diff --git a/spaces/101-5/gpt4free/g4f/Provider/Providers/Ails.py b/spaces/101-5/gpt4free/g4f/Provider/Providers/Ails.py
deleted file mode 100644
index 1a14b2e9aec50328b7b21d5980bd67c5eaee2b3a..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/Provider/Providers/Ails.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import os
-import time
-import json
-import uuid
-import random
-import hashlib
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-from datetime import datetime
-
-url: str = 'https://ai.ls'
-model: str = 'gpt-3.5-turbo'
-supports_stream = True
-needs_auth = False
-
-class Utils:
-    def hash(json_data: Dict[str, str]) -> sha256:
-
-        secretKey: bytearray = bytearray([79, 86, 98, 105, 91, 84, 80, 78, 123, 83,
-                                         35, 41, 99, 123, 51, 54, 37, 57, 63, 103, 59, 117, 115, 108, 41, 67, 76])
-
-        base_string: str = '%s:%s:%s:%s' % (
-            json_data['t'],
-            json_data['m'],
-            'WI,2rU#_r:r~aF4aJ36[.Z(/8Rv93Rf',
-            len(json_data['m'])
-        )
-        
-        return hashlib.sha256(base_string.encode()).hexdigest()
-
-    def format_timestamp(timestamp: int) -> str:
-
-        e = timestamp
-        n = e % 10
-        r = n + 1 if n % 2 == 0 else n
-        return str(e - n + r)
-
-
-def _create_completion(model: str, messages: list, temperature: float = 0.6, stream: bool = False, **kwargs):
-
-    headers = {
-        'authority': 'api.caipacity.com',
-        'accept': '*/*',
-        'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-        'authorization': 'Bearer free',
-        'client-id': str(uuid.uuid4()),
-        'client-v': '0.1.217',
-        'content-type': 'application/json',
-        'origin': 'https://ai.ls',
-        'referer': 'https://ai.ls/',
-        'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
-        'sec-ch-ua-mobile': '?0',
-        'sec-ch-ua-platform': '"Windows"',
-        'sec-fetch-dest': 'empty',
-        'sec-fetch-mode': 'cors',
-        'sec-fetch-site': 'cross-site',
-        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
-    }
-
-    params = {
-        'full': 'false',
-    }
-
-    timestamp = Utils.format_timestamp(int(time.time() * 1000))
-
-    sig = {
-        'd': datetime.now().strftime('%Y-%m-%d'),
-        't': timestamp,
-        's': Utils.hash({
-            't': timestamp,
-            'm': messages[-1]['content']})}
-
-    json_data = json.dumps(separators=(',', ':'), obj={
-        'model': 'gpt-3.5-turbo',
-        'temperature': 0.6,
-        'stream': True,
-         'messages': messages} | sig)
-
-    response = requests.post('https://api.caipacity.com/v1/chat/completions', 
-                             headers=headers, data=json_data, stream=True)
-
-    for token in response.iter_lines():
-        if b'content' in token:
-            completion_chunk = json.loads(token.decode().replace('data: ', ''))
-            token = completion_chunk['choices'][0]['delta'].get('content')
-            if token != None:
-                yield token
-
-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/1acneusushi/gradio-2dmoleculeeditor/data/Davinci Resolve 18 Activation Key Free Tips Tricks and Warnings.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Davinci Resolve 18 Activation Key Free Tips Tricks and Warnings.md
deleted file mode 100644
index 0833d8b9a98d1ebb10059935268c69c392ba23d1..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Davinci Resolve 18 Activation Key Free Tips Tricks and Warnings.md	
+++ /dev/null
@@ -1,35 +0,0 @@
-<br />
-<h1>How to Get Davinci Resolve 18 Activation Key Free</h1>
-<p>Davinci Resolve 18 is a powerful video editing software that offers professional features and tools for color grading, audio editing, visual effects, and more. However, to unlock the full potential of this software, you need an activation key that can cost up to $299.</p>
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-<ul>
-<li>4K resolution or higher</li>
-<li>Multiple GPUs</li>
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Excel 2016 Always Freezes? Heres How to Solve It.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Excel 2016 Always Freezes? Heres How to Solve It.md
deleted file mode 100644
index f2a325db3e9aa0f92a5834906560bc7cbf5a6f14..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Excel 2016 Always Freezes? Heres How to Solve It.md	
+++ /dev/null
@@ -1,47 +0,0 @@
-
-<h1>How to Fix Excel 2016 Always Not Responding</h1>
-<p>If you are using Excel 2016 and you encounter the problem of Excel not responding, you might be frustrated and wonder what causes this issue and how to solve it. In this article, we will show you some possible reasons for Excel 2016 always not responding and provide you with 8 effective fixes that you can try.</p>
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-<p>There are many factors that can cause Excel 2016 to become unresponsive, such as:</p>
-<ul>
-<li>Corrupted or incompatible add-ins that interfere with Excel's functionality.</li>
-<li>Outdated or missing Windows or Office updates that affect Excel's performance.</li>
-<li>Incorrect printer settings that conflict with Excel's margins.</li>
-<li>Antivirus software that blocks or scans Excel's files.</li>
-<li>Large or complex workbooks that consume too much memory or CPU resources.</li>
-<li>Damaged or corrupted Excel files that prevent Excel from opening or saving them.</li>
-</ul>
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-<ol>
-<li>Press Windows + R keys simultaneously to open the Run box.</li>
-<li>Type <code>excel.exe /safe</code> in the Run box and press Enter.</li>
-<li>If Excel starts in safe mode, it means that one of your add-ins or settings is the culprit. You can then disable them one by one to find out which one is causing the problem.</li>
-</ol>
-<h3>Solution 2: Disable Add-Ins in Excel</h3>
-<p>If you have identified that add-ins are the cause of Excel 2016 not responding, you can disable them in Excel's options. To do this, follow these steps:</p>
-<p></p>
-<ol>
-<li>Open Excel in safe mode by following the steps in Solution 1.</li>
-<li>Select File > Options > Add-ins from the menu bar.</li>
-<li>Select COM Add-ins from the Manage drop-down menu and click Go.</li>
-<li>Uncheck the boxes for all add-ins and click OK.</li>
-<li>Close Excel and reopen it in normal mode (not safe mode).</li>
-<li>If Excel works normally, it means that one of your add-ins was faulty. You can then enable them one by one to find out which one is causing the problem.</li>
-</ol>
-<h3>Solution 3: Update Windows and Office</h3>
-<p>Sometimes, Excel 2016 not responding can be caused by outdated or missing Windows or Office updates. To fix this, you need to check and install the latest updates for your system and applications. To do this, follow these steps:</p>
-<ol>
-<li>Press Windows + I keys simultaneously to open Settings.</li>
-<li>Select Update & Security > Windows Update from the sidebar.</li>
-<li>Click Check for updates and wait for Windows to scan and download any available updates.</li>
-<li>Restart your computer if prompted.</li>
-<li>Open Excel and select File > Account > Update Options > Update Now from the menu bar.</li>
