh-4318: passing na_values=None and
-    # keep_default_na=False yields 'None" as a na_value
-    data = """\
-A,B,C
-a,1,None
-b,2,two
-,3,None
-d,4,nan
-e,5,five
-nan,6,
-g,7,seven
-"""
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), keep_default_na=False)
-
-    expected = DataFrame(
-        {
-            "A": ["a", "b", "", "d", "e", "nan", "g"],
-            "B": [1, 2, 3, 4, 5, 6, 7],
-            "C": ["None", "two", "None", "nan", "five", "", "seven"],
-        }
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: Blocked on na_values dict support in pyarrow
-@skip_pyarrow
-def test_no_keep_default_na_dict_na_values(all_parsers):
-    # see gh-19227
-    data = "a,b\n,2"
-    parser = all_parsers
-    result = parser.read_csv(
-        StringIO(data), na_values={"b": ["2"]}, keep_default_na=False
-    )
-    expected = DataFrame({"a": [""], "b": [np.nan]})
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: Blocked on na_values dict support in pyarrow
-@skip_pyarrow
-def test_no_keep_default_na_dict_na_scalar_values(all_parsers):
-    # see gh-19227
-    #
-    # Scalar values shouldn't cause the parsing to crash or fail.
-    data = "a,b\n1,2"
-    parser = all_parsers
-    df = parser.read_csv(StringIO(data), na_values={"b": 2}, keep_default_na=False)
-    expected = DataFrame({"a": [1], "b": [np.nan]})
-    tm.assert_frame_equal(df, expected)
-
-
-# TODO: Blocked on na_values dict support in pyarrow
-@skip_pyarrow
-@pytest.mark.parametrize("col_zero_na_values", [113125, "113125"])
-def test_no_keep_default_na_dict_na_values_diff_reprs(all_parsers, col_zero_na_values):
-    # see gh-19227
-    data = """\
-113125,"blah","/blaha",kjsdkj,412.166,225.874,214.008
-729639,"qwer","",asdfkj,466.681,,252.373
-"""
-    parser = all_parsers
-    expected = DataFrame(
-        {
-            0: [np.nan, 729639.0],
-            1: [np.nan, "qwer"],
-            2: ["/blaha", np.nan],
-            3: ["kjsdkj", "asdfkj"],
-            4: [412.166, 466.681],
-            5: ["225.874", ""],
-            6: [np.nan, 252.373],
-        }
-    )
-
-    result = parser.read_csv(
-        StringIO(data),
-        header=None,
-        keep_default_na=False,
-        na_values={2: "", 6: "214.008", 1: "blah", 0: col_zero_na_values},
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: Empty null_values doesn't work properly on pyarrow
-@skip_pyarrow
-@pytest.mark.parametrize(
-    "na_filter,row_data",
-    [
-        (True, [[1, "A"], [np.nan, np.nan], [3, "C"]]),
-        (False, [["1", "A"], ["nan", "B"], ["3", "C"]]),
-    ],
-)
-def test_na_values_na_filter_override(all_parsers, na_filter, row_data):
-    data = """\
-A,B
-1,A
-nan,B
-3,C
-"""
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), na_values=["B"], na_filter=na_filter)
-
-    expected = DataFrame(row_data, columns=["A", "B"])
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: Arrow parse error
-@skip_pyarrow
-def test_na_trailing_columns(all_parsers):
-    parser = all_parsers
-    data = """Date,Currency,Symbol,Type,Units,UnitPrice,Cost,Tax
-2012-03-14,USD,AAPL,BUY,1000
-2012-05-12,USD,SBUX,SELL,500"""
-
-    # Trailing columns should be all NaN.
-    result = parser.read_csv(StringIO(data))
-    expected = DataFrame(
-        [
-            ["2012-03-14", "USD", "AAPL", "BUY", 1000, np.nan, np.nan, np.nan],
-            ["2012-05-12", "USD", "SBUX", "SELL", 500, np.nan, np.nan, np.nan],
-        ],
-        columns=[
-            "Date",
-            "Currency",
-            "Symbol",
-            "Type",
-            "Units",
-            "UnitPrice",
-            "Cost",
-            "Tax",
-        ],
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: xfail the na_values dict case
-@skip_pyarrow
-@pytest.mark.parametrize(
-    "na_values,row_data",
-    [
-        (1, [[np.nan, 2.0], [2.0, np.nan]]),
-        ({"a": 2, "b": 1}, [[1.0, 2.0], [np.nan, np.nan]]),
-    ],
-)
-def test_na_values_scalar(all_parsers, na_values, row_data):
-    # see gh-12224
-    parser = all_parsers
-    names = ["a", "b"]
-    data = "1,2\n2,1"
-
-    result = parser.read_csv(StringIO(data), names=names, na_values=na_values)
-    expected = DataFrame(row_data, columns=names)
-    tm.assert_frame_equal(result, expected)
-
-
-@skip_pyarrow
-def test_na_values_dict_aliasing(all_parsers):
-    parser = all_parsers
-    na_values = {"a": 2, "b": 1}
-    na_values_copy = na_values.copy()
-
-    names = ["a", "b"]
-    data = "1,2\n2,1"
-
-    expected = DataFrame([[1.0, 2.0], [np.nan, np.nan]], columns=names)
-    result = parser.read_csv(StringIO(data), names=names, na_values=na_values)
-
-    tm.assert_frame_equal(result, expected)
-    tm.assert_dict_equal(na_values, na_values_copy)
-
-
-@skip_pyarrow
-def test_na_values_dict_col_index(all_parsers):
-    # see gh-14203
-    data = "a\nfoo\n1"
-    parser = all_parsers
-    na_values = {0: "foo"}
-
-    result = parser.read_csv(StringIO(data), na_values=na_values)
-    expected = DataFrame({"a": [np.nan, 1]})
-    tm.assert_frame_equal(result, expected)
-
-
-@skip_pyarrow
-@pytest.mark.parametrize(
-    "data,kwargs,expected",
-    [
-        (
-            str(2**63) + "\n" + str(2**63 + 1),
-            {"na_values": [2**63]},
-            DataFrame([str(2**63), str(2**63 + 1)]),
-        ),
-        (str(2**63) + ",1" + "\n,2", {}, DataFrame([[str(2**63), 1], ["", 2]])),
-        (str(2**63) + "\n1", {"na_values": [2**63]}, DataFrame([np.nan, 1])),
-    ],
-)
-def test_na_values_uint64(all_parsers, data, kwargs, expected):
-    # see gh-14983
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), header=None, **kwargs)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_empty_na_values_no_default_with_index(all_parsers):
-    # see gh-15835
-    data = "a,1\nb,2"
-    parser = all_parsers
-    expected = DataFrame({"1": [2]}, index=Index(["b"], name="a"))
-
-    result = parser.read_csv(StringIO(data), index_col=0, keep_default_na=False)
-    tm.assert_frame_equal(result, expected)
-
-
-# TODO: Missing support for na_filter kewyord
-@skip_pyarrow
-@pytest.mark.parametrize(
-    "na_filter,index_data", [(False, ["", "5"]), (True, [np.nan, 5.0])]
-)
-def test_no_na_filter_on_index(all_parsers, na_filter, index_data):
-    # see gh-5239
-    #
-    # Don't parse NA-values in index unless na_filter=True
-    parser = all_parsers
-    data = "a,b,c\n1,,3\n4,5,6"
-
-    expected = DataFrame({"a": [1, 4], "c": [3, 6]}, index=Index(index_data, name="b"))
-    result = parser.read_csv(StringIO(data), index_col=[1], na_filter=na_filter)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_inf_na_values_with_int_index(all_parsers):
-    # see gh-17128
-    parser = all_parsers
-    data = "idx,col1,col2\n1,3,4\n2,inf,-inf"
-
-    # Don't fail with OverflowError with inf's and integer index column.
-    out = parser.read_csv(StringIO(data), index_col=[0], na_values=["inf", "-inf"])
-    expected = DataFrame(
-        {"col1": [3, np.nan], "col2": [4, np.nan]}, index=Index([1, 2], name="idx")
-    )
-    tm.assert_frame_equal(out, expected)
-
-
-@skip_pyarrow
-@pytest.mark.parametrize("na_filter", [True, False])
-def test_na_values_with_dtype_str_and_na_filter(all_parsers, na_filter):
-    # see gh-20377
-    parser = all_parsers
-    data = "a,b,c\n1,,3\n4,5,6"
-
-    # na_filter=True --> missing value becomes NaN.
-    # na_filter=False --> missing value remains empty string.
-    empty = np.nan if na_filter else ""
-    expected = DataFrame({"a": ["1", "4"], "b": [empty, "5"], "c": ["3", "6"]})
-
-    result = parser.read_csv(StringIO(data), na_filter=na_filter, dtype=str)
-    tm.assert_frame_equal(result, expected)
-
-
-@skip_pyarrow
-@pytest.mark.parametrize(
-    "data, na_values",
-    [
-        ("false,1\n,1\ntrue", None),
-        ("false,1\nnull,1\ntrue", None),
-        ("false,1\nnan,1\ntrue", None),
-        ("false,1\nfoo,1\ntrue", "foo"),
-        ("false,1\nfoo,1\ntrue", ["foo"]),
-        ("false,1\nfoo,1\ntrue", {"a": "foo"}),
-    ],
-)
-def test_cast_NA_to_bool_raises_error(all_parsers, data, na_values):
-    parser = all_parsers
-    msg = (
-        "(Bool column has NA values in column [0a])|"
-        "(cannot safely convert passed user dtype of "
-        "bool for object dtyped data in column 0)"
-    )
-    with pytest.raises(ValueError, match=msg):
-        parser.read_csv(
-            StringIO(data),
-            header=None,
-            names=["a", "b"],
-            dtype={"a": "bool"},
-            na_values=na_values,
-        )
-
-
-@skip_pyarrow
-def test_str_nan_dropped(all_parsers):
-    # see gh-21131
-    parser = all_parsers
-
-    data = """File: small.csv,,
-10010010233,0123,654
-foo,,bar
-01001000155,4530,898"""
-
-    result = parser.read_csv(
-        StringIO(data),
-        header=None,
-        names=["col1", "col2", "col3"],
-        dtype={"col1": str, "col2": str, "col3": str},
-    ).dropna()
-
-    expected = DataFrame(
-        {
-            "col1": ["10010010233", "01001000155"],
-            "col2": ["0123", "4530"],
-            "col3": ["654", "898"],
-        },
-        index=[1, 3],
-    )
-
-    tm.assert_frame_equal(result, expected)
-
-
-@skip_pyarrow
-def test_nan_multi_index(all_parsers):
-    # GH 42446
-    parser = all_parsers
-    data = "A,B,B\nX,Y,Z\n1,2,inf"
-
-    result = parser.read_csv(
-        StringIO(data), header=list(range(2)), na_values={("B", "Z"): "inf"}
-    )
-
-    expected = DataFrame(
-        {
-            ("A", "X"): [1],
-            ("B", "Y"): [2],
-            ("B", "Z"): [np.nan],
-        }
-    )
-
-    tm.assert_frame_equal(result, expected)
-
-
-@xfail_pyarrow
-def test_bool_and_nan_to_bool(all_parsers):
-    # GH#42808
-    parser = all_parsers
-    data = """0
-NaN
-True
-False
-"""
-    with pytest.raises(ValueError, match="NA values"):
-        parser.read_csv(StringIO(data), dtype="bool")
-
-
-def test_bool_and_nan_to_int(all_parsers):
-    # GH#42808
-    parser = all_parsers
-    data = """0
-NaN
-True
-False
-"""
-    with pytest.raises(ValueError, match="convert|NoneType"):
-        parser.read_csv(StringIO(data), dtype="int")
-
-
-def test_bool_and_nan_to_float(all_parsers):
-    # GH#42808
-    parser = all_parsers
-    data = """0
-NaN
-True
-False
-"""
-    result = parser.read_csv(StringIO(data), dtype="float")
-    expected = DataFrame.from_dict({"0": [np.nan, 1.0, 0.0]})
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/sbcsgroupprober.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/sbcsgroupprober.py
deleted file mode 100644
index bdeef4e15b0dc5a68220b14c9dcec1a019401106..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/sbcsgroupprober.py
+++ /dev/null
@@ -1,83 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#   Shy Shalom - original C code
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .charsetgroupprober import CharSetGroupProber
-from .hebrewprober import HebrewProber
-from .langbulgarianmodel import (ISO_8859_5_BULGARIAN_MODEL,
-                                 WINDOWS_1251_BULGARIAN_MODEL)
-from .langgreekmodel import ISO_8859_7_GREEK_MODEL, WINDOWS_1253_GREEK_MODEL
-from .langhebrewmodel import WINDOWS_1255_HEBREW_MODEL
-# from .langhungarianmodel import (ISO_8859_2_HUNGARIAN_MODEL,
-#                                  WINDOWS_1250_HUNGARIAN_MODEL)
-from .langrussianmodel import (IBM855_RUSSIAN_MODEL, IBM866_RUSSIAN_MODEL,
-                               ISO_8859_5_RUSSIAN_MODEL, KOI8_R_RUSSIAN_MODEL,
-                               MACCYRILLIC_RUSSIAN_MODEL,
-                               WINDOWS_1251_RUSSIAN_MODEL)
-from .langthaimodel import TIS_620_THAI_MODEL
-from .langturkishmodel import ISO_8859_9_TURKISH_MODEL
-from .sbcharsetprober import SingleByteCharSetProber
-
-
-class SBCSGroupProber(CharSetGroupProber):
-    def __init__(self):
-        super(SBCSGroupProber, self).__init__()
-        hebrew_prober = HebrewProber()
-        logical_hebrew_prober = SingleByteCharSetProber(WINDOWS_1255_HEBREW_MODEL,
-                                                        False, hebrew_prober)
-        # TODO: See if using ISO-8859-8 Hebrew model works better here, since
-        #       it's actually the visual one
-        visual_hebrew_prober = SingleByteCharSetProber(WINDOWS_1255_HEBREW_MODEL,
-                                                       True, hebrew_prober)
-        hebrew_prober.set_model_probers(logical_hebrew_prober,
-                                        visual_hebrew_prober)
-        # TODO: ORDER MATTERS HERE. I changed the order vs what was in master
-        #       and several tests failed that did not before. Some thought
-        #       should be put into the ordering, and we should consider making
-        #       order not matter here, because that is very counter-intuitive.
-        self.probers = [
-            SingleByteCharSetProber(WINDOWS_1251_RUSSIAN_MODEL),
-            SingleByteCharSetProber(KOI8_R_RUSSIAN_MODEL),
-            SingleByteCharSetProber(ISO_8859_5_RUSSIAN_MODEL),
-            SingleByteCharSetProber(MACCYRILLIC_RUSSIAN_MODEL),
-            SingleByteCharSetProber(IBM866_RUSSIAN_MODEL),
-            SingleByteCharSetProber(IBM855_RUSSIAN_MODEL),
-            SingleByteCharSetProber(ISO_8859_7_GREEK_MODEL),
-            SingleByteCharSetProber(WINDOWS_1253_GREEK_MODEL),
-            SingleByteCharSetProber(ISO_8859_5_BULGARIAN_MODEL),
-            SingleByteCharSetProber(WINDOWS_1251_BULGARIAN_MODEL),
-            # TODO: Restore Hungarian encodings (iso-8859-2 and windows-1250)
-            #       after we retrain model.
-            # SingleByteCharSetProber(ISO_8859_2_HUNGARIAN_MODEL),
-            # SingleByteCharSetProber(WINDOWS_1250_HUNGARIAN_MODEL),
-            SingleByteCharSetProber(TIS_620_THAI_MODEL),
-            SingleByteCharSetProber(ISO_8859_9_TURKISH_MODEL),
-            hebrew_prober,
-            logical_hebrew_prober,
-            visual_hebrew_prober,
-        ]
-        self.reset()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/file_proxy.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/file_proxy.py
deleted file mode 100644
index 3ec593a5a480b101f8d67a6bc4b2ceabc4685d8e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/file_proxy.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import io
-from typing import List, Any, IO, TYPE_CHECKING
-
-from .ansi import AnsiDecoder
-from .text import Text
-
-if TYPE_CHECKING:
-    from .console import Console
-
-
-class FileProxy(io.TextIOBase):
-    """Wraps a file (e.g. sys.stdout) and redirects writes to a console."""
-
-    def __init__(self, console: "Console", file: IO[str]) -> None:
-        self.__console = console
-        self.__file = file
-        self.__buffer: List[str] = []
-        self.__ansi_decoder = AnsiDecoder()
-
-    @property
-    def rich_proxied_file(self) -> IO[str]:
-        """Get proxied file."""
-        return self.__file
-
-    def __getattr__(self, name: str) -> Any:
-        return getattr(self.__file, name)
-
-    def write(self, text: str) -> int:
-        if not isinstance(text, str):
-            raise TypeError(f"write() argument must be str, not {type(text).__name__}")
-        buffer = self.__buffer
-        lines: List[str] = []
-        while text:
-            line, new_line, text = text.partition("\n")
-            if new_line:
-                lines.append("".join(buffer) + line)
-                del buffer[:]
-            else:
-                buffer.append(line)
-                break
-        if lines:
-            console = self.__console
-            with console:
-                output = Text("\n").join(
-                    self.__ansi_decoder.decode_line(line) for line in lines
-                )
-                console.print(output)
-        return len(text)
-
-    def flush(self) -> None:
-        buffer = self.__buffer
-        if buffer:
-            self.__console.print("".join(buffer))
-            del buffer[:]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/traceback.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/traceback.py
deleted file mode 100644
index 341f7f411474719d2c2bd13f01b4b7aae0b7b223..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/traceback.py
+++ /dev/null
@@ -1,756 +0,0 @@
-from __future__ import absolute_import
-
-import linecache
-import os
-import platform
-import sys
-from dataclasses import dataclass, field
-from traceback import walk_tb
-from types import ModuleType, TracebackType
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    Union,
-)
-
-from pygments.lexers import guess_lexer_for_filename
-from pygments.token import Comment, Keyword, Name, Number, Operator, String
-from pygments.token import Text as TextToken
-from pygments.token import Token
-from pygments.util import ClassNotFound
-
-from . import pretty
-from ._loop import loop_last
-from .columns import Columns
-from .console import Console, ConsoleOptions, ConsoleRenderable, RenderResult, group
-from .constrain import Constrain
-from .highlighter import RegexHighlighter, ReprHighlighter
-from .panel import Panel
-from .scope import render_scope
-from .style import Style
-from .syntax import Syntax
-from .text import Text
-from .theme import Theme
-
-WINDOWS = platform.system() == "Windows"
-
-LOCALS_MAX_LENGTH = 10
-LOCALS_MAX_STRING = 80
-
-
-def install(
-    *,
-    console: Optional[Console] = None,
-    width: Optional[int] = 100,
-    extra_lines: int = 3,
-    theme: Optional[str] = None,
-    word_wrap: bool = False,
-    show_locals: bool = False,
-    locals_max_length: int = LOCALS_MAX_LENGTH,
-    locals_max_string: int = LOCALS_MAX_STRING,
-    locals_hide_dunder: bool = True,
-    locals_hide_sunder: Optional[bool] = None,
-    indent_guides: bool = True,
-    suppress: Iterable[Union[str, ModuleType]] = (),
-    max_frames: int = 100,
-) -> Callable[[Type[BaseException], BaseException, Optional[TracebackType]], Any]:
-    """Install a rich traceback handler.
-
-    Once installed, any tracebacks will be printed with syntax highlighting and rich formatting.
-
-
-    Args:
-        console (Optional[Console], optional): Console to write exception to. Default uses internal Console instance.
-        width (Optional[int], optional): Width (in characters) of traceback. Defaults to 100.
-        extra_lines (int, optional): Extra lines of code. Defaults to 3.
-        theme (Optional[str], optional): Pygments theme to use in traceback. Defaults to ``None`` which will pick
-            a theme appropriate for the platform.
-        word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
-        show_locals (bool, optional): Enable display of local variables. Defaults to False.
-        locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to 10.
-        locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-        locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-        locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-        indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
-        suppress (Sequence[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
-
-    Returns:
-        Callable: The previous exception handler that was replaced.
-
-    """
-    traceback_console = Console(stderr=True) if console is None else console
-
-    locals_hide_sunder = (
-        True
-        if (traceback_console.is_jupyter and locals_hide_sunder is None)
-        else locals_hide_sunder
-    )
-
-    def excepthook(
-        type_: Type[BaseException],
-        value: BaseException,
-        traceback: Optional[TracebackType],
-    ) -> None:
-        traceback_console.print(
-            Traceback.from_exception(
-                type_,
-                value,
-                traceback,
-                width=width,
-                extra_lines=extra_lines,
-                theme=theme,
-                word_wrap=word_wrap,
-                show_locals=show_locals,
-                locals_max_length=locals_max_length,
-                locals_max_string=locals_max_string,
-                locals_hide_dunder=locals_hide_dunder,
-                locals_hide_sunder=bool(locals_hide_sunder),
-                indent_guides=indent_guides,
-                suppress=suppress,
-                max_frames=max_frames,
-            )
-        )
-
-    def ipy_excepthook_closure(ip: Any) -> None:  # pragma: no cover
-        tb_data = {}  # store information about showtraceback call
-        default_showtraceback = ip.showtraceback  # keep reference of default traceback
-
-        def ipy_show_traceback(*args: Any, **kwargs: Any) -> None:
-            """wrap the default ip.showtraceback to store info for ip._showtraceback"""
-            nonlocal tb_data
-            tb_data = kwargs
-            default_showtraceback(*args, **kwargs)
-
-        def ipy_display_traceback(
-            *args: Any, is_syntax: bool = False, **kwargs: Any
-        ) -> None:
-            """Internally called traceback from ip._showtraceback"""
-            nonlocal tb_data
-            exc_tuple = ip._get_exc_info()
-
-            # do not display trace on syntax error
-            tb: Optional[TracebackType] = None if is_syntax else exc_tuple[2]
-
-            # determine correct tb_offset
-            compiled = tb_data.get("running_compiled_code", False)
-            tb_offset = tb_data.get("tb_offset", 1 if compiled else 0)
-            # remove ipython internal frames from trace with tb_offset
-            for _ in range(tb_offset):
-                if tb is None:
-                    break
-                tb = tb.tb_next
-
-            excepthook(exc_tuple[0], exc_tuple[1], tb)
-            tb_data = {}  # clear data upon usage
-
-        # replace _showtraceback instead of showtraceback to allow ipython features such as debugging to work
-        # this is also what the ipython docs recommends to modify when subclassing InteractiveShell
-        ip._showtraceback = ipy_display_traceback
-        # add wrapper to capture tb_data
-        ip.showtraceback = ipy_show_traceback
-        ip.showsyntaxerror = lambda *args, **kwargs: ipy_display_traceback(
-            *args, is_syntax=True, **kwargs
-        )
-
-    try:  # pragma: no cover
-        # if within ipython, use customized traceback
-        ip = get_ipython()  # type: ignore[name-defined]
-        ipy_excepthook_closure(ip)
-        return sys.excepthook
-    except Exception:
-        # otherwise use default system hook
-        old_excepthook = sys.excepthook
-        sys.excepthook = excepthook
-        return old_excepthook
-
-
-@dataclass
-class Frame:
-    filename: str
-    lineno: int
-    name: str
-    line: str = ""
-    locals: Optional[Dict[str, pretty.Node]] = None
-
-
-@dataclass
-class _SyntaxError:
-    offset: int
-    filename: str
-    line: str
-    lineno: int
-    msg: str
-
-
-@dataclass
-class Stack:
-    exc_type: str
-    exc_value: str
-    syntax_error: Optional[_SyntaxError] = None
-    is_cause: bool = False
-    frames: List[Frame] = field(default_factory=list)
-
-
-@dataclass
-class Trace:
-    stacks: List[Stack]
-
-
-class PathHighlighter(RegexHighlighter):
-    highlights = [r"(?P<dim>.*/)(?P<bold>.+)"]
-
-
-class Traceback:
-    """A Console renderable that renders a traceback.
-
-    Args:
-        trace (Trace, optional): A `Trace` object produced from `extract`. Defaults to None, which uses
-            the last exception.
-        width (Optional[int], optional): Number of characters used to traceback. Defaults to 100.
-        extra_lines (int, optional): Additional lines of code to render. Defaults to 3.
-        theme (str, optional): Override pygments theme used in traceback.
-        word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
-        show_locals (bool, optional): Enable display of local variables. Defaults to False.
-        indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
-        locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to 10.
-        locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-        locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-        locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-        suppress (Sequence[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
-        max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
-
-    """
-
-    LEXERS = {
-        "": "text",
-        ".py": "python",
-        ".pxd": "cython",
-        ".pyx": "cython",
-        ".pxi": "pyrex",
-    }
-
-    def __init__(
-        self,
-        trace: Optional[Trace] = None,
-        *,
-        width: Optional[int] = 100,
-        extra_lines: int = 3,
-        theme: Optional[str] = None,
-        word_wrap: bool = False,
-        show_locals: bool = False,
-        locals_max_length: int = LOCALS_MAX_LENGTH,
-        locals_max_string: int = LOCALS_MAX_STRING,
-        locals_hide_dunder: bool = True,
-        locals_hide_sunder: bool = False,
-        indent_guides: bool = True,
-        suppress: Iterable[Union[str, ModuleType]] = (),
-        max_frames: int = 100,
-    ):
-        if trace is None:
-            exc_type, exc_value, traceback = sys.exc_info()
-            if exc_type is None or exc_value is None or traceback is None:
-                raise ValueError(
-                    "Value for 'trace' required if not called in except: block"
-                )
-            trace = self.extract(
-                exc_type, exc_value, traceback, show_locals=show_locals
-            )
-        self.trace = trace
-        self.width = width
-        self.extra_lines = extra_lines
-        self.theme = Syntax.get_theme(theme or "ansi_dark")
-        self.word_wrap = word_wrap
-        self.show_locals = show_locals
-        self.indent_guides = indent_guides
-        self.locals_max_length = locals_max_length
-        self.locals_max_string = locals_max_string
-        self.locals_hide_dunder = locals_hide_dunder
-        self.locals_hide_sunder = locals_hide_sunder
-
-        self.suppress: Sequence[str] = []
-        for suppress_entity in suppress:
-            if not isinstance(suppress_entity, str):
-                assert (
-                    suppress_entity.__file__ is not None
-                ), f"{suppress_entity!r} must be a module with '__file__' attribute"
-                path = os.path.dirname(suppress_entity.__file__)
-            else:
-                path = suppress_entity
-            path = os.path.normpath(os.path.abspath(path))
-            self.suppress.append(path)
-        self.max_frames = max(4, max_frames) if max_frames > 0 else 0
-
-    @classmethod
-    def from_exception(
-        cls,
-        exc_type: Type[Any],
-        exc_value: BaseException,
-        traceback: Optional[TracebackType],
-        *,
-        width: Optional[int] = 100,
-        extra_lines: int = 3,
-        theme: Optional[str] = None,
-        word_wrap: bool = False,
-        show_locals: bool = False,
-        locals_max_length: int = LOCALS_MAX_LENGTH,
-        locals_max_string: int = LOCALS_MAX_STRING,
-        locals_hide_dunder: bool = True,
-        locals_hide_sunder: bool = False,
-        indent_guides: bool = True,
-        suppress: Iterable[Union[str, ModuleType]] = (),
-        max_frames: int = 100,
-    ) -> "Traceback":
-        """Create a traceback from exception info
-
-        Args:
-            exc_type (Type[BaseException]): Exception type.
-            exc_value (BaseException): Exception value.
-            traceback (TracebackType): Python Traceback object.
-            width (Optional[int], optional): Number of characters used to traceback. Defaults to 100.
-            extra_lines (int, optional): Additional lines of code to render. Defaults to 3.
-            theme (str, optional): Override pygments theme used in traceback.
-            word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
-            show_locals (bool, optional): Enable display of local variables. Defaults to False.
-            indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
-            locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-                Defaults to 10.
-            locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-            locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-            locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-            suppress (Iterable[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
-            max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
-
-        Returns:
-            Traceback: A Traceback instance that may be printed.
-        """
-        rich_traceback = cls.extract(
-            exc_type,
-            exc_value,
-            traceback,
-            show_locals=show_locals,
-            locals_max_length=locals_max_length,
-            locals_max_string=locals_max_string,
-            locals_hide_dunder=locals_hide_dunder,
-            locals_hide_sunder=locals_hide_sunder,
-        )
-
-        return cls(
-            rich_traceback,
-            width=width,
-            extra_lines=extra_lines,
-            theme=theme,
-            word_wrap=word_wrap,
-            show_locals=show_locals,
-            indent_guides=indent_guides,
-            locals_max_length=locals_max_length,
-            locals_max_string=locals_max_string,
-            locals_hide_dunder=locals_hide_dunder,
-            locals_hide_sunder=locals_hide_sunder,
-            suppress=suppress,
-            max_frames=max_frames,
-        )
-
-    @classmethod
-    def extract(
-        cls,
-        exc_type: Type[BaseException],
-        exc_value: BaseException,
-        traceback: Optional[TracebackType],
-        *,
-        show_locals: bool = False,
-        locals_max_length: int = LOCALS_MAX_LENGTH,
-        locals_max_string: int = LOCALS_MAX_STRING,
-        locals_hide_dunder: bool = True,
-        locals_hide_sunder: bool = False,
-    ) -> Trace:
-        """Extract traceback information.
-
-        Args:
-            exc_type (Type[BaseException]): Exception type.
-            exc_value (BaseException): Exception value.
-            traceback (TracebackType): Python Traceback object.
-            show_locals (bool, optional): Enable display of local variables. Defaults to False.
-            locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-                Defaults to 10.
-            locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-            locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-            locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-
-        Returns:
-            Trace: A Trace instance which you can use to construct a `Traceback`.
-        """
-
-        stacks: List[Stack] = []
-        is_cause = False
-
-        from rich import _IMPORT_CWD
-
-        def safe_str(_object: Any) -> str:
-            """Don't allow exceptions from __str__ to propagate."""
-            try:
-                return str(_object)
-            except Exception:
-                return "<exception str() failed>"
-
-        while True:
-            stack = Stack(
-                exc_type=safe_str(exc_type.__name__),
-                exc_value=safe_str(exc_value),
-                is_cause=is_cause,
-            )
-
-            if isinstance(exc_value, SyntaxError):
-                stack.syntax_error = _SyntaxError(
-                    offset=exc_value.offset or 0,
-                    filename=exc_value.filename or "?",
-                    lineno=exc_value.lineno or 0,
-                    line=exc_value.text or "",
-                    msg=exc_value.msg,
-                )
-
-            stacks.append(stack)
-            append = stack.frames.append
-
-            def get_locals(
-                iter_locals: Iterable[Tuple[str, object]]
-            ) -> Iterable[Tuple[str, object]]:
-                """Extract locals from an iterator of key pairs."""
-                if not (locals_hide_dunder or locals_hide_sunder):
-                    yield from iter_locals
-                    return
-                for key, value in iter_locals:
-                    if locals_hide_dunder and key.startswith("__"):
-                        continue
-                    if locals_hide_sunder and key.startswith("_"):
-                        continue
-                    yield key, value
-
-            for frame_summary, line_no in walk_tb(traceback):
-                filename = frame_summary.f_code.co_filename
-                if filename and not filename.startswith("<"):
-                    if not os.path.isabs(filename):
-                        filename = os.path.join(_IMPORT_CWD, filename)
-                if frame_summary.f_locals.get("_rich_traceback_omit", False):
-                    continue
-
-                frame = Frame(
-                    filename=filename or "?",
-                    lineno=line_no,
-                    name=frame_summary.f_code.co_name,
-                    locals={
-                        key: pretty.traverse(
-                            value,
-                            max_length=locals_max_length,
-                            max_string=locals_max_string,
-                        )
-                        for key, value in get_locals(frame_summary.f_locals.items())
-                    }
-                    if show_locals
-                    else None,
-                )
-                append(frame)
-                if frame_summary.f_locals.get("_rich_traceback_guard", False):
-                    del stack.frames[:]
-
-            cause = getattr(exc_value, "__cause__", None)
-            if cause:
-                exc_type = cause.__class__
-                exc_value = cause
-                # __traceback__ can be None, e.g. for exceptions raised by the
-                # 'multiprocessing' module
-                traceback = cause.__traceback__
-                is_cause = True
-                continue
-
-            cause = exc_value.__context__
-            if cause and not getattr(exc_value, "__suppress_context__", False):
-                exc_type = cause.__class__
-                exc_value = cause
-                traceback = cause.__traceback__
-                is_cause = False
-                continue
-            # No cover, code is reached but coverage doesn't recognize it.
-            break  # pragma: no cover
-
-        trace = Trace(stacks=stacks)
-        return trace
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        theme = self.theme
-        background_style = theme.get_background_style()
-        token_style = theme.get_style_for_token
-
-        traceback_theme = Theme(
-            {
-                "pretty": token_style(TextToken),
-                "pygments.text": token_style(Token),
-                "pygments.string": token_style(String),
-                "pygments.function": token_style(Name.Function),
-                "pygments.number": token_style(Number),
-                "repr.indent": token_style(Comment) + Style(dim=True),
-                "repr.str": token_style(String),
-                "repr.brace": token_style(TextToken) + Style(bold=True),
-                "repr.number": token_style(Number),
-                "repr.bool_true": token_style(Keyword.Constant),
-                "repr.bool_false": token_style(Keyword.Constant),
-                "repr.none": token_style(Keyword.Constant),
-                "scope.border": token_style(String.Delimiter),
-                "scope.equals": token_style(Operator),
-                "scope.key": token_style(Name),
-                "scope.key.special": token_style(Name.Constant) + Style(dim=True),
-            },
-            inherit=False,
-        )
-
-        highlighter = ReprHighlighter()
-        for last, stack in loop_last(reversed(self.trace.stacks)):
-            if stack.frames:
-                stack_renderable: ConsoleRenderable = Panel(
-                    self._render_stack(stack),
-                    title="[traceback.title]Traceback [dim](most recent call last)",
-                    style=background_style,
-                    border_style="traceback.border",
-                    expand=True,
-                    padding=(0, 1),
-                )
-                stack_renderable = Constrain(stack_renderable, self.width)
-                with console.use_theme(traceback_theme):
-                    yield stack_renderable
-            if stack.syntax_error is not None:
-                with console.use_theme(traceback_theme):
-                    yield Constrain(
-                        Panel(
-                            self._render_syntax_error(stack.syntax_error),
-                            style=background_style,
-                            border_style="traceback.border.syntax_error",
-                            expand=True,
-                            padding=(0, 1),
-                            width=self.width,
-                        ),
-                        self.width,
-                    )
-                yield Text.assemble(
-                    (f"{stack.exc_type}: ", "traceback.exc_type"),
-                    highlighter(stack.syntax_error.msg),
-                )
-            elif stack.exc_value:
-                yield Text.assemble(
-                    (f"{stack.exc_type}: ", "traceback.exc_type"),
-                    highlighter(stack.exc_value),
-                )
-            else:
-                yield Text.assemble((f"{stack.exc_type}", "traceback.exc_type"))
-
-            if not last:
-                if stack.is_cause:
-                    yield Text.from_markup(
-                        "\n[i]The above exception was the direct cause of the following exception:\n",
-                    )
-                else:
-                    yield Text.from_markup(
-                        "\n[i]During handling of the above exception, another exception occurred:\n",
-                    )
-
-    @group()
-    def _render_syntax_error(self, syntax_error: _SyntaxError) -> RenderResult:
-        highlighter = ReprHighlighter()
-        path_highlighter = PathHighlighter()
-        if syntax_error.filename != "<stdin>":
-            if os.path.exists(syntax_error.filename):
-                text = Text.assemble(
-                    (f" {syntax_error.filename}", "pygments.string"),
-                    (":", "pygments.text"),
-                    (str(syntax_error.lineno), "pygments.number"),
-                    style="pygments.text",
-                )
-                yield path_highlighter(text)
-        syntax_error_text = highlighter(syntax_error.line.rstrip())
-        syntax_error_text.no_wrap = True
-        offset = min(syntax_error.offset - 1, len(syntax_error_text))
-        syntax_error_text.stylize("bold underline", offset, offset)
-        syntax_error_text += Text.from_markup(
-            "\n" + " " * offset + "[traceback.offset]▲[/]",
-            style="pygments.text",
-        )
-        yield syntax_error_text
-
-    @classmethod
-    def _guess_lexer(cls, filename: str, code: str) -> str:
-        ext = os.path.splitext(filename)[-1]
-        if not ext:
-            # No extension, look at first line to see if it is a hashbang
-            # Note, this is an educated guess and not a guarantee
-            # If it fails, the only downside is that the code is highlighted strangely
-            new_line_index = code.index("\n")
-            first_line = code[:new_line_index] if new_line_index != -1 else code
-            if first_line.startswith("#!") and "python" in first_line.lower():
-                return "python"
-        try:
-            return cls.LEXERS.get(ext) or guess_lexer_for_filename(filename, code).name
-        except ClassNotFound:
-            return "text"
-
-    @group()
-    def _render_stack(self, stack: Stack) -> RenderResult:
-        path_highlighter = PathHighlighter()
-        theme = self.theme
-
-        def read_code(filename: str) -> str:
-            """Read files, and cache results on filename.
-
-            Args:
-                filename (str): Filename to read
-
-            Returns:
-                str: Contents of file
-            """
-            return "".join(linecache.getlines(filename))
-
-        def render_locals(frame: Frame) -> Iterable[ConsoleRenderable]:
-            if frame.locals:
-                yield render_scope(
-                    frame.locals,
-                    title="locals",
-                    indent_guides=self.indent_guides,
-                    max_length=self.locals_max_length,
-                    max_string=self.locals_max_string,
-                )
-
-        exclude_frames: Optional[range] = None
-        if self.max_frames != 0:
-            exclude_frames = range(
-                self.max_frames // 2,
-                len(stack.frames) - self.max_frames // 2,
-            )
-
-        excluded = False
-        for frame_index, frame in enumerate(stack.frames):
-
-            if exclude_frames and frame_index in exclude_frames:
-                excluded = True
-                continue
-
-            if excluded:
-                assert exclude_frames is not None
-                yield Text(
-                    f"\n... {len(exclude_frames)} frames hidden ...",
-                    justify="center",
-                    style="traceback.error",
-                )
-                excluded = False
-
-            first = frame_index == 0
-            frame_filename = frame.filename
-            suppressed = any(frame_filename.startswith(path) for path in self.suppress)
-
-            if os.path.exists(frame.filename):
-                text = Text.assemble(
-                    path_highlighter(Text(frame.filename, style="pygments.string")),
-                    (":", "pygments.text"),
-                    (str(frame.lineno), "pygments.number"),
-                    " in ",
-                    (frame.name, "pygments.function"),
-                    style="pygments.text",
-                )
-            else:
-                text = Text.assemble(
-                    "in ",
-                    (frame.name, "pygments.function"),
-                    (":", "pygments.text"),
-                    (str(frame.lineno), "pygments.number"),
-                    style="pygments.text",
-                )
-            if not frame.filename.startswith("<") and not first:
-                yield ""
-            yield text
-            if frame.filename.startswith("<"):
-                yield from render_locals(frame)
-                continue
-            if not suppressed:
-                try:
-                    code = read_code(frame.filename)
-                    if not code:
-                        # code may be an empty string if the file doesn't exist, OR
-                        # if the traceback filename is generated dynamically
-                        continue
-                    lexer_name = self._guess_lexer(frame.filename, code)
-                    syntax = Syntax(
-                        code,
-                        lexer_name,
-                        theme=theme,
-                        line_numbers=True,
-                        line_range=(
-                            frame.lineno - self.extra_lines,
-                            frame.lineno + self.extra_lines,
-                        ),
-                        highlight_lines={frame.lineno},
-                        word_wrap=self.word_wrap,
-                        code_width=88,
-                        indent_guides=self.indent_guides,
-                        dedent=False,
-                    )
-                    yield ""
-                except Exception as error:
-                    yield Text.assemble(
-                        (f"\n{error}", "traceback.error"),
-                    )
-                else:
-                    yield (
-                        Columns(
-                            [
-                                syntax,
-                                *render_locals(frame),
-                            ],
-                            padding=1,
-                        )
-                        if frame.locals
-                        else syntax
-                    )
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from .console import Console
-
-    console = Console()
-    import sys
-
-    def bar(a: Any) -> None:  # 这是对亚洲语言支持的测试。面对模棱两可的想法，拒绝猜测的诱惑
-        one = 1
-        print(one / a)
-
-    def foo(a: Any) -> None:
-        _rich_traceback_guard = True
-        zed = {
-            "characters": {
-                "Paul Atreides",
-                "Vladimir Harkonnen",
-                "Thufir Hawat",
-                "Duncan Idaho",
-            },
-            "atomic_types": (None, False, True),
-        }
-        bar(a)
-
-    def error() -> None:
-
-        try:
-            try:
-                foo(0)
-            except:
-                slfkjsldkfj  # type: ignore[name-defined]
-        except:
-            console.print_exception(show_locals=True)
-
-    error()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/command/upload.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/command/upload.py
deleted file mode 100644
index 95e9fda186fc8f5b884215f7bea251b515e72cae..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/command/upload.py
+++ /dev/null
@@ -1,214 +0,0 @@
-"""
-distutils.command.upload
-
-Implements the Distutils 'upload' subcommand (upload package to a package
-index).
-"""
-
-import os
-import io
-import hashlib
-from base64 import standard_b64encode
-from urllib.request import urlopen, Request, HTTPError
-from urllib.parse import urlparse
-from distutils.errors import DistutilsError, DistutilsOptionError
-from distutils.core import PyPIRCCommand
-from distutils.spawn import spawn
-from distutils import log
-
-
-# PyPI Warehouse supports MD5, SHA256, and Blake2 (blake2-256)
-# https://bugs.python.org/issue40698
-_FILE_CONTENT_DIGESTS = {
-    "md5_digest": getattr(hashlib, "md5", None),
-    "sha256_digest": getattr(hashlib, "sha256", None),
-    "blake2_256_digest": getattr(hashlib, "blake2b", None),
-}
-
-
-class upload(PyPIRCCommand):
-
-    description = "upload binary package to PyPI"
-
-    user_options = PyPIRCCommand.user_options + [
-        ('sign', 's',
-         'sign files to upload using gpg'),
-        ('identity=', 'i', 'GPG identity used to sign files'),
-        ]
-
-    boolean_options = PyPIRCCommand.boolean_options + ['sign']
-
-    def initialize_options(self):
-        PyPIRCCommand.initialize_options(self)
-        self.username = ''
-        self.password = ''
-        self.show_response = 0
-        self.sign = False
-        self.identity = None
-
-    def finalize_options(self):
-        PyPIRCCommand.finalize_options(self)
-        if self.identity and not self.sign:
-            raise DistutilsOptionError(
-                "Must use --sign for --identity to have meaning"
-            )
-        config = self._read_pypirc()
-        if config != {}:
-            self.username = config['username']
-            self.password = config['password']
-            self.repository = config['repository']
-            self.realm = config['realm']
-
-        # getting the password from the distribution
-        # if previously set by the register command
-        if not self.password and self.distribution.password:
-            self.password = self.distribution.password
-
-    def run(self):
-        if not self.distribution.dist_files:
-            msg = ("Must create and upload files in one command "
-                   "(e.g. setup.py sdist upload)")
-            raise DistutilsOptionError(msg)
-        for command, pyversion, filename in self.distribution.dist_files:
-            self.upload_file(command, pyversion, filename)
-
-    def upload_file(self, command, pyversion, filename):
-        # Makes sure the repository URL is compliant
-        schema, netloc, url, params, query, fragments = \
-            urlparse(self.repository)
-        if params or query or fragments:
-            raise AssertionError("Incompatible url %s" % self.repository)
-
-        if schema not in ('http', 'https'):
-            raise AssertionError("unsupported schema " + schema)
-
-        # Sign if requested
-        if self.sign:
-            gpg_args = ["gpg", "--detach-sign", "-a", filename]
-            if self.identity:
-                gpg_args[2:2] = ["--local-user", self.identity]
-            spawn(gpg_args,
-                  dry_run=self.dry_run)
-
-        # Fill in the data - send all the meta-data in case we need to
-        # register a new release
-        f = open(filename,'rb')
-        try:
-            content = f.read()
-        finally:
-            f.close()
-
-        meta = self.distribution.metadata
-        data = {
-            # action
-            ':action': 'file_upload',
-            'protocol_version': '1',
-
-            # identify release
-            'name': meta.get_name(),
-            'version': meta.get_version(),
-
-            # file content
-            'content': (os.path.basename(filename),content),
-            'filetype': command,
-            'pyversion': pyversion,
-
-            # additional meta-data
-            'metadata_version': '1.0',
-            'summary': meta.get_description(),
-            'home_page': meta.get_url(),
-            'author': meta.get_contact(),
-            'author_email': meta.get_contact_email(),
-            'license': meta.get_licence(),
-            'description': meta.get_long_description(),
-            'keywords': meta.get_keywords(),
-            'platform': meta.get_platforms(),
-            'classifiers': meta.get_classifiers(),
-            'download_url': meta.get_download_url(),
-            # PEP 314
-            'provides': meta.get_provides(),
-            'requires': meta.get_requires(),
-            'obsoletes': meta.get_obsoletes(),
-            }
-
-        data['comment'] = ''
-
-        # file content digests
-        for digest_name, digest_cons in _FILE_CONTENT_DIGESTS.items():
-            if digest_cons is None:
-                continue
-            try:
-                data[digest_name] = digest_cons(content).hexdigest()
-            except ValueError:
-                # hash digest not available or blocked by security policy
-                pass
-
-        if self.sign:
-            with open(filename + ".asc", "rb") as f:
-                data['gpg_signature'] = (os.path.basename(filename) + ".asc",
-                                         f.read())
-
-        # set up the authentication
-        user_pass = (self.username + ":" + self.password).encode('ascii')
-        # The exact encoding of the authentication string is debated.
-        # Anyway PyPI only accepts ascii for both username or password.
-        auth = "Basic " + standard_b64encode(user_pass).decode('ascii')
-
-        # Build up the MIME payload for the POST data
-        boundary = '--------------GHSKFJDLGDS7543FJKLFHRE75642756743254'
-        sep_boundary = b'\r\n--' + boundary.encode('ascii')
-        end_boundary = sep_boundary + b'--\r\n'
-        body = io.BytesIO()
-        for key, value in data.items():
-            title = '\r\nContent-Disposition: form-data; name="%s"' % key
-            # handle multiple entries for the same name
-            if not isinstance(value, list):
-                value = [value]
-            for value in value:
-                if type(value) is tuple:
-                    title += '; filename="%s"' % value[0]
-                    value = value[1]
-                else:
-                    value = str(value).encode('utf-8')
-                body.write(sep_boundary)
-                body.write(title.encode('utf-8'))
-                body.write(b"\r\n\r\n")
-                body.write(value)
-        body.write(end_boundary)
-        body = body.getvalue()
-
-        msg = "Submitting %s to %s" % (filename, self.repository)
-        self.announce(msg, log.INFO)
-
-        # build the Request
-        headers = {
-            'Content-type': 'multipart/form-data; boundary=%s' % boundary,
-            'Content-length': str(len(body)),
-            'Authorization': auth,
-        }
-
-        request = Request(self.repository, data=body,
-                          headers=headers)
-        # send the data
-        try:
-            result = urlopen(request)
-            status = result.getcode()
-            reason = result.msg
-        except HTTPError as e:
-            status = e.code
-            reason = e.msg
-        except OSError as e:
-            self.announce(str(e), log.ERROR)
-            raise
-
-        if status == 200:
-            self.announce('Server response (%s): %s' % (status, reason),
-                          log.INFO)
-            if self.show_response:
-                text = self._read_pypi_response(result)
-                msg = '\n'.join(('-' * 75, text, '-' * 75))
-                self.announce(msg, log.INFO)
-        else:
-            msg = 'Upload failed (%s): %s' % (status, reason)
-            self.announce(msg, log.ERROR)
-            raise DistutilsError(msg)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/typer/models.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/typer/models.py
deleted file mode 100644
index 53fca273b4b84da43e8405980fedb2e4996b384d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/typer/models.py
+++ /dev/null
@@ -1,523 +0,0 @@
-import inspect
-import io
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Dict,
-    List,
-    Optional,
-    Sequence,
-    Type,
-    TypeVar,
-    Union,
-)
-
-import click
-
-from ._compat_utils import _get_click_major
-
-if TYPE_CHECKING:  # pragma: no cover
-    if _get_click_major() > 7:
-        import click.shell_completion
-
-    from .core import TyperCommand, TyperGroup
-    from .main import Typer
-
-
-NoneType = type(None)
-
-AnyType = Type[Any]
-
-Required = ...
-
-
-class Context(click.Context):
-    pass
-
-
-class FileText(io.TextIOWrapper):
-    pass
-
-
-class FileTextWrite(FileText):
-    pass
-
-
-class FileBinaryRead(io.BufferedReader):
-    pass
-
-
-class FileBinaryWrite(io.BufferedWriter):
-    pass
-
-
-class CallbackParam(click.Parameter):
-    pass
-
-
-class DefaultPlaceholder:
-    """
-    You shouldn't use this class directly.
-
-    It's used internally to recognize when a default value has been overwritten, even
-    if the new value is `None`.
-    """
-
-    def __init__(self, value: Any):
-        self.value = value
-
-    def __bool__(self) -> bool:
-        return bool(self.value)
-
-
-DefaultType = TypeVar("DefaultType")
-
-CommandFunctionType = TypeVar("CommandFunctionType", bound=Callable[..., Any])
-
-
-def Default(value: DefaultType) -> DefaultType:
-    """
-    You shouldn't use this function directly.
-
-    It's used internally to recognize when a default value has been overwritten, even
-    if the new value is `None`.
-    """
-    return DefaultPlaceholder(value)  # type: ignore
-
-
-class CommandInfo:
-    def __init__(
-        self,
-        name: Optional[str] = None,
-        *,
-        cls: Optional[Type["TyperCommand"]] = None,
-        context_settings: Optional[Dict[Any, Any]] = None,
-        callback: Optional[Callable[..., Any]] = None,
-        help: Optional[str] = None,
-        epilog: Optional[str] = None,
-        short_help: Optional[str] = None,
-        options_metavar: str = "[OPTIONS]",
-        add_help_option: bool = True,
-        no_args_is_help: bool = False,
-        hidden: bool = False,
-        deprecated: bool = False,
-        # Rich settings
-        rich_help_panel: Union[str, None] = None,
-    ):
-        self.name = name
-        self.cls = cls
-        self.context_settings = context_settings
-        self.callback = callback
-        self.help = help
-        self.epilog = epilog
-        self.short_help = short_help
-        self.options_metavar = options_metavar
-        self.add_help_option = add_help_option
-        self.no_args_is_help = no_args_is_help
-        self.hidden = hidden
-        self.deprecated = deprecated
-        # Rich settings
-        self.rich_help_panel = rich_help_panel
-
-
-class TyperInfo:
-    def __init__(
-        self,
-        typer_instance: Optional["Typer"] = Default(None),
-        *,
-        name: Optional[str] = Default(None),
-        cls: Optional[Type["TyperGroup"]] = Default(None),
-        invoke_without_command: bool = Default(False),
-        no_args_is_help: bool = Default(False),
-        subcommand_metavar: Optional[str] = Default(None),
-        chain: bool = Default(False),
-        result_callback: Optional[Callable[..., Any]] = Default(None),
-        # Command
-        context_settings: Optional[Dict[Any, Any]] = Default(None),
-        callback: Optional[Callable[..., Any]] = Default(None),
-        help: Optional[str] = Default(None),
-        epilog: Optional[str] = Default(None),
-        short_help: Optional[str] = Default(None),
-        options_metavar: str = Default("[OPTIONS]"),
-        add_help_option: bool = Default(True),
-        hidden: bool = Default(False),
-        deprecated: bool = Default(False),
-        # Rich settings
-        rich_help_panel: Union[str, None] = Default(None),
-    ):
-        self.typer_instance = typer_instance
-        self.name = name
-        self.cls = cls
-        self.invoke_without_command = invoke_without_command
-        self.no_args_is_help = no_args_is_help
-        self.subcommand_metavar = subcommand_metavar
-        self.chain = chain
-        self.result_callback = result_callback
-        self.context_settings = context_settings
-        self.callback = callback
-        self.help = help
-        self.epilog = epilog
-        self.short_help = short_help
-        self.options_metavar = options_metavar
-        self.add_help_option = add_help_option
-        self.hidden = hidden
-        self.deprecated = deprecated
-        self.rich_help_panel = rich_help_panel
-
-
-class ParameterInfo:
-    def __init__(
-        self,
-        *,
-        default: Optional[Any] = None,
-        param_decls: Optional[Sequence[str]] = None,
-        callback: Optional[Callable[..., Any]] = None,
-        metavar: Optional[str] = None,
-        expose_value: bool = True,
-        is_eager: bool = False,
-        envvar: Optional[Union[str, List[str]]] = None,
-        shell_complete: Optional[
-            Callable[
-                [click.Context, click.Parameter, str],
-                Union[List["click.shell_completion.CompletionItem"], List[str]],
-            ]
-        ] = None,
-        autocompletion: Optional[Callable[..., Any]] = None,
-        default_factory: Optional[Callable[[], Any]] = None,
-        # Custom type
-        parser: Optional[Callable[[str], Any]] = None,
-        click_type: Optional[click.ParamType] = None,
-        # TyperArgument
-        show_default: Union[bool, str] = True,
-        show_choices: bool = True,
-        show_envvar: bool = True,
-        help: Optional[str] = None,
-        hidden: bool = False,
-        # Choice
-        case_sensitive: bool = True,
-        # Numbers
-        min: Optional[Union[int, float]] = None,
-        max: Optional[Union[int, float]] = None,
-        clamp: bool = False,
-        # DateTime
-        formats: Optional[List[str]] = None,
-        # File
-        mode: Optional[str] = None,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = "strict",
-        lazy: Optional[bool] = None,
-        atomic: bool = False,
-        # Path
-        exists: bool = False,
-        file_okay: bool = True,
-        dir_okay: bool = True,
-        writable: bool = False,
-        readable: bool = True,
-        resolve_path: bool = False,
-        allow_dash: bool = False,
-        path_type: Union[None, Type[str], Type[bytes]] = None,
-        # Rich settings
-        rich_help_panel: Union[str, None] = None,
-    ):
-        # Check if user has provided multiple custom parsers
-        if parser and click_type:
-            raise ValueError(
-                "Multiple custom type parsers provided. "
-                "`parser` and `click_type` may not both be provided."
-            )
-
-        self.default = default
-        self.param_decls = param_decls
-        self.callback = callback
-        self.metavar = metavar
-        self.expose_value = expose_value
-        self.is_eager = is_eager
-        self.envvar = envvar
-        self.shell_complete = shell_complete
-        self.autocompletion = autocompletion
-        self.default_factory = default_factory
-        # Custom type
-        self.parser = parser
-        self.click_type = click_type
-        # TyperArgument
-        self.show_default = show_default
-        self.show_choices = show_choices
-        self.show_envvar = show_envvar
-        self.help = help
-        self.hidden = hidden
-        # Choice
-        self.case_sensitive = case_sensitive
-        # Numbers
-        self.min = min
-        self.max = max
-        self.clamp = clamp
-        # DateTime
-        self.formats = formats
-        # File
-        self.mode = mode
-        self.encoding = encoding
-        self.errors = errors
-        self.lazy = lazy
-        self.atomic = atomic
-        # Path
-        self.exists = exists
-        self.file_okay = file_okay
-        self.dir_okay = dir_okay
-        self.writable = writable
-        self.readable = readable
-        self.resolve_path = resolve_path
-        self.allow_dash = allow_dash
-        self.path_type = path_type
-        # Rich settings
-        self.rich_help_panel = rich_help_panel
-
-
-class OptionInfo(ParameterInfo):
-    def __init__(
-        self,
-        *,
-        # ParameterInfo
-        default: Optional[Any] = None,
-        param_decls: Optional[Sequence[str]] = None,
-        callback: Optional[Callable[..., Any]] = None,
-        metavar: Optional[str] = None,
-        expose_value: bool = True,
-        is_eager: bool = False,
-        envvar: Optional[Union[str, List[str]]] = None,
-        shell_complete: Optional[
-            Callable[
-                [click.Context, click.Parameter, str],
-                Union[List["click.shell_completion.CompletionItem"], List[str]],
-            ]
-        ] = None,
-        autocompletion: Optional[Callable[..., Any]] = None,
-        default_factory: Optional[Callable[[], Any]] = None,
-        # Custom type
-        parser: Optional[Callable[[str], Any]] = None,
-        click_type: Optional[click.ParamType] = None,
-        # Option
-        show_default: bool = True,
-        prompt: Union[bool, str] = False,
-        confirmation_prompt: bool = False,
-        prompt_required: bool = True,
-        hide_input: bool = False,
-        is_flag: Optional[bool] = None,
-        flag_value: Optional[Any] = None,
-        count: bool = False,
-        allow_from_autoenv: bool = True,
-        help: Optional[str] = None,
-        hidden: bool = False,
-        show_choices: bool = True,
-        show_envvar: bool = True,
-        # Choice
-        case_sensitive: bool = True,
-        # Numbers
-        min: Optional[Union[int, float]] = None,
-        max: Optional[Union[int, float]] = None,
-        clamp: bool = False,
-        # DateTime
-        formats: Optional[List[str]] = None,
-        # File
-        mode: Optional[str] = None,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = "strict",
-        lazy: Optional[bool] = None,
-        atomic: bool = False,
-        # Path
-        exists: bool = False,
-        file_okay: bool = True,
-        dir_okay: bool = True,
-        writable: bool = False,
-        readable: bool = True,
-        resolve_path: bool = False,
-        allow_dash: bool = False,
-        path_type: Union[None, Type[str], Type[bytes]] = None,
-        # Rich settings
-        rich_help_panel: Union[str, None] = None,
-    ):
-        super().__init__(
-            default=default,
-            param_decls=param_decls,
-            callback=callback,
-            metavar=metavar,
-            expose_value=expose_value,
-            is_eager=is_eager,
-            envvar=envvar,
-            shell_complete=shell_complete,
-            autocompletion=autocompletion,
-            default_factory=default_factory,
-            # Custom type
-            parser=parser,
-            click_type=click_type,
-            # TyperArgument
-            show_default=show_default,
-            show_choices=show_choices,
-            show_envvar=show_envvar,
-            help=help,
-            hidden=hidden,
-            # Choice
-            case_sensitive=case_sensitive,
-            # Numbers
-            min=min,
-            max=max,
-            clamp=clamp,
-            # DateTime
-            formats=formats,
-            # File
-            mode=mode,
-            encoding=encoding,
-            errors=errors,
-            lazy=lazy,
-            atomic=atomic,
-            # Path
-            exists=exists,
-            file_okay=file_okay,
-            dir_okay=dir_okay,
-            writable=writable,
-            readable=readable,
-            resolve_path=resolve_path,
-            allow_dash=allow_dash,
-            path_type=path_type,
-            # Rich settings
-            rich_help_panel=rich_help_panel,
-        )
-        self.prompt = prompt
-        self.confirmation_prompt = confirmation_prompt
-        self.prompt_required = prompt_required
-        self.hide_input = hide_input
-        self.is_flag = is_flag
-        self.flag_value = flag_value
-        self.count = count
-        self.allow_from_autoenv = allow_from_autoenv
-
-
-class ArgumentInfo(ParameterInfo):
-    def __init__(
-        self,
-        *,
-        # ParameterInfo
-        default: Optional[Any] = None,
-        param_decls: Optional[Sequence[str]] = None,
-        callback: Optional[Callable[..., Any]] = None,
-        metavar: Optional[str] = None,
-        expose_value: bool = True,
-        is_eager: bool = False,
-        envvar: Optional[Union[str, List[str]]] = None,
-        shell_complete: Optional[
-            Callable[
-                [click.Context, click.Parameter, str],
-                Union[List["click.shell_completion.CompletionItem"], List[str]],
-            ]
-        ] = None,
-        autocompletion: Optional[Callable[..., Any]] = None,
-        default_factory: Optional[Callable[[], Any]] = None,
-        # Custom type
-        parser: Optional[Callable[[str], Any]] = None,
-        click_type: Optional[click.ParamType] = None,
-        # TyperArgument
-        show_default: Union[bool, str] = True,
-        show_choices: bool = True,
-        show_envvar: bool = True,
-        help: Optional[str] = None,
-        hidden: bool = False,
-        # Choice
-        case_sensitive: bool = True,
-        # Numbers
-        min: Optional[Union[int, float]] = None,
-        max: Optional[Union[int, float]] = None,
-        clamp: bool = False,
-        # DateTime
-        formats: Optional[List[str]] = None,
-        # File
-        mode: Optional[str] = None,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = "strict",
-        lazy: Optional[bool] = None,
-        atomic: bool = False,
-        # Path
-        exists: bool = False,
-        file_okay: bool = True,
-        dir_okay: bool = True,
-        writable: bool = False,
-        readable: bool = True,
-        resolve_path: bool = False,
-        allow_dash: bool = False,
-        path_type: Union[None, Type[str], Type[bytes]] = None,
-        # Rich settings
-        rich_help_panel: Union[str, None] = None,
-    ):
-        super().__init__(
-            default=default,
-            param_decls=param_decls,
-            callback=callback,
-            metavar=metavar,
-            expose_value=expose_value,
-            is_eager=is_eager,
-            envvar=envvar,
-            shell_complete=shell_complete,
-            autocompletion=autocompletion,
-            default_factory=default_factory,
-            # Custom type
-            parser=parser,
-            click_type=click_type,
-            # TyperArgument
-            show_default=show_default,
-            show_choices=show_choices,
-            show_envvar=show_envvar,
-            help=help,
-            hidden=hidden,
-            # Choice
-            case_sensitive=case_sensitive,
-            # Numbers
-            min=min,
-            max=max,
-            clamp=clamp,
-            # DateTime
-            formats=formats,
-            # File
-            mode=mode,
-            encoding=encoding,
-            errors=errors,
-            lazy=lazy,
-            atomic=atomic,
-            # Path
-            exists=exists,
-            file_okay=file_okay,
-            dir_okay=dir_okay,
-            writable=writable,
-            readable=readable,
-            resolve_path=resolve_path,
-            allow_dash=allow_dash,
-            path_type=path_type,
-            # Rich settings
-            rich_help_panel=rich_help_panel,
-        )
-
-
-class ParamMeta:
-    empty = inspect.Parameter.empty
-
-    def __init__(
-        self,
-        *,
-        name: str,
-        default: Any = inspect.Parameter.empty,
-        annotation: Any = inspect.Parameter.empty,
-    ) -> None:
-        self.name = name
-        self.default = default
-        self.annotation = annotation
-
-
-class DeveloperExceptionConfig:
-    def __init__(
-        self,
-        *,
-        pretty_exceptions_enable: bool = True,
-        pretty_exceptions_show_locals: bool = True,
-        pretty_exceptions_short: bool = True,
-    ) -> None:
-        self.pretty_exceptions_enable = pretty_exceptions_enable
-        self.pretty_exceptions_show_locals = pretty_exceptions_show_locals
-        self.pretty_exceptions_short = pretty_exceptions_short
diff --git a/spaces/prxx/Norod78-SD15-IllusionDiffusionPattern-LoRA/README.md b/spaces/prxx/Norod78-SD15-IllusionDiffusionPattern-LoRA/README.md
deleted file mode 100644
index 3b486b02795feea3fdc9823168117066df4d5d36..0000000000000000000000000000000000000000
--- a/spaces/prxx/Norod78-SD15-IllusionDiffusionPattern-LoRA/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Norod78 SD15 IllusionDiffusionPattern LoRA
-emoji: 🏆
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pyodide-demo/self-hosted/sharedlib-test.js b/spaces/pyodide-demo/self-hosted/sharedlib-test.js
deleted file mode 100644
index f82d40d491ec4cace5143d3fc798fc1fc067bdd1..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/sharedlib-test.js
+++ /dev/null
@@ -1 +0,0 @@
-var Module=typeof globalThis.__pyodide_module!=="undefined"?globalThis.__pyodide_module:{};if(!Module.expectedDataFileDownloads){Module.expectedDataFileDownloads=0}Module.expectedDataFileDownloads++;(function(){var loadPackage=function(metadata){var PACKAGE_PATH="";if(typeof window==="object"){PACKAGE_PATH=window["encodeURIComponent"](window.location.pathname.toString().substring(0,window.location.pathname.toString().lastIndexOf("/"))+"/")}else if(typeof process==="undefined"&&typeof location!=="undefined"){PACKAGE_PATH=encodeURIComponent(location.pathname.toString().substring(0,location.pathname.toString().lastIndexOf("/"))+"/")}var PACKAGE_NAME="sharedlib-test.data";var REMOTE_PACKAGE_BASE="sharedlib-test.data";if(typeof Module["locateFilePackage"]==="function"&&!Module["locateFile"]){Module["locateFile"]=Module["locateFilePackage"];err("warning: you defined Module.locateFilePackage, that has been renamed to Module.locateFile (using your locateFilePackage for now)")}var REMOTE_PACKAGE_NAME=Module["locateFile"]?Module["locateFile"](REMOTE_PACKAGE_BASE,""):REMOTE_PACKAGE_BASE;var REMOTE_PACKAGE_SIZE=metadata["remote_package_size"];var PACKAGE_UUID=metadata["package_uuid"];function fetchRemotePackage(packageName,packageSize,callback,errback){if(typeof process==="object"){require("fs").readFile(packageName,(function(err,contents){if(err){errback(err)}else{callback(contents.buffer)}}));return}var xhr=new XMLHttpRequest;xhr.open("GET",packageName,true);xhr.responseType="arraybuffer";xhr.onprogress=function(event){var url=packageName;var size=packageSize;if(event.total)size=event.total;if(event.loaded){if(!xhr.addedTotal){xhr.addedTotal=true;if(!Module.dataFileDownloads)Module.dataFileDownloads={};Module.dataFileDownloads[url]={loaded:event.loaded,total:size}}else{Module.dataFileDownloads[url].loaded=event.loaded}var total=0;var loaded=0;var num=0;for(var download in Module.dataFileDownloads){var data=Module.dataFileDownloads[download];total+=data.total;loaded+=data.loaded;num++}total=Math.ceil(total*Module.expectedDataFileDownloads/num);if(Module["setStatus"])Module["setStatus"]("Downloading data... ("+loaded+"/"+total+")")}else if(!Module.dataFileDownloads){if(Module["setStatus"])Module["setStatus"]("Downloading data...")}};xhr.onerror=function(event){throw new Error("NetworkError for: "+packageName)};xhr.onload=function(event){if(xhr.status==200||xhr.status==304||xhr.status==206||xhr.status==0&&xhr.response){var packageData=xhr.response;callback(packageData)}else{throw new Error(xhr.statusText+" : "+xhr.responseURL)}};xhr.send(null)}function handleError(error){console.error("package error:",error)}var fetchedCallback=null;var fetched=Module["getPreloadedPackage"]?Module["getPreloadedPackage"](REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE):null;if(!fetched)fetchRemotePackage(REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE,(function(data){if(fetchedCallback){fetchedCallback(data);fetchedCallback=null}else{fetched=data}}),handleError);function runWithFS(){function assert(check,msg){if(!check)throw msg+(new Error).stack}function processPackageData(arrayBuffer){assert(arrayBuffer,"Loading data file failed.");assert(arrayBuffer instanceof ArrayBuffer,"bad input to processPackageData");var byteArray=new Uint8Array(arrayBuffer);var curr;var compressedData={data:null,cachedOffset:314,cachedIndexes:[-1,-1],cachedChunks:[null,null],offsets:[0],sizes:[314],successes:[1]};compressedData["data"]=byteArray;assert(typeof Module.LZ4==="object","LZ4 not present - was your app build with  -s LZ4=1  ?");Module.LZ4.loadPackage({metadata:metadata,compressedData:compressedData},true);Module["removeRunDependency"]("datafile_sharedlib-test.data")}Module["addRunDependency"]("datafile_sharedlib-test.data");if(!Module.preloadResults)Module.preloadResults={};Module.preloadResults[PACKAGE_NAME]={fromCache:false};if(fetched){processPackageData(fetched);fetched=null}else{fetchedCallback=processPackageData}}if(Module["calledRun"]){runWithFS()}else{if(!Module["preRun"])Module["preRun"]=[];Module["preRun"].push(runWithFS)}};loadPackage({files:[{filename:"/sharedlib-test.so",start:0,end:331,audio:0}],remote_package_size:4410,package_uuid:"d716eee0-d93e-4cc8-a631-d7515f6c0069"})})();
\ No newline at end of file
diff --git a/spaces/qi3/White-box-Cartoonization/app.py b/spaces/qi3/White-box-Cartoonization/app.py
deleted file mode 100644
index c55ced56bd87a85f59d1c8ef84b7eca87422720f..0000000000000000000000000000000000000000
--- a/spaces/qi3/White-box-Cartoonization/app.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-import argparse
-import functools
-import os
-import pathlib
-import sys
-from typing import Callable
-import uuid
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-
-from io import BytesIO
-from wbc.cartoonize import Cartoonize
-
-ORIGINAL_REPO_URL = 'https://github.com/SystemErrorWang/White-box-Cartoonization'
-TITLE = 'SystemErrorWang/White-box-Cartoonization'
-DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.
-
-"""
-ARTICLE = """
-
-"""
-
-SAFEHASH = [x for x in "0123456789-abcdefghijklmnopqrstuvwxyz_ABCDEFGHIJKLMNOPQRSTUVWXYZ"]
-def compress_UUID():
-    '''
-    根据http://www.ietf.org/rfc/rfc1738.txt,由uuid编码扩bai大字符域生成du串
-    包括:[0-9a-zA-Z\-_]共64个
-    长度:(32-2)/3*2=20
-    备注:可在地球上人zhi人都用,使用100年不重复(2^120)
-    :return:String
-    '''
-    row = str(uuid.uuid4()).replace('-', '')
-    safe_code = ''
-    for i in range(10):
-        enbin = "%012d" % int(bin(int(row[i * 3] + row[i * 3 + 1] + row[i * 3 + 2], 16))[2:], 10)
-        safe_code += (SAFEHASH[int(enbin[0:6], 2)] + SAFEHASH[int(enbin[6:12], 2)])
-    safe_code = safe_code.replace('-', '')
-    return safe_code
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    parser.add_argument('--allow-screenshot', action='store_true')
-    return parser.parse_args()
-
-def run(
-        image,
-        cartoonize : Cartoonize
-) -> tuple[PIL.Image.Image]:
-
-    out_path = compress_UUID()+'.png'
-    cartoonize.run_sigle(image.name, out_path)
-
-    return PIL.Image.open(out_path)
-
-
-def main():
-    gr.close_all()
-
-    args = parse_args()
-
-    cartoonize = Cartoonize(os.path.join(os.path.dirname(os.path.abspath(__file__)),'wbc/saved_models/'))
-
-    func = functools.partial(run, cartoonize=cartoonize)
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='file', label='Input Image'),
-        ],
-        [
-            gr.outputs.Image(
-                type='pil',
-                label='Result'),
-        ],
-        # examples=examples,
-        theme=args.theme,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        allow_screenshot=args.allow_screenshot,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (ice Age 3 Tamil Dubbed Movie Free Do).md b/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (ice Age 3 Tamil Dubbed Movie Free Do).md
deleted file mode 100644
index 631835f6a9b759ac2e60d98df6257afb9189bdfb..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (ice Age 3 Tamil Dubbed Movie Free Do).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>HD Online Player (ice age 3 tamil dubbed movie free do)</h2><br /><p><b><b>Download File</b> &#127775; <a href="https://geags.com/2uCry0">https://geags.com/2uCry0</a></b></p><br /><br />
-
-720p hd movies download 720p hd tamil movies free . at your home Get the best HD ... Watch Free Movies Online And Download 300Mb Dual Audio Hindi Dubbed ... Ip Man 3 Full Movie In Hindi Download HD Video Song Mp3. com is official ... HD Print Free Download Watch Minions 2015 Hindi Dubbed From Player 1 . 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Logisch A2 Kursbuch Pdf Free __FULL__.md b/spaces/quidiaMuxgu/Expedit-SAM/Logisch A2 Kursbuch Pdf Free __FULL__.md
deleted file mode 100644
index d69833bed33722acd6f500d563cfcfb8209fd56d..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Logisch A2 Kursbuch Pdf Free __FULL__.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-
-<h1>How to Download Logisch A2 Kursbuch Pdf Free</h1>
-<p>If you are looking for a free PDF version of Logisch A2 Kursbuch, a German textbook for young learners, you might have a hard time finding it online. Logisch A2 Kursbuch is part of a series of textbooks that lead to the levels A1, A2 and B1 of the Common European Framework of Reference for Languages (CEFR) and prepare students for the youth exams "Fit in Deutsch 1", "Fit in Deutsch 2" and "Zertifikat Deutsch für Jugendliche".</p>
-<p>Logisch A2 Kursbuch covers topics that are relevant and interesting for teenagers, such as hobbies, school, friends, family, media, sports and culture. It also provides clear explanations of grammar rules, vocabulary exercises, listening activities and cultural information. The textbook is accompanied by an audio CD and a workbook with additional practice materials.</p>
-<h2>Logisch A2 Kursbuch Pdf Free</h2><br /><p><b><b>Download</b> &#10004; <a href="https://geags.com/2uCsFQ">https://geags.com/2uCsFQ</a></b></p><br /><br />
-<p>However, Logisch A2 Kursbuch is not available for free download as a PDF file on the official website of the publisher, Klett Sprachen. The publisher only offers a sample chapter and some online resources for teachers and students who have purchased the textbook. The price of the textbook is 23.99 EUR (about 27 USD) on the publisher's website.</p>
-<p>So how can you get Logisch A2 Kursbuch Pdf Free? There are some websites that claim to offer free PDF downloads of Logisch A2 Kursbuch, but they are not reliable or legal. Some of them may contain viruses, malware or spyware that can harm your device or steal your personal information. Some of them may also violate the copyright laws and infringe on the rights of the authors and the publisher.</p>
-<p>Therefore, we do not recommend downloading Logisch A2 Kursbuch Pdf Free from any unauthorized sources. Instead, we suggest that you buy the original textbook from the publisher or a trusted online bookstore. This way, you can support the authors and the publisher who have invested their time and effort to create this quality product. You can also enjoy the full benefits of Logisch A2 Kursbuch without any risks or limitations.</p>
-<p>If you are looking for a cheaper alternative to Logisch A2 Kursbuch, you can also check out some other German textbooks for young learners that are available online. For example, you can try Logisch! Neu A2.1 Kursbuch[^3^], which is a revised edition of Logisch A2 Kursbuch with updated content and design. You can access this textbook digitally on BlinkLearning, a platform that offers interactive content and activities for various subjects and languages. You can register for a free trial on BlinkLearning and see if Logisch! Neu A2.1 Kursbuch suits your needs and preferences.</p>
-<p>We hope this article has helped you find out how to download Logisch A2 Kursbuch Pdf Free or find a suitable alternative. If you have any questions or feedback, please feel free to leave a comment below.</p>
-
-<h2>What do users say about Logisch A2 Kursbuch?</h2>
-<p>If you are wondering what other users think about Logisch A2 Kursbuch, you can check out some of the customer reviews on Amazon. Amazon is one of the most popular online marketplaces where you can buy Logisch A2 Kursbuch and other German textbooks. You can also read the opinions and feedback of other buyers who have used Logisch A2 Kursbuch and see how they rate it on different aspects, such as quality, content, design and price.</p>
-<p>Based on the customer reviews on Amazon.co.uk[^1^], Amazon.com[^2^] and Amazon.de[^3^], Logisch A2 Kursbuch has received mostly positive ratings and comments. Most users praise Logisch A2 Kursbuch for being well-structured, engaging, comprehensive and easy to follow. They also appreciate the variety of topics, exercises and activities that Logisch A2 Kursbuch offers. Some users also mention that Logisch A2 Kursbuch is suitable for self-study as well as classroom use.</p>
-<p></p>
-<p>However, some users also point out some drawbacks or limitations of Logisch A2 Kursbuch. For example, some users complain that Logisch A2 Kursbuch does not come with an answer key or a glossary. They also wish that Logisch A2 Kursbuch had more audio materials or online resources. Some users also find Logisch A2 Kursbuch too expensive or too difficult for their level.</p>
-<p>Overall, Logisch A2 Kursbuch seems to have a good reputation among its users and has received many positive reviews. However, you should also consider your own needs and preferences when choosing a German textbook. You can use the customer reviews as a reference, but you should also try out Logisch A2 Kursbuch yourself and see if it works for you.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/r2d2/decision-triptych/app.py b/spaces/r2d2/decision-triptych/app.py
deleted file mode 100644
index 6f8dc1b9b1efcba102a1542d8f0d4b05f45bc7a2..0000000000000000000000000000000000000000
--- a/spaces/r2d2/decision-triptych/app.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from imgofai import *
-
-import matplotlib.pyplot as plt
-
-import PIL
-
-import numpy as np
-from pathlib import Path
-
-from imgofai.tree import img2df, df2xy
-import pandas as pd 
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.preprocessing import FunctionTransformer
-from sklearn.pipeline import make_pipeline
-
-import streamlit as st
-
-import requests
-
-st.write("# Images of AI Demo")
-
-st.write("This page demonstrates how I created the images I submitted for [Better Images of AI project](https://betterimagesofai.org/images)")
-
-def add_radial_features(X,y=None):
-    assert isinstance(X, pd.DataFrame), "X is not a dataframe"
-    xp = X.copy()
-    xp['dim0'] = np.sqrt(((X - X.mean())**2).sum(axis=1))
-    xp['dim1'] = np.arctan2(X['dim1'],X['dim0'])
-
-    X = pd.concat([
-        X,
-        xp,
-        ],axis=1)
-
-    return X
-
-
-
-def make_tree_approximator(radial = False, max_depth=4):
-    if radial:
-        model = make_pipeline(
-            FunctionTransformer(add_radial_features),
-            DecisionTreeRegressor(max_depth=max_depth),
-        )
-    else:
-        model =  DecisionTreeRegressor(max_depth=max_depth)
-
-
-    model.fit(x_raw, y)
-    pred = PIL.Image.fromarray(
-        model.predict(x_raw).reshape(img_array.shape).round().astype("uint8")
-    )
-    score = model.score(x_raw, y)
-    return pred
-
-
-st.write("## Try it out yourself:")
-
-url = st.text_input("Image url (make sure it ends with .png/.jpg/...):", "https://images.unsplash.com/reserve/bOvf94dPRxWu0u3QsPjF_tree.jpg?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1752&q=80")
-img = PIL.Image.open(requests.get(url, stream=True).raw)
-
-img_array = np.array(img)
-df = img2df(img_array)
-x_raw, y = df2xy(df)
-
-
-
-ccol1, ccol2, _ = st.columns(3)
-
-with ccol1:
-    max_depth1 = st.slider("max depth left:",1,12,2)
-    radial1 = st.checkbox("radial features left", value=False)
-
-with ccol2:
-    max_depth2 = st.slider("max depth middle:",1,12,6)
-    radial2 = st.checkbox("radial features middle", value=False)
-
-
-st.write("## Output")
-col1, col2, col3 = st.columns(3)
-
-with col1:
-    left_img = make_tree_approximator(radial1, max_depth=max_depth1)
-    st.image(left_img)
-
-with col2:
-    mid_img = make_tree_approximator(radial2, max_depth=max_depth2)
-    st.image(mid_img)
-
-with col3:
-    st.image(img)
diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_pack/attentions.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_pack/attentions.py
deleted file mode 100644
index 679d8511efc2afd7352670ed48f86072809520be..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_pack/attentions.py
+++ /dev/null
@@ -1,414 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer.infer_libs.infer_pack import commons
-from lib.infer.infer_libs.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/r3gm/RVC_HF/infer/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py b/spaces/r3gm/RVC_HF/infer/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
deleted file mode 100644
index f9664fb1f89ef068e923211179e1c7e1ce7fdbd2..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/infer/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import numpy as np
-import pyworld
-
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-
-
-class HarvestF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.hop_length,
-            f0_ceil=self.f0_max,
-            f0_floor=self.f0_min,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/r3gm/RVC_HF/julius/resample.py b/spaces/r3gm/RVC_HF/julius/resample.py
deleted file mode 100644
index fd3b9b547d4c33ec7136d32e5f086420d0a72e14..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/julius/resample.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-Differentiable, Pytorch based resampling.
-Implementation of Julius O. Smith algorithm for resampling.
-See https://ccrma.stanford.edu/~jos/resample/ for details.
-This implementation is specially optimized for when new_sr / old_sr is a fraction
-with a small numerator and denominator when removing the gcd (e.g. new_sr = 700, old_sr = 500).
-
-Very similar to [bmcfee/resampy](https://github.com/bmcfee/resampy) except this implementation
-is optimized for the case mentioned before, while resampy is slower but more general.
-
-"""
-
-import math
-from typing import Optional
-
-import torch
-from torch.nn import functional as F
-
-from .core import sinc
-from .utils import simple_repr
-
-
-class ResampleFrac(torch.nn.Module):
-    """
-    Resampling from the sample rate `old_sr` to `new_sr`.
-    """
-    def __init__(self, old_sr: int, new_sr: int, zeros: int = 24, rolloff: float = 0.945):
-        """
-        Args:
-            old_sr (int): sample rate of the input signal x.
-            new_sr (int): sample rate of the output.
-            zeros (int): number of zero crossing to keep in the sinc filter.
-            rolloff (float): use a lowpass filter that is `rolloff * new_sr / 2`,
-                to ensure sufficient margin due to the imperfection of the FIR filter used.
-                Lowering this value will reduce anti-aliasing, but will reduce some of the
-                highest frequencies.
-
-        Shape:
-
-            - Input: `[*, T]`
-            - Output: `[*, T']` with `T' = int(new_sr * T / old_sr)
-
-
-        .. caution::
-            After dividing `old_sr` and `new_sr` by their GCD, both should be small
-            for this implementation to be fast.
-
-        >>> import torch
-        >>> resample = ResampleFrac(4, 5)
-        >>> x = torch.randn(1000)
-        >>> print(len(resample(x)))
-        1250
-        """
-        super().__init__()
-        if not isinstance(old_sr, int) or not isinstance(new_sr, int):
-            raise ValueError("old_sr and new_sr should be integers")
-        gcd = math.gcd(old_sr, new_sr)
-        self.old_sr = old_sr // gcd
-        self.new_sr = new_sr // gcd
-        self.zeros = zeros
-        self.rolloff = rolloff
-
-        self._init_kernels()
-
-    def _init_kernels(self):
-        if self.old_sr == self.new_sr:
-            return
-
-        kernels = []
-        sr = min(self.new_sr, self.old_sr)
-        # rolloff will perform antialiasing filtering by removing the highest frequencies.
-        # At first I thought I only needed this when downsampling, but when upsampling
-        # you will get edge artifacts without this, the edge is equivalent to zero padding,
-        # which will add high freq artifacts.
-        sr *= self.rolloff
-
-        # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor)
-        # using the sinc interpolation formula:
-        #   x(t) = sum_i x[i] sinc(pi * old_sr * (i / old_sr - t))
-        # We can then sample the function x(t) with a different sample rate:
-        #    y[j] = x(j / new_sr)
-        # or,
-        #    y[j] = sum_i x[i] sinc(pi * old_sr * (i / old_sr - j / new_sr))
-
-        # We see here that y[j] is the convolution of x[i] with a specific filter, for which
-        # we take an FIR approximation, stopping when we see at least `zeros` zeros crossing.
-        # But y[j+1] is going to have a different set of weights and so on, until y[j + new_sr].
-        # Indeed:
-        # y[j + new_sr] = sum_i x[i] sinc(pi * old_sr * ((i / old_sr - (j + new_sr) / new_sr))
-        #               = sum_i x[i] sinc(pi * old_sr * ((i - old_sr) / old_sr - j / new_sr))
-        #               = sum_i x[i + old_sr] sinc(pi * old_sr * (i / old_sr - j / new_sr))
-        # so y[j+new_sr] uses the same filter as y[j], but on a shifted version of x by `old_sr`.
-        # This will explain the F.conv1d after, with a stride of old_sr.
-        self._width = math.ceil(self.zeros * self.old_sr / sr)
-        # If old_sr is still big after GCD reduction, most filters will be very unbalanced, i.e.,
-        # they will have a lot of almost zero values to the left or to the right...
-        # There is probably a way to evaluate those filters more efficiently, but this is kept for
-        # future work.
-        idx = torch.arange(-self._width, self._width + self.old_sr).float()
-        for i in range(self.new_sr):
-            t = (-i/self.new_sr + idx/self.old_sr) * sr
-            t = t.clamp_(-self.zeros, self.zeros)
-            t *= math.pi
-            window = torch.cos(t/self.zeros/2)**2
-            kernel = sinc(t) * window
-            # Renormalize kernel to ensure a constant signal is preserved.
-            kernel.div_(kernel.sum())
-            kernels.append(kernel)
-
-        self.register_buffer("kernel", torch.stack(kernels).view(self.new_sr, 1, -1))
-
-    def forward(self, x: torch.Tensor, output_length: Optional[int] = None, full: bool = False):
-        """
-        Resample x.
-        Args:
-            x (Tensor): signal to resample, time should be the last dimension
-            output_length (None or int): This can be set to the desired output length
-                (last dimension). Allowed values are between 0 and
-                ceil(length * new_sr / old_sr). When None (default) is specified, the
-                floored output length will be used. In order to select the largest possible
-                size, use the `full` argument.
-            full (bool): return the longest possible output from the input. This can be useful
-                if you chain resampling operations, and want to give the `output_length` only
-                for the last one, while passing `full=True` to all the other ones.
-        """
-        if self.old_sr == self.new_sr:
-            return x
-        shape = x.shape
-        length = x.shape[-1]
-        x = x.reshape(-1, length)
-        x = F.pad(x[:, None], (self._width, self._width + self.old_sr), mode='replicate')
-        ys = F.conv1d(x, self.kernel, stride=self.old_sr)  # type: ignore
-        y = ys.transpose(1, 2).reshape(list(shape[:-1]) + [-1])
-
-        float_output_length = self.new_sr * length / self.old_sr
-        max_output_length = int(math.ceil(float_output_length))
-        default_output_length = int(float_output_length)
-        if output_length is None:
-            output_length = max_output_length if full else default_output_length
-        elif output_length < 0 or output_length > max_output_length:
-            raise ValueError(f"output_length must be between 0 and {max_output_length}")
-        else:
-            if full:
-                raise ValueError("You cannot pass both full=True and output_length")
-        return y[..., :output_length]
-
-    def __repr__(self):
-        return simple_repr(self)
-
-
-def resample_frac(x: torch.Tensor, old_sr: int, new_sr: int,
-                  zeros: int = 24, rolloff: float = 0.945,
-                  output_length: Optional[int] = None, full: bool = False):
-    """
-    Functional version of `ResampleFrac`, refer to its documentation for more information.
-
-    ..warning::
-        If you call repeatidly this functions with the same sample rates, then the
-        resampling kernel will be recomputed everytime. For best performance, you should use
-        and cache an instance of `ResampleFrac`.
-    """
-    return ResampleFrac(old_sr, new_sr, zeros, rolloff).to(x)(x, output_length, full)
-
-
-# Easier implementations for downsampling and upsampling by a factor of 2
-# Kept for testing and reference
-
-def _kernel_upsample2_downsample2(zeros):
-    # Kernel for upsampling and downsampling by a factor of 2. Interestingly,
-    # it is the same kernel used for both.
-    win = torch.hann_window(4 * zeros + 1, periodic=False)
-    winodd = win[1::2]
-    t = torch.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros)
-    t *= math.pi
-    kernel = (sinc(t) * winodd).view(1, 1, -1)
-    return kernel
-
-
-def _upsample2(x, zeros=24):
-    """
-    Upsample x by a factor of two. The output will be exactly twice as long as the input.
-    Args:
-        x (Tensor): signal to upsample, time should be the last dimension
-        zeros (int): number of zero crossing to keep in the sinc filter.
-
-    This function is kept only for reference, you should use the more generic `resample_frac`
-    one. This function does not perform anti-aliasing filtering.
-    """
-    *other, time = x.shape
-    kernel = _kernel_upsample2_downsample2(zeros).to(x)
-    out = F.conv1d(x.view(-1, 1, time), kernel, padding=zeros)[..., 1:].view(*other, time)
-    y = torch.stack([x, out], dim=-1)
-    return y.view(*other, -1)
-
-
-def _downsample2(x, zeros=24):
-    """
-    Downsample x by a factor of two. The output length is half of the input, ceiled.
-    Args:
-        x (Tensor): signal to downsample, time should be the last dimension
-        zeros (int): number of zero crossing to keep in the sinc filter.
-
-    This function is kept only for reference, you should use the more generic `resample_frac`
-    one. This function does not perform anti-aliasing filtering.