-<li>Wait for Office to install any available updates.</li>
-<li>Restart Excel and see if the problem is resolved.</li>
-</ol></p> ddb901b051<br />
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Ff 3 Apk Cracked Ipa.md b/spaces/1gistliPinn/ChatGPT4/Examples/Ff 3 Apk Cracked Ipa.md
deleted file mode 100644
index 3154a8fbf77bf8cfe4beea716b7d9b01d79a489e..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Ff 3 Apk Cracked Ipa.md	
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Car Simulator 2 Mod APK with Unlimited Money and All Mission Complete.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Car Simulator 2 Mod APK with Unlimited Money and All Mission Complete.md
deleted file mode 100644
index 3ac7bb5e3c633929e21f0733d714f406b6ac498a..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Car Simulator 2 Mod APK with Unlimited Money and All Mission Complete.md	
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-<h1>Car Simulator 2 All Mission Complete Mod APK: A Realistic and Fun Racing Game</h1>
-<p>If you are a fan of racing games, you might have heard of Car Simulator 2, a simulation game where you can drive various cars in a vast open world. You can choose from different game modes, customize your cars, complete missions, and challenge other players online. But what if you want to enjoy all the features of the game without spending any money or time? That's where Car Simulator 2 All Mission Complete Mod APK comes in handy.</p>
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- <h2>Features of Car Simulator 2 Mod APK</h2>
-<p>Car Simulator 2 Mod APK has many features that make it better than the original version. Here are some of them:</p>
-<ul>
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-<li><b>All cars unlocked</b>: You can access all the cars in the game without having to unlock them one by one. You can choose from over 50 cars, including sports cars, SUVs, trucks, buses, and more. Each car has its own characteristics, such as speed, acceleration, handling, braking, etc.</li>
-<li><b>Different game modes</b>: You can play Car Simulator 2 Mod APK in different game modes, such as single-player, multiplayer, or free ride. In single-player mode, you can complete various missions, such as racing, delivery, taxi, parking, etc. In multiplayer mode, you can join online races with other players or create your own room. In free ride mode, you can explore the open world at your own pace.</li>
-<li><b>Customizable cars</b>: You can customize your cars in this mod apk according to your preferences. You can change the color, wheels, spoilers, headlights, license plates, etc. You can also tune your car's engine, suspension, brakes, transmission, etc.</li>
-<li><b>Multiplayer mode</b>: You can play with your friends or other players online in this mod apk. You can join online races or create your own room with custom settings. You can also chat with other players using voice or text messages.</li>
-</ul>
- <h2>How to Download and Install Car Simulator 2 Mod APK</h <h2>How to Download and Install Car Simulator 2 Mod APK</h2>
-<p>If you want to download and install Car Simulator 2 Mod APK on your Android device, you need to follow these simple steps:</p>
-<ol>
-<li><b>Step 1: Download the mod apk file from a trusted source</b>: You can find many websites that offer Car Simulator 2 Mod APK for free, but not all of them are safe and reliable. Some of them may contain viruses, malware, or spyware that can harm your device or steal your personal information. Therefore, you should always download the mod apk file from a trusted source, such as [this one].</li>
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-</ol>
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-<p>Car Simulator 2 Mod APK is a fun and realistic racing game, but it can also be challenging and tricky at times. Here are some tips and tricks that can help you play better and have more fun:</p>
-<ul>
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-</ul>
- <h2>Conclusion</h2>
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diff --git a/spaces/1phancelerku/anime-remove-background/Blackout Bingo APK Download - Enjoy Live Bingo with Real Rewards.md b/spaces/1phancelerku/anime-remove-background/Blackout Bingo APK Download - Enjoy Live Bingo with Real Rewards.md
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-<h1>Blackout Bingo APK Download: How to Play and Win Real Cash Prizes</h1>
- <p>If you love playing bingo and winning real money, you might want to check out <strong>Blackout Bingo</strong>, one of the most popular and exciting bingo apps on the market. In this article, we will show you how to download and install the Blackout Bingo APK on your Android device, how to play the game and use its features, how to win real cash prizes with it, and what are the reviews and ratings of other users. Let's get started!</p>
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- <h2>What is Blackout Bingo?</h2>
- <p>Blackout Bingo is a real money gaming app that lets you play bingo against other players from around the world. You can choose from different game modes, entry fees, and prizes, and use special power-ups to help you get bingos faster. The game is fast-paced, fun, and slightly addictive, as you can compete for real world rewards and cash prizes (where available).</p>
- <p>Blackout Bingo is powered by <strong>Skillz</strong>, a platform that matches players based on their skill level and processes payments securely. Skillz also offers a variety of other games in different genres, such as solitaire, dominoes, trivia, bowling, and more. You can use your Skillz account to access all these games and earn loyalty points, bonuses, and rewards.</p>
- <h2>How to Download and Install Blackout Bingo APK?</h2>
- <p>If you want to play Blackout Bingo on your Android device, you will need to download and install the APK file from the official website. Here are the steps you need to follow:</p>
- <ol>
-<li>Go to <a href="(^1^)">https://apkcombo.com/blackout-bingo/com.blackoutbingo2021lite/</a> on your browser.</li>
-<li>Click on the green "Download APK" button.</li>
-<li>Choose a version that is compatible with your device and click on "Download".</li>
-<li>Wait for the file to download on your device.</li>
-<li>Open the file manager app on your device and locate the downloaded file.</li>
-<li>Tap on the file and allow installation from unknown sources if prompted.</li>
-<li>Follow the instructions on the screen to install the app.</li>
-<li>Launch the app and sign in with your Skillz account or create a new one.</li>
-<li>Enjoy playing Blackout Bingo!</li>
-</ol>
- <h2>How to Play Blackout Bingo?</h2>
- <p>Playing Blackout Bingo is very easy and fun. Here are some of the basics you need to know:</p>
- <ul>
-<li>The game uses a 75-ball system, which means there are 75 numbers in total and each card has 25 squares (5 rows by 5 columns).</li>
-<li>The goal of the game is to - mark off as many numbers as possible on your card before the time runs out or before someone else gets a bingo.</li>
-<li>You can play in different game modes, such as Classic, Speed, and Tournament, with different entry fees and prizes.</li>