-    """
-    if x.shape[-1] % 2 != 0:
-        x = F.pad(x, (0, 1))
-    xeven = x[..., ::2]
-    xodd = x[..., 1::2]
-    *other, time = xodd.shape
-    kernel = _kernel_upsample2_downsample2(zeros).to(x)
-    out = xeven + F.conv1d(xodd.view(-1, 1, time), kernel, padding=zeros)[..., :-1].view(
-        *other, time)
-    return out.view(*other, -1).mul(0.5)
diff --git a/spaces/ra2w/TableQandA/app.py b/spaces/ra2w/TableQandA/app.py
deleted file mode 100644
index 6bbb8a789fd7b76e92345628bae1e741502ce988..0000000000000000000000000000000000000000
--- a/spaces/ra2w/TableQandA/app.py
+++ /dev/null
@@ -1,549 +0,0 @@
-##############################################################################
-# Understanding the impact of Growth and Margin profile on B2B SaaS Valuations
-# Dataset: 106 B2B SaaS companies
-#   Author: Ramu Arunachalam (ramu@acapital.com)
-#   Created: 06/20/21
-#   Datset last updated: 06/09/21
-###############################################################################
-
-import joblib as jl
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-import statsmodels.api as sm
-import streamlit as st
-from statsmodels.stats.outliers_influence import variance_inflation_factor
-from transformers import pipeline
-from transformers import TapasTokenizer, TapasForQuestionAnswering
-import json
-import requests
-
-file_date = '2021-06-11'
-saas_filename_all = f'{file_date}-comps_B2B_ALL.csv'
-saas_filename_high_growth = f'{file_date}-comps_B2B_High_Growth.csv'
-
-
-def get_scatter_fig(df, x, y):
-    fig = px.scatter(df,
-                     x=x,
-                     y=y,
-                     hover_data=['Name'],
-                     title=f'{y} vs {x}')
-    df_r = df[[y] + [x]].dropna()
-    model = sm.OLS(df_r[y], sm.add_constant(df_r[x])).fit()
-    regline = sm.OLS(df_r[y], sm.add_constant(df_r[x])).fit().fittedvalues
-    fig.add_trace(go.Scatter(x=df_r[x],
-                             y=model.predict(),
-                             mode='lines',
-                             marker_color='black',
-                             name='Best-fit',
-                             line=dict(width=4, dash='dot')))
-    return fig
-
-
-latex_dict = {'EV / NTM Revenue': r'''\frac{EV}{Rev_{NTM}}''',
-              'EV / 2021 Revenue': r'''\frac{EV}{Rev_{2021}}''',
-              'EV / NTM Gross Profit': r'''\frac{EV}{GP_{NTM}}''',
-              'EV / 2021 Gross Profit': r'''\frac{EV}{GP_{2021}}''',
-              'NTM Revenue Growth': r'''Rev\,Growth_{NTM}''',
-              '2021 Revenue Growth': r'''Rev\,Growth_{2021}''',
-              'Growth adjusted EV / LTM Revenue': r'''\frac{EV}{Rev_{LTM}}\cdot\frac{1}{Growth_{NTM}}''',
-              'Growth adjusted EV / 2020 Revenue': r'''\frac{EV}{Rev_{2020}}\cdot\frac{1}{Growth_{2021}}'''
-              }
-
-
-class RegressionInput:
-    def __init__(self, df, x_vars, y_var):
-        self.df = df
-        self.x_vars = x_vars
-        self.y_var = y_var
-        self._hash = tuple([jl.hash(df), tuple(self.x_vars), tuple([self.y_var])])
-        return
-
-    def hash(self):
-        return self._hash
-
-
-class RegressionOutput:
-    def __init__(self, reg_input, df_r, model, df_pvalues, vif_data):
-        self.df_r = df_r
-        self.model = model
-        self.df_pvalues = df_pvalues
-        self.vif_data = vif_data
-        self.plot_figs = dict()
-
-        # Regression equation
-        self.eq_str = latex_dict.get(reg_input.y_var, reg_input.y_var) + r'''= \beta_0'''
-
-        for x, i in zip(reg_input.x_vars, range(len(reg_input.x_vars))):
-            self.eq_str += rf'''+\beta_{{{i + 1}}}\cdot {{{latex_dict.get(x, x)}}}'''
-        self.eq_str += r'''+\epsilon'''
-        self.eq_str = self.eq_str.replace("%", "\%").replace("&", "\&").replace("$", "\$")
-
-        # Compute regression plots and save them
-        # Plot residuals
-        for x in reg_input.x_vars:
-            self.plot_figs[x] = sm.graphics.plot_regress_exog(self.model, x)
-
-        self._hash = tuple([reg_input.hash(), jl.hash(df_r), id(model), jl.hash(df_pvalues), jl.hash(vif_data)])
-        return
-
-    def hash(self):
-        return self._hash
-
-
-def reg_input_hash(reg_input):
-    h = reg_input.hash()
-    # st.info(f"reg_input_hash: h = {h}")
-    return h
-
-
-def reg_output_hash(reg_output):
-    h = reg_output.hash()
-    # st.info(f'reg_output hash = {h}')
-    return h
-
-
-class Experiment:
-    id_num = 0
-
-    def __init__(self):
-        df_main = pd.read_csv(saas_filename_all)
-
-        # Clean: 2x --> 2, 80% --> 80, $3,000 --> 3000
-        df_obj = df_main[set(df_main.columns) - {'Name'}].select_dtypes(['object'])
-        df_main[df_obj.columns] = df_obj \
-            .apply(lambda x: x.str.strip('x')) \
-            .apply(lambda x: x.str.strip('%')) \
-            .replace(',', '', regex=True) \
-            .replace('\$', '', regex=True)
-
-        cols = df_main.columns
-        for c in cols:
-            try:
-                df_main[c] = pd.to_numeric(df_main[c])
-            except:
-                pass
-
-        df_main['2021 Revenue Growth'] = (df_main['2021 Analyst Revenue Estimates'].astype(float) / df_main[
-            '2020 Revenue'].astype(
-            float) - 1) * 100
-
-        df_main = df_main[df_main['Name'].notna()]
-        self.tickers_all = list(df_main[df_main['Name'].isin(['Median', 'Mean']) == False]['Name'])
-        df_main_hg = pd.read_csv(saas_filename_high_growth)
-        df_main_hg = df_main_hg[df_main_hg['Name'].notna()]
-        self.tickers_hg = list(df_main_hg[df_main_hg['Name'].isin(['Median', 'Mean']) == False]['Name'])
-        self.tickers_excl_hg = list(set(self.tickers_all) - set(self.tickers_hg))
-
-
-        self.df_main = df_main
-        self.df = df_main
-        self.reg_input = None
-        self.reg_output = None
-
-        return
-
-    def get_tickers(self, growth='High'):
-        if growth == 'High':
-            return self.tickers_hg
-        elif growth == 'Low':
-            return self.tickers_excl_hg
-        else:
-            return self.tickers_all
-
-    def filter(self, by):
-        if by == 'High growth only':
-            tickers = self.tickers_hg
-        elif by == 'All (excl. high growth)':
-            tickers = self.tickers_excl_hg
-        else:
-            tickers = self.tickers_all
-
-        self.df = self.df_main[self.df_main['Name'].isin(tickers)]  # type of dataset
-        return self
-
-    def set_fwd_timeline(self, type):
-        self.rev_g = f'{type} Revenue Growth'
-        self.rev_mult = f'EV / {type} Revenue'
-        self.gp_mult = f'EV / {type} Gross Profit'
-        self.gm = f'Gross Margin'
-
-        # To avoid double counting growth, for growth-adjusted multiples
-        # we take the forward growth rate with the current revenue multiple
-        rev_mult = 'LTM' if type == 'NTM' else '2020'
-        self.growth_adj_mult = f'Growth adjusted EV / {rev_mult} Revenue'
-        self.df[self.growth_adj_mult] = self.df[f'EV / {rev_mult} Revenue'] / self.df[self.rev_g]
-        return self
-
-    def get_y_metric_list(self):
-        return [self.rev_mult, self.gp_mult, self.growth_adj_mult, self.rev_g]
-
-    def get_x_metric_list(self):
-        return self.df.select_dtypes(['float', 'int']).columns
-
-    def to_frame(self):
-        return self.df
-
-    @st.cache(suppress_st_warning=True,
-              hash_funcs={RegressionInput: reg_input_hash, RegressionOutput: reg_output_hash})
-    def _regression(self, reg_input):
-        df = reg_input.df
-        reg_x_vars = reg_input.x_vars
-        reg_y = reg_input.y_var
-
-        if not reg_x_vars:
-            return None
-
-        df_r = df[[reg_y] + reg_x_vars].dropna()
-
-        # Run the regression
-        X = df_r[reg_x_vars]
-        X = sm.add_constant(X)
-
-        model = sm.OLS(df_r[reg_y], X).fit()
-
-        # Compute Variance Inflation Factors
-        df_v = df_r[reg_x_vars]
-        vif_data = None
-        if len(df_v.columns) >= 2:
-            # VIF dataframe
-            vif_data = pd.DataFrame()
-
-            vif_data["feature"] = df_v.columns
-            # calculating VIF for each feature
-            vif_data["VIF"] = [variance_inflation_factor(df_v.values, i)
-                               for i in range(len(df_v.columns))]
-
-        # pvalue dataframe
-        df_pvalues = model.params.to_frame().reset_index().rename(columns={'index': 'vars', 0: 'Beta'})
-        df_pvalues['p-value'] = model.pvalues.to_frame().reset_index().rename(columns={0: 'p-value'})['p-value']
-        df_pvalues['Statistical Significance'] = 'Low'
-        df_pvalues.loc[df_pvalues['p-value'] <= 0.05, 'Statistical Significance'] = 'High'
-        df_pvalues = df_pvalues[df_pvalues['vars'] != 'const']
-
-        return RegressionOutput(reg_input, df_r, model, df_pvalues, vif_data)
-
-    def regression(self, reg_x_vars, reg_y_var):
-        self.reg_input = RegressionInput(self.df, reg_x_vars, reg_y_var)
-        self.reg_output = self._regression(self.reg_input)
-        return self
-
-    def print(self, show_detail=False):
-        # Print regression equation
-        st.latex(self.reg_output.eq_str)
-
-        def highlight_significant_rows(val):
-            color = 'green' if val['p-value'] <= 0.05 else 'red'
-            return [f"color: {color}"] * len(val)
-
-        st.subheader("Summary", anchor='summary')
-        st.write(f"1. N = {len(self.reg_output.df_r)} companies")
-
-        # Assess model fit
-        if self.reg_output.model.rsquared * 100 > 30:
-            st.write(f"2. Model fit is **good** R ^ 2 = {self.reg_output.model.rsquared * 100: .2f}%")
-            if self.reg_output.model.f_pvalue < 0.05:
-                st.write(f"3. Model is **statistically significant** (F-test = {self.reg_output.model.f_pvalue:.2f})")
-            else:
-                st.write(
-                    f"3. The regression is **NOT statistically significant** (F-test = {self.reg_output.model.f_pvalue:.2f})")
-
-        else:
-            st.write(f"2. Model fit is **poor** (R ^ 2 = {self.reg_output.model.rsquared * 100: .2f}%)")
-
-        # Check for Multicolinearity
-        if (
-                self.reg_output.vif_data is not None
-                and len(self.reg_output.vif_data[self.reg_output.vif_data['VIF'] > 10]) > 0
-        ):
-            st.write("4. **Potential multicolinearity**")
-        else:
-            st.write("4. **NO multicolinearity**")
-
-        # print p-values
-        st.write('***')
-        for _, row in self.reg_output.df_pvalues.iterrows():
-            str = 'strong' if row['Statistical Significance'] == 'High' else 'weak'
-            st.write(f"* There is a **{str} relationship** between *'{self.reg_input.y_var}'* and *'{row['vars']}'*")
-
-        st.table(self.reg_output.df_pvalues.set_index('vars').style.apply(highlight_significant_rows, axis=1))
-
-        if show_detail:
-            # Show details
-            st.subheader("Details:", anchor='details')
-            # Plot residuals
-            for k, f in self.reg_output.plot_figs.items():
-                st.write(f"Plotting residuals for **{k}**")
-                st.pyplot(f)
-                # st.pyplot(f)
-            st.markdown('***')
-            st.write(self.reg_output.model.summary())
-            st.markdown('***')
-            if self.reg_output.vif_data is not None:
-                st.write("Variance Inflation Factors")
-                st.table(self.reg_output.vif_data.set_index('feature'))
-
-        return self
-
-
-def workbench(show_detail):
-    fwd_time = st.sidebar.selectbox('Timeline', ('2021', 'NTM'))
-    slice_by_growth = st.sidebar.radio("B2B SaaS Dataset", ['High growth only', 'All', 'All (excl. high growth)'])
-
-    e = Experiment().set_fwd_timeline(fwd_time).filter(slice_by_growth)
-
-    st.sidebar.write("**Regression:**")
-    y_sel = st.sidebar.radio("Target metric", e.get_y_metric_list())
-    st.sidebar.text("Select independent variable(s)")
-
-    st.header("Regression")
-    # Check if user selected revenue growth and/or gross margin
-    reg_x_cols = [i for i in [e.rev_g, e.gm] if st.sidebar.checkbox(i, value={e.rev_g: True, e.gm: True}, key=i)]
-    remaining_cols = list(set(e.get_x_metric_list()) - {e.rev_g, e.gm})
-    reg_x_cols += st.sidebar.multiselect("Additional independent variables:", remaining_cols)
-    e.regression(reg_x_vars=reg_x_cols, reg_y_var=y_sel).print(show_detail)
-
-
-    ## Plots
-    #st.header("Plots")
-    #for _, x in zip(range(4), e.reg_input.x_vars):
-    #    st.plotly_chart(get_scatter_fig(e.to_frame(), x=x, y=e.reg_input.y_var))
-    #st.plotly_chart(get_scatter_fig(e.to_frame(), x=e.gm, y=e.reg_input.y_var))
-
-    st.subheader("Dataset")
-    st.expander('Table Output') \
-        .table(e.to_frame()[['Name'] + [y_sel] + reg_x_cols]
-               .set_index('Name')
-               .sort_values(y_sel, ascending=False))
-    st.expander('Full Raw Table Output').table(e.df_main)
-    st.sidebar.info(f"""*{len(e.df)} companies selected*    
-        *Prices as of {file_date}*""")
-
-    return
-
-def get_dataset(filter='All'):
-
-   e = Experiment()
-   df = e.set_fwd_timeline('2021').to_frame()[['Name'] + ['EV / 2021 Revenue', '2021 Revenue Growth','Gross Margin']].set_index('Name')
-   
-   high_growth_tickers = e.get_tickers(growth='High')
-   low_growth_tickers = e.get_tickers(growth='Low')
-   high_growth_tickers = set(high_growth_tickers).intersection(set(df.index.values.tolist()))
-   df.loc[high_growth_tickers,'Category'] = 'High Growth'
-   low_growth_tickers = set(low_growth_tickers).intersection(set(df.index.values.tolist()))
-   df.loc[low_growth_tickers,'Category'] = 'Low Growth'
-   df = df[df['Category'].notna()]
-   if filter == 'High Growth':
-       return df.loc[high_growth_tickers]
-   elif filter == 'Low Growth':
-       return df.loc[low_growth_tickers]
-
-   return df
-
-
-def summary(e1, e2, e3, e4):
-    st.header("High Growth B2B SaaS")
-    st.markdown("""
-    For high growth B2B SaaS, ***revenue growth*** (*not profitability*) ***drives valuation***
-    * *Valuation multiples* are well explained by *revenue growth* 
-        * Model fit is good (High R^2)
-        * Revenue growth is a statistically significant factor (low p-value)
-    * *Gross Margin* does not influence *valuation multiples*
-        * Poor relationship between Revenue multiples and Gross margin (high p-value)
-    """)
-    with st.expander("More info"):
-        e1.print(True)
-
-    st.markdown("""
-        * Looking at Free Cash Flow % instead of Gross Margin yield similar results
-            * Model fit is good (High R^2)
-            * *Revenue growth* is a statistically significant factor (low p-value)
-        * *FCF Margin* does not influence *valuation multiples*
-            * Poor relationship between Revenue multiples and FCF margin (high p-value)
-        """)
-    with st.expander("More info"):
-        e2.print(True)
-
-    st.markdown('***')
-    st.header("B2B SaaS (excluding high growth)")
-    st.markdown("""
-        
-        For the rest of B2B SaaS (i.e non high growth SaaS), the picture is less clear
-        * *Revenue growth* by itself doesn't adequately explain *valuation multiples* 
-            * Model fit is poor (low R^2)
-        * But *Revenue growth* is still a statistically significant factor (low p-value)
-        * *Gross Margin* does not influence *valuation multiples*
-            * Poor relationship between Revenue multiples and Gross margin (high p-value)
-        """)
-    with st.expander("More info"):
-        e3.print(True)
-    st.markdown("""
-            * Looking at Free Cash Flow % instead of Gross Margin improves model fit
-            * FCF Margin* has a **small positive effect** on *valuation multiples*
-                * Low p-value but small Beta.
-            * But overall *revenue growth* still has a much **larger effect** on valuation multiples than profitability
-                * Low p-value and higher Beta relative to FCF %
-            """)
-    with st.expander("More info"):
-        e4.print(True)
-    return
-
-
-def make_api_call(queries, df:pd.DataFrame):
-    API_TOKEN = "api_DjJYjFpAQfQkhpfzncoRuuKuuLWrSzHdav"
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    API_URL = "https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq"
-
-    st.sidebar.info("Using ** google/tapas-large-finetuned-wtq**")
-    def query(payload):
-        response = requests.post(API_URL, headers=headers, json=payload)
-        return response.json()
-
-    table_dict = df.to_dict(orient='list')
-    output = query({
-    "inputs": {
-		"query": queries,
-        "table": table_dict
-    }})
-
-    return output
-
-def nlu_query_use_api():
- 
-    df = get_dataset(filter='High Growth').dropna().reset_index().rename(columns={'Name':'Company','EV / 2021 Revenue':'Revenue Multiple','2021 Revenue Growth':'Growth Rate'})
-
-    df['Revenue Multiple'] = df['Revenue Multiple'].round(2).apply(str)
-    df['Growth Rate'] = df['Growth Rate'].round(2).apply(str)
-    df['Gross Margin'] = df['Gross Margin'].round(2).apply(str)
-    df = df[['Company','Revenue Multiple','Growth Rate','Gross Margin']]
-
-    with st.expander("Dataset"):
-        st.table(df)
-    
-    questions = ['How many companies are in this dataset?', 
-    'Which company has the highest growth rate?',
-    'Which company has the highest gross margin?',
-    'Which company trades at the highest revenue multiple?',
-    'List all companies with growth rates greater than 40?',
-    'What is the average gross margin for companies in this dataset?',
-    'What is the average trading multiple?'
-    
-    ]
-
-    st.sidebar.write("Sample questions:")
-    st.sidebar.caption("[Copy and Paste any of these questions into the textbox below]")
-    for i in questions: st.sidebar.markdown(f"* {i}") 
-        
-    queries = st.text_area("Enter Question:", 'How many companies are in this dataset?')
-    #queries = ['Which company has the highest gross margin?']
-    output = make_api_call(queries=queries, df=df)
-    if 'error' in output:
-        st.write(output['error'])
-    else:
-        try:
-            df_output = pd.DataFrame.from_dict(output['cells'])
-            if output['aggregator'] == 'COUNT':
-                st.info(f"[COUNT] Answer: {df_output[0].count()}")
-            elif output['aggregator'] == 'SUM':
-                st.info(f"[SUM] Answer: {df_output[0].astype(float).sum().round(2)}")
-            elif output['aggregator'] == 'AVERAGE':
-                st.info(f"[AVERAGE] Answer: {df_output[0].astype(float).mean().round(2)}")
-            else:
-                st.info(f"Answer is {output['answer']}")
-        except ValueError:
-            st.write(output)
-        with st.expander("Raw Output"):
-            st.write(output)
-    return
-
-def nlu_query():
-    from torch_scatter import scatter
-
-    st.header("NLU Query")
-    model_name = 'google/tapas-large-finetuned-wtq'
-    model = TapasForQuestionAnswering.from_pretrained(model_name)
-    tokenizer = TapasTokenizer.from_pretrained(model_name)
-    df = get_dataset().dropna().reset_index().rename(columns={'Name':'Company','EV / 2021 Revenue':'Revenue Multiple','2021 Revenue Growth':'Growth Rate'})
-
-    df['Revenue Multiple'] = df['Revenue Multiple'].round(2).apply(str)
-    df['Growth Rate'] = df['Growth Rate'].round(2).apply(str)
-    df['Gross Margin'] = df['Gross Margin'].round(2).apply(str)
-    df = df[['Company','Revenue Multiple','Growth Rate','Gross Margin']]
-
-    st.table(df)
-    queries = ['How many companies are in the dataset', 'Which company has the highest growth rate?','Which company has the highest gross margin?']
-    st.write(queries)
-    #queries = st.text_area('Ask a question')
-    
-    inputs = tokenizer(table=df, queries=queries, padding='max_length', return_tensors="pt")
-    outputs = model(**inputs)
-    predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
-        inputs, 
-        outputs.logits.detach(), 
-        outputs.logits_aggregation.detach())
-
-    inputs = tokenizer(table=df, queries=queries, padding='max_length', return_tensors="pt")
-    id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
-    aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
-
-    answers = []
-    for coordinates in predicted_answer_coordinates:
-        if len(coordinates) == 1:
-            # only a single cell:
-            answers.append(df.iat[coordinates[0]])
-        else:
-            # multiple cells
-            cell_values = []
-            for coordinate in coordinates:
-                cell_values.append(df.iat[coordinate])
-            answers.append(", ".join(cell_values))
-
-    st.write(answers)
-    st.write(aggregation_predictions_string)
-    return
-
-def main():
-    #st.set_page_config(initial_sidebar_state="collapsed")
-    sel = st.sidebar.radio("Menu", ['NLU Question Answer','Summary', 'Workbench'])
-    
-    show_detail = True
-
-    # pre compute three experiments
-    # Experiment 1
-    e1 = Experiment() \
-        .set_fwd_timeline('2021') \
-        .filter('High growth only')
-    e1.regression(reg_x_vars=[e1.rev_g, e1.gm], reg_y_var=e1.rev_mult)
-
-    # Experiment 2
-    e2 = Experiment() \
-        .set_fwd_timeline('2021') \
-        .filter('High growth only')
-    e2.regression(reg_x_vars=[e2.rev_g, 'LTM FCF %'], reg_y_var=e2.rev_mult)
-
-    # Experiment 3
-    e3 = Experiment() \
-        .set_fwd_timeline('2021') \
-        .filter('All (excl. high growth)')
-    e3.regression(reg_x_vars=[e3.rev_g, e3.gm], reg_y_var=e3.rev_mult)
-
-    # Experiment 4
-    e4 = Experiment() \
-        .set_fwd_timeline('2021') \
-        .filter('All (excl. high growth)')
-    e4.regression(reg_x_vars=[e4.rev_g, 'LTM FCF %'], reg_y_var=e4.rev_mult)
-
-    if sel == 'NLU Question Answer':
-        st.title("Query Dataset")
-        return nlu_query_use_api()
-    elif sel == 'Workbench':
-        st.title('Impact of Growth and Margins on Valuation')
-        return workbench(True)
-    else:
-        st.title('Impact of Growth and Margins on Valuation')
-        return summary(e1, e2, e3, e4)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/models/__init__.py b/spaces/radames/MusicGen-Continuation/audiocraft/models/__init__.py
deleted file mode 100644
index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/models/__init__.py
+++ /dev/null
@@ -1,10 +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.
-
-# flake8: noqa
-from .musicgen import MusicGen
-from .lm import LMModel
-from .encodec import CompressionModel, EncodecModel
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Crazytalk 6 Pro Full Free Download with Working Serial and Crack Key No Surveys No Passwords No Hassle.md b/spaces/raedeXanto/academic-chatgpt-beta/Crazytalk 6 Pro Full Free Download with Working Serial and Crack Key No Surveys No Passwords No Hassle.md
deleted file mode 100644
index e0dc2bda5569c060c97a189205ee7de8019b8a86..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Crazytalk 6 Pro Full Free Download with Working Serial and Crack Key No Surveys No Passwords No Hassle.md	
+++ /dev/null
@@ -1,271 +0,0 @@
-<br />
-<h1>CrazyTalk 6 Pro: How to Download and Install It for Free</h1>
- <p>Do you want to create stunning animations with your own voice and photos? Do you want to impress your friends, family, or clients with your creativity and skills? If you answered yes, then you might be interested in CrazyTalk 6 Pro, a powerful software that allows you to transform any image into a lifelike talking character. In this article, we will show you what CrazyTalk 6 Pro is, why you might want to use it, how to download it for free, how to install it on your computer, and how to use it to create amazing animations. Let's get started!</p>
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- <h2>What is CrazyTalk 6 Pro?</h2>
- <h3>A brief introduction to the software and its features</h3>
- <p>CrazyTalk 6 Pro is a software developed by Reallusion, a company that specializes in creating realistic digital humans and animations. CrazyTalk 6 Pro is the latest version of their flagship product, which was first released in 2000. CrazyTalk 6 Pro is designed to make animation easy and fun for anyone, regardless of their experience or skill level. With CrazyTalk 6 Pro, you can:</p>
- <ul>
-<li>Turn any image into a talking character with facial animation and lip-sync.</li>
-<li>Use your own voice or text-to-speech to give your character a personality.</li>
-<li>Add emotions, expressions, gestures, and accessories to your character.</li>
-<li>Use templates, backgrounds, scenes, and effects to enhance your animation.</li>
-<li>Export your animation as a video, image sequence, or flash file.</li>
-<li>Share your animation online or offline with others.</li>
-</ul>
- <h3>Why you might want to use CrazyTalk 6 Pro</h3>
- <p>CrazyTalk 6 Pro is not only a fun and easy software to use, but also a versatile and powerful one. You can use CrazyTalk 6 Pro for various purposes, such as:</p>
- <ul>
-<li>Entertainment: You can create funny animations for yourself or others, such as jokes, pranks, greetings, stories, etc.</li>
-<li>Educational: You can create educational animations for yourself or others, such as presentations, lectures, tutorials, etc.</li>
-<li>Professional: You can create professional animations for yourself or others, such as advertisements, promotions, demos, etc.</li>
-</ul>
- <p>CrazyTalk 6 Pro can help you express your creativity, communicate your ideas, and attract your audience. It can also save you time and money compared to hiring an animator or using other software.</p>
- <h2>How to Download CrazyTalk 6 Pro for Free</h2>
- <h3>The legal and ethical issues of downloading software for free</h3>
- <p>Before we show you how to download CrazyTalk 6 Pro for free, we need to address some legal and ethical issues. Downloading software for free without paying for it is considered piracy. Piracy is illegal in most countries and can result in fines or jail time. Piracy is also unethical because it harms the developers who worked hard to create the software. Piracy can also affect the quality and security of the software. Pirated software may contain viruses, malware, spyware, or other harmful elements that can damage your computer or compromise your privacy. Pirated software may also have limited functionality or compatibility issues that can affect your user experience.</p>
- <p>We do not condone or encourage piracy in any way. We only provide this information for educational purposes only. We strongly recommend that you purchase a legitimate copy of CrazyTalk 6 Pro from Reallusion's official website or authorized resellers. By doing so, you will support the developers who created this amazing software. You will also enjoy the full features and benefits of CrazyTalk 6 Pro without any risks or problems.</p>
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- <h3>The risks and challenges of finding a working serial and crack key</h3>
- <p>If you still decide to download CrazyTalk 6 Pro for free despite the legal and ethical issues we mentioned above, you will face some risks and challenges. One of the biggest challenges is finding a working serial and crack key. A serial key is a unique code that activates the software after installation. A crack key is a modified file that bypasses the activation process. Without a working serial and crack key, you will not be able to use CrazyTalk 6 Pro at all.</p>
- <p>Finding a working serial and crack key is not easy because they are often fake or outdated. Many websites that claim to offer serial and crack keys are scams that try to trick you into downloading malware or paying money. Even if you find a genuine serial and crack key, it may not work because Reallusion updates their software regularly to prevent piracy. Reallusion may also detect your pirated copy of CrazyTalk 6 Pro and disable it remotely.</p>
- <p>You will need to spend a lot of time and effort searching for a working serial and crack key online. You will also need to be careful about what you download and install on your computer. You will need to scan every file with an antivirus program before opening it. You will also need to backup your important data in case something goes wrong.</p>
- <h3>The best sources and methods to download CrazyTalk 6 Pro for free</h3>
- <p>If you are still determined to download CrazyTalk 6 Pro for free despite the risks and challenges we mentioned above, we will show you some of the best sources and methods to do so. However, we cannot guarantee that these sources and methods will work for everyone or forever. We also cannot guarantee that these sources and methods are safe or legal. Use them at your own risk.</p>
- <p>One of the best sources to download CrazyTalk 6 Pro for free is torrent sites. Torrent sites are platforms that allow users to share files with each other using peer-to-peer technology. Torrent sites often have large collections of software files that include serial and crack keys. Some of the most popular torrent sites are The Pirate Bay (thepiratebay.org), RARBG (rarbg.to), Kickass Torrents (katcr.co), etc.</p>
- <p>To download from torrent sites, you will need a torrent client program such as BitTorrent (bittorrent.com), uTorrent (utorrent.com), qBittorrent (qbittorrent.org), etc. You will also need a VPN service such as NordVPN (nordvpn.com), ExpressVPN (expressvpn.com), CyberGhost (cyberghostvpn.com), etc., if torrenting is blocked or illegal in your country.</p>
- <p>To download CrazyTalk 6 Pro from torrent sites using a torrent client program:</p>
- <ol>
-<li>Open your VPN service and connect to a server in a country where torrenting is allowed.</li>
-<li>Open your torrent client program.</li>
-<li>Go to one of the torrent sites we mentioned above using your web browser.</li>
-<li>Search for "CrazyTalk 6 Pro" in the search bar.</li>
-<li>Select one of the results that has many seeders (users who have the complete file) and leechers (users who are downloading the file).</li>
-<li>Download the torrent file or copy the magnet link.</li>
-<li>Add the torrent file or magnet link to your torrent client program.</li>
-<li>Wait until the download is complete.</li>
-</ol>
- <p>Another source to download CrazyTalk 6 Pro for free is direct download sites. Direct download sites are platforms that allow users to upload files on their servers and share them with others using links. Direct download sites often have large collections of software files that include serial and crack keys. Some of the most popular direct download sites are Mega (mega.nz), MediaFire (mediafire.com), Zippyshare (zippyshare.com), etc. To download from direct download sites using your web browser: <ol>
-of the direct download sites we mentioned above using your web browser.</li>
-<li>Search for "CrazyTalk 6 Pro" in the search bar.</li>
-<li>Select one of the results that has many downloads and positive ratings.</li>
-<li>Click on the download button or link.</li>
-<li>Wait until the download is complete.</li>
-</ol>
- <p>A third source to download CrazyTalk 6 Pro for free is cracked software sites. Cracked software sites are platforms that specialize in providing software files that have been modified or hacked to bypass the activation process. Cracked software sites often have exclusive serial and crack keys that are not available elsewhere. Some of the most popular cracked software sites are Cracks4Win (cracks4win.com), GetIntoPC (getintopc.com), Softasm (softasm.com), etc.</p>
- <p>To download from cracked software sites using your web browser:</p>
- <ol>
-<li>Go to one of the cracked software sites we mentioned above using your web browser.</li>
-<li>Search for "CrazyTalk 6 Pro" in the search bar.</li>
-<li>Select one of the results that has many downloads and positive ratings.</li>
-<li>Read the instructions and requirements carefully.</li>
-<li>Click on the download button or link.</li>
-<li>Wait until the download is complete.</li>
-</ol>
- <h2>How to Install CrazyTalk 6 Pro on Your Computer</h2>
- <h3>The system requirements and compatibility issues of CrazyTalk 6 Pro</h3>
- <p>Before you install CrazyTalk 6 Pro on your computer, you need to make sure that your computer meets the minimum system requirements and is compatible with the software. The minimum system requirements for CrazyTalk 6 Pro are:</p>
- <ul>
-<li>Operating system: Windows XP/Vista/7/8/10</li>
-<li>Processor: Pentium IV 2 GHz or higher</li>
-<li>Memory: 512 MB RAM or higher</li>
-<li>Hard disk space: 1 GB or higher</li>
-<li>Graphics card: DirectX 9.0c compatible</li>
-<li>Sound card: Full duplex sound card</li>
-<li>Internet connection: Required for online activation and content update</li>
-</ul>
- <p>If your computer does not meet these requirements, you may experience poor performance, errors, or crashes when using CrazyTalk 6 Pro. You may also need to update your drivers, DirectX, or Windows to ensure compatibility with CrazyTalk 6 Pro. You can check your system information and update your drivers, DirectX, or Windows by following these steps:</p>
- <ol>
-<li>Click on the Start button and type "system information" in the search box.</li>
-<li>Select System Information from the list of results.</li>
-<li>Check your operating system, processor, memory, graphics card, and sound card information.</li>
-<li>If any of these components do not meet the minimum requirements or are outdated, you need to upgrade them or update their drivers.</li>
-<li>To update your drivers, go to the manufacturer's website and download the latest version of the drivers for your components.</li>
-<li>To update your DirectX, go to Microsoft's website and download the latest version of DirectX for your operating system.</li>
-<li>To update your Windows, go to Windows Update and check for any available updates for your operating system.</li>
-</ol>
- <h3>The steps to install CrazyTalk 6 Pro using the serial and crack key</h3>
- <p>After you have downloaded CrazyTalk 6 Pro from one of the sources we mentioned above and made sure that your computer meets the system requirements and is compatible with the software, you can proceed to install CrazyTalk 6 Pro using the serial and crack key. The steps to install CrazyTalk 6 Pro using the serial and crack key are:</p>
- <ol>
-<li>Extract the downloaded file using a program such as WinRAR (win-rar.com), 7-Zip (7-zip.org), or PeaZip (peazip.org).</li>
-<li>Open the extracted folder and locate the setup file (usually named "setup.exe" or "install.exe").</li>
-<li>Double-click on the setup file and follow the instructions on the screen.</li>
-<li>When prompted to enter a serial key, copy and paste one of the serial keys provided in a text file (usually named "serial.txt" or "key.txt") in the same folder as the setup file.</li>
-<li>Complete the installation process and launch CrazyTalk 6 Pro.</li>
-<li>If you see a message that says "Activation Required" or "Trial Expired", close CrazyTalk 6 Pro and go back to the extracted folder.</li>
-<li>Locate the crack key file (usually named "crack.exe", "patch.exe", or "keygen.exe") in a subfolder (usually named "crack", "patch", or "keygen") in the same folder as the setup file.</li>
-the crack key file and follow the instructions on the screen.</li>
-<li>Copy and paste the crack key file into the installation folder of CrazyTalk 6 Pro (usually located in "C:\Program Files\Reallusion\CrazyTalk 6").</li>
-<li>Replace the original file if asked.</li>
-<li>Launch CrazyTalk 6 Pro again and enjoy using it for free.</li>
-</ol>
- <h3>The common errors and troubleshooting tips for CrazyTalk 6 Pro installation</h3>
- <p>Even if you follow the steps we mentioned above carefully, you may still encounter some errors or problems when installing CrazyTalk 6 Pro using the serial and crack key. Some of the common errors and troubleshooting tips for CrazyTalk 6 Pro installation are:</p>
- <ul>
-<li>Error: "Invalid serial key" or "Serial key already used". Tip: Try using a different serial key from the text file or from another source. Make sure you enter the serial key correctly and do not include any spaces or dashes.</li>
-<li>Error: "Activation failed" or "Activation server not available". Tip: Make sure you have an internet connection and disable any firewall or antivirus program that may block the activation process. Alternatively, use the crack key file to bypass the activation process.</li>
-<li>Error: "File corrupted" or "File missing". Tip: Make sure you have extracted the downloaded file completely and correctly. Do not delete or rename any file in the extracted folder. Scan the extracted folder with an antivirus program to check for any malware or virus.</li>
-<li>Error: "Application error" or "Application crash". Tip: Make sure your computer meets the system requirements and is compatible with CrazyTalk 6 Pro. Update your drivers, DirectX, or Windows to ensure compatibility. Run CrazyTalk 6 Pro as an administrator or in compatibility mode.</li>
-</ul>
- <p>If none of these tips work, you may need to uninstall and reinstall CrazyTalk 6 Pro using a different source or method. You may also need to contact Reallusion's customer support for assistance.</p>
- <h2>How to Use CrazyTalk 6 Pro to Create Amazing Animations</h2>
- <h3>The basic functions and tools of CrazyTalk 6 Pro</h3>
- <p>After you have installed CrazyTalk 6 Pro on your computer successfully, you can start using it to create amazing animations. CrazyTalk 6 Pro has a user-friendly interface that consists of four main sections: The Menu Bar, The Toolbar, The Workspace, and The Timeline. The Menu Bar contains various options and commands that allow you to access different features and settings of CrazyTalk 6 Pro. The Toolbar contains various tools that allow you to perform different actions and tasks on your animation. The Workspace contains various panels that allow you to view and edit your animation. The Timeline contains various tracks that allow you to control and synchronize your animation.</p>
- <p>To create an animation with CrazyTalk 6 Pro, you need to follow these basic steps:</p>
- <ol>
-<li>Import an image into CrazyTalk 6 Pro. You can import any image from your computer, scanner, camera, or online source. You can also use one of the built-in images in CrazyTalk 6 Pro.</li>
-<li>Fit the facial features of your image. You need to adjust the facial fitting points and mask to match the facial features of your image. This will enable CrazyTalk 6 Pro to animate your image accurately.</li>
-<li>Add a voice to your image. You can add a voice to your image by recording your own voice, importing an audio file, or using text-to-speech. You can also edit the voice script, pitch, speed, volume, etc.</li>
-<li>Add emotions and expressions to your image. You can add emotions and expressions to your image by selecting one of the preset emotions or creating your own custom emotions. You can also edit the intensity, duration, transition, etc., of each emotion.</li>
-<li>Add gestures and accessories to your image. You can add gestures and accessories to your image by selecting one of the preset gestures or creating your own custom gestures. You can also edit the position, size, rotation, etc., of each gesture and accessory.</li>
-<li>Add backgrounds and scenes to your animation. You can add backgrounds and scenes to your animation by selecting one of the preset backgrounds or importing your own background image. You can also edit the lighting, shadow, perspective, etc., of each background and scene.</li>
-the preset effects or creating your own custom effects. You can also edit the type, color, speed, etc., of each effect and transition.</li>
-<li>Preview and export your animation. You can preview your animation by playing it in the workspace or in full screen mode. You can also export your animation as a video, image sequence, or flash file. You can also edit the format, quality, resolution, etc., of your output file.</li>
-</ol>
- <h3>The advanced features and tips of CrazyTalk 6 Pro</h3>
- <p>Besides the basic functions and tools of CrazyTalk 6 Pro, there are also some advanced features and tips that can help you create more realistic and impressive animations. Some of the advanced features and tips of CrazyTalk 6 Pro are:</p>
- <ul>
-<li>Use the auto motion feature to generate automatic animations based on the voice and emotion of your image. You can also customize the auto motion settings to suit your preferences.</li>
-<li>Use the face puppet feature to control the facial movements of your image in real time using your mouse or keyboard. You can also record and edit your face puppet performance.</li>
-<li>Use the face transform feature to change the shape and appearance of your image using sliders or presets. You can also create your own face transform presets.</li>
-<li>Use the multi-dimension feature to create 3D-like animations with different angles and perspectives. You can also adjust the depth, distance, and rotation of your image.</li>
-<li>Use the motion clip feature to save and reuse your animations as motion clips. You can also mix and blend different motion clips to create new animations.</li>
-<li>Use the layer manager feature to organize and manage your animation elements as layers. You can also lock, hide, duplicate, delete, or merge layers.</li>
-<li>Use the content manager feature to access and manage your animation content such as images, voices, emotions, gestures, accessories, backgrounds, scenes, effects, etc. You can also import or export content from or to other sources.</li>
-<li>Use the script editor feature to edit and refine your voice script using text editing tools. You can also add pauses, emphasis, punctuation, etc., to your voice script.</li>
-<li>Use the timeline editor feature to edit and synchronize your animation tracks using timeline editing tools. You can also add keyframes, markers, labels, etc., to your timeline.</li>
-</ul>
- <h3>Some examples and tutorials of CrazyTalk 6 Pro animations</h3>
- <p>To give you some inspiration and guidance on how to use CrazyTalk 6 Pro to create amazing animations, here are some examples and tutorials of CrazyTalk 6 Pro animations that you can watch and learn from:</p>
- <table>
-<tr>
-<th>Example</th>
-<th>Tutorial</th>
-</tr>
-<tr>
-<td><a href="https://www.youtube.com/watch?v=0Z8J3axc0oY">How to Create a Talking Dog Animation with CrazyTalk 6 Pro</a></td>
-<td><a href="https://www.youtube.com/watch?v=QXqfRBNbLXw">How to Create a Talking Dog Animation with CrazyTalk 6 Pro - Tutorial</a></td>
-</tr>
-<tr>
-<td><a href="https://www.youtube.com/watch?v=ZCtjtWzBfnI">How to Create a Talking Santa Animation with CrazyTalk 6 Pro</a></td>
-<td><a href="https://www.youtube.com/watch?v=1Q4LfwGQqKo">How to Create a Talking Santa Animation with CrazyTalk 6 Pro - Tutorial</a></td>
-</tr>
-<tr>
-<td><a href="https://www.youtube.com/watch?v=4MzG6lDA1A0">How to Create a Talking Obama Animation with CrazyTalk 6 Pro</a></td>
-<td><a href="https://www.youtube.com/watch?v=2QgZVYFqT7s">How to Create a Talking Obama Animation with CrazyTalk 6 Pro - Tutorial</a></td>
-</tr>
-<tr>
-<td><a href="https://www.youtube.com/watch?v=Hxu50kiDVGc">How to Create a Talking Zombie Animation with CrazyTalk 6 Pro</a></td>
-<td><a href="https://www.youtube.com/watch?v=9XwRyXn5NZY">How to Create a Talking Zombie Animation with CrazyTalk 6 Pro - Tutorial</a></td>
-</tr>
-<tr>
-<td><a href="https://www.youtube.com/watch?v=JLrTejNQGKo">How to Create a Talking Avatar Animation with CrazyTalk 6 Pro</a></td>
-<td><a href="https://www.youtube.com/watch?v=8i7uGdt7y2E">How to Create a Talking Avatar Animation with CrazyTalk 6 Pro - Tutorial</a></td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <h3>A summary of the main points of the article</h3>
- how to install it on your computer, and how to use it to create amazing animations. We have also discussed some of the legal and ethical issues, risks and challenges, and advanced features and tips of CrazyTalk 6 Pro. We hope that this article has been informative and helpful for you. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h3>A call to action for the readers</h3>
- <p>If you are interested in CrazyTalk 6 Pro and want to try it out for yourself, you can download it for free from one of the sources we mentioned above. However, we strongly recommend that you purchase a legitimate copy of CrazyTalk 6 Pro from Reallusion's official website or authorized resellers. By doing so, you will support the developers who created this amazing software. You will also enjoy the full features and benefits of CrazyTalk 6 Pro without any risks or problems.</p>
- <p>To purchase a legitimate copy of CrazyTalk 6 Pro, you can visit Reallusion's official website at <a href="https://www.reallusion.com/crazytalk/">https://www.reallusion.com/crazytalk/</a>. You can also find more information and resources about CrazyTalk 6 Pro on their website, such as features, specifications, tutorials, forums, etc. You can also check out their other products, such as iClone, Character Creator, Cartoon Animator, etc.</p>
- <p>Thank you for reading this article and we hope you have fun creating amazing animations with CrazyTalk 6 Pro!</p>
- <h2>FAQs</h2>
- <h3>What is the difference between CrazyTalk 6 Pro and CrazyTalk 8 Pro?</h3>
- <p>CrazyTalk 8 Pro is the latest version of CrazyTalk that was released in 2016. It has some new and improved features compared to CrazyTalk 6 Pro, such as:</p>
- <ul>
-<li>Support for full-body animation and 3D head creation.</li>
-<li>Support for importing and exporting 3D models and motions.</li>
-<li>Support for creating custom eyes and teeth.</li>
-<li>Support for animating multiple characters in one scene.</li>
-<li>Support for rendering realistic hair and beard.</li>
-<li>Support for VR headset and motion capture devices.</li>
-</ul>
- <p>However, CrazyTalk 8 Pro also has some drawbacks compared to CrazyTalk 6 Pro, such as:</p>
- <ul>
-<li>Higher system requirements and price.</li>
-<li>Less compatibility with older operating systems and devices.</li>
-<li>More complexity and learning curve.</li>
-</ul>
- <p>If you want to learn more about CrazyTalk 8 Pro, you can visit Reallusion's official website at <a href="https://www.reallusion.com/crazytalk/">https://www.reallusion.com/crazytalk/</a>.</p>
- <h3>How can I update my CrazyTalk 6 Pro to the latest version?</h3>
- <p>If you have purchased a legitimate copy of CrazyTalk 6 Pro from Reallusion's official website or authorized resellers, you can update your CrazyTalk 6 Pro to the latest version by following these steps:</p>
- <ol>
-<li>Launch CrazyTalk 6 Pro on your computer.</li>
-<li>Click on the Help menu and select Check for Update.</li>
-<li>If there is a new version available, click on Download Now.</li>
-<li>Follow the instructions on the screen to download and install the update.</li>
-<li>Restart CrazyTalk 6 Pro and enjoy the new features and improvements.</li>
-</ol>
- <p>If you have downloaded a pirated copy of CrazyTalk 6 Pro from one of the sources we mentioned above, you may not be able to update your CrazyTalk 6 Pro to the latest version. You may also lose your serial and crack key or encounter errors or problems after updating. We strongly recommend that you purchase a legitimate copy of CrazyTalk 6 Pro from Reallusion's official website or authorized resellers instead.</p>
- <h3>How can I get more content for my CrazyTalk 6 Pro animations?</h3>
- gestures, accessories, backgrounds, scenes, effects, etc., you have several options:</p>
- <ul>
-<li>You can create your own content using your own photos, audio files, or text. You can also use the built-in tools and features of CrazyTalk 6 Pro to customize your content.</li>
-<li>You can import content from other sources, such as your computer, scanner, camera, or online source. You can also use the built-in templates and presets of CrazyTalk 6 Pro to enhance your content.</li>
-<li>You can purchase content from Reallusion's online store, such as the Content Store or the Marketplace. You can also download free content from Reallusion's online community, such as the Forum or the Gallery.</li>
-</ul>
- <p>To access Reallusion's online store and community, you can visit their official website at <a href="https://www.reallusion.com/crazytalk/">https://www.reallusion.com/crazytalk/</a>. You can also find more information and resources about CrazyTalk 6 Pro on their website, such as features, specifications, tutorials, forums, etc.</p>
- <h3>How can I share my CrazyTalk 6 Pro animations with others?</h3>
- <p>If you want to share your CrazyTalk 6 Pro animations with others, you have several options:</p>
- <ul>
-<li>You can export your animations as a video, image sequence, or flash file. You can then upload or send your output file to any platform or device that supports it.</li>
-<li>You can upload your animations directly to YouTube or Facebook using the built-in upload feature of CrazyTalk 6 Pro. You can then share your link with others.</li>
-<li>You can embed your animations into your website or blog using the built-in embed feature of CrazyTalk 6 Pro. You can then share your URL with others.</li>
-<li>You can burn your animations onto a CD or DVD using the built-in burn feature of CrazyTalk 6 Pro. You can then distribute your disc to others.</li>
-</ul>
- <p>To use the built-in upload, embed, or burn feature of CrazyTalk 6 Pro, you need to have an internet connection and a valid account for each platform. You also need to follow the terms and conditions of each platform.</p>
- <h3>How can I learn more about CrazyTalk 6 Pro?</h3>
- <p>If you want to learn more about CrazyTalk 6 Pro, such as its features, specifications, tutorials, forums, etc., you can visit Reallusion's official website at <a href="https://www.reallusion.com/crazytalk/">https://www.reallusion.com/crazytalk/</a>. You can also contact Reallusion's customer support for assistance.</p>
- <p>Thank you for reading this article and we hope you have fun creating amazing animations with CrazyTalk 6 Pro!</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download Motu Patlu - King Of Kings Full Movie In Hindi Free.md b/spaces/raedeXanto/academic-chatgpt-beta/Download Motu Patlu - King Of Kings Full Movie In Hindi Free.md
deleted file mode 100644
index 4af0538d57cc551340fcc8e450e75fd4d129467f..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download Motu Patlu - King Of Kings Full Movie In Hindi Free.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-<br />
-<h1>Download Motu Patlu - King of Kings Full Movie in Hindi Free</h1>
-<p>If you are looking for a fun and adventurous animated movie to watch with your family or friends, then you should definitely check out <strong>Motu Patlu - King of Kings</strong>. This movie is based on the popular TV series <em>Motu Patlu</em>, which features the hilarious antics of two best friends, Motu and Patlu, who live in Furfuri Nagar. In this movie, Motu and Patlu help a vegetarian circus lion named Guddu Ghalib return to the jungle, where he becomes the king of the animals. However, they also have to face an evil poacher named Narsimha, who wants to capture Guddu and sell him to a circus. Will Motu and Patlu be able to save Guddu and the jungle from Narsimha's clutches? Find out by downloading Motu Patlu - King of Kings full movie in Hindi free from the link below.</p>
-<p>Motu Patlu - King of Kings is a 2016 Indian 3D computer-animated adventure comedy film directed by Suhas D. Kadav and produced by Ketan Mehta. The film was released on 14 October 2016 and became a successful venture at the box office. It received mixed reviews from critics, who appreciated its voice performances, humor, and animation but criticized its plot, violence and editing. The film features the voices of Saurav Chakraborty as Motu and Vinay Pathak as Guddu Ghalib. The film also has a catchy music video composed by Vishal Bhardwaj, which you can watch on YouTube Music.</p>
-<h2>download Motu Patlu - King of Kings full movie in hindi free</h2><br /><p><b><b>Download File</b> &#10084;&#10084;&#10084; <a href="https://tinourl.com/2uL2wc">https://tinourl.com/2uL2wc</a></b></p><br /><br />
-<p>To download Motu Patlu - King of Kings full movie in Hindi free, you need to follow these simple steps:</p>
-<ol>
-<li>Click on the link below to go to the download page.</li>
-<li>Select the quality and format of the movie you want to download.</li>
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-</ol>
-<p>Download link: <a href="https://example.com/download/motupatlu-kingofkings">https://example.com/download/motupatlu-kingofkings</a></p>
-<p></p>
-<p>Note: This is a fan-made article for entertainment purposes only. We do not own or claim any rights to Motu Patlu - King of Kings or any other content related to it. All rights belong to their respective owners. Please support the original creators by watching the movie legally.</p>
-
-<p>In this article, we will introduce you to some of the main characters of Motu Patlu - King of Kings and their roles in the movie. Let's get started!</p>
-<ul>
-<li><strong>Motu</strong>: Motu is a plump and bald man who loves to eat samosas. He is the best friend of Patlu and often gets into trouble because of his impulsive actions. He is brave and loyal, but also naive and gullible. He is voiced by Saurav Chakraborty.</li>
-<li><strong>Patlu</strong>: Patlu is a thin and bespectacled man who is the smartest person in Furfuri Nagar. He is the best friend of Motu and often helps him out of sticky situations. He is calm and rational, but also timid and nervous. He is voiced by Saurav Chakraborty.</li>
-<li><strong>Guddu Ghalib</strong>: Guddu Ghalib is a vegetarian circus lion who likes to eat carrots and radishes. He is the main protagonist of the movie and the king of the jungle. He is kind and gentle, but also cowardly and clumsy. He is voiced by Vinay Pathak.</li>
-<li><strong>Narsimha</strong>: Narsimha is an evil poacher who wants to capture Guddu and sell him to a circus. He is the main antagonist of the movie and the enemy of the jungle. He is cruel and greedy, but also cunning and ruthless. He is voiced by Saurav Chakraborty.</li>
-</ul>
-<p>These are some of the main characters of Motu Patlu - King of Kings. There are many other characters in the movie, such as Dr. Jhatka, Ghasitaram, Chingum, John the Don, Number 1, Number 2, Singha, Simha, etc. You can watch the movie to find out more about them and their adventures.</p>
-<p>We hope you enjoyed this article and learned something new about Motu Patlu - King of Kings. If you want to download Motu Patlu - King of Kings full movie in Hindi free, you can use the link provided above. Thank you for reading!</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rajeshradhakrishnan/english-malayalam/README.md b/spaces/rajeshradhakrishnan/english-malayalam/README.md
deleted file mode 100644
index fa6f20752db432e2c8d8a0550d2c867265a1b5f2..0000000000000000000000000000000000000000
--- a/spaces/rajeshradhakrishnan/english-malayalam/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: English Malayalam
-emoji: 🚀
-colorFrom: gray
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ramkamal2000/voice-conversion-ddp/models.py b/spaces/ramkamal2000/voice-conversion-ddp/models.py
deleted file mode 100644
index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000
--- a/spaces/ramkamal2000/voice-conversion-ddp/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-        
-        
-class SpeakerEncoder(torch.nn.Module):
-    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
-        super(SpeakerEncoder, self).__init__()
-        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
-        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, mels):
-        self.lstm.flatten_parameters()
-        _, (hidden, _) = self.lstm(mels)
-        embeds_raw = self.relu(self.linear(hidden[-1]))
-        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-        
-    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
-        mel_slices = []
-        for i in range(0, total_frames-partial_frames, partial_hop):
-            mel_range = torch.arange(i, i+partial_frames)
-            mel_slices.append(mel_range)
-            
-        return mel_slices
-    
-    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
-        mel_len = mel.size(1)
-        last_mel = mel[:,-partial_frames:]
-        
-        if mel_len > partial_frames:
-            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
-            mels = list(mel[:,s] for s in mel_slices)
-            mels.append(last_mel)
-            mels = torch.stack(tuple(mels), 0).squeeze(1)
-        
-            with torch.no_grad():
-                partial_embeds = self(mels)
-            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
-            #embed = embed / torch.linalg.norm(embed, 2)
-        else:
-            with torch.no_grad():
-                embed = self(last_mel)
-        
-        return embed
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    gin_channels,
-    ssl_dim,
-    use_spk,
-    **kwargs):
-
-    super().__init__()
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.gin_channels = gin_channels
-    self.ssl_dim = ssl_dim
-    self.use_spk = use_spk
-
-    self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) 
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-    
-    if not self.use_spk:
-      self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
-
-  def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
-    if c_lengths == None:
-      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-    if spec_lengths == None:
-      spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-      
-    if not self.use_spk:
-      g = self.enc_spk(mel.transpose(1,2))
-    g = g.unsqueeze(-1)
-      
-    _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
-    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
-    z_p = self.flow(z, spec_mask, g=g)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    
-    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, c, g=None, mel=None, c_lengths=None):
-    if c_lengths == None:
-      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-    if not self.use_spk:
-      g = self.enc_spk.embed_utterance(mel.transpose(1,2))
-    g = g.unsqueeze(-1)
-
-    z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
-    z = self.flow(z_p, c_mask, g=g, reverse=True)
-    o = self.dec(z * c_mask, g=g)
-    
-    return o
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Oleta-Adams-Discography-Torrent.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Oleta-Adams-Discography-Torrent.md
deleted file mode 100644
index 9c12e1996a9866be597cb4ebfd2ceee82dd808df..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Oleta-Adams-Discography-Torrent.md
+++ /dev/null
@@ -1,71 +0,0 @@
-## Oleta Adams Discography Torrent
-
-
-
-**Download 🆓 [https://www.google.com/url?q=https%3A%2F%2Ftinurll.com%2F2twEIS&sa=D&sntz=1&usg=AOvVaw26Z30L0llDRE2fiIRpI4KU](https://www.google.com/url?q=https%3A%2F%2Ftinurll.com%2F2twEIS&sa=D&sntz=1&usg=AOvVaw26Z30L0llDRE2fiIRpI4KU)**
-
-
-
- ```markdown 
-
-# Oleta Adams Discography Torrent: How to Download Her Albums in High Quality
-
-  
-
-Oleta Adams is a soulful singer and pianist who has been making music since the 1980s. She is best known for her collaboration with Tears for Fears on the song "Woman in Chains" and her hit single "Get Here". She has released nine studio albums, spanning pop, R&B, gospel, and jazz genres.
-
-  
-
-If you are a fan of Oleta Adams and want to download her discography torrent, you might be wondering where to find it and how to do it safely and legally. In this article, we will give you some tips and tricks on how to get her albums in high quality.
-
-  
-
-## Where to Find Oleta Adams Discography Torrent
-
-  
-
-There are many websites that offer torrent files for music, but not all of them are reliable or trustworthy. Some of them might contain viruses, malware, or fake files that can harm your computer or device. Some of them might also violate the copyright laws and infringe on the rights of the artists and labels.
-
-  
-
-Therefore, you should be careful when choosing a torrent site to download Oleta Adams discography torrent. Here are some of the factors you should consider:
-
-  
-
-- The reputation and popularity of the site. You can check online reviews, ratings, comments, and feedback from other users to see if the site is trustworthy and safe.
-
-- The availability and quality of the torrent files. You can check the number of seeders, leechers, peers, and downloads to see if the torrent files are active and fast. You can also check the file size, format, bitrate, and resolution to see if the files are high quality.
-
-- The legality and ethics of the site. You can check the terms of service, privacy policy, disclaimer, and copyright notice of the site to see if they respect the rights of the artists and labels. You can also check if they have a DMCA takedown policy or a legal disclaimer.
-
-
-
-Based on these factors, some of the possible sites where you can find Oleta Adams discography torrent are:
-
-
-
-- [Discogs](https://www.discogs.com/artist/5877-Oleta-Adams): This is a music database and marketplace that offers information, images, and audio samples of Oleta Adams albums. You can also buy or sell physical copies of her CDs, vinyls, cassettes, or digital downloads.
-
-- [AllMusic](https://www.allmusic.com/artist/oleta-adams-mn0000887806/discography): This is a music review and recommendation site that offers biographies, discographies, songs, credits, and ratings of Oleta Adams albums. You can also stream or download her songs from various platforms like Spotify, Apple Music, YouTube Music, or Amazon Music.
-
-- [Qobuz](https://www.qobuz.com/gb-en/interpreter/oleta-adams/53132): This is a music streaming and download service that offers high-resolution audio files of Oleta Adams albums. You can also access editorial content, playlists, podcasts, and radio stations related to her music.
-
-
-
-## How to Download Oleta Adams Discography Torrent
-
-  
-
-Once you have found a reliable and legal site that offers Oleta Adams discography torrent, you will need a torrent client to download it. A torrent client is a software that allows you to connect to other users who have the same torrent file and share data with them.
-
-  
-
-There are many torrent clients available online, but some of them might be unsafe or incompatible with your device. Some of the factors you should consider when choosing a torrent client are:
-
-
-
-- The compatibility and performance of the software. You should check if the software is compatible with your operating system, device, browser, or firewall. You should also check if it has any bugs, errors, or crashes that might affect your downloading experience.
-
-- The features and functions of the software. You should check if the software has any advanced features or functions that might enhance your downloading experience. For example, some software might have encryption, proxy support, bandwidth control, magnet links support, or streaming capabilities.
-
-- The security and privacy of the software. You should check if the software has any security or privacy measures that might protect your data and identity from hackers or trackers. For example, some software 1b8d091108
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Andhra Pori Telugu Full Movie Download !FULL!.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Andhra Pori Telugu Full Movie Download !FULL!.md
deleted file mode 100644
index 0aae0564d24cfd14fb4440226b2b9d9eb9863bf4..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Andhra Pori Telugu Full Movie Download !FULL!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>andhra pori telugu full movie download</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://urlgoal.com/2uCMcX">https://urlgoal.com/2uCMcX</a></b></p><br /><br />
-
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Drag Me To Hell In Hindi Torrent BEST.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Drag Me To Hell In Hindi Torrent BEST.md
deleted file mode 100644
index 10f92c38dd0b47b52e3efc57f1862832e7aa3605..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Drag Me To Hell In Hindi Torrent BEST.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Drag Me To Hell In Hindi Torrent</h2><br /><p><b><b>Download File</b> &rArr;&rArr;&rArr; <a href="https://urlgoal.com/2uCM35">https://urlgoal.com/2uCM35</a></b></p><br /><br />
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/EYE007 Nao Ayukawa REPACK.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/EYE007 Nao Ayukawa REPACK.md
deleted file mode 100644
index 5d95554d24b384df79319fc564064acfc2ad3725..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/EYE007 Nao Ayukawa REPACK.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>EYE-007 3 Mother-in-law 4 Sibling Incest Popular Nao Ayukawa (2007) [1121] / Full Movie / EYE-007 Movie A. EYE-007 Nao Ayukawa.avi, 222.37MB, 0, EYE-007 Incest, 2017-04-09 13:58:12 (2017-05-05 02:58:19) 2017-05-05 02:58:19. Movies. Nao Ayukawa @ Ayukawa Nao. Movie ID: EYE-007. EYE-007. EM051 (Re-upload) Nao Ayukawa Photos (3 pictures). EYE-007 Nao Ayukawa (2007). </p>
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\ No newline at end of file
diff --git a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/scripts/test.sh b/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/scripts/test.sh
deleted file mode 100644
index a7a3d7ec6d2a3572bbb699f935aefd8c575e768e..0000000000000000000000000000000000000000
--- a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/scripts/test.sh
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env bash
-set -ex
-
-# Training
-GPU_ID=0
-DISPLAY_ID=$((GPU_ID*10+10))
-NAME='spaces_demo'
-
-# Network configuration
-
-BATCH_SIZE=1
-MLP_DIM='257 1024 512 256 128 1'
-MLP_DIM_COLOR='513 1024 512 256 128 3'
-
-# Reconstruction resolution
-# NOTE: one can change here to reconstruct mesh in a different resolution.
-# VOL_RES=256
-
-# CHECKPOINTS_NETG_PATH='./checkpoints/net_G'
-# CHECKPOINTS_NETC_PATH='./checkpoints/net_C'
-
-# TEST_FOLDER_PATH='./sample_images'
-
-# command
-CUDA_VISIBLE_DEVICES=${GPU_ID} python ./apps/eval_spaces.py \
-    --name ${NAME} \
-    --batch_size ${BATCH_SIZE} \
-    --mlp_dim ${MLP_DIM} \
-    --mlp_dim_color ${MLP_DIM_COLOR} \
-    --num_stack 4 \
-    --num_hourglass 2 \
-    --resolution ${VOL_RES} \
-    --hg_down 'ave_pool' \
-    --norm 'group' \
-    --norm_color 'group' \
-    --load_netG_checkpoint_path ${CHECKPOINTS_NETG_PATH} \
-    --load_netC_checkpoint_path ${CHECKPOINTS_NETC_PATH} \
-    --results_path ${RESULTS_PATH} \
-    --img_path ${INPUT_IMAGE_PATH} 
\ No newline at end of file
diff --git a/spaces/riccorl/relik-entity-linking/relik/reader/pytorch_modules/span.py b/spaces/riccorl/relik-entity-linking/relik/reader/pytorch_modules/span.py
deleted file mode 100644
index 349e42cafc1dfbc583adc46e7c8cf63d1d3752d8..0000000000000000000000000000000000000000
--- a/spaces/riccorl/relik-entity-linking/relik/reader/pytorch_modules/span.py
+++ /dev/null
@@ -1,367 +0,0 @@
-import collections
-import contextlib
-import logging
-from typing import Any, Dict, Iterator, List
-
-import torch
-import transformers as tr
-from lightning_fabric.utilities import move_data_to_device
-from torch.utils.data import DataLoader, IterableDataset
-from tqdm import tqdm
-
-from relik.common.log import get_console_logger, get_logger
-from relik.common.utils import get_callable_from_string
-from relik.reader.data.relik_reader_sample import RelikReaderSample
-from relik.reader.pytorch_modules.base import RelikReaderBase
-from relik.reader.utils.special_symbols import get_special_symbols
-from relik.retriever.pytorch_modules import PRECISION_MAP
-
-console_logger = get_console_logger()
-logger = get_logger(__name__, level=logging.INFO)
-
-
-class RelikReaderForSpanExtraction(RelikReaderBase):
-    """
-    A class for the RelikReader model for span extraction.
-
-    Args:
-        transformer_model (:obj:`str` or :obj:`transformers.PreTrainedModel` or :obj:`None`, `optional`):
-            The transformer model to use. If `None`, the default model is used.
-        additional_special_symbols (:obj:`int`, `optional`, defaults to 0):
-            The number of additional special symbols to add to the tokenizer.
-        num_layers (:obj:`int`, `optional`):
-            The number of layers to use. If `None`, all layers are used.
-        activation (:obj:`str`, `optional`, defaults to "gelu"):
-            The activation function to use.
-        linears_hidden_size (:obj:`int`, `optional`, defaults to 512):
-            The hidden size of the linears.
-        use_last_k_layers (:obj:`int`, `optional`, defaults to 1):
-            The number of last layers to use.
-        training (:obj:`bool`, `optional`, defaults to False):
-            Whether the model is in training mode.
-        device (:obj:`str` or :obj:`torch.device` or :obj:`None`, `optional`):
-            The device to use. If `None`, the default device is used.
-        tokenizer (:obj:`str` or :obj:`transformers.PreTrainedTokenizer` or :obj:`None`, `optional`):
-            The tokenizer to use. If `None`, the default tokenizer is used.
-        dataset (:obj:`IterableDataset` or :obj:`str` or :obj:`None`, `optional`):
-            The dataset to use. If `None`, the default dataset is used.
-        dataset_kwargs (:obj:`Dict[str, Any]` or :obj:`None`, `optional`):
-            The keyword arguments to pass to the dataset class.
-        default_reader_class (:obj:`str` or :obj:`transformers.PreTrainedModel` or :obj:`None`, `optional`):
-            The default reader class to use. If `None`, the default reader class is used.
-        **kwargs:
-            Keyword arguments.
-    """
-
-    default_reader_class: str = (
-        "relik.reader.pytorch_modules.hf.modeling_relik.RelikReaderSpanModel"
-    )
-    default_data_class: str = "relik.reader.data.relik_reader_data.RelikDataset"
-
-    def __init__(
-        self,
-        transformer_model: str | tr.PreTrainedModel | None = None,
-        additional_special_symbols: int = 0,
-        num_layers: int | None = None,
-        activation: str = "gelu",
-        linears_hidden_size: int | None = 512,
-        use_last_k_layers: int = 1,
-        training: bool = False,
-        device: str | torch.device | None = None,
-        tokenizer: str | tr.PreTrainedTokenizer | None = None,
-        dataset: IterableDataset | str | None = None,
-        dataset_kwargs: Dict[str, Any] | None = None,
-        default_reader_class: tr.PreTrainedModel | str | None = None,
-        **kwargs,
-    ):
-        super().__init__(
-            transformer_model=transformer_model,
-            additional_special_symbols=additional_special_symbols,
-            num_layers=num_layers,
-            activation=activation,
-            linears_hidden_size=linears_hidden_size,
-            use_last_k_layers=use_last_k_layers,
-            training=training,
-            device=device,
-            tokenizer=tokenizer,
-            dataset=dataset,
-            default_reader_class=default_reader_class,
-            **kwargs,
-        )
-        # and instantiate the dataset class
-        self.dataset = dataset
-        if self.dataset is None:
-            default_data_kwargs = dict(
-                dataset_path=None,
-                materialize_samples=False,
-                transformer_model=self.tokenizer,
-                special_symbols=get_special_symbols(
-                    self.relik_reader_model.config.additional_special_symbols
-                ),
-                for_inference=True,
-            )
-            # merge the default data kwargs with the ones passed to the model
-            default_data_kwargs.update(dataset_kwargs or {})
-            self.dataset = get_callable_from_string(self.default_data_class)(
-                **default_data_kwargs
-            )
-
-    @torch.no_grad()
-    @torch.inference_mode()
-    def _read(
-        self,
-        samples: List[RelikReaderSample] | None = None,
-        input_ids: torch.Tensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        token_type_ids: torch.Tensor | None = None,
-        prediction_mask: torch.Tensor | None = None,
-        special_symbols_mask: torch.Tensor | None = None,
-        max_length: int = 1000,
-        max_batch_size: int = 128,
-        token_batch_size: int = 2048,
-        precision: str = 32,
-        annotation_type: str = "char",
-        progress_bar: bool = False,
-        *args: object,
-        **kwargs: object,
-    ) -> List[RelikReaderSample] | List[List[RelikReaderSample]]:
-        """
-        A wrapper around the forward method that returns the predicted labels for each sample.
-
-        Args:
-            samples (:obj:`List[RelikReaderSample]`, `optional`):
-                The samples to read. If provided, `text` and `candidates` are ignored.
-            input_ids (:obj:`torch.Tensor`, `optional`):
-                The input ids of the text. If `samples` is provided, this is ignored.
-            attention_mask (:obj:`torch.Tensor`, `optional`):
-                The attention mask of the text. If `samples` is provided, this is ignored.
-            token_type_ids (:obj:`torch.Tensor`, `optional`):
-                The token type ids of the text. If `samples` is provided, this is ignored.
-            prediction_mask (:obj:`torch.Tensor`, `optional`):
-                The prediction mask of the text. If `samples` is provided, this is ignored.
-            special_symbols_mask (:obj:`torch.Tensor`, `optional`):
-                The special symbols mask of the text. If `samples` is provided, this is ignored.
-            max_length (:obj:`int`, `optional`, defaults to 1000):
-                The maximum length of the text.
-            max_batch_size (:obj:`int`, `optional`, defaults to 128):
-                The maximum batch size.
-            token_batch_size (:obj:`int`, `optional`):
-                The token batch size.
-            progress_bar (:obj:`bool`, `optional`, defaults to False):
-                Whether to show a progress bar.
-            precision (:obj:`str`, `optional`, defaults to 32):
-                The precision to use for the model.
-            annotation_type (:obj:`str`, `optional`, defaults to "char"):
-                The annotation type to use. It can be either "char", "token" or "word".
-            *args:
-                Positional arguments.
-            **kwargs:
-                Keyword arguments.
-
-        Returns:
-            :obj:`List[RelikReaderSample]` or :obj:`List[List[RelikReaderSample]]`:
-                The predicted labels for each sample.
-        """
-
-        precision = precision or self.precision
-        if samples is not None:
-
-            def _read_iterator():
-                def samples_it():
-                    for i, sample in enumerate(samples):
-                        assert sample._mixin_prediction_position is None
-                        sample._mixin_prediction_position = i
-                        yield sample
-
-                next_prediction_position = 0
-                position2predicted_sample = {}
-
-                # instantiate dataset
-                if self.dataset is None:
-                    raise ValueError(
-                        "You need to pass a dataset to the model in order to predict"
-                    )
-                self.dataset.samples = samples_it()
-                self.dataset.model_max_length = max_length
-                self.dataset.tokens_per_batch = token_batch_size
-                self.dataset.max_batch_size = max_batch_size
-
-                # instantiate dataloader
-                iterator = DataLoader(
-                    self.dataset, batch_size=None, num_workers=0, shuffle=False
-                )
-                if progress_bar:
-                    iterator = tqdm(iterator, desc="Predicting with RelikReader")
-
-                # fucking autocast only wants pure strings like 'cpu' or 'cuda'
-                # we need to convert the model device to that
-                device_type_for_autocast = str(self.device).split(":")[0]
-                # autocast doesn't work with CPU and stuff different from bfloat16
-                autocast_mngr = (
-                    contextlib.nullcontext()
-                    if device_type_for_autocast == "cpu"
-                    else (
-                        torch.autocast(
-                            device_type=device_type_for_autocast,
-                            dtype=PRECISION_MAP[precision],
-                        )
-                    )
-                )
-
-                with autocast_mngr:
-                    for batch in iterator:
-                        batch = move_data_to_device(batch, self.device)
-                        batch_out = self._batch_predict(**batch)
-
-                        for sample in batch_out:
-                            if (
-                                sample._mixin_prediction_position
-                                >= next_prediction_position
-                            ):
-                                position2predicted_sample[
-                                    sample._mixin_prediction_position
-                                ] = sample
-
-                        # yield
-                        while next_prediction_position in position2predicted_sample:
-                            yield position2predicted_sample[next_prediction_position]
-                            del position2predicted_sample[next_prediction_position]
-                            next_prediction_position += 1
-
-            outputs = list(_read_iterator())
-            for sample in outputs:
-                self.dataset.merge_patches_predictions(sample)
-                self.dataset.convert_tokens_to_char_annotations(sample)
-
-        else:
-            outputs = list(
-                self._batch_predict(
-                    input_ids,
-                    attention_mask,
-                    token_type_ids,
-                    prediction_mask,
-                    special_symbols_mask,
-                    *args,
-                    **kwargs,
-                )
-            )
-        return outputs
-
-    def _batch_predict(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor,
-        token_type_ids: torch.Tensor | None = None,
-        prediction_mask: torch.Tensor | None = None,
-        special_symbols_mask: torch.Tensor | None = None,
-        sample: List[RelikReaderSample] | None = None,
-        top_k: int = 5,  # the amount of top-k most probable entities to predict
-        *args,
-        **kwargs,
-    ) -> Iterator[RelikReaderSample]:
-        """
-        A wrapper around the forward method that returns the predicted labels for each sample.
-        It also adds the predicted labels to the samples.
-
-        Args:
-            input_ids (:obj:`torch.Tensor`):
-                The input ids of the text.
-            attention_mask (:obj:`torch.Tensor`):
-                The attention mask of the text.
-            token_type_ids (:obj:`torch.Tensor`, `optional`):
-                The token type ids of the text.
-            prediction_mask (:obj:`torch.Tensor`, `optional`):
-                The prediction mask of the text.
-            special_symbols_mask (:obj:`torch.Tensor`, `optional`):
-                The special symbols mask of the text.
-            sample (:obj:`List[RelikReaderSample]`, `optional`):
-                The samples to read. If provided, `text` and `candidates` are ignored.
-            top_k (:obj:`int`, `optional`, defaults to 5):
-                The amount of top-k most probable entities to predict.
-            *args:
-                Positional arguments.
-            **kwargs:
-                Keyword arguments.
-
-        Returns:
-            The predicted labels for each sample.
-        """
-        forward_output = self.forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            prediction_mask=prediction_mask,
-            special_symbols_mask=special_symbols_mask,
-        )
-
-        ned_start_predictions = forward_output["ned_start_predictions"].cpu().numpy()
-        ned_end_predictions = forward_output["ned_end_predictions"].cpu().numpy()
-        ed_predictions = forward_output["ed_predictions"].cpu().numpy()
-        ed_probabilities = forward_output["ed_probabilities"].cpu().numpy()
-
-        batch_predictable_candidates = kwargs["predictable_candidates"]
-        patch_offset = kwargs["patch_offset"]
-        for ts, ne_sp, ne_ep, edp, edpr, pred_cands, po in zip(
-            sample,
-            ned_start_predictions,
-            ned_end_predictions,
-            ed_predictions,
-            ed_probabilities,
-            batch_predictable_candidates,
-            patch_offset,
-        ):
-            ne_start_indices = [ti for ti, c in enumerate(ne_sp[1:]) if c > 0]
-            ne_end_indices = [ti for ti, c in enumerate(ne_ep[1:]) if c > 0]
-
-            final_class2predicted_spans = collections.defaultdict(list)
-            spans2predicted_probabilities = dict()
-            for start_token_index, end_token_index in zip(
-                ne_start_indices, ne_end_indices
-            ):
-                # predicted candidate
-                token_class = edp[start_token_index + 1] - 1
-                predicted_candidate_title = pred_cands[token_class]
-                final_class2predicted_spans[predicted_candidate_title].append(
-                    [start_token_index, end_token_index]
-                )
-
-                # candidates probabilities
-                classes_probabilities = edpr[start_token_index + 1]
-                classes_probabilities_best_indices = classes_probabilities.argsort()[
-                    ::-1
-                ]
-                titles_2_probs = []
-                top_k = (
-                    min(
-                        top_k,
-                        len(classes_probabilities_best_indices),
-                    )
-                    if top_k != -1
-                    else len(classes_probabilities_best_indices)
-                )
-                for i in range(top_k):
-                    titles_2_probs.append(
-                        (
-                            pred_cands[classes_probabilities_best_indices[i] - 1],
-                            classes_probabilities[
-                                classes_probabilities_best_indices[i]
-                            ].item(),
-                        )
-                    )
-                spans2predicted_probabilities[
-                    (start_token_index, end_token_index)
-                ] = titles_2_probs
-
-            if "patches" not in ts._d:
-                ts._d["patches"] = dict()
-
-            ts._d["patches"][po] = dict()
-            sample_patch = ts._d["patches"][po]
-
-            sample_patch["predicted_window_labels"] = final_class2predicted_spans
-            sample_patch["span_title_probabilities"] = spans2predicted_probabilities
-
-            # additional info
-            sample_patch["predictable_candidates"] = pred_cands
-
-            yield ts
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/iou_loss.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/iou_loss.py
deleted file mode 100644
index bf1ed04e1903d19ee339bd131b897df5b51d311a..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/iou_loss.py
+++ /dev/null
@@ -1,474 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-import warnings
-
-import mmcv
-import torch
-import torch.nn as nn
-
-from mmdet.core import bbox_overlaps
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def iou_loss(pred, target, linear=False, mode='log', eps=1e-6):
-    """IoU loss.
-
-    Computing the IoU loss between a set of predicted bboxes and target bboxes.
-    The loss is calculated as negative log of IoU.
-
-    Args:
-        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
-        linear (bool, optional): If True, use linear scale of loss instead of
-            log scale. Default: False.
-        mode (str): Loss scaling mode, including "linear", "square", and "log".
-            Default: 'log'
-        eps (float): Eps to avoid log(0).
-
-    Return:
-        torch.Tensor: Loss tensor.
-    """
-    assert mode in ['linear', 'square', 'log']
-    if linear:
-        mode = 'linear'
-        warnings.warn('DeprecationWarning: Setting "linear=True" in '
-                      'iou_loss is deprecated, please use "mode=`linear`" '
-                      'instead.')
-    ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
-    if mode == 'linear':
-        loss = 1 - ious
-    elif mode == 'square':
-        loss = 1 - ious**2
-    elif mode == 'log':
-        loss = -ious.log()
-    else:
-        raise NotImplementedError
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
-    """BIoULoss.
-
-    This is an implementation of paper
-    `Improving Object Localization with Fitness NMS and Bounded IoU Loss.
-    <https://arxiv.org/abs/1711.00164>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted bboxes.
-        target (torch.Tensor): Target bboxes.
-        beta (float): beta parameter in smoothl1.
-        eps (float): eps to avoid NaN.
-    """
-    pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
-    pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
-    pred_w = pred[:, 2] - pred[:, 0]
-    pred_h = pred[:, 3] - pred[:, 1]
-    with torch.no_grad():
-        target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
-        target_ctry = (target[:, 1] + target[:, 3]) * 0.5
-        target_w = target[:, 2] - target[:, 0]
-        target_h = target[:, 3] - target[:, 1]
-
-    dx = target_ctrx - pred_ctrx
-    dy = target_ctry - pred_ctry
-
-    loss_dx = 1 - torch.max(
-        (target_w - 2 * dx.abs()) /
-        (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
-    loss_dy = 1 - torch.max(
-        (target_h - 2 * dy.abs()) /
-        (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
-    loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
-                            (target_w + eps))
-    loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
-                            (target_h + eps))
-    # view(..., -1) does not work for empty tensor
-    loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
-                            dim=-1).flatten(1)
-
-    loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
-                       loss_comb - 0.5 * beta)
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def giou_loss(pred, target, eps=1e-7):
-    r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
-    Box Regression <https://arxiv.org/abs/1902.09630>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
-        eps (float): Eps to avoid log(0).
-
-    Return:
-        Tensor: Loss tensor.
-    """
-    gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps)
-    loss = 1 - gious
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def diou_loss(pred, target, eps=1e-7):
-    r"""`Implementation of Distance-IoU Loss: Faster and Better
-    Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.
-
-    Code is modified from https://github.com/Zzh-tju/DIoU.
-
-    Args:
-        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (Tensor): Corresponding gt bboxes, shape (n, 4).
-        eps (float): Eps to avoid log(0).
-    Return:
-        Tensor: Loss tensor.
-    """
-    # overlap
-    lt = torch.max(pred[:, :2], target[:, :2])
-    rb = torch.min(pred[:, 2:], target[:, 2:])
-    wh = (rb - lt).clamp(min=0)
-    overlap = wh[:, 0] * wh[:, 1]
-
-    # union
-    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
-    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
-    union = ap + ag - overlap + eps
-
-    # IoU
-    ious = overlap / union
-
-    # enclose area
-    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
-    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
-    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
-    cw = enclose_wh[:, 0]
-    ch = enclose_wh[:, 1]
-
-    c2 = cw**2 + ch**2 + eps
-
-    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
-    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
-    b2_x1, b2_y1 = target[:, 0], target[:, 1]
-    b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
-    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
-    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
-    rho2 = left + right
-
-    # DIoU
-    dious = ious - rho2 / c2
-    loss = 1 - dious
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def ciou_loss(pred, target, eps=1e-7):
-    r"""`Implementation of paper `Enhancing Geometric Factors into
-    Model Learning and Inference for Object Detection and Instance
-    Segmentation <https://arxiv.org/abs/2005.03572>`_.
-
-    Code is modified from https://github.com/Zzh-tju/CIoU.
-
-    Args:
-        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (Tensor): Corresponding gt bboxes, shape (n, 4).
-        eps (float): Eps to avoid log(0).
-    Return:
-        Tensor: Loss tensor.
-    """
-    # overlap
-    lt = torch.max(pred[:, :2], target[:, :2])
-    rb = torch.min(pred[:, 2:], target[:, 2:])
-    wh = (rb - lt).clamp(min=0)
-    overlap = wh[:, 0] * wh[:, 1]
-
-    # union
-    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
-    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
-    union = ap + ag - overlap + eps
-
-    # IoU
-    ious = overlap / union
-
-    # enclose area
-    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
-    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
-    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
-    cw = enclose_wh[:, 0]
-    ch = enclose_wh[:, 1]
-
-    c2 = cw**2 + ch**2 + eps
-
-    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
-    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
-    b2_x1, b2_y1 = target[:, 0], target[:, 1]
-    b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
-    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
-    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-
-    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
-    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
-    rho2 = left + right
-
-    factor = 4 / math.pi**2
-    v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
-
-    with torch.no_grad():
-        alpha = (ious > 0.5).float() * v / (1 - ious + v)
-
-    # CIoU
-    cious = ious - (rho2 / c2 + alpha * v)
-    loss = 1 - cious.clamp(min=-1.0, max=1.0)
-    return loss
-
-
-@LOSSES.register_module()
-class IoULoss(nn.Module):
-    """IoULoss.
-
-    Computing the IoU loss between a set of predicted bboxes and target bboxes.
-
-    Args:
-        linear (bool): If True, use linear scale of loss else determined
-            by mode. Default: False.
-        eps (float): Eps to avoid log(0).
-        reduction (str): Options are "none", "mean" and "sum".
-        loss_weight (float): Weight of loss.
-        mode (str): Loss scaling mode, including "linear", "square", and "log".
-            Default: 'log'
-    """
-
-    def __init__(self,
-                 linear=False,
-                 eps=1e-6,
-                 reduction='mean',
-                 loss_weight=1.0,
-                 mode='log'):
-        super(IoULoss, self).__init__()
-        assert mode in ['linear', 'square', 'log']
-        if linear:
-            mode = 'linear'
-            warnings.warn('DeprecationWarning: Setting "linear=True" in '
-                          'IOULoss is deprecated, please use "mode=`linear`" '
-                          'instead.')
-        self.mode = mode
-        self.linear = linear
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction.
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None. Options are "none", "mean" and "sum".