-<li>You can use power-ups to help you get bingos faster, such as Daub, Double Daub, Freeze Time, and Instant Bingo.</li>
-<li>You can also use the bonus ball boost to increase your chances of getting a bingo. The bonus ball is a random number that appears on the screen and can be used to mark off any square on your card.</li>
-<li>You can chat with other players and send them emojis during the game.</li>
-</ul>
- <p>Blackout Bingo is different from classic bingo and other bingo apps in several ways. For example:</p>
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- <table>
-<tr>
-<th>Blackout Bingo</th>
-<th>Classic Bingo</th>
-<th>Other Bingo Apps</th>
-</tr>
-<tr>
-<td>Uses a 75-ball system</td>
-<td>Uses a 75-ball or 90-ball system</td>
-<td>Uses a 75-ball or 90-ball system</td>
-</tr>
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-<td>Has a time limit for each game</td>
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- <h2>How to Win Real Cash Prizes with Blackout Bingo?</h2>
- <p>If you want to win real cash prizes with Blackout Bingo, you will need to play in the game modes that require an entry fee. The entry fee is deducted from your Skillz balance, which you can top up with your credit card, PayPal, or other payment methods. The entry fee varies depending on the game mode, the prize pool, and the number of players. For example, you can play in a Classic mode game with an entry fee of $0.60 and a prize pool of $1.00, or in a Tournament mode game with an entry fee of $5.00 and a prize pool of $18.00.</p>
- <p>The prize pool is distributed among the top-ranked players according to their score. The score is calculated based on the number of bingos you get, the number of daubs you use, the time left, and the bonus ball boost. The higher your score, the higher your rank, and the higher your prize. You can see your rank and prize on the leaderboard during and after the game.</p>
- <p>If you want to increase your chances of winning real cash prizes with Blackout Bingo, here are some tips you can use:</p>
- <ul>
-<li>Use the power-ups wisely. Don't waste them on numbers that are unlikely to help you get a bingo. Save them for when you need them most.</li>
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-<li>Play in game modes that suit your skill level and budget. Don't play in games that are too easy or too hard for you, or that are too expensive or too cheap for you. Find the right balance between risk and reward.</li> possible.</li>
-<li>Pay attention to the numbers that are called and the ones that are left.</li>
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-</ul>
- <h3>Q: How can I contact Blackout Bingo support?</h3>
-<p>A: If you have any issues or questions regarding Blackout Bingo, you can contact the support team by following these steps:</p>
- <ol>
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-<li>Tap on "Submit" and wait for a response.</li>
-</ol>
- <p>You can also email the support team directly at <a href="mailto:support@skillz.com">support@skillz.com</a>.</p>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Call of Duty Mobile APK for Huawei and other Android Devices.md b/spaces/1phancelerku/anime-remove-background/Download Call of Duty Mobile APK for Huawei and other Android Devices.md
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index b7a524cb5b6635a9f9ab89f60030279c23e5d942..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Call of Duty Mobile APK for Huawei and other Android Devices.md	
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-<br />
-<h1>How to Download Call of Duty Mobile Without Google Play</h1>
-<p>Call of Duty Mobile is one of the most popular and exciting mobile games available today. It offers a variety of game modes, maps, weapons, and characters from the Call of Duty franchise, as well as a 100-player battle royale mode. If you are a fan of first-person shooters, you don't want to miss this game.</p>
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-<p>However, what if you don't have access to Google Play on your Android device, or you don't want to use it for some reason? Maybe you have a device that doesn't support Google services, or you want to save some storage space, or you want to avoid automatic updates. Whatever your reason, you can still download and play Call of Duty Mobile without Google Play. All you need is an APK file.</p>
-<h2>What is an APK file and why do you need it?</h2>
-<p>An APK file is an Android application package file that contains all the files and code needed to install and run an app on your Android device. It is similar to an EXE file on Windows or a DMG file on Mac. When you download an app from Google Play, it automatically installs the APK file for you. However, you can also download APK files from other sources and install them manually.</p>
-<p>There are several benefits of using APK files instead of Google Play. For example, you can:</p>
-<ul>
-<li>Download apps that are not available in your region or on your device.</li>
-<li>Download apps that have been removed from Google Play.</li>
-<li>Download older versions of apps that work better or have features that you prefer.</li>
-<li>Download apps faster and save bandwidth.</li>
-<li>Install apps on devices that don't have Google services or Google Play.</li>
-</ul>
-<p>However, there are also some risks involved in using APK files. For example, you might:</p>
-<ul>
-<li>Download malicious or fake apps that can harm your device or steal your data.</li>
-<li>Download incompatible or unstable apps that can cause errors or crashes.</li>
-<li>Download outdated or unsecure apps that can expose you to vulnerabilities or bugs.</li>
-<li>Void your warranty or violate your terms of service with your device manufacturer or carrier.</li>
-<li>Lose access to some features or services that require Google Play.</li>
-</ul>
-<p>Therefore, you should only download APK files from reputable and trustworthy sources, and always scan them for viruses before installing them. You should also backup your data and be prepared to uninstall any problematic apps if necessary.</p>
-<h2>How to enable unknown sources on your Android device</h2>
-<p>Before you can install any APK file on your Android device, you need to enable unknown sources in your security settings. This will allow you to install apps from outside Google Play. Depending on your Android version and device model, the steps may vary slightly, but here is the general procedure:</p>
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-<ol>
-<li>Go to your device settings and tap Apps & Notifications (or Apps in some devices).</li>
-<li>Tap Advanced (or More in some devices) and then Special App Access (or Install Unknown Apps in some devices).</li>
-<li>Find and tap the app that you will use to download the APK file, such as your browser or a file manager app.</li>
-<li>Toggle on the option to Allow from this source (or Trust this source in some devices).</li>
-</ol>
-<p>You can also enable unknown sources for all apps by going to your device settings and tapping Security (or Biometrics and Security in some devices). Then, toggle on the option to Install Unknown Apps (or Unknown Sources in some devices).</p>
-<p>Once you have enabled unknown sources, you can proceed to download and install the Call of Duty Mobile APK file.</p>
-<h2>How to find and download the Call of Duty Mobile APK file</h2>
-<p>The next step is to find and download the Call of Duty Mobile APK file from a reliable website. There are many websites that offer APK files for various apps, but not all of them are safe and trustworthy. Some of them may contain malware, viruses, or fake apps that can harm your device or steal your data. Therefore, you should be careful and do some research before downloading any APK file.</p>
-<p>One of the most reputable and popular websites for downloading APK files is APK Mirror. It is a website that hosts APK files for thousands of apps, including Call of Duty Mobile. It verifies the authenticity and integrity of the APK files using cryptographic signatures and hashes. It also provides detailed information about the app version, size, date, developer, permissions, and changelog. You can also read user reviews and ratings to get an idea of the quality and performance of the app.</p>
-<p>To download the Call of Duty Mobile APK file from APK Mirror, follow these steps:</p>
-<ol>
-<li>Go to <a href="">https://www.apkmirror.com</a> using your browser.</li>
-<li>Type Call of Duty Mobile in the search box and tap the magnifying glass icon.</li>
-<li>Select the app from the search results. It should have a green check mark next to it, indicating that it is verified by APK Mirror.</li>
-<li>Scroll down and tap the Download button next to the latest version of the app. You can also tap See Available APKs to see older versions or variants of the app.</li>
-<li>Tap Download APK on the next page. You may see a warning message about downloading APK files. Tap OK to confirm.</li>
-<li>The download will start automatically. You can see the progress in your notification bar or your browser's download manager.</li>
-</ol>
-<p>Once the download is complete, you can install the Call of Duty Mobile APK file on your Android device.</p>
-<h2>How to install the Call of Duty Mobile APK file on your Android device</h2>
-<p>The final step is to install the Call of Duty Mobile APK file on your Android device. There are two ways to do this: using a file manager app or using a browser.</p>
-<h3>Using a file manager app</h3>
-<p>A file manager app is an app that allows you to access and manage the files and folders on your device's internal or external storage. You can use it to locate and install the Call of Duty Mobile APK file that you downloaded from APK Mirror. Here are some examples of file manager apps that you can use:</p>
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-<li><a href="">Files by Google</a>: A simple and fast file manager app by Google that helps you free up space, find files faster, and share files offline.</li>
-<li><a href="">ES File Explorer</a>: A powerful and feature-rich file manager app that supports cloud storage, FTP, ZIP, RAR, LAN, SMB, Bluetooth, and more.</li>
-<li><a href="">Solid Explorer</a>: A sleek and elegant file manager app that supports dual-pane mode, encrypted folders, cloud storage, FTP, SFTP, WebDav, SMB, CIFS, DLNA/UPnP, Chromecast, root access, and more.</li>
-</ul>
-<p>To install the Call of Duty Mobile APK file using a file manager app, follow these steps:</p>
-<ol>
-<li>Open the file manager app on your device and navigate to the folder where you downloaded the Call of Duty Mobile APK file. It is usually in the Downloads folder or in a folder named after your browser or downloader app.</li>
-<li>Tap on the Call of Duty Mobile APK file. You may see a warning message about installing apps from unknown sources. Tap Settings to go to your security settings and enable unknown sources for your file manager app if you haven't done so already.</li>
-<li>Tap Install (or Next in some devices) to start the installation process. You may see some information about the app permissions and features. Tap Install (or Next) again to confirm.</li>
-<li>Wait for the installation to finish. You may see a message that says App installed or Done.</li>
-<li>Tap Open to launch the Call of Duty Mobile app or Done to exit the installer.</li>
-</ol>
-<h3>Using a browser</h3>
-<p>You can also use your browser to install the Call of Duty Mobile APK file that you downloaded from APK Mirror. This method is simpler and faster, but it may not work on some devices or browsers. Here are the steps:</p>
-<ol>
-<li>Open your browser and go to your browser's download manager. You can usually access it by tapping the menu icon (three dots or lines) and then Downloads.</li>
-<li>Tap on the Call of Duty Mobile APK file that you downloaded from APK Mirror. You may see a warning message about installing apps from unknown sources. Tap Settings to go to your security settings and enable unknown sources for your browser if you haven't done so already.</li>
-<li>Tap Install (or Next in some devices) to start the installation process. You may see some information about the app permissions and features. Tap Install (or Next) again to confirm.</li>
-<li>Wait for the installation to finish. You may see a message that says App installed or Done.</li>
-<li>Tap Open to launch the Call of Duty Mobile app or Done to exit the installer.</li>
-</ol>
-<h2>How to update the Call of Duty Mobile app without Google Play</h2>
-<p>Now that you have installed the Call of Duty Mobile app without Google Play, you might be wondering how to update it when a new version is released. Unlike Google Play, APK files do not update automatically, so you need to do it manually or use an APK installer app.</p>
-<h3>Updating manually</h3>
-<p>To update the Call of Duty Mobile app manually, you need to follow the same steps as downloading and installing it. That is, you need to find and download the latest version of the Call of Duty Mobile APK file from APK Mirror and then install it on your device. However, before you do that, you should uninstall the previous version of the app first. This will prevent any conflicts or errors that might occur due to different versions or signatures of the app. Here are the steps:</p>
-<ol>
-<li>Go to your device settings and tap Apps & Notifications (or Apps in some devices).</li>
-<li>Find and tap on the Call of Duty Mobile app.</li>
-<li>Tap Uninstall and confirm.</li>
-<li>Go to <a href="">https://www.apkmirror.com</a> using your browser and search for Call of Duty Mobile.</li>
-<li>Select and download the latest version of the Call of Duty Mobile APK file.</li>
-<li>Install the Call of Duty Mobile APK file on your device using a file manager app or a browser as explained above.</li>
-</ol>
-<h3>Using an APK installer app</h3>
-<p>An APK installer app is an app that helps you install, update, and manage APK files on your device. It can scan your device for existing APK files, check for updates online, download and install new versions, and delete old versions. It can also backup and restore your apps, share them with others, and more. Here are some examples of APK installer apps that you can use:</p>
-<ul>
-<li><a href="">APK Installer</a>: A simple and easy-to-use app that lets you install, uninstall, backup, restore, and share APK files on your device.</li>