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if (weight is not None) and (not torch.any(weight > 0)) and (
-                reduction != 'none'):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # iou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * iou_loss(
-            pred,
-            target,
-            weight,
-            mode=self.mode,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class BoundedIoULoss(nn.Module):
-
-    def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0):
-        super(BoundedIoULoss, self).__init__()
-        self.beta = beta
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss = self.loss_weight * bounded_iou_loss(
-            pred,
-            target,
-            weight,
-            beta=self.beta,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class GIoULoss(nn.Module):
-
-    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
-        super(GIoULoss, self).__init__()
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # giou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * giou_loss(
-            pred,
-            target,
-            weight,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class DIoULoss(nn.Module):
-
-    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
-        super(DIoULoss, self).__init__()
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # giou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * diou_loss(
-            pred,
-            target,
-            weight,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class CIoULoss(nn.Module):
-
-    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
-        super(CIoULoss, self).__init__()
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # giou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * ciou_loss(
-            pred,
-            target,
-            weight,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
diff --git a/spaces/rorallitri/biomedical-language-models/logs/David Tejeda Joins The Last Resort Learn More About His Role and Background as a Licensed Therapist.md b/spaces/rorallitri/biomedical-language-models/logs/David Tejeda Joins The Last Resort Learn More About His Role and Background as a Licensed Therapist.md
deleted file mode 100644
index 16317e0a1031cb968cb6c77f77906ac9b329a94e..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/David Tejeda Joins The Last Resort Learn More About His Role and Background as a Licensed Therapist.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-
-<p>There are several more projects in the works that could in one way or another expand the Northeast ski roster. The most exciting of these is up at Big Squaw, Maine, where new owners \u2013 if they can ever get the deal done \u2013 would at long last re-open the dormant upper mountain (and rename the ski area), increasing the vertical drop from its current 600 feet to 1,700. Les Otten continues his quest to re-open the former Wilderness ski area as The Balsams and transform it into the largest resort in New England. It looks amazing:</p>
-<p>Unfortunately, the last owner, Jeff Katofsky, insisted Sugar Loaf could only be resurrected as some kind of luxury resort, and went to great expense to strip the five old double chairs before selling the place in late 2020 to a mysterious \u201Cprivate buyer.\u201D That owner, \u201Cknown only as SPV 45 LLC,\u201D according to the Traverse City Record Eagle, finally tore down the derelict base buildings late last year. Their intentions beyond that are unclear.</p>
-<h2>David Tejeda Joins The Last Resort</h2><br /><p><b><b>Download Zip</b> ---> <a href="https://tinurll.com/2uzmiz">https://tinurll.com/2uzmiz</a></b></p><br /><br />
-<p><strong>Tim: </strong>Your simplification is pretty spot on. Minimum infrastructure to get going again would be a new/used lift, say $2 million will cover that with installation. For snowmaking pipe/pond/pumps/electrical for the frontside (Round About, Nastar, Waful, k2 and Awful Awful) 25 acres or so (that\u2019s twice the size of Waterville Valley\u2019s high country) feed that with 2,000 gpm giving an acre foot of snow every 1.33 hours, so 33 hours at full blast to cover the area with a foot of snow. To blast that type of snow the area would need at least 20 guns but I would recommend 40 as maxing out requires extreme cold. That\u2019s 500k in guns, 500k in pumping, $1.5M for pipe, hydrants, electrical (bit of a WAG as I don\u2019t know what is still there for primary electrical). A steel building lodge like Crotched runs about $2.5 but a yurt village would be doable for a few 100k so let say $1M for now. Parking is still in OK shape but I think the thing that would kill the project is the demo of the old lodge, but the county or state may help as it is a public nuisance at this point, so let\u2019s say the fire department does a controlled burn \uD83D\uDD25 All said and done on a shoe string $5M should get it up and running. But if it was me I would go all in as I think the area could support a higher end resort, just need the right money man \uD83D\uDE0E. This is off the top of my head, some day it would be fun to run these numbers down and see if something could really work.</p>
-<p>Ski area operators are fairly secretive about their finances, but the basic structure is this: every time you swipe your Ikon or Epic or Indy or Mountain Collective Pass at Schweitzer or Telluride or Jay Peak or Grand Targhee, the mountain gets a payout. This payout, or \u201Cyield\u201D is a percentage of the so-called \u201Crack rate,\u201D or walk-up lift ticket price. Several sources with knowledge of the machinery behind these things have indicated to me that Mountain Collective and Indy tend to pay the highest yields. Indy Pass founder Doug Fish told me last week that Powder Mountain received \u201Cnear rack rate\u201D for weekend redemptions this season. Telluride\u2019s then-CEO Bill Jensen said a few years back that each Epic Pass swipe yielded roughly the same payout as a guest buying a multi-day lift ticket (though I can\u2019t seem to locate that article). Reciprocal lift tickets of the sort you find on Ski Cooper\u2019s season pass are a whole different thing. When Ski Cooper and Holiday Valley agree to give one another\u2019s passholders three lift tickets each, no money is exchanged between resorts. They are just comp tickets.</p>
-<p>KSL Capital has owned Camelback since 2019, and, last year, also purchased nearby Blue Mountain. Its KSL Resorts division manages both properties. Curiously, KSL Capital is, along with Aspen owners Henry Crown, a co-owner of Alterra Mountain Company, overlord of the Ikon Pass. KSL reportedly stuck their Poconos mountains under the purview of their luxury resort manager, rather than Alterra, because they were \u201Ctoo small,\u201D which is nonsense. Vail owns at least a dozen resorts smaller than Camelback. Alterra CEO Rusty Gregory did, however, tell me on the podcast last year that we could see Camelback (and, hopefully, by extension, Blue), on the Ikon Pass at some point. Although when Boyne opted to keep its new Shawnee Peak ski area off the pass for next season, it signaled, along with Ikon\u2019s dramatic access changes, that volume management is crucial to the pass\u2019 self-image at the moment. While I don\u2019t expect to see Camelback and Blue on Ikon anytime soon, they could, and should, at least launch a joint season pass, as the two ski areas are only 30 miles apart.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Desktop Reminder 2 Pro Crack Hit.md b/spaces/rorallitri/biomedical-language-models/logs/Desktop Reminder 2 Pro Crack Hit.md
deleted file mode 100644
index 048d433fe755f770ee21b0169ad33991e1de2484..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Desktop Reminder 2 Pro Crack Hit.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>desktop reminder 2 pro crack hit</h2><br /><p><b><b>Download File</b> &rarr; <a href="https://tinurll.com/2uzlE8">https://tinurll.com/2uzlE8</a></b></p><br /><br />
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download Xforce Keygen TruPlan 2017 64 Bit Patch __FULL__.md b/spaces/rorallitri/biomedical-language-models/logs/Download Xforce Keygen TruPlan 2017 64 Bit Patch __FULL__.md
deleted file mode 100644
index 7725eb3b24023ac5f785e3dbd1fc415b32632df0..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download Xforce Keygen TruPlan 2017 64 Bit Patch __FULL__.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<br />
-```
-<h1>How to Download Xforce Keygen TruPlan 2017 64 Bit Patch</h1>
-<p>If you are looking for a way to activate TruPlan 2017, a software for planning and optimizing manufacturing processes, you might have heard of Xforce Keygen. Xforce Keygen is a tool that can generate serial keys and activation codes for various software products. However, downloading and using Xforce Keygen is not recommended, as it may contain malware, viruses, or other harmful components that can damage your computer or compromise your data. In this article, we will explain why you should avoid Xforce Keygen and what are some alternative ways to get TruPlan 2017.</p>
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-<h2>Why You Should Avoid Xforce Keygen</h2>
-<p>Xforce Keygen is a crack tool that claims to be able to bypass the security measures of software products and generate valid activation codes. However, there are several reasons why you should not trust Xforce Keygen or any other similar tools:</p>
-<ul>
-<li>Xforce Keygen is illegal. Using Xforce Keygen or any other crack tool to activate software products without paying for them is a violation of the software license agreement and the intellectual property rights of the software developers. You may face legal consequences if you are caught using pirated software.</li>
-<li>Xforce Keygen is unsafe. Downloading Xforce Keygen from unknown sources may expose your computer to malware, viruses, or other harmful components that can infect your system, steal your personal information, or damage your files. You may also encounter fake or malicious links that can redirect you to phishing sites or download unwanted programs.</li>
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-</ul>
-<h2>How to Get TruPlan 2017 Legally</h2>
-<p>If you want to use TruPlan 2017, a software for planning and optimizing manufacturing processes, you should get it legally from the official website of Autodesk, the software developer. Here are some benefits of getting TruPlan 2017 legally:</p>
-<ul>
-<li>You will get a valid license and activation code that will allow you to use the software without any limitations or risks.</li>
-<li>You will get access to technical support and customer service from Autodesk in case you encounter any problems or issues with the software.</li>
-<li>You will get regular updates, patches, and features that will improve the performance and functionality of the software.</li>
-<li>You will support the software developer and contribute to the development and innovation of the software industry.</li>
-</ul>
-<p>To get TruPlan 2017 legally from Autodesk, you need to follow these steps:</p>
-<p></p>
-<ol>
-<li>Go to the official website of Autodesk at <a href="https://www.autodesk.com/">https://www.autodesk.com/</a>.</li>
-<li>Click on the Products tab and select TruPlan from the list of software products.</li>
-<li>Click on the Buy button and choose the subscription plan that suits your needs and budget.</li>
-<li>Fill in your personal and payment details and complete the purchase process.</li>
-<li>Download and install TruPlan 2017 on your computer using the instructions provided by Autodesk.</li>
-<li>Activate TruPlan 2017 using the license and activation code that you received from Autodesk.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In conclusion, downloading Xforce Keygen TruPlan 2017 64 bit patch is not a good idea, as it may cause legal, security, or reliability issues. Instead, you should get TruPlan 2017 legally from Autodesk, which will provide you with a valid license and activation code, technical support and customer service, regular updates and patches, and other benefits. We hope this article has helped you understand why you should avoid Xforce Keygen and what are some alternative ways to get TruPlan 2017.</p>
-
-```</p> d5da3c52bf<br />
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\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Full Dhol Movies Why You Should Watch These Funny Films Right Now.md b/spaces/rorallitri/biomedical-language-models/logs/Full Dhol Movies Why You Should Watch These Funny Films Right Now.md
deleted file mode 100644
index ad6bf81de2d512fb2aac22d55f9d7155af2071b8..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Full Dhol Movies Why You Should Watch These Funny Films Right Now.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>What follows next is predictable of Priyadarshan's movies. The hawan scene is hilarious, as the four boys start pouring water on her home, as they think it is on fire when in truth, there is a havan pooja happening. Another hilarious moment is the office scene with Asrani where he pretends to be a Tanushree's prospective bridegroom.</p>
-<p>As if on cue, Tanushree Dutta moves into the house next door along with her elderly grandparents. Even as they vie for her attention, each trying to outdo the other, our frightfully foolish foursome find themselves inadvertently involved in a mystery of unexplained suicides and believe it or not, a missing drum, thus justifying the title of this mindless movie, <i>Dhol</i>.</p>
-<h2>full Dhol movies</h2><br /><p><b><b>Download File</b> &#10001; &#10001; &#10001; <a href="https://tinurll.com/2uzo7H">https://tinurll.com/2uzo7H</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/rstallman/Contract-AI/README.md b/spaces/rstallman/Contract-AI/README.md
deleted file mode 100644
index ad8cbc02369db2f29a44ebacdae64862ab293a05..0000000000000000000000000000000000000000
--- a/spaces/rstallman/Contract-AI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Contract AI
-emoji: 🐨
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-duplicated_from: ullasmrnva/LawBerta
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/ruangguru/rg-ds-chatbot-gradio/client/schema.py b/spaces/ruangguru/rg-ds-chatbot-gradio/client/schema.py
deleted file mode 100644
index ff23bcf55e2cd275b0f4caa1e305a914f3c1399c..0000000000000000000000000000000000000000
--- a/spaces/ruangguru/rg-ds-chatbot-gradio/client/schema.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from pydantic import BaseModel
-from typing import Union, Any, Optional, Dict
-
-
-class EventRgDsChatbotApiGPT(BaseModel):
-    uuid: str
-    uuid_conversation: str
-    prompt: str
-    model: str
-    completion: str
-    token_usage: Dict[str, float]
-    service_name: str
-
-
-class PostEventTrackingRequestData(BaseModel):
-    id: Optional[str] = None
-    sessionId: Optional[str] = None
-    cookiesId: Optional[str] = None
-    deviceId: Optional[str] = None
-    memberId: Optional[str] = None
-    isLogged: bool = False
-    eventType: str
-    clientDevice: Optional[str] = None
-    clientUA: Optional[str] = None
-    appVersion: Optional[str] = None
-    clientOS: Optional[str] = None
-    clientOSVersion: Optional[str] = None
-    clientTimestamp: Optional[str] = None
-    context: Union[str, Any]
-    connectionType: Optional[str] = None
-    source: str = "backend"
diff --git a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/__init__.py b/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/safi842/FashionGen/netdissect/edit.html b/spaces/safi842/FashionGen/netdissect/edit.html
deleted file mode 100644
index 9aac30bb08171c4c58eb936f9ba382e85a184803..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/netdissect/edit.html
+++ /dev/null
@@ -1,805 +0,0 @@
-<!doctype html>
-<html>
-<head>
-<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
-<script src="https://code.jquery.com/jquery-3.3.1.js" integrity="sha256-2Kok7MbOyxpgUVvAk/HJ2jigOSYS2auK4Pfzbm7uH60=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js" integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q" crossorigin="anonymous"></script>
-<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js" integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/vue/2.5.16/vue.js" integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ=" crossorigin="anonymous"></script>
-<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.js" integrity="sha256-qwbDmNVLiCqkqRBpF46q5bjYH11j5cd+K+Y6D3/ja28=" crossorigin="anonymous"></script>
-<script src="https://unpkg.com/split.js@1.3.5/split.js" integrity="sha384-zUUFqW6+QulITI/GAXB5UnHBryKHEZp4G+eNNC1/3Z3IJ+6CZRSFcdl4f7gCa957" crossorigin="anonymous"></script>
-<style>
-[v-cloak] {
-  display: none;
-}
-body, html { height: 100vh; }
-#app {
-  border: 0;
-  margin: 0;
-  padding: 0;
-  height: 100vh;
-  width: 100vw;
-}
-table.pane {
-  float: left;
-  display: inline-table;
-  border: 0;
-  margin: 0;
-  padding: 0;
-  border-spacing: 0;
-  height: 100vh;
-  width: 50vw;
-}
-.gutter-horizontal {
-  float: left;
-  height: 100vh;
-  background-color: #eee;
-  cursor: ew-resize;
-}
-.palette, .preview {
-  height: 100%;
-  overflow-y: scroll;
-  text-align: center;
-}
-.unitviz, .unitviz .modal-header, .unitviz .modal-body, .unitviz .modal-footer {
-  font-family: Arial;
-  font-size: 15px;
-}
-.unitgrid {
-  text-align: center;
-  border-spacing: 5px;
-  border-collapse: separate;
-  max-height: 100%;
-}
-.unitgrid .info {
-  text-align: left;
-  display: block;
-  position: absolute;
-  top: 0;
-  color: white;
-}
-.unitgrid .layername {
-}
-.unitgrid .unitnum {
-}
-.unitlabel {
-  font-weight: bold;
-  line-height: 1;
-  position: absolute;
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-  color: white;
-  white-space: nowrap;
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-.lowscore .unitlabel {
-   color: silver;
-}
-.thumbcrop {
-  overflow: hidden;
-  width: 72px;
-  height: 72px;
-}
-.thumbcrop img, .img-scroller img {
-  image-rendering: pixelated;
-}
-.unit {
-  display: inline-block;
-  background: white;
-  padding: 0;
-  margin: 2px;
-  box-shadow: 0 5px 12px grey;
-  overflow: hidden;
-  height: 72px;
-  width: 72px;
-  position: relative;
-  user-select: none;
-}
-.unit.ablated .ablationmark::after,
-.selmodeablation .unit.dragged .ablationmark::after {
-  content: '\2716';
-  bottom: 0;
-}
-.unit.inserted .insertionmark::after,
-.selmodeinsertion .unit.dragged .insertionmark::after {
-  content: '\2713';
-  font-weight: bold;
-  text-shadow: 0 0 #000;
-  bottom: 10px;
-}
-.ablationmark::after, .insertionmark::after {
-  line-height: 1;
-  position: absolute;
-  right: 0;
-}
-.unit.ablated .ablationmark {
-  color: red;
-}
-.unit.inserted .insertionmark {
-  color: lime;
-}
-.unit.dragged {
-  opacity: 0.8;
-}
-.selmodeablation .unit.dragged .ablationmark,
-.selmodeinsertion .unit.dragged .insertionmark {
-  color: yellow;
-}
-.iou {
-  display: inline-block;
-  float: right;
-}
-.modal .big-modal {
-  width:auto;
-  max-width:90%;
-  max-height:80%;
-}
-.modal-title {
-  display: inline-block;
-}
-.footer-caption {
-  float: left;
-  width: 100%;
-}
-.histogram {
-  text-align: center;
-  margin-top: 3px;
-}
-.img-wrapper {
-  text-align: center;
-}
-.big-modal img {
-  max-height: 60vh;
-}
-.img-scroller {
-  overflow-x: scroll;
-}
-.img-scroller .img-fluid {
-  max-width: initial;
-}
-.gridheader {
-  font-size: 12px;
-  margin-bottom: 10px;
-  margin-left: 30px;
-  margin-right: 30px;
-}
-.gridheader:after {
-  content: '';
-  display: table;
-  clear: both;
-}
-.sortheader {
-  float: right;
-  cursor: default;
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-.layerinfo {
-  float: left;
-}
-.sortby {
-  text-decoration: underline;
-  cursor: pointer;
-}
-.sortby.currentsort {
-  text-decoration: none;
-  font-weight: bold;
-  cursor: default;
-}
-.bg-inverse {
-  background: #021B54;
-}
-.layout {
-  width: 50%;
-  vertical-align: top;
-}
-.chooser-cell {
-  height: 100px;
-  background: #021B54;
-  color: white;
-  padding: 5px 5px 0;
-}
-.examplepair {
-  display: inline-block;
-  margin: 2px;
-  position: relative;
-  line-height: 0;
-}
-.exampleid {
-  position: absolute;
-  line-height: 1;
-  font-size: large;
-  font-weight: bold;
-  text-shadow: -1px 2px #000, 1px 2px #000;
-  bottom: 0;
-  color: white;
-  z-index: 1;
-}
-.querymark, .editmark {
-  position: absolute;
-  background: rgba(255,0,0,0.5);
-  width: 10px;
-  height: 10px;
-  margin: -5px;
-  border-radius: 5px;
-  top: 0;
-  left: 0;
-  pointer-events: none;
-}
-.editmark {
-  background: rgba(0,255,0,0.5);
-}
-.chooser-cell input[type=button] {
-  border-radius: 5px; padding: 0 5px;
-}
-</style>
-</head>
-<body class="unitviz">
-<div id="app" :class="['selmode' + selection_mode]" v-cloak>
-<table class="pane" id="leftpane">
-<tr>
-<td class="chooser-cell">
-<div v-if="dissect">
-<p>
-<label for="selmode-ablation-radio">
-<input type="radio" id="selmode-ablation-radio"
-       value="ablation" v-model="selection_mode">
-<span style="color:red">&#x2716;</span>
-Choose units to ablate.</label>
-<label for="selmode-insertion-radio">
-<input type="radio" id="selmode-insertion-radio"
-       value="insertion" v-model="selection_mode">
-<span style="color:lime;font-weight:bold">&#x2713;</span>
-Choose units to insert.</label>
-</p>
-<label for="ranking-radio">
-<input type="radio" id="ranking-radio" value="ranking" v-model="palette_mode">
-Show all units in
-<select v-model="palette_layer" v-on:change="palette_mode='ranking'">
-<option v-for="(lrec, lind) in dissect.layers" :value="lind"
->{{lrec.layer}}</option>
-</select>,
-sorted by
-<template v-for="lrec in [dissect.layers[palette_layer]]">
-<select v-model="sort_order" v-on:change="palette_mode='ranking'">
-<option v-for="rank in lrec['rankings']" :value="rank.name"
->{{rank.name}}</option>
-</select>
-</template>
-</label><br>
-<label for="ablation-radio">
-<input type="radio" id="ablation-radio" value="ablation" v-model="palette_mode">
-Show current ablation
-        ({{ _.sum(_.map(selected_ablation,
-              function(x) { return _.sum(x)} ))
-          }} units ablated)</label>
-<input type="button" value="Reset" v-on:click="resetselection('ablation')">
-<input type="button" :value="'Invert ' + dissect.layers[palette_layer].layer"
-       v-on:click="invertselection('ablation')"><br>
-<label for="insertion-radio">
-<input type="radio" id="insertion-radio" value="insertion"
-       v-model="palette_mode">
-Show current insertion
-        ({{ _.sum(_.map(selected_insertion,
-              function(x) { return _.sum(x)} ))
-          }} units inserted)</label>
-<input type="button" value="Reset" v-on:click="resetselection('insertion')">
-<input type="button" :value="'Invert ' + dissect.layers[palette_layer].layer"
-       v-on:click="invertselection('insertion')"><br>
-<label for="query-radio">
-<input type="radio" id="query-radio" value="query" v-model="palette_mode"
- :disabled="!(dissect.layers[palette_layer].layer in query_ranking)">
-Units in {{ dissect.layers[palette_layer].layer }}
-by 
-<select v-model="query_stat">
-<option value="mean_quantile">quantile of average</option>
-<option value="max_quantile">quantile of maximum</option>
-<option value="mean">average activation</option>
-<option value="max">maximum activation</option>
-</select>
-activation on <span style="color:red">&#x25cf;</span> selected pixels of
-{{ currentquery.map(x => 'image #' + x.id).join(', ') || 'an image' }}.
-</label>
-</div>
-</td>
-</tr>
-
-<tr>
-<td class="layout">
-
-<div class="palette" v-if="dissect">
-
-<div class="unitgrid"
-><div :class="{unit: true, lowscore: !urec.interp,
-               ablated: (selected_ablation[urec.layer][urec.unit] > 0),
-               inserted: (selected_insertion[urec.layer][urec.unit] > 0),
-               dragged: (dragging.active &&
-                ((dragging.first <= ordernum && ordernum <= dragging.last) ||
-                 (dragging.last <= ordernum && ordernum <= dragging.first)))
-               }"
-      v-for="urec, ordernum in palette_units"
-      :data-ordernum="ordernum"
-      >
-<template v-if="sort_order.indexOf('-') < 0"
-          ><div v-if="'iou_label' in urec" class="unitlabel"
-          >{{urec.iou_label}}</div></template>
-<div class="info"
- ><span class="layername">{{urec.layer}}</span
- > <span class="unitnum">{{urec.unit}}</span>
-</div>
-<div class="thumbcrop"
-><img
- :src="urec.dirname + '/s-image/' + urec.unit + '-top.jpg'"
- height="72"
-></div>
-
-<div class="ablationmark"></div>
-<div class="insertionmark"></div>
-</div> <!-- end unit -->
-
-</div> <!-- end unitgrid -->
-
-</div> <!-- end palette -->
-
-</td>
-</tr>
-</table> <!-- end pane -->
-
-<table class="pane" id="rightpane">
-<tr>
-<td rowspan="2" class="layout">
-
-<div class="preview" v-if="dissect">
-<p>Seeds to generate <input size="30" v-model="image_numbers"></p>
-<p style="text-align: left">
-To transfer activations from one pixel to another (1) click on a source pixel
-on the left image and (2) click on a target pixel on a right image,
-then (3) choose a set of units to insert in the palette.</p>
-<div v-for="ex in examples" class="examplepair">
-<div class="exampleid">#{{ ex.id }}</div>
-<img :src="ex.baseline" style="max-width:50%;"
-     :data-imgid="ex.id"
-     data-side="left"
-     v-on:click="clickexample"
-><img :src="ex.modified" style="max-width:50%;"
-     :data-imgid="ex.id"
-     data-side="right"
-     v-on:click="clickexample"
-><div class="querymark" v-if="ex.mask" :style="{
-   top: ((ex.mask.bitbounds[0] + ex.mask.bitbounds[2]) / 0.02
-          / ex.mask.shape[0]) + '%',
-   left: ((ex.mask.bitbounds[1] + ex.mask.bitbounds[3]) / 0.04
-          / ex.mask.shape[1]) + '%'}"
-></div><div class="editmark" v-if="ex.edit" :style="{
-   top: ((ex.edit.bitbounds[0] + ex.edit.bitbounds[2]) / 0.02
-          / ex.edit.shape[0]) + '%',
-   left: ((ex.edit.bitbounds[1] + ex.edit.bitbounds[3]) / 0.04
-          / ex.edit.shape[1]) + 50 + '%'}"
-></div>
-</div>
-
-</div>
-
-</td>
-</tr>
-</table>
-
-</div> <!-- end app -->
-<div class="modal" id="lightbox">
-  <div class="modal-dialog big-modal" role="document">
-    <div class="modal-content">
-      <div class="modal-header">
-        <h5 class="modal-title"></h5>
-        <button type="button" class="close"
-             data-dismiss="modal" aria-label="Close">
-          <span aria-hidden="true">&times;</span>
-        </button>
-      </div>
-      <div class="modal-body">
-        <div class="img-wrapper img-scroller">
-          <img class="fullsize img-fluid">
-        </div>
-      </div>
-      <div class="modal-footer">
-        <div class="footer-caption">
-        </div>
-      </div>
-    </div>
-  </div>
-</div>
-<script>
-$(document).on('click', '[data-toggle=lightbox]', function(event) {
-    $('#lightbox img').attr('src', $(this).attr('href'));
-    $('#lightbox .modal-title').text($(this).data('title') ||
-       $(this).closest('.unit').find('.unitlabel').text());
-    $('#lightbox .footer-caption').text($(this).data('footer') ||
-       $(this).closest('.unit').find('.info').text());
-    event.preventDefault();
-    $('#lightbox').modal();
-    $('#lightbox img').closest('div').scrollLeft(0);
-});
-$(document).on('mousedown', '.unit', function(event) {
-  if (!(event.buttons & 1)) return;
-  var layer = $(event.currentTarget).find('.info .layername').text();
-  var unit = $(event.currentTarget).closest('.unit').data('ordernum');
-  theapp.dragging.active = true;
-  theapp.dragging.first = unit;
-  theapp.dragging.last = unit;
-  event.preventDefault();
-});
-$(document).on('mouseup', function(event) {
-  if (!theapp.dragging.active) { return; }
-  theapp.dragging.active = false;
-  var first_sel = $('.unit').filter(function(i, elt) {
-                     return elt.dataset.ordernum == theapp.dragging.first;
-                  }).map(function(i, elt) {
-                        return {layer: $(elt).find('.layername').text(),
-                                unit: $(elt).find('.unitnum').text()}; })[0];
-  var selected = $('.unit').filter(function(i, elt) {
-         return (theapp.dragging.first <= elt.dataset.ordernum &&
-                 elt.dataset.ordernum <= theapp.dragging.last) ||
-                (theapp.dragging.last <= elt.dataset.ordernum &&
-                 elt.dataset.ordernum <= theapp.dragging.first); })
-                .map(function(i, elt) {
-                        return {layer: $(elt).find('.layername').text(),
-                                unit: $(elt).find('.unitnum').text()}; });
-  if (selected.length) {
-    var selection = 'selected_' + theapp.selection_mode;
-    var mode = 1 - theapp[selection][first_sel.layer][first_sel.unit];
-    for (u of selected) {
-      theapp[selection][u.layer].splice(u.unit, 1, mode);
-    }
-    theapp.selectionchange();
-  }
-});
-$(document).on('mouseenter', '.unit', function(event) {
-  if (!(event.buttons & 1)) { theapp.dragging.active = false; }
-  if (!theapp.dragging.active) return;
-  theapp.dragging.last = 
-      $(event.currentTarget).closest('.unit').data('ordernum');
-});
-$(function() {
-  window.Split(['#leftpane', '#rightpane'], {
-              sizes: [50, 50],
-              minSize: 280,
-              snapOffset: 0,
-  });
-});
-var theapp = new Vue({
-  el: '#app',
-  data: {
-    palette_mode: 'ranking',
-    sort_order: 'unit',
-    palette_layer: null,
-    selected_ablation: null,
-    selected_insertion: null,
-    selection_mode: 'ablation',
-    dragging: { active: false, first: null, last: null },
-    recipe: null,
-    dissect: null,
-    image_numbers: '10-19',
-    examples: _.range(10, 20).map(function(x) {
-        return {id: x, baseline: '', modified: '', mask: null, edit: null}; }),
-    query_stat: 'mean_quantile',
-    query_ranking: {},
-  },
-  created: function() {
-    var self = this;
-    $.getJSON('dissect.json?' + Math.random(), function(d) {
-      self.selected_ablation = {};
-      self.selected_insertion = {};
-      for (var layer of d.layers) {
-        // Preprocess ranking records to sort them.
-        for (var rank of layer.rankings) {
-          if (!('ranking' in rank)) {
-            rank.ranking = rank.score.map((score, index) => [score, index])
-                .sort(([score1], [score2]) => score1 - score2)
-                .map(([, index]) => index);
-          }
-        }
-        // Note layer in each unit record to simplify mixing.
-        for (var urec of layer.units) {
-          urec.layer = layer.layer;
-          urec.dirname = layer.dirname;
-        }
-        Vue.set(self.selected_ablation, layer.layer,
-                        _.fill(Array(layer.units.length), 0));
-        Vue.set(self.selected_insertion, layer.layer,
-                        _.fill(Array(layer.units.length), 0));
-      }
-      self.dissect = d;
-      self.sort_order = d.default_ranking;
-      self.palette_layer = Math.floor((d.layers.length - 1) / 2);
-    });
-    this.selectionchange();
-  },
-  computed: {
-    currentquery: function() {
-      var regions = [];
-      for (var ex of this.examples) {
-        if (ex.mask) {
-          regions.push({id: ex.id, mask: ex.mask});
-        }
-      }
-      return regions;
-    },
-    palette_units: function() {
-      if (this.palette_mode == 'ranking') {
-        // Order units according to an iou-matching sort order.
-        var lrec = this.dissect.layers[this.palette_layer];
-        var ranking = _.find(lrec.rankings, x=>x.name == this.sort_order);
-        return ranking.ranking.map(x => lrec.units[x]);
-      } else if (this.palette_mode == 'ablation' ||
-                 this.palette_mode == 'insertion') {
-        // Show units involved in the edit
-        var result = [];
-        var selectionname = 'selected_' + this.palette_mode;
-        for (var lrec of this.dissect.layers) {
-          var sel = this[selectionname][lrec.layer];
-          for (var u in sel) {
-            if (sel[u] > 0) {
-              result.push(lrec.units[u]);
-            }
-          }
-        }
-        return result;
-      } else if (this.palette_mode == 'query') {
-        // Order units according to query ranking
-        var lrec = this.dissect.layers[this.palette_layer];
-        var ranking = this.query_ranking[lrec.layer];
-        return ranking.ranking.map(x => lrec.units[x]);
-      }
-    }
-  },
-  watch: {
-    query_stat: function() {
-      this.querychange();
-    },
-    palette_layer: function(val) {
-      // If sort_order is not available at this layer, reset it to default.
-      var self = this;
-      if (!_.find(self.dissect.layers[val].rankings,
-            function(x) { return x.name == self.sort_order; })) {
-        self.sort_order = self.dissect.default_ranking;
-      }
-    },
-    image_numbers: function(val) {
-      // Parse a series of image numbers
-      var max_examples = 1000;
-      var rs = val.replace(/[^-\d]+/g, ' ').replace(/\s*-\s*/g, '-').split(' ');
-      var indexes = [];
-      for (var r of rs) {
-        if (r.match(/\d+-\d+/)) {
-          for (var i = parseInt(r.split('-')[0]);
-                          i <= parseInt(r.split('-')[1]); i++) {
-            indexes.push(i);
-          }
-        } else if (r.match(/\d+/)) {
-          indexes.push(parseInt(r));
-        } else if (indexes.length == 0) {
-          indexes.push(0);
-        }
-        if (indexes.length >= max_examples) { break; }
-      }
-      // Update examples to match.
-      var modified = false;
-      var examples = this.examples;
-      for (i = 0; i < indexes.length; i++) {
-        if (i >= examples.length) {
-          examples.push({id: indexes[i], baseline: '', modified: '',
-             mask: null });
-          modified = true
-        } else if (examples[i].id != indexes[i]) {
-          examples[i].id = indexes[i];
-          examples[i].baseline = '';
-          examples[i].modified = '';
-          examples[i].mask = null;
-          examples[i].edit = null;
-          modified = true
-        }
-      }
-      if (examples.length > indexes.length) {
-        examples.splice(indexes.length, examples.length - indexes.length);
-        modified = true
-      }
-      if (modified) {
-        this.generate_examples([], _.range(examples.length), true);
-        this.selectionchange();
-      }
-    }
-  },
-  methods: {
-    hashchange: function() {
-      this.palette_layer = +window.location.hash.substr(1) || 0;
-    },
-    resetselection: function(mode) {
-      var selection = 'selected_' + (mode);
-      console.log(selection);
-      for (var layer in this[selection]) {
-        this[selection][layer] = _.fill(Array(this[selection][layer].length),0);
-      }
-      this.selectionchange();
-    },
-    invertselection: function(mode) {
-      var layer = this.dissect.layers[this.palette_layer].layer;
-      var selection = 'selected_' + (mode);
-      var sel = this[selection][layer];
-      for (var u in sel) {
-        sel.splice(u, 1, 1 - sel[u]);
-      }
-      this.selectionchange();
-    },
-    selectionchange: function() {
-      var edited_indices = [];
-      var unedited_indices = [];
-      var ca = this.currentablations();
-      for (var i in this.examples) {
-        var ci = this.currentinsertion(ca, i);
-        if (ci) {
-          edited_indices.push({intervention: ci, index: i});
-        } else{
-          unedited_indices.push(i);
-        }
-      }
-      if (!window.skipupdate && unedited_indices.length) {
-        this.generate_examples(ca, unedited_indices, false);
-      }
-      for (var r of edited_indices) {
-        this.generate_examples(r.intervention, [r.index], false);
-      }
-    },
-    currentablations: function() {
-      var ablations = [];
-      if (this.selected_ablation) {
-        for (var layer in this.selected_ablation) {
-          for (var unit in this.selected_ablation[layer]) {
-            if (this.selected_ablation[layer][unit] > 0) {
-              ablations.push({
-                  layer: layer,
-                  unit: parseInt(unit),
-                  alpha: this.selected_ablation[layer][unit],
-              });
-            }
-          }
-        }
-      }
-      return ablations.length ? [{ablations: ablations}] : [];
-    },
-    currentinsertion: function(currentablations, exindex) {
-      if (!this.examples[exindex].edit || !this.selected_insertion) {
-        return null;
-      }
-      var insertions = [];
-      for (var layer in this.selected_insertion) {
-        for (var unit in this.selected_insertion[layer]) {
-          if (this.selected_insertion[layer][unit] > 0) {
-            insertions.push({
-                layer: layer,
-                unit: parseInt(unit),
-                alpha: this.selected_insertion[layer][unit],
-                value: this.query_ranking && layer in this.query_ranking &&
-                       this.query_ranking[layer].activation[unit] || 0
-            });
-          }
-        }
-      }
-      var result = _.clone(currentablations);
-      result.push({ablations: insertions, mask: this.examples[exindex].edit});
-      return result;
-    },
-    querychange: function() {
-      this.query_regions(this.currentquery, this.currentablations());
-    },
-    query_regions: function(regions, intervention) {
-      if (!_.keys(regions).length) {
-        return;
-      }
-      var ids = regions.map(x => x.id);
-      var masks = regions.map(x => x.mask);
-      var self = this;
-      $.post({
-        url: '/api/features',
-        data: JSON.stringify({
-          project: this.currentproject(),
-          ids: ids,
-          masks: masks,
-          // layers: [this.dissect.layers[this.palette_layer].layer],
-          layers: this.dissect.layers.map(x => x.layer),
-          // interventions: intervention,
-        }),
-        headers: {
-          "Content-type": "application/json; charset=UTF-8"
-        },
-        success: function(resp) {
-          var statname = self.query_stat;
-          var actname = statname.replace('_quantile', '');
-          var stats = resp.res;
-          var result = {};
-          for (var layer in stats) {
-            result[layer] = {
-              score: stats[layer][statname],
-              ranking: stats[layer][statname]
-                .map((score, index) => [score, index])
-                .sort(([score1], [score2]) => score2 - score1)
-                .map(([, index]) => index),
-              activation: stats[layer][actname]};
-          }
-          self.query_ranking = result;
-          self.palette_mode = 'query';
-          // If there are any insertions, now the edit has changed
-          if (_.sum(_.map(self.selected_insertion, x => _.sum(x))) > 0) {
-            self.selectionchange();
-          }
-        },
-        dataType: 'json'
-      });
-    },
-    clickexample: function(ev) {
-      var elt = ev.currentTarget;
-      var imgid = elt.dataset.imgid * 1;
-      var side = elt.dataset.side;
-      var w = elt.naturalWidth;
-      var h = elt.naturalHeight;
-      var x = Math.round((ev.pageX - $(elt).offset().left) * (w / elt.width));
-      var y = Math.round((ev.pageY - $(elt).offset().top) * (h / elt.height));
-      // Clear all masks and leave one mask with one pixel chosen.
-      var field = (side == 'right') ? 'edit' : 'mask'
-      for (var ex of this.examples) {
-        if (ex.id != imgid) {
-          Vue.set(ex, field, null);
-        } else {
-          Vue.set(ex, field, {
-            shape: [h, w],
-            bitbounds: [y, x, y+1, x+1],
-            bitstring: '1'
-          });
-        }
-      }
-      if (field == 'edit') {
-        this.selection_mode = 'insertion';
-        this.selectionchange();
-      } else {
-        this.querychange();
-      }
-    },
-    generate_examples: function(intervention, example_indexes, baseline_only) {
-      var self = this;
-      var ids = example_indexes.map(x => self.examples[x].id);
-      $.post({
-        url: '/api/generate',
-        data: JSON.stringify({
-          project: this.currentproject(),
-          wantz: 0,
-          ids: ids,
-          interventions: intervention
-        }),
-        headers: {
-          "Content-type": "application/json; charset=UTF-8"
-        },
-        success: function(resp) {
-          for (var j in resp.res) {
-            var i = example_indexes[j];
-            if (self.examples[i].id == ids[j]) {
-              if (!baseline_only) {
-                self.examples[i].modified = resp.res[j].d;
-              }
-              if (!intervention || !intervention.length) {
-                self.examples[i].baseline = resp.res[j].d;
-              }
-            }
-          }
-        },
-        dataType: 'json'
-      });
-    },
-    currentproject: function() {
-      return location.pathname.match(/\/data\/([^\/]*)/)[1];
-    }
-  },
-  filters: {
-    fixed: function(value, digits, truncate) {
-       if (typeof value != 'number') return value;
-       var fixed = value.toFixed(digits);
-       return truncate ? +fixed : fixed;
-    }
-  }
-});
-</script>
-</body>
-</html>
diff --git a/spaces/sasaro/webui/app.py b/spaces/sasaro/webui/app.py
deleted file mode 100644
index 3ca07f943647936c9ba52ee64796e47ce74136d4..0000000000000000000000000000000000000000
--- a/spaces/sasaro/webui/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import os
-from subprocess import getoutput
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-
-os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/                show_progress=False,/                show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/inputs=\[component\],/&\\n                queue=False,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/outputs=\[token_counter\]/outputs=[token_counter], queue=False/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
-os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-
-if "IS_SHARED_UI" in os.environ:
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-    os.system(f"python launch.py --force-enable-xformers --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
-    # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
-    os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-    
-    # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
-    #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
-    os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
-    os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-    
-    # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
-    #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
-    #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
-    #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
-    #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
-    #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-    #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
-    #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-    os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")
diff --git a/spaces/sayakpaul/demo-custom-css/app.py b/spaces/sayakpaul/demo-custom-css/app.py
deleted file mode 100644
index 8b9c69dc535c248ed24a1f4d9e94e7e819e70d08..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/demo-custom-css/app.py
+++ /dev/null
@@ -1,232 +0,0 @@
-import importlib
-from functools import partial
-from typing import List
-
-import gradio as gr
-import numpy as np
-import torch
-from diffusers import StableDiffusionPipeline
-from PIL import Image
-from torchmetrics.functional.multimodal import clip_score
-from torchmetrics.image.inception import InceptionScore
-
-SEED = 0
-WEIGHT_DTYPE = torch.float16
-
-TITLE = "Evaluate Schedulers with StableDiffusionPipeline 🧨"
-DESCRIPTION = """
-This Space allows you to quantitatively compare [different noise schedulers](https://huggingface.co/docs/diffusers/using-diffusers/schedulers) with a [`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).
-
-One of the applications of this Space could be to evaluate different schedulers for a certain Stable Diffusion checkpoint for a fixed number of inference steps.
-
-Here's how it works:
-
-* The evaluator first sets a seed and then generates the initial noise which is passed as the initial latent to start the image generation process. It is done to ensure fair comparison.
-* This initial latent is used every time the pipeline is run (with different schedulers).
-* To quantify the quality of the generated images we use:
-    * [Inception Score](https://en.wikipedia.org/wiki/Inception_score)
-    * [Clip Score](https://arxiv.org/abs/2104.08718)
-
-**Notes**:
-
-* The default scheduler associated with the provided checkpoint is always used for reporting the scores.
-* Increasing both the number of images per prompt and the number of inference steps could quickly build up the inference queue and thus
-resulting in slowdowns.
-"""
-
-
-inception_score_fn = InceptionScore(normalize=True)
-torch.manual_seed(SEED)
-clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16")
-
-
-def make_grid(images, rows, cols):
-    w, h = images[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-    for i, image in enumerate(images):
-        grid.paste(image, box=(i % cols * w, i // cols * h))
-    return grid
-
-
-# Copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L814
-def numpy_to_pil(images):
-    """
-    Convert a numpy image or a batch of images to a PIL image.
-    """
-    if images.ndim == 3:
-        images = images[None, ...]
-    images = (images * 255).round().astype("uint8")
-    if images.shape[-1] == 1:
-        # special case for grayscale (single channel) images
-        pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
-    else:
-        pil_images = [Image.fromarray(image) for image in images]
-
-    return pil_images
-
-
-def prepare_report(scheduler_name: str, results: dict):
-    image_grid = results["images"]
-    scores = results["scores"]
-    img_str = ""
-
-    image_name = f"{scheduler_name}_images.png"
-    image_grid.save(image_name)
-    img_str = img_str = f"![img_grid_{scheduler_name}](/file=./{image_name})\n"
-
-    report_str = f"""
-\n\n## {scheduler_name}
-
-### Sample images
-
-{img_str}
-
-### Scores
-
-{scores}
-\n\n
-    """
-
-    return report_str
-
-
-def initialize_pipeline(checkpoint: str):
-    sd_pipe = StableDiffusionPipeline.from_pretrained(
-        checkpoint, torch_dtype=WEIGHT_DTYPE
-    )
-    sd_pipe = sd_pipe.to("cuda")
-    original_scheduler_config = sd_pipe.scheduler.config
-    return sd_pipe, original_scheduler_config
-
-
-def get_scheduler(scheduler_name: str):
-    schedulers_lib = importlib.import_module("diffusers", package="schedulers")
-    scheduler_abs = getattr(schedulers_lib, scheduler_name)
-
-    return scheduler_abs
-
-
-def get_latents(num_images_per_prompt: int, seed=SEED):
-    generator = torch.manual_seed(seed)
-    latents = np.random.RandomState(seed).standard_normal(
-        (num_images_per_prompt, 4, 64, 64)
-    )
-    latents = torch.from_numpy(latents).to(device="cuda", dtype=WEIGHT_DTYPE)
-    return latents
-
-
-def compute_metrics(images: np.ndarray, prompts: List[str]):
-    inception_score_fn.update(torch.from_numpy(images).permute(0, 3, 1, 2))
-    inception_score = inception_score_fn.compute()
-
-    images_int = (images * 255).astype("uint8")
-    clip_score = clip_score_fn(
-        torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts
-    ).detach()
-    return {
-        "inception_score (⬆️)": {
-            "mean": round(float(inception_score[0]), 4),
-            "std": round(float(inception_score[1]), 4),
-        },
-        "clip_score (⬆️)": round(float(clip_score), 4),
-    }
-
-
-def run(
-    prompt: str,
-    num_images_per_prompt: int,
-    num_inference_steps: int,
-    checkpoint: str,
-    schedulers_to_test: List[str],
-):
-    all_images = {}
-
-    sd_pipeline, original_scheduler_config = initialize_pipeline(checkpoint)
-    latents = get_latents(num_images_per_prompt)
-    prompts = [prompt] * num_images_per_prompt
-
-    images = sd_pipeline(
-        prompts,
-        latents=latents,
-        num_inference_steps=num_inference_steps,
-        output_type="numpy",
-    ).images
-    original_scheduler_name = original_scheduler_config._class_name
-
-    all_images.update(
-        {
-            original_scheduler_name: {
-                "images": make_grid(numpy_to_pil(images), 1, num_images_per_prompt),
-                "scores": compute_metrics(images, prompts),
-            }
-        }
-    )
-    # print("First scheduler complete.")
-
-    for scheduler_name in schedulers_to_test:
-        if scheduler_name == original_scheduler_name:
-            continue
-        scheduler_cls = get_scheduler(scheduler_name)
-        current_scheduler = scheduler_cls.from_config(original_scheduler_config)
-        sd_pipeline.scheduler = current_scheduler
-
-        cur_scheduler_images = sd_pipeline(
-            prompts, num_inference_steps=num_inference_steps, output_type="numpy"
-        ).images
-        all_images.update(
-            {
-                scheduler_name: {
-                    "images": make_grid(
-                        numpy_to_pil(cur_scheduler_images), 1, num_images_per_prompt
-                    ),
-                    "scores": compute_metrics(cur_scheduler_images, prompts),
-                }
-            }
-        )
-        # print(f"{scheduler_name} complete.")
-
-    output_str = ""
-    for scheduler_name in all_images:
-        # print(f"scheduler_name: {scheduler_name}")
-        output_str += prepare_report(scheduler_name, all_images[scheduler_name])
-    # print(output_str)
-    return output_str
-
-
-with gr.Blocks() as demo:
-    gr.HTML(f"<div align='center'{TITLE}</div>")
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Text(max_lines=1, placeholder="a painting of a dog")
-            num_images_per_prompt = gr.Slider(3, 10, value=3, step=1)
-            num_inference_steps = gr.Slider(10, 100, value=50, step=1)
-            model_ckpt = gr.Dropdown(
-                [
-                    "CompVis/stable-diffusion-v1-4",
-                    "runwayml/stable-diffusion-v1-5",
-                    "stabilityai/stable-diffusion-2-base",
-                    "Other"
-                ],
-                value="CompVis/stable-diffusion-v1-4",
-                multiselect=False,
-                interactive=True,
-            )
-            other_finedtuned_checkpoints = gr.Text(visible=False, placeholder="valhalla/sd-pokemon-model")
-            model_ckpt.change(lambda x: gr.Dropdown.update(visible=x=="Other"), model_ckpt, other_finedtuned_checkpoints)
-            schedulers_to_test = gr.Dropdown(
-                [
-                    "EulerDiscreteScheduler",
-                    "PNDMScheduler",
-                    "LMSDiscreteScheduler",
-                    "DPMSolverMultistepScheduler",
-                    "DDIMScheduler",
-                ],
-                value=["LMSDiscreteScheduler"],
-                multiselect=True,
-            )
-            evaluation_button = gr.Button(value="Submit")
-        
-        with gr.Column():
-            report = gr.Markdown(label="Evaluation Report")
-           
-demo.launch()
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/Dogar Surgery Book Free !!BETTER!! 648.md b/spaces/scedlatioru/img-to-music/example/Dogar Surgery Book Free !!BETTER!! 648.md
deleted file mode 100644
index 4dd7ed5f24309ef9b13050e9e80d447d49a4cd14..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Dogar Surgery Book Free !!BETTER!! 648.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-
-<h1>How to Download Abdul Wahab Dogar Surgery Books for Free</h1>
-<p>If you are looking for a comprehensive and concise review of general and systemic surgery, you might be interested in Abdul Wahab Dogar surgery books. These books are written by a renowned Pakistani surgeon and cover all the essential topics for the exams and clinical practice. In this article, we will show you how to download Abdul Wahab Dogar surgery books for free using direct links.</p>
-<h2>dogar surgery book free 648</h2><br /><p><b><b>Download File</b> &#10037;&#10037;&#10037; <a href="https://gohhs.com/2uEz5E">https://gohhs.com/2uEz5E</a></b></p><br /><br />
-<h2>Abdul Wahab Dogar General Surgery PDF</h2>
-<p>This book is one of the best books for quick review of general surgery. It covers all the basic principles and techniques of surgery, as well as common surgical conditions and procedures. It also includes questions and answers after each chapter and a summary of key points. The book is updated with the latest advances and guidelines in general surgery.</p>
-<p>To download Abdul Wahab Dogar General Surgery PDF for free, you can use the following link[^1^]:</p>
-<pre><code>Download it from MediaFire
-</code></pre>
-<h2>Abdul Wahab Dogar Systemic Surgery 2nd Edition PDF</h2>
-<p>This book is another excellent resource for systemic surgery. It covers all the major organ systems and their surgical diseases and management. It also provides a comprehensive approach to the principles of systemic surgery, with diagrams, tables, and illustrations. The book is revised and updated with the latest information and evidence-based practices.</p>
-<p></p>
-<p>To download Abdul Wahab Dogar Systemic Surgery 2nd Edition PDF for free, you can use the following link[^2^]:</p>
-<pre><code>Download Link (Google Drive) OR Download Link (Mega Drive)
-</code></pre>
-<h3>Disclaimer</h3>
-<p>Please note that we do not own the copyrights to these books and we are only sharing them for educational purposes. We are not hosting any copyrighted contents on our servers, it’s a catalog of links that already found on the internet. If you are the owner of these books and want us to remove them, please contact us at medicalstudyzone.com.</p><h4>Why Abdul Wahab Dogar Surgery Books are Useful</h4>
-<p>Abdul Wahab Dogar surgery books are useful for several reasons. First of all, they are written by an experienced and qualified surgeon who has a vast knowledge and expertise in the field of surgery. Second, they are concise and comprehensive, covering all the important topics and concepts in a clear and easy-to-understand manner. Third, they are updated and revised with the latest developments and standards in surgery, ensuring that you get the most accurate and relevant information. Fourth, they are designed to help you prepare for the exams and clinical practice, with questions and answers, summaries, and key points. Fifth, they are available for free download using direct links, saving you time and money.</p>
-<h4>How to Use Abdul Wahab Dogar Surgery Books Effectively</h4>
-<p>To use Abdul Wahab Dogar surgery books effectively, you should follow some tips and strategies. First of all, you should read the books thoroughly and carefully, paying attention to the details and explanations. Second, you should review the books regularly and frequently, using the questions and answers and summaries to reinforce your learning. Third, you should practice the techniques and procedures described in the books, using models, simulators, or real patients if possible. Fourth, you should compare the books with other sources of information, such as textbooks, journals, websites, or lectures. Fifth, you should seek feedback and guidance from your teachers, mentors, or peers, who can help you improve your knowledge and skills.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf For FlashTool __EXCLUSIVE__.md b/spaces/scedlatioru/img-to-music/example/Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf For FlashTool __EXCLUSIVE__.md
deleted file mode 100644
index c40b3c20267b66ee22a22a030cec0ed61d5c146d..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf For FlashTool __EXCLUSIVE__.md	
+++ /dev/null
@@ -1,61 +0,0 @@
-<br />
-<h1>Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf for FlashTool: A Complete Guide</h1>
-
-<p>If you are looking for a way to update or restore your Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 smartphone, you may need to download and install the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool. This is a file that contains the original Android operating system and other system files for your device. By flashing this file, you can fix various issues such as bootloop, lag, battery drain, or performance problems on your Sony Xperia Z3 Plus (Z4 Dual SIM) E6533.</p>
-<h2>Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf for FlashTool</h2><br /><p><b><b>Download</b> &#8250; <a href="https://gohhs.com/2uEAzm">https://gohhs.com/2uEAzm</a></b></p><br /><br />
-
-<p>In this article, we will show you how to download and install the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool step by step. We will also provide you with the necessary tools and drivers to complete the process. Before you proceed, make sure you have backed up your important data and have at least 50% battery charge on your device.</p>
-
-<h2>What is FlashTool and Why Do You Need It?</h2>
-
-<p>FlashTool is a software application that allows you to flash firmware files on Sony Xperia devices. It supports various file formats such as .ftf, .sin, .ta, .elf, etc. You can use FlashTool to upgrade or downgrade your device's firmware, unlock the bootloader, root your device, or install custom ROMs.</p>
-
-<p>You need FlashTool to flash the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool because it is compatible with your device model and firmware version. Flashing the wrong file can brick your device or cause other problems. FlashTool also has a user-friendly interface and a fast flashing speed.</p>
-
-<h2>How to Download Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf for FlashTool</h2>
-
-<p>To download the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool, you can use one of the following links:</p>
-
-<ul>
-<li><a href="https://byltly.com/2b6oo6">Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf for FlashTool</a></li>
-<li><a href="https://orirom.com/sony-xperia-z3-e6553-firmware-ftf-for-flashtool/">Sony Xperia Z3+ (Z4 Single SIM) E6553 Firmware .ftf For FlashTool</a></li>
-<li><a href="https://forum.xda-developers.com/t/official-nougat-marshmallow-lollipop-firmware-for-z3-e6553-6533.3545481/">Official Nougat/Marshmallow/Lollipop Firmware for Z3+ (E6553/6533)</a></li>
-<li><a href="https://forum.xda-developers.com/t/ftf-z4-z3-stock-rom-e6553-sov31-402so-so-03g.3136161/">[FTF][Z4/Z3+] Stock ROM E6553, SOV31, 402SO, SO-03G</a></li>
-</ul>
-
-<p>Choose the link that matches your device model and firmware version. The file size may vary depending on the link you choose. After downloading the file, extract it to a folder on your computer using a tool like WinRAR or 7-Zip.</p>
-<p></p>
-
-<h2>How to Install Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf for FlashTool</h2>
-
-<p>To install the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool, you will need to follow these steps:</p>
-
-<ol>
-<li>Download and install the latest version of <a href="https://flashtool.net/">FlashTool</a> on your computer.</li>
-<li>Download and install the latest <a href="https://developer.sony.com/develop/drivers/">Sony USB drivers</a> on your computer.</li>
-<li>Enable USB debugging mode on your device by going to Settings > Developer options > USB debugging. If you don't see Developer options, go to Settings > About phone > Build number and tap it seven times.</li>
-<li>Turn off your device and press and hold the Volume Down button while connecting it to your computer using a USB cable.</li>
-<li>Open FlashTool on your computer and click on the lightning icon on the top left corner.</li>
-<li>Select Flashmode from the pop-up menu and click OK.</li>
-<li>Browse to the folder where you extracted the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool and select it.</li>
-<li>Make sure all options are unchecked except Wipe APPS_LOG and Wipe USERDATA.</li>
-<li>Click on Flash button and wait for the process to complete.</li>
-<li>When the flashing is done, disconnect your device from your computer and reboot it.</li>
-</ol>
-
-<p>Congratulations! You have successfully installed the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool on your device. You can now enjoy a smooth and stable performance on your Sony Xperia Z3 Plus (Z4 Dual SIM) E6533.</p>
-
-<h2>Tips and Tricks for Official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 Stock Rom .ftf for FlashTool</h2>
-
-<p>Here are some tips and tricks that can help you get the most out of your official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool:</p>
-
-<ul>
-<li>If you encounter any errors or problems during or after flashing, try using a different USB port or cable, reinstalling the drivers, or using a different computer.</li>
-<li>If you want to root your device after flashing, you can use <a href="https://kingroot.net/">KingRoot</a> or <a href="https://www.kingoapp.com/">KingoRoot</a> apps that can root your device without a computer.</li>
-<li>If you want to install custom ROMs or mods on your device after flashing, you can use <a href="https://twrp.me/">TWRP</a> or <a href="https://www.cyanogenmods.org/cwm-recovery-download/">CWM</a> recovery that can flash zip files from your SD card or internal storage.</li>
-<li>If you want to backup or restore your device's data before or after flashing, you can use <a href="https://play.google.com/store/apps/details?id=com.koushikdutta.backup&hl=en&gl=US">Helium</a> or <a href="https://play.google.com/store/apps/details?id=com.idea.backup.smscontacts&hl=en&gl=US">Super Backup & Restore</a> apps that can backup or restore your contacts, messages, call logs, apps, etc.</li>
-</ul>
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-<p>We hope this article has helped you learn how to download and install the official Sony Xperia Z3 Plus (Z4 Dual SIM) E6533 stock rom .ftf for FlashTool on your device. If you have any questions or feedback, feel free to leave a comment below.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/model/group_modules.py b/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/model/group_modules.py
deleted file mode 100644
index a9bf64c51613be619705bee2da9b7508378cbb46..0000000000000000000000000000000000000000
--- a/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/model/group_modules.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from typing import Optional
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from tracker.model.channel_attn import CAResBlock
-
-
-def interpolate_groups(g: torch.Tensor, ratio: float, mode: str,
-                       align_corners: bool) -> torch.Tensor:
-    batch_size, num_objects = g.shape[:2]
-    g = F.interpolate(g.flatten(start_dim=0, end_dim=1),
-                      scale_factor=ratio,
-                      mode=mode,
-                      align_corners=align_corners)
-    g = g.view(batch_size, num_objects, *g.shape[1:])
-    return g
-
-
-def upsample_groups(g: torch.Tensor,
-                    ratio: float = 2,
-                    mode: str = 'bilinear',
-                    align_corners: bool = False) -> torch.Tensor:
-    return interpolate_groups(g, ratio, mode, align_corners)
-
-
-def downsample_groups(g: torch.Tensor,
-                      ratio: float = 1 / 2,
-                      mode: str = 'area',
-                      align_corners: bool = None) -> torch.Tensor:
-    return interpolate_groups(g, ratio, mode, align_corners)
-
-
-class GConv2d(nn.Conv2d):
-    def forward(self, g: torch.Tensor) -> torch.Tensor:
-        batch_size, num_objects = g.shape[:2]
-        g = super().forward(g.flatten(start_dim=0, end_dim=1))
-        return g.view(batch_size, num_objects, *g.shape[1:])
-
-
-class GroupResBlock(nn.Module):
-    def __init__(self, in_dim: int, out_dim: int):
-        super().__init__()
-
-        if in_dim == out_dim:
-            self.downsample = nn.Identity()
-        else:
-            self.downsample = GConv2d(in_dim, out_dim, kernel_size=1)
-
-        self.conv1 = GConv2d(in_dim, out_dim, kernel_size=3, padding=1)
-        self.conv2 = GConv2d(out_dim, out_dim, kernel_size=3, padding=1)
-
-    def forward(self, g: torch.Tensor) -> torch.Tensor:
-        out_g = self.conv1(F.relu(g))
-        out_g = self.conv2(F.relu(out_g))
-
-        g = self.downsample(g)
-
-        return out_g + g
-
-
-class MainToGroupDistributor(nn.Module):
-    def __init__(self,
-                 x_transform: Optional[nn.Module] = None,
-                 g_transform: Optional[nn.Module] = None,
-                 method: str = 'cat',
-                 reverse_order: bool = False):
-        super().__init__()
-
-        self.x_transform = x_transform
-        self.g_transform = g_transform
-        self.method = method
-        self.reverse_order = reverse_order
-
-    def forward(self, x: torch.Tensor, g: torch.Tensor, skip_expand: bool = False) -> torch.Tensor:
-        num_objects = g.shape[1]
-
-        if self.x_transform is not None:
-            x = self.x_transform(x)
-
-        if self.g_transform is not None:
-            g = self.g_transform(g)
-
-        if not skip_expand:
-            x = x.unsqueeze(1).expand(-1, num_objects, -1, -1, -1)
-        if self.method == 'cat':
-            if self.reverse_order:
-                g = torch.cat([g, x], 2)
-            else:
-                g = torch.cat([x, g], 2)
-        elif self.method == 'add':
-            g = x + g
-        elif self.method == 'mulcat':
-            g = torch.cat([x * g, g], dim=2)
-        elif self.method == 'muladd':
-            g = x * g + g
-        else:
-            raise NotImplementedError
-
-        return g
-
-
-class GroupFeatureFusionBlock(nn.Module):
-    def __init__(self, x_in_dim: int, g_in_dim: int, out_dim: int):
-        super().__init__()
-
-        x_transform = nn.Conv2d(x_in_dim, out_dim, kernel_size=1)
-        g_transform = GConv2d(g_in_dim, out_dim, kernel_size=1)
-
-        self.distributor = MainToGroupDistributor(x_transform=x_transform,
-                                                  g_transform=g_transform,
-                                                  method='add')
-        self.block1 = CAResBlock(out_dim, out_dim)
-        self.block2 = CAResBlock(out_dim, out_dim)
-
-    def forward(self, x: torch.Tensor, g: torch.Tensor) -> torch.Tensor:
-        batch_size, num_objects = g.shape[:2]
-
-        g = self.distributor(x, g)
-
-        g = g.flatten(start_dim=0, end_dim=1)
-
-        g = self.block1(g)
-        g = self.block2(g)
-
-        g = g.view(batch_size, num_objects, *g.shape[1:])
-
-        return g
\ No newline at end of file
diff --git a/spaces/sdeeas/ChuanhuChatGPT/modules/webui_locale.py b/spaces/sdeeas/ChuanhuChatGPT/modules/webui_locale.py
deleted file mode 100644
index 1ce4d97b9b41cbb2d9be3fdadc4c85f6ef897604..0000000000000000000000000000000000000000
--- a/spaces/sdeeas/ChuanhuChatGPT/modules/webui_locale.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import locale
-import commentjson as json
-
-class I18nAuto:
-    def __init__(self):
-        if os.path.exists("config.json"):
-            with open("config.json", "r", encoding='utf-8') as f:
-                config = json.load(f)
-        else:
-            config = {}
-        lang_config = config.get("language", "auto")
-        language = os.environ.get("LANGUAGE", lang_config)
-        if language == "auto":
-            language = locale.getdefaultlocale()[0] # get the language code of the system (ex. zh_CN)
-        self.language_map = {}
-        self.file_is_exists = os.path.isfile(f"./locale/{language}.json")
-        if self.file_is_exists:
-            with open(f"./locale/{language}.json", "r", encoding="utf-8") as f:
-                self.language_map.update(json.load(f))
-
-    def __call__(self, key):
-        if self.file_is_exists and key in self.language_map:
-            return self.language_map[key]
-        else:
-            return key
diff --git a/spaces/segments-tobias/conex/espnet/asr/pytorch_backend/asr.py b/spaces/segments-tobias/conex/espnet/asr/pytorch_backend/asr.py
deleted file mode 100644
index 32c7c5b7d180717557ca2e68814e92fa7685fcda..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/asr/pytorch_backend/asr.py
+++ /dev/null
@@ -1,1500 +0,0 @@
-# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Training/decoding definition for the speech recognition task."""
-
-import copy
-import json
-import logging
-import math
-import os
-import sys
-
-from chainer import reporter as reporter_module
-from chainer import training
-from chainer.training import extensions
-from chainer.training.updater import StandardUpdater
-import numpy as np
-from tensorboardX import SummaryWriter
-import torch
-from torch.nn.parallel import data_parallel
-
-from espnet.asr.asr_utils import adadelta_eps_decay
-from espnet.asr.asr_utils import add_results_to_json
-from espnet.asr.asr_utils import CompareValueTrigger
-from espnet.asr.asr_utils import format_mulenc_args
-from espnet.asr.asr_utils import get_model_conf
-from espnet.asr.asr_utils import plot_spectrogram
-from espnet.asr.asr_utils import restore_snapshot
-from espnet.asr.asr_utils import snapshot_object
-from espnet.asr.asr_utils import torch_load
-from espnet.asr.asr_utils import torch_resume
-from espnet.asr.asr_utils import torch_snapshot
-from espnet.asr.pytorch_backend.asr_init import freeze_modules
-from espnet.asr.pytorch_backend.asr_init import load_trained_model
-from espnet.asr.pytorch_backend.asr_init import load_trained_modules
-import espnet.lm.pytorch_backend.extlm as extlm_pytorch
-from espnet.nets.asr_interface import ASRInterface
-from espnet.nets.beam_search_transducer import BeamSearchTransducer
-from espnet.nets.pytorch_backend.e2e_asr import pad_list
-import espnet.nets.pytorch_backend.lm.default as lm_pytorch
-from espnet.nets.pytorch_backend.streaming.segment import SegmentStreamingE2E
-from espnet.nets.pytorch_backend.streaming.window import WindowStreamingE2E
-from espnet.transform.spectrogram import IStft
-from espnet.transform.transformation import Transformation
-from espnet.utils.cli_writers import file_writer_helper
-from espnet.utils.dataset import ChainerDataLoader
-from espnet.utils.dataset import TransformDataset
-from espnet.utils.deterministic_utils import set_deterministic_pytorch
-from espnet.utils.dynamic_import import dynamic_import
-from espnet.utils.io_utils import LoadInputsAndTargets
-from espnet.utils.training.batchfy import make_batchset
-from espnet.utils.training.evaluator import BaseEvaluator
-from espnet.utils.training.iterators import ShufflingEnabler
-from espnet.utils.training.tensorboard_logger import TensorboardLogger
-from espnet.utils.training.train_utils import check_early_stop
-from espnet.utils.training.train_utils import set_early_stop
-
-import matplotlib
-
-matplotlib.use("Agg")
-
-if sys.version_info[0] == 2:
-    from itertools import izip_longest as zip_longest
-else:
-    from itertools import zip_longest as zip_longest
-
-
-def _recursive_to(xs, device):
-    if torch.is_tensor(xs):
-        return xs.to(device)
-    if isinstance(xs, tuple):
-        return tuple(_recursive_to(x, device) for x in xs)
-    return xs
-
-
-class CustomEvaluator(BaseEvaluator):
-    """Custom Evaluator for Pytorch.
-
-    Args:
-        model (torch.nn.Module): The model to evaluate.
-        iterator (chainer.dataset.Iterator) : The train iterator.
-
-        target (link | dict[str, link]) :Link object or a dictionary of
-            links to evaluate. If this is just a link object, the link is
-            registered by the name ``'main'``.
-
-        device (torch.device): The device used.
-        ngpu (int): The number of GPUs.
-
-    """
-
-    def __init__(self, model, iterator, target, device, ngpu=None):
-        super(CustomEvaluator, self).__init__(iterator, target)
-        self.model = model
-        self.device = device
-        if ngpu is not None:
-            self.ngpu = ngpu
-        elif device.type == "cpu":
-            self.ngpu = 0
-        else:
-            self.ngpu = 1
-
-    # The core part of the update routine can be customized by overriding
-    def evaluate(self):
-        """Main evaluate routine for CustomEvaluator."""
-        iterator = self._iterators["main"]
-
-        if self.eval_hook:
-            self.eval_hook(self)
-
-        if hasattr(iterator, "reset"):
-            iterator.reset()
-            it = iterator
-        else:
-            it = copy.copy(iterator)
-
-        summary = reporter_module.DictSummary()
-
-        self.model.eval()
-        with torch.no_grad():
-            for batch in it:
-                x = _recursive_to(batch, self.device)
-                observation = {}
-                with reporter_module.report_scope(observation):
-                    # read scp files
-                    # x: original json with loaded features
-                    #    will be converted to chainer variable later
-                    if self.ngpu == 0:
-                        self.model(*x)
-                    else:
-                        # apex does not support torch.nn.DataParallel
-                        data_parallel(self.model, x, range(self.ngpu))
-
-                summary.add(observation)
-        self.model.train()
-
-        return summary.compute_mean()
-
-
-class CustomUpdater(StandardUpdater):
-    """Custom Updater for Pytorch.
-
-    Args:
-        model (torch.nn.Module): The model to update.
-        grad_clip_threshold (float): The gradient clipping value to use.
-        train_iter (chainer.dataset.Iterator): The training iterator.
-        optimizer (torch.optim.optimizer): The training optimizer.
-
-        device (torch.device): The device to use.
-        ngpu (int): The number of gpus to use.
-        use_apex (bool): The flag to use Apex in backprop.
-
-    """
-
-    def __init__(
-        self,
-        model,
-        grad_clip_threshold,
-        train_iter,
-        optimizer,
-        device,
-        ngpu,
-        grad_noise=False,
-        accum_grad=1,
-        use_apex=False,
-    ):
-        super(CustomUpdater, self).__init__(train_iter, optimizer)
-        self.model = model
-        self.grad_clip_threshold = grad_clip_threshold
-        self.device = device
-        self.ngpu = ngpu
-        self.accum_grad = accum_grad
-        self.forward_count = 0
-        self.grad_noise = grad_noise
-        self.iteration = 0
-        self.use_apex = use_apex
-
-    # The core part of the update routine can be customized by overriding.
-    def update_core(self):
-        """Main update routine of the CustomUpdater."""
-        # When we pass one iterator and optimizer to StandardUpdater.__init__,
-        # they are automatically named 'main'.
-        train_iter = self.get_iterator("main")
-        optimizer = self.get_optimizer("main")
-        epoch = train_iter.epoch
-
-        # Get the next batch (a list of json files)
-        batch = train_iter.next()
-        # self.iteration += 1 # Increase may result in early report,
-        # which is done in other place automatically.
-        x = _recursive_to(batch, self.device)
-        is_new_epoch = train_iter.epoch != epoch
-        # When the last minibatch in the current epoch is given,
-        # gradient accumulation is turned off in order to evaluate the model
-        # on the validation set in every epoch.
-        # see details in https://github.com/espnet/espnet/pull/1388
-
-        # Compute the loss at this time step and accumulate it
-        if self.ngpu == 0:
-            loss = self.model(*x).mean() / self.accum_grad
-        else:
-            # apex does not support torch.nn.DataParallel
-            loss = (
-                data_parallel(self.model, x, range(self.ngpu)).mean() / self.accum_grad
-            )
-        if self.use_apex:
-            from apex import amp
-
-            # NOTE: for a compatibility with noam optimizer
-            opt = optimizer.optimizer if hasattr(optimizer, "optimizer") else optimizer
-            with amp.scale_loss(loss, opt) as scaled_loss:
-                scaled_loss.backward()
-        else:
-            loss.backward()
-        # gradient noise injection
-        if self.grad_noise:
-            from espnet.asr.asr_utils import add_gradient_noise
-
-            add_gradient_noise(
-                self.model, self.iteration, duration=100, eta=1.0, scale_factor=0.55
-            )
-
-        # update parameters
-        self.forward_count += 1
-        if not is_new_epoch and self.forward_count != self.accum_grad:
-            return
-        self.forward_count = 0
-        # compute the gradient norm to check if it is normal or not
-        grad_norm = torch.nn.utils.clip_grad_norm_(
-            self.model.parameters(), self.grad_clip_threshold
-        )
-        logging.info("grad norm={}".format(grad_norm))
-        if math.isnan(grad_norm):
-            logging.warning("grad norm is nan. Do not update model.")
-        else:
-            optimizer.step()
-        optimizer.zero_grad()
-
-    def update(self):
-        self.update_core()
-        # #iterations with accum_grad > 1
-        # Ref.: https://github.com/espnet/espnet/issues/777
-        if self.forward_count == 0:
-            self.iteration += 1
-
-
-class CustomConverter(object):
-    """Custom batch converter for Pytorch.
-
-    Args:
-        subsampling_factor (int): The subsampling factor.
-        dtype (torch.dtype): Data type to convert.
-
-    """
-
-    def __init__(self, subsampling_factor=1, dtype=torch.float32):
-        """Construct a CustomConverter object."""
-        self.subsampling_factor = subsampling_factor
-        self.ignore_id = -1
-        self.dtype = dtype
-
-    def __call__(self, batch, device=torch.device("cpu")):
-        """Transform a batch and send it to a device.
-
-        Args:
-            batch (list): The batch to transform.
-            device (torch.device): The device to send to.
-
-        Returns:
-            tuple(torch.Tensor, torch.Tensor, torch.Tensor)
-
-        """
-        # batch should be located in list
-        assert len(batch) == 1
-        xs, ys = batch[0]
-
-        # perform subsampling
-        if self.subsampling_factor > 1:
-            xs = [x[:: self.subsampling_factor, :] for x in xs]
-
-        # get batch of lengths of input sequences
-        ilens = np.array([x.shape[0] for x in xs])
-
-        # perform padding and convert to tensor
-        # currently only support real number
-        if xs[0].dtype.kind == "c":
-            xs_pad_real = pad_list(
-                [torch.from_numpy(x.real).float() for x in xs], 0
-            ).to(device, dtype=self.dtype)
-            xs_pad_imag = pad_list(
-                [torch.from_numpy(x.imag).float() for x in xs], 0
-            ).to(device, dtype=self.dtype)
-            # Note(kamo):
-            # {'real': ..., 'imag': ...} will be changed to ComplexTensor in E2E.
-            # Don't create ComplexTensor and give it E2E here
-            # because torch.nn.DataParellel can't handle it.
-            xs_pad = {"real": xs_pad_real, "imag": xs_pad_imag}
-        else:
-            xs_pad = pad_list([torch.from_numpy(x).float() for x in xs], 0).to(
-                device, dtype=self.dtype
-            )
-
-        ilens = torch.from_numpy(ilens).to(device)
-        # NOTE: this is for multi-output (e.g., speech translation)
-        ys_pad = pad_list(
-            [
-                torch.from_numpy(
-                    np.array(y[0][:]) if isinstance(y, tuple) else y
-                ).long()
-                for y in ys
-            ],
-            self.ignore_id,
-        ).to(device)
-
-        return xs_pad, ilens, ys_pad
-
-
-class CustomConverterMulEnc(object):
-    """Custom batch converter for Pytorch in multi-encoder case.
-
-    Args:
-        subsampling_factors (list): List of subsampling factors for each encoder.
-        dtype (torch.dtype): Data type to convert.
-
-    """
-
-    def __init__(self, subsamping_factors=[1, 1], dtype=torch.float32):
-        """Initialize the converter."""
-        self.subsamping_factors = subsamping_factors
-        self.ignore_id = -1
-        self.dtype = dtype
-        self.num_encs = len(subsamping_factors)
-
-    def __call__(self, batch, device=torch.device("cpu")):
-        """Transform a batch and send it to a device.
-
-        Args:
-            batch (list): The batch to transform.
-            device (torch.device): The device to send to.
-
-        Returns:
-            tuple( list(torch.Tensor), list(torch.Tensor), torch.Tensor)
-
-        """
-        # batch should be located in list
-        assert len(batch) == 1
-        xs_list = batch[0][: self.num_encs]
-        ys = batch[0][-1]
-
-        # perform subsampling
-        if np.sum(self.subsamping_factors) > self.num_encs:
-            xs_list = [
-                [x[:: self.subsampling_factors[i], :] for x in xs_list[i]]
-                for i in range(self.num_encs)
-            ]
-
-        # get batch of lengths of input sequences
-        ilens_list = [
-            np.array([x.shape[0] for x in xs_list[i]]) for i in range(self.num_encs)
-        ]
-
-        # perform padding and convert to tensor
-        # currently only support real number
-        xs_list_pad = [
-            pad_list([torch.from_numpy(x).float() for x in xs_list[i]], 0).to(
-                device, dtype=self.dtype
-            )
-            for i in range(self.num_encs)
-        ]
-
-        ilens_list = [
-            torch.from_numpy(ilens_list[i]).to(device) for i in range(self.num_encs)
-        ]
-        # NOTE: this is for multi-task learning (e.g., speech translation)
-        ys_pad = pad_list(
-            [
-                torch.from_numpy(np.array(y[0]) if isinstance(y, tuple) else y).long()
-                for y in ys
-            ],
-            self.ignore_id,
-        ).to(device)
-
-        return xs_list_pad, ilens_list, ys_pad
-
-
-def train(args):
-    """Train with the given args.
-
-    Args:
-        args (namespace): The program arguments.
-
-    """
-    set_deterministic_pytorch(args)
-    if args.num_encs > 1:
-        args = format_mulenc_args(args)
-
-    # check cuda availability
-    if not torch.cuda.is_available():
-        logging.warning("cuda is not available")
-
-    # get input and output dimension info
-    with open(args.valid_json, "rb") as f:
-        valid_json = json.load(f)["utts"]
-    utts = list(valid_json.keys())
-    idim_list = [
-        int(valid_json[utts[0]]["input"][i]["shape"][-1]) for i in range(args.num_encs)
-    ]
-    odim = int(valid_json[utts[0]]["output"][0]["shape"][-1])
-    for i in range(args.num_encs):
-        logging.info("stream{}: input dims : {}".format(i + 1, idim_list[i]))
-    logging.info("#output dims: " + str(odim))
-
-    # specify attention, CTC, hybrid mode
-    if "transducer" in args.model_module:
-        if (
-            getattr(args, "etype", False) == "custom"
-            or getattr(args, "dtype", False) == "custom"
-        ):
-            mtl_mode = "custom_transducer"
-        else:
-            mtl_mode = "transducer"
-        logging.info("Pure transducer mode")
-    elif args.mtlalpha == 1.0:
-        mtl_mode = "ctc"
-        logging.info("Pure CTC mode")
-    elif args.mtlalpha == 0.0:
-        mtl_mode = "att"
-        logging.info("Pure attention mode")
-    else:
-        mtl_mode = "mtl"
-        logging.info("Multitask learning mode")
-
-    if (args.enc_init is not None or args.dec_init is not None) and args.num_encs == 1:
-        model = load_trained_modules(idim_list[0], odim, args)
-    else:
-        model_class = dynamic_import(args.model_module)
-        model = model_class(
-            idim_list[0] if args.num_encs == 1 else idim_list, odim, args
-        )
-    assert isinstance(model, ASRInterface)
-    total_subsampling_factor = model.get_total_subsampling_factor()
-
-    logging.info(
-        " Total parameter of the model = "
-        + str(sum(p.numel() for p in model.parameters()))
-    )
-
-    if args.rnnlm is not None:
-        rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
-        rnnlm = lm_pytorch.ClassifierWithState(
-            lm_pytorch.RNNLM(len(args.char_list), rnnlm_args.layer, rnnlm_args.unit)
-        )
-        torch_load(args.rnnlm, rnnlm)
-        model.rnnlm = rnnlm
-
-    # write model config
-    if not os.path.exists(args.outdir):
-        os.makedirs(args.outdir)
-    model_conf = args.outdir + "/model.json"
-    with open(model_conf, "wb") as f:
-        logging.info("writing a model config file to " + model_conf)
-        f.write(
-            json.dumps(
-                (idim_list[0] if args.num_encs == 1 else idim_list, odim, vars(args)),
-                indent=4,
-                ensure_ascii=False,
-                sort_keys=True,
-            ).encode("utf_8")
-        )
-    for key in sorted(vars(args).keys()):
-        logging.info("ARGS: " + key + ": " + str(vars(args)[key]))
-
-    reporter = model.reporter
-
-    # check the use of multi-gpu
-    if args.ngpu > 1:
-        if args.batch_size != 0:
-            logging.warning(
-                "batch size is automatically increased (%d -> %d)"
-                % (args.batch_size, args.batch_size * args.ngpu)
-            )
-            args.batch_size *= args.ngpu
-        if args.num_encs > 1:
-            # TODO(ruizhili): implement data parallel for multi-encoder setup.
-            raise NotImplementedError(
-                "Data parallel is not supported for multi-encoder setup."
-            )
-
-    # set torch device
-    device = torch.device("cuda" if args.ngpu > 0 else "cpu")
-    if args.train_dtype in ("float16", "float32", "float64"):
-        dtype = getattr(torch, args.train_dtype)
-    else:
-        dtype = torch.float32
-    model = model.to(device=device, dtype=dtype)
-
-    if args.freeze_mods:
-        model, model_params = freeze_modules(model, args.freeze_mods)
-    else:
-        model_params = model.parameters()
-
-    logging.warning(
-        "num. model params: {:,} (num. trained: {:,} ({:.1f}%))".format(
-            sum(p.numel() for p in model.parameters()),
-            sum(p.numel() for p in model.parameters() if p.requires_grad),
-            sum(p.numel() for p in model.parameters() if p.requires_grad)
-            * 100.0
-            / sum(p.numel() for p in model.parameters()),
-        )
-    )
-
-    # Setup an optimizer
-    if args.opt == "adadelta":
-        optimizer = torch.optim.Adadelta(
-            model_params, rho=0.95, eps=args.eps, weight_decay=args.weight_decay
-        )
-    elif args.opt == "adam":
-        optimizer = torch.optim.Adam(model_params, weight_decay=args.weight_decay)
-    elif args.opt == "noam":
-        from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt
-
-        # For transformer-transducer, adim declaration is within the block definition.
-        # Thus, we need retrieve the most dominant value (d_hidden) for Noam scheduler.
-        if hasattr(args, "enc_block_arch") or hasattr(args, "dec_block_arch"):
-            adim = model.most_dom_dim
-        else:
-            adim = args.adim
-
-        optimizer = get_std_opt(
-            model_params, adim, args.transformer_warmup_steps, args.transformer_lr
-        )
-    else:
-        raise NotImplementedError("unknown optimizer: " + args.opt)
-
-    # setup apex.amp
-    if args.train_dtype in ("O0", "O1", "O2", "O3"):
-        try:
-            from apex import amp
-        except ImportError as e:
-            logging.error(
-                f"You need to install apex for --train-dtype {args.train_dtype}. "
-                "See https://github.com/NVIDIA/apex#linux"
-            )
-            raise e
-        if args.opt == "noam":
-            model, optimizer.optimizer = amp.initialize(
-                model, optimizer.optimizer, opt_level=args.train_dtype
-            )
-        else:
-            model, optimizer = amp.initialize(
-                model, optimizer, opt_level=args.train_dtype
-            )
-        use_apex = True
-
-        from espnet.nets.pytorch_backend.ctc import CTC
-
-        amp.register_float_function(CTC, "loss_fn")
-        amp.init()
-        logging.warning("register ctc as float function")
-    else:
-        use_apex = False
-
-    # FIXME: TOO DIRTY HACK
-    setattr(optimizer, "target", reporter)
-    setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
-
-    # Setup a converter
-    if args.num_encs == 1:
-        converter = CustomConverter(subsampling_factor=model.subsample[0], dtype=dtype)
-    else:
-        converter = CustomConverterMulEnc(
-            [i[0] for i in model.subsample_list], dtype=dtype
-        )
-
-    # read json data
-    with open(args.train_json, "rb") as f:
-        train_json = json.load(f)["utts"]
-    with open(args.valid_json, "rb") as f:
-        valid_json = json.load(f)["utts"]
-
-    use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
-    # make minibatch list (variable length)
-    train = make_batchset(
-        train_json,
-        args.batch_size,
-        args.maxlen_in,
-        args.maxlen_out,
-        args.minibatches,
-        min_batch_size=args.ngpu if args.ngpu > 1 else 1,
-        shortest_first=use_sortagrad,
-        count=args.batch_count,
-        batch_bins=args.batch_bins,
-        batch_frames_in=args.batch_frames_in,
-        batch_frames_out=args.batch_frames_out,
-        batch_frames_inout=args.batch_frames_inout,
-        iaxis=0,
-        oaxis=0,
-    )
-    valid = make_batchset(
-        valid_json,
-        args.batch_size,
-        args.maxlen_in,
-        args.maxlen_out,
-        args.minibatches,
-        min_batch_size=args.ngpu if args.ngpu > 1 else 1,
-        count=args.batch_count,
-        batch_bins=args.batch_bins,
-        batch_frames_in=args.batch_frames_in,
-        batch_frames_out=args.batch_frames_out,
-        batch_frames_inout=args.batch_frames_inout,
-        iaxis=0,
-        oaxis=0,
-    )
-
-    load_tr = LoadInputsAndTargets(
-        mode="asr",
-        load_output=True,
-        preprocess_conf=args.preprocess_conf,
-        preprocess_args={"train": True},  # Switch the mode of preprocessing
-    )
-    load_cv = LoadInputsAndTargets(
-        mode="asr",
-        load_output=True,
-        preprocess_conf=args.preprocess_conf,
-        preprocess_args={"train": False},  # Switch the mode of preprocessing
-    )
-    # hack to make batchsize argument as 1
-    # actual bathsize is included in a list
-    # default collate function converts numpy array to pytorch tensor
-    # we used an empty collate function instead which returns list
-    train_iter = ChainerDataLoader(
-        dataset=TransformDataset(train, lambda data: converter([load_tr(data)])),
-        batch_size=1,
-        num_workers=args.n_iter_processes,
-        shuffle=not use_sortagrad,
-        collate_fn=lambda x: x[0],
-    )
-    valid_iter = ChainerDataLoader(
-        dataset=TransformDataset(valid, lambda data: converter([load_cv(data)])),
-        batch_size=1,
-        shuffle=False,
-        collate_fn=lambda x: x[0],
-        num_workers=args.n_iter_processes,
-    )
-
-    # Set up a trainer
-    updater = CustomUpdater(
-        model,
-        args.grad_clip,
-        {"main": train_iter},
-        optimizer,
-        device,
-        args.ngpu,
-        args.grad_noise,
-        args.accum_grad,
-        use_apex=use_apex,
-    )
-    trainer = training.Trainer(updater, (args.epochs, "epoch"), out=args.outdir)
-
-    if use_sortagrad:
-        trainer.extend(
-            ShufflingEnabler([train_iter]),
-            trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs, "epoch"),
-        )
-
-    # Resume from a snapshot
-    if args.resume:
-        logging.info("resumed from %s" % args.resume)
-        torch_resume(args.resume, trainer)
-
-    # Evaluate the model with the test dataset for each epoch
-    if args.save_interval_iters > 0:
-        trainer.extend(
-            CustomEvaluator(model, {"main": valid_iter}, reporter, device, args.ngpu),
-            trigger=(args.save_interval_iters, "iteration"),
-        )
-    else:
-        trainer.extend(
-            CustomEvaluator(model, {"main": valid_iter}, reporter, device, args.ngpu)
-        )
-
-    # Save attention weight each epoch
-    is_attn_plot = (
-        "transformer" in args.model_module
-        or "conformer" in args.model_module
-        or mtl_mode in ["att", "mtl", "custom_transducer"]
-    )
-
-    if args.num_save_attention > 0 and is_attn_plot:
-        data = sorted(
-            list(valid_json.items())[: args.num_save_attention],
-            key=lambda x: int(x[1]["input"][0]["shape"][1]),
-            reverse=True,
-        )
-        if hasattr(model, "module"):
-            att_vis_fn = model.module.calculate_all_attentions
-            plot_class = model.module.attention_plot_class
-        else:
-            att_vis_fn = model.calculate_all_attentions
-            plot_class = model.attention_plot_class
-        att_reporter = plot_class(
-            att_vis_fn,
-            data,
-            args.outdir + "/att_ws",
-            converter=converter,
-            transform=load_cv,
-            device=device,
-            subsampling_factor=total_subsampling_factor,
-        )
-        trainer.extend(att_reporter, trigger=(1, "epoch"))
-    else:
-        att_reporter = None
-
-    # Save CTC prob at each epoch
-    if mtl_mode in ["ctc", "mtl"] and args.num_save_ctc > 0:
-        # NOTE: sort it by output lengths
-        data = sorted(
-            list(valid_json.items())[: args.num_save_ctc],
-            key=lambda x: int(x[1]["output"][0]["shape"][0]),
-            reverse=True,
-        )
-        if hasattr(model, "module"):
-            ctc_vis_fn = model.module.calculate_all_ctc_probs
-            plot_class = model.module.ctc_plot_class
-        else:
-            ctc_vis_fn = model.calculate_all_ctc_probs
-            plot_class = model.ctc_plot_class
-        ctc_reporter = plot_class(
-            ctc_vis_fn,
-            data,
-            args.outdir + "/ctc_prob",
-            converter=converter,
-            transform=load_cv,
-            device=device,
-            subsampling_factor=total_subsampling_factor,
-        )
-        trainer.extend(ctc_reporter, trigger=(1, "epoch"))
-    else:
-        ctc_reporter = None
-
-    # Make a plot for training and validation values
-    if args.num_encs > 1:
-        report_keys_loss_ctc = [
-            "main/loss_ctc{}".format(i + 1) for i in range(model.num_encs)
-        ] + ["validation/main/loss_ctc{}".format(i + 1) for i in range(model.num_encs)]
-        report_keys_cer_ctc = [
-            "main/cer_ctc{}".format(i + 1) for i in range(model.num_encs)
-        ] + ["validation/main/cer_ctc{}".format(i + 1) for i in range(model.num_encs)]
-
-    if hasattr(model, "is_rnnt"):
-        trainer.extend(
-            extensions.PlotReport(
-                [
-                    "main/loss",
-                    "validation/main/loss",
-                    "main/loss_trans",
-                    "validation/main/loss_trans",
-                    "main/loss_ctc",
-                    "validation/main/loss_ctc",
-                    "main/loss_lm",
-                    "validation/main/loss_lm",
-                    "main/loss_aux_trans",
-                    "validation/main/loss_aux_trans",
-                    "main/loss_aux_symm_kl",
-                    "validation/main/loss_aux_symm_kl",
-                ],
-                "epoch",
-                file_name="loss.png",
-            )
-        )
-    else:
-        trainer.extend(
-            extensions.PlotReport(
-                [
-                    "main/loss",
-                    "validation/main/loss",
-                    "main/loss_ctc",
-                    "validation/main/loss_ctc",
-                    "main/loss_att",
-                    "validation/main/loss_att",
-                ]
-                + ([] if args.num_encs == 1 else report_keys_loss_ctc),
-                "epoch",
-                file_name="loss.png",
-            )
-        )
-
-    trainer.extend(
-        extensions.PlotReport(
-            ["main/acc", "validation/main/acc"], "epoch", file_name="acc.png"
-        )
-    )
-    trainer.extend(
-        extensions.PlotReport(
-            ["main/cer_ctc", "validation/main/cer_ctc"]
-            + ([] if args.num_encs == 1 else report_keys_loss_ctc),
-            "epoch",
-            file_name="cer.png",
-        )
-    )
-
-    # Save best models
-    trainer.extend(
-        snapshot_object(model, "model.loss.best"),
-        trigger=training.triggers.MinValueTrigger("validation/main/loss"),
-    )
-    if mtl_mode not in ["ctc", "transducer", "custom_transducer"]:
-        trainer.extend(
-            snapshot_object(model, "model.acc.best"),
-            trigger=training.triggers.MaxValueTrigger("validation/main/acc"),
-        )
-
-    # save snapshot which contains model and optimizer states
-    if args.save_interval_iters > 0:
-        trainer.extend(
-            torch_snapshot(filename="snapshot.iter.{.updater.iteration}"),
-            trigger=(args.save_interval_iters, "iteration"),
-        )
-
-    # save snapshot at every epoch - for model averaging
-    trainer.extend(torch_snapshot(), trigger=(1, "epoch"))
-
-    # epsilon decay in the optimizer
-    if args.opt == "adadelta":
-        if args.criterion == "acc" and mtl_mode != "ctc":
-            trainer.extend(
-                restore_snapshot(
-                    model, args.outdir + "/model.acc.best", load_fn=torch_load
-                ),
-                trigger=CompareValueTrigger(
-                    "validation/main/acc",
-                    lambda best_value, current_value: best_value > current_value,
-                ),
-            )
-            trainer.extend(
-                adadelta_eps_decay(args.eps_decay),
-                trigger=CompareValueTrigger(
-                    "validation/main/acc",
-                    lambda best_value, current_value: best_value > current_value,
-                ),
-            )
-        elif args.criterion == "loss":
-            trainer.extend(
-                restore_snapshot(
-                    model, args.outdir + "/model.loss.best", load_fn=torch_load
-                ),
-                trigger=CompareValueTrigger(
-                    "validation/main/loss",
-                    lambda best_value, current_value: best_value < current_value,
-                ),
-            )
-            trainer.extend(
-                adadelta_eps_decay(args.eps_decay),
-                trigger=CompareValueTrigger(
-                    "validation/main/loss",
-                    lambda best_value, current_value: best_value < current_value,
-                ),
-            )
-        # NOTE: In some cases, it may take more than one epoch for the model's loss
-        # to escape from a local minimum.
-        # Thus, restore_snapshot extension is not used here.
-        # see details in https://github.com/espnet/espnet/pull/2171
-        elif args.criterion == "loss_eps_decay_only":
-            trainer.extend(
-                adadelta_eps_decay(args.eps_decay),
-                trigger=CompareValueTrigger(
-                    "validation/main/loss",
-                    lambda best_value, current_value: best_value < current_value,
-                ),
-            )
-
-    # Write a log of evaluation statistics for each epoch
-    trainer.extend(
-        extensions.LogReport(trigger=(args.report_interval_iters, "iteration"))
-    )
-
-    if hasattr(model, "is_rnnt"):
-        report_keys = [
-            "epoch",
-            "iteration",
-            "main/loss",
-            "main/loss_trans",
-            "main/loss_ctc",
-            "main/loss_lm",
-            "main/loss_aux_trans",
-            "main/loss_aux_symm_kl",
-            "validation/main/loss",
-            "validation/main/loss_trans",
-            "validation/main/loss_ctc",
-            "validation/main/loss_lm",
-            "validation/main/loss_aux_trans",
-            "validation/main/loss_aux_symm_kl",
-            "elapsed_time",
-        ]
-    else:
-        report_keys = [
-            "epoch",
-            "iteration",
-            "main/loss",
-            "main/loss_ctc",
-            "main/loss_att",
-            "validation/main/loss",
-            "validation/main/loss_ctc",
-            "validation/main/loss_att",
-            "main/acc",
-            "validation/main/acc",
-            "main/cer_ctc",
-            "validation/main/cer_ctc",
-            "elapsed_time",
-        ] + ([] if args.num_encs == 1 else report_keys_cer_ctc + report_keys_loss_ctc)
-
-    if args.opt == "adadelta":
-        trainer.extend(
-            extensions.observe_value(
-                "eps",
-                lambda trainer: trainer.updater.get_optimizer("main").param_groups[0][
-                    "eps"
-                ],
-            ),
-            trigger=(args.report_interval_iters, "iteration"),
-        )
-        report_keys.append("eps")
-    if args.report_cer:
-        report_keys.append("validation/main/cer")
-    if args.report_wer:
-        report_keys.append("validation/main/wer")
-    trainer.extend(
-        extensions.PrintReport(report_keys),
-        trigger=(args.report_interval_iters, "iteration"),
-    )
-
-    trainer.extend(extensions.ProgressBar(update_interval=args.report_interval_iters))
-    set_early_stop(trainer, args)
-
-    if args.tensorboard_dir is not None and args.tensorboard_dir != "":
-        trainer.extend(
-            TensorboardLogger(
-                SummaryWriter(args.tensorboard_dir),
-                att_reporter=att_reporter,
-                ctc_reporter=ctc_reporter,
-            ),
-            trigger=(args.report_interval_iters, "iteration"),
-        )
-    # Run the training
-    trainer.run()
-    check_early_stop(trainer, args.epochs)
-
-
-def recog(args):
-    """Decode with the given args.
-
-    Args:
-        args (namespace): The program arguments.