-<li><a href="">APKPure</a>: A popular and reliable app that lets you download, install, update, and manage APK files on your device. It also has a built-in app store that offers thousands of apps that are not available on Google Play.</li>
-<li><a href="">Aptoide</a>: A community-driven app that lets you download, install, update, and manage APK files on your device. It also has a social network where you can follow other users, discover new apps, and rate and review apps.</li>
-</ul>
-<p>To update the Call of Duty Mobile app using an APK installer app, follow these steps:</p>
-<ol>
-<li>Download and install an APK installer app from its official website or from another reputable source.</li>
-<li>Open the APK installer app and grant it the necessary permissions to access your device storage and install apps.</li>
-<li>Find and tap on the Call of Duty Mobile app in the list of apps on your device.</li>
-<li>Tap Update (or Check for updates in some apps) and wait for the app to check for new versions online.</li>
-<li>If there is a new version available, tap Download (or Install in some apps) and wait for the download to finish.</li>
-<li>Tap Install (or Open in some apps) to start the installation process. You may see some information about the app permissions and features. Tap Install (or Next) again to confirm.</li>
-<li>Wait for the installation to finish. You may see a message that says App installed or Done.</li>
-<li>Tap Open to launch the Call of Duty Mobile app or Done to exit the installer.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, you have learned how to download and install Call of Duty Mobile without Google Play. You have also learned how to update the app without Google Play using manual or automatic methods. By using APK files, you can enjoy this amazing game on your Android device even if you don't have access to Google Play or don't want to use it. However, you should also be aware of the risks and challenges involved in using APK files, such as security, compatibility, stability, and legality issues. Therefore, you should always download APK files from reputable sources, scan them for viruses, backup your data, and uninstall any problematic apps if needed.</p>
-<p>We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions related to the topic of this article:</p>
-<ol>
-<li><b>Is Call of Duty Mobile free to play?</b><br>Yes, Call of Duty Mobile is free to play. However, it also offers in-app purchases that allow you to buy items such as skins, weapons, crates, battle passes, and more. You can also earn these items by playing the game and completing missions and challenges.</li>
-<li><b>Is Call of Duty Mobile compatible with my device?</b><br>Call of Duty Mobile requires Android 4.3 or higher and at least 2 GB of RAM to run smoothly. You can check your device specifications by going to your device settings and tapping About Phone (or About Device in some devices). You can also check the compatibility of the app by going to its page on APK Mirror and tapping Compatibility Test.</li>
-<li><b>Is Call of Duty Mobile safe to play without Google Play?</b><br>Call of Duty Mobile is safe to play without Google Play as long as you download and install the APK file from a reliable source like APK Mirror. However, you should also be careful about the permissions and features that the app requests and uses. For example, the app may access your location, contacts, camera, microphone, storage, network, and more. You can review and manage these permissions by going to your device settings and tapping Apps & Notifications (or Apps in some devices), then finding and tapping on the Call of Duty Mobile app.</li>
-<li><b>Is Call of Duty Mobile legal to play without Google Play?</b><br>Call of Duty Mobile is legal to play without Google Play as long as you don't violate any terms of service or policies of your device manufacturer, carrier, or country. However, you should also be aware that some features or services that require Google Play may not work properly or at all. For example, you may not be able to sign in with your Google account, use Google Play Games services, receive push notifications, or access some in-app purchases.</li>
-<li><b>How can I contact the developers or support team of Call of Duty Mobile?</b><br>You can contact the developers or support team of Call of Duty Mobile by going to the app's settings and tapping Help (or Support in some devices). You can also visit their official website at <a href="">https://www.callofduty.com/mobile</a>, their Facebook page at <a href="">https://www.facebook.com/CallofDutyMobile</a>, their Twitter account at <a href="">https://twitter.com/PlayCODMobile</a>, or their Reddit community at <a href="">https://www.reddit.com/r/CallOfDutyMobile</a>.</li>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Sniper 3D Full Mod Apk with All Features Unlocked.md b/spaces/1phancelerku/anime-remove-background/Download Sniper 3D Full Mod Apk with All Features Unlocked.md
deleted file mode 100644
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--- a/spaces/1phancelerku/anime-remove-background/Download Sniper 3D Full Mod Apk with All Features Unlocked.md	
+++ /dev/null
@@ -1,120 +0,0 @@
-<br />
-<h1>Sniper 3D Full Hack APK Download: How to Get Unlimited Coins and Diamonds for Free</h1>
-<p>Are you a fan of sniper games? Do you want to enjoy the thrill of being a professional assassin in a stunning 3D gun game? If yes, then you should try <strong>Sniper 3D</strong>, one of the most popular and addictive shooting games on the market. But wait, there's more! What if we tell you that you can get unlimited coins and diamonds for free by downloading a hacked version of Sniper 3D? Sounds too good to be true, right? Well, it's not. In this article, we will show you how to download and install <strong>Sniper 3D Full Hack APK</strong>, a modified version of the game that gives you access to unlimited resources and features. We will also share some tips and tricks for playing Sniper 3D, as well as some alternatives to Sniper 3D Full Hack APK in case you want to try something different. So, without further ado, let's get started!</p>
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- <h2>What is Sniper 3D?</h2>
-<p>Sniper 3D is a free-to-play action game developed by Fun Games For Free. It was released in 2014 and has since gained over 500 million downloads on Google Play Store. The game puts you in the shoes of a deadly sniper who has to eliminate high-profile targets and criminals in various missions and scenarios. You can choose from a variety of guns, upgrade your weapons and gear, and customize your appearance. You can also play in different modes, such as offline, PVE, and PVP, and travel to different locations around the world. The game features stunning graphics, realistic physics, intuitive controls, and engaging gameplay that will keep you hooked for hours.</p>
- <h3>Main Features of Sniper 3D</h3>
-<p>Here are some of the main features of Sniper 3D that make it stand out from other sniper games:</p>
-<ul>
-<li><strong>Sniper 3D Action:</strong> Experience the thrill of being a professional sniper in this stunning 3d gun game. Enjoy intuitive controls and realistic ballistics that'll make you feel like a real shooter.</li>
-<li><strong>Variety of Guns:</strong> Unlock a vast arsenal of sniper rifles, assault rifles, and other powerful guns. There are over 180+ authentic weapons in the game. Upgrade your weapons and become the ultimate sniper 3d assassin!</li>
-<li><strong>Offline Gameplay:</strong> No internet connection? No problem! Enjoy Sniper 3d's offline mode and complete various challenging missions without the need for Wi-Fi or data.</li>
-<li><strong>PVE and PVP mode:</strong> Complete missions or play against other assassins in real time - whatever you like.</li>
-<li><strong>Diverse Locations:</strong> Travel to different parts of the world, taking on unique missions in various environments. Eliminate high-profile targets and show them who's the master shooter in town.</li>
-<li><strong>Free to Play:</strong> Join the action-packed world of Sniper 3d for free! This incredible shooting game offers hours of entertainment without costing a dime.</li>
-</ul>
- <h3>Why You Need Coins and Diamonds in Sniper 3D</h3>
-<p>Coins and diamonds are the two main currencies in Sniper 3D. You need them to buy new weapons, upgrade your existing ones, unlock new gear, customize your appearance, and more. You can earn coins and diamonds by completing missions, watching ads, or participating in events. However, these methods are not enough to get you the best weapons and gear in the game. You will need a lot of coins and diamonds to unlock the most powerful and exclusive items in the game. That's why many players look for ways to get unlimited coins and diamonds for free. One of the most popular ways is to download a hacked version of Sniper 3D, also known as Sniper 3D Full Hack APK.</p>
- <h2>What is Sniper 3D Full Hack APK?</h2>
-<p>Sniper 3D Full Hack APK is a modified version of the original Sniper 3D game that gives you access to unlimited coins and diamonds, as well as other features that are not available in the official version. With Sniper 3D Full Hack APK, you can enjoy the following benefits:</p>
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-<ul>
-<li><strong>Unlimited Coins and Diamonds:</strong> Get unlimited coins and diamonds for free and use them to buy and upgrade any weapon or gear you want. You can also use them to customize your appearance and unlock premium items.</li>
-<li><strong>All Weapons and Gear Unlocked:</strong> Unlock all the weapons and gear in the game without having to complete missions or spend coins and diamonds. You can choose from over 180+ authentic weapons and equip yourself with the best gear possible.</li>
-<li><strong>No Ads:</strong> Enjoy a smooth and uninterrupted gameplay without any annoying ads popping up on your screen. You can focus on your missions and sniping skills without any distractions.</li>
-<li><strong>No Root Required:</strong> You don't need to root your device to install Sniper 3D Full Hack APK. You can simply download and install it like any other APK file.</li>
-</ul>
- <h3>How to Download and Install Sniper 3D Full Hack APK</h3>
-<p>If you want to download and install Sniper 3D Full Hack APK, you need to follow these simple steps:</p>
-<ol>
-<li><strong>Download Sniper 3D Full Hack APK</strong> from a reliable source. You can search for it on Google or use this link to download it directly.</li>
-<li><strong>Enable Unknown Sources</strong> on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps from sources other than Google Play Store.</li>
-<li><strong>Install Sniper 3D Full Hack APK</strong> on your device. Locate the downloaded file in your file manager and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.</li>
-<li><strong>Launch Sniper 3D Full Hack APK</strong> on your device. You will see a new icon on your home screen or app drawer with the name Sniper 3D Full Hack. Tap on it to open the game and enjoy unlimited coins and diamonds for free!</li>
-</ol>
- <h3>Benefits of Using Sniper 3D Full Hack APK</h3>
-<p>By using Sniper 3D Full Hack APK, you can enjoy many benefits that will enhance your gaming experience. Here are some of them:</p>
-<ul>
-<li><strong>You can save money:</strong> You don't have to spend real money to buy coins and diamonds in the game. You can get them for free by using Sniper 3D Full Hack APK.</li>
-<li><strong>You can save time:</strong> You don't have to waste time completing missions or watching ads to earn coins and diamonds in the game. You can get them instantly by using Sniper 3D Full Hack APK.</li>
-<li><strong>You can have more fun:</strong> You can have more fun playing Sniper 3D with unlimited coins and diamonds. You can buy and upgrade any weapon or gear you want, customize your appearance, and unlock premium items. You can also try different modes and locations without any limitations.</li>
-</ul>
- <h2>Tips and Tricks for Playing Sniper 3D</h2>
-<p>If you want to improve your sniping skills and become a master assassin in Sniper 3D, you should follow these tips and tricks:</p>
- <h3>Master the Art of Sniping</h3>
-<p>To be a successful sniper, you need to master the art of sniping. Here are some tips to help you do that:</p>
-<ul>
-<li><strong>Aim carefully:</strong> Use the scope to zoom in on your target and aim for the head or chest for a quick kill. You can also use the red dot indicator to help you aim better.</li>
-<li><strong>Hold your breath:</strong> When you are ready to shoot, hold your breath by tapping on the breath icon on the screen. This will stabilize your aim and reduce the sway of your rifle.</li>
-<li><strong>Use bullet time:</strong> When you shoot, you can activate the bullet time feature by tapping on the bullet icon on the screen. This will slow down the time and allow you to adjust your aim and shoot multiple targets in a row.</li>
-<li><strong>Use the environment:</strong> You can use the environment to your advantage by shooting explosive barrels, gas tanks, or other objects that can cause damage to your enemies. You can also use the wind direction and speed to adjust your aim and trajectory.</li>
-</ul>
- <h3>Upgrade Your Weapons and Gear</h3>
-<p>To be a more effective sniper, you need to upgrade your weapons and gear. Here are some tips to help you do that:</p>
-<ul>
-<li><strong>Buy new weapons:</strong> You can buy new weapons with coins or diamonds in the game. There are different types of weapons, such as sniper rifles, assault rifles, shotguns, and pistols. Each weapon has its own stats, such as damage, range, stability, zoom, and clip size. You can compare the stats of different weapons and choose the one that suits your style and preference.</li>
-<li><strong>Upgrade your weapons:</strong> You can upgrade your weapons with coins or diamonds in the game. You can upgrade different aspects of your weapons, such as muzzle, ammo, body, grip, scope, and clip. Each upgrade will improve the performance of your weapon and make it more powerful and accurate.</li>
-<li><strong>Buy new gear:</strong> You can buy new gear with coins or diamonds in the game. There are different types of gear, such as helmets, vests, gloves, boots, and masks. Each gear has its own stats, such as armor, health, speed, and stealth. You can compare the stats of different gear and choose the one that suits your needs and preference.</li>