-
-    """
-    set_deterministic_pytorch(args)
-    model, train_args = load_trained_model(args.model, training=False)
-    assert isinstance(model, ASRInterface)
-    model.recog_args = args
-
-    if args.streaming_mode and "transformer" in train_args.model_module:
-        raise NotImplementedError("streaming mode for transformer is not implemented")
-    logging.info(
-        " Total parameter of the model = "
-        + str(sum(p.numel() for p in model.parameters()))
-    )
-
-    # read rnnlm
-    if args.rnnlm:
-        rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
-        if getattr(rnnlm_args, "model_module", "default") != "default":
-            raise ValueError(
-                "use '--api v2' option to decode with non-default language model"
-            )
-        rnnlm = lm_pytorch.ClassifierWithState(
-            lm_pytorch.RNNLM(
-                len(train_args.char_list),
-                rnnlm_args.layer,
-                rnnlm_args.unit,
-                getattr(rnnlm_args, "embed_unit", None),  # for backward compatibility
-            )
-        )
-        torch_load(args.rnnlm, rnnlm)
-        rnnlm.eval()
-    else:
-        rnnlm = None
-
-    if args.word_rnnlm:
-        rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
-        word_dict = rnnlm_args.char_list_dict
-        char_dict = {x: i for i, x in enumerate(train_args.char_list)}
-        word_rnnlm = lm_pytorch.ClassifierWithState(
-            lm_pytorch.RNNLM(
-                len(word_dict),
-                rnnlm_args.layer,
-                rnnlm_args.unit,
-                getattr(rnnlm_args, "embed_unit", None),  # for backward compatibility
-            )
-        )
-        torch_load(args.word_rnnlm, word_rnnlm)
-        word_rnnlm.eval()
-
-        if rnnlm is not None:
-            rnnlm = lm_pytorch.ClassifierWithState(
-                extlm_pytorch.MultiLevelLM(
-                    word_rnnlm.predictor, rnnlm.predictor, word_dict, char_dict
-                )
-            )
-        else:
-            rnnlm = lm_pytorch.ClassifierWithState(
-                extlm_pytorch.LookAheadWordLM(
-                    word_rnnlm.predictor, word_dict, char_dict
-                )
-            )
-
-    # gpu
-    if args.ngpu == 1:
-        gpu_id = list(range(args.ngpu))
-        logging.info("gpu id: " + str(gpu_id))
-        model.cuda()
-        if rnnlm:
-            rnnlm.cuda()
-
-    # read json data
-    with open(args.recog_json, "rb") as f:
-        js = json.load(f)["utts"]
-    new_js = {}
-
-    load_inputs_and_targets = LoadInputsAndTargets(
-        mode="asr",
-        load_output=False,
-        sort_in_input_length=False,
-        preprocess_conf=train_args.preprocess_conf
-        if args.preprocess_conf is None
-        else args.preprocess_conf,
-        preprocess_args={"train": False},
-    )
-
-    # load transducer beam search
-    if hasattr(model, "is_rnnt"):
-        if hasattr(model, "dec"):
-            trans_decoder = model.dec
-        else:
-            trans_decoder = model.decoder
-        joint_network = model.joint_network
-
-        beam_search_transducer = BeamSearchTransducer(
-            decoder=trans_decoder,
-            joint_network=joint_network,
-            beam_size=args.beam_size,
-            nbest=args.nbest,
-            lm=rnnlm,
-            lm_weight=args.lm_weight,
-            search_type=args.search_type,
-            max_sym_exp=args.max_sym_exp,
-            u_max=args.u_max,
-            nstep=args.nstep,
-            prefix_alpha=args.prefix_alpha,
-            score_norm=args.score_norm,
-        )
-
-    if args.batchsize == 0:
-        with torch.no_grad():
-            for idx, name in enumerate(js.keys(), 1):
-                logging.info("(%d/%d) decoding " + name, idx, len(js.keys()))
-                batch = [(name, js[name])]
-                feat = load_inputs_and_targets(batch)
-                feat = (
-                    feat[0][0]
-                    if args.num_encs == 1
-                    else [feat[idx][0] for idx in range(model.num_encs)]
-                )
-                if args.streaming_mode == "window" and args.num_encs == 1:
-                    logging.info(
-                        "Using streaming recognizer with window size %d frames",
-                        args.streaming_window,
-                    )
-                    se2e = WindowStreamingE2E(e2e=model, recog_args=args, rnnlm=rnnlm)
-                    for i in range(0, feat.shape[0], args.streaming_window):
-                        logging.info(
-                            "Feeding frames %d - %d", i, i + args.streaming_window
-                        )
-                        se2e.accept_input(feat[i : i + args.streaming_window])
-                    logging.info("Running offline attention decoder")
-                    se2e.decode_with_attention_offline()
-                    logging.info("Offline attention decoder finished")
-                    nbest_hyps = se2e.retrieve_recognition()
-                elif args.streaming_mode == "segment" and args.num_encs == 1:
-                    logging.info(
-                        "Using streaming recognizer with threshold value %d",
-                        args.streaming_min_blank_dur,
-                    )
-                    nbest_hyps = []
-                    for n in range(args.nbest):
-                        nbest_hyps.append({"yseq": [], "score": 0.0})
-                    se2e = SegmentStreamingE2E(e2e=model, recog_args=args, rnnlm=rnnlm)
-                    r = np.prod(model.subsample)
-                    for i in range(0, feat.shape[0], r):
-                        hyps = se2e.accept_input(feat[i : i + r])
-                        if hyps is not None:
-                            text = "".join(
-                                [
-                                    train_args.char_list[int(x)]
-                                    for x in hyps[0]["yseq"][1:-1]
-                                    if int(x) != -1
-                                ]
-                            )
-                            text = text.replace(
-                                "\u2581", " "
-                            ).strip()  # for SentencePiece
-                            text = text.replace(model.space, " ")
-                            text = text.replace(model.blank, "")
-                            logging.info(text)
-                            for n in range(args.nbest):
-                                nbest_hyps[n]["yseq"].extend(hyps[n]["yseq"])
-                                nbest_hyps[n]["score"] += hyps[n]["score"]
-                elif hasattr(model, "is_rnnt"):
-                    nbest_hyps = model.recognize(feat, beam_search_transducer)
-                else:
-                    nbest_hyps = model.recognize(
-                        feat, args, train_args.char_list, rnnlm
-                    )
-                new_js[name] = add_results_to_json(
-                    js[name], nbest_hyps, train_args.char_list
-                )
-
-    else:
-
-        def grouper(n, iterable, fillvalue=None):
-            kargs = [iter(iterable)] * n
-            return zip_longest(*kargs, fillvalue=fillvalue)
-
-        # sort data if batchsize > 1
-        keys = list(js.keys())
-        if args.batchsize > 1:
-            feat_lens = [js[key]["input"][0]["shape"][0] for key in keys]
-            sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
-            keys = [keys[i] for i in sorted_index]
-
-        with torch.no_grad():
-            for names in grouper(args.batchsize, keys, None):
-                names = [name for name in names if name]
-                batch = [(name, js[name]) for name in names]
-                feats = (
-                    load_inputs_and_targets(batch)[0]
-                    if args.num_encs == 1
-                    else load_inputs_and_targets(batch)
-                )
-                if args.streaming_mode == "window" and args.num_encs == 1:
-                    raise NotImplementedError
-                elif args.streaming_mode == "segment" and args.num_encs == 1:
-                    if args.batchsize > 1:
-                        raise NotImplementedError
-                    feat = feats[0]
-                    nbest_hyps = []
-                    for n in range(args.nbest):
-                        nbest_hyps.append({"yseq": [], "score": 0.0})
-                    se2e = SegmentStreamingE2E(e2e=model, recog_args=args, rnnlm=rnnlm)
-                    r = np.prod(model.subsample)
-                    for i in range(0, feat.shape[0], r):
-                        hyps = se2e.accept_input(feat[i : i + r])
-                        if hyps is not None:
-                            text = "".join(
-                                [
-                                    train_args.char_list[int(x)]
-                                    for x in hyps[0]["yseq"][1:-1]
-                                    if int(x) != -1
-                                ]
-                            )
-                            text = text.replace(
-                                "\u2581", " "
-                            ).strip()  # for SentencePiece
-                            text = text.replace(model.space, " ")
-                            text = text.replace(model.blank, "")
-                            logging.info(text)
-                            for n in range(args.nbest):
-                                nbest_hyps[n]["yseq"].extend(hyps[n]["yseq"])
-                                nbest_hyps[n]["score"] += hyps[n]["score"]
-                    nbest_hyps = [nbest_hyps]
-                else:
-                    nbest_hyps = model.recognize_batch(
-                        feats, args, train_args.char_list, rnnlm=rnnlm
-                    )
-
-                for i, nbest_hyp in enumerate(nbest_hyps):
-                    name = names[i]
-                    new_js[name] = add_results_to_json(
-                        js[name], nbest_hyp, train_args.char_list
-                    )
-
-    with open(args.result_label, "wb") as f:
-        f.write(
-            json.dumps(
-                {"utts": new_js}, indent=4, ensure_ascii=False, sort_keys=True
-            ).encode("utf_8")
-        )
-
-
-def enhance(args):
-    """Dumping enhanced speech and mask.
-
-    Args:
-        args (namespace): The program arguments.
-    """
-    set_deterministic_pytorch(args)
-    # read training config
-    idim, odim, train_args = get_model_conf(args.model, args.model_conf)
-
-    # TODO(ruizhili): implement enhance for multi-encoder model
-    assert args.num_encs == 1, "number of encoder should be 1 ({} is given)".format(
-        args.num_encs
-    )
-
-    # load trained model parameters
-    logging.info("reading model parameters from " + args.model)
-    model_class = dynamic_import(train_args.model_module)
-    model = model_class(idim, odim, train_args)
-    assert isinstance(model, ASRInterface)
-    torch_load(args.model, model)
-    model.recog_args = args
-
-    # gpu
-    if args.ngpu == 1:
-        gpu_id = list(range(args.ngpu))
-        logging.info("gpu id: " + str(gpu_id))
-        model.cuda()
-
-    # read json data
-    with open(args.recog_json, "rb") as f:
-        js = json.load(f)["utts"]
-
-    load_inputs_and_targets = LoadInputsAndTargets(
-        mode="asr",
-        load_output=False,
-        sort_in_input_length=False,
-        preprocess_conf=None,  # Apply pre_process in outer func
-    )
-    if args.batchsize == 0:
-        args.batchsize = 1
-
-    # Creates writers for outputs from the network
-    if args.enh_wspecifier is not None:
-        enh_writer = file_writer_helper(args.enh_wspecifier, filetype=args.enh_filetype)
-    else:
-        enh_writer = None
-
-    # Creates a Transformation instance
-    preprocess_conf = (
-        train_args.preprocess_conf
-        if args.preprocess_conf is None
-        else args.preprocess_conf
-    )
-    if preprocess_conf is not None:
-        logging.info(f"Use preprocessing: {preprocess_conf}")
-        transform = Transformation(preprocess_conf)
-    else:
-        transform = None
-
-    # Creates a IStft instance
-    istft = None
-    frame_shift = args.istft_n_shift  # Used for plot the spectrogram
-    if args.apply_istft:
-        if preprocess_conf is not None:
-            # Read the conffile and find stft setting
-            with open(preprocess_conf) as f:
-                # Json format: e.g.
-                #    {"process": [{"type": "stft",
-                #                  "win_length": 400,
-                #                  "n_fft": 512, "n_shift": 160,
-                #                  "window": "han"},
-                #                 {"type": "foo", ...}, ...]}
-                conf = json.load(f)
-                assert "process" in conf, conf
-                # Find stft setting
-                for p in conf["process"]:
-                    if p["type"] == "stft":
-                        istft = IStft(
-                            win_length=p["win_length"],
-                            n_shift=p["n_shift"],
-                            window=p.get("window", "hann"),
-                        )
-                        logging.info(
-                            "stft is found in {}. "
-                            "Setting istft config from it\n{}".format(
-                                preprocess_conf, istft
-                            )
-                        )
-                        frame_shift = p["n_shift"]
-                        break
-        if istft is None:
-            # Set from command line arguments
-            istft = IStft(
-                win_length=args.istft_win_length,
-                n_shift=args.istft_n_shift,
-                window=args.istft_window,
-            )
-            logging.info(
-                "Setting istft config from the command line args\n{}".format(istft)
-            )
-
-    # sort data
-    keys = list(js.keys())
-    feat_lens = [js[key]["input"][0]["shape"][0] for key in keys]
-    sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
-    keys = [keys[i] for i in sorted_index]
-
-    def grouper(n, iterable, fillvalue=None):
-        kargs = [iter(iterable)] * n
-        return zip_longest(*kargs, fillvalue=fillvalue)
-
-    num_images = 0
-    if not os.path.exists(args.image_dir):
-        os.makedirs(args.image_dir)
-
-    for names in grouper(args.batchsize, keys, None):
-        batch = [(name, js[name]) for name in names]
-
-        # May be in time region: (Batch, [Time, Channel])
-        org_feats = load_inputs_and_targets(batch)[0]
-        if transform is not None:
-            # May be in time-freq region: : (Batch, [Time, Channel, Freq])
-            feats = transform(org_feats, train=False)
-        else:
-            feats = org_feats
-
-        with torch.no_grad():
-            enhanced, mask, ilens = model.enhance(feats)
-
-        for idx, name in enumerate(names):
-            # Assuming mask, feats : [Batch, Time, Channel. Freq]
-            #          enhanced    : [Batch, Time, Freq]
-            enh = enhanced[idx][: ilens[idx]]
-            mas = mask[idx][: ilens[idx]]
-            feat = feats[idx]
-
-            # Plot spectrogram
-            if args.image_dir is not None and num_images < args.num_images:
-                import matplotlib.pyplot as plt
-
-                num_images += 1
-                ref_ch = 0
-
-                plt.figure(figsize=(20, 10))
-                plt.subplot(4, 1, 1)
-                plt.title("Mask [ref={}ch]".format(ref_ch))
-                plot_spectrogram(
-                    plt,
-                    mas[:, ref_ch].T,
-                    fs=args.fs,
-                    mode="linear",
-                    frame_shift=frame_shift,
-                    bottom=False,
-                    labelbottom=False,
-                )
-
-                plt.subplot(4, 1, 2)
-                plt.title("Noisy speech [ref={}ch]".format(ref_ch))
-                plot_spectrogram(
-                    plt,
-                    feat[:, ref_ch].T,
-                    fs=args.fs,
-                    mode="db",
-                    frame_shift=frame_shift,
-                    bottom=False,
-                    labelbottom=False,
-                )
-
-                plt.subplot(4, 1, 3)
-                plt.title("Masked speech [ref={}ch]".format(ref_ch))
-                plot_spectrogram(
-                    plt,
-                    (feat[:, ref_ch] * mas[:, ref_ch]).T,
-                    frame_shift=frame_shift,
-                    fs=args.fs,
-                    mode="db",
-                    bottom=False,
-                    labelbottom=False,
-                )
-
-                plt.subplot(4, 1, 4)
-                plt.title("Enhanced speech")
-                plot_spectrogram(
-                    plt, enh.T, fs=args.fs, mode="db", frame_shift=frame_shift
-                )
-
-                plt.savefig(os.path.join(args.image_dir, name + ".png"))
-                plt.clf()
-
-            # Write enhanced wave files
-            if enh_writer is not None:
-                if istft is not None:
-                    enh = istft(enh)
-                else:
-                    enh = enh
-
-                if args.keep_length:
-                    if len(org_feats[idx]) < len(enh):
-                        # Truncate the frames added by stft padding
-                        enh = enh[: len(org_feats[idx])]
-                    elif len(org_feats) > len(enh):
-                        padwidth = [(0, (len(org_feats[idx]) - len(enh)))] + [
-                            (0, 0)
-                        ] * (enh.ndim - 1)
-                        enh = np.pad(enh, padwidth, mode="constant")
-
-                if args.enh_filetype in ("sound", "sound.hdf5"):
-                    enh_writer[name] = (args.fs, enh)
-                else:
-                    # Hint: To dump stft_signal, mask or etc,
-                    # enh_filetype='hdf5' might be convenient.
-                    enh_writer[name] = enh
-
-            if num_images >= args.num_images and enh_writer is None:
-                logging.info("Breaking the process.")
-                break
-
-
-def ctc_align(args):
-    """CTC forced alignments with the given args.
-
-    Args:
-        args (namespace): The program arguments.
-    """
-
-    def add_alignment_to_json(js, alignment, char_list):
-        """Add N-best results to json.
-
-        Args:
-            js (dict[str, Any]): Groundtruth utterance dict.
-            alignment (list[int]): List of alignment.
-            char_list (list[str]): List of characters.
-
-        Returns:
-            dict[str, Any]: N-best results added utterance dict.
-
-        """
-        # copy old json info
-        new_js = dict()
-        new_js["ctc_alignment"] = []
-
-        alignment_tokens = []
-        for idx, a in enumerate(alignment):
-            alignment_tokens.append(char_list[a])
-        alignment_tokens = " ".join(alignment_tokens)
-
-        new_js["ctc_alignment"] = alignment_tokens
-
-        return new_js
-
-    set_deterministic_pytorch(args)
-    model, train_args = load_trained_model(args.model)
-    assert isinstance(model, ASRInterface)
-    model.eval()
-
-    load_inputs_and_targets = LoadInputsAndTargets(
-        mode="asr",
-        load_output=True,
-        sort_in_input_length=False,
-        preprocess_conf=train_args.preprocess_conf
-        if args.preprocess_conf is None
-        else args.preprocess_conf,
-        preprocess_args={"train": False},
-    )
-
-    if args.ngpu > 1:
-        raise NotImplementedError("only single GPU decoding is supported")
-    if args.ngpu == 1:
-        device = "cuda"
-    else:
-        device = "cpu"
-    dtype = getattr(torch, args.dtype)
-    logging.info(f"Decoding device={device}, dtype={dtype}")
-    model.to(device=device, dtype=dtype).eval()
-
-    # read json data
-    with open(args.align_json, "rb") as f:
-        js = json.load(f)["utts"]
-    new_js = {}
-    if args.batchsize == 0:
-        with torch.no_grad():
-            for idx, name in enumerate(js.keys(), 1):
-                logging.info("(%d/%d) aligning " + name, idx, len(js.keys()))
-                batch = [(name, js[name])]
-                feat, label = load_inputs_and_targets(batch)
-                feat = feat[0]
-                label = label[0]
-                enc = model.encode(torch.as_tensor(feat).to(device)).unsqueeze(0)
-                alignment = model.ctc.forced_align(enc, label)
-                new_js[name] = add_alignment_to_json(
-                    js[name], alignment, train_args.char_list
-                )
-    else:
-        raise NotImplementedError("Align_batch is not implemented.")
-
-    with open(args.result_label, "wb") as f:
-        f.write(
-            json.dumps(
-                {"utts": new_js}, indent=4, ensure_ascii=False, sort_keys=True
-            ).encode("utf_8")
-        )
diff --git a/spaces/segments-tobias/conex/espnet/nets/chainer_backend/rnn/attentions.py b/spaces/segments-tobias/conex/espnet/nets/chainer_backend/rnn/attentions.py
deleted file mode 100644
index e9a776e5b2ee67150fc778eefe3cb21a8c5582c3..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/chainer_backend/rnn/attentions.py
+++ /dev/null
@@ -1,280 +0,0 @@
-import chainer
-import chainer.functions as F
-import chainer.links as L
-
-import numpy as np
-
-
-# dot product based attention
-class AttDot(chainer.Chain):
-    """Compute attention based on dot product.
-
-    Args:
-        eprojs (int | None): Dimension of input vectors from encoder.
-        dunits (int | None): Dimension of input vectors for decoder.
-        att_dim (int): Dimension of input vectors for attention.
-
-    """
-
-    def __init__(self, eprojs, dunits, att_dim):
-        super(AttDot, self).__init__()
-        with self.init_scope():
-            self.mlp_enc = L.Linear(eprojs, att_dim)
-            self.mlp_dec = L.Linear(dunits, att_dim)
-
-        self.dunits = dunits
-        self.eprojs = eprojs
-        self.att_dim = att_dim
-        self.h_length = None
-        self.enc_h = None
-        self.pre_compute_enc_h = None
-
-    def reset(self):
-        """Reset states."""
-        self.h_length = None
-        self.enc_h = None
-        self.pre_compute_enc_h = None
-
-    def __call__(self, enc_hs, dec_z, att_prev, scaling=2.0):
-        """Compute AttDot forward layer.
-
-        Args:
-            enc_hs (chainer.Variable | N-dimensional array):
-                Input variable from encoder.
-            dec_z (chainer.Variable | N-dimensional array): Input variable of decoder.
-            scaling (float): Scaling weight to make attention sharp.
-
-        Returns:
-            chainer.Variable: Weighted sum over flames.
-            chainer.Variable: Attention weight.
-
-        """
-        batch = len(enc_hs)
-        # pre-compute all h outside the decoder loop
-        if self.pre_compute_enc_h is None:
-            self.enc_h = F.pad_sequence(enc_hs)  # utt x frame x hdim
-            self.h_length = self.enc_h.shape[1]
-            # utt x frame x att_dim
-            self.pre_compute_enc_h = F.tanh(self.mlp_enc(self.enc_h, n_batch_axes=2))
-
-        if dec_z is None:
-            dec_z = chainer.Variable(
-                self.xp.zeros((batch, self.dunits), dtype=np.float32)
-            )
-        else:
-            dec_z = dec_z.reshape(batch, self.dunits)
-
-        # <phi (h_t), psi (s)> for all t
-        u = F.broadcast_to(
-            F.expand_dims(F.tanh(self.mlp_dec(dec_z)), 1), self.pre_compute_enc_h.shape
-        )
-        e = F.sum(self.pre_compute_enc_h * u, axis=2)  # utt x frame
-        # Applying a minus-large-number filter
-        # to make a probability value zero for a padded area
-        # simply degrades the performance, and I gave up this implementation
-        # Apply a scaling to make an attention sharp
-        w = F.softmax(scaling * e)
-        # weighted sum over flames
-        # utt x hdim
-        c = F.sum(
-            self.enc_h * F.broadcast_to(F.expand_dims(w, 2), self.enc_h.shape), axis=1
-        )
-
-        return c, w
-
-
-# location based attention
-class AttLoc(chainer.Chain):
-    """Compute location-based attention.
-
-    Args:
-        eprojs (int | None): Dimension of input vectors from encoder.
-        dunits (int | None): Dimension of input vectors for decoder.
-        att_dim (int): Dimension of input vectors for attention.
-        aconv_chans (int): Number of channels of output arrays from convolutional layer.
-        aconv_filts (int): Size of filters of convolutional layer.
-
-    """
-
-    def __init__(self, eprojs, dunits, att_dim, aconv_chans, aconv_filts):
-        super(AttLoc, self).__init__()
-        with self.init_scope():
-            self.mlp_enc = L.Linear(eprojs, att_dim)
-            self.mlp_dec = L.Linear(dunits, att_dim, nobias=True)
-            self.mlp_att = L.Linear(aconv_chans, att_dim, nobias=True)
-            self.loc_conv = L.Convolution2D(
-                1, aconv_chans, ksize=(1, 2 * aconv_filts + 1), pad=(0, aconv_filts)
-            )
-            self.gvec = L.Linear(att_dim, 1)
-
-        self.dunits = dunits
-        self.eprojs = eprojs
-        self.att_dim = att_dim
-        self.h_length = None
-        self.enc_h = None
-        self.pre_compute_enc_h = None
-        self.aconv_chans = aconv_chans
-
-    def reset(self):
-        """Reset states."""
-        self.h_length = None
-        self.enc_h = None
-        self.pre_compute_enc_h = None
-
-    def __call__(self, enc_hs, dec_z, att_prev, scaling=2.0):
-        """Compute AttLoc forward layer.
-
-        Args:
-            enc_hs (chainer.Variable | N-dimensional array):
-                Input variable from encoders.
-            dec_z (chainer.Variable | N-dimensional array): Input variable of decoder.
-            att_prev (chainer.Variable | None): Attention weight.
-            scaling (float): Scaling weight to make attention sharp.
-
-        Returns:
-            chainer.Variable: Weighted sum over flames.
-            chainer.Variable: Attention weight.
-
-        """
-        batch = len(enc_hs)
-        # pre-compute all h outside the decoder loop
-        if self.pre_compute_enc_h is None:
-            self.enc_h = F.pad_sequence(enc_hs)  # utt x frame x hdim
-            self.h_length = self.enc_h.shape[1]
-            # utt x frame x att_dim
-            self.pre_compute_enc_h = self.mlp_enc(self.enc_h, n_batch_axes=2)
-
-        if dec_z is None:
-            dec_z = chainer.Variable(
-                self.xp.zeros((batch, self.dunits), dtype=np.float32)
-            )
-        else:
-            dec_z = dec_z.reshape(batch, self.dunits)
-
-        # initialize attention weight with uniform dist.
-        if att_prev is None:
-            att_prev = [
-                self.xp.full(hh.shape[0], 1.0 / hh.shape[0], dtype=np.float32)
-                for hh in enc_hs
-            ]
-            att_prev = [chainer.Variable(att) for att in att_prev]
-            att_prev = F.pad_sequence(att_prev)
-
-        # att_prev: utt x frame -> utt x 1 x 1 x frame
-        # -> utt x att_conv_chans x 1 x frame
-        att_conv = self.loc_conv(att_prev.reshape(batch, 1, 1, self.h_length))
-        # att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans
-        att_conv = F.swapaxes(F.squeeze(att_conv, axis=2), 1, 2)
-        # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim
-        att_conv = self.mlp_att(att_conv, n_batch_axes=2)
-
-        # dec_z_tiled: utt x frame x att_dim
-        dec_z_tiled = F.broadcast_to(
-            F.expand_dims(self.mlp_dec(dec_z), 1), self.pre_compute_enc_h.shape
-        )
-
-        # dot with gvec
-        # utt x frame x att_dim -> utt x frame
-        # TODO(watanabe) use batch_matmul
-        e = F.squeeze(
-            self.gvec(
-                F.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled), n_batch_axes=2
-            ),
-            axis=2,
-        )
-        # Applying a minus-large-number filter
-        # to make a probability value zero for a padded area
-        # simply degrades the performance, and I gave up this implementation
-        # Apply a scaling to make an attention sharp
-        w = F.softmax(scaling * e)
-
-        # weighted sum over flames
-        # utt x hdim
-        c = F.sum(
-            self.enc_h * F.broadcast_to(F.expand_dims(w, 2), self.enc_h.shape), axis=1
-        )
-
-        return c, w
-
-
-class NoAtt(chainer.Chain):
-    """Compute non-attention layer.
-
-    This layer is a dummy attention layer to be compatible with other
-    attention-based models.
-
-    """
-
-    def __init__(self):
-        super(NoAtt, self).__init__()
-        self.h_length = None
-        self.enc_h = None
-        self.pre_compute_enc_h = None
-        self.c = None
-
-    def reset(self):
-        """Reset states."""
-        self.h_length = None
-        self.enc_h = None
-        self.pre_compute_enc_h = None
-        self.c = None
-
-    def __call__(self, enc_hs, dec_z, att_prev):
-        """Compute NoAtt forward layer.
-
-        Args:
-            enc_hs (chainer.Variable | N-dimensional array):
-                Input variable from encoders.
-            dec_z: Dummy.
-            att_prev (chainer.Variable | None): Attention weight.
-
-        Returns:
-            chainer.Variable: Sum over flames.
-            chainer.Variable: Attention weight.
-
-        """
-        # pre-compute all h outside the decoder loop
-        if self.pre_compute_enc_h is None:
-            self.enc_h = F.pad_sequence(enc_hs)  # utt x frame x hdim
-            self.h_length = self.enc_h.shape[1]
-
-        # initialize attention weight with uniform dist.
-        if att_prev is None:
-            att_prev = [
-                self.xp.full(hh.shape[0], 1.0 / hh.shape[0], dtype=np.float32)
-                for hh in enc_hs
-            ]
-            att_prev = [chainer.Variable(att) for att in att_prev]
-            att_prev = F.pad_sequence(att_prev)
-            self.c = F.sum(
-                self.enc_h
-                * F.broadcast_to(F.expand_dims(att_prev, 2), self.enc_h.shape),
-                axis=1,
-            )
-
-        return self.c, att_prev
-
-
-def att_for(args):
-    """Returns an attention layer given the program arguments.
-
-    Args:
-        args (Namespace): The arguments.
-
-    Returns:
-        chainer.Chain: The corresponding attention module.
-
-    """
-    if args.atype == "dot":
-        att = AttDot(args.eprojs, args.dunits, args.adim)
-    elif args.atype == "location":
-        att = AttLoc(
-            args.eprojs, args.dunits, args.adim, args.aconv_chans, args.aconv_filts
-        )
-    elif args.atype == "noatt":
-        att = NoAtt()
-    else:
-        raise NotImplementedError(
-            "chainer supports only noatt, dot, and location attention."
-        )
-    return att
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/__init__.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/__init__.py
deleted file mode 100644
index b7f177368e62a5578b8706300e101f831a3972ac..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Initialize sub package."""
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/multi_layer_conv.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/multi_layer_conv.py
deleted file mode 100644
index 5fb0717b060d5815d44c83b711f8fc4659987f3a..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/multi_layer_conv.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Layer modules for FFT block in FastSpeech (Feed-forward Transformer)."""
-
-import torch
-
-
-class MultiLayeredConv1d(torch.nn.Module):
-    """Multi-layered conv1d for Transformer block.
-
-    This is a module of multi-leyered conv1d designed
-    to replace positionwise feed-forward network
-    in Transforner block, which is introduced in
-    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
-
-    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
-        https://arxiv.org/pdf/1905.09263.pdf
-
-    """
-
-    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
-        """Initialize MultiLayeredConv1d module.
-
-        Args:
-            in_chans (int): Number of input channels.
-            hidden_chans (int): Number of hidden channels.
-            kernel_size (int): Kernel size of conv1d.
-            dropout_rate (float): Dropout rate.
-
-        """
-        super(MultiLayeredConv1d, self).__init__()
-        self.w_1 = torch.nn.Conv1d(
-            in_chans,
-            hidden_chans,
-            kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-        )
-        self.w_2 = torch.nn.Conv1d(
-            hidden_chans,
-            in_chans,
-            kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-        )
-        self.dropout = torch.nn.Dropout(dropout_rate)
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (torch.Tensor): Batch of input tensors (B, T, in_chans).
-
-        Returns:
-            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
-
-        """
-        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
-        return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
-
-
-class Conv1dLinear(torch.nn.Module):
-    """Conv1D + Linear for Transformer block.
-
-    A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
-
-    """
-
-    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
-        """Initialize Conv1dLinear module.
-
-        Args:
-            in_chans (int): Number of input channels.
-            hidden_chans (int): Number of hidden channels.
-            kernel_size (int): Kernel size of conv1d.
-            dropout_rate (float): Dropout rate.
-
-        """
-        super(Conv1dLinear, self).__init__()
-        self.w_1 = torch.nn.Conv1d(
-            in_chans,
-            hidden_chans,
-            kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-        )
-        self.w_2 = torch.nn.Linear(hidden_chans, in_chans)
-        self.dropout = torch.nn.Dropout(dropout_rate)
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (torch.Tensor): Batch of input tensors (B, T, in_chans).
-
-        Returns:
-            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
-
-        """
-        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
-        return self.w_2(self.dropout(x))
diff --git a/spaces/segments-tobias/conex/espnet2/samplers/sorted_batch_sampler.py b/spaces/segments-tobias/conex/espnet2/samplers/sorted_batch_sampler.py
deleted file mode 100644
index 4649f9a4fd748f8911cdaab94b8f07326905643f..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/samplers/sorted_batch_sampler.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import logging
-from typing import Iterator
-from typing import Tuple
-
-from typeguard import check_argument_types
-
-from espnet2.fileio.read_text import load_num_sequence_text
-from espnet2.samplers.abs_sampler import AbsSampler
-
-
-class SortedBatchSampler(AbsSampler):
-    """BatchSampler with sorted samples by length.
-
-    Args:
-        batch_size:
-        shape_file:
-        sort_in_batch: 'descending', 'ascending' or None.
-        sort_batch:
-    """
-
-    def __init__(
-        self,
-        batch_size: int,
-        shape_file: str,
-        sort_in_batch: str = "descending",
-        sort_batch: str = "ascending",
-        drop_last: bool = False,
-    ):
-        assert check_argument_types()
-        assert batch_size > 0
-        self.batch_size = batch_size
-        self.shape_file = shape_file
-        self.sort_in_batch = sort_in_batch
-        self.sort_batch = sort_batch
-        self.drop_last = drop_last
-
-        # utt2shape: (Length, ...)
-        #    uttA 100,...
-        #    uttB 201,...
-        utt2shape = load_num_sequence_text(shape_file, loader_type="csv_int")
-        if sort_in_batch == "descending":
-            # Sort samples in descending order (required by RNN)
-            keys = sorted(utt2shape, key=lambda k: -utt2shape[k][0])
-        elif sort_in_batch == "ascending":
-            # Sort samples in ascending order
-            keys = sorted(utt2shape, key=lambda k: utt2shape[k][0])
-        else:
-            raise ValueError(
-                f"sort_in_batch must be either one of "
-                f"ascending, descending, or None: {sort_in_batch}"
-            )
-        if len(keys) == 0:
-            raise RuntimeError(f"0 lines found: {shape_file}")
-
-        # Apply max(, 1) to avoid 0-batches
-        N = max(len(keys) // batch_size, 1)
-        if not self.drop_last:
-            # Split keys evenly as possible as. Note that If N != 1,
-            # the these batches always have size of batch_size at minimum.
-            self.batch_list = [
-                keys[i * len(keys) // N : (i + 1) * len(keys) // N] for i in range(N)
-            ]
-        else:
-            self.batch_list = [
-                tuple(keys[i * batch_size : (i + 1) * batch_size]) for i in range(N)
-            ]
-
-        if len(self.batch_list) == 0:
-            logging.warning(f"{shape_file} is empty")
-
-        if sort_in_batch != sort_batch:
-            if sort_batch not in ("ascending", "descending"):
-                raise ValueError(
-                    f"sort_batch must be ascending or descending: {sort_batch}"
-                )
-            self.batch_list.reverse()
-
-        if len(self.batch_list) == 0:
-            raise RuntimeError("0 batches")
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}("
-            f"N-batch={len(self)}, "
-            f"batch_size={self.batch_size}, "
-            f"shape_file={self.shape_file}, "
-            f"sort_in_batch={self.sort_in_batch}, "
-            f"sort_batch={self.sort_batch})"
-        )
-
-    def __len__(self):
-        return len(self.batch_list)
-
-    def __iter__(self) -> Iterator[Tuple[str, ...]]:
-        return iter(self.batch_list)
diff --git a/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/generate.py b/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/generate.py
deleted file mode 100644
index 4255c8cb0a16817b3f4d60783456bfa5cd15d018..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/generate.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import argparse
-
-import torch
-from torchvision import utils
-from model import Generator
-from tqdm import tqdm
-def generate(args, g_ema, device, mean_latent):
-
-    with torch.no_grad():
-        g_ema.eval()
-        for i in tqdm(range(args.pics)):
-           sample_z = torch.randn(args.sample, args.latent, device=device)
-
-           sample, _ = g_ema([sample_z], truncation=args.truncation, truncation_latent=mean_latent)
-           
-           utils.save_image(
-            sample,
-            f'sample/{str(i).zfill(6)}.png',
-            nrow=1,
-            normalize=True,
-            range=(-1, 1),
-        )
-
-if __name__ == '__main__':
-    device = 'cuda'
-
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--size', type=int, default=1024)
-    parser.add_argument('--sample', type=int, default=1)
-    parser.add_argument('--pics', type=int, default=20)
-    parser.add_argument('--truncation', type=float, default=1)
-    parser.add_argument('--truncation_mean', type=int, default=4096)
-    parser.add_argument('--ckpt', type=str, default="stylegan2-ffhq-config-f.pt")
-    parser.add_argument('--channel_multiplier', type=int, default=2)
-
-    args = parser.parse_args()
-
-    args.latent = 512
-    args.n_mlp = 8
-
-    g_ema = Generator(
-        args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier
-    ).to(device)
-    checkpoint = torch.load(args.ckpt)
-
-    g_ema.load_state_dict(checkpoint['g_ema'])
-
-    if args.truncation < 1:
-        with torch.no_grad():
-            mean_latent = g_ema.mean_latent(args.truncation_mean)
-    else:
-        mean_latent = None
-
-    generate(args, g_ema, device, mean_latent)
diff --git a/spaces/silencewing/server/youyou/.history/math_20230613231807.html b/spaces/silencewing/server/youyou/.history/math_20230613231807.html
deleted file mode 100644
index 95b23250d23830e1f01c28cc26fcc4072475142e..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/.history/math_20230613231807.html
+++ /dev/null
@@ -1,234 +0,0 @@
-<!-- /*
- * @Author: Chauncey Yuan 
- * @Date: 2019-08-01 18:25:30 
- * @Last Modified by:   Chauncey Yuan 
- * @Last Modified time: 2019-08-03 08:24:27 
- */ -->
-
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <meta http-equiv="X-UA-Compatible" content="ie=edge">
-    <title>Document</title>
-    <style>
-        html {
-            height: 100%;
-            background: #b3b4a6;
-        }
-        .cal_game {
-            width: 100vw;
-            margin: 0 auto;
-        }
-        
-        table {
-            margin: 0 auto;
-        }
-        
-        td {
-            width: 150px;
-            height: 50px;
-            text-align: center;
-            line-height: 40px;
-            border: 1px solid #2b7e71;
-        }
-        
-        /* .btn {
-            width: 150px;
-            height: 50px;
-            color: #2b7e71;;
-            border: 1px solid #2b7e71;
-            background-color: transparent;
-        }
-        
-        .btn:hover {
-            background-color: #2b7e71;
-            color: #fff;
-            cursor: pointer;
-        } */
-        
-        input {
-            height: 30px;
-            border: 1px solid #999999;
-            background-color: transparent;
-
-        }
-
-        input:hover {
-            border: 3px solid #314834;
-
-        }
-    </style>
-</head>
-
-<body>
-    <div class="cal_game">
-        <table>
-            <tr>
-                <td id="output" colspan="3"></td>
-            </tr>
-            <tr>
-                <td>题目</td>
-                <td id="eq" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>答案</td>
-                <td colspan="2"><input type="text" name="" id="input" placeholder="请输入计算结果："></td>
-                <!-- <td><button class="btn" onclick="xun()">计算</button></td> -->
-            </tr>
-            <tr>
-                <td>正误</td>
-                <td id="result" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>得分</td>
-                <td id="score" colspan="2"></td>
-            </tr>
-            <tr>
-                <td id="accuracy" colspan="3"></td>
-            </tr>
-        </table>
-    </div>
-    <script>
-        // 定义题号，使用时+1
-        var i = 0;
-        // 定义计算正确的次数
-        var right_times = 0;
-        // 定义分数
-        var score = 0;
-        // 定义正确率
-        var accuracy = 0;
-        // 定义加减法符号数组，用于后边产生0或1的随机数，来确定加减法
-        var sign_operation_list = ["+", "-"];
-        // 生成0或者1的随机数，确定加减法
-        var sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-
-        var max_num = 20;
-        // 显示题号
-        document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-        document.onkeydown = function (e) {
-            if(e.which == "13" && document.getElementById("input").value){
-                xun()
-            }
-            else{
-                document.getElementById("input").focus()
-            }
-        }
-
-        // 如果是减法
-        if (sign_operation == 1) {
-            // 则被减数应该小于减数，结果才不会为负数
-            // 减数范围为0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 被减数范围为0-减数
-            var num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-        }
-        // 如果是加法
-        if (sign_operation == 0) {
-            // 两数和应小于max_num
-            // 第一个数的范围是0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 第二个数是0-（max_num-第一个数）
-            var num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-        }
-        // 组成算式，显示给用户看
-        var eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-        // 页面中显示算式
-        document.getElementById("eq").innerHTML = eq;
-        // console.log(eq);
-
-        // 定义函数，当按钮按下是执行一次
-        function xun() {
-            // console.log(num1, num2);
-            // 题号加1
-            i++;
-            // 获取用户输入的结果
-            var input = Number(document.getElementById("input").value);
-            // console.log(input, num1, num2);
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 定义真实结果
-                var calResult = num1 + num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            } // 如果是减法
-            if (sign_operation == 1) {
-                // 定义真实结果
-                var calResult = num1 - num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误:" + str(calResult);
-                }
-            }
-            // 判断结果后，用户输入框清空
-            document.getElementById("input").value = "";
-            // 显示分数
-            document.getElementById("score").innerHTML = score;
-            // 显示正确率
-            document.getElementById("accuracy").innerHTML = (((right_times / i) * 100).toFixed(2)) + "%";
-
-            if (score >= 100) {
-                window.location.href = "game.html";
-            }
-            // 生成0或者1的随机数，确定加减法
-            sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-            // 显示题号
-            document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-            // 如果是减法
-            if (sign_operation == 1) {
-                // 则被减数应该小于减数，结果才不会为负数
-                // 减数范围为0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 被减数范围为0-减数
-                num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-            }
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 两数和应小于max_num
-                // 第一个数的范围是0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 第二个数是0-（max_num-第一个数）
-                num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-            }
-            // 组成算式，显示给用户看
-            eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-            // 页面中显示算式
-            document.getElementById("eq").innerHTML = eq;
-            // console.log(eq);
-        }
-    </script>
-</body>
-
-</html>
diff --git a/spaces/simonguest/cs-tutor/levels/1/starter_code.py b/spaces/simonguest/cs-tutor/levels/1/starter_code.py
deleted file mode 100644
index 9b540611933b6a381c7430dd789d6945cda3650f..0000000000000000000000000000000000000000
--- a/spaces/simonguest/cs-tutor/levels/1/starter_code.py
+++ /dev/null
@@ -1,4 +0,0 @@
-def reverse_string(string):
-  return string
-
-print(reverse_string("Hello, world!"))
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Descargar Mortal Kombat X para Android APK  OBB para una experiencia de juego increble.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Descargar Mortal Kombat X para Android APK  OBB para una experiencia de juego increble.md
deleted file mode 100644
index 6738414cd960e0812e96b1d67b3b40292df38555..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Descargar Mortal Kombat X para Android APK  OBB para una experiencia de juego increble.md	
+++ /dev/null
@@ -1,102 +0,0 @@
-<br />
-<h1>Descargar Mortal Kombat X para Android APK + DATOS OBB</h1>
-<p>¿Te gustan los juegos de lucha? ¿Quieres disfrutar de uno de los más populares y espectaculares del género en tu dispositivo Android? Entonces no te pierdas este artículo, donde te explicaremos cómo descargar e instalar Mortal Kombat X, un juego que combina una presentación cinematográfica con un gameplay dinámico y variado. Además, te daremos algunos consejos y trucos para que puedas sacarle el máximo partido a este título.</p>
-<h2>¿Qué es Mortal Kombat X?</h2>
-<p>Mortal Kombat X es un juego de lucha desarrollado por NetherRealm Studios y publicado por Warner Bros. Interactive Entertainment en 2015. Es la décima entrega principal de la franquicia Mortal Kombat y una secuela del juego de 2011. El juego se ambienta 25 años después de los eventos del anterior, y presenta a varios personajes nuevos y veteranos, cada uno con tres variaciones que afectan tanto a su estrategia como a su estilo de lucha. El juego también cuenta con varios modos, como un modo historia, varios modos torre con desafíos cambiantes, numerosos modos online y la cripta, un modo en primera persona donde los jugadores exploran las áreas desbloqueando una variedad de objetos del juego.</p>
-<h2>descargar mortal kombat x para android apk y obb</h2><br /><p><b><b>DOWNLOAD</b> &gt; <a href="https://ssurll.com/2uNRd3">https://ssurll.com/2uNRd3</a></b></p><br /><br />
-<p>Como en los anteriores juegos de Mortal Kombat, el gameplay consiste en que dos jugadores, o uno y la CPU, se enfrenten entre sí con su personaje seleccionado, usando una gran variedad de ataques específicos de cada personaje. El juego también incluye los movimientos finales característicos de la serie, las Fatalities, que son ejecuciones brutales y sangrientas que se pueden realizar al finalizar un combate.</p>
-<h2>¿Cómo descargar e instalar Mortal Kombat X en tu dispositivo Android?</h2>
-<p>Para poder jugar a Mortal Kombat X en tu dispositivo Android, necesitas descargar dos archivos: el archivo APK y el archivo OBB. El archivo APK es el que contiene la aplicación del juego, mientras que el archivo OBB es el que contiene los datos adicionales del juego, como los gráficos, los sonidos y las animaciones. Estos archivos no están disponibles en la tienda oficial de Google Play, por lo que tendrás que buscarlos en otras fuentes. A continuación, te explicamos los pasos que debes seguir para descargar e instalar Mortal Kombat X en tu dispositivo Android.</p>
-<h3>Descarga el archivo APK de Mortal Kombat X</h3>
-<p>El primer paso es descargar el archivo APK de Mortal Kombat X, que es el que contiene la aplicación del juego. Para ello, puedes usar el siguiente enlace, que te llevará a una página donde podrás descargar el archivo APK de forma segura y gratuita. El tamaño del archivo APK es de unos 25 MB, por lo que no debería tardar mucho en descargarse.</p>
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-<h3>Descarga el archivo OBB de Mortal Kombat X</h3>
-<p>El segundo paso es descargar el archivo OBB de Mortal Kombat X, que es el que contiene los datos adicionales del juego. Para ello, puedes usar el siguiente enlace, que te llevará a una página donde podrás descargar el archivo OBB de forma segura y gratuita. El tamaño del archivo OBB es de unos 1.5 GB, por lo que puede tardar bastante en descargarse dependiendo de tu velocidad de conexión.</p>
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-<h3>Instala el archivo APK de Mortal Kombat X</h3>
-<p>El tercer paso es instalar el archivo APK de Mortal Kombat X en tu dispositivo Android. Para ello, tendrás que habilitar la instalación de aplicaciones desde fuentes desconocidas, ya que el archivo APK no proviene de la tienda oficial de Google Play. Para habilitar esta opción, sigue estos pasos:</p>
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-<ul>
-<li>Ve a los ajustes de tu dispositivo Android.</li>
-<li>Busca la opción de seguridad o privacidad.</li>
-<li>Activa la opción de orígenes desconocidos o fuentes desconocidas.</li>
-</ul>
-<p>Una vez que hayas habilitado esta opción, podrás instalar el archivo APK de Mortal Kombat X. Para ello, sigue estos pasos:</p>
-<ul>
-<li>Localiza el archivo APK que has descargado en tu dispositivo Android. Puedes usar un explorador de archivos para encontrarlo.</li>
-<li>Toca el archivo APK para iniciar la instalación.</li>
-<li>Sigue las instrucciones que aparecen en la pantalla para completar la instalación.</li>
-</ul>
-<h3>Copia el archivo OBB de Mortal Kombat X en la carpeta adecuada</h3>
-<p>El cuarto y último paso es copiar el archivo OBB de Mortal Kombat X en la carpeta adecuada de tu dispositivo Android. Para ello, sigue estos pasos:</p>
-<ul>
-<li>Localiza el archivo OBB que has descargado en tu dispositivo Android. Puedes usar un explorador de archivos para encontrarlo.</li>
-<li>Copia el archivo OBB y pégalo en la siguiente ruta: /sdcard/Android/obb/com.wb.goog.mkx/</li>
-<li>Si no existe la carpeta com.wb.goog.mkx, créala manualmente.</li>
-</ul>
-<p>Con esto, ya habrás completado la instalación de Mortal Kombat X en tu dispositivo Android. Ahora solo te queda disfrutar del juego.</p> <h2>¿Cómo jugar a Mortal Kombat X en tu dispositivo Android?</h2>
-<p>Una vez que hayas instalado Mortal Kombat X en tu dispositivo Android, podrás acceder al juego desde el menú de aplicaciones. Al iniciar el juego, tendrás que aceptar los términos y condiciones, y luego podrás elegir entre varios modos de juego. Estos son algunos de los modos disponibles:</p>
-<ul>
-<li>Modo historia: En este modo, podrás seguir la trama del juego a través de una serie de capítulos, donde controlarás a diferentes personajes y te enfrentarás a diversos enemigos y jefes. El modo historia te permitirá conocer más sobre el universo de Mortal Kombat y sus personajes.</li>
-<li>Modo torre: En este modo, podrás elegir entre varias torres con diferentes desafíos y recompensas. Cada torre tiene un número determinado de combates, y algunos de ellos tienen condiciones especiales, como tiempo límite, salud reducida, modificadores aleatorios, etc. El modo torre te permitirá poner a prueba tus habilidades y ganar monedas y almas para desbloquear objetos en la cripta.</li>
-<li>Modo online: En este modo, podrás conectarte con otros jugadores de todo el mundo y competir en partidas clasificatorias o amistosas. El modo online te permitirá demostrar tu nivel y subir en el ranking global.</li>
-<li>Cripta: En este modo, podrás explorar las áreas de la cripta en primera persona, donde encontrarás una gran variedad de objetos que podrás desbloquear con las monedas y las almas que hayas ganado en los otros modos. Estos objetos incluyen nuevos trajes, fatalities, brutalities, arte conceptual, música, etc.</li>
-</ul>
-<p>Para jugar a Mortal Kombat X en tu dispositivo Android, tendrás que usar los controles táctiles que aparecen en la pantalla. Estos son los controles básicos:</p>
-<ul>
-<li>Toca la parte izquierda de la pantalla para moverte hacia adelante o hacia atrás.</li>
-<li>Toca la parte derecha de la pantalla para atacar con golpes o patadas.</li>
-<li>Desliza el dedo hacia arriba o hacia abajo en la parte derecha de la pantalla para realizar un ataque rápido o un ataque fuerte.</li>
-<li>Desliza el dedo hacia adelante o hacia atrás en la parte derecha de la pantalla para realizar un ataque especial.</li>
-<li>Toca los iconos que aparecen en la parte inferior de la pantalla para activar los ataques X-Ray o las fatalities.</li>
-</ul>
-<p>En Mortal Kombat X, podrás elegir entre más de 30 personajes, cada uno con tres variaciones que cambian sus habilidades y movimientos. Algunos de los personajes son: Scorpion, Sub-Zero, Raiden, Liu Kang, Kitana, Sonya Blade, Johnny Cage, Kano, Cassie Cage, Jacqui Briggs, Kung Lao, Jax, Kenshi, Ermac, Reptile, Mileena, D'Vorah, Kotal Kahn, Ferra/Torr, Quan Chi, Shinnok, etc. También hay algunos personajes invitados de otras franquicias, como Jason Voorhees, Predator, Alien y Leatherface.</p> <h2>Consejos y trucos para mejorar tu experiencia con Mortal Kombat X</h2>
-<p>Para terminar, te vamos a dar algunos consejos y trucos que te ayudarán a mejorar tu experiencia con Mortal Kombat X y a convertirte en un maestro de la lucha. Estos son algunos de ellos:</p>
-<ul>
-<li>Aprende los combos de cada personaje y variación. Los combos son secuencias de ataques que se pueden encadenar para infligir más daño al oponente. Cada personaje y variación tiene sus propios combos, que puedes consultar en el menú de pausa. Practica los combos en el modo entrenamiento hasta que los domines.</li>
-<li>Usa los ataques especiales con inteligencia. Los ataques especiales son movimientos únicos de cada personaje que consumen una parte de la barra de energía. Estos ataques pueden ser muy efectivos para sorprender al oponente o para rematar un combo. Sin embargo, no abuses de ellos, ya que también necesitas la barra de energía para realizar los ataques X-Ray o las defensas mejoradas.</li>
-<li>Usa el entorno a tu favor. En Mortal Kombat X, los escenarios no son solo decorados, sino que también son elementos interactivos que puedes usar para atacar o escapar del oponente. Por ejemplo, puedes lanzar objetos, saltar sobre plataformas, romper paredes, etc. Observa el entorno y aprovecha las oportunidades que te ofrece.</li>
-<li>No te olvides de las fatalities. Las fatalities son los movimientos finales más emblemáticos de Mortal Kombat, que consisten en ejecutar al oponente de forma brutal y sangrienta. Para realizar una fatality, debes estar cerca o lejos del oponente (dependiendo de la fatality) y pulsar una combinación de botones cuando aparezca el mensaje "Finish Him/Her". Cada personaje tiene dos fatalities, que puedes desbloquear en la cripta o consultar en el menú de pausa.</li>
-<li>Diviértete y experimenta. Lo más importante es que disfrutes del juego y que pruebes diferentes personajes, variaciones, modos y opciones. No te frustres si pierdes algún combate o si no consigues realizar algún movimiento. Recuerda que se trata de un juego de lucha, donde la práctica y la diversión son esenciales.</li>
-</ul>
-<h2>Conclusión</h2>
-<p>En este artículo, te hemos explicado cómo descargar e instalar Mortal Kombat X en tu dispositivo Android, cómo jugar a este increíble juego de lucha y cómo mejorar tu experiencia con algunos consejos y trucos. Esperamos que te haya sido útil y que te animes a probar este juego que te hará sentir la adrenalina y la emoción de los combates más épicos y sangrientos. ¿A qué esperas para descargar Mortal Kombat X y enfrentarte a tus amigos o a jugadores de todo el mundo? ¡No te arrepentirás!</p>
-<h2>Preguntas frecuentes</h2>
-<p>A continuación, te respondemos a algunas preguntas frecuentes sobre Mortal Kombat X:</p>
-<ul>
-<li><b>¿Mortal Kombat X es compatible con todos los dispositivos Android?</b></li>
-<p>No, Mortal Kombat X requiere unos requisitos mínimos para funcionar correctamente en tu dispositivo Android. Estos requisitos son: tener al menos 1 GB de RAM, tener al menos 2 GB de espacio libre en el almacenamiento interno o externo y tener una versión de Android igual o superior a la 4.0.</p>
-<li><b>¿Mortal Kombat X necesita conexión a internet para jugar?</b></li>
-<p>Sí, Mortal Kombat X necesita conexión a internet para jugar, ya que el juego se sincroniza con los servidores de Warner Bros. Interactive Entertainment para guardar tu progreso, tus logros, tus estadísticas y tus objetos desbloqueados. Además, la conexión a internet es necesaria para acceder al modo online y a algunos modos torre que se actualizan periódicamente.</p>
-<li><b>¿Mortal Kombat X se puede jugar online con otros jugadores?</b></li>
-<p>Sí, Mortal Kombat X se puede jugar online con otros jugadores de todo el mundo en partidas clasificatorias o amistosas. También se puede crear o unirse a facciones, que son grupos de jugadores que compiten entre sí y contra otras facciones, ganando puntos de facción y recompensas exclusivas.</p>
-<li><b>¿Mortal Kombat X tiene compras integradas?</b></li>
-<p>Sí, Mortal Kombat X tiene compras integradas que te permiten adquirir monedas, almas, personajes, trajes, fatalities y otros objetos del juego con dinero real. Sin embargo, estas compras no son obligatorias, ya que también puedes obtener estos objetos jugando al juego y completando los diferentes modos y desafíos.</p>
-<li><b>¿Mortal Kombat X tiene contenido violento o inapropiado?</b></li>
-<p>Sí, Mortal Kombat X tiene contenido violento e inapropiado que puede herir la sensibilidad de algunas personas. El juego muestra escenas de combates muy sangrientos y crueles, donde se ven huesos rotos, órganos destrozados, mutilaciones y decapitaciones. El juego también tiene un lenguaje soez y referencias sexuales. Por estas razones, el juego está clasificado como para mayores de 18 años.</p> 401be4b1e0<br />
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\ No newline at end of file
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-<br />
-<h1>Angry Birds Classic APK IOS: How to Download and Play the Classic Bird Flinging Game on Your iPhone or iPad</h1>
-<p>Angry Birds Classic is one of the most popular mobile games of all time. It was released in 2009 by Rovio Entertainment and has been downloaded over a billion times. The game features a flock of angry birds who are trying to get their eggs back from the greedy pigs who stole them. The player has to use a slingshot to launch the birds at the pigs' fortresses and destroy them. The game has a simple but addictive gameplay, a physics-based puzzle system, and a colorful cartoon style.</p>
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-<p>If you are a fan of Angry Birds Classic and want to play it on your iPhone or iPad, you might be wondering how to download and install it. The game is no longer available on the official App Store, but you can still get it from other sources. In this article, we will show you how to download Angry Birds Classic APK IOS from the App Store or TapTap, how to install and launch it on your device, and what features it offers. We will also give you some tips and tricks for playing it, as well as some pros and cons of the game. Let's get started!</p>
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-<p>There are two ways to download Angry Birds Classic APK IOS on your iPhone or iPad. One is to use the App Store, which is the official source of apps for iOS devices. The other is to use TapTap, which is a third-party app store that offers games that are not available on the App Store.</p>
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-<p>After downloading Angry Birds Classic APK IOS from the App Store or TapTap, you need to install and launch it on your device. The installation process may vary depending on the source of the app and the version of your iOS system.</p>
-<p>If you downloaded Angry Birds Classic APK IOS from the App Store, you can install it like any other app by tapping on the icon and waiting for it to finish. Then, you can launch it by tapping on the icon again.</p>
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-<p>Angry Birds Classic APK IOS is a fun and addictive game that offers many features for the players. Here are some of the main features of the game:</p>
-<ul>
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-<li><b>Mighty League mode to compete with other players</b>: The game has a Mighty League mode that allows you to play against other players from around the world. You can join a league and compete in daily challenges to earn stars and coins. You can also climb up the leaderboards and win trophies and rewards.</li>
-<li><b>Powerups to boost your birds' destructive strength</b>: The game has powerups that you can use to enhance your birds' abilities and cause more damage to the pigs. You can use powerups such as Sling Scope, King Sling, Super Seeds, Birdquake, and TNT Drop. You can also use the Mighty Eagle, which is a powerful bird that can destroy everything on the screen.</li>
-<li><b>Offline gameplay and free download</b>: The game can be played offline without an internet connection. You can also download it for free from the App Store or TapTap.</li>
-</ul>
-<h2>Tips and Tricks for Playing Angry Birds Classic APK IOS</h2>
-<p>Angry Birds Classic APK IOS is a game that requires skill and strategy to complete. Here are some tips and tricks for playing it:</p>
-<ul>
-<li><b>How to use the slingshot and aim at the pigs' defenses</b>: To use the slingshot, you need to tap and hold on a bird and drag it back. You will see a dotted line that shows the trajectory of the bird. You can adjust the angle and power of the shot by moving your finger up or down. To release the bird, you need to lift your finger off the screen. To aim at the pigs' defenses, you need to consider the physics of the game, such as gravity, wind, bounce, and friction. You also need to look for weak spots in the structures, such as cracks, joints, or explosives.</li>
-<li><b>How to use the unique powers of each bird</b>: Each bird has a unique power that can help you destroy more pigs and structures. To activate the power, you need to tap on the screen while the bird is in mid-air. Here are the powers of each bird:</li>
-<ul>
-<li><b>Red</b>: Red is the basic bird that has no special power. However, he can still cause damage with his speed and weight.</li>
-<li><b>Blue</b>: Blue can split into three smaller birds that can cover more area and hit more targets.</li>
-<li><b>Yellow</b>: Yellow can speed up and smash through wood and glass with ease.</li>
-<li><b>Black</b>: Black can explode and cause massive damage to everything around him.</li>
-<li><b>White</b>: White can drop an egg bomb that can destroy anything below him.</li>
-<li><b>Green</b>: Green can boomerang back and hit targets behind him.</li>
-<li><b>Big Brother</b>: Big Brother is a larger version of Red that has more force and can break through stone and metal.</li>
-<li><b>Orange</b>: Orange can inflate and push away anything around him.</li>
-<li><b>Pink</b>: Pink can create bubbles that can lift up objects and pigs.</li>
-<li><b>Ice</b>: Ice can freeze anything he touches and make it easier to break.</li>
-<li><b>Stella</b>: Stella can create a vortex that can suck in objects and pigs.</li>
-<li><b>Bubbles</b>: Bubbles can inflate and pop, causing a shockwave that can destroy nearby objects and pigs.</li>
-<li><b>Hal</b>: Hal can spin like a boomerang and hit targets behind him.</li>
-<li><b>Terence</b>: Terence is a larger version of Big Brother that has more force and can break through anything.</li>
-<li><b>Mighty Eagle</b>: Mighty Eagle is a special bird that can be used once per hour. He can destroy everything on the screen with his powerful dive.</li>
-</ul>
-<li><b>How to earn stars and unlock new levels</b>: To earn stars, you need to score as many points as possible by destroying the pigs and their structures. The more damage you cause, the more points you get. You can earn up to three stars per level, depending on your score. To unlock new levels, you need to complete the previous levels with at least one star. You can also unlock new episodes by collecting golden eggs that are hidden in some levels or by completing special tasks.</li>
-<li><b>How to use the Mighty Eagle and other special birds</b>: To use the Mighty Eagle, you need to buy a can of sardines with coins or real money. You can use the can of sardines once per hour by tapping on it and then tapping on the screen. The Mighty Eagle will fly over the screen and dive down, destroying everything in his path. You can also use other special birds, such as Silver, Matilda, Bomb, Chuck, Red, and the Blues, by buying them with coins or real money. You can use these birds once per level by tapping on their icons and then tapping on the screen. They will replace the current bird in the slingshot and use their special powers.</li>
-</ul>
-<h2>Pros and Cons of Angry Birds Classic APK IOS</h2>
-<p>Angry Birds Classic APK IOS is a game that has many pros and cons. Here are some of them:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- Fun and satisfying gameplay that lets you fling birds at pigs and watch them explode.</td>
-<td>- Repetitive gameplay that can get boring after a while.</td>
-</tr>
-<tr>
-<td>- Challenging physics-based puzzles that require skill and strategy to solve.</td>
-<td>- Ads and in-app purchases that can interrupt the game or tempt you to spend money.</td>
-</tr>
-<tr>
-<td>- Colorful graphics and sound effects that create a cartoon-like atmosphere.</td>
-<td>- Compatibility issues with some devices that may cause crashes or glitches.</td>
-</tr>
-<tr>
-<td>- Nostalgic appeal that reminds you of the original Angry Birds game.</td>
-<td>- None</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>Angry Birds Classic APK IOS is a game that lets you enjoy the classic bird flinging game on your iPhone or iPad. You can download it from the App Store or TapTap, install it on your device, and launch it to play. You can also enjoy its features, such as 15 original episodes with over 680 levels, Mighty League mode to compete with other players, powerups to boost your birds' destructive strength, and offline gameplay and free download. You can also use some tips and tricks to play it better, such as how to use the slingshot and aim at the pigs' defenses, how to use the unique powers of each bird, how to earn stars and unlock new levels, and how to use the Mighty Eagle and other special birds. However, you should also be aware of its pros and cons, such as fun and satisfying gameplay, challenging physics-based puzzles, colorful graphics and sound effects, nostalgic appeal, repetitive gameplay, ads and in-app purchases, and compatibility issues with some devices. We hope this article has helped you learn more about Angry Birds Classic APK IOS and how to play it on your iPhone or iPad. Have fun!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Angry Birds Classic APK IOS:</p>
- <ul>
- <li><b>Q1: What is the difference between Angry Birds Classic and Angry Birds 2?</b></li>
- <p>A1: Angry Birds Classic is the original game that was released in 2009 by Rovio Entertainment and has 15 episodes with over 680 levels. It has a simple but addictive gameplay, a physics-based puzzle system, and a colorful cartoon style. Angry Birds 2 is the sequel that was released in 2015 by Rovio Entertainment and has more episodes and levels. It has a more complex and dynamic gameplay, a card-based system to choose your birds, and a more realistic and detailed style.</p>
- <li><b>Q2: How can I get rid of ads in Angry Birds Classic?</b></li>
- <p>A2: There are two ways to get rid of ads in Angry Birds Classic. One is to buy the ad-free version of the game for $0.99 from the App Store or TapTap. The other is to turn off your internet connection before launching the game. However, this will also disable some features, such as Mighty League mode and powerups.</p>
- <li><b>Q3: How can I restore my progress in Angry Birds Classic?</b></li>
- <p>A3: To restore your progress in Angry Birds Classic, you need to use the Rovio Account feature. You can create a Rovio Account by tapping on the settings icon on the main menu and then tapping on the Rovio Account button. You can sign up with your email or Facebook account. Then, you can sync your progress across different devices by logging in with your Rovio Account. You can also backup your progress by tapping on the cloud icon on the settings menu.</p>
- <li><b>Q4: How can I contact the developer of Angry Birds Classic?</b></li>
- <p>A4: To contact the developer of Angry Birds Classic, you can use the following methods:</p>
-<ul>
-<li>Email: support@rovio.com</li>
-<li>Website: [1](https://www.rovio.com)</li>
-<li>Facebook: [0](https://www.facebook.com/angrybirds)</li>
-<li>Twitter: [@AngryBirds](https://twitter.com/AngryBirds)</li>
-</ul>
- <li><b>Q5: What are some alternatives to Angry Birds Classic?</b></li>
- <p>A5: If you are looking for some alternatives to Angry Birds Classic, you can try these games:</p>
-<ul>
-<li><b>Cut the Rope</b>: A game that involves cutting ropes to feed candy to a cute monster named Om Nom.</li>
-<li><b>Plants vs Zombies</b>: A game that involves planting plants to defend your house from zombies.</li>
-<li><b>Fruit Ninja</b>: A game that involves slicing fruits with your finger as they fly across the screen.</li>
-<li><b>Bad Piggies</b>: A game that involves building vehicles and machines to help the pigs reach their goals.</li>
-<li><b>Angry Birds Star Wars</b>: A game that combines Angry Birds with Star Wars characters and themes.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skavya/youtube_transcript_summarizer/app.py b/spaces/skavya/youtube_transcript_summarizer/app.py
deleted file mode 100644
index a0aef7b442989179e45a73215dada86526eefd3c..0000000000000000000000000000000000000000
--- a/spaces/skavya/youtube_transcript_summarizer/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-import transformers
-import youtube_transcript_api
-from transformers import pipeline
-from youtube_transcript_api import YouTubeTranscriptApi
-from datasets import Dataset
-
-summarizer = pipeline("summarization",model="facebook/bart-large-cnn")
-
-def greet(link):
-  try:
-      unique_id = link.split("=")[-1]
-      sub = YouTubeTranscriptApi.get_transcript(unique_id)  
-      subtitle = " ".join([w['text'] for w in sub])
-      summary = summarizer(subtitle, max_length=180, min_length=30, do_sample=False)
-      return summary[0]['summary_text']
-  except:
-      return 'Invalid URL'
-
-demo=gr.Interface(fn=greet, inputs="text", outputs="text")
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/unet.py b/spaces/skf15963/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/unet.py
deleted file mode 100644
index 187b6c9737fda143f70e8dae365c35b690820466..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/unet.py
+++ /dev/null
@@ -1,975 +0,0 @@
-from abc import abstractmethod
-
-import math
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .fp16_util import convert_module_to_f16, convert_module_to_f32
-from .nn import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-
-from transformers import PreTrainedModel, PretrainedConfig
-
-
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
-        )
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        ch = input_ch = int(channel_mult[0] * model_channels)
-        self.input_blocks = nn.ModuleList(
-            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
-        )
-        self._feature_size = ch
-        input_block_chans = [ch]
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(mult * model_channels),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(mult * model_channels)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(model_channels * mult),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(model_channels * mult)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps, y=None):
-        """
-        Apply the model to an input batch.
-
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-
-        hs = []
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            hs.append(h)
-        h = self.middle_block(h, emb)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb)
-        h = h.type(x.dtype)
-        return self.out(h)
-
-
-class SuperResModel(UNetModel):
-    """
-    A UNetModel that performs super-resolution.
-
-    Expects an extra kwarg `low_res` to condition on a low-resolution image.
-    """
-
-    def __init__(self, image_size, in_channels, *args, **kwargs):
-        super().__init__(image_size, in_channels * 2, *args, **kwargs)
-
-    def forward(self, x, timesteps, low_res=None, **kwargs):
-        _, _, new_height, new_width = x.shape
-        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
-        x = th.cat([x, upsampled], dim=1)
-        return super().forward(x, timesteps, **kwargs)
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        pool="adaptive",
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        ch = int(channel_mult[0] * model_channels)
-        self.input_blocks = nn.ModuleList(
-            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
-        )
-        self._feature_size = ch
-        input_block_chans = [ch]
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(mult * model_channels),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(mult * model_channels)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
-
-
-class UNetConfig(PretrainedConfig):
-    def __init__(
-        self,
-        image_size=512,
-        in_channels=3,
-        model_channels=256,
-        out_channels=6,
-        num_res_blocks=2,
-        attention_resolutions=[16, 32, 64],
-        dropout=0.0,
-        channel_mult=(0.5, 1, 1, 2, 2, 4, 4),
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=True,
-        num_heads=4,
-        num_head_channels=64,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=True,
-        resblock_updown=True,
-        use_new_attention_order=False,
-        **kwargs
-    ):
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.use_fp16 = use_fp16
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.use_scale_shift_norm = use_scale_shift_norm
-        self.resblock_updown = resblock_updown
-        self.use_new_attention_order = use_new_attention_order
-        super().__init__(**kwargs)
-
-
-class HFUNetModel(PreTrainedModel):
-    config_class = UNetConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = UNetModel(
-            image_size=config.image_size,
-            in_channels=config.in_channels,
-            model_channels=config.model_channels,
-            out_channels=config.out_channels,
-            num_res_blocks=config.num_res_blocks,
-            attention_resolutions=config.attention_resolutions,
-            dropout=config.dropout,
-            channel_mult=config.channel_mult,
-            num_classes=config.num_classes,
-            use_checkpoint=config.use_checkpoint,
-            use_fp16=config.use_fp16,
-            num_heads=config.num_heads,
-            num_head_channels=config.num_head_channels,
-            num_heads_upsample=config.num_heads_upsample,
-            use_scale_shift_norm=config.use_scale_shift_norm,
-            resblock_updown=config.resblock_updown,
-            use_new_attention_order=config.use_new_attention_order,
-        )
-
-    def forward(self, x, timesteps, y=None):
-        return self.model.forward(x, timesteps, y)
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.model.input_blocks.apply(convert_module_to_f16)
-        self.model.middle_block.apply(convert_module_to_f16)
-        self.model.output_blocks.apply(convert_module_to_f16)
diff --git a/spaces/snoop2head/KoGPT-Conditional-Generation/README.md b/spaces/snoop2head/KoGPT-Conditional-Generation/README.md
deleted file mode 100644
index 44b0f8287e5d3415519b9643342d7c0c03b7829d..0000000000000000000000000000000000000000
--- a/spaces/snoop2head/KoGPT-Conditional-Generation/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: KoGPT-Conditional-Generation
-emoji: 🎭
-colorFrom: yellow
-colorTo: blue
-sdk: streamlit
-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.
diff --git a/spaces/spacerini/imdb-search/app.py b/spaces/spacerini/imdb-search/app.py
deleted file mode 100644
index 688b1eaac4a94c2ec5217c2c3e17c2a7efca0408..0000000000000000000000000000000000000000
--- a/spaces/spacerini/imdb-search/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import gradio as gr
-from datasets import load_from_disk
-from pyserini.search.lucene import LuceneSearcher
-
-searcher = LuceneSearcher("index")
-ds = load_from_disk("data")
-PAGINATION_VISIBLE = True
-NUM_PAGES = 10 # STATIC. THIS CAN'T CHANGE BECAUSE GRADIO CAN'T DYNAMICALLY CREATE COMPONENTS. 