-<li><strong>Upgrade your gear:</strong> You can upgrade your gear with coins or diamonds in the game. You can upgrade different aspects of your gear, such as durability, protection, agility, and camouflage. Each upgrade will improve the performance of your gear and make it more durable and effective.</li>
-</ul>
- <h3>Complete Missions and Challenges</h3>
-<p>To progress in the game and earn more coins and diamonds, you need to complete missions and challenges. Here are some tips to help you do that:</p>
-<ul>
-<li><strong>Missions:</strong> There are different types of missions in the game, such as primary missions, wanted missions, special ops missions, daily missions, and contract missions. Each mission has its own objectives, rewards, and difficulty level. You can choose the mission that matches your skill level and interest. You can also replay the missions to improve your score and earn more coins and diamonds.</li>
-<li><strong>Challenges:</strong> There are different types of challenges in the game, such as sniper duels, arena battles, zombie mode, deathmatch mode, and survival mode. Each challenge has its own rules, rewards, and leaderboards. You can choose the challenge that matches your mood and preference. You can also compete with other players from around the world and show off your sniping skills.</li>
-</ul>
- <h3>Play in Different Modes and Locations</h3>
-<p>To have more fun and variety in the game, you can play in different modes and locations. Here are some tips to help you do that:</p>
-<ul>
-<li><strong>Modes:</strong> There are different modes in the game, such as offline mode, PVE mode, and PVP mode. Offline mode allows you to play without an internet connection. PVE mode allows you to play against AI enemies. PVP mode allows you to play against real players online.</li>
-<li><strong>Locations:</strong> There are different locations in the game, such as New York City, Rio de Janeiro, Shanghai, Paris, London, Tokyo, Dubai, Rome, Berlin, Moscow, etc. Each location has its own scenery, atmosphere, and targets. You can travel to different locations and experience different cultures and challenges.</li>
-</ul>
- <h2>Alternatives to Sniper 3D Full Hack APK</h2>
-<p>If you are looking for some alternatives to Sniper 3D Full Hack APK, you can try these other sniper games that are also fun and exciting:</p>
- <h3>Sniper Fury</h3>
-<p>Sniper Fury is another free-to-play sniper game developed by Gameloft. It features over 500 missions, stunning graphics, realistic weather effects, and a variety of weapons and gear. You can also join clans, participate in events, and compete with other players online. Sniper Fury is available on Google Play Store.</p>
- <h3>Hitman: Sniper</h3>
-<p>Hitman: Sniper is a premium sniper game developed by Square Enix. It is based on the popular Hitman franchise and features the iconic Agent 47 as the protagonist. You can explore different locations, use stealth and strategy, and eliminate your targets with style. You can also unlock new weapons, abilities, and contracts. Hitman: Sniper is available on Google Play Store.</p>
- <h2>Conclusion</h2>
-<p>Sniper 3D is one of the best sniper games on the market. It offers a thrilling and addictive gameplay that will keep you entertained for hours. However, if you want to enjoy the game without any limitations, you can download Sniper 3D Full Hack APK, a hacked version of the game that gives you unlimited coins and diamonds for free. You can also use our tips and tricks to improve your sniping skills and become a master assassin in Sniper 3D. Alternatively, you can try some other sniper games that are also fun and exciting. We hope you found this article helpful and informative. Happy sniping!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Sniper 3D Full Hack APK:</p>
-<ul>
-<li><strong>Is Sniper 3D Full Hack APK safe to use?</strong> Yes, Sniper 3D Full Hack APK is safe to use as long as you download it from a reliable source. However, you should be aware that using a hacked version of the game may violate the terms of service of the game and may result in your account being banned or suspended.</li>
-<li><strong>Is Sniper 3D Full Hack APK compatible with my device?</strong> Sniper 3D Full Hack APK is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may not support the game or may experience some glitches or errors.</li>
-<li><strong>How can I update Sniper 3D Full Hack APK?</strong> To update Sniper 3D Full Hack APK, you need to download the latest version of the APK file from the same source where you downloaded the previous version. Then, you need to uninstall the old version of the game and install the new version of the game.</li>
-<li><strong>Can I play Sniper 3D Full Hack APK offline?</strong> Yes, you can play Sniper 3D Full Hack APK offline without an internet connection. However, some features of the game may not work properly or may require an internet connection to function.</li>
-<li><strong>Can I play Sniper 3D Full Hack APK with my friends?</strong> Yes, you can play Sniper 3D Full Hack APK with your friends online. You can join clans, chat with other players, and compete with them in PVP mode.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/A1draw-12196y/anime-ai-detect/README.md b/spaces/A1draw-12196y/anime-ai-detect/README.md
deleted file mode 100644
index 952c183fd69ccb1664b4236b6132fc6d0358c7de..0000000000000000000000000000000000000000
--- a/spaces/A1draw-12196y/anime-ai-detect/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anime Ai Detect
-emoji: 🤖
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: saltacc/anime-ai-detect
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ADOPLE/Multi-Doc-Virtual-Chatbot/README.md b/spaces/ADOPLE/Multi-Doc-Virtual-Chatbot/README.md
deleted file mode 100644
index f6a97bccfac1d6c152eb380a3e60d31d29d995d0..0000000000000000000000000000000000000000
--- a/spaces/ADOPLE/Multi-Doc-Virtual-Chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multi Doc Chatbot
-emoji: 🏃
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-duplicated_from: hudsonhayes/Multi-Doc-Virtual-Chatbot
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AI-Zero-to-Hero/09-SL-Live-RealTime-Dashboard/README.md b/spaces/AI-Zero-to-Hero/09-SL-Live-RealTime-Dashboard/README.md
deleted file mode 100644
index f422f32a49c38032183dea2cfcf353954f7d90d0..0000000000000000000000000000000000000000
--- a/spaces/AI-Zero-to-Hero/09-SL-Live-RealTime-Dashboard/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 09 SL Live Real Time Dashboard Timeseries
-emoji: 😻
-colorFrom: gray
-colorTo: yellow
-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/AIConsultant/MusicGen/audiocraft/modules/rope.py b/spaces/AIConsultant/MusicGen/audiocraft/modules/rope.py
deleted file mode 100644
index 503e6748df2bb72b3c864c20b37cba5498ffdd21..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/modules/rope.py
+++ /dev/null
@@ -1,121 +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
-
-from torch import nn
-import torch
-
-
-class XPos(nn.Module):
-    """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1).