-RESULTS_PER_PAGE = 10 
-
-def result_html(result, meta):
-    return (
-    f"<div style=\"color:#2a5cb3;font-weight: 500\"><u>{meta}</u></div><br>"
-    f"<div><details><summary>{result[:250]}...</summary><p>{result[250:]}</p></details></div><br><hr><br>"
-    )
-
-def format_results(results, meta_field):
-    return "\n".join([result_html(result, meta) for result,meta in zip(results['text'], results[meta_field])])
-    
-def page_0(query):
-    hits = searcher.search(query, k=NUM_PAGES*RESULTS_PER_PAGE)
-    ix = [int(hit.docid) for hit in hits]
-    results = ds.select(ix).shard(num_shards=NUM_PAGES, index=0, contiguous=True) # no need to shard. split ix in batches instead. (would make sense if results was cacheable)
-    results = format_results(results, "label")
-    return results, [ix]
-
-def page_i(i, ix):
-    ix = ix[0]
-    results = ds.select(ix).shard(num_shards=NUM_PAGES, index=i, contiguous=True)
-    results = format_results(results, "label")
-    return results, [ix]
-    
-with gr.Blocks(css="#b {min-width:15px;background:transparent;border:white;box-shadow:none;}") as demo: #
-    with gr.Row():
-        gr.Markdown(value="""## <p style="text-align: center;"> IMDB Reviews Dataset Search </p>""")  
-    with gr.Row():
-        with gr.Column(scale=1):
-            result_list = gr.Dataframe(type="array", visible=False, col_count=1)      
-        with gr.Column(scale=13):
-            query = gr.Textbox(lines=1, max_lines=1, placeholder="Search…", label="")
-        with gr.Column(scale=1):
-            with gr.Row(scale=1):
-                pass
-            with gr.Row(scale=1):    
-                submit_btn = gr.Button("🔍", elem_id="b").style(full_width=False)
-            with gr.Row(scale=1):
-                pass
-                
-    with gr.Row():
-        with gr.Column(scale=1):
-            pass
-        with gr.Column(scale=13):
-            c = gr.HTML(label="Results")
-            with gr.Row():
-                # left = gr.Button(value="◀", elem_id="b", visible=False).style(full_width=True)
-                page_1 = gr.Button(value="1", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_2 = gr.Button(value="2", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_3 = gr.Button(value="3", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_4 = gr.Button(value="4", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_5 = gr.Button(value="5", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_6 = gr.Button(value="6", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_7 = gr.Button(value="7", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_8 = gr.Button(value="8", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_9 = gr.Button(value="9", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                page_10 = gr.Button(value="10", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True)
-                # right = gr.Button(value="▶", elem_id="b", visible=False).style(full_width=True)
-        with gr.Column(scale=1):
-            pass
-    query.submit(fn=page_0, inputs=[query], outputs=[c, result_list])
-    submit_btn.click(page_0, inputs=[query], outputs=[c, result_list])
-    with gr.Box(visible=False):
-        nums = [gr.Number(i, visible=False, precision=0) for i in range(NUM_PAGES)]
-    page_1.click(fn=page_i, inputs=[nums[0], result_list], outputs=[c, result_list])
-    page_2.click(fn=page_i, inputs=[nums[1], result_list], outputs=[c, result_list])
-    page_3.click(fn=page_i, inputs=[nums[2], result_list], outputs=[c, result_list])
-    page_4.click(fn=page_i, inputs=[nums[3], result_list], outputs=[c, result_list])
-    page_5.click(fn=page_i, inputs=[nums[4], result_list], outputs=[c, result_list])
-    page_6.click(fn=page_i, inputs=[nums[5], result_list], outputs=[c, result_list])
-    page_7.click(fn=page_i, inputs=[nums[6], result_list], outputs=[c, result_list])
-    page_8.click(fn=page_i, inputs=[nums[7], result_list], outputs=[c, result_list])
-    page_9.click(fn=page_i, inputs=[nums[8], result_list], outputs=[c, result_list])
-    page_10.click(fn=page_i, inputs=[nums[9], result_list], outputs=[c, result_list])
-demo.launch(enable_queue=True, debug=True)
diff --git a/spaces/sparanoid/milky-green-sovits-4/app.py b/spaces/sparanoid/milky-green-sovits-4/app.py
deleted file mode 100644
index f48f9183ced57842d6bda2f6a51c9e78c658fa64..0000000000000000000000000000000000000000
--- a/spaces/sparanoid/milky-green-sovits-4/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import io
-import os
-
-os.system("wget -P hubert/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-model = Svc("logs/44k/G_140000.pth", "configs/config.json", cluster_model_path="logs/44k/kmeans_10000.pt")
-
-
-
-def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, noise_scale):
-    if input_audio is None:
-        return "You need to upload an audio", None
-    sampling_rate, audio = input_audio
-    # print(audio.shape,sampling_rate)
-    duration = audio.shape[0] / sampling_rate
-    if duration > 45:
-        return "请上传小于45s的音频，需要转换长音频请本地进行转换", None
-    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-    if len(audio.shape) > 1:
-        audio = librosa.to_mono(audio.transpose(1, 0))
-    if sampling_rate != 16000:
-        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-    print(audio.shape)
-    out_wav_path = "temp.wav"
-    soundfile.write(out_wav_path, audio, 16000, format="wav")
-    print( cluster_ratio, auto_f0, noise_scale)
-    out_audio, out_sr = model.infer(sid, vc_transform, out_wav_path,
-                                   cluster_infer_ratio=cluster_ratio,
-                                   auto_predict_f0=auto_f0,
-                                   noice_scale=noise_scale
-                                   )
-    return "Success", (44100, out_audio.numpy())
-
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("Basic"):
-            gr.Markdown(value="""
-                SoVITS 4.0 在线 demo，基于 https://github.com/innnky/so-vits-svc/tree/4.0
-
-                此 demo 为预训练底模在线 demo，使用数据：明前奶绿
-                """)
-            spks = list(model.spk2id.keys())
-            sid = gr.Dropdown(label="音色", choices=["mqnl-covers-v3"], value="mqnl-covers-v3")
-            vc_input3 = gr.Audio(label="上传音频（长度小于45秒）")
-            vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
-            cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
-            auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False)
-            noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
-            vc_submit = gr.Button("转换", variant="primary")
-            vc_output1 = gr.Textbox(label="Output Message")
-            vc_output2 = gr.Audio(label="Output Audio")
-        vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, noise_scale], [vc_output1, vc_output2])
-
-    app.launch()
diff --git a/spaces/starlit7/KorPoliticsTTS/text/korean.py b/spaces/starlit7/KorPoliticsTTS/text/korean.py
deleted file mode 100644
index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000
--- a/spaces/starlit7/KorPoliticsTTS/text/korean.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄳ', 'ㄱㅅ'),
-    ('ㄵ', 'ㄴㅈ'),
-    ('ㄶ', 'ㄴㅎ'),
-    ('ㄺ', 'ㄹㄱ'),
-    ('ㄻ', 'ㄹㅁ'),
-    ('ㄼ', 'ㄹㅂ'),
-    ('ㄽ', 'ㄹㅅ'),
-    ('ㄾ', 'ㄹㅌ'),
-    ('ㄿ', 'ㄹㅍ'),
-    ('ㅀ', 'ㄹㅎ'),
-    ('ㅄ', 'ㅂㅅ'),
-    ('ㅘ', 'ㅗㅏ'),
-    ('ㅙ', 'ㅗㅐ'),
-    ('ㅚ', 'ㅗㅣ'),
-    ('ㅝ', 'ㅜㅓ'),
-    ('ㅞ', 'ㅜㅔ'),
-    ('ㅟ', 'ㅜㅣ'),
-    ('ㅢ', 'ㅡㅣ'),
-    ('ㅑ', 'ㅣㅏ'),
-    ('ㅒ', 'ㅣㅐ'),
-    ('ㅕ', 'ㅣㅓ'),
-    ('ㅖ', 'ㅣㅔ'),
-    ('ㅛ', 'ㅣㅗ'),
-    ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', '에이'),
-    ('b', '비'),
-    ('c', '시'),
-    ('d', '디'),
-    ('e', '이'),
-    ('f', '에프'),
-    ('g', '지'),
-    ('h', '에이치'),
-    ('i', '아이'),
-    ('j', '제이'),
-    ('k', '케이'),
-    ('l', '엘'),
-    ('m', '엠'),
-    ('n', '엔'),
-    ('o', '오'),
-    ('p', '피'),
-    ('q', '큐'),
-    ('r', '아르'),
-    ('s', '에스'),
-    ('t', '티'),
-    ('u', '유'),
-    ('v', '브이'),
-    ('w', '더블유'),
-    ('x', '엑스'),
-    ('y', '와이'),
-    ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('t͡ɕ','ʧ'),
-    ('d͡ʑ','ʥ'),
-    ('ɲ','n^'),
-    ('ɕ','ʃ'),
-    ('ʷ','w'),
-    ('ɭ','l`'),
-    ('ʎ','ɾ'),
-    ('ɣ','ŋ'),
-    ('ɰ','ɯ'),
-    ('ʝ','j'),
-    ('ʌ','ə'),
-    ('ɡ','g'),
-    ('\u031a','#'),
-    ('\u0348','='),
-    ('\u031e',''),
-    ('\u0320',''),
-    ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
-    for regex, replacement in _latin_to_hangul:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def divide_hangul(text):
-    text = j2hcj(h2j(text))
-    for regex, replacement in _hangul_divided:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def hangul_number(num, sino=True):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    num = re.sub(',', '', num)
-
-    if num == '0':
-        return '영'
-    if not sino and num == '20':
-        return '스무'
-
-    digits = '123456789'
-    names = '일이삼사오육칠팔구'
-    digit2name = {d: n for d, n in zip(digits, names)}
-
-    modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-    decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-    digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-    digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-    spelledout = []
-    for i, digit in enumerate(num):
-        i = len(num) - i - 1
-        if sino:
-            if i == 0:
-                name = digit2name.get(digit, '')
-            elif i == 1:
-                name = digit2name.get(digit, '') + '십'
-                name = name.replace('일십', '십')
-        else:
-            if i == 0:
-                name = digit2mod.get(digit, '')
-            elif i == 1:
-                name = digit2dec.get(digit, '')
-        if digit == '0':
-            if i % 4 == 0:
-                last_three = spelledout[-min(3, len(spelledout)):]
-                if ''.join(last_three) == '':
-                    spelledout.append('')
-                    continue
-            else:
-                spelledout.append('')
-                continue
-        if i == 2:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 3:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 4:
-            name = digit2name.get(digit, '') + '만'
-            name = name.replace('일만', '만')
-        elif i == 5:
-            name = digit2name.get(digit, '') + '십'
-            name = name.replace('일십', '십')
-        elif i == 6:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 7:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 8:
-            name = digit2name.get(digit, '') + '억'
-        elif i == 9:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 10:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 11:
-            name = digit2name.get(digit, '') + '천'
-        elif i == 12:
-            name = digit2name.get(digit, '') + '조'
-        elif i == 13:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 14:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 15:
-            name = digit2name.get(digit, '') + '천'
-        spelledout.append(name)
-    return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-    for token in tokens:
-        num, classifier = token
-        if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-            spelledout = hangul_number(num, sino=False)
-        else:
-            spelledout = hangul_number(num, sino=True)
-        text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-    # digit by digit for remaining digits
-    digits = '0123456789'
-    names = '영일이삼사오육칠팔구'
-    for d, n in zip(digits, names):
-        text = text.replace(d, n)
-    return text
-
-
-def korean_to_lazy_ipa(text):
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
-    for regex, replacement in _ipa_to_lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def korean_to_ipa(text):
-    text = korean_to_lazy_ipa(text)
-    return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Bebe And Cece Winans Lost Without You Mp3 UPD Download.md b/spaces/stomexserde/gpt4-ui/Examples/Bebe And Cece Winans Lost Without You Mp3 UPD Download.md
deleted file mode 100644
index 492eaf0af37f2086ffb31b8ca22fab6e152508e2..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Bebe And Cece Winans Lost Without You Mp3 UPD Download.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-<br />
-<h1>How to Download Bebe and Cece Winans Lost Without You MP3 for Free</h1>
-<p>Bebe and Cece Winans are a legendary gospel duo who have won numerous awards and accolades for their soulful music. One of their most popular songs is "Lost Without You", a beautiful ballad that expresses their love and devotion to each other and to God. If you are a fan of Bebe and Cece Winans, you might be wondering how to download "Lost Without You" MP3 for free. In this article, we will show you some easy and legal ways to get this song on your device.</p>
-<h2>Why Download Bebe and Cece Winans Lost Without You MP3?</h2>
-<p>"Lost Without You" is a song that can touch your heart and uplift your spirit. It was released in 1996 as part of Bebe and Cece Winans' album "Still". The song was written by Bebe Winans and Keith Thomas, and produced by Thomas. It features Bebe and Cece's harmonious vocals over a smooth R&B groove. The lyrics talk about how they depend on each other and on God's grace to overcome any challenges in life.</p>
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-<p>"Lost Without You" was a huge hit, reaching number one on the Billboard Hot R&B/Hip-Hop Songs chart and number 15 on the Billboard Hot 100 chart. It also earned them a Grammy nomination for Best R&B Performance by a Duo or Group with Vocals. The song has been covered by many artists, such as Ruben Studdard, Donnie McClurkin, Yolanda Adams, and Kirk Franklin.</p>
-<p>If you love "Lost Without You", you might want to download it as an MP3 file so that you can listen to it anytime and anywhere. MP3 is a digital audio format that compresses sound data without losing much quality. It is compatible with most devices, such as computers, smartphones, tablets, and music players. By downloading "Lost Without You" MP3, you can enjoy this song offline, without worrying about internet connection or streaming fees.</p>
-<h2>How to Download Bebe and Cece Winans Lost Without You MP3 for Free?</h2>
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-<p>To avoid these risks, we recommend that you use only trusted and reputable sources to download "Lost Without You" MP3 for free. Here are some of them:</p>
-<ul>
-<li><a href="https://www.youtube.com/watch?v=Z0GFRcFm-aY">YouTube</a>: YouTube is the largest video-sharing platform in the world, where you can find millions of videos of various genres and topics. You can also find the official music video of "Lost Without You" by Bebe and Cece Winans on YouTube. To download it as an MP3 file, you need to use a third-party tool such as <a href="https://ytmp3.cc/en13/">ytmp3.cc</a>, which allows you to convert YouTube videos to MP3 files for free. Just copy the URL of the video, paste it into the converter box, click "Convert", and then click "Download".</li>
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-<p></p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Beware Of BIOS Update 0373 For Intel NUCs [PATCHED].md b/spaces/stomexserde/gpt4-ui/Examples/Beware Of BIOS Update 0373 For Intel NUCs [PATCHED].md
deleted file mode 100644
index 72756bcac14a3bcd4a45aeb0cd571529b24abdac..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Beware Of BIOS Update 0373 For Intel NUCs [PATCHED].md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-<h1>Beware Of BIOS Update 0373 For Intel NUCs: Here's What You Need To Know</h1>
-<p>If you own an Intel NUC mini PC, you may want to think twice before updating to the latest BIOS version 0373. According to several reports from users, this update can cause serious issues such as boot failures, fan noise, and performance degradation.</p>
-<p>BIOS stands for Basic Input/Output System and it is the software that controls the communication between the hardware and the operating system of your computer. Updating the BIOS can sometimes improve the stability and compatibility of your system, but it can also introduce bugs and glitches that can render your device unusable.</p>
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-<p>According to Intel's release notes, BIOS version 0373 was supposed to fix some security vulnerabilities and improve the fan control algorithm for NUC models NUC8i3CYSM, NUC8i3CYSN, NUC8i5INH, and NUC8i7INH. However, many users who installed this update have reported that their NUCs failed to boot or became extremely slow and noisy after the update.</p>
-<p>Some users have managed to revert to the previous BIOS version 0366 using a USB flash drive and the recovery mode, but others have not been so lucky and had to contact Intel's customer support for assistance. Intel has acknowledged the issue and has temporarily removed the BIOS update 0373 from its download center. The company has also advised users to not install this update until a new version is available.</p>
-<p>If you have already installed BIOS update 0373 on your Intel NUC and are experiencing problems, you can try the following steps to restore your system:</p>
-<p></p>
-<ul>
-<li>Power off your NUC and unplug the power cord.</li>
-<li>Plug in a USB flash drive with the BIOS file 0366 on it. You can download it from <a href="https://downloadcenter.intel.com/download/30533/BIOS-Update-BECFL357-86A-">here</a>.</li>
-<li>Press and hold the power button for three seconds to enter the recovery mode.</li>
-<li>Follow the on-screen instructions to select the USB flash drive and the BIOS file.</li>
-<li>Wait for the BIOS update process to complete and reboot your NUC.</li>
-</ul>
-<p>If these steps do not work or you are unsure how to perform them, you should contact Intel's customer support for further guidance. You can also check the <a href="https://community.intel.com/t5/Intel-NUCs/bd-p/NUC">Intel NUC community forum</a> for more information and updates on this issue.</p>
-<p>We hope that Intel will soon release a new BIOS update that will fix the problems caused by version 0373 and improve the performance and security of its NUC mini PCs. Until then, beware of BIOS update 0373 for Intel NUCs and do not install it on your device.</p>
-
-<p>Intel NUCs are small but powerful mini PCs that can be used for various purposes such as gaming, entertainment, productivity, and education. They come in different models and configurations to suit different needs and budgets. They also have a compact and sleek design that can fit in any space and environment.</p>
-<p>However, like any other computer, Intel NUCs are not immune to software and hardware issues that can affect their performance and functionality. One of the most common and critical issues that can occur is a faulty BIOS update that can prevent your NUC from booting or cause other problems such as overheating, freezing, or crashing.</p>
-<p>A BIOS update is a risky procedure that should only be done when necessary and with caution. You should always backup your data and settings before updating the BIOS and follow the instructions provided by Intel carefully. You should also check the compatibility of the BIOS update with your NUC model and operating system before installing it.</p>
-<p>If you encounter any issues after updating the BIOS on your Intel NUC, you should not panic or try to fix them yourself. You should contact Intel's customer support or visit their website for troubleshooting tips and solutions. You can also join the Intel NUC community forum and seek help from other users who may have faced similar issues.</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Christianity Before Christ John G Jackson Pdf 123 _VERIFIED_.md b/spaces/stomexserde/gpt4-ui/Examples/Christianity Before Christ John G Jackson Pdf 123 _VERIFIED_.md
deleted file mode 100644
index 41c7be36b424407b2128cccf308c47f3f00434c1..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Christianity Before Christ John G Jackson Pdf 123 _VERIFIED_.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-
-<h1>Review of Christianity Before Christ by John G. Jackson</h1>
-<p>Christianity Before Christ is a book by John G. Jackson, an African-American historian and freethinker, published in 1985 by the American Atheist Press. The book challenges the historical and theological claims of Christianity by tracing its origins to ancient religions and myths that predated the Christian era. Jackson argues that Christianity is not a unique revelation from God, but a syncretic amalgamation of various pagan beliefs and practices that were adapted and modified over time.</p>
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-<p>Christianity Before Christ is a provocative and controversial book that challenges the conventional wisdom and dogma of Christianity. It exposes the historical inaccuracies, contradictions, and absurdities of the Christian faith, and reveals its human and cultural origins. The book is not intended to offend or ridicule Christians, but to stimulate critical thinking and inquiry into the foundations of their beliefs. Jackson writes in a clear and engaging style, with a wealth of references and illustrations. The book is a valuable resource for anyone interested in learning more about the history and evolution of Christianity.</p>
-
-<p>One of the main sources that Jackson uses to support his arguments is the work of Gerald Massey, a 19th-century English poet and self-taught Egyptologist. Massey wrote several books on the parallels between ancient Egyptian religion and Christianity, such as The Natural Genesis and Ancient Egypt: The Light of the World. Jackson quotes extensively from Massey's writings, and agrees with his conclusion that Christianity is a "revised and remodeled edition" of the Egyptian religion.</p>
-<p></p>
-<p>Another source that Jackson relies on is the work of Kersey Graves, a 19th-century American abolitionist and freethinker. Graves wrote The World's Sixteen Crucified Saviors, a book that compares the stories and teachings of Jesus with those of 16 other savior figures from various cultures and religions, such as Krishna, Buddha, Mithra, Horus, Quetzalcoatl, and Odin. Jackson cites Graves' book as evidence that the story of Jesus is not original or historical, but a common mythological motif that was borrowed and adapted by the early Christians.</p>
-<p>Jackson also draws on the work of modern biblical scholars and historians, such as Albert Schweitzer, Rudolf Bultmann, John M. Allegro, Robert M. Price, and Earl Doherty. These scholars question the historicity and authenticity of the New Testament documents, and propose alternative explanations for the origin and development of Christianity. Jackson summarizes their findings and arguments, and shows how they challenge the traditional Christian claims and doctrines.</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Free YouTube Download 4.1.88.1229 Crack !!TOP!!.md b/spaces/stomexserde/gpt4-ui/Examples/Free YouTube Download 4.1.88.1229 Crack !!TOP!!.md
deleted file mode 100644
index c2fb6223084038f70f35043dfa8d2f0c4d65ce4b..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Free YouTube Download 4.1.88.1229 Crack !!TOP!!.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
-<h1>Free YouTube Download 4.1.88.1229 Crack: How to Download and Install It</h1>
-<p>Free YouTube Download is a popular software that allows you to download videos from YouTube and other platforms with ease. You can choose from various formats and quality options, and even convert the downloaded videos to MP3 or other audio formats.</p>
-<h2>Free YouTube Download 4.1.88.1229 Crack</h2><br /><p><b><b>Download</b> ===== <a href="https://urlgoal.com/2uI7HD">https://urlgoal.com/2uI7HD</a></b></p><br /><br />
-<p>However, if you want to enjoy the full features of this software, you need to activate it with a license key. Otherwise, you will face some limitations, such as ads, watermarks, and restricted download speed.</p>
-<p>That's why some people look for a crack version of Free YouTube Download 4.1.88.1229, which is the latest update as of April 2023. A crack version is a modified version of the software that bypasses the activation process and unlocks all the features for free.</p>
-<p>But is it safe and legal to use a crack version of Free YouTube Download? And how can you download and install it on your computer? In this article, we will answer these questions and provide you with a step-by-step guide on how to use Free YouTube Download 4.1.88.1229 Crack.</p>
-<h2>Is Free YouTube Download 4.1.88.1229 Crack Safe and Legal?</h2>
-<p>The short answer is no. Using a crack version of any software is not safe or legal, and it can expose you to various risks.</p>
-<p></p>
-<p>First of all, cracking a software is a form of piracy, which is illegal in most countries. You are violating the terms and conditions of the software developer and infringing their intellectual property rights. This can result in legal actions against you, such as fines or lawsuits.</p>
-<p>Secondly, downloading a crack version of Free YouTube Download from an unknown source can be dangerous for your computer and your personal data. You never know what kind of malware or viruses are hidden in the crack file, which can infect your system and compromise your security. You may end up losing your files, having your identity stolen, or even having your computer hijacked by hackers.</p>
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-<ol>
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-</ul>
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Iso14004pdf[PORTABLE] Freedownload.md b/spaces/stomexserde/gpt4-ui/Examples/Iso14004pdf[PORTABLE] Freedownload.md
deleted file mode 100644
index f92892d6108bab020219ff8f41a4848e6372ec2d..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Iso14004pdf[PORTABLE] Freedownload.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-<br />
-<h1>How to Download ISO 14004 PDF for Free</h1>
-<p>ISO 14004 is a standard that provides guidance for an organization on the establishment, implementation, maintenance and improvement of a robust, credible and reliable environmental management system[^1^]. It is intended for an organization seeking to manage its environmental responsibilities in a systematic manner that contributes to the environmental pillar of sustainability[^1^].</p>
-<p>If you are looking for a way to download ISO 14004 PDF for free, you may be disappointed to find out that this standard is not available for free download from the official ISO website or any other authorized source. ISO standards are protected by copyright and require a purchase or a subscription to access them legally[^2^]. However, there are some ways you can get a glimpse of what ISO 14004 contains without paying for it.</p>
-<h2>iso14004pdffreedownload</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://urlgoal.com/2uI90K">https://urlgoal.com/2uI90K</a></b></p><br /><br />
-<p>One option is to read the abstract or the preview of ISO 14004 on the ISO website[^1^]. This will give you an overview of the scope, terms and definitions, and main clauses of the standard. You can also see the table of contents and some excerpts from the introduction and the foreword. However, this will not give you the full text or the details of the guidance provided by ISO 14004.</p>
-<p>Another option is to read some articles or reports that summarize or explain ISO 14004. For example, you can find a brief introduction to ISO 14004 on the International Organization for Standardization (ISO) website[^3^]. This will give you some background information on the history and purpose of ISO 14004, as well as some examples of how it can be used by organizations. However, this will not give you the complete or accurate information on ISO 14004.</p>
-<p>A third option is to search for unofficial sources that claim to offer ISO 14004 PDF for free download. For example, you can find some websites that host PDF files of ISO 14004[^4^]. However, these sources are not authorized by ISO and may not be reliable or trustworthy. They may contain outdated, incomplete, inaccurate, or corrupted versions of ISO 14004. They may also pose security risks such as malware or viruses. Therefore, we do not recommend using these sources.</p>
-<p>The best option to download ISO 14004 PDF for free is to use a legitimate source that offers a free trial or a sample of ISO 14004. For example, you can use iTeh Standards Store[^2^], which is an online platform that provides access to thousands of standards from various organizations. You can register for a free account and get a 7-day trial that allows you to download up to 10 standards for free. You can also request a free sample of any standard before purchasing it. This way, you can download ISO 14004 PDF for free legally and safely.</p>
-<p></p>
-<p>To summarize, ISO 14004 is a valuable standard that can help organizations improve their environmental performance and sustainability. However, it is not available for free download from official sources. You can either read some abstracts or summaries of ISO 14004 online, or use a legitimate source that offers a free trial or a sample of ISO 14004. We hope this article has helped you understand how to download ISO 14004 PDF for free.</p> 7b8c122e87<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Jeeva Video Songs Hd 1080p Blu-ray Tamil Movies Download.md b/spaces/stomexserde/gpt4-ui/Examples/Jeeva Video Songs Hd 1080p Blu-ray Tamil Movies Download.md
deleted file mode 100644
index faa25740f1355e36330e97f33a43208bff56703b..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Jeeva Video Songs Hd 1080p Blu-ray Tamil Movies Download.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-
-<h1>How to Download Jeeva Video Songs in HD 1080p Blu-ray Quality from Tamil Movies</h1>
-
-<p>Jeeva is a popular Tamil actor who has starred in many hit movies such as Ko, Nanban, Rangam, Endrendrum Punnagai and more. His fans love to watch his video songs in high definition quality on their screens. But how can you download Jeeva video songs in HD 1080p Blu-ray quality from Tamil movies? Here are some tips and tricks to help you out.</p>
-<h2>Jeeva Video Songs Hd 1080p Blu-ray Tamil Movies Download</h2><br /><p><b><b>Download File</b> &#128279; <a href="https://urlgoal.com/2uIbQ1">https://urlgoal.com/2uIbQ1</a></b></p><br /><br />
-
-<h2>Use a Reliable Video Downloader Software or Website</h2>
-
-<p>One of the easiest ways to download Jeeva video songs in HD 1080p Blu-ray quality from Tamil movies is to use a reliable video downloader software or website. There are many such tools available online that can help you download videos from various sources such as YouTube, Dailymotion, Vimeo and more. Some of the popular ones are:</p>
-
-<ul>
-<li><a href="https://tgstat.com/channel/@Tamil_1080p_Video_Songs">Tamil Bluray , 4k HD Video Songs</a>: This is a Telegram channel that offers Tamil video songs in 4K, 1080p and 720p quality. You can join the channel and download the songs you want from the links provided[^1^].</li>
-<li><a href="https://www.youtube.com/playlist?list=PLzKnN1unLDQa6np9t1sORH7x-Sir_s5gM">Tamil Latest Songs 2023 HD BluRay 1080p</a>: This is a YouTube playlist that contains new hit Tamil songs with 4K HD BluRay 1080p quality. You can use a YouTube video downloader software or website to download the songs you want from the playlist[^2^].</li>
-<li><a href="https://soundcloud.com/cieticisamp1980/jeeva-video-songs-hd-1080p-blu-ray-tamil-movies-better-download/sets">Jeeva Video Songs Hd 1080p Blu-ray Tamil Movies BETTER Download</a>: This is a SoundCloud playlist that contains Jeeva video songs in HD 1080p Blu-ray quality from various Tamil movies. You can use a SoundCloud downloader software or website to download the songs you want from the playlist[^3^].</li>
-</ul>
-
-<p>Make sure to check the quality and format of the videos before downloading them. You may need to convert them to Blu-ray compatible format if they are not already.</p>
-
-<h2>Use a Torrent Client or Website</h2>
-
-<p>Another way to download Jeeva video songs in HD 1080p Blu-ray quality from Tamil movies is to use a torrent client or website. Torrents are files that contain information about other files that are shared by users over a peer-to-peer network. You can use a torrent client such as BitTorrent, uTorrent, qBittorrent or Vuze to download the files you want from the torrent websites. Some of the popular torrent websites for Tamil movies and songs are:</p>
-<p></p>
-
-<ul>
-<li><a href="https://tamilrockers.ws/">TamilRockers</a>: This is one of the most notorious torrent websites for Tamil movies and songs. It offers a wide range of content in various qualities and formats. You can find Jeeva video songs in HD 1080p Blu-ray quality from many of his movies on this website.</li>
-<li><a href="https://tamilmv.unblockit.onl/">TamilMV</a>: This is another popular torrent website for Tamil movies and songs. It also offers a variety of content in different qualities and formats. You can search for Jeeva video songs in HD 1080p Blu-ray quality from his movies on this website.</li>
-<li><a href="https://isaiminiweb.com/">Isaimini</a>: This is a torrent website that specializes in Tamil mp3 songs and video songs. It has a large collection of Jeeva video songs in HD 1080p Blu-ray quality from his movies. You can download them easily from this website.</li>
-</ul>
-
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\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/tests/metagpt/actions/__init__.py b/spaces/sub314xxl/MetaGPT/tests/metagpt/actions/__init__.py
deleted file mode 100644
index ee4b0e690a311cb8341f794293425a29dc3f0c67..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/tests/metagpt/actions/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 19:35
-@Author  : alexanderwu
-@File    : __init__.py
-"""
diff --git "a/spaces/suchun/chatGPT_acdemic/crazy_functions/\344\270\213\350\275\275arxiv\350\256\272\346\226\207\347\277\273\350\257\221\346\221\230\350\246\201.py" "b/spaces/suchun/chatGPT_acdemic/crazy_functions/\344\270\213\350\275\275arxiv\350\256\272\346\226\207\347\277\273\350\257\221\346\221\230\350\246\201.py"
deleted file mode 100644
index 3da831fd07e361a532777c83bb02cff265b94abd..0000000000000000000000000000000000000000
--- "a/spaces/suchun/chatGPT_acdemic/crazy_functions/\344\270\213\350\275\275arxiv\350\256\272\346\226\207\347\277\273\350\257\221\346\221\230\350\246\201.py"
+++ /dev/null
@@ -1,194 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file, get_conf
-import re, requests, unicodedata, os
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-def download_arxiv_(url_pdf):
-    if 'arxiv.org' not in url_pdf:
-        if ('.' in url_pdf) and ('/' not in url_pdf):
-            new_url = 'https://arxiv.org/abs/'+url_pdf
-            print('下载编号：', url_pdf, '自动定位：', new_url)
-            # download_arxiv_(new_url)
-            return download_arxiv_(new_url)
-        else:
-            print('不能识别的URL！')
-            return None
-    if 'abs' in url_pdf:
-        url_pdf = url_pdf.replace('abs', 'pdf')
-        url_pdf = url_pdf + '.pdf'
-
-    url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')
-    title, other_info = get_name(_url_=url_abs)
-
-    paper_id = title.split()[0]  # '[1712.00559]'
-    if '2' in other_info['year']:
-        title = other_info['year'] + ' ' + title
-
-    known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']
-    for k in known_conf:
-        if k in other_info['comment']:
-            title = k + ' ' + title
-
-    download_dir = './gpt_log/arxiv/'
-    os.makedirs(download_dir, exist_ok=True)
-
-    title_str = title.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-
-    requests_pdf_url = url_pdf
-    file_path = download_dir+title_str
-    # if os.path.exists(file_path):
-    #     print('返回缓存文件')
-    #     return './gpt_log/arxiv/'+title_str
-
-    print('下载中')
-    proxies, = get_conf('proxies')
-    r = requests.get(requests_pdf_url, proxies=proxies)
-    with open(file_path, 'wb+') as f:
-        f.write(r.content)
-    print('下载完成')
-
-    # print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
-    # subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
-
-    x = "%s  %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
-    x = x.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-    return './gpt_log/arxiv/'+title_str, other_info
-
-
-def get_name(_url_):
-    import os
-    from bs4 import BeautifulSoup
-    print('正在获取文献名！')
-    print(_url_)
-
-    # arxiv_recall = {}
-    # if os.path.exists('./arxiv_recall.pkl'):
-    #     with open('./arxiv_recall.pkl', 'rb') as f:
-    #         arxiv_recall = pickle.load(f)
-
-    # if _url_ in arxiv_recall:
-    #     print('在缓存中')
-    #     return arxiv_recall[_url_]
-
-    proxies, = get_conf('proxies')
-    res = requests.get(_url_, proxies=proxies)
-
-    bs = BeautifulSoup(res.text, 'html.parser')
-    other_details = {}
-
-    # get year
-    try:
-        year = bs.find_all(class_='dateline')[0].text
-        year = re.search(r'(\d{4})', year, re.M | re.I).group(1)
-        other_details['year'] = year
-        abstract = bs.find_all(class_='abstract mathjax')[0].text
-        other_details['abstract'] = abstract
-    except:
-        other_details['year'] = ''
-        print('年份获取失败')
-
-    # get author
-    try:
-        authors = bs.find_all(class_='authors')[0].text
-        authors = authors.split('Authors:')[1]
-        other_details['authors'] = authors
-    except:
-        other_details['authors'] = ''
-        print('authors获取失败')
-
-    # get comment
-    try:
-        comment = bs.find_all(class_='metatable')[0].text
-        real_comment = None
-        for item in comment.replace('\n', ' ').split('   '):
-            if 'Comments' in item:
-                real_comment = item
-        if real_comment is not None:
-            other_details['comment'] = real_comment
-        else:
-            other_details['comment'] = ''
-    except:
-        other_details['comment'] = ''
-        print('年份获取失败')
-
-    title_str = BeautifulSoup(
-        res.text, 'html.parser').find('title').contents[0]
-    print('获取成功：', title_str)
-    # arxiv_recall[_url_] = (title_str+'.pdf', other_details)
-    # with open('./arxiv_recall.pkl', 'wb') as f:
-    #     pickle.dump(arxiv_recall, f)
-
-    return title_str+'.pdf', other_details
-
-
-
-@CatchException
-def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-
-    CRAZY_FUNCTION_INFO = "下载arxiv论文并翻译摘要，函数插件作者[binary-husky]。正在提取摘要并下载PDF文档……"
-    import glob
-    import os
-
-    # 基本信息：功能、贡献者
-    chatbot.append(["函数插件功能？", CRAZY_FUNCTION_INFO])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import pdfminer, bs4
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 提取摘要，下载PDF文档
-    try:
-        pdf_path, info = download_arxiv_(txt)
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"下载pdf文件未成功")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    # 翻译摘要等
-    i_say =            f"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}"
-    i_say_show_user =  f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'
-    chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    msg = '正常'
-    # ** gpt request **
-    # 单线，获取文章meta信息
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say,
-        inputs_show_user=i_say_show_user,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot, history=[],
-        sys_prompt="Your job is to collect information from materials and translate to Chinese。",
-    )
-
-    chatbot[-1] = (i_say_show_user, gpt_say)
-    history.append(i_say_show_user); history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    # 写入文件
-    import shutil
-    # 重置文件的创建时间
-    shutil.copyfile(pdf_path, f'./gpt_log/{os.path.basename(pdf_path)}'); os.remove(pdf_path)
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res + "\n\nPDF文件也已经下载"))
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
diff --git a/spaces/sunil448832/retrieval-augment-generation/vector_store/faiss_vector_store.py b/spaces/sunil448832/retrieval-augment-generation/vector_store/faiss_vector_store.py
deleted file mode 100644
index 8ade592f66104651ab1225a2f9bf65ca7bb27dc3..0000000000000000000000000000000000000000
--- a/spaces/sunil448832/retrieval-augment-generation/vector_store/faiss_vector_store.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import faiss
-import numpy as np
-import os
-import pickle
-from tqdm import tqdm
-
-# Create a class for a flat index
-class IndexFlat:
-    def __init__(self, dimension):
-        # Initialize a Faiss flat index with L2 distance
-        self.index = faiss.IndexFlatL2(dimension)
-
-    def add(self, vectors):
-        # Add vectors to the index
-        self.index.add(np.array(vectors))
-
-    def delete(self, ids):
-        # Remove vectors from the index by their IDs
-        self.index.remove_ids(np.array(ids))
-
-    def search(self, vectors, k):
-        # Search for the k-nearest neighbors of the given vectors
-        return self.index.search(np.array(vectors), k)
-
-# Create a class for an IVF (Inverted File) index
-class IndexIVF:
-    def __init__(self, dimension, nlists=100, nprobe=10):
-        # Initialize a Faiss flat index and an IVF index with inner product metric
-        self.index_flat = faiss.IndexFlatL2(dimension)  
-        self.index = faiss.IndexIVFFlat(self.index_flat, dimension, nlists, faiss.METRIC_INNER_PRODUCT)
-        self.index.nprobe = nprobe
-
-    def add(self, vectors):
-        # Train and add vectors to the index
-        self.index.train(np.array(vectors))
-        self.index.add(np.array(vectors))
-
-    def delete(self, ids):
-        # Remove vectors from the index by their IDs
-        self.index.remove_ids(np.array(ids))
-
-    def search(self, vectors, k):
-        # Search for the k-nearest neighbors of the given vectors
-        return self.index.search(np.array(vectors), k)
-
-# Create a class for managing Faiss vector storage
-class FaissVectorStore:
-    def __init__(self, dimension=324, nlists=100, nprobe=10):
-        self.dimension = dimension
-        self.nlists = nlists
-        self.nprobe = nprobe
-        self.index = None
-        self.documents_db = {}
-
-    def add(self, documents):
-        ids = range(0, len(self.documents_db) + len(documents))
-        db_vectors, db_documents, db_docs_ids = [], [], []
-
-        # Collect existing document vectors and documents
-        for doc_id in self.documents_db:
-            db_vectors.append(self.documents_db[doc_id]['vector'])
-            db_documents.append(self.documents_db[doc_id]['document'])
-            db_docs_ids.append(doc_id)
-
-        # Add new document vectors and documents
-        for doc_id in documents:
-            db_vectors.append(documents[doc_id]['vector'])
-            db_documents.append(documents[doc_id]['document'])
-            db_docs_ids.append(doc_id)
-
-        if len(db_vectors) < 10000:
-            self.index = IndexFlat(self.dimension)
-        else:
-            self.index = IndexIVF(self.dimension, self.nlists, self.nprobe)
-
-        self.index.add(np.array(db_vectors))
-        self.documents_db = {}
-        for i, doc_id in enumerate(db_docs_ids):
-            self.documents_db[doc_id] = {'vector': db_vectors[i], 'document': db_documents[i], 'index_id': i}
-        
-    def delete(self, documents_ids):
-        # Delete vectors from the index by document IDs
-        index_ids_to_delete = []
-        for doc_id in documents_ids:
-            if doc_id in self.documents_db:
-                index_ids_to_delete.append(self.documents_db[doc_id]['index_id'])
-        self.index.delete(index_ids_to_delete)
-        self.documents_db = {k: v for k, v in self.documents_db.items() if k not in documents_ids}
-        
-
-    def query(self, query_vector, k):
-        # Query for the top k nearest neighbors to the query_vector
-        _, I = self.index.search(query_vector, k)
-        documents = []
-        for doc_id in self.documents_db:
-            if self.documents_db[doc_id]['index_id'] in I[0]:
-                documents.append(self.documents_db[doc_id]['document'])
-        return documents
-
-    def write(self,database_path):
-        # Save the index and documents to files
-        if not os.path.exists(database_path):
-            os.makedirs(database_path)
-        faiss_path = os.path.join(database_path, 'index.faiss')
-        document_path = os.path.join(database_path, 'documents.pkl')
-        faiss.write_index(self.index.index, faiss_path)
-        with open(document_path, 'wb') as f:
-            pickle.dump(self.documents_db, f)
-
-    def read(self,database_path):
-        # Read the index and documents from files
-        faiss_path = os.path.join(database_path, 'index.faiss')
-        document_path = os.path.join(database_path, 'documents.pkl')
-        self.index = faiss.read_index(faiss_path)
-        with open(document_path, 'rb') as f:
-            self.documents_db = pickle.load(f)
-
-    @classmethod
-    def from_documents(cls, documents, dimension, nlists, nprobe):
-        vector_store = cls(dimension, nlists, nprobe)
-        vector_store.add(documents)
-        return vector_store
-
-    @classmethod
-    def as_retriever(cls, database_path):
-        vector_store = cls()
-        vector_store.read(database_path)
-        return vector_store
-
-if __name__ == '__main__':
-    nb = 20000
-    d = 50
-    database_path = 'db_path'
-
-    if not os.path.exists(database_path):
-        os.makedirs(database_path)
-
-    documents = {}
-    for i in range(nb):
-        id = f'id_{i}'
-        texts = f'text_{i}'
-        vectors = np.random.random((d)).astype('float32')
-        documents[id] = {'document': texts, 'vector': vectors}
-
-    vector_store = FaissVectorStore.from_documents(documents, dimension=50, nlists=100, nprobe=10)
-    query_vector = np.random.random((1, d)).astype('float32')
-    nearest_neighbors = vector_store.query(query_vector, k=5)
-    print(nearest_neighbors)
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download __HOT__ Crack Mobtime Cell Phone Manager 2007.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download __HOT__ Crack Mobtime Cell Phone Manager 2007.md
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--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download __HOT__ Crack Mobtime Cell Phone Manager 2007.md	
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Save Money and Time with Office 2016 EADO for Windows 10.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Save Money and Time with Office 2016 EADO for Windows 10.md
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index b472315ebc083f952b909d760e1abed082a8e72a..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Save Money and Time with Office 2016 EADO for Windows 10.md	
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-<p>If you are looking for a way to download and install Office 2016 EADO for Windows 10, you have come to the right place. Office 2016 EADO is a version of Microsoft Office that allows you to access your files and documents from anywhere, anytime, and on any device. It also includes the latest features and updates for Word, Excel, PowerPoint, Outlook, and more.</p>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/99 Download APK A Safe and Easy Way to Access Millions of Android Content.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/99 Download APK A Safe and Easy Way to Access Millions of Android Content.md
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index d3dd4ee81d2d615be74acf948b3c134ae1104038..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/99 Download APK A Safe and Easy Way to Access Millions of Android Content.md	
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-<p>99 is an app that connects passengers and drivers in more than 1,600 cities in Brazil. You can choose from different categories of vehicles, such as taxi, private car, or voucher, and pay with cash or directly through the app. You can also enjoy features like real-time tracking, driver rating, safety alerts, and customer support 24/7.</p>
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-<h3>Step 3: Install the APK file and launch the app</h3>
-<p>Once you have downloaded the APK file, locate it in your downloads folder or notification bar and tap on it. You will see a prompt asking you to confirm the installation. Tap on Install and wait for the process to finish. After that, tap on Open and launch the app.</p>
-<h2>How to use 99 app to book a ride</h2>
-<p> <p>Now that you have installed the app, you can use it to book a ride in a few simple steps:</p>
-<h3>Step 1: Choose your destination and pick-up location</h3>
-<p>Open the app and enter your destination in the search bar. You can also use the map to select your destination by tapping on it. The app will automatically detect your current location as your pick-up point, but you can change it if you want by tapping on the location icon.</p>
-<h3>Step 2: Select your preferred category and payment method</h3>
-<p>Swipe left or right to choose the category of vehicle you want, such as taxi, private car, or voucher. You can also see the estimated fare and time for each category. Tap on the payment icon to select your payment method, such as cash or card. You can also add a coupon code if you have one.</p>
-<h3>Step 3: Confirm your ride and track your driver</h3>
-<p>Tap on the request button to confirm your ride. The app will find the nearest available driver for you and show you their name, photo, rating, and car details. You can also track their location and arrival time on the map. You can also contact them by phone or chat if you need to.</p>
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-<p>If you want to save money on your rides, you can take advantage of the discounts and promotions that 99 offers. Here are some ways to do that:</p>
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-<p>You can invite your friends to use 99 and get R$10 off your next ride for each friend who signs up and takes their first ride. To do this, go to Menu > Invite Friends and share your referral code with them. You can also see how many credits you have earned in the same section.</p>
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-<p>In conclusion, 99 is a great app for ridesharing in Brazil that offers convenience, safety, affordability, and quality. You can download and install 99 APK on your Android device easily by following the steps we have explained. You can also use the app to book a ride in a few simple steps by choosing your destination, category, and payment method. Moreover, you can get discounts and promotions with 99 app by referring friends, using coupons, and checking the app for deals.</p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Adjustment Program Epson Xp 225.md b/spaces/tioseFevbu/cartoon-converter/scripts/Adjustment Program Epson Xp 225.md
deleted file mode 100644
index 5ce9196a2c62ba9ac4d6e078cdef0367dffbeef6..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Adjustment Program Epson Xp 225.md	
+++ /dev/null
@@ -1,83 +0,0 @@
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-<h1>Adjustment Program Epson Xp 225: How to Reset Your Printer and Solve Common Problems</h1>
- <p>If you own an Epson Expression Home XP-225 printer, you may encounter some issues that require you to reset your printer or use an adjustment program. In this article, we will explain what an adjustment program is, how to download and use it, and how to solve some common problems with your Epson XP-225 printer.</p>
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- <p>An adjustment program is a software tool that allows you to perform various maintenance tasks on your Epson printer, such as resetting the waste ink pad counter, cleaning the print head, checking the ink level, and more. An adjustment program can help you extend the life of your printer and improve its performance.</p>
- <h3>Why Do You Need an Adjustment Program for Epson XP-225?</h3>
- <p>The Epson XP-225 is a compact and affordable all-in-one printer that offers wireless printing, scanning, and copying. However, like any other printer, it may experience some problems over time, such as:</p>
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-<li>The waste ink pad counter reaching its limit and displaying an error message.</li>
-<li>The print quality deteriorating due to clogged or misaligned print head nozzles.</li>
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- <p>These problems can be solved by using an adjustment program for Epson XP-225. The adjustment program can reset the waste ink pad counter, clean and align the print head, check and reset the ink level, and perform other functions that can fix your printer issues.</p>
- <h2>How to Download and Use an Adjustment Program for Epson XP-225?</h2>
- <p>To download and use an adjustment program for Epson XP-225, you need to follow these steps:</p>
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- <h3>Step 1: Download the Adjustment Program</h3>
- <p>You can find various websites that offer the adjustment program for Epson XP-225 for free or for a fee. However, not all of them are reliable or safe. Some of them may contain viruses or malware that can harm your computer or printer. Therefore, you should be careful when choosing a source to download the adjustment program from.</p>
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- <h3>Step 2: Extract and Run the Adjustment Program</h3>
- <p>After downloading the adjustment program file, you need to extract it using a software like WinRAR or 7-Zip. You will get a folder containing several files, including AdjProg.exe. This is the main executable file of the adjustment program.</p>
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- <p>When you run the adjustment program, you will see a window with several options. You need to select "Particular adjustment mode" from the menu on the left side. Then, you need to select your printer model (XP-225) and port (usually USB001) from the drop-down menus on the right side. Click "OK" to proceed.</p>
- <h3>Step 4: Choose the Function You Want to Perform</h3>
- <p>In the next window, you will see a list of functions that you can perform with the adjustment program. Depending on your printer issue, you can choose one or more of these functions:</p>
- <ul>
-<li>Waste ink pad counter: This function allows you to reset the waste ink pad counter that keeps track of how much ink is absorbed by the waste ink pads inside your printer. When the counter reaches its limit, your printer will stop working and display an error message. To reset the counter, you need to click on "Waste ink pad counter" and then "OK". In the next window, check both "Main pad counter" and "Platen pad counter" boxes and click "Check". The current value of the counter will be displayed. Then click "Initialization". The counter will be reset to zero. Click "Finish" to exit.</li>
-<li>Head cleaning: This function allows you to clean the print head nozzles that may be clogged or blocked by dried ink. This can improve the print quality and prevent streaks or missing colors. To clean the print head, you need to click on "Head cleaning" and then "OK". The adjustment program will prompt you to load a few sheets of paper in the printer and then start the cleaning process. It may take a few minutes. When it is done, you can print a nozzle check pattern to see if the cleaning was successful.</li>
-<li>Head alignment: This function allows you to align the print head nozzles that may be misaligned due to various factors, such as paper jam, ink leakage, or printer movement. This can also improve the print quality and prevent blurry or distorted images. To align the print head, you need to click on "Head alignment" and then "OK". The adjustment program will prompt you to load a sheet of paper in the printer and then print an alignment pattern. You need to check the pattern and select the best option for each column. Then click "Next" and repeat the process for the next pattern. When it is done, click "Finish" to exit.</li>
-<li>Ink charge: This function allows you to charge the ink in the cartridges or refillable ink tanks that may be low or empty. This can prevent air bubbles or ink starvation from affecting the print quality. To charge the ink, you need to click on "Ink charge" and then "OK". The adjustment program will prompt you to make sure that the ink cartridges or tanks are installed properly and have enough ink. Then it will start the ink charging process. It may take a few minutes. When it is done, click "Finish" to exit.</li>
-<li>EEPROM data copy: This function allows you to copy the data from the EEPROM (Electrically Erasable Programmable Read-Only Memory) chip of your printer to another printer of the same model. This can help you transfer your printer settings, such as serial number, region code, ink level, etc., to a new or replacement printer. To copy the EEPROM data, you need to click on "EEPROM data copy" and then "OK". The adjustment program will prompt you to connect both printers to your computer via USB cables and turn them on. Then it will start the data copying process. It may take a few seconds. When it is done, click "Finish" to exit.</li>
-</ul>
- <h2>How to Solve Some Common Problems with Epson XP-225?</h2>
- <p>Besides using an adjustment program for Epson XP-225, you can also try some simple troubleshooting steps to solve some common problems with your printer, such as:</p>
- <h3>Problem: Printer Not Printing or Printing Blank Pages</h3>
- <p>Solution: There are several possible causes for this problem, such as:</p>
- <ul>
-<li>The printer is not connected properly to your computer or network. Check the USB cable or wireless connection and make sure they are working.</li>
-<li>The printer driver is not installed correctly or is outdated. Reinstall or update the printer driver from the Epson website.</li>
-<li>The printer settings are incorrect or incompatible with your document or application. Check the paper size, orientation, margins, quality, etc., and adjust them accordingly.</li>
-<li>The ink cartridges or tanks are empty or not recognized by the printer. Replace or refill them with genuine Epson ink and make sure they are installed correctly.</li>
-<li>The print head is clogged or misaligned. Use an adjustment program for Epson XP-225 to clean and align the print head.</li>
-</ul>
- <h3>Problem: Printer Displaying Error Messages</h3>
- <p>Solution: There are different types of error messages that your printer may display, such as:</p>
- <ul>
-<li>E-01: Paper jam. Remove any jammed paper from the paper feed or output tray carefully and press the power button to resume printing.</li>
-<li>E-02: Scanner error. Turn off the printer and check if there is any obstruction on the scanner glass or cover. Turn on the printer again and try scanning again.</li>
-<li>E-10: Ink low. The ink level in one or more cartridges or tanks is low. Replace or refill them with genuine Epson ink soon.</li>
-<li>E-11: Ink out. The ink level in one or more cartridges or tanks is empty. Replace or refill them with genuine Epson ink immediately.</li>
-<li>E-13: Ink cartridge not recognized. The printer does not recognize one or more cartridges or tanks. Remove and reinstall them correctly and make sure they are genuine Epson ink.</li>
-<li>E-14: Ink cartridge incompatible. The printer does not support one or more cartridges or tanks that are installed. Remove and replace them with compatible ones and make sure they are genuine Epson ink.</li>
-<li>E-15: Ink cartridge not installed. The printer does not detect one or more cartridges or tanks that are installed. Remove and reinstall them correctly and make sure they are genuine Epson ink.</li>
-<li>E-91: Service required. The waste ink pad counter has reached its limit and the printer needs to be serviced. Use an adjustment program for Epson XP-225 to reset the waste ink pad counter.</li>
-</ul>
- <h3>Problem: Printer Making Noise or Vibrating</h3>
- <p>Solution: There are several possible causes for this problem, such as:</p>
- <ul>
-<li>The printer is placed on an uneven or unstable surface. Move the printer to a flat and stable surface and make sure it is not tilted or shaken.</li>
-<li>The printer is dirty or dusty. Clean the exterior and interior of the printer with a soft cloth and a mild detergent. Do not use alcohol, solvents, or abrasive cleaners.</li>
-<li>The printer is overheated or overworked. Turn off the printer and let it cool down for a while. Do not use the printer continuously for a long time or in a hot or humid environment.</li>
-<li>The printer is malfunctioning or damaged. Contact Epson support or an authorized service center for assistance.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>The adjustment program for Epson XP-225 is a useful tool that can help you perform various maintenance tasks on your printer, such as resetting the waste ink pad counter, cleaning and aligning the print head, checking and resetting the ink level, and more. By using this tool, you can extend the life of your printer and improve its performance.</p>
- <p>However, the adjustment program is not a magic solution that can fix all your printer problems. You still need to take good care of your printer and follow some basic troubleshooting steps when you encounter some common issues, such as printer not printing, displaying error messages, making noise, or vibrating.</p>
- <p>If you have any questions or concerns about your Epson XP-225 printer or the adjustment program, you can contact Epson support or visit their website for more information and guidance.</p>
- <h2>FAQs</h2>
- <h3>Q: Where can I find the serial number of my Epson XP-225 printer?</h3>
- <p>A: You can find the serial number on a label at the back of your printer. It is a 10-digit alphanumeric code that starts with "S" or "X". You may need to provide this number when you contact Epson support or use an adjustment program.</p>
- <h3>Q: How often do I need to use an adjustment program for Epson XP-225?</h3>
- <p>A: There is no fixed rule on how often you need to use an adjustment program for Epson XP-225. It depends on how frequently you use your printer and what kind of issues you face. Generally, you should use an adjustment program when you notice a decline in print quality, an error message related to the waste ink pad counter, or an inaccurate ink level indicator.</p>
- <h3>Q: Is it safe to use an adjustment program for Epson XP-225?</h3>
- <p>A: Yes, it is safe to use an adjustment program for Epson XP-225 as long as you download it from a reliable source and follow the instructions carefully. However, you should be aware that using an adjustment program may void your warranty or affect your service contract with Epson. Therefore, you should use it at your own risk and responsibility.</p>
- <h3>Q: Can I use an adjustment program for Epson XP-225 on other models of Epson printers?</h3>
- <p>A: No, you cannot use an adjustment program for Epson XP-225 on other models of Epson printers. Each model of Epson printer has its own specific adjustment program that is designed to work with its features and functions. Using an incompatible adjustment program may damage your printer or cause unexpected errors.</p>
- <h3>Q: How can I get more help with my Epson XP-225 printer or the adjustment program?</h3>
- <p>A: You can get more help with your Epson XP-225 printer or the adjustment program by visiting the [Epson website] or contacting [Epson support]. You can also check the user manual that came with your printer or download it from the website.</p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Epic Mickey 2 The Power Of Two [Multi6][PCDVD][Wait Crack] PC.md b/spaces/tioseFevbu/cartoon-converter/scripts/Epic Mickey 2 The Power Of Two [Multi6][PCDVD][Wait Crack] PC.md
deleted file mode 100644
index f9b62aa7316491007feea3c70007a9eef497d390..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Epic Mickey 2 The Power Of Two [Multi6][PCDVD][Wait Crack] PC.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-
-Here is what I created:
-
-<h1>Epic Mickey 2: The Power of Two - A Magical Adventure for PC</h1>
-<p>Epic Mickey 2: The Power of Two is a platform video game that features the iconic Disney characters Mickey Mouse and Oswald the Lucky Rabbit. The game is a sequel to the 2010 game Epic Mickey, and it was released for various platforms, including PC, in 2012.</p>
-<p>The game follows the adventures of Mickey and Oswald as they explore the Wasteland, a world where forgotten and rejected Disney creations live. The game features a co-operative mode, where one player controls Mickey and his magical paintbrush, and the other controls Oswald and his remote control. Together, they can use their abilities to solve puzzles, fight enemies, and restore the Wasteland.</p>
-<h2>Epic Mickey 2 The Power Of Two [Multi6][PCDVD][Wait Crack] PC</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://urlcod.com/2uHxdp">https://urlcod.com/2uHxdp</a></b></p><br /><br />
-<p>The game also features a musical element, where the characters sing and dance along with the soundtrack. The game has six different languages to choose from: English, Spanish, French, Italian, German, and Portuguese. The game requires a crack to run on PC, which is not yet available.</p>
-<p>Epic Mickey 2: The Power of Two is a game that will appeal to fans of Disney and platform games. It offers a colorful and charming experience that can be enjoyed alone or with a friend. The game is available on PC DVD and can be downloaded from various sources online.</p>
-<p></p>
-Here is what I created:
-
-<p>The game is set after the events of the first Epic Mickey, where Mickey accidentally created a monster called the Shadow Blot that threatened the Wasteland. Mickey managed to defeat the Shadow Blot and restore some of the Wasteland's beauty, but he also caused some damage and made some enemies along the way. Oswald, who is the ruler of the Wasteland, forgives Mickey and invites him to return as a hero.</p>
-<p>However, when Mickey arrives, he finds out that the Wasteland is in trouble again. A series of earthquakes have been shaking the land, and a mysterious voice has been convincing some of the residents to rebel against Oswald. Mickey and Oswald team up to find out who is behind this plot and stop them before it's too late. Along the way, they meet old friends and foes, such as Gus the Gremlin, Ortensia the Cat, Clarabelle Cow, Horace Horsecollar, and the Mad Doctor.</p>
-<p>The game features various locations from Disney's history, such as Mean Street, OsTown, Ventureland, Autotopia, and Rainbow Caverns. The game also pays homage to some of Disney's classic films and attractions, such as Fantasia, Sleeping Beauty, Pirates of the Caribbean, and Haunted Mansion. The game allows the player to choose how they want to play: they can either use Mickey's paint to create and befriend things, or use his thinner to erase and destroy things. The game also adapts to the player's choices and actions, resulting in different outcomes and consequences.</p>
-Here is what I created:
-
-<p>The game also features a variety of gameplay modes and challenges, such as side-scrolling levels, racing mini-games, boss battles, and hidden collectibles. The game supports both keyboard and mouse controls, as well as gamepad controllers. The game also has an online mode, where the player can join another player's game or invite a friend to join their game. The online mode allows the players to chat and cooperate with each other, as well as compete for the best score and time.</p>
-<p>Epic Mickey 2: The Power of Two is a game that celebrates the legacy and magic of Disney. It is a game that offers a rich and immersive story, a vibrant and diverse world, and a fun and engaging gameplay. It is a game that can be enjoyed by anyone who loves Disney, adventure, and creativity. The game is currently waiting for a crack to run on PC, but it is expected to be available soon.</p> e93f5a0c3f<br />
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diff --git a/spaces/tomofi/MMOCR/configs/_base_/recog_datasets/MJ_train.py b/spaces/tomofi/MMOCR/configs/_base_/recog_datasets/MJ_train.py
deleted file mode 100644
index 37fd7814385e0ba9e3c6d12cf5e05fd9950752e5..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/_base_/recog_datasets/MJ_train.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: Syn90k
-
-train_root = 'data/mixture/Syn90k'
-
-train_img_prefix = f'{train_root}/mnt/ramdisk/max/90kDICT32px'
-train_ann_file = f'{train_root}/label.lmdb'
-
-train = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix,
-    ann_file=train_ann_file,
-    loader=dict(
-        type='LmdbLoader',
-        repeat=1,
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=False)
-
-train_list = [train]
diff --git a/spaces/tomofi/MMOCR/mmocr/datasets/pipelines/textdet_targets/base_textdet_targets.py b/spaces/tomofi/MMOCR/mmocr/datasets/pipelines/textdet_targets/base_textdet_targets.py
deleted file mode 100644
index b86d85402a1873a5619a61d62d3b7249a3b12c31..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/datasets/pipelines/textdet_targets/base_textdet_targets.py
+++ /dev/null
@@ -1,168 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import sys
-
-import cv2
-import numpy as np
-import pyclipper
-from mmcv.utils import print_log
-from shapely.geometry import Polygon as plg
-
-import mmocr.utils.check_argument as check_argument
-
-
-class BaseTextDetTargets:
-    """Generate text detector ground truths."""
-
-    def __init__(self):
-        pass
-
-    def point2line(self, xs, ys, point_1, point_2):
-        """Compute the distance from point to a line. This is adapted from
-        https://github.com/MhLiao/DB.
-
-        Args:
-            xs (ndarray): The x coordinates of size hxw.
-            ys (ndarray): The y coordinates of size hxw.
-            point_1 (ndarray): The first point with shape 1x2.
-            point_2 (ndarray): The second point with shape 1x2.
-
-        Returns:
-            result (ndarray): The distance matrix of size hxw.
-        """
-        # suppose a triangle with three edge abc with c=point_1 point_2
-        # a^2
-        a_square = np.square(xs - point_1[0]) + np.square(ys - point_1[1])
-        # b^2
-        b_square = np.square(xs - point_2[0]) + np.square(ys - point_2[1])
-        # c^2
-        c_square = np.square(point_1[0] - point_2[0]) + np.square(point_1[1] -
-                                                                  point_2[1])
-        # -cosC=(c^2-a^2-b^2)/2(ab)
-        neg_cos_c = (
-            (c_square - a_square - b_square) /
-            (np.finfo(np.float32).eps + 2 * np.sqrt(a_square * b_square)))
-        # sinC^2=1-cosC^2
-        square_sin = 1 - np.square(neg_cos_c)
-        square_sin = np.nan_to_num(square_sin)
-        # distance=a*b*sinC/c=a*h/c=2*area/c
-        result = np.sqrt(a_square * b_square * square_sin /
-                         (np.finfo(np.float32).eps + c_square))
-        # set result to minimum edge if C<pi/2
-        result[neg_cos_c < 0] = np.sqrt(np.fmin(a_square,
-                                                b_square))[neg_cos_c < 0]
-        return result
-
-    def polygon_area(self, polygon):
-        """Compute the polygon area. Please refer to Green's theorem.
-        https://en.wikipedia.org/wiki/Green%27s_theorem. This is adapted from
-        https://github.com/MhLiao/DB.
-
-        Args:
-            polygon (ndarray): The polygon boundary points.
-        """
-
-        polygon = polygon.reshape(-1, 2)
-        edge = 0
-        for i in range(polygon.shape[0]):
-            next_index = (i + 1) % polygon.shape[0]
-            edge += (polygon[next_index, 0] - polygon[i, 0]) * (
-                polygon[next_index, 1] + polygon[i, 1])
-
-        return edge / 2.
-
-    def polygon_size(self, polygon):
-        """Estimate the height and width of the minimum bounding box of the
-        polygon.
-
-        Args:
-            polygon (ndarray): The polygon point sequence.
-
-        Returns:
-            size (tuple): The height and width of the minimum bounding box.
-        """
-        poly = polygon.reshape(-1, 2)
-        rect = cv2.minAreaRect(poly.astype(np.int32))
-        size = rect[1]
-        return size
-
-    def generate_kernels(self,
-                         img_size,
-                         text_polys,
-                         shrink_ratio,
-                         max_shrink=sys.maxsize,
-                         ignore_tags=None):
-        """Generate text instance kernels for one shrink ratio.
-
-        Args:
-            img_size (tuple(int, int)): The image size of (height, width).
-            text_polys (list[list[ndarray]]: The list of text polygons.
-            shrink_ratio (float): The shrink ratio of kernel.
-
-        Returns:
-            text_kernel (ndarray): The text kernel mask of (height, width).
-        """
-        assert isinstance(img_size, tuple)
-        assert check_argument.is_2dlist(text_polys)
-        assert isinstance(shrink_ratio, float)
-
-        h, w = img_size
-        text_kernel = np.zeros((h, w), dtype=np.float32)
-
-        for text_ind, poly in enumerate(text_polys):
-            instance = poly[0].reshape(-1, 2).astype(np.int32)
-            area = plg(instance).area
-            peri = cv2.arcLength(instance, True)
-            distance = min(
-                int(area * (1 - shrink_ratio * shrink_ratio) / (peri + 0.001) +
-                    0.5), max_shrink)
-            pco = pyclipper.PyclipperOffset()
-            pco.AddPath(instance, pyclipper.JT_ROUND,
-                        pyclipper.ET_CLOSEDPOLYGON)
-            shrunk = np.array(pco.Execute(-distance))
-
-            # check shrunk == [] or empty ndarray
-            if len(shrunk) == 0 or shrunk.size == 0:
-                if ignore_tags is not None:
-                    ignore_tags[text_ind] = True
-                continue
-            try:
-                shrunk = np.array(shrunk[0]).reshape(-1, 2)
-
-            except Exception as e:
-                print_log(f'{shrunk} with error {e}')
-                if ignore_tags is not None:
-                    ignore_tags[text_ind] = True
-                continue
-            cv2.fillPoly(text_kernel, [shrunk.astype(np.int32)], text_ind + 1)
-        return text_kernel, ignore_tags
-
-    def generate_effective_mask(self, mask_size: tuple, polygons_ignore):
-        """Generate effective mask by setting the ineffective regions to 0 and
-        effective regions to 1.
-
-        Args:
-            mask_size (tuple): The mask size.
-            polygons_ignore (list[[ndarray]]: The list of ignored text
-                polygons.
-
-        Returns:
-            mask (ndarray): The effective mask of (height, width).
-        """
-
-        assert check_argument.is_2dlist(polygons_ignore)
-
-        mask = np.ones(mask_size, dtype=np.uint8)
-
-        for poly in polygons_ignore:
-            instance = poly[0].reshape(-1,
-                                       2).astype(np.int32).reshape(1, -1, 2)
-            cv2.fillPoly(mask, instance, 0)
-
-        return mask
-
-    def generate_targets(self, results):
-        raise NotImplementedError
-
-    def __call__(self, results):
-        results = self.generate_targets(results)
-        return results
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
deleted file mode 100644
index 0cfc7d78a79836ed06cf242f5f5c32af7f065249..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py
deleted file mode 100644
index efbedc863c7eeeaef331121416141334906fef3d..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,50 +0,0 @@
-_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
-# model settings
-model = dict(
-    neck=[
-        dict(
-            type='FPN',
-            in_channels=[256, 512, 1024, 2048],
-            out_channels=256,
-            num_outs=5),
-        dict(
-            type='BFP',
-            in_channels=256,
-            num_levels=5,
-            refine_level=2,
-            refine_type='non_local')
-    ],
-    roi_head=dict(
-        bbox_head=dict(
-            loss_bbox=dict(
-                _delete_=True,
-                type='BalancedL1Loss',
-                alpha=0.5,
-                gamma=1.5,
-                beta=1.0,
-                loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rcnn=dict(
-            sampler=dict(
-                _delete_=True,
-                type='CombinedSampler',
-                num=512,
-                pos_fraction=0.25,
-                add_gt_as_proposals=True,
-                pos_sampler=dict(type='InstanceBalancedPosSampler'),
-                neg_sampler=dict(
-                    type='IoUBalancedNegSampler',
-                    floor_thr=-1,
-                    floor_fraction=0,
-                    num_bins=3)))))
-# dataset settings
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-data = dict(
-    train=dict(proposal_file=data_root +
-               'libra_proposals/rpn_r50_fpn_1x_train2017.pkl'),
-    val=dict(proposal_file=data_root +
-             'libra_proposals/rpn_r50_fpn_1x_val2017.pkl'),
-    test=dict(proposal_file=data_root +
-              'libra_proposals/rpn_r50_fpn_1x_val2017.pkl'))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ndl/cascade_mask_rcnn_r50_fpn_1x_ndl_1024.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ndl/cascade_mask_rcnn_r50_fpn_1x_ndl_1024.py
deleted file mode 100644
index 4bf74e50216ef0e28e528d4b71944f6eb7987a69..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ndl/cascade_mask_rcnn_r50_fpn_1x_ndl_1024.py
+++ /dev/null
@@ -1,24 +0,0 @@
-_base_ = [
-    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
-    './ndl_instance_1024.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-num_classes = 16
-
-model = dict(
-    roi_head=dict(
-        bbox_head=[
-            dict(
-                type='Shared2FCBBoxHead',
-                num_classes=num_classes),
-            dict(
-                type='Shared2FCBBoxHead',
-                num_classes=num_classes),
-            dict(
-                type='Shared2FCBBoxHead',
-                num_classes=num_classes)],
-    mask_head=dict(num_classes=num_classes)))
-
-checkpoint_config = dict(interval=10)
-runner = dict(type='EpochBasedRunner', max_epochs=150)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
deleted file mode 100644
index 8d5013d30a059f067c71e877dbc0bcef94790154..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
-model = dict(
-    bbox_head=dict(transform_method='minmax', use_grid_points=True),
-    # training and testing settings
-    train_cfg=dict(
-        init=dict(
-            assigner=dict(
-                _delete_=True,
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.4,
-                min_pos_iou=0,
-                ignore_iof_thr=-1))))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/custom.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/custom.py
deleted file mode 100644
index 2942eccb59a3ec536b2cfcc05ac3e1f379ec1618..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/custom.py
+++ /dev/null
@@ -1,361 +0,0 @@
-import os.path as osp
-import warnings
-from collections import OrderedDict
-
-import mmcv
-import numpy as np
-from mmcv.utils import print_log
-from terminaltables import AsciiTable
-from torch.utils.data import Dataset
-
-from mmdet.core import eval_map, eval_recalls
-from .builder import DATASETS
-from .pipelines import Compose
-
-
-@DATASETS.register_module()
-class CustomDataset(Dataset):
-    """Custom dataset for detection.
-
-    The annotation format is shown as follows. The `ann` field is optional for
-    testing.
-
-    .. code-block:: none
-
-        [
-            {
-                'filename': 'a.jpg',
-                'width': 1280,
-                'height': 720,
-                'ann': {
-                    'bboxes': <np.ndarray> (n, 4) in (x1, y1, x2, y2) order.
-                    'labels': <np.ndarray> (n, ),
-                    'bboxes_ignore': <np.ndarray> (k, 4), (optional field)
-                    'labels_ignore': <np.ndarray> (k, 4) (optional field)
-                }
-            },
-            ...
-        ]
-
-    Args:
-        ann_file (str): Annotation file path.
-        pipeline (list[dict]): Processing pipeline.
-        classes (str | Sequence[str], optional): Specify classes to load.
-            If is None, ``cls.CLASSES`` will be used. Default: None.
-        data_root (str, optional): Data root for ``ann_file``,
-            ``img_prefix``, ``seg_prefix``, ``proposal_file`` if specified.
-        test_mode (bool, optional): If set True, annotation will not be loaded.
-        filter_empty_gt (bool, optional): If set true, images without bounding
-            boxes of the dataset's classes will be filtered out. This option
-            only works when `test_mode=False`, i.e., we never filter images
-            during tests.
-    """
-
-    CLASSES = None
-
-    def __init__(self,
-                 ann_file,
-                 pipeline,
-                 classes=None,
-                 data_root=None,
-                 img_prefix='',
-                 seg_prefix=None,
-                 proposal_file=None,
-                 test_mode=False,
-                 filter_empty_gt=True):
-        self.ann_file = ann_file
-        self.data_root = data_root
-        self.img_prefix = img_prefix
-        self.seg_prefix = seg_prefix
-        self.proposal_file = proposal_file
-        self.test_mode = test_mode
-        self.filter_empty_gt = filter_empty_gt
-        self.CLASSES = self.get_classes(classes)
-
-        # join paths if data_root is specified
-        if self.data_root is not None:
-            if not osp.isabs(self.ann_file):
-                self.ann_file = osp.join(self.data_root, self.ann_file)
-            if not (self.img_prefix is None or osp.isabs(self.img_prefix)):
-                self.img_prefix = osp.join(self.data_root, self.img_prefix)
-            if not (self.seg_prefix is None or osp.isabs(self.seg_prefix)):
-                self.seg_prefix = osp.join(self.data_root, self.seg_prefix)
-            if not (self.proposal_file is None
-                    or osp.isabs(self.proposal_file)):
-                self.proposal_file = osp.join(self.data_root,
-                                              self.proposal_file)
-        # load annotations (and proposals)
-        self.data_infos = self.load_annotations(self.ann_file)
-
-        if self.proposal_file is not None:
-            self.proposals = self.load_proposals(self.proposal_file)
-        else:
-            self.proposals = None
-
-        # filter images too small and containing no annotations
-        if not test_mode:
-            valid_inds = self._filter_imgs()
-            self.data_infos = [self.data_infos[i] for i in valid_inds]
-            if self.proposals is not None:
-                self.proposals = [self.proposals[i] for i in valid_inds]
-            # set group flag for the sampler
-            self._set_group_flag()
-
-        # processing pipeline
-        self.pipeline = Compose(pipeline)
-
-    def __len__(self):
-        """Total number of samples of data."""
-        return len(self.data_infos)
-
-    def load_annotations(self, ann_file):
-        """Load annotation from annotation file."""
-        return mmcv.load(ann_file)
-
-    def load_proposals(self, proposal_file):
-        """Load proposal from proposal file."""
-        return mmcv.load(proposal_file)
-
-    def get_ann_info(self, idx):
-        """Get annotation by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Annotation info of specified index.
-        """
-
-        return self.data_infos[idx]['ann']
-
-    def get_cat_ids(self, idx):
-        """Get category ids by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            list[int]: All categories in the image of specified index.
-        """
-
-        return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist()
-
-    def pre_pipeline(self, results):
-        """Prepare results dict for pipeline."""
-        results['img_prefix'] = self.img_prefix
-        results['seg_prefix'] = self.seg_prefix
-        results['proposal_file'] = self.proposal_file
-        results['bbox_fields'] = []
-        results['mask_fields'] = []
-        results['seg_fields'] = []
-
-    def _filter_imgs(self, min_size=32):
-        """Filter images too small."""
-        if self.filter_empty_gt:
-            warnings.warn(
-                'CustomDataset does not support filtering empty gt images.')
-        valid_inds = []
-        for i, img_info in enumerate(self.data_infos):
-            if min(img_info['width'], img_info['height']) >= min_size:
-                valid_inds.append(i)
-        return valid_inds
-
-    def _set_group_flag(self):
-        """Set flag according to image aspect ratio.
-
-        Images with aspect ratio greater than 1 will be set as group 1,
-        otherwise group 0.
-        """
-        self.flag = np.zeros(len(self), dtype=np.uint8)
-        for i in range(len(self)):
-            img_info = self.data_infos[i]
-            if img_info['width'] / img_info['height'] > 1:
-                self.flag[i] = 1
-
-    def _rand_another(self, idx):
-        """Get another random index from the same group as the given index."""
-        pool = np.where(self.flag == self.flag[idx])[0]
-        return np.random.choice(pool)
-
-    def __getitem__(self, idx):
-        """Get training/test data after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Training/test data (with annotation if `test_mode` is set \
-                True).
-        """
-
-        if self.test_mode:
-            return self.prepare_test_img(idx)
-        while True:
-            data = self.prepare_train_img(idx)
-            if data is None:
-                idx = self._rand_another(idx)
-                continue
-            return data
-
-    def prepare_train_img(self, idx):
-        """Get training data and annotations after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Training data and annotation after pipeline with new keys \
-                introduced by pipeline.
-        """
-
-        img_info = self.data_infos[idx]
-        ann_info = self.get_ann_info(idx)
-        results = dict(img_info=img_info, ann_info=ann_info)
-        if self.proposals is not None:
-            results['proposals'] = self.proposals[idx]
-        self.pre_pipeline(results)
-        return self.pipeline(results)
-
-    def prepare_test_img(self, idx):
-        """Get testing data  after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Testing data after pipeline with new keys introduced by \
-                pipeline.
-        """
-
-        img_info = self.data_infos[idx]
-        results = dict(img_info=img_info)
-        if self.proposals is not None:
-            results['proposals'] = self.proposals[idx]
-        self.pre_pipeline(results)
-        return self.pipeline(results)
-
-    @classmethod
-    def get_classes(cls, classes=None):
-        """Get class names of current dataset.
-
-        Args:
-            classes (Sequence[str] | str | None): If classes is None, use
-                default CLASSES defined by builtin dataset. If classes is a
-                string, take it as a file name. The file contains the name of
-                classes where each line contains one class name. If classes is
-                a tuple or list, override the CLASSES defined by the dataset.
-
-        Returns:
-            tuple[str] or list[str]: Names of categories of the dataset.
-        """
-        if classes is None:
-            return cls.CLASSES
-
-        if isinstance(classes, str):
-            # take it as a file path
-            class_names = mmcv.list_from_file(classes)
-        elif isinstance(classes, (tuple, list)):
-            class_names = classes
-        else:
-            raise ValueError(f'Unsupported type {type(classes)} of classes.')
-
-        return class_names
-
-    def format_results(self, results, **kwargs):
-        """Place holder to format result to dataset specific output."""
-
-    def evaluate(self,
-                 results,
-                 metric='mAP',
-                 logger=None,
-                 proposal_nums=(100, 300, 1000),
-                 iou_thr=0.5,
-                 scale_ranges=None):
-        """Evaluate the dataset.
-
-        Args:
-            results (list): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated.
-            logger (logging.Logger | None | str): Logger used for printing
-                related information during evaluation. Default: None.
-            proposal_nums (Sequence[int]): Proposal number used for evaluating
-                recalls, such as recall@100, recall@1000.
-                Default: (100, 300, 1000).
-            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
-            scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
-                Default: None.
-        """
-
-        if not isinstance(metric, str):
-            assert len(metric) == 1
-            metric = metric[0]
-        allowed_metrics = ['mAP', 'recall']
-        if metric not in allowed_metrics:
-            raise KeyError(f'metric {metric} is not supported')
-        annotations = [self.get_ann_info(i) for i in range(len(self))]
-        eval_results = OrderedDict()
-        iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
-        if metric == 'mAP':
-            assert isinstance(iou_thrs, list)
-            mean_aps = []
-            for iou_thr in iou_thrs:
-                print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
-                mean_ap, _ = eval_map(
-                    results,
-                    annotations,
-                    scale_ranges=scale_ranges,
-                    iou_thr=iou_thr,
-                    dataset=self.CLASSES,
-                    logger=logger)
-                mean_aps.append(mean_ap)
-                eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
-            eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
-        elif metric == 'recall':
-            gt_bboxes = [ann['bboxes'] for ann in annotations]
-            recalls = eval_recalls(
-                gt_bboxes, results, proposal_nums, iou_thr, logger=logger)
-            for i, num in enumerate(proposal_nums):
-                for j, iou in enumerate(iou_thrs):
-                    eval_results[f'recall@{num}@{iou}'] = recalls[i, j]
-            if recalls.shape[1] > 1:
-                ar = recalls.mean(axis=1)
-                for i, num in enumerate(proposal_nums):
-                    eval_results[f'AR@{num}'] = ar[i]
-        return eval_results
-
-    def __repr__(self):
-        """Print the number of instance number."""
-        dataset_type = 'Test' if self.test_mode else 'Train'
-        result = (f'\n{self.__class__.__name__} {dataset_type} dataset '
-                  f'with number of images {len(self)}, '
-                  f'and instance counts: \n')
-        if self.CLASSES is None:
-            result += 'Category names are not provided. \n'
-            return result
-        instance_count = np.zeros(len(self.CLASSES) + 1).astype(int)
-        # count the instance number in each image
-        for idx in range(len(self)):
-            label = self.get_ann_info(idx)['labels']
-            unique, counts = np.unique(label, return_counts=True)
-            if len(unique) > 0:
-                # add the occurrence number to each class
-                instance_count[unique] += counts
-            else:
-                # background is the last index
-                instance_count[-1] += 1
-        # create a table with category count
-        table_data = [['category', 'count'] * 5]
-        row_data = []
-        for cls, count in enumerate(instance_count):
-            if cls < len(self.CLASSES):
-                row_data += [f'{cls} [{self.CLASSES[cls]}]', f'{count}']
-            else:
-                # add the background number
-                row_data += ['-1 background', f'{count}']
-            if len(row_data) == 10:
-                table_data.append(row_data)
-                row_data = []
-
-        table = AsciiTable(table_data)
-        result += table.table
-        return result
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/builder.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/builder.py
deleted file mode 100644
index 85dc2562da1e70d68a80619b3f16abd2fb666407..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/builder.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import warnings
-
-from mmcv.cnn import MODELS as MMCV_MODELS
-from mmcv.utils import Registry
-
-MODELS = Registry('models', parent=MMCV_MODELS)
-
-BACKBONES = MODELS
-NECKS = MODELS
-ROI_EXTRACTORS = MODELS
-SHARED_HEADS = MODELS
-HEADS = MODELS
-LOSSES = MODELS
-DETECTORS = MODELS
-
-
-def build_backbone(cfg):
-    """Build backbone."""
-    return BACKBONES.build(cfg)
-
-
-def build_neck(cfg):
-    """Build neck."""
-    return NECKS.build(cfg)
-
-
-def build_roi_extractor(cfg):
-    """Build roi extractor."""
-    return ROI_EXTRACTORS.build(cfg)
-
-
-def build_shared_head(cfg):
-    """Build shared head."""
-    return SHARED_HEADS.build(cfg)
-
-
-def build_head(cfg):
-    """Build head."""
-    return HEADS.build(cfg)
-
-
-def build_loss(cfg):
-    """Build loss."""
-    return LOSSES.build(cfg)
-
-
-def build_detector(cfg, train_cfg=None, test_cfg=None):
-    """Build detector."""
-    if train_cfg is not None or test_cfg is not None:
-        warnings.warn(
-            'train_cfg and test_cfg is deprecated, '
-            'please specify them in model', UserWarning)
-    assert cfg.get('train_cfg') is None or train_cfg is None, \
-        'train_cfg specified in both outer field and model field '
-    assert cfg.get('test_cfg') is None or test_cfg is None, \
-        'test_cfg specified in both outer field and model field '
-    return DETECTORS.build(
-        cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/dense_heads/paa_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/dense_heads/paa_head.py
deleted file mode 100644
index e067b0121cf8b8230c0c9c6b8cfd41f56be4e298..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/dense_heads/paa_head.py
+++ /dev/null
@@ -1,671 +0,0 @@
-import numpy as np
-import torch
-from mmcv.runner import force_fp32
-
-from mmdet.core import multi_apply, multiclass_nms
-from mmdet.core.bbox.iou_calculators import bbox_overlaps
-from mmdet.models import HEADS
-from mmdet.models.dense_heads import ATSSHead
-
-EPS = 1e-12
-try:
-    import sklearn.mixture as skm
-except ImportError:
-    skm = None
-
-
-def levels_to_images(mlvl_tensor):
-    """Concat multi-level feature maps by image.
-
-    [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
-    Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
-    (N, H*W , C), then split the element to N elements with shape (H*W, C), and
-    concat elements in same image of all level along first dimension.
-
-    Args:
-        mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
-            corresponding level. Each element is of shape (N, C, H, W)
-
-    Returns:
-        list[torch.Tensor]: A list that contains N tensors and each tensor is
-            of shape (num_elements, C)
-    """
-    batch_size = mlvl_tensor[0].size(0)
-    batch_list = [[] for _ in range(batch_size)]
-    channels = mlvl_tensor[0].size(1)
-    for t in mlvl_tensor:
-        t = t.permute(0, 2, 3, 1)
-        t = t.view(batch_size, -1, channels).contiguous()
-        for img in range(batch_size):
-            batch_list[img].append(t[img])
-    return [torch.cat(item, 0) for item in batch_list]
-
-
-@HEADS.register_module()
-class PAAHead(ATSSHead):
-    """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
-    Prediction for Object Detection.
-
-    Code is modified from the `official github repo
-    <https://github.com/kkhoot/PAA/blob/master/paa_core
-    /modeling/rpn/paa/loss.py>`_.
-
-    More details can be found in the `paper
-    <https://arxiv.org/abs/2007.08103>`_ .
-
-    Args:
-        topk (int): Select topk samples with smallest loss in
-            each level.
-        score_voting (bool): Whether to use score voting in post-process.
-        covariance_type : String describing the type of covariance parameters
-            to be used in :class:`sklearn.mixture.GaussianMixture`.
-            It must be one of:
-
-            - 'full': each component has its own general covariance matrix
-            - 'tied': all components share the same general covariance matrix
-            - 'diag': each component has its own diagonal covariance matrix
-            - 'spherical': each component has its own single variance
-            Default: 'diag'. From 'full' to 'spherical', the gmm fitting
-            process is faster yet the performance could be influenced. For most
-            cases, 'diag' should be a good choice.
-    """
-
-    def __init__(self,
-                 *args,
-                 topk=9,
-                 score_voting=True,
-                 covariance_type='diag',
-                 **kwargs):
-        # topk used in paa reassign process
-        self.topk = topk
-        self.with_score_voting = score_voting
-        self.covariance_type = covariance_type
-        super(PAAHead, self).__init__(*args, **kwargs)
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             iou_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            iou_preds (list[Tensor]): iou_preds for each scale
-                level with shape (N, num_anchors * 1, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
-                boxes can be ignored when are computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss gmm_assignment.
-        """
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-        )
-        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
-         pos_gt_index) = cls_reg_targets
-        cls_scores = levels_to_images(cls_scores)
-        cls_scores = [
-            item.reshape(-1, self.cls_out_channels) for item in cls_scores
-        ]
-        bbox_preds = levels_to_images(bbox_preds)
-        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
-        iou_preds = levels_to_images(iou_preds)
-        iou_preds = [item.reshape(-1, 1) for item in iou_preds]
-        pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
-                                       cls_scores, bbox_preds, labels,
-                                       labels_weight, bboxes_target,
-                                       bboxes_weight, pos_inds)
-
-        with torch.no_grad():
-            reassign_labels, reassign_label_weight, \
-                reassign_bbox_weights, num_pos = multi_apply(
-                    self.paa_reassign,
-                    pos_losses_list,
-                    labels,
-                    labels_weight,
-                    bboxes_weight,
-                    pos_inds,
-                    pos_gt_index,
-                    anchor_list)
-            num_pos = sum(num_pos)
-        # convert all tensor list to a flatten tensor
-        cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
-        bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
-        iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))
-        labels = torch.cat(reassign_labels, 0).view(-1)
-        flatten_anchors = torch.cat(
-            [torch.cat(item, 0) for item in anchor_list])
-        labels_weight = torch.cat(reassign_label_weight, 0).view(-1)
-        bboxes_target = torch.cat(bboxes_target,
-                                  0).view(-1, bboxes_target[0].size(-1))
-
-        pos_inds_flatten = ((labels >= 0)
-                            &
-                            (labels < self.num_classes)).nonzero().reshape(-1)
-
-        losses_cls = self.loss_cls(
-            cls_scores,
-            labels,
-            labels_weight,
-            avg_factor=max(num_pos, len(img_metas)))  # avoid num_pos=0
-        if num_pos:
-            pos_bbox_pred = self.bbox_coder.decode(
-                flatten_anchors[pos_inds_flatten],
-                bbox_preds[pos_inds_flatten])
-            pos_bbox_target = bboxes_target[pos_inds_flatten]
-            iou_target = bbox_overlaps(
-                pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
-            losses_iou = self.loss_centerness(
-                iou_preds[pos_inds_flatten],
-                iou_target.unsqueeze(-1),
-                avg_factor=num_pos)
-            losses_bbox = self.loss_bbox(
-                pos_bbox_pred,
-                pos_bbox_target,
-                iou_target.clamp(min=EPS),
-                avg_factor=iou_target.sum())
-        else:
-            losses_iou = iou_preds.sum() * 0
-            losses_bbox = bbox_preds.sum() * 0
-
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
-
-    def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight,
-                     bbox_target, bbox_weight, pos_inds):
-        """Calculate loss of all potential positive samples obtained from first
-        match process.
-
-        Args:
-            anchors (list[Tensor]): Anchors of each scale.
-            cls_score (Tensor): Box scores of single image with shape
-                (num_anchors, num_classes)
-            bbox_pred (Tensor): Box energies / deltas of single image
-                with shape (num_anchors, 4)
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_target (dict): Regression target of each anchor with
-                shape (num_anchors, 4).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-
-        Returns:
-            Tensor: Losses of all positive samples in single image.
-        """
-        if not len(pos_inds):
-            return cls_score.new([]),
-        anchors_all_level = torch.cat(anchors, 0)
-        pos_scores = cls_score[pos_inds]
-        pos_bbox_pred = bbox_pred[pos_inds]
-        pos_label = label[pos_inds]
-        pos_label_weight = label_weight[pos_inds]
-        pos_bbox_target = bbox_target[pos_inds]
-        pos_bbox_weight = bbox_weight[pos_inds]
-        pos_anchors = anchors_all_level[pos_inds]
-        pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
-
-        # to keep loss dimension
-        loss_cls = self.loss_cls(
-            pos_scores,
-            pos_label,
-            pos_label_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-
-        loss_bbox = self.loss_bbox(
-            pos_bbox_pred,
-            pos_bbox_target,
-            pos_bbox_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-
-        loss_cls = loss_cls.sum(-1)
-        pos_loss = loss_bbox + loss_cls
-        return pos_loss,
-
-    def paa_reassign(self, pos_losses, label, label_weight, bbox_weight,
-                     pos_inds, pos_gt_inds, anchors):
-        """Fit loss to GMM distribution and separate positive, ignore, negative
-        samples again with GMM model.
-
-        Args:
-            pos_losses (Tensor): Losses of all positive samples in
-                single image.
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-            pos_gt_inds (Tensor): Gt_index of all positive samples got
-                from first assign process.
-            anchors (list[Tensor]): Anchors of each scale.
-
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - label (Tensor): classification target of each anchor after
-                  paa assign, with shape (num_anchors,)
-                - label_weight (Tensor): Classification loss weight of each
-                  anchor after paa assign, with shape (num_anchors).
-                - bbox_weight (Tensor): Bbox weight of each anchor with shape
-                  (num_anchors, 4).
-                - num_pos (int): The number of positive samples after paa
-                  assign.
-        """
-        if not len(pos_inds):
-            return label, label_weight, bbox_weight, 0
-        label = label.clone()
-        label_weight = label_weight.clone()
-        bbox_weight = bbox_weight.clone()
-        num_gt = pos_gt_inds.max() + 1
-        num_level = len(anchors)
-        num_anchors_each_level = [item.size(0) for item in anchors]
-        num_anchors_each_level.insert(0, 0)
-        inds_level_interval = np.cumsum(num_anchors_each_level)
-        pos_level_mask = []
-        for i in range(num_level):
-            mask = (pos_inds >= inds_level_interval[i]) & (
-                pos_inds < inds_level_interval[i + 1])
-            pos_level_mask.append(mask)
-        pos_inds_after_paa = [label.new_tensor([])]
-        ignore_inds_after_paa = [label.new_tensor([])]
-        for gt_ind in range(num_gt):
-            pos_inds_gmm = []
-            pos_loss_gmm = []
-            gt_mask = pos_gt_inds == gt_ind
-            for level in range(num_level):
-                level_mask = pos_level_mask[level]
-                level_gt_mask = level_mask & gt_mask
-                value, topk_inds = pos_losses[level_gt_mask].topk(
-                    min(level_gt_mask.sum(), self.topk), largest=False)
-                pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds])
-                pos_loss_gmm.append(value)
-            pos_inds_gmm = torch.cat(pos_inds_gmm)
-            pos_loss_gmm = torch.cat(pos_loss_gmm)
-            # fix gmm need at least two sample
-            if len(pos_inds_gmm) < 2:
-                continue
-            device = pos_inds_gmm.device
-            pos_loss_gmm, sort_inds = pos_loss_gmm.sort()
-            pos_inds_gmm = pos_inds_gmm[sort_inds]
-            pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy()
-            min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max()
-            means_init = np.array([min_loss, max_loss]).reshape(2, 1)
-            weights_init = np.array([0.5, 0.5])
-            precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1)  # full
-            if self.covariance_type == 'spherical':
-                precisions_init = precisions_init.reshape(2)
-            elif self.covariance_type == 'diag':
-                precisions_init = precisions_init.reshape(2, 1)
-            elif self.covariance_type == 'tied':
-                precisions_init = np.array([[1.0]])
-            if skm is None:
-                raise ImportError('Please run "pip install sklearn" '
-                                  'to install sklearn first.')
-            gmm = skm.GaussianMixture(
-                2,
-                weights_init=weights_init,
-                means_init=means_init,
-                precisions_init=precisions_init,
-                covariance_type=self.covariance_type)
-            gmm.fit(pos_loss_gmm)
-            gmm_assignment = gmm.predict(pos_loss_gmm)
-            scores = gmm.score_samples(pos_loss_gmm)
-            gmm_assignment = torch.from_numpy(gmm_assignment).to(device)
-            scores = torch.from_numpy(scores).to(device)
-
-            pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme(
-                gmm_assignment, scores, pos_inds_gmm)
-            pos_inds_after_paa.append(pos_inds_temp)
-            ignore_inds_after_paa.append(ignore_inds_temp)
-
-        pos_inds_after_paa = torch.cat(pos_inds_after_paa)
-        ignore_inds_after_paa = torch.cat(ignore_inds_after_paa)
-        reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1)
-        reassign_ids = pos_inds[reassign_mask]
-        label[reassign_ids] = self.num_classes
-        label_weight[ignore_inds_after_paa] = 0
-        bbox_weight[reassign_ids] = 0
-        num_pos = len(pos_inds_after_paa)
-        return label, label_weight, bbox_weight, num_pos
-
-    def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm):
-        """A general separation scheme for gmm model.
-
-        It separates a GMM distribution of candidate samples into three
-        parts, 0 1 and uncertain areas, and you can implement other
-        separation schemes by rewriting this function.
-
-        Args:
-            gmm_assignment (Tensor): The prediction of GMM which is of shape
-                (num_samples,). The 0/1 value indicates the distribution
-                that each sample comes from.
-            scores (Tensor): The probability of sample coming from the
-                fit GMM distribution. The tensor is of shape (num_samples,).
-            pos_inds_gmm (Tensor): All the indexes of samples which are used
-                to fit GMM model. The tensor is of shape (num_samples,)
-
-        Returns:
-            tuple[Tensor]: The indices of positive and ignored samples.
-
-                - pos_inds_temp (Tensor): Indices of positive samples.
-                - ignore_inds_temp (Tensor): Indices of ignore samples.
-        """
-        # The implementation is (c) in Fig.3 in origin paper instead of (b).
-        # You can refer to issues such as
-        # https://github.com/kkhoot/PAA/issues/8 and
-        # https://github.com/kkhoot/PAA/issues/9.
-        fgs = gmm_assignment == 0
-        pos_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        ignore_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        if fgs.nonzero().numel():
-            _, pos_thr_ind = scores[fgs].topk(1)
-            pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1]
-            ignore_inds_temp = pos_inds_gmm.new_tensor([])
-        return pos_inds_temp, ignore_inds_temp
-
-    def get_targets(
-        self,
-        anchor_list,
-        valid_flag_list,
-        gt_bboxes_list,
-        img_metas,
-        gt_bboxes_ignore_list=None,
-        gt_labels_list=None,
-        label_channels=1,
-        unmap_outputs=True,
-    ):
-        """Get targets for PAA head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. We direct
-        return the results from _get_targets_single instead map it to levels
-        by images_to_levels function.
-
-        Args:
-            anchor_list (list[list[Tensor]]): Multi level anchors of each
-                image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, 4).
-            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
-                each image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, )
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
-                ignored.
-            gt_labels_list (list[Tensor]): Ground truth labels of each box.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - labels (list[Tensor]): Labels of all anchors, each with
-                    shape (num_anchors,).
-                - label_weights (list[Tensor]): Label weights of all anchor.
-                    each with shape (num_anchors,).
-                - bbox_targets (list[Tensor]): BBox targets of all anchors.
-                    each with shape (num_anchors, 4).
-                - bbox_weights (list[Tensor]): BBox weights of all anchors.
-                    each with shape (num_anchors, 4).
-                - pos_inds (list[Tensor]): Contains all index of positive
-                    sample in all anchor.
-                - gt_inds (list[Tensor]): Contains all gt_index of positive
-                    sample in all anchor.
-        """
-
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-        concat_anchor_list = []
-        concat_valid_flag_list = []
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            concat_anchor_list.append(torch.cat(anchor_list[i]))
-            concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        results = multi_apply(
-            self._get_targets_single,
-            concat_anchor_list,
-            concat_valid_flag_list,
-            gt_bboxes_list,
-            gt_bboxes_ignore_list,
-            gt_labels_list,
-            img_metas,
-            label_channels=label_channels,
-            unmap_outputs=unmap_outputs)
-
-        (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds,
-         valid_neg_inds, sampling_result) = results
-
-        # Due to valid flag of anchors, we have to calculate the real pos_inds
-        # in origin anchor set.
-        pos_inds = []
-        for i, single_labels in enumerate(labels):
-            pos_mask = (0 <= single_labels) & (
-                single_labels < self.num_classes)
-            pos_inds.append(pos_mask.nonzero().view(-1))
-
-        gt_inds = [item.pos_assigned_gt_inds for item in sampling_result]
-        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
-                gt_inds)
-
-    def _get_targets_single(self,
-                            flat_anchors,
-                            valid_flags,
-                            gt_bboxes,
-                            gt_bboxes_ignore,
-                            gt_labels,
-                            img_meta,
-                            label_channels=1,
-                            unmap_outputs=True):
-        """Compute regression and classification targets for anchors in a
-        single image.
-
-        This method is same as `AnchorHead._get_targets_single()`.
-        """
-        assert unmap_outputs, 'We must map outputs back to the original' \
-            'set of anchors in PAAhead'
-        return super(ATSSHead, self)._get_targets_single(
-            flat_anchors,
-            valid_flags,
-            gt_bboxes,
-            gt_bboxes_ignore,
-            gt_labels,
-            img_meta,
-            label_channels=1,
-            unmap_outputs=True)
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    iou_preds,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        This method is almost same as `ATSSHead._get_bboxes()`.
-        We use sqrt(iou_preds * cls_scores) in NMS process instead of just
-        cls_scores. Besides, score voting is used when `` score_voting``
-        is set to True.
-        """
-        assert with_nms, 'PAA only supports "with_nms=True" now'
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        batch_size = cls_scores[0].shape[0]
-
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_iou_preds = []
-        for cls_score, bbox_pred, iou_preds, anchors in zip(
-                cls_scores, bbox_preds, iou_preds, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3,
-                                          1).reshape(batch_size, -1, 4)
-            iou_preds = iou_preds.permute(0, 2, 3, 1).reshape(batch_size,
-                                                              -1).sigmoid()
-
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[1] > nms_pre:
-                max_scores, _ = (scores * iou_preds[..., None]).sqrt().max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-                iou_preds = iou_preds[batch_inds, topk_inds]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_iou_preds.append(iou_preds)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        # Add a dummy background class to the backend when using sigmoid
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-        batch_mlvl_iou_preds = torch.cat(mlvl_iou_preds, dim=1)
-        batch_mlvl_nms_scores = (batch_mlvl_scores *
-                                 batch_mlvl_iou_preds[..., None]).sqrt()
-
-        det_results = []
-        for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
-                                              batch_mlvl_nms_scores):
-            det_bbox, det_label = multiclass_nms(
-                mlvl_bboxes,
-                mlvl_scores,
-                cfg.score_thr,
-                cfg.nms,
-                cfg.max_per_img,
-                score_factors=None)
-            if self.with_score_voting and len(det_bbox) > 0:
-                det_bbox, det_label = self.score_voting(
-                    det_bbox, det_label, mlvl_bboxes, mlvl_scores,
-                    cfg.score_thr)
-            det_results.append(tuple([det_bbox, det_label]))
-
-        return det_results
-
-    def score_voting(self, det_bboxes, det_labels, mlvl_bboxes,
-                     mlvl_nms_scores, score_thr):
-        """Implementation of score voting method works on each remaining boxes
-        after NMS procedure.
-
-        Args:
-            det_bboxes (Tensor): Remaining boxes after NMS procedure,
-                with shape (k, 5), each dimension means
-                (x1, y1, x2, y2, score).
-            det_labels (Tensor): The label of remaining boxes, with shape
-                (k, 1),Labels are 0-based.
-            mlvl_bboxes (Tensor): All boxes before the NMS procedure,
-                with shape (num_anchors,4).
-            mlvl_nms_scores (Tensor): The scores of all boxes which is used
-                in the NMS procedure, with shape (num_anchors, num_class)
-            mlvl_iou_preds (Tensor): The predictions of IOU of all boxes
-                before the NMS procedure, with shape (num_anchors, 1)
-            score_thr (float): The score threshold of bboxes.
-
-        Returns:
-            tuple: Usually returns a tuple containing voting results.
-
-                - det_bboxes_voted (Tensor): Remaining boxes after
-                    score voting procedure, with shape (k, 5), each
-                    dimension means (x1, y1, x2, y2, score).
-                - det_labels_voted (Tensor): Label of remaining bboxes
-                    after voting, with shape (num_anchors,).
-        """
-        candidate_mask = mlvl_nms_scores > score_thr
-        candidate_mask_nonzeros = candidate_mask.nonzero()
-        candidate_inds = candidate_mask_nonzeros[:, 0]
-        candidate_labels = candidate_mask_nonzeros[:, 1]
-        candidate_bboxes = mlvl_bboxes[candidate_inds]
-        candidate_scores = mlvl_nms_scores[candidate_mask]
-        det_bboxes_voted = []
-        det_labels_voted = []
-        for cls in range(self.cls_out_channels):
-            candidate_cls_mask = candidate_labels == cls
-            if not candidate_cls_mask.any():
-                continue
-            candidate_cls_scores = candidate_scores[candidate_cls_mask]
-            candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask]
-            det_cls_mask = det_labels == cls
-            det_cls_bboxes = det_bboxes[det_cls_mask].view(
-                -1, det_bboxes.size(-1))
-            det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4],
-                                               candidate_cls_bboxes)
-            for det_ind in range(len(det_cls_bboxes)):
-                single_det_ious = det_candidate_ious[det_ind]
-                pos_ious_mask = single_det_ious > 0.01
-                pos_ious = single_det_ious[pos_ious_mask]
-                pos_bboxes = candidate_cls_bboxes[pos_ious_mask]
-                pos_scores = candidate_cls_scores[pos_ious_mask]
-                pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) *
-                       pos_scores)[:, None]
-                voted_box = torch.sum(
-                    pis * pos_bboxes, dim=0) / torch.sum(
-                        pis, dim=0)
-                voted_score = det_cls_bboxes[det_ind][-1:][None, :]
-                det_bboxes_voted.append(
-                    torch.cat((voted_box[None, :], voted_score), dim=1))
-                det_labels_voted.append(cls)
-
-        det_bboxes_voted = torch.cat(det_bboxes_voted, dim=0)
-        det_labels_voted = det_labels.new_tensor(det_labels_voted)
-        return det_bboxes_voted, det_labels_voted
diff --git a/spaces/trttung1610/musicgen/audiocraft/adversarial/discriminators/msd.py b/spaces/trttung1610/musicgen/audiocraft/adversarial/discriminators/msd.py
deleted file mode 100644
index c4e67e29b46ab22f6ffeec85ffc64d8b99800b1b..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/adversarial/discriminators/msd.py
+++ /dev/null
@@ -1,126 +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 typing as tp
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from ...modules import NormConv1d
-from .base import MultiDiscriminator, MultiDiscriminatorOutputType
-
-
-class ScaleDiscriminator(nn.Module):
-    """Waveform sub-discriminator.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        kernel_sizes (Sequence[int]): Kernel sizes for first and last convolutions.
-        filters (int): Number of initial filters for convolutions.
-        max_filters (int): Maximum number of filters.
-        downsample_scales (Sequence[int]): Scale for downsampling implemented as strided convolutions.
-        inner_kernel_sizes (Sequence[int] or None): Kernel sizes for inner convolutions.
-        groups (Sequence[int] or None): Groups for inner convolutions.
-        strides (Sequence[int] or None): Strides for inner convolutions.
-        paddings (Sequence[int] or None): Paddings for inner convolutions.
-        norm (str): Normalization method.
-        activation (str): Activation function.
-        activation_params (dict): Parameters to provide to the activation function.
-        pad (str): Padding for initial convolution.
-        pad_params (dict): Parameters to provide to the padding module.
-    """
-    def __init__(self, in_channels=1, out_channels=1, kernel_sizes: tp.Sequence[int] = [5, 3],
-                 filters: int = 16, max_filters: int = 1024, downsample_scales: tp.Sequence[int] = [4, 4, 4, 4],
-                 inner_kernel_sizes: tp.Optional[tp.Sequence[int]] = None, groups: tp.Optional[tp.Sequence[int]] = None,
-                 strides: tp.Optional[tp.Sequence[int]] = None, paddings: tp.Optional[tp.Sequence[int]] = None,
-                 norm: str = 'weight_norm', activation: str = 'LeakyReLU',
-                 activation_params: dict = {'negative_slope': 0.2}, pad: str = 'ReflectionPad1d',
-                 pad_params: dict = {}):
-        super().__init__()
-        assert len(kernel_sizes) == 2
-        assert kernel_sizes[0] % 2 == 1
-        assert kernel_sizes[1] % 2 == 1
-        assert (inner_kernel_sizes is None or len(inner_kernel_sizes) == len(downsample_scales))
-        assert (groups is None or len(groups) == len(downsample_scales))
-        assert (strides is None or len(strides) == len(downsample_scales))
-        assert (paddings is None or len(paddings) == len(downsample_scales))
-        self.activation = getattr(torch.nn, activation)(**activation_params)
-        self.convs = nn.ModuleList()
-        self.convs.append(
-            nn.Sequential(
-                getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
-                NormConv1d(in_channels, filters, kernel_size=np.prod(kernel_sizes), stride=1, norm=norm)
-            )
-        )
-
-        in_chs = filters
-        for i, downsample_scale in enumerate(downsample_scales):
-            out_chs = min(in_chs * downsample_scale, max_filters)
-            default_kernel_size = downsample_scale * 10 + 1
-            default_stride = downsample_scale
-            default_padding = (default_kernel_size - 1) // 2
-            default_groups = in_chs // 4
-            self.convs.append(
-                NormConv1d(in_chs, out_chs,
-                           kernel_size=inner_kernel_sizes[i] if inner_kernel_sizes else default_kernel_size,
-                           stride=strides[i] if strides else default_stride,
-                           groups=groups[i] if groups else default_groups,
-                           padding=paddings[i] if paddings else default_padding,
-                           norm=norm))
-            in_chs = out_chs
-
-        out_chs = min(in_chs * 2, max_filters)
-        self.convs.append(NormConv1d(in_chs, out_chs, kernel_size=kernel_sizes[0], stride=1,
-                                     padding=(kernel_sizes[0] - 1) // 2, norm=norm))
-        self.conv_post = NormConv1d(out_chs, out_channels, kernel_size=kernel_sizes[1], stride=1,
-                                    padding=(kernel_sizes[1] - 1) // 2, norm=norm)
-
-    def forward(self, x: torch.Tensor):
-        fmap = []
-        for layer in self.convs:
-            x = layer(x)
-            x = self.activation(x)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        # x = torch.flatten(x, 1, -1)
-        return x, fmap
-
-
-class MultiScaleDiscriminator(MultiDiscriminator):
-    """Multi-Scale (MSD) Discriminator,
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        downsample_factor (int): Downsampling factor between the different scales.
-        scale_norms (Sequence[str]): Normalization for each sub-discriminator.
-        **kwargs: Additional args for ScaleDiscriminator.
-    """
-    def __init__(self, in_channels: int = 1, out_channels: int = 1, downsample_factor: int = 2,
-                 scale_norms: tp.Sequence[str] = ['weight_norm', 'weight_norm', 'weight_norm'], **kwargs):
-        super().__init__()
-        self.discriminators = nn.ModuleList([
-            ScaleDiscriminator(in_channels, out_channels, norm=norm, **kwargs) for norm in scale_norms
-        ])
-        self.downsample = nn.AvgPool1d(downsample_factor * 2, downsample_factor, padding=downsample_factor)
-
-    @property
-    def num_discriminators(self):
-        return len(self.discriminators)
-
-    def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
-        logits = []
-        fmaps = []
-        for i, disc in enumerate(self.discriminators):
-            if i != 0:
-                self.downsample(x)
-            logit, fmap = disc(x)
-            logits.append(logit)
-            fmaps.append(fmap)
-        return logits, fmaps
diff --git a/spaces/varun500/flan-alpaca-base/README.md b/spaces/varun500/flan-alpaca-base/README.md
deleted file mode 100644
index 392bf6b43fa4cf1a2298c1bf58c860ed0ad49d16..0000000000000000000000000000000000000000
--- a/spaces/varun500/flan-alpaca-base/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Flan Alpaca Base
-emoji: 📈
-colorFrom: gray
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/victor/autotrain-advanced-dreambooth/README.md b/spaces/victor/autotrain-advanced-dreambooth/README.md
deleted file mode 100644
index f2ab61407bd237e6ad48c2525dbde9489f7c8c55..0000000000000000000000000000000000000000
--- a/spaces/victor/autotrain-advanced-dreambooth/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Dreambooth
-emoji: 📚
-colorFrom: pink
-colorTo: gray
-sdk: docker
-pinned: false
-duplicated_from: autotrain-projects/dreambooth
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vijv/VV-06-SL-AI-Image-Music-Video-UI-UX-URL/Article.md b/spaces/vijv/VV-06-SL-AI-Image-Music-Video-UI-UX-URL/Article.md
deleted file mode 100644
index c7f042e4c9c0f401731f009842a325e2d1386bf5..0000000000000000000000000000000000000000
--- a/spaces/vijv/VV-06-SL-AI-Image-Music-Video-UI-UX-URL/Article.md
+++ /dev/null
@@ -1,51 +0,0 @@
-
-# Image Generation for Art, Marketing, Ideation, Design, and Use in Business
-
-A number of multiple AI pipeline element strategies have evolved on the open market which allow you to generate images using a combination of image prompts and word prompts.  This brief analysis gives an idea of the prompting capabilities as well as image rendering techniques that are used in the strategy to generate art from human understanding of images and text used to describe a scene.
-
-First a top five list on state of the art generators both free and paid is worth consideration.
-
-1) Midjourney - a Discord server based chatboat AI that allows /imagine prompts which can generate multiple images at a time.  This is best at parallel creation, high accuracy even photo real creations.
-2) Artbreeder - A multiple capability tool which now features a Collager which assists in starting image composition.  By far the most innovative approach which does great to combine the right partial elements in a scene.
-3) Dreamstudio - A Huggingface derived art program in beta which uses stable diffusion to create highly accurate art and images.
-4) Nightcafe - A credit based creation AI app that can do generation of video dives into an AI art piece which can produce some of the best experiences in Video.
-5) RunwayML - a quintessential tool in processing morph audio and video tracks which rival most high end video edit tools.
-
-These 5 tools make up some of the best AI pipeline programs that are cloud based that allow anyone to begin easily building their portfolio of art.
-
-The prompting capabilities often involve having a set of text based prompts to get started.  Most also feature a starter image which could be an example of what you would like to create.
-
-URL Links:
-1) Collager:  https://www.artbreeder.com/beta/collage
-2) NightCafe: https://creator.nightcafe.studio/explore
-3) Midjourney: https://www.midjourney.com/app/users/779773261440614430/
-4) Dreamstudio: https://beta.dreamstudio.ai/dream
-5) RunwayML: https://app.runwayml.com/
-
-## Getting Started and Organizing Your AI Pipeline and Process
-
-Any great strategy has a number of steps that combine all capabilities at your disposal.  It is useful to note how you can easily fir these together into a process that works for you.
-
-The techniques worth noted are listed below.  Consider how you will use them will make your pipeline easier and more automated to allow you to spend the majority of your time curating what you have made, and ideating what you want to create next.
-
-1) Source materials: Since prompting requires text and text examples can quickly help you compose good input, its worth considering and documenting some effective prompts.  Nightcafe with its integration into email, sends you a copy of your creation plus the prompting text so one option is to use your email account to keep a record of which prompts work for which outputs.
-2) Source materials:  Discord since its a public chat format allows you to easily see what others are using for prompts in bulk.  There are a number of chat channels designed for people new to the platform and often you can copy and paste if you see very effective prompts with material you are looking for.
-3) Source materials: Collager is unique in its ability to add additive parts and then dial in the percent of AI you would like with that.  This allows you to add a few image elements which help start out your generation.  
-4) Source materials: Since images and prompts are going to be your mainstay for inputs its worth considering an open standard for storing and retrieving these from anywhere.  Github is a good place since markdown language can involve text in table or list format and includes a capability to reference uploaded images within markdown.  This is also a good form for portability since you can later fork and download your repository with a few clicks from anywhere.
-5) Source materials: Google drive is integrated into the Artbreeder Collager workflow which allows you easily expand your work and even compose albums of the ones you like to place in Google photo albums.  The portfolio you save on different sites have different degrees of ease when aggregating your collections.  Collager for instance allows right click save for instant saving of your creation.  Dreamstudio features a history.  Midjourney features a profile site for you to store and review creations even triggering Upscales which important to use to get the highest resolution output for your creations.
-
-##  Social Media integration
-
-Depending on your target "safe for work" exports of your work, it is sometimes important to know your accepted social media outlets that you can integrate.  Cloud based interactions are the key to successful audiences if you want to scale and share your process with others.
-
-The key social media outlets supported for these tools are here in a sorted link list which start with public open source first:
-
-1) Github - Github is open at most companies and allow creation of a free space to share your content.
-2) LinkedIn - LinkedIn is acceptable use at nearly every company.
-3) Twitter - Twitter is supported as a social media outlet at most companies yet can also be used with security restrictions which might limit posting but allow read access.
-4) Facebook - Meta's Facebook is a good outlet since it allows creation of large folios of your images along with stories.  This venue however is locked down at many organizations.
-5) Instagram - Instagram is supported as an output channel for many tools yet has decreased in popularity due to high frequency of ads and pay for likes models.  While it can still be one of the best places for domain specific arrangements of images it is likely locked down in most secure organizations.
-6) Youtube - For video uploads with automated captioning and long term storage of short and long form video this is an essential for any creation you compose as video.  It is also useful to review and compose playlists of videos here for yourself that speed up your learning - Spend some time at Youtube university and keep a record of keyword searches there sometimes along with your playlists to accelerate learning.
-7) Gmail - With the baility to move email in and out its useful to create and wrap up details within email.  Most email policies come with a content limitation (for example no files larger than 25MB.  For this reason get used to creating pproject wrap up archives with winzip or compression software.  With the convenience of keyword searching you can usually use this as a base.
-8) Last a worth mention is Huggingface.com.  Like github as you become more sophisticated in your public open source capabilities, HuggingFace can allow you to wrap up using one of three software development kits which are gadio, streamlit, and HTML5 each with unique AI and UI integration components and features.  If you want to create your own AI pipelines this one also has the open source code and models ready to go to help you on your journey.
-
diff --git a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/visualizer.py b/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/visualizer.py
deleted file mode 100644
index 7a1b7b101e9b73f75f9136bc67f2063c7c1cf1c1..0000000000000000000000000000000000000000
--- a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/visualizer.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@File    :   visualizer.py
-@Time    :   2022/04/05 11:39:33
-@Author  :   Shilong Liu 
-@Contact :   slongliu86@gmail.com
-"""
-
-import datetime
-import os
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from matplotlib import transforms
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-from pycocotools import mask as maskUtils
-
-
-def renorm(
-    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-) -> torch.FloatTensor:
-    # img: tensor(3,H,W) or tensor(B,3,H,W)
-    # return: same as img
-    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
-    if img.dim() == 3:
-        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
-            img.size(0),
-            str(img.size()),
-        )
-        img_perm = img.permute(1, 2, 0)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(2, 0, 1)
-    else:  # img.dim() == 4
-        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
-            img.size(1),
-            str(img.size()),
-        )
-        img_perm = img.permute(0, 2, 3, 1)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(0, 3, 1, 2)
-
-
-class ColorMap:
-    def __init__(self, basergb=[255, 255, 0]):
-        self.basergb = np.array(basergb)
-
-    def __call__(self, attnmap):
-        # attnmap: h, w. np.uint8.
-        # return: h, w, 4. np.uint8.
-        assert attnmap.dtype == np.uint8
-        h, w = attnmap.shape
-        res = self.basergb.copy()
-        res = res[None][None].repeat(h, 0).repeat(w, 1)  # h, w, 3
-        attn1 = attnmap.copy()[..., None]  # h, w, 1
-        res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)
-        return res
-
-
-def rainbow_text(x, y, ls, lc, **kw):
-    """
-    Take a list of strings ``ls`` and colors ``lc`` and place them next to each
-    other, with text ls[i] being shown in color lc[i].
-
-    This example shows how to do both vertical and horizontal text, and will
-    pass all keyword arguments to plt.text, so you can set the font size,
-    family, etc.
-    """
-    t = plt.gca().transData
-    fig = plt.gcf()
-    plt.show()
-
-    # horizontal version
-    for s, c in zip(ls, lc):
-        text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw)
-        text.draw(fig.canvas.get_renderer())
-        ex = text.get_window_extent()
-        t = transforms.offset_copy(text._transform, x=ex.width, units="dots")
-
-    # #vertical version
-    # for s,c in zip(ls,lc):
-    #     text = plt.text(x,y," "+s+" ",color=c, transform=t,
-    #             rotation=90,va='bottom',ha='center',**kw)
-    #     text.draw(fig.canvas.get_renderer())
-    #     ex = text.get_window_extent()
-    #     t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
-
-
-class COCOVisualizer:
-    def __init__(self, coco=None, tokenlizer=None) -> None:
-        self.coco = coco
-
-    def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
-        """
-        img: tensor(3, H, W)
-        tgt: make sure they are all on cpu.
-            must have items: 'image_id', 'boxes', 'size'
-        """
-        plt.figure(dpi=dpi)
-        plt.rcParams["font.size"] = "5"
-        ax = plt.gca()
-        img = renorm(img).permute(1, 2, 0)
-        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-        #     import ipdb; ipdb.set_trace()
-        ax.imshow(img)
-
-        self.addtgt(tgt)
-
-        if tgt is None:
-            image_id = 0
-        elif "image_id" not in tgt:
-            image_id = 0
-        else:
-            image_id = tgt["image_id"]
-
-        if caption is None:
-            savename = "{}/{}-{}.png".format(
-                savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
-            )
-        else:
-            savename = "{}/{}-{}-{}.png".format(
-                savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
-            )
-        print("savename: {}".format(savename))
-        os.makedirs(os.path.dirname(savename), exist_ok=True)
-        plt.savefig(savename)
-        plt.close()
-
-    def addtgt(self, tgt):
-        """ """
-        if tgt is None or not "boxes" in tgt:
-            ax = plt.gca()
-
-            if "caption" in tgt:
-                ax.set_title(tgt["caption"], wrap=True)
-
-            ax.set_axis_off()
-            return
-
-        ax = plt.gca()
-        H, W = tgt["size"]
-        numbox = tgt["boxes"].shape[0]
-
-        color = []
-        polygons = []
-        boxes = []
-        for box in tgt["boxes"].cpu():
-            unnormbbox = box * torch.Tensor([W, H, W, H])
-            unnormbbox[:2] -= unnormbbox[2:] / 2
-            [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()
-            boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])
-            poly = [
-                [bbox_x, bbox_y],
-                [bbox_x, bbox_y + bbox_h],
-                [bbox_x + bbox_w, bbox_y + bbox_h],
-                [bbox_x + bbox_w, bbox_y],
-            ]
-            np_poly = np.array(poly).reshape((4, 2))
-            polygons.append(Polygon(np_poly))
-            c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
-            color.append(c)
-
-        p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)
-        ax.add_collection(p)
-        p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
-        ax.add_collection(p)
-
-        if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0:
-            assert (
-                len(tgt["strings_positive"]) == numbox
-            ), f"{len(tgt['strings_positive'])} = {numbox}, "
-            for idx, strlist in enumerate(tgt["strings_positive"]):
-                cate_id = int(tgt["labels"][idx])
-                _string = str(cate_id) + ":" + " ".join(strlist)
-                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
-                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
-                ax.text(
-                    bbox_x,
-                    bbox_y,
-                    _string,
-                    color="black",
-                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
-                )
-
-        if "box_label" in tgt:
-            assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
-            for idx, bl in enumerate(tgt["box_label"]):
-                _string = str(bl)
-                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
-                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
-                ax.text(
-                    bbox_x,
-                    bbox_y,
-                    _string,
-                    color="black",
-                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
-                )
-
-        if "caption" in tgt:
-            ax.set_title(tgt["caption"], wrap=True)
-            # plt.figure()
-            # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(),
-            #         ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])
-
-        if "attn" in tgt:
-            # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-            #     import ipdb; ipdb.set_trace()
-            if isinstance(tgt["attn"], tuple):
-                tgt["attn"] = [tgt["attn"]]
-            for item in tgt["attn"]:
-                attn_map, basergb = item
-                attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
-                attn_map = (attn_map * 255).astype(np.uint8)
-                cm = ColorMap(basergb)
-                heatmap = cm(attn_map)
-                ax.imshow(heatmap)
-        ax.set_axis_off()
-
-    def showAnns(self, anns, draw_bbox=False):
-        """
-        Display the specified annotations.
-        :param anns (array of object): annotations to display
-        :return: None
-        """
-        if len(anns) == 0:
-            return 0
-        if "segmentation" in anns[0] or "keypoints" in anns[0]:
-            datasetType = "instances"
-        elif "caption" in anns[0]:
-            datasetType = "captions"
-        else:
-            raise Exception("datasetType not supported")
-        if datasetType == "instances":
-            ax = plt.gca()
-            ax.set_autoscale_on(False)
-            polygons = []
-            color = []
-            for ann in anns:
-                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
-                if "segmentation" in ann:
-                    if type(ann["segmentation"]) == list:
-                        # polygon
-                        for seg in ann["segmentation"]:
-                            poly = np.array(seg).reshape((int(len(seg) / 2), 2))
-                            polygons.append(Polygon(poly))
-                            color.append(c)
-                    else:
-                        # mask
-                        t = self.imgs[ann["image_id"]]
-                        if type(ann["segmentation"]["counts"]) == list:
-                            rle = maskUtils.frPyObjects(
-                                [ann["segmentation"]], t["height"], t["width"]
-                            )
-                        else:
-                            rle = [ann["segmentation"]]
-                        m = maskUtils.decode(rle)
-                        img = np.ones((m.shape[0], m.shape[1], 3))
-                        if ann["iscrowd"] == 1:
-                            color_mask = np.array([2.0, 166.0, 101.0]) / 255
-                        if ann["iscrowd"] == 0:
-                            color_mask = np.random.random((1, 3)).tolist()[0]
-                        for i in range(3):
-                            img[:, :, i] = color_mask[i]
-                        ax.imshow(np.dstack((img, m * 0.5)))
-                if "keypoints" in ann and type(ann["keypoints"]) == list:
-                    # turn skeleton into zero-based index
-                    sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
-                    kp = np.array(ann["keypoints"])
-                    x = kp[0::3]
-                    y = kp[1::3]
-                    v = kp[2::3]
-                    for sk in sks:
-                        if np.all(v[sk] > 0):
-                            plt.plot(x[sk], y[sk], linewidth=3, color=c)
-                    plt.plot(
-                        x[v > 0],
-                        y[v > 0],
-                        "o",
-                        markersize=8,
-                        markerfacecolor=c,
-                        markeredgecolor="k",
-                        markeredgewidth=2,
-                    )
-                    plt.plot(
-                        x[v > 1],
-                        y[v > 1],
-                        "o",
-                        markersize=8,
-                        markerfacecolor=c,
-                        markeredgecolor=c,
-                        markeredgewidth=2,
-                    )
-
-                if draw_bbox:
-                    [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
-                    poly = [
-                        [bbox_x, bbox_y],
-                        [bbox_x, bbox_y + bbox_h],
-                        [bbox_x + bbox_w, bbox_y + bbox_h],
-                        [bbox_x + bbox_w, bbox_y],
-                    ]
-                    np_poly = np.array(poly).reshape((4, 2))
-                    polygons.append(Polygon(np_poly))
-                    color.append(c)
-
-            # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
-            # ax.add_collection(p)
-            p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
-            ax.add_collection(p)
-        elif datasetType == "captions":
-            for ann in anns:
-                print(ann["caption"])
diff --git a/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/docs/modelzoo.md b/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/docs/modelzoo.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/wangguanlin/vits_Kazari/MoeGoe.py b/spaces/wangguanlin/vits_Kazari/MoeGoe.py
deleted file mode 100644
index 0f3baad5145af17d70dbb008c08b5da30f32c295..0000000000000000000000000000000000000000
--- a/spaces/wangguanlin/vits_Kazari/MoeGoe.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import sys, re
-from torch import no_grad, LongTensor
-import logging
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text import text_to_sequence, _clean_text
-from mel_processing import spectrogram_torch
-
-from scipy.io.wavfile import write
-
-def ex_print(text,escape=False):
-    if escape:
-        print(text.encode('unicode_escape').decode())
-    else:
-        print(text)
-
-def get_text(text, hps, cleaned=False):
-    if cleaned:
-        text_norm = text_to_sequence(text, hps.symbols, [])
-    else:
-        text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm
-
-def ask_if_continue():
-    while True:
-        answer = input('Continue? (y/n): ')
-        if answer == 'y':
-            break
-        elif answer == 'n':
-            sys.exit(0)
-
-def print_speakers(speakers,escape=False):
-    print('ID\tSpeaker')
-    for id, name in enumerate(speakers):
-        ex_print(str(id) + '\t' + name,escape)
-
-def get_speaker_id(message):
-    speaker_id = input(message)
-    try:
-        speaker_id = int(speaker_id)
-    except:
-        print(str(speaker_id) + ' is not a valid ID!')
-        sys.exit(1)
-    return speaker_id
-
-def get_label_value(text, label, default, warning_name='value'):
-    value=re.search(rf'\[{label}=(.+?)\]',text)
-    if value:
-        try:
-            text=re.sub(rf'\[{label}=(.+?)\]','',text,1)
-            value=float(value.group(1))
-        except:
-            print(f'Invalid {warning_name}!')
-            sys.exit(1)
-    else:
-        value=default
-    return value, text
-
-def get_label(text,label):
-    if f'[{label}]' in text:
-        return True,text.replace(f'[{label}]','')
-    else:
-        return False,text
-
-if __name__ == '__main__':
-    if '--escape' in sys.argv:
-        escape=True
-    else:
-        escape=False
-    
-    model = input('Path of a VITS model: ')
-    config = input('Path of a config file: ')
-    
-    hps_ms = utils.get_hparams_from_file(config)
-    n_speakers = hps_ms.data.n_speakers if 'n_speakers' in hps_ms.data.keys() else 0
-    n_symbols = len(hps_ms.symbols) if 'symbols' in hps_ms.keys() else 0
-    speakers = hps_ms.speakers if 'speakers' in hps_ms.keys() else ['0']
-    use_f0 = hps_ms.data.use_f0 if 'use_f0' in hps_ms.data.keys() else False
-
-    net_g_ms = SynthesizerTrn(
-        n_symbols,
-        hps_ms.data.filter_length // 2 + 1,
-        hps_ms.train.segment_size // hps_ms.data.hop_length,
-        n_speakers=n_speakers,
-        **hps_ms.model)
-    _ = net_g_ms.eval()
-    utils.load_checkpoint(model, net_g_ms)
-    
-    if n_symbols!=0:
-        while True:
-            choice = input('TTS or VC? (t/v):')
-            if choice == 't':
-                text = input('Text to read: ')
-                if text=='[ADVANCED]':
-                    text = input('Raw text:')
-                    print('Cleaned text is:')
-                    ex_print(_clean_text(text, hps_ms.data.text_cleaners),escape)
-                    continue
-                
-                length_scale,text=get_label_value(text,'LENGTH',1,'length scale')
-                noise_scale,text=get_label_value(text,'NOISE',0.667,'noise scale')
-                noise_scale_w,text=get_label_value(text,'NOISEW',0.8,'deviation of noise')
-                cleaned,text=get_label(text,'CLEANED')
-
-                stn_tst = get_text(text, hps_ms, cleaned=cleaned)
-                
-                print_speakers(speakers,escape)
-                speaker_id = get_speaker_id('Speaker ID: ')
-                out_path = input('Path to save: ')
-
-                with no_grad():
-                    x_tst = stn_tst.unsqueeze(0)
-                    x_tst_lengths = LongTensor([stn_tst.size(0)])
-                    sid = LongTensor([speaker_id])
-                    audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-                write(out_path, hps_ms.data.sampling_rate, audio)
-                
-                print('Successfully saved!')
-                ask_if_continue()
-                
-                    
-            elif choice == 'v':
-                audio_path = input('Path of an audio file to convert:\n')
-                print_speakers(speakers)
-                audio = utils.load_audio_to_torch(audio_path, hps_ms.data.sampling_rate)
-
-                originnal_id = get_speaker_id('Original speaker ID: ')
-                target_id = get_speaker_id('Target speaker ID: ')
-                out_path = input('Path to save: ')
-
-                y = audio.unsqueeze(0)
-
-                spec = spectrogram_torch(y, hps_ms.data.filter_length,
-                    hps_ms.data.sampling_rate, hps_ms.data.hop_length, hps_ms.data.win_length,
-                    center=False)
-                spec_lengths = LongTensor([spec.size(-1)])
-                sid_src = LongTensor([originnal_id])
-
-                with no_grad():
-                    sid_tgt = LongTensor([target_id])
-                    audio = net_g_ms.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][0,0].data.cpu().float().numpy()
-                write(out_path, hps_ms.data.sampling_rate, audio)
-                        
-                print('Successfully saved!')
-                ask_if_continue()
-    else:
-        model = input('Path of a hubert-soft model: ')
-        from hubert_model import hubert_soft
-        hubert = hubert_soft(model)
-
-        while True:
-            audio_path = input('Path of an audio file to convert:\n')
-            print_speakers(speakers)
-            
-            import librosa
-            if use_f0:
-                audio, sampling_rate = librosa.load(audio_path, sr=hps_ms.data.sampling_rate, mono=True)
-                audio16000 = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            else:
-                audio16000, sampling_rate = librosa.load(audio_path, sr=16000, mono=True)
-            
-            target_id = get_speaker_id('Target speaker ID: ')
-            out_path = input('Path to save: ')
-            length_scale,out_path=get_label_value(out_path,'LENGTH',1,'length scale')
-            noise_scale,out_path=get_label_value(out_path,'NOISE',0.1,'noise scale')
-            noise_scale_w,out_path=get_label_value(out_path,'NOISEW',0.1,'deviation of noise')
-                
-            from torch import inference_mode, FloatTensor
-            import numpy as np
-            with inference_mode():
-                units = hubert.units(FloatTensor(audio16000).unsqueeze(0).unsqueeze(0)).squeeze(0).numpy()
-                if use_f0:
-                    f0_scale,out_path = get_label_value(out_path,'F0',1,'f0 scale')
-                    f0 = librosa.pyin(audio, sr=sampling_rate,
-                        fmin=librosa.note_to_hz('C0'),
-                        fmax=librosa.note_to_hz('C7'),
-                        frame_length=1780)[0]
-                    target_length = len(units[:, 0])
-                    f0 = np.nan_to_num(np.interp(np.arange(0, len(f0)*target_length, len(f0))/target_length,
-                        np.arange(0, len(f0)), f0)) * f0_scale
-                    units[:, 0] = f0 / 10
-            
-            stn_tst = FloatTensor(units)
-            with no_grad():
-                x_tst = stn_tst.unsqueeze(0)
-                x_tst_lengths = LongTensor([stn_tst.size(0)])
-                sid = LongTensor([target_id])
-                audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0, 0].data.float().numpy()
-            write(out_path, hps_ms.data.sampling_rate, audio)
-            
-            print('Successfully saved!')
-            ask_if_continue()
diff --git a/spaces/wanghuoto/gogoai/src/components/button-scroll-to-bottom.tsx b/spaces/wanghuoto/gogoai/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/wanghuoto/gogoai/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
-  const isAtBottom = useAtBottom()
-
-  return (
-    <Button
-      variant="outline"
-      size="icon"
-      className={cn(
-        'fixed right-4 bottom-24 z-50 bg-background transition-opacity duration-300 sm:right-20',
-        isAtBottom ? 'opacity-0' : 'opacity-100',
-        className
-      )}
-      onClick={() =>
-        window.scrollTo({
-          top: document.body.offsetHeight,
-          behavior: 'smooth'
-        })
-      }
-      {...props}
-    >
-      <IconArrowDown />
-      <span className="sr-only">Scroll to bottom</span>
-    </Button>
-  )
-}
diff --git a/spaces/wanghuoto/gogoai/src/pages/api/sydney.ts b/spaces/wanghuoto/gogoai/src/pages/api/sydney.ts
deleted file mode 100644
index 0e7bbf23d77c2e1a6635185a060eeee58b8c8e66..0000000000000000000000000000000000000000
--- a/spaces/wanghuoto/gogoai/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const conversationContext = req.body
-  const headers = createHeaders(req.cookies)
-  debug(headers)
-  res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
-  const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
-    headers: {
-      ...headers,
-      'accept-language': 'zh-CN,zh;q=0.9',
-      'cache-control': 'no-cache',
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      pragma: 'no-cache',
-    }
-  })
-
-  const closeDog = new WatchDog()
-  const timeoutDog = new WatchDog()
-  ws.onmessage = (event) => {
-    timeoutDog.watch(() => {
-      ws.send(websocketUtils.packMessage({ type: 6 }))
-    }, 1500)
-    closeDog.watch(() => {
-      ws.close()
-    }, 10000)
-    res.write(event.data)
-    if (/\{"type":([367])\}/.test(String(event.data))) {
-      const type = parseInt(RegExp.$1, 10)
-      debug('connection type', type)
-      if (type === 3) {
-        ws.close()
-      } else {
-        ws.send(websocketUtils.packMessage({ type }))
-      }
-    }
-  }
-
-  ws.onclose = () => {
-    timeoutDog.reset()
-    closeDog.reset()
-    debug('connection close')
-    res.end()
-  }
-
-  await new Promise((resolve) => ws.onopen = resolve)
-  ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
-  ws.send(websocketUtils.packMessage({ type: 6 }))
-  ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
-  req.socket.once('close', () => {
-    ws.close()
-    if (!res.closed) {
-      res.end()
-    }
-  })
-}
diff --git a/spaces/weishao2019/ChuanhuChatGPT/llama_func.py b/spaces/weishao2019/ChuanhuChatGPT/llama_func.py
deleted file mode 100644
index c71027dd4e6f99c0c12626cbbf276f407877be04..0000000000000000000000000000000000000000
--- a/spaces/weishao2019/ChuanhuChatGPT/llama_func.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import logging
-
-from llama_index import GPTSimpleVectorIndex
-from llama_index import download_loader
-from llama_index import (
-    Document,
-    LLMPredictor,
-    PromptHelper,
-    QuestionAnswerPrompt,
-    RefinePrompt,
-)
-from langchain.llms import OpenAI
-import colorama
-
-
-from presets import *
-from utils import *
-
-
-def get_documents(file_src):
-    documents = []
-    index_name = ""
-    logging.debug("Loading documents...")
-    logging.debug(f"file_src: {file_src}")
-    for file in file_src:
-        logging.debug(f"file: {file.name}")
-        index_name += file.name
-        if os.path.splitext(file.name)[1] == ".pdf":
-            logging.debug("Loading PDF...")
-            CJKPDFReader = download_loader("CJKPDFReader")
-            loader = CJKPDFReader()
-            documents += loader.load_data(file=file.name)
-        elif os.path.splitext(file.name)[1] == ".docx":
-            logging.debug("Loading DOCX...")
-            DocxReader = download_loader("DocxReader")
-            loader = DocxReader()
-            documents += loader.load_data(file=file.name)
-        elif os.path.splitext(file.name)[1] == ".epub":
-            logging.debug("Loading EPUB...")
-            EpubReader = download_loader("EpubReader")
-            loader = EpubReader()
-            documents += loader.load_data(file=file.name)
-        else:
-            logging.debug("Loading text file...")
-            with open(file.name, "r", encoding="utf-8") as f:
-                text = add_space(f.read())
-                documents += [Document(text)]
-    index_name = sha1sum(index_name)
-    return documents, index_name
-
-
-def construct_index(
-    api_key,
-    file_src,
-    max_input_size=4096,
-    num_outputs=1,
-    max_chunk_overlap=20,
-    chunk_size_limit=600,
-    embedding_limit=None,
-    separator=" ",
-    num_children=10,
-    max_keywords_per_chunk=10,
-):
-    os.environ["OPENAI_API_KEY"] = api_key
-    chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
-    embedding_limit = None if embedding_limit == 0 else embedding_limit
-    separator = " " if separator == "" else separator
-
-    llm_predictor = LLMPredictor(
-        llm=OpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key)
-    )
-    prompt_helper = PromptHelper(
-        max_input_size,
-        num_outputs,
-        max_chunk_overlap,
-        embedding_limit,
-        chunk_size_limit,
-        separator=separator,
-    )
-    documents, index_name = get_documents(file_src)
-    if os.path.exists(f"./index/{index_name}.json"):
-        logging.info("找到了缓存的索引文件，加载中……")
-        return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
-    else:
-        try:
-            logging.debug("构建索引中……")
-            index = GPTSimpleVectorIndex(
-                documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
-            )
-            os.makedirs("./index", exist_ok=True)
-            index.save_to_disk(f"./index/{index_name}.json")
-            return index
-        except Exception as e:
-            print(e)
-            return None
-
-
-def chat_ai(
-    api_key,
-    index,
-    question,
-    context,
-    chatbot,
-):
-    os.environ["OPENAI_API_KEY"] = api_key
-
-    logging.info(f"Question: {question}")
-
-    response, chatbot_display, status_text = ask_ai(
-        api_key,
-        index,
-        question,
-        replace_today(PROMPT_TEMPLATE),
-        REFINE_TEMPLATE,
-        SIM_K,
-        INDEX_QUERY_TEMPRATURE,
-        context,
-    )
-    if response is None:
-        status_text = "查询失败，请换个问法试试"
-        return context, chatbot
-    response = response
-
-    context.append({"role": "user", "content": question})
-    context.append({"role": "assistant", "content": response})
-    chatbot.append((question, chatbot_display))
-
-    os.environ["OPENAI_API_KEY"] = ""
-    return context, chatbot, status_text
-
-
-def ask_ai(
-    api_key,
-    index,
-    question,
-    prompt_tmpl,
-    refine_tmpl,
-    sim_k=1,
-    temprature=0,
-    prefix_messages=[],
-):
-    os.environ["OPENAI_API_KEY"] = api_key
-
-    logging.debug("Index file found")
-    logging.debug("Querying index...")
-    llm_predictor = LLMPredictor(
-        llm=OpenAI(
-            temperature=temprature,
-            model_name="gpt-3.5-turbo-0301",
-            prefix_messages=prefix_messages,
-        )
-    )
-
-    response = None  # Initialize response variable to avoid UnboundLocalError
-    qa_prompt = QuestionAnswerPrompt(prompt_tmpl)
-    rf_prompt = RefinePrompt(refine_tmpl)
-    response = index.query(
-        question,
-        llm_predictor=llm_predictor,
-        similarity_top_k=sim_k,
-        text_qa_template=qa_prompt,
-        refine_template=rf_prompt,
-        response_mode="compact",
-    )
-
-    if response is not None:
-        logging.info(f"Response: {response}")
-        ret_text = response.response
-        nodes = []
-        for index, node in enumerate(response.source_nodes):
-            brief = node.source_text[:25].replace("\n", "")
-            nodes.append(
-                f"<details><summary>[{index+1}]\t{brief}...</summary><p>{node.source_text}</p></details>"
-            )
-        new_response = ret_text + "\n----------\n" + "\n\n".join(nodes)
-        logging.info(
-            f"Response: {colorama.Fore.BLUE}{ret_text}{colorama.Style.RESET_ALL}"
-        )
-        os.environ["OPENAI_API_KEY"] = ""
-        return ret_text, new_response, f"查询消耗了{llm_predictor.last_token_usage} tokens"
-    else:
-        logging.warning("No response found, returning None")
-        os.environ["OPENAI_API_KEY"] = ""
-        return None
-
-
-def add_space(text):
-    punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
-    for cn_punc, en_punc in punctuations.items():
-        text = text.replace(cn_punc, en_punc)
-    return text
diff --git a/spaces/wendys-llc/panoptic-segment-anything/app.py b/spaces/wendys-llc/panoptic-segment-anything/app.py
deleted file mode 100644
index eda9a0c1cd0523b4a8f7e5d63cdec61b10b09c7f..0000000000000000000000000000000000000000
--- a/spaces/wendys-llc/panoptic-segment-anything/app.py
+++ /dev/null
@@ -1,601 +0,0 @@
-import subprocess, os, sys
-
-result = subprocess.run(["pip", "install", "-e", "GroundingDINO"], check=True)
-print(f"pip install GroundingDINO = {result}")
-
-result = subprocess.run(["pip", "install", "gradio==3.27.0"], check=True)
-print(f"pip install gradio==3.27.0 = {result}")
-
-sys.path.insert(0, "./GroundingDINO")
-
-if not os.path.exists("./sam_vit_h_4b8939.pth"):
-    result = subprocess.run(
-        [
-            "wget",
-            "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
-        ],
-        check=True,
-    )
-    print(f"wget sam_vit_h_4b8939.pth result = {result}")
-
-
-import argparse
-import random
-import warnings
-import json
-
-import gradio as gr
-import numpy as np
-import torch
-from torch import nn
-import torch.nn.functional as F
-from scipy import ndimage
-from PIL import Image
-from huggingface_hub import hf_hub_download
-from segments.utils import bitmap2file
-
-# Grounding DINO
-import GroundingDINO.groundingdino.datasets.transforms as T
-from GroundingDINO.groundingdino.models import build_model
-from GroundingDINO.groundingdino.util import box_ops
-from GroundingDINO.groundingdino.util.slconfig import SLConfig
-from GroundingDINO.groundingdino.util.utils import (
-    clean_state_dict,
-)
-from GroundingDINO.groundingdino.util.inference import annotate, predict
-
-# segment anything
-from segment_anything import build_sam, SamPredictor
-
-# CLIPSeg
-from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-
-
-def load_model_hf(model_config_path, repo_id, filename, device):
-    args = SLConfig.fromfile(model_config_path)
-    model = build_model(args)
-    args.device = device
-
-    cache_file = hf_hub_download(repo_id=repo_id, filename=filename)
-    checkpoint = torch.load(cache_file, map_location=device)
-    log = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
-    print("Model loaded from {} \n => {}".format(cache_file, log))
-    _ = model.eval()
-    model = model.to(device)
-    return model
-
-
-def load_image_for_dino(image):
-    transform = T.Compose(
-        [
-            T.RandomResize([800], max_size=1333),
-            T.ToTensor(),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ]
-    )
-    dino_image, _ = transform(image, None)
-    return dino_image
-
-
-def dino_detection(
-    model,
-    image,
-    image_array,
-    category_names,
-    category_name_to_id,
-    box_threshold,
-    text_threshold,
-    device,
-    visualize=False,
-):
-    detection_prompt = " . ".join(category_names)
-    dino_image = load_image_for_dino(image)
-    dino_image = dino_image.to(device)
-    with torch.no_grad():
-        boxes, logits, phrases = predict(
-            model=model,
-            image=dino_image,
-            caption=detection_prompt,
-            box_threshold=box_threshold,
-            text_threshold=text_threshold,
-            device=device,
-        )
-    category_ids = [category_name_to_id[phrase] for phrase in phrases]
-
-    if visualize:
-        annotated_frame = annotate(
-            image_source=image_array, boxes=boxes, logits=logits, phrases=phrases
-        )
-        annotated_frame = annotated_frame[..., ::-1]  # BGR to RGB
-        visualization = Image.fromarray(annotated_frame)
-        return boxes, category_ids, visualization
-    else:
-        return boxes, category_ids, phrases
-
-
-def sam_masks_from_dino_boxes(predictor, image_array, boxes, device):
-    # box: normalized box xywh -> unnormalized xyxy
-    H, W, _ = image_array.shape
-    boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])
-    transformed_boxes = predictor.transform.apply_boxes_torch(
-        boxes_xyxy, image_array.shape[:2]
-    ).to(device)
-    thing_masks, _, _ = predictor.predict_torch(
-        point_coords=None,
-        point_labels=None,
-        boxes=transformed_boxes,
-        multimask_output=False,
-    )
-    return thing_masks
-
-
-def preds_to_semantic_inds(preds, threshold):
-    flat_preds = preds.reshape((preds.shape[0], -1))
-    # Initialize a dummy "unlabeled" mask with the threshold
-    flat_preds_with_treshold = torch.full(
-        (preds.shape[0] + 1, flat_preds.shape[-1]), threshold
-    )
-    flat_preds_with_treshold[1 : preds.shape[0] + 1, :] = flat_preds
-
-    # Get the top mask index for each pixel
-    semantic_inds = torch.topk(flat_preds_with_treshold, 1, dim=0).indices.reshape(
-        (preds.shape[-2], preds.shape[-1])
-    )
-
-    return semantic_inds
-
-
-def clipseg_segmentation(
-    processor, model, image, category_names, background_threshold, device
-):
-    inputs = processor(
-        text=category_names,
-        images=[image] * len(category_names),
-        padding="max_length",
-        return_tensors="pt",
-    ).to(device)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    logits = outputs.logits
-    if len(logits.shape) == 2:
-        logits = logits.unsqueeze(0)
-    # resize the outputs
-    upscaled_logits = nn.functional.interpolate(
-        logits.unsqueeze(1),
-        size=(image.size[1], image.size[0]),
-        mode="bilinear",
-    )
-    preds = torch.sigmoid(upscaled_logits.squeeze(dim=1))
-    semantic_inds = preds_to_semantic_inds(preds, background_threshold)
-    return preds, semantic_inds
-
-
-def semantic_inds_to_shrunken_bool_masks(
-    semantic_inds, shrink_kernel_size, num_categories
-):
-    shrink_kernel = np.ones((shrink_kernel_size, shrink_kernel_size))
-
-    bool_masks = torch.zeros((num_categories, *semantic_inds.shape), dtype=bool)
-    for category in range(num_categories):
-        binary_mask = semantic_inds == category
-        shrunken_binary_mask_array = (
-            ndimage.binary_erosion(binary_mask.numpy(), structure=shrink_kernel)
-            if shrink_kernel_size > 0
-            else binary_mask.numpy()
-        )
-        bool_masks[category] = torch.from_numpy(shrunken_binary_mask_array)
-
-    return bool_masks
-
-
-def clip_and_shrink_preds(semantic_inds, preds, shrink_kernel_size, num_categories):
-    # convert semantic_inds to shrunken bool masks
-    bool_masks = semantic_inds_to_shrunken_bool_masks(
-        semantic_inds, shrink_kernel_size, num_categories
-    ).to(preds.device)
-
-    sizes = [
-        torch.sum(bool_masks[i].int()).item() for i in range(1, bool_masks.size(0))
-    ]
-    max_size = max(sizes)
-    relative_sizes = [size / max_size for size in sizes] if max_size > 0 else sizes
-
-    # use bool masks to clip preds
-    clipped_preds = torch.zeros_like(preds)
-    for i in range(1, bool_masks.size(0)):
-        float_mask = bool_masks[i].float()
-        clipped_preds[i - 1] = preds[i - 1] * float_mask
-
-    return clipped_preds, relative_sizes
-
-
-def sample_points_based_on_preds(preds, N):
-    height, width = preds.shape
-    weights = preds.ravel()
-    indices = np.arange(height * width)
-
-    # Randomly sample N indices based on the weights
-    sampled_indices = random.choices(indices, weights=weights, k=N)
-
-    # Convert the sampled indices into (col, row) coordinates
-    sampled_points = [(index % width, index // width) for index in sampled_indices]
-
-    return sampled_points
-
-
-def upsample_pred(pred, image_source):
-    pred = pred.unsqueeze(dim=0)
-    original_height = image_source.shape[0]
-    original_width = image_source.shape[1]
-
-    larger_dim = max(original_height, original_width)
-    aspect_ratio = original_height / original_width
-
-    # upsample the tensor to the larger dimension
-    upsampled_tensor = F.interpolate(
-        pred, size=(larger_dim, larger_dim), mode="bilinear", align_corners=False
-    )
-
-    # remove the padding (at the end) to get the original image resolution
-    if original_height > original_width:
-        target_width = int(upsampled_tensor.shape[3] * aspect_ratio)
-        upsampled_tensor = upsampled_tensor[:, :, :, :target_width]
-    else:
-        target_height = int(upsampled_tensor.shape[2] * aspect_ratio)
-        upsampled_tensor = upsampled_tensor[:, :, :target_height, :]
-    return upsampled_tensor.squeeze(dim=1)
-
-
-def sam_mask_from_points(predictor, image_array, points):
-    points_array = np.array(points)
-    # we only sample positive points, so labels are all 1
-    points_labels = np.ones(len(points))
-    # we don't use predict_torch here cause it didn't seem to work...
-    _, _, logits = predictor.predict(
-        point_coords=points_array,
-        point_labels=points_labels,
-    )
-    # max over the 3 segmentation levels
-    total_pred = torch.max(torch.sigmoid(torch.tensor(logits)), dim=0)[0].unsqueeze(
-        dim=0
-    )
-    # logits are 256x256 -> upsample back to image shape
-    upsampled_pred = upsample_pred(total_pred, image_array)
-    return upsampled_pred
-
-
-def inds_to_segments_format(
-    panoptic_inds, thing_category_ids, stuff_category_names, category_name_to_id
-):
-    panoptic_inds_array = panoptic_inds.numpy().astype(np.uint32)
-    bitmap_file = bitmap2file(panoptic_inds_array, is_segmentation_bitmap=True)
-    segmentation_bitmap = Image.open(bitmap_file)
-
-    stuff_category_ids = [
-        category_name_to_id[stuff_category_name]
-        for stuff_category_name in stuff_category_names
-    ]
-
-    unique_inds = np.unique(panoptic_inds_array)
-    stuff_annotations = [
-        {"id": i, "category_id": stuff_category_ids[i - 1]}
-        for i in range(1, len(stuff_category_names) + 1)
-        if i in unique_inds
-    ]
-    thing_annotations = [
-        {"id": len(stuff_category_names) + 1 + i, "category_id": thing_category_id}
-        for i, thing_category_id in enumerate(thing_category_ids)
-    ]
-    annotations = stuff_annotations + thing_annotations
-
-    return segmentation_bitmap, annotations
-
-
-def generate_panoptic_mask(
-    image,
-    thing_category_names_string,
-    stuff_category_names_string,
-    dino_box_threshold=0.3,
-    dino_text_threshold=0.25,
-    segmentation_background_threshold=0.1,
-    shrink_kernel_size=20,
-    num_samples_factor=1000,
-    task_attributes_json="",
-):
-    if task_attributes_json != "":
-        task_attributes = json.loads(task_attributes_json)
-        categories = task_attributes["categories"]
-        category_name_to_id = {
-            category["name"]: category["id"] for category in categories
-        }
-        # split the categories into "stuff" categories (regions w/o instances)
-        # and "thing" categories (objects/instances)
-        stuff_categories = [
-            category
-            for category in categories
-            if "has_instances" not in category or not category["has_instances"]
-        ]
-        thing_categories = [
-            category
-            for category in categories
-            if "has_instances" in category and category["has_instances"]
-        ]
-        stuff_category_names = [category["name"] for category in stuff_categories]
-        thing_category_names = [category["name"] for category in thing_categories]
-        category_names = thing_category_names + stuff_category_names
-    else:
-        # parse inputs
-        thing_category_names = [
-            thing_category_name.strip()
-            for thing_category_name in thing_category_names_string.split(",")
-        ]
-        stuff_category_names = [
-            stuff_category_name.strip()
-            for stuff_category_name in stuff_category_names_string.split(",")
-        ]
-        category_names = thing_category_names + stuff_category_names
-        category_name_to_id = {
-            category_name: i for i, category_name in enumerate(category_names)
-        }
-
-    image = image.convert("RGB")
-    image_array = np.asarray(image)
-
-    # compute SAM image embedding
-    sam_predictor.set_image(image_array)
-
-    # detect boxes for "thing" categories using Grounding DINO
-    thing_category_ids = []
-    thing_masks = []
-    thing_boxes = []
-    detected_thing_category_names = []
-    if len(thing_category_names) > 0:
-        thing_boxes, thing_category_ids, detected_thing_category_names = dino_detection(
-            dino_model,
-            image,
-            image_array,
-            thing_category_names,
-            category_name_to_id,
-            dino_box_threshold,
-            dino_text_threshold,
-            device,
-        )
-        if len(thing_boxes) > 0:
-            # get segmentation masks for the thing boxes
-            thing_masks = sam_masks_from_dino_boxes(
-                sam_predictor, image_array, thing_boxes, device
-            )
-    if len(stuff_category_names) > 0:
-        # get rough segmentation masks for "stuff" categories using CLIPSeg
-        clipseg_preds, clipseg_semantic_inds = clipseg_segmentation(
-            clipseg_processor,
-            clipseg_model,
-            image,
-            stuff_category_names,
-            segmentation_background_threshold,
-            device,
-        )
-        # remove things from stuff masks
-        clipseg_semantic_inds_without_things = clipseg_semantic_inds.clone()
-        if len(thing_boxes) > 0:
-            combined_things_mask = torch.any(thing_masks, dim=0)
-            clipseg_semantic_inds_without_things[combined_things_mask[0]] = 0
-        # clip CLIPSeg preds based on non-overlapping semantic segmentation inds (+ optionally shrink the mask of each category)
-        # also returns the relative size of each category
-        clipsed_clipped_preds, relative_sizes = clip_and_shrink_preds(
-            clipseg_semantic_inds_without_things,
-            clipseg_preds,
-            shrink_kernel_size,
-            len(stuff_category_names) + 1,
-        )
-        # get finer segmentation masks for the "stuff" categories using SAM
-        sam_preds = torch.zeros_like(clipsed_clipped_preds)
-        for i in range(clipsed_clipped_preds.shape[0]):
-            clipseg_pred = clipsed_clipped_preds[i]
-            # for each "stuff" category, sample points in the rough segmentation mask
-            num_samples = int(relative_sizes[i] * num_samples_factor)
-            if num_samples == 0:
-                continue
-            points = sample_points_based_on_preds(
-                clipseg_pred.cpu().numpy(), num_samples
-            )
-            if len(points) == 0:
-                continue
-            # use SAM to get mask for points
-            pred = sam_mask_from_points(sam_predictor, image_array, points)
-            sam_preds[i] = pred
-        sam_semantic_inds = preds_to_semantic_inds(
-            sam_preds, segmentation_background_threshold
-        )
-
-    # combine the thing inds and the stuff inds into panoptic inds
-    panoptic_inds = (
-        sam_semantic_inds.clone()
-        if len(stuff_category_names) > 0
-        else torch.zeros(image_array.shape[0], image_array.shape[1], dtype=torch.long)
-    )
-    ind = len(stuff_category_names) + 1
-    for thing_mask in thing_masks:
-        # overlay thing mask on panoptic inds
-        panoptic_inds[thing_mask.squeeze(dim=0)] = ind
-        ind += 1
-
-    panoptic_bool_masks = (
-        semantic_inds_to_shrunken_bool_masks(panoptic_inds, 0, ind + 1)
-        .numpy()
-        .astype(int)
-    )
-    panoptic_names = (
-        ["unlabeled"] + stuff_category_names + detected_thing_category_names
-    )
-    subsection_label_pairs = [
-        (panoptic_bool_masks[i], panoptic_name)
-        for i, panoptic_name in enumerate(panoptic_names)
-    ]
-
-    segmentation_bitmap, annotations = inds_to_segments_format(
-        panoptic_inds, thing_category_ids, stuff_category_names, category_name_to_id
-    )
-    annotations_json = json.dumps(annotations)
-
-    return (image_array, subsection_label_pairs), segmentation_bitmap, annotations_json
-
-
-config_file = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py"
-ckpt_repo_id = "ShilongLiu/GroundingDINO"
-ckpt_filename = "groundingdino_swint_ogc.pth"
-sam_checkpoint = "./sam_vit_h_4b8939.pth"
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print("Using device:", device)
-
-if device != "cpu":
-    try:
-        from GroundingDINO.groundingdino import _C
-    except:
-        warnings.warn(
-            "Failed to load custom C++ ops. Running on CPU mode Only in groundingdino!"
-        )
-
-# initialize groundingdino model
-dino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filename, device)
-
-# initialize SAM
-sam = build_sam(checkpoint=sam_checkpoint)
-sam.to(device=device)
-sam_predictor = SamPredictor(sam)
-
-clipseg_processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-clipseg_model = CLIPSegForImageSegmentation.from_pretrained(
-    "CIDAS/clipseg-rd64-refined"
-)
-clipseg_model.to(device)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser("Panoptic Segment Anything demo", add_help=True)
-    parser.add_argument("--debug", action="store_true", help="using debug mode")
-    parser.add_argument("--share", action="store_true", help="share the app")
-    args = parser.parse_args()
-
-    print(f"args = {args}")
-
-    block = gr.Blocks(title="Panoptic Segment Anything").queue()
-    with block:
-        with gr.Column():
-            title = gr.Markdown(
-                "# [Panoptic Segment Anything](https://github.com/segments-ai/panoptic-segment-anything)"
-            )
-            description = gr.Markdown(
-                "Demo for zero-shot panoptic segmentation using Segment Anything, Grounding DINO, and CLIPSeg."
-            )
-            with gr.Row():
-                with gr.Column():
-                    input_image = gr.Image(source="upload", type="pil")
-                    thing_category_names_string = gr.Textbox(
-                        label="Thing categories (i.e. categories with instances), comma-separated",
-                        placeholder="E.g. car, bus, person",
-                    )
-                    stuff_category_names_string = gr.Textbox(
-                        label="Stuff categories (i.e. categories without instances), comma-separated",
-                        placeholder="E.g. sky, road, buildings",
-                    )
-                    run_button = gr.Button(label="Run")
-                    with gr.Accordion("Advanced options", open=False):
-                        box_threshold = gr.Slider(
-                            label="Grounding DINO box threshold",
-                            minimum=0.0,
-                            maximum=1.0,
-                            value=0.3,
-                            step=0.001,
-                        )
-                        text_threshold = gr.Slider(
-                            label="Grounding DINO text threshold",
-                            minimum=0.0,
-                            maximum=1.0,
-                            value=0.25,
-                            step=0.001,
-                        )
-                        segmentation_background_threshold = gr.Slider(
-                            label="Segmentation background threshold (under this threshold, a pixel is considered background/unlabeled)",
-                            minimum=0.0,
-                            maximum=1.0,
-                            value=0.1,
-                            step=0.001,
-                        )
-                        shrink_kernel_size = gr.Slider(
-                            label="Shrink kernel size (how much to shrink the mask before sampling points)",
-                            minimum=0,
-                            maximum=100,
-                            value=20,
-                            step=1,
-                        )
-                        num_samples_factor = gr.Slider(
-                            label="Number of samples factor (how many points to sample in the largest category)",
-                            minimum=0,
-                            maximum=1000,
-                            value=1000,
-                            step=1,
-                        )
-                        task_attributes_json = gr.Textbox(
-                            label="Task attributes JSON",
-                        )
-
-                with gr.Column():
-                    annotated_image = gr.AnnotatedImage()
-                    with gr.Accordion("Segmentation bitmap", open=False):
-                        segmentation_bitmap_text = gr.Markdown(
-                            """
-The segmentation bitmap is a 32-bit RGBA png image which contains the segmentation masks.
-The alpha channel is set to 255, and the remaining 24-bit values in the RGB channels correspond to the object ids in the annotations list.
-Unlabeled regions have a value of 0.
-Because of the large dynamic range, the segmentation bitmap appears black in the image viewer.
-"""
-                        )
-                        segmentation_bitmap = gr.Image(
-                            type="pil", label="Segmentation bitmap"
-                        )
-                        annotations_json = gr.Textbox(
-                            label="Annotations JSON",
-                        )
-
-            examples = gr.Examples(
-                examples=[
-                    [
-                        "a2d2.png",
-                        "car, bus, person",
-                        "road, sky, buildings, sidewalk",
-                    ],
-                    [
-                        "bxl.png",
-                        "car, tram, motorcycle, person",
-                        "road, buildings, sky",
-                    ],
-                ],
-                fn=generate_panoptic_mask,
-                inputs=[
-                    input_image,
-                    thing_category_names_string,
-                    stuff_category_names_string,
-                ],
-                outputs=[annotated_image, segmentation_bitmap, annotations_json],
-                cache_examples=True,
-            )
-
-        run_button.click(
-            fn=generate_panoptic_mask,
-            inputs=[
-                input_image,
-                thing_category_names_string,
-                stuff_category_names_string,
-                box_threshold,
-                text_threshold,
-                segmentation_background_threshold,
-                shrink_kernel_size,
-                num_samples_factor,
-                task_attributes_json,
-            ],
-            outputs=[annotated_image, segmentation_bitmap, annotations_json],
-            api_name="segment",
-        )
-
-    block.launch(server_name="0.0.0.0", debug=args.debug, share=args.share)
diff --git a/spaces/wendys-llc/roboflow2huggingface/app.py b/spaces/wendys-llc/roboflow2huggingface/app.py
deleted file mode 100644
index 7cc115acee64666945a5a4a6d5e98035368ab9fe..0000000000000000000000000000000000000000
--- a/spaces/wendys-llc/roboflow2huggingface/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import gradio as gr
-from roboflow2huggingface import roboflow_to_huggingface_pipeline
-
-# https://pypi.org/project/roboflow2huggingface/
-def process(rf_public_url, hf_api_key, rf_api_key, dataset_name):
-    if not rf_public_url.startswith("https://universe.roboflow.com"):
-        return "URL should start with https://universe.roboflow.com"
-    if not hf_api_key:
-        return "Hugging Face API key required, find it at https://huggingface.co/settings/tokens"
-    if not rf_api_key:
-        return "Roboflow API key required"
-    if not dataset_name:
-        dataset_name = roboflow_universe_url.split("/")[:-3]
-        
-    roboflow_to_huggingface_pipeline(
-        roboflow_universe_url=rf_public_url,
-        roboflow_api_key=rf_api_key,
-        hf_dataset_id=dataset_name,
-        hf_write_token=hf_api_key,
-    )
-
-    return "Done"
-
-iface = gr.Interface(fn=process,
-                     inputs=[
-                         gr.Textbox(label="Roboflow Universe Public URL"),
-                         gr.Textbox(label="Hugging Face API key"),
-                         gr.Textbox(label="Roboflow API key"),
-                         gr.Textbox(label="Dataset name for Hugging Face side")
-                     ],
-                     outputs=["text"])
-iface.launch()
diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/management/skill_manager.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/management/skill_manager.py
deleted file mode 100644
index 4f141832a8e547d9a31205a8f2d96125a7658fb2..0000000000000000000000000000000000000000
--- a/spaces/wffcyrus/MetaGPT-v1/metagpt/management/skill_manager.py
+++ /dev/null
@@ -1,79 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/6/5 01:44
-@Author  : alexanderwu
-@File    : skill_manager.py
-@Modified By: mashenquan, 2023/8/20. Remove useless `_llm`
-"""
-from metagpt.actions import Action
-from metagpt.const import PROMPT_PATH
-from metagpt.document_store.chromadb_store import ChromaStore
-from metagpt.logs import logger
-
-Skill = Action
-
-
-class SkillManager:
-    """用来管理所有技能"""
-
-    def __init__(self):
-        self._store = ChromaStore('skill_manager')
-        self._skills: dict[str: Skill] = {}
-
-    def add_skill(self, skill: Skill):
-        """
-        增加技能，将技能加入到技能池与可检索的存储中
-        :param skill: 技能
-        :return:
-        """
-        self._skills[skill.name] = skill
-        self._store.add(skill.desc, {}, skill.name)
-
-    def del_skill(self, skill_name: str):
-        """
-        删除技能，将技能从技能池与可检索的存储中移除
-        :param skill_name: 技能名
-        :return:
-        """
-        self._skills.pop(skill_name)
-        self._store.delete(skill_name)
-
-    def get_skill(self, skill_name: str) -> Skill:
-        """
-        通过技能名获得精确的技能
-        :param skill_name: 技能名
-        :return: 技能
-        """
-        return self._skills.get(skill_name)
-
-    def retrieve_skill(self, desc: str, n_results: int = 2) -> list[Skill]:
-        """
-        通过检索引擎获得技能
-        :param desc: 技能描述
-        :return: 技能（多个）
-        """
-        return self._store.search(desc, n_results=n_results)['ids'][0]
-
-    def retrieve_skill_scored(self, desc: str, n_results: int = 2) -> dict:
-        """
-        通过检索引擎获得技能
-        :param desc: 技能描述
-        :return: 技能与分数组成的字典
-        """
-        return self._store.search(desc, n_results=n_results)
-
-    def generate_skill_desc(self, skill: Skill) -> str:
-        """
-        为每个技能生成对应的描述性文本
-        :param skill:
-        :return:
-        """
-        path = PROMPT_PATH / "generate_skill.md"
-        text = path.read_text()
-        logger.info(text)
-
-
-if __name__ == '__main__':
-    manager = SkillManager()
-    manager.generate_skill_desc(Action())
diff --git a/spaces/whgwd2023/bingo/src/components/ui/input.tsx b/spaces/whgwd2023/bingo/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/whgwd2023/bingo/src/lib/bots/bing/types.ts b/spaces/whgwd2023/bingo/src/lib/bots/bing/types.ts
deleted file mode 100644
index 5a9813b797d13b592ec17b45cfac4bd46510d883..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,261 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
-  Creative = 'Creative',
-  Balanced = 'Balanced',
-  Precise = 'Precise'
-}
-
-export enum ErrorCode {
-  CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
-  BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
-  BING_IP_FORBIDDEN = 'BING_IP_FORBIDDEN',
-  BING_TRY_LATER = 'BING_TRY_LATER',
-  BING_FORBIDDEN = 'BING_FORBIDDEN',
-  BING_CAPTCHA = 'BING_CAPTCHA',
-  THROTTLE_LIMIT = 'THROTTLE_LIMIT',
-  NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
-  UNKOWN_ERROR = 'UNKOWN_ERROR',
-  NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
-  code: ErrorCode
-  constructor(message: string, code: ErrorCode) {
-    super(message)
-    this.code = code
-  }
-}
-
-export type ChatMessageModel = {
-  id: string
-  author: Author
-  text: string
-  error?: ChatError
-  throttling?: Throttling
-  sourceAttributions?: SourceAttribution[]
-  suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
-  messages: ChatMessageModel[]
-}
-
-export type Event =
-  | {
-      type: 'UPDATE_ANSWER'
-      data: {
-        text: string
-        spokenText?: string
-        sourceAttributions?: SourceAttribution[]
-        suggestedResponses?: SuggestedResponse[]
-        throttling?: Throttling
-      }
-    }
-  | {
-      type: 'DONE'
-    }
-  | {
-      type: 'ERROR'
-      error: ChatError
-    }
-
-export interface SendMessageParams<T> {
-  prompt: string
-  imageUrl?: string
-  options: T
-  onEvent: (event: Event) => void
-  signal?: AbortSignal
-}
-
-export interface ConversationResponse {
-  conversationId: string
-  clientId: string
-  conversationSignature: string
-  result: {
-    value: string
-    message?: string
-  }
-}
-
-export interface Telemetry {
-  metrics?: null
-  startTime: string
-}
-
-export interface ChatUpdateArgument {
-  messages?: ChatResponseMessage[]
-  throttling?: Throttling
-  requestId: string
-  result: null
-}
-
-export type ChatUpdateCompleteResponse = {
-  type: 2
-  invocationId: string
-  item: ChatResponseItem
-} | {
-  type: 1
-  target: string
-  arguments: ChatUpdateArgument[]
-} | {
-  type: 3
-  invocationId: string
-} | {
-  type: 6 | 7
-}
-
-export interface ChatRequestResult {
-  value: string
-  serviceVersion: string
-  error?: string
-}
-
-export interface ChatResponseItem {
-  messages: ChatResponseMessage[]
-  firstNewMessageIndex: number
-  suggestedResponses: null
-  conversationId: string
-  requestId: string
-  conversationExpiryTime: string
-  telemetry: Telemetry
-  result: ChatRequestResult
-  throttling: Throttling
-}
-export enum InvocationEventType {
-  Invocation = 1,
-  StreamItem = 2,
-  Completion = 3,
-  StreamInvocation = 4,
-  CancelInvocation = 5,
-  Ping = 6,
-  Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
-  conversationId: string
-  clientId: string
-  conversationSignature: string
-  invocationId: number
-  conversationStyle: BingConversationStyle
-  prompt: string
-  imageUrl?: string
-}
-
-export interface BingChatResponse {
-  conversationSignature: string
-  conversationId: string
-  clientId: string
-  invocationId: number
-  conversationExpiryTime: Date
-  response: string
-  details: ChatResponseMessage
-}
-
-export interface Throttling {
-  maxNumLongDocSummaryUserMessagesInConversation: number
-  maxNumUserMessagesInConversation: number
-  numLongDocSummaryUserMessagesInConversation: number
-  numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
-  text: string
-  spokenText?: string
-  author: string
-  createdAt: Date
-  timestamp: Date
-  messageId: string
-  requestId: string
-  offense: string
-  adaptiveCards: AdaptiveCard[]
-  sourceAttributions: SourceAttribution[]
-  feedback: Feedback
-  contentOrigin: string
-  messageType?: string
-  contentType?: string
-  privacy: null
-  suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
-  type: string
-  version: string
-  body: Body[]
-}
-
-export interface Body {
-  type: string
-  text: string
-  wrap: boolean
-  size?: string
-}
-
-export interface Feedback {
-  tag: null
-  updatedOn: null
-  type: string
-}
-
-export interface SourceAttribution {
-  providerDisplayName: string
-  seeMoreUrl: string
-  searchQuery: string
-}
-
-export interface SuggestedResponse {
-  text: string
-  author?: Author
-  createdAt?: Date
-  timestamp?: Date
-  messageId?: string
-  messageType?: string
-  offense?: string
-  feedback?: Feedback
-  contentOrigin?: string
-  privacy?: null
-}
-
-export interface KBlobRequest {
-  knowledgeRequest: KnowledgeRequestContext
-  imageBase64?: string
-}
-
-export interface KBlobResponse {
-  blobId: string
-  processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
-  imageInfo:        ImageInfo;
-  knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
-  url?: string;
-}
-
-export interface KnowledgeRequest {
-  invokedSkills:            string[];
-  subscriptionId:           string;
-  invokedSkillsRequestData: InvokedSkillsRequestData;
-  convoData:                ConvoData;
-}
-
-export interface ConvoData {
-  convoid:   string;
-  convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
-  enableFaceBlur: boolean;
-}
-
-export interface FileItem {
-  url: string;
-  status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/wuhuik/bingo/src/lib/hooks/use-at-bottom.tsx b/spaces/wuhuik/bingo/src/lib/hooks/use-at-bottom.tsx
deleted file mode 100644
index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/lib/hooks/use-at-bottom.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
-  const [isAtBottom, setIsAtBottom] = React.useState(false)
-
-  React.useEffect(() => {
-    const handleScroll = () => {
-      setIsAtBottom(
-        window.innerHeight + window.scrollY >=
-          document.body.offsetHeight - offset
-      )
-    }
-
-    window.addEventListener('scroll', handleScroll, { passive: true })
-    handleScroll()
-
-    return () => {
-      window.removeEventListener('scroll', handleScroll)
-    }
-  }, [offset])
-
-  return isAtBottom
-}
diff --git a/spaces/wuhuik/bingo/src/lib/hooks/use-bing.ts b/spaces/wuhuik/bingo/src/lib/hooks/use-bing.ts
deleted file mode 100644
index dcdb1667ced0cba299b0825c0e91c4732411308c..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/lib/hooks/use-bing.ts
+++ /dev/null
@@ -1,173 +0,0 @@
-'use client'
-
-import { useState, useCallback, useEffect, useMemo } from 'react'
-import { useAtom, useAtomValue } from 'jotai'
-import { chatFamily, bingConversationStyleAtom, GreetMessages, hashAtom, voiceAtom } from '@/state'
-import { setConversationMessages } from './chat-history'
-import { ChatMessageModel, BotId, FileItem } from '@/lib/bots/bing/types'
-import { nanoid } from '../utils'
-import { TTS } from '../bots/bing/tts'
-
-export function useBing(botId: BotId = 'bing') {
-  const chatAtom = useMemo(() => chatFamily({ botId, page: 'singleton' }), [botId])
-  const [enableTTS] = useAtom(voiceAtom)
-  const speaker = useMemo(() => new TTS(), [])
-  const [hash, setHash] = useAtom(hashAtom)
-  const bingConversationStyle = useAtomValue(bingConversationStyleAtom)
-  const [chatState, setChatState] = useAtom(chatAtom)
-  const [input, setInput] = useState('')
-  const [attachmentList, setAttachmentList] = useState<FileItem[]>([])
-
-  const updateMessage = useCallback(
-    (messageId: string, updater: (message: ChatMessageModel) => void) => {
-      setChatState((draft) => {
-        const message = draft.messages.find((m) => m.id === messageId)
-        if (message) {
-          updater(message)
-        }
-      })
-    },
-    [setChatState],
-  )
-
-  const sendMessage = useCallback(
-    async (input: string, options = {}) => {
-      const botMessageId = nanoid()
-      const imageUrl = attachmentList?.[0]?.status === 'loaded' ? attachmentList[0].url : undefined
-      setChatState((draft) => {
-        const text = imageUrl ? `${input}\n\n![image](${imageUrl})` : input
-        draft.messages.push({ id: nanoid(), text, author: 'user' }, { id: botMessageId, text: '', author: 'bot' })
-        setAttachmentList([])
-      })
-      const abortController = new AbortController()
-      setChatState((draft) => {
-        draft.generatingMessageId = botMessageId
-        draft.abortController = abortController
-      })
-      speaker.reset()
-      await chatState.bot.sendMessage({
-        prompt: input,
-        imageUrl: /\?bcid=([^&]+)/.test(imageUrl ?? '') ? `https://www.bing.com/images/blob?bcid=${RegExp.$1}` : imageUrl,
-        options: {
-          ...options,
-          bingConversationStyle,
-        },
-        signal: abortController.signal,
-        onEvent(event) {
-          if (event.type === 'UPDATE_ANSWER') {
-            updateMessage(botMessageId, (message) => {
-              if (event.data.text.length > message.text.length) {
-                message.text = event.data.text
-              }
-
-              if (event.data.spokenText && enableTTS) {
-                speaker.speak(event.data.spokenText)
-              }
-
-              message.throttling = event.data.throttling || message.throttling
-              message.sourceAttributions = event.data.sourceAttributions || message.sourceAttributions
-              message.suggestedResponses = event.data.suggestedResponses || message.suggestedResponses
-            })
-          } else if (event.type === 'ERROR') {
-            updateMessage(botMessageId, (message) => {
-              message.error = event.error
-            })
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          } else if (event.type === 'DONE') {
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          }
-        },
-      })
-    },
-    [botId, attachmentList, chatState.bot, setChatState, updateMessage],
-  )
-
-  const uploadImage = useCallback(async (imgUrl: string) => {
-    setAttachmentList([{ url: imgUrl, status: 'loading' }])
-    const response = await chatState.bot.uploadImage(imgUrl, bingConversationStyle)
-    if (response?.blobId) {
-      setAttachmentList([{ url: `/api/blob?bcid=${response.blobId}`, status: 'loaded' }])
-    } else {
-      setAttachmentList([{ url: imgUrl, status: 'error' }])
-    }
-  }, [chatState.bot])
-
-  const resetConversation = useCallback(() => {
-    chatState.bot.resetConversation()
-    speaker.abort()
-    setChatState((draft) => {
-      draft.abortController = undefined
-      draft.generatingMessageId = ''
-      draft.messages = [{ author: 'bot', text: GreetMessages[Math.floor(GreetMessages.length * Math.random())], id: nanoid() }]
-      draft.conversationId = nanoid()
-    })
-  }, [chatState.bot, setChatState])
-
-  const stopGenerating = useCallback(() => {
-    chatState.abortController?.abort()
-    if (chatState.generatingMessageId) {
-      updateMessage(chatState.generatingMessageId, (message) => {
-        if (!message.text && !message.error) {
-          message.text = 'Cancelled'
-        }
-      })
-    }
-    setChatState((draft) => {
-      draft.generatingMessageId = ''
-    })
-  }, [chatState.abortController, chatState.generatingMessageId, setChatState, updateMessage])
-
-  useEffect(() => {
-    if (chatState.messages.length) {
-      setConversationMessages(botId, chatState.conversationId, chatState.messages)
-    }
-  }, [botId, chatState.conversationId, chatState.messages])
-
-  useEffect(() => {
-    if (hash === 'reset') {
-      resetConversation()
-      setHash('')
-    }
-  }, [hash, setHash])
-
-  const chat = useMemo(
-    () => ({
-      botId,
-      bot: chatState.bot,
-      isSpeaking: speaker.isSpeaking,
-      messages: chatState.messages,
-      sendMessage,
-      setInput,
-      input,
-      resetConversation,
-      generating: !!chatState.generatingMessageId,
-      stopGenerating,
-      uploadImage,
-      setAttachmentList,
-      attachmentList,
-    }),
-    [
-      botId,
-      bingConversationStyle,
-      chatState.bot,
-      chatState.generatingMessageId,
-      chatState.messages,
-      speaker.isSpeaking,
-      setInput,
-      input,
-      setAttachmentList,
-      attachmentList,
-      resetConversation,
-      sendMessage,
-      stopGenerating,
-    ],
-  )
-
-  return chat
-}
diff --git a/spaces/wy213/213a/src/components/ui/voice/index.tsx b/spaces/wy213/213a/src/components/ui/voice/index.tsx
deleted file mode 100644
index 4adcb632226bfced8b97092782811edf08b56569..0000000000000000000000000000000000000000
--- a/spaces/wy213/213a/src/components/ui/voice/index.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import './index.scss'
-
-export interface VoiceProps extends CSSPropertyRule {
-  num?: number;
-  duration?: number;
-}
-export default function Voice({ duration = 400, num = 7, ...others }) {
-  return (
-    <div className="voice-button" { ...others }>
-      {Array.from({ length: num }).map((_, index) => {
-        const randomDuration = Math.random() * 100 + duration
-        const initialDelay = Math.random() * 2 * duration
-        const initialScale = Math.sin((index + 1) * Math.PI / num)
-        return (
-          <div
-            className="voice-button-item"
-            key={index}
-            style={{
-              animationDelay: initialDelay + 'ms',
-              animationDuration: randomDuration + 'ms',
-              transform: `scale(${initialScale})`
-            }}
-          />
-        )
-      })}
-    </div>
-  )
-}
diff --git a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/body/encoder/transformer_encoder_fpn.py b/spaces/xdecoder/Instruct-X-Decoder/xdecoder/body/encoder/transformer_encoder_fpn.py
deleted file mode 100644
index 16e449fd3ac19a5d143d4fc61cbafc16158b0654..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/body/encoder/transformer_encoder_fpn.py
+++ /dev/null
@@ -1,324 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
-from torch.cuda.amp import autocast
-
-import fvcore.nn.weight_init as weight_init
-from detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm
-
-from .registry import register_encoder
-from ..transformer_blocks import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn
-from ...modules import PositionEmbeddingSine
-from ...utils import configurable
-
-# from ..layers import Conv2d, DeformConv, ShapeSpec, get_norm
-
-# This is a modified FPN decoder.
-class BasePixelDecoder(nn.Module):
-    def __init__(
-        self,
-        input_shape: Dict[str, ShapeSpec],
-        *,
-        conv_dim: int,
-        mask_dim: int,
-        mask_on: bool,
-        norm: Optional[Union[str, Callable]] = None,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            input_shape: shapes (channels and stride) of the input features
-            conv_dims: number of output channels for the intermediate conv layers.
-            mask_dim: number of output channels for the final conv layer.
-            norm (str or callable): normalization for all conv layers
-        """
-        super().__init__()
-
-        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
-        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
-        feature_channels = [v.channels for k, v in input_shape]
-
-        lateral_convs = []
-        output_convs = []
-
-        use_bias = norm == ""
-        for idx, in_channels in enumerate(feature_channels):
-            if idx == len(self.in_features) - 1:
-                output_norm = get_norm(norm, conv_dim)
-                output_conv = Conv2d(
-                    in_channels,
-                    conv_dim,
-                    kernel_size=3,
-                    stride=1,
-                    padding=1,
-                    bias=use_bias,
-                    norm=output_norm,
-                    activation=F.relu,
-                )
-                weight_init.c2_xavier_fill(output_conv)
-                self.add_module("layer_{}".format(idx + 1), output_conv)
-
-                lateral_convs.append(None)
-                output_convs.append(output_conv)
-            else:
-                lateral_norm = get_norm(norm, conv_dim)
-                output_norm = get_norm(norm, conv_dim)
-
-                lateral_conv = Conv2d(
-                    in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
-                )
-                output_conv = Conv2d(
-                    conv_dim,
-                    conv_dim,
-                    kernel_size=3,
-                    stride=1,
-                    padding=1,
-                    bias=use_bias,
-                    norm=output_norm,
-                    activation=F.relu,
-                )
-                weight_init.c2_xavier_fill(lateral_conv)
-                weight_init.c2_xavier_fill(output_conv)
-                self.add_module("adapter_{}".format(idx + 1), lateral_conv)
-                self.add_module("layer_{}".format(idx + 1), output_conv)
-
-                lateral_convs.append(lateral_conv)
-                output_convs.append(output_conv)
-        # Place convs into top-down order (from low to high resolution)
-        # to make the top-down computation in forward clearer.
-        self.lateral_convs = lateral_convs[::-1]
-        self.output_convs = output_convs[::-1]
-
-        self.mask_on = mask_on
-        if self.mask_on:
-            self.mask_dim = mask_dim
-            self.mask_features = Conv2d(
-                conv_dim,
-                mask_dim,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            )
-            weight_init.c2_xavier_fill(self.mask_features)
-
-        self.maskformer_num_feature_levels = 3  # always use 3 scales
-
-    @classmethod
-    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
-        enc_cfg = cfg['MODEL']['ENCODER']
-        ret = {}
-        ret["input_shape"] = {
-            k: v for k, v in input_shape.items() if k in enc_cfg['IN_FEATURES']
-        }
-        ret["conv_dim"] = enc_cfg['CONVS_DIM']
-        ret["mask_dim"] = enc_cfg['MASK_DIM']
-        ret["norm"] = enc_cfg['NORM']
-        return ret
-
-    def forward_features(self, features):
-        multi_scale_features = []
-        num_cur_levels = 0
-        # Reverse feature maps into top-down order (from low to high resolution)
-        for idx, f in enumerate(self.in_features[::-1]):
-            x = features[f]
-            lateral_conv = self.lateral_convs[idx]
-            output_conv = self.output_convs[idx]
-            if lateral_conv is None:
-                y = output_conv(x)
-            else:
-                cur_fpn = lateral_conv(x)
-                # Following FPN implementation, we use nearest upsampling here
-                y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
-                y = output_conv(y)
-            if num_cur_levels < self.maskformer_num_feature_levels:
-                multi_scale_features.append(y)
-                num_cur_levels += 1
-        
-        mask_features = self.mask_features(y) if self.mask_on else None
-        return mask_features, None, multi_scale_features
-
-    def forward(self, features, targets=None):
-        logger = logging.getLogger(__name__)
-        logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.")
-        return self.forward_features(features)
-
-
-class TransformerEncoderOnly(nn.Module):
-    def __init__(
-        self,
-        d_model=512,
-        nhead=8,
-        num_encoder_layers=6,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        normalize_before=False,
-    ):
-        super().__init__()
-
-        encoder_layer = TransformerEncoderLayer(
-            d_model, nhead, dim_feedforward, dropout, activation, normalize_before
-        )
-        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-        self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
-
-        self._reset_parameters()
-
-        self.d_model = d_model
-        self.nhead = nhead
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(self, src, mask, pos_embed):
-        # flatten NxCxHxW to HWxNxC
-        bs, c, h, w = src.shape
-        src = src.flatten(2).permute(2, 0, 1)
-        pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
-        if mask is not None:
-            mask = mask.flatten(1)
-
-        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
-        return memory.permute(1, 2, 0).view(bs, c, h, w)
-
-
-# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.
-class TransformerEncoderPixelDecoder(BasePixelDecoder):
-    @configurable
-    def __init__(
-        self,
-        input_shape: Dict[str, ShapeSpec],
-        *,
-        transformer_dropout: float,
-        transformer_nheads: int,
-        transformer_dim_feedforward: int,
-        transformer_enc_layers: int,
-        transformer_pre_norm: bool,
-        conv_dim: int,
-        mask_dim: int,
-        mask_on: int,
-        norm: Optional[Union[str, Callable]] = None,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            input_shape: shapes (channels and stride) of the input features
-            transformer_dropout: dropout probability in transformer
-            transformer_nheads: number of heads in transformer
-            transformer_dim_feedforward: dimension of feedforward network
-            transformer_enc_layers: number of transformer encoder layers
-            transformer_pre_norm: whether to use pre-layernorm or not
-            conv_dims: number of output channels for the intermediate conv layers.
-            mask_dim: number of output channels for the final conv layer.
-            norm (str or callable): normalization for all conv layers
-        """
-        super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm, mask_on=mask_on)
-
-        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
-        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
-        feature_strides = [v.stride for k, v in input_shape]
-        feature_channels = [v.channels for k, v in input_shape]
-
-        in_channels = feature_channels[len(self.in_features) - 1]
-        self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)
-        weight_init.c2_xavier_fill(self.input_proj)
-        self.transformer = TransformerEncoderOnly(
-            d_model=conv_dim,
-            dropout=transformer_dropout,
-            nhead=transformer_nheads,
-            dim_feedforward=transformer_dim_feedforward,
-            num_encoder_layers=transformer_enc_layers,
-            normalize_before=transformer_pre_norm,
-        )
-        N_steps = conv_dim // 2
-        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
-
-        # update layer
-        use_bias = norm == ""
-        output_norm = get_norm(norm, conv_dim)
-        output_conv = Conv2d(
-            conv_dim,
-            conv_dim,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=use_bias,
-            norm=output_norm,
-            activation=F.relu,
-        )
-        weight_init.c2_xavier_fill(output_conv)
-        delattr(self, "layer_{}".format(len(self.in_features)))
-        self.add_module("layer_{}".format(len(self.in_features)), output_conv)
-        self.output_convs[0] = output_conv
-
-    @classmethod
-    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
-        enc_cfg = cfg['MODEL']['ENCODER']
-        dec_cfg = cfg['MODEL']['DECODER']
-
-        ret = super().from_config(cfg, input_shape)
-        ret["transformer_dropout"] = dec_cfg['DROPOUT']
-        ret["transformer_nheads"] = dec_cfg['NHEADS']
-        ret["transformer_dim_feedforward"] = dec_cfg['DIM_FEEDFORWARD']
-        ret["transformer_enc_layers"] = enc_cfg['TRANSFORMER_ENC_LAYERS']  # a separate config
-        ret["transformer_pre_norm"] = dec_cfg['PRE_NORM']
-
-        ret['mask_on'] = cfg['MODEL']['DECODER']['MASK']
-        return ret
-
-    def forward_features(self, features):
-        multi_scale_features = []
-        num_cur_levels = 0
-        
-        # Reverse feature maps into top-down order (from low to high resolution)
-        for idx, f in enumerate(self.in_features[::-1]):
-            x = features[f]
-            lateral_conv = self.lateral_convs[idx]
-            output_conv = self.output_convs[idx]
-            if lateral_conv is None:
-                transformer = self.input_proj(x)
-                pos = self.pe_layer(x)
-                transformer = self.transformer(transformer, None, pos)
-                y = output_conv(transformer)
-                # save intermediate feature as input to Transformer decoder
-                transformer_encoder_features = transformer
-            else:
-                cur_fpn = lateral_conv(x)
-                # Following FPN implementation, we use nearest upsampling here
-                y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
-                y = output_conv(y)
-            if num_cur_levels < self.maskformer_num_feature_levels:
-                multi_scale_features.append(y)
-                num_cur_levels += 1
-
-        mask_features = self.mask_features(y) if self.mask_on else None
-        return mask_features, transformer_encoder_features, multi_scale_features
-
-    def forward(self, features, targets=None):
-        logger = logging.getLogger(__name__)
-        logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.")
-        return self.forward_features(features)
-
-
-
-@register_encoder
-def get_transformer_encoder_fpn(cfg, input_shape):
-    """
-    Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`.
-    """
-    model = TransformerEncoderPixelDecoder(cfg, input_shape)    
-    forward_features = getattr(model, "forward_features", None)
-    if not callable(forward_features):
-        raise ValueError(
-            "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
-            f"Please implement forward_features for {name} to only return mask features."
-        )
-    return model
\ No newline at end of file
diff --git a/spaces/xiang-wuu/yolov5/.github/PULL_REQUEST_TEMPLATE.md b/spaces/xiang-wuu/yolov5/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index f25b017ace8b67408aad972c35fbcdcc2ff185ce..0000000000000000000000000000000000000000
--- a/spaces/xiang-wuu/yolov5/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,9 +0,0 @@
-<!--
-Thank you for submitting a YOLOv5 🚀 Pull Request! We want to make contributing to YOLOv5 as easy and transparent as possible. A few tips to get you started:
-
-- Search existing YOLOv5 [PRs](https://github.com/ultralytics/yolov5/pull) to see if a similar PR already exists.
-- Link this PR to a YOLOv5 [issue](https://github.com/ultralytics/yolov5/issues) to help us understand what bug fix or feature is being implemented.
-- Provide before and after profiling/inference/training results to help us quantify the improvement your PR provides (if applicable).
-
-Please see our ✅ [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) for more details.
--->
diff --git a/spaces/yaelvinker/CLIPasso/CLIP_/setup.py b/spaces/yaelvinker/CLIPasso/CLIP_/setup.py
deleted file mode 100644
index c9ea7d0d2f3d2fcf66d6f6e2aa0eb1a97a524bb6..0000000000000000000000000000000000000000
--- a/spaces/yaelvinker/CLIPasso/CLIP_/setup.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-
-import pkg_resources
-from setuptools import setup, find_packages
-
-setup(
-    name="clip",
-    py_modules=["clip"],
-    version="1.0",
-    description="",
-    author="OpenAI",
-    packages=find_packages(exclude=["tests*"]),
-    install_requires=[
-        str(r)
-        for r in pkg_resources.parse_requirements(
-            open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
-        )
-    ],
-    include_package_data=True,
-    extras_require={'dev': ['pytest']},
-)
diff --git a/spaces/yangheng/Super-Resolution-Anime-Diffusion/Waifu2x/Readme.md b/spaces/yangheng/Super-Resolution-Anime-Diffusion/Waifu2x/Readme.md
deleted file mode 100644
index bc528c3474faeff4784aecfc44c9fd8aeac092b6..0000000000000000000000000000000000000000
--- a/spaces/yangheng/Super-Resolution-Anime-Diffusion/Waifu2x/Readme.md
+++ /dev/null
@@ -1,167 +0,0 @@
-# Waifu2x
-
- Re-implementation on the original [waifu2x](https://github.com/nagadomi/waifu2x) in PyTorch with additional super resolution models. This repo is mainly used to explore interesting super resolution models. User-friendly tools may not be available now ><.  
-
-## Dependencies 
-* Python 3x
-* [PyTorch](https://pytorch.org/) >= 1 ( > 0.41 shall also work, but not guarantee)
-* [Nvidia/Apex](https://github.com/NVIDIA/apex/) (used for mixed precision training, you may use the [python codes](https://github.com/NVIDIA/apex/tree/master/apex/fp16_utils) directly)
-
-Optinal: Nvidia GPU. Model inference (32 fp only) can run in cpu only. 
-
-## What's New
-* Add [CARN Model (Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network)](https://github.com/nmhkahn/CARN-pytorch). Model Codes are adapted from the authors's [github repo](https://github.com/nmhkahn/CARN-pytorch). I add [Spatial Channel Squeeze Excitation](https://arxiv.org/abs/1709.01507) and swap all 1x1 convolution with 3x3 standard convolutions. The model is trained in fp 16 with Nvidia's [apex](https://github.com/NVIDIA/apex). Details and plots on model variant can be found in [docs/CARN](./docs/CARN)
-
-* Dilated Convolution seems less effective (if not make the model worse) in super resolution, though it brings some improvement in image segmentation, especially when dilated rate increases and then decreases. Further investigation is needed. 
-
-## How to Use 
-Compare the input image and upscaled image
-```python
-from utils.prepare_images import *
-from Models import *
-from torchvision.utils import save_image
-model_cran_v2 = CARN_V2(color_channels=3, mid_channels=64, conv=nn.Conv2d,
-                        single_conv_size=3, single_conv_group=1,
-                        scale=2, activation=nn.LeakyReLU(0.1),
-                        SEBlock=True, repeat_blocks=3, atrous=(1, 1, 1))
-                        
-model_cran_v2 = network_to_half(model_cran_v2)
-checkpoint = "model_check_points/CRAN_V2/CARN_model_checkpoint.pt"
-model_cran_v2.load_state_dict(torch.load(checkpoint, 'cpu'))
-# if use GPU, then comment out the next line so it can use fp16. 
-model_cran_v2 = model_cran_v2.float() 
-
-demo_img = "input_image.png"
-img = Image.open(demo_img).convert("RGB")
-
-# origin
-img_t = to_tensor(img).unsqueeze(0) 
-
-# used to compare the origin
-img = img.resize((img.size[0] // 2, img.size[1] // 2), Image.BICUBIC) 
-
-# overlapping split
-# if input image is too large, then split it into overlapped patches 
-# details can be found at [here](https://github.com/nagadomi/waifu2x/issues/238)
-img_splitter = ImageSplitter(seg_size=64, scale_factor=2, boarder_pad_size=3)
-img_patches = img_splitter.split_img_tensor(img, scale_method=None, img_pad=0)
-with torch.no_grad():
-    out = [model_cran_v2(i) for i in img_patches]
-img_upscale = img_splitter.merge_img_tensor(out)
-
-final = torch.cat([img_t, img_upscale])
-save_image(final, 'out.png', nrow=2)
-```
-
- ## Training
- 
- If possible, fp16 training is preferred because it is much faster with minimal quality decrease. 
- 
- Sample training script is available in `train.py`, but you may need to change some liens. 
- 
- ###  Image Processing
- Original images are all at least 3k x 3K. I downsample them  by LANCZOS so that  one side has at most 2048, then I randomly cut them into 256x256 patches as target  and use 128x128 with jpeg noise as input images. All input patches have at least 14 kb, and they are stored in SQLite with BLOB format. SQlite seems to have [better performance](https://www.sqlite.org/intern-v-extern-blob.html) than file system for small objects. H5 file format may not be optimal because of its larger size. 
- 
- Although convolutions can take in any sizes of images, the content of image matters. For real life images, small patches may maintain color,brightness, etc variances in small regions, but for digital drawn images, colors are added in block areas. A small patch may end up showing entirely one color, and the model has little to learn. 
- 
- For example, the following two plots come from CARN and have the same settings, including initial parameters. Both training loss and ssim are lower for 64x64, but they perform worse in test time compared to 128x128. 
- 
- ![loss](docs/CARN/plots/128_vs_64_model_loss.png)
- ![ssim](docs/CARN/plots/128_vs_64_model_ssim.png)
-  
-
-Downsampling methods  are uniformly chosen among ```[PIL.Image.BILINEAR, PIL.Image.BICUBIC, PIL.Image.LANCZOS]``` , so different patches in the same image might be down-scaled in different ways. 
-
-Image noise are from JPEG format only. They are added by re-encoding PNG images into PIL's JPEG data with various quality. Noise level 1 means quality ranges uniformly from [75, 95]; level 2 means quality ranges uniformly from [50, 75]. 
- 
-
- ## Models
- Models are tuned and modified with extra features. 
- 
- 
-* [DCSCN 12](https://github.com/jiny2001/dcscn-super-resolution) 
-
-* [CRAN](https://github.com/nmhkahn/CARN-pytorch)
- 
- #### From [Waifu2x](https://github.com/nagadomi/waifu2x)
- * [Upconv7](https://github.com/nagadomi/waifu2x/blob/7d156917ae1113ab847dab15c75db7642231e7fa/lib/srcnn.lua#L360)
- 
- * [Vgg_7](https://github.com/nagadomi/waifu2x/blob/7d156917ae1113ab847dab15c75db7642231e7fa/lib/srcnn.lua#L334)
- 
- * [Cascaded Residual U-Net with SEBlock](https://github.com/nagadomi/waifu2x/blob/7d156917ae1113ab847dab15c75db7642231e7fa/lib/srcnn.lua#L514) (PyTorch codes are not available and under testing)
- 
- #### Models Comparison
-   Images are from [Key: サマボケ(Summer Pocket)](http://key.visualarts.gr.jp/summer/).
- 
- The left column is the original image, and the right column is bicubic, DCSCN, CRAN_V2
- 
-![img](docs/demo_bicubic_model_comparison.png)
-
-
-![img](docs/demo_true_bicubic_dcscn_upconv.png)
-
-
- 
- ##### Scores
- The list will be updated after I add more models. 
- 
-Images are twitter icons (PNG) from [Key: サマボケ(Summer Pocket)](http://key.visualarts.gr.jp/summer/). They are cropped into non-overlapping 96x96 patches and down-scaled by 2. Then images are re-encoded into JPEG format with quality from [75, 95]. Scores are PSNR and MS-SSIM. 
-
-|       | Total Parameters | BICUBIC  | Random* |
-| :---: | :---:   | :---:  |  :---:  |
-| CRAN V2| 2,149,607 | 34.0985 (0.9924) |  34.0509 (0.9922) |
-| DCSCN 12 |1,889,974 | 31.5358 (0.9851) | 31.1457 (0.9834) |   
-| Upconv 7| 552,480|  31.4566 (0.9788) |   30.9492 (0.9772)   |
-
-*uniformly select down scale methods from Image.BICUBIC, Image.BILINEAR, Image.LANCZOS.
-            
-
-
- 
-
- #### DCSCN
-[Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network](https://github.com/jiny2001/dcscn-super-resolution#fast-and-accurate-image-super-resolution-by-deep-cnn-with-skip-connection-and-network-in-network)
- 
- DCSCN is very interesting as it  has relatively quick forward computation, and  both the shallow model (layerr 8) and deep model (layer 12) are quick to train. The settings are different from the paper. 
- 
- * I use exponential decay to decrease the number of feature filters in each layer. [Here](https://github.com/jiny2001/dcscn-super-resolution/blob/a868775930c6b36922897b0203468f3f1481e935/DCSCN.py#L204) is the original filter decay method. 
- 
- * I also increase the reconstruction filters from 48 to 128. 
- 
- * All activations are replaced by SELU. Dropout and weight decay are not added neither because they significantly increase the training time. 
- 
- * The loss function is changed from MSE to L1. 
-   According to [Loss Functions for Image Restoration with Neural
-Networks](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0ahUKEwi7kuGt_7_bAhXrqVQKHRqhCcUQFghUMAM&url=http%3A%2F%2Fresearch.nvidia.com%2Fsites%2Fdefault%2Ffiles%2Fpubs%2F2017-03_Loss-Functions-for%2Fcomparison_tci.pdf&usg=AOvVaw1p0ndOKRH2ZaEsumO7d_bA),  L1 seems to be more robust and converges faster than MSE.  But the authors find the results from L1 and MSE are [similar](https://github.com/jiny2001/dcscn-super-resolution/issues/29). 
- 
- 
- I need to thank jiny2001 (one of the paper's author) to test the difference of SELU and PRELU. SELU seems more stable and has fewer parameters to train. It is a good drop in replacement
- >layers=8, filters=96 and dataset=yang91+bsd200. 
- ![](docs/DCSCN_comparison/selu_prelu.png)
- The details can be found in [here]( https://github.com/jiny2001/dcscn-super-resolution/issues/29). 
- 
- 
- 
- A pre-trained 12-layer model as well as model parameters are available. The model run time is around 3-5 times of Waifu2x. The output quality is usually visually indistinguishable, but its PSNR and SSIM are  bit higher. Though, such comparison is not fair since the 12-layer model has around 1,889,974 parameters, 5 times more than waifu2x's Upconv_7 model. 
- 
- #### CARN
- Channels are set to 64 across all blocks, so residual adds are very effective. Increase the channels to 128 lower the loss curve a little bit but doubles the total parameters from 0.9 Millions to 3 Millions. 32 Channels has much worse performance. Increasing the number of cascaded blocks from 3 to 5 doesn't lower the loss a lot. 
-  
- SE Blocks seems to have the most obvious improvement without increasing the computation a lot. Partial based padding seems have little effect if not decrease the quality. Atrous convolution is slower about 10%-20% than normal convolution in Pytorch 1.0, but there are no obvious improvement. 
-
-Another more effective model is to add upscaled input image to the final convolution. A simple bilinear upscaled image seems sufficient. 
-
-More examples on model configurations can be found in [docs/CARN folder](./docs/CARN/carn_plot_loss.md)
-
-![img](docs/CARN/plots/CARN_Compare.png)
-
-![img](docs/CARN/plots/CARN_Compare_Res_Add.png)
-
-### Waifu2x Original Models 
-Models can load waifu2x's pre-trained weights.  The function ```forward_checkpoint```  sets the ```nn.LeakyReLU``` to compute data inplace.
-
-#### Upconv_7
-Original waifu2x's model. PyTorch's implementation with cpu only is around 5 times longer for large images.  The output images have very close PSNR and SSIM scores compared to images generated from the [caffe version](https://github.com/lltcggie/waifu2x-caffe) , thought they are not identical. 
-
-#### Vgg_7
-Not tested yet, but it is ready to use. 
diff --git a/spaces/ybelkada/interfacegan_pp/torch_utils/ops/upfirdn2d.h b/spaces/ybelkada/interfacegan_pp/torch_utils/ops/upfirdn2d.h
deleted file mode 100644
index 2793daf874492af01e8634a7863c036e17b6731f..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/interfacegan_pp/torch_utils/ops/upfirdn2d.h
+++ /dev/null
@@ -1,59 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/yerfor/SyntaSpeech/modules/commons/rel_transformer.py b/spaces/yerfor/SyntaSpeech/modules/commons/rel_transformer.py
deleted file mode 100644
index 3d83cb1c9bd8df6f9ddc6f373e299336ec55f08c..0000000000000000000000000000000000000000
--- a/spaces/yerfor/SyntaSpeech/modules/commons/rel_transformer.py
+++ /dev/null
@@ -1,378 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from modules.commons.layers import Embedding
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-class Encoder(nn.Module):
-    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
-                 window_size=None, block_length=None, pre_ln=False, **kwargs):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.pre_ln = pre_ln
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size,
-                                   p_dropout=p_dropout, block_length=block_length))
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-        if pre_ln:
-            self.last_ln = LayerNorm(hidden_channels)
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        for i in range(self.n_layers):
-            x = x * x_mask
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_1[i](x)
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_1[i](x)
-
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_2[i](x)
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_2[i](x)
-        if self.pre_ln:
-            x = self.last_ln(x)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.,
-                 block_length=None, proximal_bias=False, proximal_init=False):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.p_dropout = p_dropout
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels ** -0.5
-            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        if proximal_init:
-            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
-            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
-        if self.window_size is not None:
-            assert t_s == t_t, "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
-            rel_logits = self._relative_position_to_absolute_position(rel_logits)
-            scores_local = rel_logits / math.sqrt(self.k_channels)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-                scores = scores * block_mask + -1e4 * (1 - block_mask)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
-        x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(x * x_mask)
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        return x * x_mask
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-4):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        n_dims = len(x.shape)
-        mean = torch.mean(x, 1, keepdim=True)
-        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
-
-        x = (x - mean) * torch.rsqrt(variance + self.eps)
-
-        shape = [1, -1] + [1] * (n_dims - 2)
-        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
-        return x
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(
-            nn.ReLU(),
-            nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class RelTransformerEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout=0.0,
-                 window_size=4,
-                 block_length=None,
-                 prenet=True,
-                 pre_ln=True,
-                 ):
-
-        super().__init__()
-
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.prenet = prenet
-        if n_vocab > 0:
-            self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
-
-        if prenet:
-            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
-                                    kernel_size=5, n_layers=3, p_dropout=0)
-        self.encoder = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-    def forward(self, x, x_mask=None):
-        if self.n_vocab > 0:
-            x_lengths = (x > 0).long().sum(-1)
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        else:
-            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        if self.prenet:
-            x = self.pre(x, x_mask)
-        x = self.encoder(x, x_mask)
-        return x.transpose(1, 2)
diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/modules/layers/simswap/pg_modules/projector.py b/spaces/ygtxr1997/ReliableSwap_Demo/modules/layers/simswap/pg_modules/projector.py
deleted file mode 100644
index 34a4521b3c67aee16fdcca10df2d518aaea043be..0000000000000000000000000000000000000000
--- a/spaces/ygtxr1997/ReliableSwap_Demo/modules/layers/simswap/pg_modules/projector.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import torch
-import torch.nn as nn
-import timm
-from modules.layers.simswap.pg_modules.blocks import FeatureFusionBlock
-
-
-def _make_scratch_ccm(scratch, in_channels, cout, expand=False):
-    # shapes
-    out_channels = [cout, cout*2, cout*4, cout*8] if expand else [cout]*4
-
-    scratch.layer0_ccm = nn.Conv2d(in_channels[0], out_channels[0], kernel_size=1, stride=1, padding=0, bias=True)
-    scratch.layer1_ccm = nn.Conv2d(in_channels[1], out_channels[1], kernel_size=1, stride=1, padding=0, bias=True)
-    scratch.layer2_ccm = nn.Conv2d(in_channels[2], out_channels[2], kernel_size=1, stride=1, padding=0, bias=True)
-    scratch.layer3_ccm = nn.Conv2d(in_channels[3], out_channels[3], kernel_size=1, stride=1, padding=0, bias=True)
-
-    scratch.CHANNELS = out_channels
-
-    return scratch
-
-
-def _make_scratch_csm(scratch, in_channels, cout, expand):
-    scratch.layer3_csm = FeatureFusionBlock(in_channels[3], nn.ReLU(False), expand=expand, lowest=True)
-    scratch.layer2_csm = FeatureFusionBlock(in_channels[2], nn.ReLU(False), expand=expand)
-    scratch.layer1_csm = FeatureFusionBlock(in_channels[1], nn.ReLU(False), expand=expand)
-    scratch.layer0_csm = FeatureFusionBlock(in_channels[0], nn.ReLU(False))
-
-    # last refinenet does not expand to save channels in higher dimensions
-    scratch.CHANNELS = [cout, cout, cout*2, cout*4] if expand else [cout]*4
-
-    return scratch
-
-
-def _make_efficientnet(model):
-    pretrained = nn.Module()
-    pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, *model.blocks[0:2])
-    pretrained.layer1 = nn.Sequential(*model.blocks[2:3])
-    pretrained.layer2 = nn.Sequential(*model.blocks[3:5])
-    pretrained.layer3 = nn.Sequential(*model.blocks[5:9])
-    return pretrained
-
-
-def calc_channels(pretrained, inp_res=224):
-    channels = []
-    tmp = torch.zeros(1, 3, inp_res, inp_res)
-
-    # forward pass
-    tmp = pretrained.layer0(tmp)
-    channels.append(tmp.shape[1])
-    tmp = pretrained.layer1(tmp)
-    channels.append(tmp.shape[1])
-    tmp = pretrained.layer2(tmp)
-    channels.append(tmp.shape[1])
-    tmp = pretrained.layer3(tmp)
-    channels.append(tmp.shape[1])
-
-    return channels
-
-
-def _make_projector(im_res, cout, proj_type, expand=False):
-    assert proj_type in [0, 1, 2], "Invalid projection type"
-
-    ### Build pretrained feature network
-    model = timm.create_model('tf_efficientnet_lite0', pretrained=False,
-                              checkpoint_path='/gavin/code/FaceSwapping/modules/third_party/efficientnet/'
-                                              'tf_efficientnet_lite0-0aa007d2.pth')
-    pretrained = _make_efficientnet(model)
-
-    # determine resolution of feature maps, this is later used to calculate the number
-    # of down blocks in the discriminators. Interestingly, the best results are achieved
-    # by fixing this to 256, ie., we use the same number of down blocks per discriminator
-    # independent of the dataset resolution
-    im_res = 256
-    pretrained.RESOLUTIONS = [im_res//4, im_res//8, im_res//16, im_res//32]
-    pretrained.CHANNELS = calc_channels(pretrained)
-
-    if proj_type == 0: return pretrained, None
-
-    ### Build CCM
-    scratch = nn.Module()
-    scratch = _make_scratch_ccm(scratch, in_channels=pretrained.CHANNELS, cout=cout, expand=expand)
-    pretrained.CHANNELS = scratch.CHANNELS
-
-    if proj_type == 1: return pretrained, scratch
-
-    ### build CSM
-    scratch = _make_scratch_csm(scratch, in_channels=scratch.CHANNELS, cout=cout, expand=expand)
-
-    # CSM upsamples x2 so the feature map resolution doubles
-    pretrained.RESOLUTIONS = [res*2 for res in pretrained.RESOLUTIONS]
-    pretrained.CHANNELS = scratch.CHANNELS
-
-    return pretrained, scratch
-
-
-class F_RandomProj(nn.Module):
-    def __init__(
-        self,
-        im_res=256,
-        cout=64,
-        expand=True,
-        proj_type=2,  # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
-        **kwargs,
-    ):
-        super().__init__()
-        self.proj_type = proj_type
-        self.cout = cout
-        self.expand = expand
-
-        # build pretrained feature network and random decoder (scratch)
-        self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand)
-        self.CHANNELS = self.pretrained.CHANNELS
-        self.RESOLUTIONS = self.pretrained.RESOLUTIONS
-
-    def forward(self, x, get_features=False):
-        # predict feature maps
-        out0 = self.pretrained.layer0(x)
-        out1 = self.pretrained.layer1(out0)
-        out2 = self.pretrained.layer2(out1)
-        out3 = self.pretrained.layer3(out2)
-
-        # start enumerating at the lowest layer (this is where we put the first discriminator)
-        backbone_features = {
-            '0': out0,
-            '1': out1,
-            '2': out2,
-            '3': out3,
-        }
-        if get_features:
-            return backbone_features
-
-        if self.proj_type == 0: return backbone_features
-
-        out0_channel_mixed = self.scratch.layer0_ccm(backbone_features['0'])
-        out1_channel_mixed = self.scratch.layer1_ccm(backbone_features['1'])
-        out2_channel_mixed = self.scratch.layer2_ccm(backbone_features['2'])
-        out3_channel_mixed = self.scratch.layer3_ccm(backbone_features['3'])
-
-        out = {
-            '0': out0_channel_mixed,
-            '1': out1_channel_mixed,
-            '2': out2_channel_mixed,
-            '3': out3_channel_mixed,
-        }
-
-        if self.proj_type == 1: return out
-
-        # from bottom to top
-        out3_scale_mixed = self.scratch.layer3_csm(out3_channel_mixed)
-        out2_scale_mixed = self.scratch.layer2_csm(out3_scale_mixed, out2_channel_mixed)
-        out1_scale_mixed = self.scratch.layer1_csm(out2_scale_mixed, out1_channel_mixed)
-        out0_scale_mixed = self.scratch.layer0_csm(out1_scale_mixed, out0_channel_mixed)
-
-        out = {
-            '0': out0_scale_mixed,
-            '1': out1_scale_mixed,
-            '2': out2_scale_mixed,
-            '3': out3_scale_mixed,
-        }
-
-        return out, backbone_features
diff --git a/spaces/yhshin/latex-ocr/README.md b/spaces/yhshin/latex-ocr/README.md
deleted file mode 100644
index ea4018355eca89a0efa9d5570a7fc52f2cb5321e..0000000000000000000000000000000000000000
--- a/spaces/yhshin/latex-ocr/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Latex Ocr
-emoji: 👀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/open_llama/configuration_open_llama.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/open_llama/configuration_open_llama.py
deleted file mode 100644
index 93e1394ab6d9d6e9ceefd5629691bec6fdeeb2c9..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/open_llama/configuration_open_llama.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# coding=utf-8
-# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# 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.
-""" Open-Llama model configuration"""
-
-from ....configuration_utils import PretrainedConfig
-from ....utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
-}
-
-
-class OpenLlamaConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`OpenLlamaModel`]. It is used to instantiate an
-    Open-Llama model according to the specified arguments, defining the model architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the
-    [s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1).
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the Open-Llama model. Defines the number of different tokens that can be represented by
-            the `inputs_ids` passed when calling [`OpenLlamaModel`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 11008):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 2048):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
-            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
-            these scaling strategies behave:
-            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
-            experimental feature, subject to breaking API changes in future versions.
-
-        Example:
-
-    ```python
-    >>> from transformers import OpenLlamaModel, OpenLlamaConfig
-
-    >>> # Initializing a Open-Llama open_llama-7b style configuration
-    >>> configuration = OpenLlamaConfig()
-
-    >>> # Initializing a model from the open_llama-7b style configuration
-    >>> model = OpenLlamaModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "open-llama"
-
-    def __init__(
-        self,
-        vocab_size=100000,
-        hidden_size=4096,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        hidden_act="silu",
-        max_position_embeddings=2048,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        use_memory_efficient_attention=True,
-        hidden_dropout_prob=0.1,
-        attention_dropout_prob=0.1,
-        use_stable_embedding=True,
-        shared_input_output_embedding=True,
-        rope_scaling=None,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.use_memory_efficient_attention = kwargs.pop(
-            "use_memorry_efficient_attention", use_memory_efficient_attention
-        )
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_dropout_prob = attention_dropout_prob
-        self.use_stable_embedding = use_stable_embedding
-        self.shared_input_output_embedding = shared_input_output_embedding
-        self.rope_scaling = rope_scaling
-        self._rope_scaling_validation()
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
-                f"got {self.rope_scaling}"
-            )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_factor = self.rope_scaling.get("factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
-            )
-        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/tapex/tokenization_tapex.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/tapex/tokenization_tapex.py
deleted file mode 100644
index a5ee093c56bd2680ca480713674a40bdc68483a6..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/tapex/tokenization_tapex.py
+++ /dev/null
@@ -1,1487 +0,0 @@
-# coding=utf-8
-# Copyright 2022 Microsoft Research and The HuggingFace Inc. 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.
-"""Tokenization classes for TAPEX."""
-
-import json
-import os
-import random
-from functools import lru_cache
-from typing import Dict, List, Optional, Tuple, Union
-
-import regex as re
-
-from ....file_utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available
-from ....tokenization_utils import AddedToken, PreTrainedTokenizer
-from ....tokenization_utils_base import ENCODE_KWARGS_DOCSTRING, BatchEncoding, TextInput, TruncationStrategy
-from ....utils import logging
-
-
-if is_pandas_available():
-    import pandas as pd
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/vocab.json",
-    },
-    "merges_file": {
-        "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/merges.txt",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "microsoft/tapex-base": 512,
-}
-
-PRETRAINED_INIT_CONFIGURATION = {
-    "microsoft/tapex-base": {"do_lower_case": True},
-}
-
-
-class TapexTruncationStrategy(ExplicitEnum):
-    """
-    Possible values for the `truncation` argument in [`~TapasTokenizer.__call__`]. Useful for tab-completion in an IDE.
-    """
-
-    DROP_ROWS_TO_FIT = "drop_rows_to_fit"
-
-
-TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
-            add_special_tokens (`bool`, *optional*, defaults to `True`):
-                Whether or not to encode the sequences with the special tokens relative to their model.
-            padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
-                Activates and controls padding. Accepts the following values:
-
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            truncation (`bool`, `str`, [`TapexTruncationStrategy`] or [`~tokenization_utils_base.TruncationStrategy`],
-                   *optional*, defaults to `False`):
-
-                Activates and controls truncation. Accepts the following values:
-
-                - `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will truncate
-                  row by row, removing rows from the table.
-                - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
-                  to the maximum acceptable input length for the model if that argument is not provided. This will
-                  truncate token by token, removing a token from the longest sequence in the pair if a pair of
-                  sequences (or a batch of pairs) is provided.
-                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
-                  greater than the model maximum admissible input size).
-            max_length (`int`, *optional*):
-                Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
-                `None`, this will use the predefined model maximum length if a maximum length is required by one of the
-                truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
-                truncation/padding to a maximum length will be deactivated.
-            stride (`int`, *optional*, defaults to 0):
-                If set to a number along with `max_length`, the overflowing tokens returned when
-                `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
-                returned to provide some overlap between truncated and overflowing sequences. The value of this
-                argument defines the number of overlapping tokens.
-            pad_to_multiple_of (`int`, *optional*):
-                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
-                the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
-                If set, will return tensors instead of list of python integers. Acceptable values are:
-
-                - `'tf'`: Return TensorFlow `tf.constant` objects.
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return Numpy `np.ndarray` objects.
-"""
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
-    characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
-    of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
-    you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
-    vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """
-    Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
-    strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class IndexedRowTableLinearize:
-    """
-    FORMAT: col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ...
-    """
-
-    def process_table(self, table_content: Dict):
-        """
-        Given a table, TableLinearize aims at converting it into a flatten sequence with special symbols.
-        """
-        assert "header" in table_content and "rows" in table_content, self.PROMPT_MESSAGE
-        # process header
-        table_str = self.process_header(table_content["header"]) + " "
-        # process rows
-        for i, row_example in enumerate(table_content["rows"]):
-            # NOTE: the row should start from row 1 instead of 0
-            table_str += self.process_row(row_example, row_index=i + 1) + " "
-        return table_str.strip()
-
-    def process_header(self, headers: List):
-        """
-        Given a list of headers, TableLinearize aims at converting it into a flatten sequence with special symbols.
-        """
-        return "col : " + " | ".join(headers)
-
-    def process_row(self, row: List, row_index: int):
-        """
-        Given a row, TableLinearize aims at converting it into a flatten sequence with special symbols.
-        """
-        row_str = ""
-        row_cell_values = []
-        for cell_value in row:
-            if isinstance(cell_value, int):
-                row_cell_values.append(str(cell_value))
-            else:
-                row_cell_values.append(cell_value)
-        row_str += " | ".join(row_cell_values)
-        return "row " + str(row_index) + " : " + row_str
-
-
-class TapexTokenizer(PreTrainedTokenizer):
-    r"""
-    Construct a TAPEX tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
-
-    This tokenizer can be used to flatten one or more table(s) and concatenate them with one or more related sentences
-    to be used by TAPEX models. The format that the TAPEX tokenizer creates is the following:
-
-    sentence col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ...
-
-    The tokenizer supports a single table + single query, a single table and multiple queries (in which case the table
-    will be duplicated for every query), a single query and multiple tables (in which case the query will be duplicated
-    for every table), and multiple tables and queries. In other words, you can provide a batch of tables + questions to
-    the tokenizer for instance to prepare them for the model.
-
-    Tokenization itself is based on the BPE algorithm. It is identical to the one used by BART, RoBERTa and GPT-2.
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-        do_lower_case (`bool`, *optional*, defaults to `True`):
-            Whether or not to lowercase the input when tokenizing.
-        errors (`str`, *optional*, defaults to `"replace"`):
-            Paradigm to follow when decoding bytes to UTF-8. See
-            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
-        bos_token (`str`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the beginning of
-            sequence. The token used is the `cls_token`.
-
-            </Tip>
-
-        eos_token (`str`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
-            The token used is the `sep_token`.
-
-            </Tip>
-
-        sep_token (`str`, *optional*, defaults to `"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        cls_token (`str`, *optional*, defaults to `"<s>"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (`str`, *optional*, defaults to `"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        mask_token (`str`, *optional*, defaults to `"<mask>"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        add_prefix_space (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
-            other word. (BART tokenizer detect beginning of words by the preceding space).
-        max_cell_length (`int`, *optional*, defaults to 15):
-            Maximum number of characters per cell when linearizing a table. If this number is exceeded, truncation
-            takes place.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        merges_file,
-        do_lower_case=True,
-        errors="replace",
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        add_prefix_space=False,
-        max_cell_length=15,
-        **kwargs,
-    ):
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
-        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
-
-        # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
-
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.encoder = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        with open(merges_file, encoding="utf-8") as merges_handle:
-            bpe_merges = merges_handle.read().split("\n")[1:-1]
-        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-        self.add_prefix_space = add_prefix_space
-        self.do_lower_case = do_lower_case
-
-        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
-
-        # additional properties
-
-        super().__init__(
-            vocab_file=vocab_file,
-            merges_file=merges_file,
-            do_lower_case=do_lower_case,
-            errors=errors,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            sep_token=sep_token,
-            cls_token=cls_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            add_prefix_space=add_prefix_space,
-            max_cell_length=max_cell_length,
-            **kwargs,
-        )
-
-        self.max_cell_length = max_cell_length
-        self.table_linearize = IndexedRowTableLinearize()
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A TAPEX sequence has the following format:
-        - single sequence: `<s> X </s>`
-        - pair of sequences: `<s> A </s></s> B </s>`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-        if token_ids_1 is None:
-            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-        cls = [self.cls_token_id]
-        sep = [self.sep_token_id]
-        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Args:
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Args:
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. TAPEX does not:
-        make use of token type ids, therefore a list of zeros is returned.
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-        Returns:
-            `List[int]`: List of zeros.
-        """
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
-
-    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
-        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
-        if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
-            text = " " + text
-        return (text, kwargs)
-
-    @property
-    def vocab_size(self):
-        return len(self.encoder)
-
-    def get_vocab(self):
-        return dict(self.encoder, **self.added_tokens_encoder)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                except ValueError:
-                    new_word.extend(word[i:])
-                    break
-                else:
-                    new_word.extend(word[i:j])
-                    i = j
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def _tokenize(self, text):
-        """Tokenize a string."""
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            token = "".join(
-                self.byte_encoder[b] for b in token.encode("utf-8")
-            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
-        return bpe_tokens
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        text = "".join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
-        return text
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-        merge_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write("#version: 0.2\n")
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning(
-                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
-                        " Please check that the tokenizer is not corrupted!"
-                    )
-                    index = token_index
-                writer.write(" ".join(bpe_tokens) + "\n")
-                index += 1
-
-        return vocab_file, merge_file
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def __call__(
-        self,
-        table: Union["pd.DataFrame", List["pd.DataFrame"]] = None,
-        query: Optional[Union[TextInput, List[TextInput]]] = None,
-        answer: Union[str, List[str]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Main method to tokenize and prepare for the model one or several table-sequence pair(s).
-
-        Args:
-            table (`pd.DataFrame`, `List[pd.DataFrame]`):
-                Table(s) containing tabular data.
-            query (`str` or `List[str]`, *optional*):
-                Sentence or batch of sentences related to one or more table(s) to be encoded. Note that the number of
-                sentences must match the number of tables.
-            answer (`str` or `List[str]`, *optional*):
-                Optionally, the corresponding answer to the questions as supervision.
-        """
-
-        if table is not None:
-            return self.source_call_func(
-                table=table,
-                query=query,
-                answer=answer,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        elif answer is not None:
-            return self.target_call_func(
-                answer=answer,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        else:
-            raise ValueError("You need to provide either a `table` or an `answer`.")
-
-    def source_call_func(
-        self,
-        table: Union["pd.DataFrame", List["pd.DataFrame"]],
-        query: Optional[Union[TextInput, List[TextInput]]] = None,
-        answer: Union[str, List[str]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        # Input type checking for clearer error
-        valid_table = False
-        valid_query = False
-
-        # Check that table have a valid type
-        if isinstance(table, pd.DataFrame):
-            valid_table = True
-        elif isinstance(table, (list, tuple)) and isinstance(table[0], pd.DataFrame):
-            valid_table = True
-
-        # Check that query have a valid type
-        if query is None or isinstance(query, str):
-            valid_query = True
-        elif isinstance(query, (list, tuple)):
-            if len(query) == 0 or isinstance(query[0], str):
-                valid_query = True
-
-        if not valid_table:
-            raise ValueError(
-                "table input must of type `pd.DataFrame` (single example), `List[pd.DataFrame]` (batch of examples). "
-            )
-        if not valid_query:
-            raise ValueError("query input must of type `str` (single example), `List[str]` (batch of examples). ")
-        is_batched = isinstance(table, (list, tuple)) or isinstance(query, (list, tuple))
-
-        if is_batched:
-            return self.batch_encode_plus(
-                table=table,
-                query=query,
-                answer=answer,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        else:
-            return self.encode_plus(
-                table=table,
-                query=query,
-                answer=answer,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def batch_encode_plus(
-        self,
-        table: Union["pd.DataFrame", List["pd.DataFrame"]],
-        query: Optional[List[TextInput]] = None,
-        answer: List[str] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str] = None,
-        max_length: Optional[int] = None,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        <Tip warning={true}>
-
-        This method is deprecated, `__call__` should be used instead.
-
-        </Tip>
-        """
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._batch_encode_plus(
-            table=table,
-            query=query,
-            answer=answer,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    def _batch_encode_plus(
-        self,
-        table: Union["pd.DataFrame", List["pd.DataFrame"]],
-        query: Optional[List[TextInput]] = None,
-        answer: Optional[List[str]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast."
-            )
-
-        if isinstance(table, pd.DataFrame) and isinstance(query, (list, tuple)):
-            # single table, many queries case
-            # duplicate table for every query
-            table = [table] * len(query)
-        if isinstance(table, (list, tuple)) and isinstance(query, str):
-            # many tables, single query case
-            # duplicate query for every table
-            query = [query] * len(table)
-
-        batch_outputs = self._batch_prepare_for_model(
-            table=table,
-            query=query,
-            answer=answer,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            return_tensors=return_tensors,
-            verbose=verbose,
-        )
-
-        return BatchEncoding(batch_outputs)
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def _batch_prepare_for_model(
-        self,
-        table: Union["pd.DataFrame", List["pd.DataFrame"]],
-        query: Optional[Union[TextInput, List[TextInput]]] = None,
-        answer: Optional[Union[str, List[str]]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-    ) -> BatchEncoding:
-        """
-        This method adds special tokens, truncates sequences if overflowing while taking into account the special
-        tokens and manages a moving window (with user defined stride) for overflowing tokens.
-        """
-        batch_outputs = {}
-        if answer is None:
-            answer = [None] * len(table)
-        for _table, _query, _answer in zip(table, query, answer):
-            text = self.prepare_table_query(
-                _table, _query, _answer, truncation_strategy=truncation_strategy, max_length=max_length
-            )
-
-            if self.do_lower_case:
-                text = text.lower()
-
-            tokens = self.tokenize(text)
-            outputs = self.prepare_for_model(
-                ids=self.convert_tokens_to_ids(tokens),
-                add_special_tokens=add_special_tokens,
-                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterwards
-                truncation=truncation_strategy.value,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=None,  # we pad in batch afterwards
-                return_attention_mask=False,  # we pad in batch afterwards
-                return_token_type_ids=return_token_type_ids,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_length=return_length,
-                return_tensors=None,  # We convert the whole batch to tensors at the end
-                prepend_batch_axis=False,
-                verbose=verbose,
-            )
-
-            for key, value in outputs.items():
-                if key not in batch_outputs:
-                    batch_outputs[key] = []
-                batch_outputs[key].append(value)
-
-        batch_outputs = self.pad(
-            batch_outputs,
-            padding=padding_strategy.value,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-        )
-
-        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
-
-        return batch_outputs
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
-    def encode(
-        self,
-        table: "pd.DataFrame",
-        query: Optional[TextInput] = None,
-        answer: Optional[str] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
-    ) -> List[int]:
-        """
-        Prepare a table, a string and possible answer for the model. This method does not return token type IDs,
-        attention masks, etc. which are necessary for the model to work correctly. Use this method if you want to build
-        your processing on your own, otherwise refer to `__call__`.
-        """
-        encoded_inputs = self.encode_plus(
-            table,
-            query=query,
-            answer=answer,
-            add_special_tokens=add_special_tokens,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            return_tensors=return_tensors,
-            **kwargs,
-        )
-
-        return encoded_inputs["input_ids"]
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def encode_plus(
-        self,
-        table: "pd.DataFrame",
-        query: Optional[TextInput] = None,
-        answer: Optional[str] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str] = None,
-        max_length: Optional[int] = None,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._encode_plus(
-            table=table,
-            query=query,
-            answer=answer,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    def _encode_plus(
-        self,
-        table: "pd.DataFrame",
-        query: Optional[TextInput] = None,
-        answer: Optional[str] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast. "
-                "More information on available tokenizers at "
-                "https://github.com/huggingface/transformers/pull/2674"
-            )
-
-        text = self.prepare_table_query(
-            table, query, answer, truncation_strategy=truncation_strategy, max_length=max_length
-        )
-
-        # if necessary, perform lower case
-        if self.do_lower_case:
-            text = text.lower()
-
-        tokens = self.tokenize(text)
-
-        return self.prepare_for_model(
-            ids=self.convert_tokens_to_ids(tokens),
-            add_special_tokens=add_special_tokens,
-            padding=padding_strategy.value,
-            truncation=truncation_strategy.value,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            prepend_batch_axis=True,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            verbose=verbose,
-        )
-
-    def target_call_func(
-        self,
-        answer: Union[str, List[str]],
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        The method tokenizes and prepares the answer label for the model.
-
-        Args:
-            answer (`str` or `List[str]`):
-                Corresponding answer supervision to the queries for training the model.
-        """
-        is_batched = isinstance(answer, (list, tuple))
-
-        if is_batched:
-            return self.target_batch_encode_plus(
-                answer=answer,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        else:
-            return self.target_encode_plus(
-                answer=answer,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-
-    def target_batch_encode_plus(
-        self,
-        answer: List[str],
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str] = None,
-        max_length: Optional[int] = None,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Prepare answer strings for the model.
-
-        Args:
-            answer `List[str]`:
-                Corresponding answer supervision to the queries for training the model.
-        """
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._target_batch_encode_plus(
-            answer=answer,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    def _target_batch_encode_plus(
-        self,
-        answer: List[str],
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        batch_outputs = {}
-        for text in answer:
-            if self.do_lower_case:
-                text = text.lower()
-
-            tokens = self.tokenize(text)
-            outputs = self.prepare_for_model(
-                ids=self.convert_tokens_to_ids(tokens),
-                add_special_tokens=add_special_tokens,
-                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterwards
-                truncation=truncation_strategy.value,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=None,  # we pad in batch afterwards
-                return_attention_mask=False,  # we pad in batch afterwards
-                return_token_type_ids=return_token_type_ids,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_length=return_length,
-                return_tensors=None,  # We convert the whole batch to tensors at the end
-                prepend_batch_axis=False,
-                verbose=verbose,
-            )
-
-            for key, value in outputs.items():
-                if key not in batch_outputs:
-                    batch_outputs[key] = []
-                batch_outputs[key].append(value)
-
-        batch_outputs = self.pad(
-            batch_outputs,
-            padding=padding_strategy.value,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-        )
-
-        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
-
-        return BatchEncoding(batch_outputs)
-
-    def target_encode(
-        self,
-        answer: str,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
-    ) -> List[int]:
-        """
-        Prepare the answer string for the model. This method does not return token type IDs, attention masks, etc.
-        which are necessary for the model to work correctly. Use this method if you want to build your processing on
-        your own, otherwise refer to `__call__`.
-
-        Args:
-            answer `str`:
-                Corresponding answer supervision to the queries for training the model
-        """
-        encoded_outputs = self.target_encode_plus(
-            answer=answer,
-            add_special_tokens=add_special_tokens,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            return_tensors=return_tensors,
-            **kwargs,
-        )
-
-        return encoded_outputs["input_ids"]
-
-    def target_encode_plus(
-        self,
-        answer: str,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str] = None,
-        max_length: Optional[int] = None,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Prepare a answer string for the model.
-
-        Args:
-            answer `str`:
-                Corresponding answer supervision to the queries for training the model.
-        """
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._target_encode_plus(
-            answer=answer,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    def _target_encode_plus(
-        self,
-        answer: str,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast. "
-                "More information on available tokenizers at "
-                "https://github.com/huggingface/transformers/pull/2674"
-            )
-
-        text = answer
-
-        # if necessary, perform lower case
-        if self.do_lower_case:
-            text = text.lower()
-
-        tokens = self.tokenize(text)
-
-        return self.prepare_for_model(
-            ids=self.convert_tokens_to_ids(tokens),
-            add_special_tokens=add_special_tokens,
-            padding=padding_strategy.value,
-            truncation=truncation_strategy.value,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            prepend_batch_axis=True,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            verbose=verbose,
-        )
-
-    def prepare_table_query(
-        self,
-        table,
-        query,
-        answer=None,
-        truncation_strategy=Union[str, TruncationStrategy, TapexTruncationStrategy],
-        max_length=None,
-    ):
-        """
-        This method can be used to linearize a table and add a corresponding query.
-
-        Optionally, it also handles truncation of the table (cells).
-
-        An answer can be provided for more precise truncation.
-        """
-        if not table.empty:
-            # step 1: create table dictionary
-            table_content = {"header": list(table.columns), "rows": [list(row.values) for i, row in table.iterrows()]}
-
-            # step 2: modify table internally
-            # always truncate table cells based on self.max_cell_length
-            # optionally truncate rows if truncation_strategy is set to it
-            self.truncate_table_cells(table_content, query, answer)
-            if truncation_strategy == TapexTruncationStrategy.DROP_ROWS_TO_FIT:
-                self.truncate_table_rows(table_content, query, answer, max_length=max_length)
-
-            # step 3: linearize table
-            linear_table = self.table_linearize.process_table(table_content)
-        else:
-            linear_table = ""
-
-        if linear_table == "":
-            logger.warning(
-                "You provide an empty table, or all cells contain much tokens (e.g., >= 1024 tokens). "
-                + f"Please carefully check the corresponding table with the query : {query}."
-            )
-        if query == "":
-            logger.warning("You provide nothing to query with respect to the table.")
-        # step 4: concatenate query with linear_table
-        separator = " " if query and linear_table else ""
-        joint_input = (query + separator + linear_table) if query else linear_table
-
-        return joint_input
-
-    def truncate_table_cells(self, table_content: Dict, question: str, answer: List):
-        # TODO (Qian): is it possible to revert the original cell if it is in the final answer?
-        cell_mapping = {}
-        for row in table_content["rows"]:
-            for i, cell in enumerate(row):
-                truncate_cell = self.truncate_cell(cell)
-                if truncate_cell is not None:
-                    cell_mapping[cell] = truncate_cell
-                    row[i] = truncate_cell
-
-        # modify the answer list
-        if answer is not None:
-            for i, case in enumerate(answer):
-                if case in cell_mapping.keys():
-                    answer[i] = cell_mapping[case]
-
-    def truncate_cell(self, cell_value):
-        # do not process on these cases
-        if isinstance(cell_value, int) or isinstance(cell_value, float):
-            return cell_value
-        if cell_value.strip() != "":
-            try_tokens = self.tokenize(cell_value)
-            if len(try_tokens) >= self.max_cell_length:
-                retain_tokens = try_tokens[: self.max_cell_length]
-                retain_cell_value = self.convert_tokens_to_string(retain_tokens)
-                return retain_cell_value
-            else:
-                return None
-        else:
-            return cell_value
-
-    def truncate_table_rows(
-        self, table_content: Dict, question: str, answer: Optional[Union[str, List[str]]] = None, max_length=None
-    ):
-        """
-        Args:
-        table_content:
-            {"header": xxx, "rows": xxx, "id" (Optionally): xxx}
-
-        question:
-            natural language sentence
-
-        answer:
-            if for training, is the supervision; otherwise will be empty
-        """
-        delete_ratio, remain_token_len = self.estimate_delete_ratio(table_content, question, max_length)
-        # randomly delete unrelated rows
-        self.delete_unrelated_rows(table_content, question, answer, delete_ratio)
-        # guarantee the result < max_length
-        maximum_keep_rows = 0
-        for ind, row_example in enumerate(table_content["rows"]):
-            value_string = self.table_linearize.process_row(row_example, ind + 1)
-            value_token_len = len(self.tokenize(value_string))
-            # over the size limit, and take action
-            if value_token_len > remain_token_len:
-                break
-            remain_token_len -= value_token_len
-            maximum_keep_rows += 1
-        del table_content["rows"][maximum_keep_rows:]
-
-    def estimate_delete_ratio(self, table_content: Dict, question: str, max_length=None):
-        if "header" not in table_content or "rows" not in table_content:
-            raise ValueError("The table content should contain both 'header' and 'rows' keys.")
-        # calculate the tokens of header, special tokens will only be pre-prepended into question
-        question_tokens = self.tokenize(question, add_special_tokens=True)
-        # calculate the tokens of header
-        header_string = self.table_linearize.process_header(table_content["header"])
-        header_tokens = self.tokenize(header_string, add_special_tokens=False)
-        # split all cell values into tokens and see how many can be accommodated
-        used_token_len = len(question_tokens) + len(header_tokens)
-        # remaining token space for rows
-        remain_token_len = max_length - used_token_len
-
-        value_string = ""
-        for _, row_example in enumerate(table_content["rows"]):
-            # use a general index to roughly estimate the overall token len
-            value_string += self.table_linearize.process_row(row_example, 100) + " "
-        value_token_len = len(self.tokenize(value_string))
-
-        if value_token_len < remain_token_len:
-            # no row will be deleted
-            return 0.0, remain_token_len
-        else:
-            # calc a roughly delete rate
-            return 1.0 - remain_token_len / value_token_len, remain_token_len
-
-    def delete_unrelated_rows(self, table_content: Dict, question: str, answer: List, delete_ratio: float):
-        """
-        The argument answer is used only during training.
-        """
-        truncated_unrelated_indices = []
-        related_indices = []
-        if answer is None or len(answer) == 0:
-            answer_set = set()
-        else:
-            answer_set = {ans_ex.lower() for ans_ex in answer}
-        # add question key words into answer set
-        if question is not None:
-            answer_set.update(question.split())
-        question_set = set(question.strip("?!.,").split(" "))
-        row_max_len = len(table_content["rows"])
-        for _row_idx, row in enumerate(table_content["rows"]):
-            lower_row = {str(cell).lower() for cell in row}
-            if len(lower_row & answer_set) == 0 and len(lower_row & question_set) == 0:
-                truncated_unrelated_indices.append(_row_idx)
-            else:
-                # add neighbours to preserve information aggressively
-                related_indices.extend([_row_idx - 2, _row_idx - 1, _row_idx, _row_idx + 1, _row_idx + 2])
-
-        # remove the neighbours
-        truncated_unrelated_indices = [
-            _row_idx for _row_idx in truncated_unrelated_indices if _row_idx not in related_indices
-        ]
-        # select some cases to drop
-        drop_items = min(len(truncated_unrelated_indices), int(len(table_content["rows"]) * delete_ratio))
-        drop_row_indices = random.choices(truncated_unrelated_indices, k=drop_items)
-
-        for _row_idx in reversed(range(row_max_len)):
-            if _row_idx in drop_row_indices:
-                del table_content["rows"][_row_idx]
-
-        # only when the drop ratio is too large, logging for warning.
-        if "id" in table_content and len(drop_row_indices) > 0:
-            logger.warning("Delete {:.2f} rows in table {}".format(len(drop_row_indices), table_content["id"]))
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/perceiver/modeling_perceiver.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/perceiver/modeling_perceiver.py
deleted file mode 100644
index bb7ac2bc3139e13c7cc99213c9563f11c7831016..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/perceiver/modeling_perceiver.py
+++ /dev/null
@@ -1,3437 +0,0 @@
-# coding=utf-8
-# Copyright 2021 Deepmind and The HuggingFace Inc. 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.
-""" PyTorch Perceiver model."""
-
-import abc
-import math
-from dataclasses import dataclass
-from functools import reduce
-from operator import __add__
-from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutputWithCrossAttentions
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, meshgrid, prune_linear_layer
-from ...utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_perceiver import PerceiverConfig
-
-
-ModalitySizeType = Mapping[str, int]
-PreprocessorOutputType = Tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor]
-PreprocessorType = Callable[..., PreprocessorOutputType]
-PostprocessorType = Callable[..., Any]
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "deepmind/language-perceiver"
-_CONFIG_FOR_DOC = "PerceiverConfig"
-
-PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "deepmind/language-perceiver",
-    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
-]
-
-
-@dataclass
-class PerceiverModelOutput(ModelOutput):
-    """
-    Base class for Perceiver base model's outputs, with potential hidden states, attentions and cross-attentions.
-
-    Args:
-        logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-    """
-
-    logits: torch.FloatTensor = None
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class PerceiverDecoderOutput(ModelOutput):
-    """
-    Base class for Perceiver decoder outputs, with potential cross-attentions.
-
-    Args:
-        logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
-            Output of the basic decoder.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-    """
-
-    logits: torch.FloatTensor = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class PerceiverMaskedLMOutput(ModelOutput):
-    """
-    Base class for Perceiver's masked language model outputs.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Masked language modeling (MLM) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, num_latents,
-            num_latents)`. Attentions weights after the attention softmax, used to compute the weighted average in the
-            self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class PerceiverClassifierOutput(ModelOutput):
-    """
-    Base class for Perceiver's outputs of sequence/image classification models, optical flow and multimodal
-    autoencoding.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification (or regression if config.num_labels==1) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-class PerceiverEmbeddings(nn.Module):
-    """Construct the latent embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.latents = nn.Parameter(torch.randn(config.num_latents, config.d_latents))
-
-    def forward(self, batch_size: int):
-        return self.latents.expand(batch_size, -1, -1)  # Thanks, Phil Wang
-
-
-class PerceiverSelfAttention(nn.Module):
-    """Multi-headed {cross, self}-attention. Can be used both in the encoder as well as in the decoder."""
-
-    def __init__(
-        self,
-        config,
-        is_cross_attention=False,
-        qk_channels=None,
-        v_channels=None,
-        num_heads=1,
-        q_dim=None,
-        kv_dim=None,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        # Q and K must have the same number of channels.
-        # Default to preserving Q's input's shape.
-        if qk_channels is None:
-            qk_channels = q_dim
-        # V's num_channels determines the shape of the output of QKV-attention.
-        # Default to the same number of channels used in the key-query operation.
-        if v_channels is None:
-            v_channels = qk_channels
-        if qk_channels % num_heads != 0:
-            raise ValueError(f"qk_channels ({qk_channels}) must be divisible by num_heads ({num_heads}).")
-        if v_channels % num_heads != 0:
-            raise ValueError(f"v_channels ({v_channels}) must be divisible by num_heads ({num_heads}).")
-
-        self.qk_channels = qk_channels
-        self.v_channels = v_channels
-        self.qk_channels_per_head = self.qk_channels // num_heads
-        self.v_channels_per_head = self.v_channels // num_heads
-
-        # Layer normalization
-        self.layernorm1 = nn.LayerNorm(q_dim)
-        self.layernorm2 = nn.LayerNorm(kv_dim) if is_cross_attention else nn.Identity()
-
-        # Projection matrices
-        self.query = nn.Linear(q_dim, qk_channels)
-        self.key = nn.Linear(kv_dim, qk_channels)
-        self.value = nn.Linear(kv_dim, v_channels)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x, channels_per_head):
-        new_x_shape = x.size()[:-1] + (self.num_heads, channels_per_head)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs: Optional[torch.FloatTensor] = None,
-        inputs_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        hidden_states = self.layernorm1(hidden_states)
-        inputs = self.layernorm2(inputs)
-
-        # Project queries, keys and values to a common feature dimension. If this is instantiated as a cross-attention module,
-        # the keys and values come from the inputs; the attention mask needs to be such that the inputs's non-relevant tokens are not attended to.
-        is_cross_attention = inputs is not None
-        queries = self.query(hidden_states)
-
-        if is_cross_attention:
-            keys = self.key(inputs)
-            values = self.value(inputs)
-            attention_mask = inputs_mask
-        else:
-            keys = self.key(hidden_states)
-            values = self.value(hidden_states)
-
-        # Reshape channels for multi-head attention.
-        # We reshape from (batch_size, time, channels) to (batch_size, num_heads, time, channels per head)
-        queries = self.transpose_for_scores(queries, self.qk_channels_per_head)
-        keys = self.transpose_for_scores(keys, self.qk_channels_per_head)
-        values = self.transpose_for_scores(values, self.v_channels_per_head)
-
-        # Take the dot product between the queries and keys to get the raw attention scores.
-        attention_scores = torch.matmul(queries, keys.transpose(-1, -2))
-
-        batch_size, num_heads, seq_len, q_head_dim = queries.shape
-        _, _, _, v_head_dim = values.shape
-        hiddens = self.num_heads * v_head_dim
-
-        attention_scores = attention_scores / math.sqrt(q_head_dim)
-
-        if attention_mask is not None:
-            # Apply the attention mask (precomputed for all layers in PerceiverModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, values)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (hiddens,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        return outputs
-
-
-class PerceiverSelfOutput(nn.Module):
-    def __init__(self, config, input_channels, output_channels):
-        super().__init__()
-        self.dense = nn.Linear(input_channels, output_channels)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        return hidden_states
-
-
-class PerceiverAttention(nn.Module):
-    """Attention module, including a dense block."""
-
-    def __init__(
-        self,
-        config,
-        is_cross_attention=False,
-        qk_channels=None,
-        v_channels=None,
-        num_heads=1,
-        q_dim=None,
-        kv_dim=None,
-        use_query_residual=True,
-    ):
-        super().__init__()
-        # MultiHead attention
-        if is_cross_attention and qk_channels is None:
-            if config.cross_attention_shape_for_attention == "q":
-                qk_channels = q_dim
-            elif config.cross_attention_shape_for_attention == "kv":
-                qk_channels = kv_dim
-            else:
-                raise ValueError(
-                    f"Unknown value {config.cross_attention_shape_for_attention} for "
-                    "cross_attention_shape_for_attention."
-                )
-        else:
-            if qk_channels is None:
-                qk_channels = q_dim
-            if v_channels is None:
-                v_channels = qk_channels
-        self.self = PerceiverSelfAttention(
-            config,
-            is_cross_attention=is_cross_attention,
-            qk_channels=qk_channels,
-            v_channels=v_channels,
-            num_heads=num_heads,
-            q_dim=q_dim,
-            kv_dim=kv_dim,
-        )
-        # dense block
-        output_channels = None
-        if is_cross_attention:
-            output_channels = q_dim
-        else:
-            if output_channels is None:
-                output_channels = v_channels
-        self.output = PerceiverSelfOutput(config, input_channels=self.self.v_channels, output_channels=output_channels)
-        self.use_query_residual = use_query_residual
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs: Optional[torch.FloatTensor] = None,
-        inputs_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            inputs,
-            inputs_mask,
-            output_attentions,
-        )
-
-        # Output projection
-        attention_output = self.output(self_outputs[0])
-
-        # Optionally include a residual to the original queries.
-        # Consider omitting the residual if the semantics of query and output
-        # are different, e.g. if queries are positions and outputs are pixels.
-        if self.use_query_residual:
-            attention_output = attention_output + hidden_states
-
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class PerceiverMLP(nn.Module):
-    """A Transformer-style dense module to follow attention."""
-
-    def __init__(self, config, input_size, widening_factor):
-        super().__init__()
-        self.dense1 = nn.Linear(input_size, widening_factor * input_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-        self.dense2 = nn.Linear(widening_factor * input_size, input_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense1(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        hidden_states = self.dense2(hidden_states)
-        return hidden_states
-
-
-class PerceiverLayer(nn.Module):
-    def __init__(
-        self,
-        config,
-        is_cross_attention=False,
-        qk_channels=None,
-        v_channels=None,
-        num_heads=1,
-        q_dim=None,
-        kv_dim=None,
-        widening_factor=4,
-        use_query_residual=True,
-    ):
-        super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = PerceiverAttention(
-            config,
-            is_cross_attention=is_cross_attention,
-            qk_channels=qk_channels,
-            v_channels=v_channels,
-            num_heads=num_heads,
-            q_dim=q_dim,
-            kv_dim=kv_dim,
-            use_query_residual=use_query_residual,
-        )
-        self.layernorm = nn.LayerNorm(q_dim)
-        self.mlp = PerceiverMLP(config, input_size=q_dim, widening_factor=widening_factor)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs: Optional[torch.FloatTensor] = None,
-        inputs_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            inputs,
-            inputs_mask,
-            output_attentions,
-        )
-        attention_output = attention_outputs[0]
-
-        outputs = attention_outputs[1:]  # add attentions if we output attention weights
-
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-
-        layer_output = layer_output + attention_output  # residual connection
-
-        outputs = (layer_output,) + outputs
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        layer_output = self.layernorm(attention_output)
-        layer_output = self.mlp(layer_output)
-        return layer_output
-
-
-class PerceiverEncoder(nn.Module):
-    """The Perceiver Encoder: a scalable, fully attentional encoder."""
-
-    def __init__(self, config, kv_dim=None):
-        super().__init__()
-        self.config = config
-
-        # Check that we can use multihead-attention with these shapes.
-        if config.d_latents % config.num_self_attention_heads != 0:
-            raise ValueError(
-                f"num_z_channels ({config.d_latents}) must be divisible by"
-                f" num_self_attend_heads ({config.num_self_attention_heads})."
-            )
-        if config.d_latents % config.num_cross_attention_heads != 0:
-            raise ValueError(
-                f"num_z_channels ({config.d_latents}) must be divisible by"
-                f" num_cross_attend_heads ({config.num_cross_attention_heads})."
-            )
-
-        # Construct the cross attention layer.
-        self.cross_attention = PerceiverLayer(
-            config,
-            is_cross_attention=True,
-            qk_channels=config.qk_channels,
-            v_channels=config.v_channels,
-            num_heads=config.num_cross_attention_heads,
-            q_dim=config.d_latents,
-            kv_dim=kv_dim,
-            widening_factor=config.cross_attention_widening_factor,
-            use_query_residual=config.use_query_residual,
-        )
-
-        # Construct a single block of self-attention layers.
-        # We get deeper architectures by applying this block more than once.
-        self_attention_layers = []
-        for _ in range(config.num_self_attends_per_block):
-            layer = PerceiverLayer(
-                config,
-                is_cross_attention=False,
-                qk_channels=config.qk_channels,
-                v_channels=config.v_channels,
-                num_heads=config.num_self_attention_heads,
-                q_dim=config.d_latents,
-                kv_dim=config.d_latents,
-                widening_factor=config.self_attention_widening_factor,
-            )
-            self_attention_layers.append(layer)
-
-        self.self_attends = nn.ModuleList(self_attention_layers)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs: Optional[torch.FloatTensor] = None,
-        inputs_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-        output_hidden_states: Optional[bool] = False,
-        return_dict: Optional[bool] = True,
-    ) -> Union[Tuple, BaseModelOutputWithCrossAttentions]:
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions else None
-
-        # Apply the cross-attention between the latents (hidden_states) and inputs:
-        layer_outputs = self.cross_attention(
-            hidden_states,
-            attention_mask=attention_mask,
-            head_mask=None,
-            inputs=inputs,
-            inputs_mask=inputs_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = layer_outputs[0]
-
-        if output_attentions:
-            all_cross_attentions = all_cross_attentions + (layer_outputs[1],)
-
-        # Apply the block of self-attention layers more than once:
-        for _ in range(self.config.num_blocks):
-            for i, layer_module in enumerate(self.self_attends):
-                if output_hidden_states:
-                    all_hidden_states = all_hidden_states + (hidden_states,)
-
-                layer_head_mask = head_mask[i] if head_mask is not None else None
-
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    head_mask=layer_head_mask,
-                    output_attentions=output_attentions,
-                )
-
-                hidden_states = layer_outputs[0]
-                if output_attentions:
-                    all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, all_hidden_states, all_self_attentions, all_cross_attentions]
-                if v is not None
-            )
-        return BaseModelOutputWithCrossAttentions(
-            last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class PerceiverPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = PerceiverConfig
-    base_model_prefix = "perceiver"
-    main_input_name = "inputs"
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif hasattr(module, "latents"):
-            module.latents.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif hasattr(module, "position_embeddings") and isinstance(module, PerceiverTrainablePositionEncoding):
-            module.position_embeddings.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.ParameterDict):
-            for modality in module.keys():
-                module[modality].data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-
-PERCEIVER_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
-    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`PerceiverConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-PERCEIVER_MODEL_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
-    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`PerceiverConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-        decoder (*DecoderType*, *optional*):
-            Optional decoder to use to decode the latent representation of the encoder. Examples include
-            *transformers.models.perceiver.modeling_perceiver.PerceiverBasicDecoder*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverClassificationDecoder*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder*.
-        input_preprocessor (*PreprocessorType*, *optional*):
-            Optional input preprocessor to use. Examples include
-            *transformers.models.perceiver.modeling_perceiver.PerceiverImagePreprocessor*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverAudioPreprocessor*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverTextPreprocessor*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor*.
-        output_postprocessor (*PostprocessorType*, *optional*):
-            Optional output postprocessor to use. Examples include
-            *transformers.models.perceiver.modeling_perceiver.PerceiverImagePostprocessor*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverAudioPostprocessor*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverClassificationPostprocessor*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverProjectionPostprocessor*,
-            *transformers.models.perceiver.modeling_perceiver.PerceiverMultimodalPostprocessor*.
-
-        Note that you can define your own decoders, preprocessors and/or postprocessors to fit your use-case.
-"""
-
-PERCEIVER_INPUTS_DOCSTRING = r"""
-    Args:
-        inputs (`torch.FloatTensor`):
-            Inputs to the perceiver. Can be anything: images, text, audio, video, etc.
-        attention_mask (`torch.FloatTensor` of shape `{0}`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    """The Perceiver: a scalable, fully attentional architecture.""",
-    PERCEIVER_MODEL_START_DOCSTRING,
-)
-class PerceiverModel(PerceiverPreTrainedModel):
-    def __init__(
-        self,
-        config,
-        decoder=None,
-        input_preprocessor: PreprocessorType = None,
-        output_postprocessor: PostprocessorType = None,
-    ):
-        super().__init__(config)
-        self.config = config
-
-        self.input_preprocessor = input_preprocessor
-        self.output_postprocessor = output_postprocessor
-        self.embeddings = PerceiverEmbeddings(config)
-        self.encoder = PerceiverEncoder(
-            config, kv_dim=input_preprocessor.num_channels if input_preprocessor is not None else config.d_model
-        )
-        self.decoder = decoder
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings.latents
-
-    def set_input_embeddings(self, value):
-        self.embeddings.latents = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
-    @replace_return_docstrings(output_type=PerceiverModelOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: torch.FloatTensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        subsampled_output_points: Optional[Dict[str, torch.Tensor]] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PerceiverModelOutput]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import PerceiverConfig, PerceiverTokenizer, PerceiverImageProcessor, PerceiverModel
-        >>> from transformers.models.perceiver.modeling_perceiver import (
-        ...     PerceiverTextPreprocessor,
-        ...     PerceiverImagePreprocessor,
-        ...     PerceiverClassificationDecoder,
-        ... )
-        >>> import torch
-        >>> import requests
-        >>> from PIL import Image
-
-        >>> # EXAMPLE 1: using the Perceiver to classify texts
-        >>> # - we define a TextPreprocessor, which can be used to embed tokens
-        >>> # - we define a ClassificationDecoder, which can be used to decode the
-        >>> # final hidden states of the latents to classification logits
-        >>> # using trainable position embeddings
-        >>> config = PerceiverConfig()
-        >>> preprocessor = PerceiverTextPreprocessor(config)
-        >>> decoder = PerceiverClassificationDecoder(
-        ...     config,
-        ...     num_channels=config.d_latents,
-        ...     trainable_position_encoding_kwargs=dict(num_channels=config.d_latents, index_dims=1),
-        ...     use_query_residual=True,
-        ... )
-        >>> model = PerceiverModel(config, input_preprocessor=preprocessor, decoder=decoder)
-
-        >>> # you can then do a forward pass as follows:
-        >>> tokenizer = PerceiverTokenizer()
-        >>> text = "hello world"
-        >>> inputs = tokenizer(text, return_tensors="pt").input_ids
-
-        >>> with torch.no_grad():
-        ...     outputs = model(inputs=inputs)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 2]
-
-        >>> # to train, one can train the model using standard cross-entropy:
-        >>> criterion = torch.nn.CrossEntropyLoss()
-
-        >>> labels = torch.tensor([1])
-        >>> loss = criterion(logits, labels)
-
-        >>> # EXAMPLE 2: using the Perceiver to classify images
-        >>> # - we define an ImagePreprocessor, which can be used to embed images
-        >>> config = PerceiverConfig(image_size=224)
-        >>> preprocessor = PerceiverImagePreprocessor(
-        ...     config,
-        ...     prep_type="conv1x1",
-        ...     spatial_downsample=1,
-        ...     out_channels=256,
-        ...     position_encoding_type="trainable",
-        ...     concat_or_add_pos="concat",
-        ...     project_pos_dim=256,
-        ...     trainable_position_encoding_kwargs=dict(
-        ...         num_channels=256,
-        ...         index_dims=config.image_size**2,
-        ...     ),
-        ... )
-
-        >>> model = PerceiverModel(
-        ...     config,
-        ...     input_preprocessor=preprocessor,
-        ...     decoder=PerceiverClassificationDecoder(
-        ...         config,
-        ...         num_channels=config.d_latents,
-        ...         trainable_position_encoding_kwargs=dict(num_channels=config.d_latents, index_dims=1),
-        ...         use_query_residual=True,
-        ...     ),
-        ... )
-
-        >>> # you can then do a forward pass as follows:
-        >>> image_processor = PerceiverImageProcessor()
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-        >>> inputs = image_processor(image, return_tensors="pt").pixel_values
-
-        >>> with torch.no_grad():
-        ...     outputs = model(inputs=inputs)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 2]
-
-        >>> # to train, one can train the model using standard cross-entropy:
-        >>> criterion = torch.nn.CrossEntropyLoss()
-
-        >>> labels = torch.tensor([1])
-        >>> loss = criterion(logits, labels)
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if self.input_preprocessor is not None:
-            inputs, modality_sizes, inputs_without_pos = self.input_preprocessor(inputs)
-        else:
-            modality_sizes = None
-            inputs_without_pos = None
-            if inputs.size()[-1] != self.config.d_model:
-                raise ValueError(
-                    f"Last dimension of the inputs: {inputs.size()[-1]} doesn't correspond to config.d_model:"
-                    f" {self.config.d_model}. Make sure to set config.d_model appropriately."
-                )
-
-        batch_size, seq_length, _ = inputs.size()
-        device = inputs.device
-
-        # If no attention mask is provided, make them all ones
-        if attention_mask is None:
-            attention_mask = torch.ones((batch_size, seq_length), device=device)
-        # Make the attention mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        extended_attention_mask = self.invert_attention_mask(attention_mask)
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_blocks x num_heads]
-        # and head_mask is converted to shape [num_blocks x batch x num_heads x N x N]
-        head_mask = self.get_head_mask(head_mask, self.config.num_blocks * self.config.num_self_attends_per_block)
-
-        embedding_output = self.embeddings(batch_size=batch_size)
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=None,
-            head_mask=head_mask,
-            inputs=inputs,
-            inputs_mask=extended_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-
-        logits = None
-        if self.decoder:
-            if subsampled_output_points is not None:
-                output_modality_sizes = {
-                    "audio": subsampled_output_points["audio"].shape[0],
-                    "image": subsampled_output_points["image"].shape[0],
-                    "label": 1,
-                }
-            else:
-                output_modality_sizes = modality_sizes
-            decoder_query = self.decoder.decoder_query(
-                inputs, modality_sizes, inputs_without_pos, subsampled_points=subsampled_output_points
-            )
-            decoder_outputs = self.decoder(
-                decoder_query,
-                z=sequence_output,
-                query_mask=extended_attention_mask,
-                output_attentions=output_attentions,
-            )
-            logits = decoder_outputs.logits
-
-            # add cross-attentions of decoder
-            if output_attentions and decoder_outputs.cross_attentions is not None:
-                if return_dict:
-                    encoder_outputs.cross_attentions = (
-                        encoder_outputs.cross_attentions + decoder_outputs.cross_attentions
-                    )
-                else:
-                    encoder_outputs = encoder_outputs + decoder_outputs.cross_attentions
-
-            if self.output_postprocessor:
-                logits = self.output_postprocessor(logits, modality_sizes=output_modality_sizes)
-
-        if not return_dict:
-            if logits is not None:
-                return (logits, sequence_output) + encoder_outputs[1:]
-            else:
-                return (sequence_output,) + encoder_outputs[1:]
-
-        return PerceiverModelOutput(
-            logits=logits,
-            last_hidden_state=sequence_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings("""Example use of Perceiver for masked language modeling.""", PERCEIVER_START_DOCSTRING)
-class PerceiverForMaskedLM(PerceiverPreTrainedModel):
-    def __init__(self, config: PerceiverConfig):
-        super().__init__(config)
-
-        text_preprocessor = PerceiverTextPreprocessor(config)
-
-        trainable_position_encoding_kwargs_decoder = {
-            "num_channels": text_preprocessor.num_channels,
-            "index_dims": config.max_position_embeddings,
-        }
-
-        self.perceiver = PerceiverModel(
-            config,
-            input_preprocessor=text_preprocessor,
-            decoder=PerceiverBasicDecoder(
-                config,
-                output_num_channels=config.d_latents,
-                output_index_dims=config.max_position_embeddings,  # we need to define the seq_len of the inputs beforehand
-                num_channels=text_preprocessor.num_channels,
-                qk_channels=8 * 32,
-                v_channels=text_preprocessor.num_channels,
-                num_heads=8,
-                use_query_residual=False,
-                final_project=False,
-                trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder,
-            ),
-        )
-        self.embedding_decoder = PerceiverEmbeddingDecoder(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=PerceiverMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-        input_ids: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, PerceiverMaskedLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
-            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, PerceiverForMaskedLM
-        >>> import torch
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("deepmind/language-perceiver")
-        >>> model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver")
-
-        >>> # training
-        >>> text = "This is an incomplete sentence where some words are missing."
-        >>> inputs = tokenizer(text, padding="max_length", return_tensors="pt")
-        >>> # mask " missing."
-        >>> inputs["input_ids"][0, 52:61] = tokenizer.mask_token_id
-        >>> labels = tokenizer(text, padding="max_length", return_tensors="pt").input_ids
-
-        >>> outputs = model(**inputs, labels=labels)
-        >>> loss = outputs.loss
-        >>> round(loss.item(), 2)
-        19.87
-
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 2048, 262]
-
-        >>> # inference
-        >>> text = "This is an incomplete sentence where some words are missing."
-        >>> encoding = tokenizer(text, padding="max_length", return_tensors="pt")
-
-        >>> # mask bytes corresponding to " missing.". Note that the model performs much better if the masked span starts with a space.
-        >>> encoding["input_ids"][0, 52:61] = tokenizer.mask_token_id
-
-        >>> # forward pass
-        >>> with torch.no_grad():
-        ...     outputs = model(**encoding)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 2048, 262]
-
-        >>> masked_tokens_predictions = logits[0, 52:61].argmax(dim=-1).tolist()
-        >>> tokenizer.decode(masked_tokens_predictions)
-        ' missing.'
-        ```"""
-        if inputs is not None and input_ids is not None:
-            raise ValueError("You cannot use both `inputs` and `input_ids`")
-        elif inputs is None and input_ids is not None:
-            inputs = input_ids
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.perceiver(
-            inputs=inputs,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        logits = self.embedding_decoder(
-            outputs.logits if return_dict else outputs[0], embedding_layer=self.perceiver.input_preprocessor.embeddings
-        )
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()  # -100 index = padding token
-            masked_lm_loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return PerceiverMaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings("""Example use of Perceiver for text classification.""", PERCEIVER_START_DOCSTRING)
-class PerceiverForSequenceClassification(PerceiverPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1}
-
-        self.num_labels = config.num_labels
-        self.perceiver = PerceiverModel(
-            config,
-            input_preprocessor=PerceiverTextPreprocessor(config),
-            decoder=PerceiverClassificationDecoder(
-                config,
-                num_channels=config.d_latents,
-                trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder,
-                use_query_residual=True,
-            ),
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-        input_ids: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, PerceiverClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the classification/regression loss. Indices should be in `[0, ..., config.num_labels -
-            1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels >
-            1` a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, PerceiverForSequenceClassification
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("deepmind/language-perceiver")
-        >>> model = PerceiverForSequenceClassification.from_pretrained("deepmind/language-perceiver")
-
-        >>> text = "hello world"
-        >>> inputs = tokenizer(text, return_tensors="pt").input_ids
-        >>> outputs = model(inputs=inputs)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 2]
-        ```"""
-        if inputs is not None and input_ids is not None:
-            raise ValueError("You cannot use both `inputs` and `input_ids`")
-        elif inputs is None and input_ids is not None:
-            inputs = input_ids
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.perceiver(
-            inputs=inputs,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        logits = outputs.logits if return_dict else outputs[0]
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return PerceiverClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-Example use of Perceiver for image classification, for tasks such as ImageNet.
-
-This model uses learned position embeddings. In other words, this model is not given any privileged information about
-the structure of images. As shown in the paper, this model can achieve a top-1 accuracy of 72.7 on ImageNet.
-
-[`PerceiverForImageClassificationLearned`] uses [`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`]
-(with `prep_type="conv1x1"`) to preprocess the input images, and
-[`~models.perceiver.modeling_perceiver.PerceiverClassificationDecoder`] to decode the latent representation of
-[`PerceiverModel`] into classification logits.
-""",
-    PERCEIVER_START_DOCSTRING,
-)
-class PerceiverForImageClassificationLearned(PerceiverPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        trainable_position_encoding_kwargs_preprocessor = {"num_channels": 256, "index_dims": config.image_size**2}
-        trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1}
-
-        self.num_labels = config.num_labels
-        self.perceiver = PerceiverModel(
-            config,
-            input_preprocessor=PerceiverImagePreprocessor(
-                config,
-                prep_type="conv1x1",
-                spatial_downsample=1,
-                out_channels=256,
-                position_encoding_type="trainable",
-                concat_or_add_pos="concat",
-                project_pos_dim=256,
-                trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_preprocessor,
-            ),
-            decoder=PerceiverClassificationDecoder(
-                config,
-                num_channels=config.d_latents,
-                trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder,
-                use_query_residual=True,
-            ),
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-        pixel_values: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, PerceiverClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, PerceiverForImageClassificationLearned
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("deepmind/vision-perceiver-learned")
-        >>> model = PerceiverForImageClassificationLearned.from_pretrained("deepmind/vision-perceiver-learned")
-
-        >>> inputs = image_processor(images=image, return_tensors="pt").pixel_values
-        >>> outputs = model(inputs=inputs)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 1000]
-
-        >>> # model predicts one of the 1000 ImageNet classes
-        >>> predicted_class_idx = logits.argmax(-1).item()
-        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
-        Predicted class: tabby, tabby cat
-        ```"""
-        if inputs is not None and pixel_values is not None:
-            raise ValueError("You cannot use both `inputs` and `pixel_values`")
-        elif inputs is None and pixel_values is not None:
-            inputs = pixel_values
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.perceiver(
-            inputs=inputs,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        logits = outputs.logits if return_dict else outputs[0]
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return PerceiverClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-Example use of Perceiver for image classification, for tasks such as ImageNet.
-
-This model uses fixed 2D Fourier position embeddings. As shown in the paper, this model can achieve a top-1 accuracy of
-79.0 on ImageNet, and 84.5 when pre-trained on a large-scale dataset (i.e. JFT).
-
-[`PerceiverForImageClassificationLearned`] uses [`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`]
-(with `prep_type="pixels"`) to preprocess the input images, and
-[`~models.perceiver.modeling_perceiver.PerceiverClassificationDecoder`] to decode the latent representation of
-[`PerceiverModel`] into classification logits.
-""",
-    PERCEIVER_START_DOCSTRING,
-)
-class PerceiverForImageClassificationFourier(PerceiverPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        fourier_position_encoding_kwargs_preprocessor = {
-            "concat_pos": True,
-            "max_resolution": (224, 224),
-            "num_bands": 64,
-            "sine_only": False,
-        }
-        trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1}
-
-        self.num_labels = config.num_labels
-        self.perceiver = PerceiverModel(
-            config,
-            input_preprocessor=PerceiverImagePreprocessor(
-                config,
-                prep_type="pixels",
-                spatial_downsample=1,
-                fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_preprocessor,
-            ),
-            decoder=PerceiverClassificationDecoder(
-                config,
-                num_channels=config.d_latents,
-                trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder,
-                use_query_residual=True,
-            ),
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-        pixel_values: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, PerceiverClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, PerceiverForImageClassificationFourier
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("deepmind/vision-perceiver-fourier")
-        >>> model = PerceiverForImageClassificationFourier.from_pretrained("deepmind/vision-perceiver-fourier")
-
-        >>> inputs = image_processor(images=image, return_tensors="pt").pixel_values
-        >>> outputs = model(inputs=inputs)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 1000]
-
-        >>> # model predicts one of the 1000 ImageNet classes
-        >>> predicted_class_idx = logits.argmax(-1).item()
-        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
-        Predicted class: tabby, tabby cat
-        ```"""
-        if inputs is not None and pixel_values is not None:
-            raise ValueError("You cannot use both `inputs` and `pixel_values`")
-        elif inputs is None and pixel_values is not None:
-            inputs = pixel_values
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.perceiver(
-            inputs=inputs,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        logits = outputs.logits if return_dict else outputs[0]
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return PerceiverClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-Example use of Perceiver for image classification, for tasks such as ImageNet.
-
-This model uses a 2D conv+maxpool preprocessing network. As shown in the paper, this model can achieve a top-1 accuracy
-of 82.1 on ImageNet.
-
-[`PerceiverForImageClassificationLearned`] uses [`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`]
-(with `prep_type="conv"`) to preprocess the input images, and
-[`~models.perceiver.modeling_perceiver.PerceiverClassificationDecoder`] to decode the latent representation of
-[`PerceiverModel`] into classification logits.
-""",
-    PERCEIVER_START_DOCSTRING,
-)
-class PerceiverForImageClassificationConvProcessing(PerceiverPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        fourier_position_encoding_kwargs_preprocessor = {
-            "concat_pos": True,
-            "max_resolution": (56, 56),
-            "num_bands": 64,
-            "sine_only": False,
-        }
-        trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1}
-
-        self.num_labels = config.num_labels
-        self.perceiver = PerceiverModel(
-            config,
-            input_preprocessor=PerceiverImagePreprocessor(
-                config,
-                prep_type="conv",
-                spatial_downsample=1,
-                position_encoding_type="fourier",
-                fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_preprocessor,
-            ),
-            decoder=PerceiverClassificationDecoder(
-                config,
-                num_channels=config.d_latents,
-                trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder,
-                use_query_residual=True,
-            ),
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-        pixel_values: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, PerceiverClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, PerceiverForImageClassificationConvProcessing
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("deepmind/vision-perceiver-conv")
-        >>> model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv")
-
-        >>> inputs = image_processor(images=image, return_tensors="pt").pixel_values
-        >>> outputs = model(inputs=inputs)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 1000]
-
-        >>> # model predicts one of the 1000 ImageNet classes
-        >>> predicted_class_idx = logits.argmax(-1).item()
-        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
-        Predicted class: tabby, tabby cat
-        ```"""
-        if inputs is not None and pixel_values is not None:
-            raise ValueError("You cannot use both `inputs` and `pixel_values`")
-        elif inputs is None and pixel_values is not None:
-            inputs = pixel_values
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.perceiver(
-            inputs=inputs,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        logits = outputs.logits if return_dict else outputs[0]
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return PerceiverClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-Example use of Perceiver for optical flow, for tasks such as Sintel and KITTI. [`PerceiverForOpticalFlow`] uses
-[`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`] (with *prep_type="patches"*) to preprocess the
-input images, and [`~models.perceiver.modeling_perceiver.PerceiverOpticalFlowDecoder`] to decode the latent
-representation of [`PerceiverModel`].
-
-As input, one concatenates 2 subsequent frames along the channel dimension and extract a 3 x 3 patch around each pixel
-(leading to 3 x 3 x 3 x 2 = 54 values for each pixel). Fixed Fourier position encodings are used to encode the position
-of each pixel in the patch. Next, one applies the Perceiver encoder. To decode, one queries the latent representation
-using the same encoding used for the input.
-""",
-    PERCEIVER_START_DOCSTRING,
-)
-class PerceiverForOpticalFlow(PerceiverPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        fourier_position_encoding_kwargs_preprocessor = {
-            "num_bands": 64,
-            "max_resolution": config.train_size,
-            "sine_only": False,
-            "concat_pos": True,
-        }
-        fourier_position_encoding_kwargs_decoder = {
-            "concat_pos": True,
-            "max_resolution": config.train_size,
-            "num_bands": 64,
-            "sine_only": False,
-        }
-
-        image_preprocessor = PerceiverImagePreprocessor(
-            config,
-            prep_type="patches",
-            spatial_downsample=1,
-            conv_after_patching=True,
-            conv_after_patching_in_channels=54,
-            temporal_downsample=2,
-            position_encoding_type="fourier",
-            # position_encoding_kwargs
-            fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_preprocessor,
-        )
-
-        self.perceiver = PerceiverModel(
-            config,
-            input_preprocessor=image_preprocessor,
-            decoder=PerceiverOpticalFlowDecoder(
-                config,
-                num_channels=image_preprocessor.num_channels,
-                output_image_shape=config.train_size,
-                rescale_factor=100.0,
-                # decoder kwargs
-                use_query_residual=False,
-                output_num_channels=2,
-                # We query the decoder using the first frame features
-                # rather than a standard decoder position encoding.
-                position_encoding_type="fourier",
-                fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_decoder,
-            ),
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PerceiverClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the optical flow loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import PerceiverForOpticalFlow
-        >>> import torch
-
-        >>> model = PerceiverForOpticalFlow.from_pretrained("deepmind/optical-flow-perceiver")
-
-        >>> # in the Perceiver IO paper, the authors extract a 3 x 3 patch around each pixel,
-        >>> # leading to 3 x 3 x 3 = 27 values for each pixel (as each pixel also has 3 color channels)
-        >>> # patches have shape (batch_size, num_frames, num_channels, height, width)
-        >>> # the authors train on resolutions of 368 x 496
-        >>> patches = torch.randn(1, 2, 27, 368, 496)
-        >>> outputs = model(inputs=patches)
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 368, 496, 2]
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.perceiver(
-            inputs=inputs,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        logits = outputs.logits if return_dict else outputs[0]
-
-        loss = None
-        if labels is not None:
-            raise NotImplementedError("Optical flow training is not yet supported")
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return PerceiverClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-Example use of Perceiver for multimodal (video) autoencoding, for tasks such as Kinetics-700.
-
-[`PerceiverForMultimodalAutoencoding`] uses [`~models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor`] to
-preprocess the 3 modalities: images, audio and class labels. This preprocessor uses modality-specific preprocessors to
-preprocess every modality separately, after which they are concatenated. Trainable position embeddings are used to pad
-each modality to the same number of channels to make concatenation along the time dimension possible. Next, one applies
-the Perceiver encoder.
-
-[`~models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder`] is used to decode the latent representation of
-[`PerceiverModel`]. This decoder uses each modality-specific decoder to construct queries. The decoder queries are
-created based on the inputs after preprocessing. However, autoencoding an entire video in a single forward pass is
-computationally infeasible, hence one only uses parts of the decoder queries to do cross-attention with the latent
-representation. This is determined by the subsampled indices for each modality, which can be provided as additional
-input to the forward pass of [`PerceiverForMultimodalAutoencoding`].
-
-[`~models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder`] also pads the decoder queries of the different
-modalities to the same number of channels, in order to concatenate them along the time dimension. Next, cross-attention
-is performed with the latent representation of [`PerceiverModel`].
-
-Finally, [`~models.perceiver.modeling_perceiver.PerceiverMultiModalPostprocessor`] is used to turn this tensor into an
-actual video. It first splits up the output into the different modalities, and then applies the respective
-postprocessor for each modality.
-
-Note that, by masking the classification label during evaluation (i.e. simply providing a tensor of zeros for the
-"label" modality), this auto-encoding model becomes a Kinetics 700 video classifier.
-""",
-    PERCEIVER_START_DOCSTRING,
-)
-class PerceiverForMultimodalAutoencoding(PerceiverPreTrainedModel):
-    def __init__(self, config: PerceiverConfig):
-        super().__init__(config)
-
-        n_audio_samples = config.num_frames * config.audio_samples_per_frame
-
-        input_preprocessor = PerceiverMultimodalPreprocessor(
-            min_padding_size=4,
-            modalities={
-                "audio": PerceiverAudioPreprocessor(
-                    config,
-                    position_encoding_type="fourier",
-                    fourier_position_encoding_kwargs={
-                        "num_bands": 192,
-                        "max_resolution": (n_audio_samples,),
-                        "sine_only": False,
-                        "concat_pos": True,
-                    },
-                    prep_type="patches",
-                    samples_per_patch=config.samples_per_patch,
-                ),
-                "image": PerceiverImagePreprocessor(
-                    config,
-                    position_encoding_type="fourier",
-                    fourier_position_encoding_kwargs={
-                        "num_bands": 32,
-                        "max_resolution": (config.num_frames, config.image_size, config.image_size),
-                        "sine_only": False,
-                        "concat_pos": True,
-                    },
-                    prep_type="patches",
-                    spatial_downsample=4,
-                    temporal_downsample=1,
-                ),
-                "label": PerceiverOneHotPreprocessor(config),
-            },
-            mask_probs={"image": 0.0, "audio": 0.0, "label": 1.0},
-        )
-
-        image_decoder = PerceiverBasicVideoAutoencodingDecoder(
-            config,
-            # Autoencoding, don't pass inputs to the queries.
-            concat_preprocessed_input=False,
-            output_shape=config.output_shape,
-            output_num_channels=config.output_num_channels,
-            use_query_residual=False,
-            position_encoding_only=True,
-            position_encoding_type="fourier",
-            fourier_position_encoding_kwargs={
-                "num_bands": 32,
-                "max_resolution": (config.num_frames, config.image_size, config.image_size),
-                "sine_only": False,
-                "concat_pos": True,
-            },
-        )
-
-        decoder = PerceiverMultimodalDecoder(
-            config,
-            # Autoencoding, don't pass inputs to the queries.
-            concat_preprocessed_input=False,
-            # Modality specific decoders are used ONLY to generate queries.
-            # All modalties are decoded together using a unified decoder.
-            modalities={
-                "audio": PerceiverBasicDecoder(
-                    config,
-                    # Autoencoding, don't pass inputs to the queries.
-                    concat_preprocessed_input=False,
-                    output_index_dims=(n_audio_samples // config.samples_per_patch,),
-                    output_num_channels=config.output_num_channels,
-                    use_query_residual=False,
-                    position_encoding_only=True,
-                    position_encoding_type="fourier",
-                    fourier_position_encoding_kwargs={
-                        "num_bands": 192,
-                        "max_resolution": (n_audio_samples,),
-                        "sine_only": False,
-                        "concat_pos": True,
-                    },
-                ),
-                "image": image_decoder,
-                "label": PerceiverClassificationDecoder(
-                    config,
-                    # Autoencoding, don't pass inputs to the queries.
-                    concat_preprocessed_input=False,
-                    use_query_residual=False,
-                    position_encoding_only=True,
-                    position_encoding_type="trainable",
-                    trainable_position_encoding_kwargs={
-                        "num_channels": config._label_trainable_num_channels,
-                        "index_dims": 1,
-                    },
-                ),
-            },
-            num_outputs=None,
-            output_num_channels=config.output_num_channels,
-            use_query_residual=False,
-        )
-
-        output_postprocessor = PerceiverMultimodalPostprocessor(
-            modalities={
-                "audio": PerceiverAudioPostprocessor(config, in_channels=config.output_num_channels),
-                "image": PerceiverProjectionPostprocessor(in_channels=config.output_num_channels, out_channels=3),
-                "label": PerceiverClassificationPostprocessor(config, in_channels=config.output_num_channels),
-            }
-        )
-
-        self.perceiver = PerceiverModel(
-            config,
-            input_preprocessor=input_preprocessor,
-            decoder=decoder,
-            output_postprocessor=output_postprocessor,
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PERCEIVER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        subsampled_output_points: Optional[Dict[str, torch.Tensor]] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PerceiverClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import PerceiverForMultimodalAutoencoding
-        >>> import torch
-        >>> import numpy as np
-
-        >>> # create multimodal inputs
-        >>> images = torch.randn((1, 16, 3, 224, 224))
-        >>> audio = torch.randn((1, 30720, 1))
-        >>> inputs = dict(image=images, audio=audio, label=torch.zeros((images.shape[0], 700)))
-
-        >>> model = PerceiverForMultimodalAutoencoding.from_pretrained("deepmind/multimodal-perceiver")
-
-        >>> # in the Perceiver IO paper, videos are auto-encoded in chunks
-        >>> # each chunk subsamples different index dimensions of the image and audio modality decoder queries
-        >>> nchunks = 128
-        >>> image_chunk_size = np.prod((16, 224, 224)) // nchunks
-        >>> audio_chunk_size = audio.shape[1] // model.config.samples_per_patch // nchunks
-        >>> # process the first chunk
-        >>> chunk_idx = 0
-        >>> subsampling = {
-        ...     "image": torch.arange(image_chunk_size * chunk_idx, image_chunk_size * (chunk_idx + 1)),
-        ...     "audio": torch.arange(audio_chunk_size * chunk_idx, audio_chunk_size * (chunk_idx + 1)),
-        ...     "label": None,
-        ... }
-
-        >>> outputs = model(inputs=inputs, subsampled_output_points=subsampling)
-        >>> logits = outputs.logits
-        >>> list(logits["audio"].shape)
-        [1, 240]
-
-        >>> list(logits["image"].shape)
-        [1, 6272, 3]
-
-        >>> list(logits["label"].shape)
-        [1, 700]
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.perceiver(
-            inputs=inputs,
-            attention_mask=attention_mask,
-            subsampled_output_points=subsampled_output_points,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        logits = outputs.logits if return_dict else outputs[0]
-
-        loss = None
-        if labels is not None:
-            raise NotImplementedError("Multimodal autoencoding training is not yet supported")
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return PerceiverClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-# Below: position encodings
-
-
-def build_position_encoding(
-    position_encoding_type,
-    out_channels=None,
-    project_pos_dim=-1,
-    trainable_position_encoding_kwargs=None,
-    fourier_position_encoding_kwargs=None,
-):
-    """
-    Builds the position encoding.
-
-    Args:
-    - out_channels: refers to the number of channels of the position encodings.
-    - project_pos_dim: if specified, will project the position encodings to this dimension.
-
-    """
-
-    if position_encoding_type == "trainable":
-        if not trainable_position_encoding_kwargs:
-            raise ValueError("Make sure to pass trainable_position_encoding_kwargs")
-        output_pos_enc = PerceiverTrainablePositionEncoding(**trainable_position_encoding_kwargs)
-    elif position_encoding_type == "fourier":
-        # We don't use the index_dims argument, as this is only known during the forward pass
-        if not fourier_position_encoding_kwargs:
-            raise ValueError("Make sure to pass fourier_position_encoding_kwargs")
-        output_pos_enc = PerceiverFourierPositionEncoding(**fourier_position_encoding_kwargs)
-    else:
-        raise ValueError(f"Unknown position encoding type: {position_encoding_type}.")
-
-    # Optionally, project the position encoding to a target dimension:
-    positions_projection = nn.Linear(out_channels, project_pos_dim) if project_pos_dim > 0 else nn.Identity()
-
-    return output_pos_enc, positions_projection
-
-
-# Below: Perceiver decoders
-
-
-class PerceiverAbstractDecoder(nn.Module, metaclass=abc.ABCMeta):
-    """Perceiver abstract decoder."""
-
-    @abc.abstractmethod
-    def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None):
-        raise NotImplementedError
-
-    @property
-    @abc.abstractmethod
-    def num_query_channels(self):
-        raise NotImplementedError
-
-    @abc.abstractmethod
-    def forward(self, query, z, query_mask=None):
-        raise NotImplementedError
-
-
-class PerceiverProjectionDecoder(PerceiverAbstractDecoder):
-    """
-    Baseline projection decoder (no cross-attention).
-
-    Args:
-        config ([`PerceiverConfig`]):
-            Model configuration.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.classifier = nn.Linear(config.d_latents, config.num_labels)
-
-    def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None):
-        return None
-
-    def forward(
-        self, query: torch.Tensor, z: torch.FloatTensor, query_mask: Optional[torch.FloatTensor] = None
-    ) -> torch.FloatTensor:
-        # (batch_size, num_latents, d_latents) -> (batch_size, d_latents)
-        z = torch.mean(z, dim=1)
-        # (batch_size, d_latents) -> (batch_size, config.num_labels)
-        logits = self.classifier(z)
-        return logits
-
-
-class PerceiverBasicDecoder(PerceiverAbstractDecoder):
-    """
-    Cross-attention-based decoder. This class can be used to decode the final hidden states of the latents using a
-    cross-attention operation, in which the latents produce keys and values.
-
-    The shape of the output of this class depends on how one defines the output queries (also called decoder queries).
-
-    Args:
-        config ([*PerceiverConfig*]):
-            Model configuration.
-        output_num_channels (`int`, *optional*):
-            The number of channels in the output. Will only be used in case *final_project* is set to `True`.
-        position_encoding_type (`str`, *optional*, defaults to "trainable"):
-            The type of position encoding to use. Can be either "trainable", "fourier", or "none".
-        output_index_dims (`int`, *optional*):
-            The number of dimensions of the output queries. Ignored if 'position_encoding_type' == 'none'.
-        num_channels (`int`, *optional*, defaults to 128):
-            The number of channels of the decoder queries. Ignored if 'position_encoding_type' == 'none'.
-        qk_channels (`int`, *optional*):
-            The number of channels of the queries and keys in the cross-attention layer.
-        v_channels (`int`, *optional*):
-            The number of channels of the values in the cross-attention layer.
-        num_heads (`int`, *optional*, defaults to 1):
-            The number of attention heads in the cross-attention layer.
-        widening_factor (`int`, *optional*, defaults to 1):
-            The widening factor of the cross-attention layer.
-        use_query_residual (`bool`, *optional*, defaults to `False`):
-            Whether to use a residual connection between the query and the output of the cross-attention layer.
-        concat_preprocessed_input (`bool`, *optional*, defaults to `False`):
-            Whether to concatenate the preprocessed input to the query.
-        final_project (`bool`, *optional*, defaults to `True`):
-            Whether to project the output of the cross-attention layer to a target dimension.
-        position_encoding_only (`bool`, *optional*, defaults to `False`):
-            Whether to only use this class to define output queries.
-    """
-
-    def __init__(
-        self,
-        config: PerceiverConfig,
-        output_num_channels: int,
-        position_encoding_type: Optional[str] = "trainable",
-        # The following 2 arguments are ignored if position_encoding_type == 'none':
-        output_index_dims: Optional[int] = None,
-        num_channels: Optional[int] = 128,
-        subsampled_index_dims: Optional[int] = None,
-        qk_channels: Optional[int] = None,
-        v_channels: Optional[int] = None,
-        num_heads: Optional[int] = 1,
-        widening_factor: Optional[int] = 1,
-        use_query_residual: Optional[bool] = False,
-        concat_preprocessed_input: Optional[bool] = False,
-        final_project: Optional[bool] = True,
-        position_encoding_only: Optional[bool] = False,
-        **position_encoding_kwargs,
-    ) -> None:
-        super().__init__()
-
-        self.output_num_channels = output_num_channels
-        # If `none`, the decoder will not construct any position encodings.
-        # You should construct your own when querying the decoder.
-        self.output_position_encodings = None
-        self.position_encoding_type = position_encoding_type
-        self.position_encoding_kwargs = position_encoding_kwargs
-        if position_encoding_type != "none":
-            self.output_position_encodings, self.positions_projection = build_position_encoding(
-                position_encoding_type=position_encoding_type, **position_encoding_kwargs
-            )
-
-        self.output_index_dims = output_index_dims
-        self.num_channels = num_channels
-        if subsampled_index_dims is None:
-            subsampled_index_dims = output_index_dims
-        self.subsampled_index_dims = subsampled_index_dims
-        self.concat_preprocessed_input = concat_preprocessed_input
-        self.final_project = final_project
-        self.position_encoding_only = position_encoding_only
-
-        # for multimodal autoencoding, we don't need the decoder cross-attention and final layer
-        # so then we will set position_encoding_only to True
-        if not self.position_encoding_only:
-            self.decoding_cross_attention = PerceiverLayer(
-                config,
-                is_cross_attention=True,
-                qk_channels=qk_channels,
-                v_channels=v_channels,
-                num_heads=num_heads,
-                q_dim=num_channels,
-                kv_dim=config.d_latents,
-                widening_factor=widening_factor,
-                use_query_residual=use_query_residual,
-            )
-            self.final_layer = nn.Linear(num_channels, output_num_channels) if final_project else nn.Identity()
-
-    @property
-    def num_query_channels(self) -> int:
-        if self.position_encoding_type == "none":  # Queries come from elsewhere
-            raise ValueError(
-                "You cannot calculate number of decoder query channels when position_encoding_type is set to none"
-            )
-        if self.position_encoding_only:
-            if "project_pos_dim" in self.position_encoding_kwargs:
-                return self.position_encoding_kwargs["project_pos_dim"]
-            return self.output_position_encodings.output_size()
-        if self.final_project:
-            return self.output_num_channels
-        return self.num_channels
-
-    def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None):
-        if self.position_encoding_type == "none":  # Queries come from elsewhere
-            raise ValueError("You cannot construct decoder queries when position_encoding_type is set to none")
-        if subsampled_points is not None:
-            # subsampled_points are the indices if the inputs would be flattened
-            # however, the inputs aren't flattened, that's why we use unravel_index
-            # to get the indices for the unflattened array
-            # unravel_index returns a tuple (x_idx, y_idx, ...)
-            # stack to get the [n, d] tensor of coordinates
-            indices = [torch.from_numpy(x) for x in np.unravel_index(subsampled_points.cpu(), self.output_index_dims)]
-            pos = torch.stack(indices, dim=1)
-            batch_size = inputs.shape[0]
-            # Map these coordinates to [-1, 1]
-            pos = -1 + 2 * pos / torch.tensor(self.output_index_dims)[None, :]
-            pos = torch.broadcast_to(pos[None], [batch_size, pos.shape[0], pos.shape[1]])
-            # Construct the position encoding.
-            if self.position_encoding_type == "trainable":
-                pos_emb = self.output_position_encodings(batch_size)
-            elif self.position_encoding_type == "fourier":
-                pos_emb = self.output_position_encodings(
-                    self.output_index_dims, batch_size=batch_size, device=inputs.device, dtype=inputs.dtype, pos=pos
-                )
-
-            # Optionally project them to a target dimension.
-            pos_emb = self.positions_projection(pos_emb)
-            pos_emb = torch.reshape(pos_emb, [pos_emb.shape[0], -1, pos_emb.shape[-1]])
-        else:
-            batch_size = inputs.shape[0]
-            index_dims = inputs.shape[2:]
-
-            # Construct the position encoding.
-            if self.position_encoding_type == "trainable":
-                pos_emb = self.output_position_encodings(batch_size)
-            elif self.position_encoding_type == "fourier":
-                pos_emb = self.output_position_encodings(
-                    index_dims, batch_size, device=inputs.device, dtype=inputs.dtype
-                )
-
-            # Optionally project them to a target dimension.
-            pos_emb = self.positions_projection(pos_emb)
-
-        if self.concat_preprocessed_input:
-            if inputs_without_pos is None:
-                raise ValueError("Value is required for inputs_without_pos if concat_preprocessed_input is True")
-            pos_emb = torch.cat([inputs_without_pos, pos_emb], dim=-1)
-
-        return pos_emb
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        z: torch.FloatTensor,
-        query_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> PerceiverDecoderOutput:
-        # Cross-attention decoding.
-        # key, value: B x N x K; query: B x M x K
-        # Attention maps -> B x N x M
-        # Output -> B x M x K
-        cross_attentions = () if output_attentions else None
-
-        layer_outputs = self.decoding_cross_attention(
-            query,
-            attention_mask=query_mask,
-            head_mask=None,
-            inputs=z,
-            inputs_mask=None,
-            output_attentions=output_attentions,
-        )
-        output = layer_outputs[0]
-
-        if output_attentions:
-            cross_attentions = cross_attentions + (layer_outputs[1],)
-
-        logits = self.final_layer(output)
-
-        return PerceiverDecoderOutput(logits=logits, cross_attentions=cross_attentions)
-
-
-class PerceiverClassificationDecoder(PerceiverAbstractDecoder):
-    """
-    Cross-attention based classification decoder. Light-weight wrapper of [`PerceiverBasicDecoder`] for logit output.
-    Will turn the output of the Perceiver encoder which is of shape (batch_size, num_latents, d_latents) to a tensor of
-    shape (batch_size, num_labels). The queries are of shape (batch_size, 1, num_labels).
-
-    Args:
-        config ([`PerceiverConfig`]):
-            Model configuration.
-    """
-
-    def __init__(self, config, **decoder_kwargs):
-        super().__init__()
-
-        self.num_labels = config.num_labels
-        self.decoder = PerceiverBasicDecoder(
-            config,
-            output_num_channels=self.num_labels,
-            output_index_dims=1,  # Predict a single logit array.
-            **decoder_kwargs,
-        )
-
-    @property
-    def num_query_channels(self) -> int:
-        return self.decoder.num_query_channels
-
-    def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None):
-        return self.decoder.decoder_query(
-            inputs, modality_sizes, inputs_without_pos, subsampled_points=subsampled_points
-        )
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        z: torch.FloatTensor,
-        query_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> PerceiverDecoderOutput:
-        decoder_outputs = self.decoder(query, z, output_attentions=output_attentions)
-
-        # B x 1 x num_classes -> B x num_classes
-        logits = decoder_outputs.logits[:, 0, :]
-
-        return PerceiverDecoderOutput(logits=logits, cross_attentions=decoder_outputs.cross_attentions)
-
-
-class PerceiverOpticalFlowDecoder(PerceiverAbstractDecoder):
-    """Cross-attention based optical flow decoder."""
-
-    def __init__(self, config, output_image_shape, output_num_channels=2, rescale_factor=100.0, **decoder_kwargs):
-        super().__init__()
-
-        self.output_image_shape = output_image_shape
-        self.output_num_channels = output_num_channels
-        self.rescale_factor = rescale_factor
-        self.decoder = PerceiverBasicDecoder(config, output_num_channels=output_num_channels, **decoder_kwargs)
-
-    @property
-    def num_query_channels(self) -> int:
-        return self.decoder.num_query_channels
-
-    def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None):
-        if subsampled_points is not None:
-            raise ValueError("FlowDecoder doesn't support subsampling yet.")
-        return inputs
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        z: torch.FloatTensor,
-        query_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> PerceiverDecoderOutput:
-        decoder_outputs = self.decoder(query, z, output_attentions=output_attentions)
-        preds = decoder_outputs.logits
-        # Output flow and rescale.
-        preds /= self.rescale_factor
-        preds = preds.reshape([preds.shape[0]] + list(self.output_image_shape) + [preds.shape[-1]])
-        return PerceiverDecoderOutput(logits=preds, cross_attentions=decoder_outputs.cross_attentions)
-
-
-class PerceiverBasicVideoAutoencodingDecoder(PerceiverAbstractDecoder):
-    """
-    Cross-attention based video-autoencoding decoder. Light-weight wrapper of [*PerceiverBasicDecoder*] with video
-    reshaping logic.
-
-    Args:
-        config ([*PerceiverConfig*]):
-            Model configuration.
-        output_shape (`List[int]`):
-            Shape of the output as (batch_size, num_frames, height, width), excluding the channel dimension.
-        position_encoding_type (`str`):
-            The type of position encoding to use. Can be either "trainable", "fourier", or "none".
-    """
-
-    def __init__(
-        self, config: PerceiverConfig, output_shape: List[int], position_encoding_type: str, **decoder_kwargs
-    ) -> None:
-        super().__init__()
-        if len(output_shape) != 4:  # B, T, H, W
-            raise ValueError(f"Expected rank 4 output_shape, got {output_shape}.")
-        # Build the decoder components:
-        self.output_shape = output_shape
-        self.output_num_channels = decoder_kwargs["output_num_channels"]
-
-        self.decoder = PerceiverBasicDecoder(
-            config,
-            output_index_dims=self.output_shape[1:4],  # T*H*W
-            position_encoding_type=position_encoding_type,
-            **decoder_kwargs,
-        )
-
-    @property
-    def num_query_channels(self) -> int:
-        return self.decoder.num_query_channels
-
-    def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None):
-        return self.decoder.decoder_query(
-            inputs,
-            modality_sizes=modality_sizes,
-            inputs_without_pos=inputs_without_pos,
-            subsampled_points=subsampled_points,
-        )
-
-    def forward(
-        self, query: torch.Tensor, z: torch.FloatTensor, query_mask: Optional[torch.FloatTensor] = None
-    ) -> PerceiverDecoderOutput:
-        decoder_outputs = self.decoder(query, z)
-        logits = decoder_outputs.logits
-
-        logits = torch.reshape(logits, self.output_shape + [logits.shape[-1]])
-        return PerceiverDecoderOutput(logits=logits, cross_attentions=decoder_outputs.cross_attentions)
-
-
-def restructure(modality_sizes: ModalitySizeType, inputs: torch.Tensor) -> Mapping[str, torch.Tensor]:
-    """
-    Partitions a [B, N, C] tensor into tensors for each modality.
-
-    Args:
-        modality_sizes
-            dict specifying the size of the modality
-        inputs:
-            input tensor
-
-    Returns:
-        dict mapping name of modality to its associated tensor.
-    """
-    outputs = {}
-    index = 0
-    # Apply a predictable ordering to the modalities
-    for modality in sorted(modality_sizes.keys()):
-        size = modality_sizes[modality]
-        inp = inputs[:, index : index + size]
-        index += size
-        outputs[modality] = inp
-    return outputs
-
-
-class PerceiverMultimodalDecoder(PerceiverAbstractDecoder):
-    """
-    Multimodal decoding by composing uni-modal decoders. The *modalities* argument of the constructor is a dictionary
-    mapping modality name to the decoder of that modality. That decoder will be used to construct queries for that
-    modality. Modality-specific queries are padded with trainable modality-specific parameters, after which they are
-    concatenated along the time dimension.
-
-    Next, there is a shared cross attention operation across all modalities.
-
-    Args:
-        config ([*PerceiverConfig*]):
-            Model configuration.
-        modalities (`Dict[str, PerceiverAbstractDecoder]`):
-            Dictionary mapping modality name to the decoder of that modality.
-        num_outputs (`int`):
-            The number of outputs of the decoder.
-        output_num_channels (`int`):
-            The number of channels in the output.
-        min_padding_size (`int`, *optional*, defaults to 2):
-            The minimum padding size for all modalities. The final output will have num_channels equal to the maximum
-            channels across all modalities plus min_padding_size.
-        subsampled_index_dims (`Dict[str, PerceiverAbstractDecoder]`, *optional*):
-            Dictionary mapping modality name to the subsampled index dimensions to use for the decoder query of that
-            modality.
-    """
-
-    def __init__(
-        self,
-        config: PerceiverConfig,
-        modalities: Dict[str, PerceiverAbstractDecoder],
-        num_outputs: int,
-        output_num_channels: int,
-        min_padding_size: Optional[int] = 2,
-        subsampled_index_dims: Optional[Dict[str, PerceiverAbstractDecoder]] = None,
-        **decoder_kwargs,
-    ) -> None:
-        super().__init__()
-        self.modalities = nn.ModuleDict(modalities)
-        self.subsampled_index_dims = subsampled_index_dims
-        self.min_padding_size = min_padding_size
-        self.output_num_channels = output_num_channels
-        self.num_outputs = num_outputs
-        self.decoder = PerceiverBasicDecoder(
-            config,
-            output_index_dims=(num_outputs,),
-            output_num_channels=output_num_channels,
-            position_encoding_type="none",
-            num_channels=self.num_query_channels,
-            **decoder_kwargs,
-        )
-        self.padding = nn.ParameterDict(
-            {
-                modality: nn.Parameter(torch.randn(1, self.num_query_channels - decoder.num_query_channels))
-                for modality, decoder in modalities.items()
-            }
-        )
-
-    @property
-    def num_query_channels(self) -> int:
-        max_channel_size = max(decoder.num_query_channels for _, decoder in self.modalities.items())
-        common_channel_size = max_channel_size + self.min_padding_size
-        return common_channel_size
-
-    def decoder_query(self, inputs, modality_sizes, inputs_without_pos=None, subsampled_points=None):
-        # Partition the flat inputs among the different modalities
-        inputs = restructure(modality_sizes, inputs)
-
-        # Obtain modality-specific decoders' queries
-        subsampled_points = subsampled_points or {}
-
-        decoder_queries = {}
-        for modality, decoder in self.modalities.items():
-            # Get input_without_pos for this modality if it exists.
-            input_without_pos = None
-            if inputs_without_pos is not None:
-                input_without_pos = inputs_without_pos.get(modality, None)
-            query = decoder.decoder_query(
-                inputs=inputs[modality],
-                modality_sizes=None,
-                inputs_without_pos=input_without_pos,
-                subsampled_points=subsampled_points.get(modality, None),
-            )
-            decoder_queries[modality] = query
-
-        # Pad all queries with trainable position encodings to make them have the same channels
-
-        def embed(modality, x):
-            x = torch.reshape(x, [x.shape[0], np.prod(x.shape[1:-1]), x.shape[-1]])
-            pos = self.padding[modality]
-            pos = torch.broadcast_to(pos, [x.shape[0], x.shape[1], self.num_query_channels - x.shape[2]])
-            return torch.cat([x, pos], dim=2)
-
-        # Apply a predictable ordering to the modalities
-        return torch.cat(
-            [embed(modality, decoder_queries[modality]) for modality in sorted(self.modalities.keys())], dim=1
-        )
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        z: torch.FloatTensor,
-        query_mask: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> torch.Tensor:
-        # B x 1 x num_classes -> B x num_classes
-        decoder_outputs = self.decoder(query, z, output_attentions=output_attentions)
-
-        return decoder_outputs
-
-
-# Below: IO pre- and post-processor classes for Perceiver.
-def space_to_depth(frames: torch.Tensor, temporal_block_size: int = 1, spatial_block_size: int = 1) -> torch.Tensor:
-    """
-    Space to depth transform. Rearranges blocks of spatial data, into depth.
-
-    This function assumes the channels to be first, but will place the channels last after transformation.
-
-    Based on https://discuss.pytorch.org/t/is-there-any-layer-like-tensorflows-space-to-depth-function/3487/15.
-    """
-    if len(frames.shape) == 4:
-        batch_size, num_channels, height, width = frames.shape
-        # split up dimensions (height by spatial_block_size, width by spatial_block_size)
-        frames = frames.view(
-            batch_size,
-            num_channels,
-            height // spatial_block_size,
-            spatial_block_size,
-            width // spatial_block_size,
-            spatial_block_size,
-        )
-        # move blocks to last dimension: (batch_size, H//bs, W//bs, bs, bs, C)
-        frames = frames.permute(0, 2, 4, 3, 5, 1).contiguous()
-        # concatenate blocks along channel dimension: (batch_size, H//bs, W//bs, bs*bs*C)
-        frames = frames.view(
-            batch_size,
-            height // spatial_block_size,
-            width // spatial_block_size,
-            (spatial_block_size**2) * num_channels,
-        )
-        return frames
-    elif len(frames.shape) == 5:
-        batch_size, time, num_channels, height, width = frames.shape
-        # split up dimensions (time by temporal_block_size, height by spatial_block_size, width by spatial_block_size)
-        frames = frames.view(
-            batch_size,
-            time // temporal_block_size,
-            temporal_block_size,
-            num_channels,
-            height // spatial_block_size,
-            spatial_block_size,
-            width // spatial_block_size,
-            spatial_block_size,
-        )
-        # move blocks to last dimension: (batch_size, T//ts, H//bs, W//bs, ts, bs, bs, C)
-        frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous()
-        # concatenate blocks along channel dimension: (batch_size, T//ts, H//bs, W//bs, ts*bs*bs*C)
-        frames = frames.view(
-            batch_size,
-            time // temporal_block_size,
-            height // spatial_block_size,
-            width // spatial_block_size,
-            temporal_block_size * (spatial_block_size**2) * num_channels,
-        )
-        return frames
-    else:
-        raise ValueError(
-            "Frames should be of rank 4 (batch, channels, height, width)"
-            " or rank 5 (batch, time, channels, height, width)"
-        )
-
-
-class Conv2dSamePadding(nn.Conv2d):
-    """
-    Conv2d layer with padding="same" support. Source:
-    https://gist.github.com/sumanmichael/4de9dee93f972d47c80c4ade8e149ea6
-    """
-
-    def __init__(self, *args, **kwargs):
-        super(Conv2dSamePadding, self).__init__(*args, **kwargs)
-        self.zero_pad_2d = nn.ZeroPad2d(
-            reduce(__add__, [(k // 2 + (k - 2 * (k // 2)) - 1, k // 2) for k in self.kernel_size[::-1]])
-        )
-
-    def forward(self, input):
-        return self._conv_forward(self.zero_pad_2d(input), self.weight, self.bias)
-
-
-class Conv2DDownsample(nn.Module):
-    """Downsamples 4x by applying a 2D convolution and doing max pooling."""
-
-    def __init__(
-        self,
-        num_layers: int = 1,
-        in_channels: int = 3,
-        out_channels: int = 64,
-        use_batchnorm: bool = True,
-    ):
-        """
-        Constructs a Conv2DDownsample model.
-
-        Args:
-          in_channels (`int`, *optional*, defaults to 3):
-            The number of input channels.
-          out_channels (`int`, *optional*, defaults to 64):
-            The number of conv output channels.
-          use_batchnorm (`bool`, *optional*, defaults to `True`):
-            Whether to use batchnorm.
-        """
-        super().__init__()
-
-        self.conv = Conv2dSamePadding(
-            in_channels=in_channels, out_channels=out_channels, kernel_size=7, stride=2, bias=False
-        )
-        self.batchnorm = nn.BatchNorm2d(num_features=out_channels) if use_batchnorm else nn.Identity()
-        self.relu = nn.ReLU()
-        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2)
-
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        out = self.conv(inputs)
-        out = self.batchnorm(out)
-        out = self.relu(out)
-        out = self.max_pool(out)
-        return out
-
-
-def generate_fourier_features(pos, num_bands, max_resolution=(224, 224), concat_pos=True, sine_only=False):
-    """
-    Generate a Fourier frequency position encoding with linear spacing.
-
-    Args:
-      pos (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`):
-        The Tensor containing the position of n points in d dimensional space.
-      num_bands (`int`):
-        The number of frequency bands (K) to use.
-      max_resolution (`Tuple[int]`, *optional*, defaults to (224, 224)):
-        The maximum resolution (i.e. the number of pixels per dim). A tuple representing resolution for each dimension.
-      concat_pos (`bool`, *optional*, defaults to `True`):
-        Whether to concatenate the input position encoding to the Fourier features.
-      sine_only (`bool`, *optional*, defaults to `False`):
-        Whether to use a single phase (sin) or two (sin/cos) for each frequency band.
-
-    Returns:
-      `torch.FloatTensor` of shape `(batch_size, sequence_length, n_channels)`: The Fourier position embeddings. If
-      `concat_pos` is `True` and `sine_only` is `False`, output dimensions are ordered as: [dim_1, dim_2, ..., dim_d,
-      sin(pi*f_1*dim_1), ..., sin(pi*f_K*dim_1), ..., sin(pi*f_1*dim_d), ..., sin(pi*f_K*dim_d), cos(pi*f_1*dim_1),
-      ..., cos(pi*f_K*dim_1), ..., cos(pi*f_1*dim_d), ..., cos(pi*f_K*dim_d)], where dim_i is pos[:, i] and f_k is the
-      kth frequency band.
-    """
-
-    batch_size = pos.shape[0]
-
-    min_freq = 1.0
-    # Nyquist frequency at the target resolution:
-    freq_bands = torch.stack(
-        [torch.linspace(start=min_freq, end=res / 2, steps=num_bands) for res in max_resolution], dim=0
-    )
-
-    # Get frequency bands for each spatial dimension.
-    # Output is size [n, d * num_bands]
-    per_pos_features = pos[0, :, :][:, :, None] * freq_bands[None, :, :]
-    per_pos_features = torch.reshape(per_pos_features, [-1, np.prod(per_pos_features.shape[1:])])
-
-    if sine_only:
-        # Output is size [n, d * num_bands]
-        per_pos_features = torch.sin(np.pi * (per_pos_features))
-    else:
-        # Output is size [n, 2 * d * num_bands]
-        per_pos_features = torch.cat(
-            [torch.sin(np.pi * per_pos_features), torch.cos(np.pi * per_pos_features)], dim=-1
-        )
-    # Concatenate the raw input positions.
-    if concat_pos:
-        # Adds d bands to the encoding.
-        per_pos_features = torch.cat([pos, per_pos_features.expand(batch_size, -1, -1)], dim=-1)
-    return per_pos_features
-
-
-def build_linear_positions(index_dims, output_range=(-1.0, 1.0)):
-    """
-    Generate an array of position indices for an N-D input array.
-
-    Args:
-      index_dims (`List[int]`):
-        The shape of the index dimensions of the input array.
-      output_range (`Tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`):
-        The min and max values taken by each input index dimension.
-
-    Returns:
-      `torch.FloatTensor` of shape `(index_dims[0], index_dims[1], .., index_dims[-1], N)`.
-    """
-
-    def _linspace(n_xels_per_dim):
-        return torch.linspace(start=output_range[0], end=output_range[1], steps=n_xels_per_dim, dtype=torch.float32)
-
-    dim_ranges = [_linspace(n_xels_per_dim) for n_xels_per_dim in index_dims]
-    array_index_grid = meshgrid(*dim_ranges, indexing="ij")
-
-    return torch.stack(array_index_grid, dim=-1)
-
-
-class PerceiverAbstractPositionEncoding(nn.Module, metaclass=abc.ABCMeta):
-    """Perceiver abstract position encoding."""
-
-    @property
-    @abc.abstractmethod
-    def num_dimensions(self) -> int:
-        raise NotImplementedError
-
-    @abc.abstractmethod
-    def output_size(self, *args, **kwargs) -> int:
-        raise NotImplementedError
-
-    @abc.abstractmethod
-    def forward(self, batch_size, pos):
-        raise NotImplementedError
-
-
-class PerceiverTrainablePositionEncoding(PerceiverAbstractPositionEncoding):
-    """Trainable position encoding."""
-
-    def __init__(self, index_dims, num_channels=128):
-        super().__init__()
-        self._num_channels = num_channels
-        self._index_dims = index_dims
-        index_dim = np.prod(index_dims)
-        self.position_embeddings = nn.Parameter(torch.randn(index_dim, num_channels))
-
-    @property
-    def num_dimensions(self) -> int:
-        if isinstance(self._index_dims, int):
-            return 1
-        return len(self._index_dims)
-
-    def output_size(self, *args, **kwargs) -> int:
-        return self._num_channels
-
-    def forward(self, batch_size: int) -> torch.Tensor:
-        position_embeddings = self.position_embeddings
-
-        if batch_size is not None:
-            position_embeddings = position_embeddings.expand(batch_size, -1, -1)
-        return position_embeddings
-
-
-def _check_or_build_spatial_positions(pos, index_dims, batch_size):
-    """
-    Checks or builds spatial position features (x, y, ...).
-
-    Args:
-      pos (`torch.FloatTensor`):
-        None, or an array of position features. If None, position features are built. Otherwise, their size is checked.
-      index_dims (`List[int]`):
-        An iterable giving the spatial/index size of the data to be featurized.
-      batch_size (`int`):
-        The batch size of the data to be featurized.
-
-    Returns:
-        `torch.FloatTensor` of shape `(batch_size, prod(index_dims))` an array of position features.
-    """
-    if pos is None:
-        pos = build_linear_positions(index_dims)
-        # equivalent to `torch.broadcast_to(pos[None], (batch_size,) + pos.shape)`
-        # but `torch.broadcast_to` cannot be converted to ONNX
-        pos = pos[None].expand((batch_size,) + pos.shape)
-        pos = torch.reshape(pos, [batch_size, np.prod(index_dims), -1])
-    else:
-        # Just a warning label: you probably don't want your spatial features to
-        # have a different spatial layout than your pos coordinate system.
-        # But feel free to override if you think it'll work!
-        if pos.shape[-1] != len(index_dims):
-            raise ValueError("Spatial features have the wrong number of dimensions.")
-    return pos
-
-
-class PerceiverFourierPositionEncoding(PerceiverAbstractPositionEncoding):
-    """Fourier (Sinusoidal) position encoding."""
-
-    def __init__(self, num_bands, max_resolution, concat_pos=True, sine_only=False):
-        super().__init__()
-        self.num_bands = num_bands
-        self.max_resolution = max_resolution
-        self.concat_pos = concat_pos
-        self.sine_only = sine_only
-
-    @property
-    def num_dimensions(self) -> int:
-        return len(self.max_resolution)
-
-    def output_size(self):
-        """Returns size of positional encodings last dimension."""
-        num_dims = len(self.max_resolution)
-        encoding_size = self.num_bands * num_dims
-        if not self.sine_only:
-            encoding_size *= 2
-        if self.concat_pos:
-            encoding_size += self.num_dimensions
-
-        return encoding_size
-
-    def forward(
-        self,
-        index_dims: List[int],
-        batch_size: int,
-        device: torch.device,
-        dtype: torch.dtype,
-        pos: torch.FloatTensor = None,
-    ) -> torch.FloatTensor:
-        pos = _check_or_build_spatial_positions(pos, index_dims, batch_size)
-        fourier_pos_enc = generate_fourier_features(
-            pos,
-            num_bands=self.num_bands,
-            max_resolution=self.max_resolution,
-            concat_pos=self.concat_pos,
-            sine_only=self.sine_only,
-        ).to(device=device, dtype=dtype)
-        return fourier_pos_enc
-
-
-class AbstractPreprocessor(nn.Module):
-    @property
-    def num_channels(self) -> int:
-        """Returns size of preprocessor output."""
-        raise NotImplementedError()
-
-
-class PerceiverTextPreprocessor(AbstractPreprocessor):
-    """
-    Text preprocessing for Perceiver Encoder. Can be used to embed `inputs` and add positional encodings.
-
-    The dimensionality of the embeddings is determined by the `d_model` attribute of the configuration.
-
-    Args:
-        config ([`PerceiverConfig`]):
-            Model configuration.
-    """
-
-    def __init__(self, config: PerceiverConfig) -> None:
-        super().__init__()
-        self.config = config
-        self.embeddings = nn.Embedding(num_embeddings=config.vocab_size, embedding_dim=config.d_model)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.d_model)
-
-    @property
-    def num_channels(self) -> int:
-        return self.config.d_model
-
-    def forward(self, inputs: torch.LongTensor, pos: Optional[torch.Tensor] = None, network_input_is_1d: bool = True):
-        embeddings_without_pos = self.embeddings(inputs)
-
-        seq_length = inputs.shape[1]
-        position_ids = torch.arange(0, seq_length, device=inputs.device)
-        embeddings = embeddings_without_pos + self.position_embeddings(position_ids)
-
-        return embeddings, None, embeddings_without_pos
-
-
-class PerceiverEmbeddingDecoder(nn.Module):
-    """
-    Module to decode embeddings (for masked language modeling).
-
-    Args:
-        config ([`PerceiverConfig`]):
-            Model configuration.
-    """
-
-    def __init__(self, config: PerceiverConfig) -> None:
-        super().__init__()
-        self.config = config
-        self.vocab_size = config.vocab_size
-        self.bias = nn.Parameter(torch.zeros(self.vocab_size))
-
-    def forward(self, hidden_states: torch.Tensor, embedding_layer: torch.Tensor) -> torch.Tensor:
-        batch_size, seq_len, d_model = hidden_states.shape
-        # Flatten batch dim
-        output = torch.matmul(hidden_states.reshape([-1, d_model]), embedding_layer.weight.transpose(0, 1))
-        output = output + self.bias
-
-        return output.reshape([batch_size, seq_len, self.vocab_size])
-
-
-class PerceiverMultimodalPostprocessor(nn.Module):
-    """
-    Multimodal postprocessing for Perceiver. Can be used to combine modality-specific postprocessors into a single
-    postprocessor.
-
-    Args:
-          modalities (`Mapping[str, PostprocessorType]`):
-            Dictionary mapping modality name to postprocessor class for that modality.
-          input_is_dict (`bool`, *optional*, defaults to `False`):
-            If True, input is assumed to be dictionary structured, and outputs keep the same dictionary shape. If
-            False, input is a tensor which is sliced up during postprocessing by *modality_sizes*.
-    """
-
-    def __init__(self, modalities: Mapping[str, PostprocessorType], input_is_dict: bool = False):
-        super().__init__()
-        self.modalities = nn.ModuleDict(modalities)
-        self.input_is_dict = input_is_dict
-
-    def forward(
-        self, inputs: torch.Tensor, pos: Optional[torch.Tensor] = None, modality_sizes=None
-    ) -> Mapping[str, torch.Tensor]:
-        if not self.input_is_dict:
-            # Slice up modalities by their sizes.
-            if modality_sizes is None:
-                raise ValueError("Modality sizes should be specified if input is not a dictionary.")
-            inputs = restructure(modality_sizes=modality_sizes, inputs=inputs)
-
-        outputs = {
-            modality: postprocessor(inputs[modality], pos=pos, modality_sizes=None)
-            for modality, postprocessor in self.modalities.items()
-        }
-        return outputs
-
-
-class PerceiverClassificationPostprocessor(nn.Module):
-    """
-    Classification postprocessing for Perceiver. Can be used to convert the decoder output to classification logits.
-
-    Args:
-        config ([*PerceiverConfig*]):
-            Model configuration.
-        in_channels (`int`):
-            Number of channels in the input.
-    """
-
-    def __init__(self, config: PerceiverConfig, in_channels: int) -> None:
-        super().__init__()
-        self.classifier = nn.Linear(in_channels, config.num_labels)
-
-    def forward(self, inputs, pos: Optional[torch.Tensor] = None, modality_sizes=None) -> torch.Tensor:
-        logits = self.classifier(inputs)
-        return logits[:, 0, :]
-
-
-class PerceiverAudioPostprocessor(nn.Module):
-    """
-    Audio postprocessing for Perceiver. Can be used to convert the decoder output to audio features.
-
-    Args:
-        config ([*PerceiverConfig*]):
-            Model configuration.
-        in_channels (`int`):
-            Number of channels in the input.
-        postproc_type (`str`, *optional*, defaults to `"patches"`):
-            Postprocessor type to use. Currently, only "patches" is supported.
-    """
-
-    def __init__(self, config: PerceiverConfig, in_channels: int, postproc_type: str = "patches") -> None:
-        super().__init__()
-
-        if postproc_type not in ("patches",):  # to be supported: 'conv', 'patches', 'pixels'
-            raise ValueError("Invalid postproc_type!")
-
-        # Architecture parameters:
-        self.classifier = nn.Linear(in_channels, config.samples_per_patch)
-
-    def forward(self, inputs: torch.Tensor, pos: Optional[torch.Tensor] = None, modality_sizes=None) -> torch.Tensor:
-        logits = self.classifier(inputs)
-        return torch.reshape(logits, [inputs.shape[0], -1])
-
-
-class PerceiverProjectionPostprocessor(nn.Module):
-    """
-    Projection postprocessing for Perceiver. Can be used to project the channels of the decoder output to a lower
-    dimension.
-
-    Args:
-        in_channels (`int`):
-            Number of channels in the input.
-        out_channels (`int`):
-            Number of channels in the output.
-    """
-
-    def __init__(self, in_channels: int, out_channels: int) -> None:
-        super().__init__()
-        self.classifier = nn.Linear(in_channels, out_channels)
-
-    def forward(self, inputs: torch.Tensor, pos: Optional[torch.Tensor] = None, modality_sizes=None) -> torch.Tensor:
-        logits = self.classifier(inputs)
-        return logits
-
-
-class PerceiverImagePreprocessor(AbstractPreprocessor):
-    """
-    Image preprocessing for Perceiver Encoder.
-
-    Note: the *out_channels* argument refers to the output channels of a convolutional layer, if *prep_type* is set to
-    "conv1x1" or "conv". If one adds absolute position embeddings, one must make sure the *num_channels* of the
-    position encoding kwargs are set equal to the *out_channels*.
-
-    Args:
-        config ([*PerceiverConfig*]):
-            Model configuration.
-        prep_type (`str`, *optional*, defaults to `"conv"`):
-            Preprocessing type. Can be "conv1x1", "conv", "patches", "pixels".
-        spatial_downsample (`int`, *optional*, defaults to 4):
-            Spatial downsampling factor.
-        temporal_downsample (`int`, *optional*, defaults to 1):
-            Temporal downsampling factor (only relevant in case a time dimension is present).
-        position_encoding_type (`str`, *optional*, defaults to `"fourier"`):
-            Position encoding type. Can be "fourier" or "trainable".
-        in_channels (`int`, *optional*, defaults to 3):
-            Number of channels in the input.
-        out_channels (`int`, *optional*, defaults to 64):
-            Number of channels in the output.
-        conv_after_patching (`bool`, *optional*, defaults to `False`):
-            Whether to apply a convolutional layer after patching.
-        conv_after_patching_in_channels (`int`, *optional*, defaults to 54):
-            Number of channels in the input of the convolutional layer after patching.
-        conv2d_use_batchnorm (`bool`, *optional*, defaults to `True`):
-            Whether to use batch normalization in the convolutional layer.
-        concat_or_add_pos (`str`, *optional*, defaults to `"concat"`):
-            How to concatenate the position encoding to the input. Can be "concat" or "add".
-        project_pos_dim (`int`, *optional*, defaults to -1):
-            Dimension of the position encoding to project to. If -1, no projection is applied.
-        **position_encoding_kwargs (`Dict`, *optional*):
-            Keyword arguments for the position encoding.
-    """
-
-    def __init__(
-        self,
-        config,
-        prep_type="conv",
-        spatial_downsample: int = 4,
-        temporal_downsample: int = 1,
-        position_encoding_type: str = "fourier",
-        in_channels: int = 3,
-        out_channels: int = 64,
-        conv_after_patching: bool = False,
-        conv_after_patching_in_channels: int = 54,  # only relevant when conv_after_patching = True
-        conv2d_use_batchnorm: bool = True,
-        concat_or_add_pos: str = "concat",
-        project_pos_dim: int = -1,
-        **position_encoding_kwargs,
-    ):
-        super().__init__()
-        self.config = config
-
-        if prep_type not in ("conv", "patches", "pixels", "conv1x1"):
-            raise ValueError(f"Prep_type {prep_type} is invalid")
-
-        if concat_or_add_pos not in ["concat", "add"]:
-            raise ValueError(f"Invalid value {concat_or_add_pos} for concat_or_add_pos.")
-
-        self.in_channels = in_channels
-        self.prep_type = prep_type
-        self.spatial_downsample = spatial_downsample
-        self.temporal_downsample = temporal_downsample
-        self.position_encoding_type = position_encoding_type
-        self.concat_or_add_pos = concat_or_add_pos
-        self.conv_after_patching = conv_after_patching
-        self.out_channels = out_channels
-
-        if self.prep_type == "conv":
-            # Downsampling with conv is currently restricted
-            convnet_num_layers = math.log(spatial_downsample, 4)
-            convnet_num_layers_is_int = convnet_num_layers == np.round(convnet_num_layers)
-            if not convnet_num_layers_is_int or temporal_downsample != 1:
-                raise ValueError(
-                    "Only powers of 4 expected for spatial and 1 expected for temporal downsampling with conv."
-                )
-            self.convnet = Conv2DDownsample(
-                in_channels=in_channels,
-                num_layers=int(convnet_num_layers),
-                out_channels=out_channels,
-                use_batchnorm=conv2d_use_batchnorm,
-            )
-
-        elif self.prep_type == "conv1x1":
-            if temporal_downsample != 1:
-                raise ValueError("Conv1x1 does not downsample in time.")
-            self.convnet_1x1 = nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(1, 1),
-                # spatial_downsample is unconstrained for 1x1 convolutions.
-                stride=(spatial_downsample, spatial_downsample),
-            )
-
-        # Position embeddings
-        self.project_pos_dim = project_pos_dim
-        self.position_embeddings, self.positions_projection = build_position_encoding(
-            position_encoding_type=position_encoding_type,
-            out_channels=out_channels,
-            project_pos_dim=project_pos_dim,
-            **position_encoding_kwargs,
-        )
-
-        # Optional convolutional layer after patches.
-        self.conv_after_patches = (
-            nn.Linear(conv_after_patching_in_channels, self.out_channels) if conv_after_patching else nn.Identity()
-        )
-
-    @property
-    def num_channels(self) -> int:
-        # Let's assume that the number of resolutions (in the context of image preprocessing)
-        # of the input data is 2 or 3 depending on whether we are processing image or video respectively.
-        # In this case, for convenience, we will declare is_temporal variable,
-        # which will show whether the data has a temporal dimension or not.
-        is_temporal = self.position_embeddings.num_dimensions > 2
-
-        # position embedding
-        if self.project_pos_dim > 0:
-            pos_dim = self.project_pos_dim
-        else:
-            pos_dim = self.position_embeddings.output_size()
-        if self.concat_or_add_pos == "add":
-            return pos_dim
-
-        # inputs
-        if self.conv_after_patching or self.prep_type in ("conv1x1", "conv"):
-            inp_dim = self.out_channels
-        elif self.prep_type == "pixels":
-            inp_dim = self.in_channels
-            if not is_temporal:
-                inp_dim = math.ceil(inp_dim / self.spatial_downsample)
-        elif self.prep_type == "patches":
-            if self.conv_after_patching:
-                inp_dim = self.out_channels
-            else:
-                inp_dim = self.in_channels * self.spatial_downsample**2
-                if is_temporal:
-                    inp_dim *= self.temporal_downsample
-
-        return inp_dim + pos_dim
-
-    def _build_network_inputs(self, inputs: torch.Tensor, network_input_is_1d: bool = True):
-        """
-        Construct the final input, including position encoding.
-
-        This method expects the inputs to always have channels as last dimension.
-
-        """
-        batch_size = inputs.shape[0]
-        index_dims = inputs.shape[1:-1]
-        indices = np.prod(index_dims)
-
-        # Flatten input features to a 1D index dimension if necessary.
-        if len(inputs.shape) > 3 and network_input_is_1d:
-            inputs = torch.reshape(inputs, [batch_size, indices, -1])
-
-        # Construct the position encoding.
-        if self.position_encoding_type == "trainable":
-            pos_enc = self.position_embeddings(batch_size)
-        elif self.position_encoding_type == "fourier":
-            pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device, dtype=inputs.dtype)
-
-        # Optionally project them to a target dimension.
-        pos_enc = self.positions_projection(pos_enc)
-
-        if not network_input_is_1d:
-            # Reshape pos to match the input feature shape
-            # if the network takes non-1D inputs
-            sh = inputs.shape
-            pos_enc = torch.reshape(pos_enc, list(sh)[:-1] + [-1])
-        if self.concat_or_add_pos == "concat":
-            inputs_with_pos = torch.cat([inputs, pos_enc], dim=-1)
-        elif self.concat_or_add_pos == "add":
-            inputs_with_pos = inputs + pos_enc
-        return inputs_with_pos, inputs
-
-    def forward(self, inputs: torch.Tensor, pos: Optional[torch.Tensor] = None, network_input_is_1d: bool = True):
-        if self.prep_type == "conv":
-            # Convnet image featurization.
-            # Downsamples spatially by a factor of 4
-            inputs = self.convnet(inputs)
-
-        elif self.prep_type == "conv1x1":
-            # map inputs to self.out_channels
-            inputs = self.convnet_1x1(inputs)
-
-        elif self.prep_type == "pixels":
-            # if requested, downsamples in the crudest way
-            if inputs.ndim == 4:
-                inputs = inputs[:: self.spatial_downsample, :: self.spatial_downsample]
-            elif inputs.ndim == 5:
-                inputs = inputs[
-                    :, :: self.temporal_downsample, :, :: self.spatial_downsample, :: self.spatial_downsample
-                ]
-            else:
-                raise ValueError("Unsupported data format for pixels.")
-
-        elif self.prep_type == "patches":
-            # Space2depth featurization.
-            # Video: B x T x C x H x W
-            inputs = space_to_depth(
-                inputs, temporal_block_size=self.temporal_downsample, spatial_block_size=self.spatial_downsample
-            )
-
-            if inputs.ndim == 5 and inputs.shape[1] == 1:
-                # for flow
-                inputs = inputs.squeeze(dim=1)
-
-            # Optionally apply conv layer.
-            inputs = self.conv_after_patches(inputs)
-
-        if self.prep_type != "patches":
-            # move channels to last dimension, as the _build_network_inputs method below expects this
-            if inputs.ndim == 4:
-                inputs = inputs.permute(0, 2, 3, 1)
-            elif inputs.ndim == 5:
-                inputs = inputs.permute(0, 1, 3, 4, 2)
-            else:
-                raise ValueError("Unsupported data format for conv1x1.")
-
-        inputs, inputs_without_pos = self._build_network_inputs(inputs, network_input_is_1d)
-        modality_sizes = None  # Size for each modality, only needed for multimodal
-
-        return inputs, modality_sizes, inputs_without_pos
-
-
-class PerceiverOneHotPreprocessor(AbstractPreprocessor):
-    """
-    One-hot preprocessor for Perceiver Encoder. Can be used to add a dummy index dimension to the input.
-
-    Args:
-        config ([`PerceiverConfig`]):
-            Model configuration.
-    """
-
-    def __init__(self, config: PerceiverConfig) -> None:
-        super().__init__()
-        self.config: PerceiverConfig = config
-
-    @property
-    def num_channels(self) -> int:
-        return self.config.num_labels
-
-    def forward(self, inputs: torch.Tensor, pos: Optional[torch.Tensor] = None, network_input_is_1d: bool = True):
-        # Add a dummy index dimension.
-        inputs = inputs[:, None, :]
-
-        # No position encodings, so the 1st (input) and 3rd (inputs_without_pos)
-        # outputs are identical.
-        return inputs, None, inputs
-
-
-class PerceiverAudioPreprocessor(AbstractPreprocessor):
-    """
-    Audio preprocessing for Perceiver Encoder.
-
-    Args:
-        config ([*PerceiverConfig*]):
-            Model configuration.
-        prep_type (`str`, *optional*, defaults to `"patches"`):
-            Preprocessor type to use. Only "patches" is supported.
-        samples_per_patch (`int`, *optional*, defaults to 96):
-            Number of samples per patch.
-        position_encoding_type (`str`, *optional*, defaults to `"fourier"`):
-            Type of position encoding to use. Can be "trainable" or "fourier".
-        concat_or_add_pos (`str`, *optional*, defaults to `"concat"`):
-            How to concatenate the position encoding to the input. Can be "concat" or "add".
-        out_channels (`int`, *optional*, defaults to 64):
-            Number of channels in the output.
-        project_pos_dim (`int`, *optional*, defaults to -1):
-            Dimension of the position encoding to project to. If -1, no projection is applied.
-        **position_encoding_kwargs (`Dict`, *optional*):
-            Keyword arguments for the position encoding.
-    """
-
-    def __init__(
-        self,
-        config,
-        prep_type: str = "patches",
-        samples_per_patch: int = 96,
-        position_encoding_type: str = "fourier",
-        concat_or_add_pos: str = "concat",
-        out_channels=64,
-        project_pos_dim=-1,
-        **position_encoding_kwargs,
-    ):
-        super().__init__()
-        self.config = config
-
-        if prep_type not in ("patches",):
-            raise ValueError(f"Prep_type {prep_type} is invalid, can only be 'patches'.")
-
-        if concat_or_add_pos not in ["concat", "add"]:
-            raise ValueError(f"Concat_or_pos {concat_or_add_pos} is invalid, can only be 'concat' or 'add'.")
-
-        self.samples_per_patch = samples_per_patch
-        self.position_encoding_type = position_encoding_type
-        self.concat_or_add_pos = concat_or_add_pos
-        self.project_pos_dim = project_pos_dim
-
-        # Position embeddings
-        self.position_embeddings, self.positions_projection = build_position_encoding(
-            position_encoding_type=position_encoding_type,
-            out_channels=out_channels,
-            project_pos_dim=project_pos_dim,
-            **position_encoding_kwargs,
-        )
-
-    @property
-    def num_channels(self) -> int:
-        # position embedding
-        if self.project_pos_dim > 0:
-            pos_dim = self.project_pos_dim
-        else:
-            pos_dim = self.position_embeddings.output_size()
-        if self.concat_or_add_pos == "add":
-            return pos_dim
-        return self.samples_per_patch + pos_dim
-
-    def _build_network_inputs(self, inputs):
-        """Construct the final input, including position encoding."""
-        batch_size = inputs.shape[0]
-        index_dims = inputs.shape[1:-1]
-
-        # Construct the position encoding.
-        if self.position_encoding_type == "trainable":
-            pos_enc = self.position_embeddings(batch_size)
-        elif self.position_encoding_type == "fourier":
-            pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device, dtype=inputs.dtype)
-
-        # Optionally project them to a target dimension.
-        pos_enc = self.positions_projection(pos_enc)
-
-        if self.concat_or_add_pos == "concat":
-            inputs_with_pos = torch.cat([inputs, pos_enc], dim=-1)
-        elif self.concat_or_add_pos == "add":
-            inputs_with_pos = inputs + pos_enc
-
-        return inputs_with_pos, inputs
-
-    def forward(self, inputs: torch.Tensor, pos: Optional[torch.Tensor] = None, network_input_is_1d: bool = True):
-        inputs = torch.reshape(inputs, [inputs.shape[0], -1, self.samples_per_patch])
-
-        inputs, inputs_without_pos = self._build_network_inputs(inputs)
-        modality_sizes = None  # Size for each modality, only needed for multimodal
-
-        return inputs, modality_sizes, inputs_without_pos
-
-
-class PerceiverMultimodalPreprocessor(AbstractPreprocessor):
-    """
-    Multimodal preprocessing for Perceiver Encoder.
-
-    Inputs for each modality are preprocessed, then padded with trainable position embeddings to have the same number
-    of channels.
-
-    Args:
-        modalities (`Mapping[str, PreprocessorType]`):
-            Dict mapping modality name to preprocessor.
-        mask_probs (`Dict[str, float]`):
-            Dict mapping modality name to masking probability of that modality.
-        min_padding_size (`int`, *optional*, defaults to 2):
-            The minimum padding size for all modalities. The final output will have num_channels equal to the maximum
-            channels across all modalities plus min_padding_size.
-    """
-
-    def __init__(
-        self,
-        modalities: Mapping[str, PreprocessorType],
-        mask_probs: Optional[Mapping[str, float]] = None,
-        min_padding_size: int = 2,
-    ):
-        super().__init__()
-        self.modalities = nn.ModuleDict(modalities)
-        self.min_padding_size = min_padding_size
-        self.mask_probs = mask_probs if mask_probs is not None else {}
-        self.padding = nn.ParameterDict(
-            {
-                modality: nn.Parameter(torch.randn(1, self.num_channels - preprocessor.num_channels))
-                for modality, preprocessor in modalities.items()
-            }
-        )
-        self.mask = nn.ParameterDict(
-            {modality: nn.Parameter(torch.randn(1, self.num_channels)) for modality, _ in self.mask_probs.items()}
-        )
-
-    @property
-    def num_channels(self) -> int:
-        max_channel_size = max(processor.num_channels for _, processor in self.modalities.items())
-        common_channel_size = max_channel_size + self.min_padding_size
-        return common_channel_size
-
-    def forward(
-        self, inputs: Mapping[str, torch.Tensor], pos: Optional[torch.Tensor] = None, network_input_is_1d: bool = True
-    ) -> PreprocessorOutputType:
-        padded = {}
-        modality_sizes = {}
-        inputs_without_pos = {}
-        for modality, preprocessor in self.modalities.items():
-            # preprocess each modality using the respective preprocessor.
-            output, _, inputs_without_pos[modality] = preprocessor(
-                inputs[modality], pos=pos, network_input_is_1d=network_input_is_1d
-            )
-
-            # pad to the same common_channel_size.
-            batch_size, num_samples, num_channels = output.shape
-            pos_enc = self.padding[modality].expand(batch_size, -1, -1)
-
-            padding = torch.broadcast_to(
-                pos_enc,
-                [batch_size, num_samples, self.num_channels - num_channels],
-            )
-            output_padded = torch.cat([output, padding], dim=2)
-
-            # mask if required
-            if modality in self.mask_probs:
-                mask_token = self.mask[modality].expand(batch_size, -1, -1)
-                mask_prob = self.mask_probs[modality]
-                mask = torch.bernoulli(torch.full([batch_size, num_samples], mask_prob))
-                mask = torch.unsqueeze(mask, dim=2).to(mask_token.device)
-                output_padded = (1 - mask) * output_padded + mask * mask_token
-
-            padded[modality] = output_padded
-            modality_sizes[modality] = output_padded.shape[1]
-
-        # Apply a predictable ordering to the modalities
-        padded_ls = [padded[k] for k in sorted(padded.keys())]
-
-        # Finally, concatenate along the time dimension
-        final_inputs = torch.cat(padded_ls, dim=1)
-
-        return final_inputs, modality_sizes, inputs_without_pos
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/vencoder/ContentVec768L12.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/vencoder/ContentVec768L12.py
deleted file mode 100644
index 0d1591c8843b920d5685e822354e8e6adc9a9e19..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/vencoder/ContentVec768L12.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from vencoder.encoder import SpeechEncoder
-import torch
-from fairseq import checkpoint_utils
-
-class ContentVec768L12(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None):
-        print("load model(s) from {}".format(vec_path))
-        self.hidden_dim = 768
-        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-          [vec_path],
-          suffix="",
-        )
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.model = models[0].to(self.dev)
-        self.model.eval()
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        inputs = {
-          "source": feats.to(wav.device),
-          "padding_mask": padding_mask.to(wav.device),
-          "output_layer": 12,  # layer 12
-        }
-        with torch.no_grad():
-          logits = self.model.extract_features(**inputs)
-        return logits[0].transpose(1, 2)
\ No newline at end of file
diff --git a/spaces/yo2266911/DeepDanbooru_string/README.md b/spaces/yo2266911/DeepDanbooru_string/README.md
deleted file mode 100644
index 4330b6f969246dc764a34ea254d2e807159f1c55..0000000000000000000000000000000000000000
--- a/spaces/yo2266911/DeepDanbooru_string/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: DeepDanbooru String
-emoji: 💬
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-duplicated_from: NoCrypt/DeepDanbooru_string
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/zakiu/Personal-TTS/pretrain_work_dir/orig_model/README.md b/spaces/zakiu/Personal-TTS/pretrain_work_dir/orig_model/README.md
deleted file mode 100644
index d57bc2cf0baf064e0fdd0ac1ca3eb93ff6bfab6d..0000000000000000000000000000000000000000
--- a/spaces/zakiu/Personal-TTS/pretrain_work_dir/orig_model/README.md
+++ /dev/null
@@ -1,581 +0,0 @@
----
-tasks:
-- text-to-speech
-studios:
-- damo/personal_tts
-domain:
-- audio
-frameworks:
-- pytorch
-backbone:
-- transformer
-metrics:
-- MOS
-license: Apache License 2.0
-tags:
-- Alibaba
-- tts
-- personal
-- hifigan
-- nsf
-- sambert
-- text-to-speech
-- pretrain
-- zhcn
-- 16k
-widgets:
-  - task: text-to-speech
-    inputs:
-      - type: text 
-        name: input 
-        title: 文本 
-        validator: 
-          max_words: 300
-    examples:
-      - name: 1
-        title: 示例1 
-        inputs:
-          - name: input
-            data: 北京今天天气怎么样
-    inferencespec:
-      cpu: 4 #CPU数量
-      memory: 8192 
-      gpu: 1 #GPU数量
-      gpu_memory: 8192 
----
-
-
-## SAMBERT个性化语音合成模型介绍
-本文将介绍SAMBERT个性化语音合成模型, 包括其模型结构, 如何在ModelScope上体验模型效果, 以及如何使用开源代码训练SAMBERT个性化语音合成模型.
-
-## 个性化语音合成
-语音合成(Text-to-Speech, TTS) 是指将输入文字合成为对应语音信号的功能，即赋予计算机“说”的能力，是人机交互中重要的一环。现代语音合成最早可以追溯到1939年贝尔实验室制造的第一个电子语音合成器，后来历经共振峰合成、PSOLA合成、Unit Selection波形拼接、统计参数合成几代的发展，在2016年随着WaveNet的出现步入了深度学习合成时代，此时语音合成的效果已经表现出了比拟真人的水准。
-
-提到个性化定制语音，大家并不陌生，许多平台会选择一些大家耳熟能详的明星，进行声音定制，并普遍应用在语音导航，文字播报，小说阅读等场景中。这项技术来自文本到语音的服务，一般来说，语音合成数据需要专业播音员在录音棚录制，且数据量都是以500-1000句话起步，这种标准定制的流程，无论是对播音员、录制条件、录制数量和成本都提出了较高的要求。
-
-Personal TTS，即个性化语音合成，是通过身边的一些常见录音设备（手机、电脑、录音笔等），录取目标说话人的少量语音片段后，构建出这个人的语音合成系统。相比于标准定制，个性化定制的技术难点在于，数据量有限（20句话）、数据质量不佳和流程全自动化。而它的意义在于进一步降低语音合成的定制门槛，能够将语音合成定制推广到普通用户。
-
-学术界有很多关于声音克隆的工作，论文陈述效果很好。考虑到落地应用场景的效果，达摩院以自研语音合成系统KAN-TTS的迁移学习能力为基础，设计了一套较为完善的个性化语音合成方案。用户只需要录制20句话，经过几分钟的训练，就能够获得一个较好的个性化声音。
-
-<div align=center>
-    <figure>
-        <img src="description/ptts_normal.png">
-        <figcaption>定制方案比较</figcaption>
-    </figure>
-</div>
-
-
-## 模型框架
-
-模型框架主要由三个部分组成：
-1. 数据自动化处理和标注
-2. 韵律建模SAMBERT声学模型
-3. 基于说话人特征信息的个性化语音合成
-
-<div align=center>
-    <figure>
-        <img src="description/ptts_structure.png">
-        <figcaption>个性化定制流程</figcaption>
-    </figure>
-</div>
-   
-### 数据自动化处理和标注
-在用户录制完音频之后，我们只有<文本，音频>，而语音合成是需要一些额外的标注信息：韵律标注、音素时长标注。为了获得较好的标注信息，我们采用了一种融合了多种原子能力的全自动化处理和标注流程，包括，韵律预测、ASR、VAD和语音增强等。通过测试集测试，该自动化流程产生的标注信息，在准确度能够满足个性化的需求。
-
-此外，我们也在modelscope上发布了**TTS-Autolabel自动化数据标注工具**，旨在降低TTS数据标注门槛，使开发者更便捷的定制个性化语音合成模型，具体使用方式及相关教程请进入[TTS-AutoLabel工具主页](https://modelscope.cn/models/damo/speech_ptts_autolabel_16k/summary)获取。
-
-### 韵律建模SAMBERT声学模型
-在整个链路中，和效果最相关的模块就是声学模型。在语音合成领域，类似FastSpeech的Parallel模型是目前的主流，它针对基频（pitch）、能量（energy）和时长（duration）三种韵律表征分别建模。但是，该类模型普遍存在一些效果和性能上的问题，例如，独立建模时长、基频、能量，忽视了其内在联系；完全非自回归的网络结构，无法满足工业级实时合成需求；帧级别基频和能量预测不稳定。 因此达摩院语音实验室设计了SAMBERT，一种基于Parallel结构的改良版TTS模型，它具有以下优点：
-
-```
-1. Backbone采用Self-Attention-Mechanism(SAM)，提升模型建模能力。
-2. Encoder部分采用BERT进行初始化，引入更多文本信息，提升合成韵律。
-3. Variance Adaptor对音素级别的韵律(基频、能量、时长)轮廓进行粗粒度的预测，再通过decoder进行帧级别细粒度的建模;并在时长预测时考虑到其与基频、能量的关联信息，结合自回归结构，进一步提升韵律自然度.
-4. Decoder部分采用PNCA AR-Decoder[@li2020robutrans]，自然支持流式合成。
-```
-
-<div align=center>
-    <figure>
-        <img src="description/sambert.jpg">
-        <figcaption>SAMBERT模型结构</figcaption>
-    </figure>
-</div>
-
-### 基于说话人特征信息的个性化语音合成
-如果需要进行迁移学习，那么需要先构建多说话人的声学模型，不同说话人是通过可训练的说话人编码（speaker embedding）进行区分的。给定新的一个说话人，一般通过随机初始化一个speaker embedding，然后再基于这个说话人的数据进行更新（见下图说话人空间1）。对于个性化语音合成来说，发音人的数据量比较少，学习难度很大，最终合成声音的相似度就无法保证。因此，我们采用说话人特征信息来表示每个说话人，此时，以少量说话人数据初始化的 speaker embedding 距离实际的目标说话人更近得多（见下图说话人空间2），学习难度小，此时合成声音的相似度就比较高。采用基于说话人特征信息的个性化语音合成，使得在20句条件下，依旧能够有较好的相似度。
-
-<div align=center>
-    <figure>
-        <img src="description/speaker_embedding.png">
-        <figcaption>说话人特征空间</figcaption>
-    </figure>
-</div>
-
-## Notebook最佳实践
-
-首先我们从[sample_test_female](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/test_female.zip)或者[sample_test_male](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/test_male.zip)获得示例音频文件, 这里以`sample_test_male`为例。
-
-解压完成后得到如下文件结构:
-```
-test_male
-├── 01_000001.wav
-├── 01_000002.wav
-├── 01_000003.wav
-├── 01_000004.wav
-├── 01_000005.wav
-├── 01_000006.wav
-├── 01_000007.wav
-├── 01_000008.wav
-├── 01_000009.wav
-├── 01_000010.wav
-├── 01_000011.wav
-├── 01_000012.wav
-├── 01_000013.wav
-├── 01_000014.wav
-├── 01_000015.wav
-├── 01_000016.wav
-├── 01_000017.wav
-├── 01_000018.wav
-├── 01_000019.wav
-└── 01_000020.wav
-```
-
-<div align=center>
-    <figure>
-        <img src="description/notebook_steps/upload_wavs.jpg">
-        <figcaption>上传音频至Notebook</figcaption>
-    </figure>
-</div>
-
-接着选择这些wav文件上传至Notebook
-
-### 前置工作
-使用AutoLabeling需要用到`nltk`, 由于国内访问nltk资源较慢，所以我们建议先按下面的步骤手动下载nltk资源。
-
-在Terminal界面下使用`wget`获取`nltk_data`并解压至`home`目录
-```
-# 切换至home目录
-cd ~
-# 安装zip
-apt install zip
-# 下载并解压nltk_data
-wget https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/nltk_data.zip
-unzip nltk_data.zip
-```
-<div align=center>
-    <figure>
-        <img src="description/notebook_steps/fetch_nltk_data.jpg">
-        <figcaption>手动下载nltk资源</figcaption>
-    </figure>
-</div>
-
-### 运行TTS-AutoLabel自动标注
-首先在Notebook左侧新建TTS-AutoLabel工作文件夹`output_training_data`, 然后在Notebook code block中输入如下代码并运行, 由于Notebook运行环境已经安装了`modelscope`和`tts-autolabel`此处可以直接导入。
-```python
-from modelscope.tools import run_auto_label
-
-input_wav = "./test_wavs/"
-output_data = "./output_training_data/"
-
-ret, report = run_auto_label(input_wav=input_wav, work_dir=output_data, resource_revision="v1.0.4")
-```
-<div align=center>
-    <figure>
-        <img src="description/notebook_steps/autolabel_done.jpg">
-        <figcaption>Notebook运行TTS—AutoLabel自动标注数据</figcaption>
-    </figure>
-</div>
-二十条音频标注时间在2～4分钟。标注完成后在`output_training_data`中可以获取音频对应的TTS训练数据。
-
-### 基于PTTS-basemodel微调
-获得标注好的训练数据后，我们进行模型微调，新建一个Notebook代码块，并输入如下代码运行
-```python
-from modelscope.metainfo import Trainers
-from modelscope.trainers import build_trainer
-from modelscope.utils.audio.audio_utils import TtsTrainType
-
-pretrained_model_id = 'damo/speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k'
-
-dataset_id = "./output_training_data/"
-pretrain_work_dir = "./pretrain_work_dir/"
-        
-# 训练信息，用于指定需要训练哪个或哪些模型，这里展示AM和Vocoder模型皆进行训练
-# 目前支持训练：TtsTrainType.TRAIN_TYPE_SAMBERT, TtsTrainType.TRAIN_TYPE_VOC
-# 训练SAMBERT会以模型最新step作为基础进行finetune
-train_info = {
-    TtsTrainType.TRAIN_TYPE_SAMBERT: {  # 配置训练AM（sambert）模型
-        'train_steps': 202,               # 训练多少个step 
-        'save_interval_steps': 200,       # 每训练多少个step保存一次checkpoint
-        'log_interval': 10               # 每训练多少个step打印一次训练日志
-    }
-}
-
-# 配置训练参数，指定数据集，临时工作目录和train_info
-kwargs = dict(
-    model=pretrained_model_id,                  # 指定要finetune的模型
-    model_revision = "v1.0.5",
-    work_dir=pretrain_work_dir,                 # 指定临时工作目录
-    train_dataset=dataset_id,                   # 指定数据集id
-    train_type=train_info                       # 指定要训练类型及参数
-)
-
-trainer = build_trainer(Trainers.speech_kantts_trainer,
-                        default_args=kwargs)
-
-trainer.train()
-```
-
-<div align=center>
-    <figure>
-        <img src="description/notebook_steps/training.jpg">
-        <figcaption>在Notebook中使用trainer微调模型</figcaption>
-    </figure>
-</div>
-
-微调过程大概需要5分钟的时间，请耐心等待。
-
-
-### 体验模型合成效果
-使用上一步微调得到的模型合成音频，新建一个Notebook代码块, 输入以下代码
-```python
-import os
-from modelscope.models.audio.tts import SambertHifigan
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-model_dir = os.path.abspath("./pretrain_work_dir")
-
-custom_infer_abs = {
-    'voice_name':
-    'F7',
-    'am_ckpt':
-    os.path.join(model_dir, 'tmp_am', 'ckpt'),
-    'am_config':
-    os.path.join(model_dir, 'tmp_am', 'config.yaml'),
-    'voc_ckpt':
-    os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan', 'ckpt'),
-    'voc_config':
-    os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan',
-             'config.yaml'),
-    'audio_config':
-    os.path.join(model_dir, 'data', 'audio_config.yaml'),
-    'se_file':
-    os.path.join(model_dir, 'data', 'se', 'se.npy')
-}
-kwargs = {'custom_ckpt': custom_infer_abs}
-
-model_id = SambertHifigan(os.path.join(model_dir, "orig_model"), **kwargs)
-
-inference = pipeline(task=Tasks.text_to_speech, model=model_id)
-output = inference(input="今天的天气真不错")
-
-import IPython.display as ipd
-ipd.Audio(output["output_wav"], rate=16000)
-```
-
-<div align=center>
-    <figure>
-        <img src="description/notebook_steps/inference_play.jpg">
-        <figcaption>在Notebook中使用微调好的模型合成音频</figcaption>
-    </figure>
-</div>
-
-点击播放控件即可体验合成音频。
-
-## KAN-TTS最佳实践
-### 环境搭建
-获取KAN-TTS源码， 后续操作默认在代码库根目录下执行
-
-```bash
-git clone -b develop https://github.com/alibaba-damo-academy/KAN-TTS.git
-cd KAN-TTS
-```
-
-我们推荐使用Anaconda来搭建Python虚拟环境，使用以下命令创建(目前只兼容Linux x86系统)：
-
-```bash
-# 防止使用pip安装时出现网络问题，建议切换国内pip源
-pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
-
-# 创建虚拟环境
-conda env create -f environment.yaml
-
-# 激活虚拟环境
-conda activate maas
-```
-
-
-### 拉取预训练模型
-ModelScope中文个性化语音合成模型是达摩院语音实验室在1000多小时4000多人数据集上训练产出的预训练模型，我们以此为basemodel做后续微调。
-使用git命令拉取模型，在拉取前，首先你需要安装git-lfs, 具体的安装教程见Git Large File Storage，安装完成后执行以下命令：
-
-```bash
-# 克隆预训练模型
- git clone https://www.modelscope.cn/damo/speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k.git
-```
-
-### 数据获取与自动标注
-
-目前KAN-TTS个性化语音合成支持达摩院TTS标准数据格式和普通音频格式。
-其中达摩院TTS标准数据格式，wav文件夹下存放了音频文件，prosody文件夹下的.txt文件对应的是音频文件的文本标注，interval文件夹下存放的是音素级别的时间戳标注，如下:
-```
-.
-├── interval
-│   ├── 500001.interval
-│   ├── 500002.interval
-│   ├── 500003.interval
-│   ├── ...
-│   └── 500020.interval
-├── prosody
-│   └── prosody.txt
-└── wav
-    ├── 500001.wav
-    ├── 500002.wav
-    ├── ...
-    └── 500020.wav
-```
-
-普通音频数据，不携带时间戳标注和prosody标注，如下：
-```
-.
-└── wav
-    ├── 1.wav
-    ├── 2.wav
-    ├── ...
-    └── 20.wav
-```
-
-**快速开始**：你可以从ModelScope下载经过阿里标准格式处理的[AISHELL-3开源语音合成数据集](https://modelscope.cn/datasets/speech_tts/AISHELL-3/summary)，用来进行后续操作。如果你只有普通音频格式的数据，那么可以采用PTTS Autolabel自动化标注工具进行格式转换，下面介绍使用Notebook 进行PTTS Autolable自动化标注的方式。
-
-#### PTTS Autolable自动化标注
-
-打开右上角的Notebook，上传[sample_test_male](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/test_male.zip)的20条音频文件到目录如/mnt/workspace/Data/ptts_spk0_wav下面
-``` 
-ptts_spk0_wav
-├── 01_000001.wav
-├── 01_000002.wav
-├── 01_000003.wav
-├── 01_000004.wav
-├── 01_000005.wav
-├── 01_000006.wav
-├── 01_000007.wav
-├── 01_000008.wav
-├── 01_000009.wav
-├── 01_000010.wav
-├── 01_000011.wav
-├── 01_000012.wav
-├── 01_000013.wav
-├── 01_000014.wav
-├── 01_000015.wav
-├── 01_000016.wav
-├── 01_000017.wav
-├── 01_000018.wav
-├── 01_000019.wav
-└── 01_000020.wav
-```
-在Notebook的Jupyter中安装tts-autolabel
-
-``` Python
-# 运行此代码块安装tts-autolabel
-import sys
-!{sys.executable} -m pip install tts-autolabel -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
-```
-``` Python
-# 导入run_auto_label工具, 初次运行会下载相关库文件
-from modelscope.tools import run_auto_label
-``` 
-
-``` Python
-# 运行 autolabel进行自动标注，20句音频的自动标注约4分钟
-import os 
-
-input_wav = '/mnt/workspace/Data/ptts_spk0_wav' # wav audio path
-work_dir = '/mnt/workspace/Data/ptts_spk0_autolabel' # output path
-os.makedirs(work_dir, exist_ok=True)
-
-ret, report = run_auto_label(input_wav = input_wav,
-                             work_dir = work_dir,
-                            resource_revision='v1.0.4')
-print(report)
-``` 
-
-完成数据标注后，下载对应文件夹`/mnt/workspace/Data/ptts_spk0_autolabel`, 使用`KAN-TTS`的数据前处理脚本做训练前数据准备。
-
-我们选择个性化语音合成配置文件进行特征提取操作，这里我们以提供的16k采样率为例kantts/configs/audio_config_se_16k.yaml
-运行以下命令来进行特征提取，其中--speaker代表该数据集对应发音人的名称，用户可以随意命名。
-```bash
-# 特征提取
-python kantts/preprocess/data_process.py --voice_input_dir ptts_spk0_autolabel --voice_output_dir training_stage/test_male_ptts_feats --audio_config kantts/configs/audio_config_se_16k.yaml --speaker F7 --se_model speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k/basemodel_16k/speaker_embedding/se.*     
-
-# 扩充epoch
-stage0=training_stage
-voice=test_male_ptts_feats
-
-cat $stage0/$voice/am_valid.lst  >> $stage0/$voice/am_train.lst
-lines=0
-while [ $lines -lt 400 ]
-do
-    shuf $stage0/$voice/am_train.lst >> $stage0/$voice/am_train.lst.tmp
-    lines=$(wc -l < "$stage0/$voice/am_train.lst.tmp")
-done
-mv $stage0/$voice/am_train.lst.tmp $stage0/$voice/am_train.lst
-```
-
-个性化语音合成只需要20句目标说话人的语音，特征提取只需要运行一小段时间，提取完毕后你会在training_stage/test_male_ptts_feats目录下得到如下结构的文件：
-```
-# 基于阿里标准格式数据所提取出的特征目录
-├── am_train.lst
-├── am_valid.lst
-├── audio_config.yaml
-├── badlist.txt
-├── data_process_stdout.log
-├── duration
-├── energy
-├── f0
-├── frame_energy
-├── frame_f0
-├── frame_uv
-├── mel
-├── raw_duration
-├── raw_metafile.txt
-├── Script.xml
-├── se
-├── train.lst
-├── valid.lst
-└── wav
-```
-至此数据准备工作就算完成了。
-
-微调声学模型
-KAN-TTS的训练脚本是配置驱动的，我们使用预训练模型中的speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k/basemodel_16k/sambert/config.yaml作为训练配置文件。
-我们提供默认的PTTS 默认使用的finetune参数值，希望在basemodel的基础上继续微调一定步数并保存，即train_max_steps配置项。
-
-```bash
-# 将train_max_steps改为 2400301
-...
-train_max_steps: 2400301
-...
-```
-完成上述必要的配置项修改后，我们就可以使用以下命令训练声学模型了：
-```bash
-# 训练声学模型
-CUDA_VISIBLE_DEVICES=0 python kantts/bin/train_sambert.py --model_config speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k/basemodel_16k/sambert/config.yaml --root_dir  training_stage/test_male_ptts_feats --stage_dir training_stage/test_male_ptts_sambert_ckpt --resume_path speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k/basemodel_16k/sambert/ckpt/checkpoint_*.pth
-```
-
-体验微调模型效果
-在声学模型微调完毕后，我们就可以使用产出的模型文件和预训练的声码器来合成语音了，在此之前需要做一些准备工作。
-
-将我们想要合成的文本写入一个文件test.txt，每句话按行分隔，如下所示
-```
-徐玠诡谲多智，善揣摩，知道徐知询不可辅佐，掌握着他的短处以归附徐知诰。
-许乐夫生于山东省临朐县杨善镇大辛庄，毕业于抗大一分校。
-宣统元年（1909年），顺德绅士冯国材在香山大黄圃成立安洲农务分会，管辖东海十六沙，冯国材任总理。
-学生们大多住在校区宿舍，通过参加不同的体育文化俱乐部及社交活动，形成一个友谊长存的社会圈。
-学校的“三节一会”（艺术节、社团节、科技节、运动会）是显示青春才华的盛大活动。
-雪是先天自闭症患者，不懂与人沟通，却拥有灵敏听觉，而且对复杂动作过目不忘。
-勋章通过一柱状螺孔和螺钉附着在衣物上。
-雅恩雷根斯堡足球俱乐部（）是一家位于德国雷根斯堡的足球俱乐部，处于德国足球丙级联赛。
-亚历山大·格罗滕迪克于1957年证明了一个深远的推广，现在叫做格罗滕迪克–黎曼–罗赫定理。
-```
-
-运行以下命令进行合成，其中se_file为特征提取环节抽取的speaker embedding，voc_ckpt为basemodel_16k中的预训练模型：
-
-``` bash
-# 运行合成语音
-CUDA_VISIBLE_DEVICES=0 python kantts/bin/text_to_wav.py --txt test.txt --output_dir res/test_male_ptts_syn --res_zip speech_sambert-hifigan_tts_zh-cn_multisp_pretrain_16k/resource.zip --am_ckpt training_stage/test_male_ptts_sambert_ckpt/ckpt/checkpoint_2400300.pth --voc_ckpt speech_sambert-hifigan_tts_zh-cn_multisp_pretrain_16k/hifigan/ckpt/checkpoint_2400000.pth --se_file training_stage/test_male_ptts_feats/se/se.npy
-```
-
-完成后在res/test_male_ptts_syn/res_wavs文件夹下就可以获得合成结果
-```
-.
-├── 0.wav
-├── 1.wav
-├── 2.wav
-├── 3.wav
-├── 4.wav
-├── 5.wav
-├── 6.wav
-├── 7.wav
-└── 8.wav
-```
-
-合成样音
-
-<audio id="audio" controls="" preload="none">
-      <source src="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/ptts_modelcard_assets/wavs/synthesis/7.wav">
-</audio>
-<audio id="audio" controls="" preload="none">
-      <source src="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/ptts_modelcard_assets/wavs/synthesis/8.wav">
-</audio>
-<audio id="audio" controls="" preload="none">
-      <source src="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/ptts_modelcard_assets/wavs/synthesis/9.wav">
-</audio>
-
-原始录音
-
-<audio id="audio" controls="" preload="none">
-      <source src="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/ptts_modelcard_assets/wavs/recordings/01_000003.wav">
-</audio>
-<audio id="audio" controls="" preload="none">
-      <source id="mp3" src="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/ptts_modelcard_assets/wavs/recordings/01_000004.wav">
-</audio>
-<audio id="audio" controls="" preload="none">
-      <source id="mp3" src="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/ptts_modelcard_assets/wavs/recordings/01_000005.wav">
-</audio>
-
-## 模型局限性以及可能的偏差
-* 该发音人支持中文及英文混合，TN规则为中文
-
-## 训练数据介绍
-使用约4000个不同发音人，共计约1000小时数据训练, 主要为中文语料, 包含少量英文语料。
-
-## 模型训练流程
-模型所需训练数据格式为：音频(.wav), 文本标注(.txt), 音素时长标注(.interval), 个性化语音合成模型微调训练时间需要2～5分钟。
-
-### 预处理
-模型训练需对音频文件提取声学特征(梅尔频谱)；音素时长根据配置项中的帧长将时间单位转换成帧数；文本标注，根据配置项中的音素集、音调分类、边界分类转换成对应的one-hot编号；
-
-
-## 引用
-如果你觉得这个该模型对有所帮助，请考虑引用下面的相关的论文：
-
-```BibTeX
-@inproceedings{li2020robutrans,
-  title={Robutrans: A robust transformer-based text-to-speech model},
-  author={Li, Naihan and Liu, Yanqing and Wu, Yu and Liu, Shujie and Zhao, Sheng and Liu, Ming},
-  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
-  volume={34},
-  number={05},
-  pages={8228--8235},
-  year={2020}
-}
-```
-
-```BibTeX
-@article{devlin2018bert,
-  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
-  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
-  journal={arXiv preprint arXiv:1810.04805},
-  year={2018}
-}
-```
-```BibTeX
-@article{kong2020hifi,
-  title={Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis},
-  author={Kong, Jungil and Kim, Jaehyeon and Bae, Jaekyoung},
-  journal={Advances in Neural Information Processing Systems},
-  volume={33},
-  pages={17022--17033},
-  year={2020}
-}
-```
-
-本模型参考了以下实现
-- [1] [ming024's FastSpeech2 Implementation](https://github.com/ming024/FastSpeech2)
-- [2] [jik876/hifi-gan](https://github.com/jik876/hifi-gan)
-- [3] [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)
-- [4] [mozilla/TTS](https://github.com/mozilla/TTS)
-- [5] [espnet/espnet](https://github.com/espnet/espnet)
-
-
-
diff --git a/spaces/zhang-wei-jian/docker/node_modules/brace-expansion/README.md b/spaces/zhang-wei-jian/docker/node_modules/brace-expansion/README.md
deleted file mode 100644
index 6b4e0e16409152451eb2b55e083a88e3396c23b1..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/brace-expansion/README.md
+++ /dev/null
@@ -1,129 +0,0 @@
-# brace-expansion
-
-[Brace expansion](https://www.gnu.org/software/bash/manual/html_node/Brace-Expansion.html), 
-as known from sh/bash, in JavaScript.
-
-[![build status](https://secure.travis-ci.org/juliangruber/brace-expansion.svg)](http://travis-ci.org/juliangruber/brace-expansion)
-[![downloads](https://img.shields.io/npm/dm/brace-expansion.svg)](https://www.npmjs.org/package/brace-expansion)
-[![Greenkeeper badge](https://badges.greenkeeper.io/juliangruber/brace-expansion.svg)](https://greenkeeper.io/)
-
-[![testling badge](https://ci.testling.com/juliangruber/brace-expansion.png)](https://ci.testling.com/juliangruber/brace-expansion)
-
-## Example
-
-```js
-var expand = require('brace-expansion');
-
-expand('file-{a,b,c}.jpg')
-// => ['file-a.jpg', 'file-b.jpg', 'file-c.jpg']
-
-expand('-v{,,}')
-// => ['-v', '-v', '-v']
-
-expand('file{0..2}.jpg')
-// => ['file0.jpg', 'file1.jpg', 'file2.jpg']
-
-expand('file-{a..c}.jpg')
-// => ['file-a.jpg', 'file-b.jpg', 'file-c.jpg']
-
-expand('file{2..0}.jpg')
-// => ['file2.jpg', 'file1.jpg', 'file0.jpg']
-
-expand('file{0..4..2}.jpg')
-// => ['file0.jpg', 'file2.jpg', 'file4.jpg']
-
-expand('file-{a..e..2}.jpg')
-// => ['file-a.jpg', 'file-c.jpg', 'file-e.jpg']
-
-expand('file{00..10..5}.jpg')
-// => ['file00.jpg', 'file05.jpg', 'file10.jpg']
-
-expand('{{A..C},{a..c}}')
-// => ['A', 'B', 'C', 'a', 'b', 'c']
-
-expand('ppp{,config,oe{,conf}}')
-// => ['ppp', 'pppconfig', 'pppoe', 'pppoeconf']
-```
-
-## API
-
-```js
-var expand = require('brace-expansion');
-```
-
-### var expanded = expand(str)
-
-Return an array of all possible and valid expansions of `str`. If none are
-found, `[str]` is returned.
-
-Valid expansions are:
-
-```js
-/^(.*,)+(.+)?$/
-// {a,b,...}
-```
-
-A comma separated list of options, like `{a,b}` or `{a,{b,c}}` or `{,a,}`.
-
-```js
-/^-?\d+\.\.-?\d+(\.\.-?\d+)?$/
-// {x..y[..incr]}
-```
-
-A numeric sequence from `x` to `y` inclusive, with optional increment.
-If `x` or `y` start with a leading `0`, all the numbers will be padded
-to have equal length. Negative numbers and backwards iteration work too.
-
-```js
-/^-?\d+\.\.-?\d+(\.\.-?\d+)?$/
-// {x..y[..incr]}
-```
-
-An alphabetic sequence from `x` to `y` inclusive, with optional increment.
-`x` and `y` must be exactly one character, and if given, `incr` must be a
-number.
-
-For compatibility reasons, the string `${` is not eligible for brace expansion.
-
-## Installation
-
-With [npm](https://npmjs.org) do:
-
-```bash
-npm install brace-expansion
-```
-
-## Contributors
-
-- [Julian Gruber](https://github.com/juliangruber)
-- [Isaac Z. Schlueter](https://github.com/isaacs)
-
-## Sponsors
-
-This module is proudly supported by my [Sponsors](https://github.com/juliangruber/sponsors)!
-
-Do you want to support modules like this to improve their quality, stability and weigh in on new features? Then please consider donating to my [Patreon](https://www.patreon.com/juliangruber). Not sure how much of my modules you're using? Try [feross/thanks](https://github.com/feross/thanks)!
-
-## License
-
-(MIT)
-
-Copyright (c) 2013 Julian Gruber &lt;julian@juliangruber.com&gt;
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
-of the Software, and to permit persons to whom the Software is furnished to do
-so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/spaces/zhang-wei-jian/docker/node_modules/parseurl/HISTORY.md b/spaces/zhang-wei-jian/docker/node_modules/parseurl/HISTORY.md
deleted file mode 100644
index 8e409541d855de232cc24d29524282441d11cbc7..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/parseurl/HISTORY.md
+++ /dev/null
@@ -1,58 +0,0 @@
-1.3.3 / 2019-04-15
-==================
-
-  * Fix Node.js 0.8 return value inconsistencies
-
-1.3.2 / 2017-09-09
-==================
-
-  * perf: reduce overhead for full URLs
-  * perf: unroll the "fast-path" `RegExp`
-
-1.3.1 / 2016-01-17
-==================
-
-  * perf: enable strict mode
-
-1.3.0 / 2014-08-09
-==================
-
-  * Add `parseurl.original` for parsing `req.originalUrl` with fallback
-  * Return `undefined` if `req.url` is `undefined`
-
-1.2.0 / 2014-07-21
-==================
-
-  * Cache URLs based on original value
-  * Remove no-longer-needed URL mis-parse work-around
-  * Simplify the "fast-path" `RegExp`
-
-1.1.3 / 2014-07-08
-==================
-
-  * Fix typo
-
-1.1.2 / 2014-07-08
-==================
-
-  * Seriously fix Node.js 0.8 compatibility
-
-1.1.1 / 2014-07-08
-==================
-
-  * Fix Node.js 0.8 compatibility
-
-1.1.0 / 2014-07-08
-==================
-
-  * Incorporate URL href-only parse fast-path
-
-1.0.1 / 2014-03-08
-==================
-
-  * Add missing `require`
-
-1.0.0 / 2014-03-08
-==================
-
-  * Genesis from `connect`
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/lib/utils.ts b/spaces/zhaoys/wfms-kuiwenc/src/lib/utils.ts
deleted file mode 100644
index 99c8b3ea04ecb917ececacd7e9c71e4c7dcc0e9c..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/lib/utils.ts
+++ /dev/null
@@ -1,199 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import md5 from 'md5'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-import dayjs from 'dayjs'
-import { lookup } from 'dns'
-// @ts-ignore
-import imei from 'node-imei'
-// @ts-ignore
-// @ts-ignore
-import randomip from 'random-ip'
-import cidr from './cidr.json'
-import { debug } from './isomorphic'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export function formatDate(date: number) {
-  const time = dayjs(date)
-  if (time > dayjs().startOf('day')) {
-    return dayjs(time).format('H:mm')
-  } else if (time > dayjs().subtract(1, 'day').startOf('day')) {
-    return '昨天'
-  } else if (time > dayjs().startOf('year')) {
-    return dayjs(time).format('M-DD')
-  } else {
-    return dayjs(time).format('YYYY-MM-DD')
-  }
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function muid() {
-  return md5(imei.random()).toUpperCase()
-}
-
-export function random (start: number, end: number) {
-  return start + Math.floor(Math.random() * (end - start))
-}
-
-export function randomString(length: number = 32) {
-  const char = 'ABCDEFGHJKMNPQRSTWXYZ1234567890';
-  
-  return Array.from({ length }, () => char.charAt(random(0, char.length))).join('')
-}
-
-export function randomIP() {
-  // return `104.${random(0, 21)}.${random(0, 127)}.${random(1, 255)}`
-  const [ip, range] = cidr.at(random(1, cidr.length))?.split('/')!
-  return randomip(ip, range)
-}
-export const lookupPromise = async function (domain: string) {
-  return new Promise((resolve, reject) => {
-    lookup(domain, (err, address, family) => {
-      if (err) resolve('')
-      resolve(address)
-    })
-  })
-}
-
-export const defaultUID = ''
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.split('curl ')[1]?.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-  return headers
-}
-
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch (e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  if (!cookie || !cookieName) return ''
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function setCookie(key: string, value?: string) {
-  const cookie = value === undefined ? key : `${key}=${value || ''}`
-  const maxAge = value === '' ? 0 : 86400 * 30
-  const cookieSuffix = location.protocol === 'http:' ? '' : 'SameSite=None; Secure'
-  document.cookie = `${cookie}; Path=/; Max-Age=${maxAge}; ${cookieSuffix}`
-}
-
-export function getCookie(cookieName: string) {
-  const re = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`)
-  return re.test(document.cookie) ? RegExp.$1 : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-export function resetCookies() {
-  [...ChunkKeys, 'BING_COOKIE', 'BING_UA', '_U', 'BING_IP', 'MUID'].forEach(key => setCookie(key, ''))
-}
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36 Edg/117.0.0.0'
-export const DEFAULT_UA_MOBILE = `Mozilla/5.0 (iPhone; CPU iPhone OS 15_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.7 Mobile/15E148 Safari/605.1.15 BingSapphire/1.0.410427012`
-export function parseUA(ua?: string, default_ua = DEFAULT_UA_MOBILE) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function mockUser(cookies: Partial<{ [key: string]: string }>) {
-  const {
-    BING_HEADER,
-    BING_UA = process.env.BING_UA,
-    
-    BING_IP = '',
-   
-  } = cookies
-  const ua = parseUA(BING_UA)
-    const { _U, MUID } = parseCookies(extraHeadersFromCookie({
-    BING_HEADER,
-    ...cookies,
-  }).cookie, ['MUID'])
-  
-  return {
-    'x-forwarded-for': BING_IP || randomIP(),
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.3 OS/Win32',
-    'referer': 'https://www.bing.com/search?showconv=1&sendquery=1&q=Bing%20AI&form=MY02CJ&OCID=MY02CJ&OCID=MY02CJ&pl=launch',
-    cookie: `_U=${_U || defaultUID}; MUID=${MUID || randomString(32)}`,
-  }
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, useMock?: boolean) {
-  let {
-    BING_HEADER = process.env.BING_HEADER,
-    BING_IP = '',
-    IMAGE_ONLY = process.env.IMAGE_ONLY ?? '1',
-  } = cookies || {}
-   useMock = useMock ?? /^(1|true|yes)$/i.test(String(IMAGE_ONLY))
-  if (!BING_HEADER || useMock) {
-    return mockUser(cookies)
-  }
-  const headers = extraHeadersFromCookie({
-    BING_HEADER,
-    ...cookies,
-  })
-  headers['x-forwarded-for'] = BING_IP || randomIP()
-  headers['user-agent'] = parseUA(headers['user-agent'])
-  headers['referer'] = 'https://www.bing.com/search?showconv=1&sendquery=1&q=Bing%20AI&form=MY02CJ&OCID=MY02CJ&OCID=MY02CJ&pl=launch'
-  headers['x-ms-useragent'] = headers['x-ms-useragent'] || 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.3 OS/Win32'
-  return headers
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}
diff --git a/spaces/zhuyuheng/IMossGPT/modules/overwrites.py b/spaces/zhuyuheng/IMossGPT/modules/overwrites.py
deleted file mode 100644
index 035a4a52722d66ee28af1c05231ad1cea3339ef5..0000000000000000000000000000000000000000
--- a/spaces/zhuyuheng/IMossGPT/modules/overwrites.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from __future__ import annotations
-import logging
-
-from llama_index import Prompt
-from typing import List, Tuple
-import mdtex2html
-from gradio_client import utils as client_utils
-
-from modules.presets import *
-from modules.llama_func import *
-
-
-def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
-    logging.debug("Compacting text chunks...🚀🚀🚀")
-    combined_str = [c.strip() for c in text_chunks if c.strip()]
-    combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
-    combined_str = "\n\n".join(combined_str)
-    # resplit based on self.max_chunk_overlap
-    text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
-    return text_splitter.split_text(combined_str)
-
-
-def postprocess(
-        self,
-        y: List[List[str | Tuple[str] | Tuple[str, str] | None] | Tuple],
-    ) -> List[List[str | Dict | None]]:
-        """
-        Parameters:
-            y: List of lists representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.  It can also be a tuple whose first element is a string filepath or URL to an image/video/audio, and second (optional) element is the alt text, in which case the media file is displayed. It can also be None, in which case that message is not displayed.
-        Returns:
-            List of lists representing the message and response. Each message and response will be a string of HTML, or a dictionary with media information. Or None if the message is not to be displayed.
-        """
-        if y is None:
-            return []
-        processed_messages = []
-        for message_pair in y:
-            assert isinstance(
-                message_pair, (tuple, list)
-            ), f"Expected a list of lists or list of tuples. Received: {message_pair}"
-            assert (
-                len(message_pair) == 2
-            ), f"Expected a list of lists of length 2 or list of tuples of length 2. Received: {message_pair}"
-
-            processed_messages.append(
-                [
-                    self._postprocess_chat_messages(message_pair[0], "user"),
-                    self._postprocess_chat_messages(message_pair[1], "bot"),
-                ]
-            )
-        return processed_messages
-
-def postprocess_chat_messages(
-        self, chat_message: str | Tuple | List | None, message_type: str
-    ) -> str | Dict | None:
-        if chat_message is None:
-            return None
-        elif isinstance(chat_message, (tuple, list)):
-            filepath = chat_message[0]
-            mime_type = client_utils.get_mimetype(filepath)
-            filepath = self.make_temp_copy_if_needed(filepath)
-            return {
-                "name": filepath,
-                "mime_type": mime_type,
-                "alt_text": chat_message[1] if len(chat_message) > 1 else None,
-                "data": None,  # These last two fields are filled in by the frontend
-                "is_file": True,
-            }
-        elif isinstance(chat_message, str):
-            if message_type == "bot":
-                if not detect_converted_mark(chat_message):
-                    chat_message = convert_mdtext(chat_message)
-            elif message_type == "user":
-                if not detect_converted_mark(chat_message):
-                    chat_message = convert_asis(chat_message)
-            return chat_message
-        else:
-            raise ValueError(f"Invalid message for Chatbot component: {chat_message}")
-
-with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
-    customJS = f.read()
-    kelpyCodos = f2.read()
-
-def reload_javascript():
-    print("Reloading javascript...")
-    js = f'<script>{customJS}</script><script>{kelpyCodos}</script>'
-    def template_response(*args, **kwargs):
-        res = GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gr.routes.templates.TemplateResponse = template_response
-
-GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse
\ No newline at end of file