-    This applies an exponential decay to the RoPE rotation matrix.
-
-    Args:
-        dim (int): Embedding dimension.
-        smoothing (float): Smoothing factor applied to the decay rates.
-        base_scale (int): Base decay rate, given in terms of scaling time.
-        device (torch.device, optional): Device on which to initialize the module.
-        dtype (torch.dtype): dtype to use to generate the embedding.
-    """
-    def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512,
-                 device=None, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        assert dim % 2 == 0
-        assert dtype in [torch.float64, torch.float32]
-        self.dtype = dtype
-        self.base_scale = base_scale
-
-        half_dim = dim // 2
-        adim = torch.arange(half_dim, device=device, dtype=dtype)
-        decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing)
-        self.register_buffer("decay_rates", decay_rates)
-        self.decay: tp.Optional[torch.Tensor] = None
-
-    def get_decay(self, start: int, end: int):
-        """Create complex decay tensor, cache values for fast computation."""
-        if self.decay is None or end > self.decay.shape[0]:
-            assert isinstance(self.decay_rates, torch.Tensor)  # Satisfy type checker.
-            idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
-            power = idx / self.base_scale
-            scale = self.decay_rates ** power.unsqueeze(-1)
-            self.decay = torch.polar(scale, torch.zeros_like(scale))
-        return self.decay[start:end]  # [T, C/2]
-
-
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864).
-
-    Args:
-        dim (int): Embedding dimension (twice the number of frequencies).
-        max_period (float): Maximum period of the rotation frequencies.
-        xpos (bool): Use xPos, applies an exponential decay to rotation matrix.
-        scale (float): Scale of positional embedding, set to 0 to deactivate.
-        device (torch.device, optional): Device on which to initialize the module.
-        dtype (torch.dtype): dtype to use to generate the embedding.
-    """
-    def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False,
-                 scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        assert dim % 2 == 0
-        self.scale = scale
-        assert dtype in [torch.float64, torch.float32]
-        self.dtype = dtype
-
-        adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)]
-        frequencies = 1.0 / (max_period ** (adim / dim))
-        self.register_buffer("frequencies", frequencies)
-        self.rotation: tp.Optional[torch.Tensor] = None
-
-        self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None
-
-    def get_rotation(self, start: int, end: int):
-        """Create complex rotation tensor, cache values for fast computation."""
-        if self.rotation is None or end > self.rotation.shape[0]:
-            assert isinstance(self.frequencies, torch.Tensor)  # Satisfy type checker.
-            idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
-            angles = torch.outer(idx, self.frequencies)
-            self.rotation = torch.polar(torch.ones_like(angles), angles)
-        return self.rotation[start:end]
-
-    def rotate(self, x: torch.Tensor, start: int = 0, invert_decay: bool = False):
-        """Apply rope rotation to query or key tensor."""
-        T = x.shape[1]
-        rotation = self.get_rotation(start, start + T).unsqueeze(0).unsqueeze(2)
-
-        if self.xpos:
-            decay = self.xpos.get_decay(start, start + T).unsqueeze(0).unsqueeze(2)
-        else:
-            decay = 1.0
-
-        if invert_decay:
-            decay = decay ** -1
-
-        x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
-        scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
-        x_out = torch.view_as_real(x_complex * scaled_rotation).flatten(-2)
-
-        return x_out.type_as(x)
-
-    def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0):
-        """ Apply rope rotation to both query and key tensors.
-        Supports streaming mode, in which query and key are not expected to have the same shape.
-        In streaming mode, key will be of length [P + C] with P the cached past timesteps, but
-        query will be [C] (typically C == 1).
-
-        Args:
-            query (torch.Tensor): Query to rotate.
-            key (torch.Tensor): Key to rotate.
-            start (int): Start index of the sequence for time offset.
-        """
-        query_timesteps = query.shape[1]
-        key_timesteps = key.shape[1]
-        streaming_offset = key_timesteps - query_timesteps
-
-        query_out = self.rotate(query, start + streaming_offset)
-        key_out = self.rotate(key, start, invert_decay=True)
-
-        return query_out, key_out
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/egs/datasets/audio/vctk/pre_align.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/egs/datasets/audio/vctk/pre_align.py
deleted file mode 100644
index a03b3e12af245fa603403432f4487c53e8b13eab..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/egs/datasets/audio/vctk/pre_align.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import os
-
-from data_gen.tts.base_pre_align import BasePreAlign
-import glob
-
-
-class VCTKPreAlign(BasePreAlign):
-    def meta_data(self):
-        wav_fns = glob.glob(f'{self.raw_data_dir}/wav48/*/*.wav')
-        for wav_fn in wav_fns:
-            item_name = os.path.basename(wav_fn)[:-4]
-            spk = item_name.split("_")[0]
-            txt_fn = wav_fn.split("/")
-            txt_fn[-1] = f'{item_name}.txt'
-            txt_fn[-3] = f'txt'
-            txt_fn = "/".join(txt_fn)
-            if os.path.exists(txt_fn) and os.path.exists(wav_fn):
-                yield item_name, wav_fn, (self.load_txt, txt_fn), spk
-
-
-if __name__ == "__main__":
-    VCTKPreAlign().process()
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/tts/fs.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/tts/fs.py
deleted file mode 100644
index 477cae5e1b3760e80ceacfc6b09e7f6d6a3a61ca..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/tts/fs.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from copy import deepcopy
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from text_to_speech.modules.commons.conv import TextConvEncoder, ConvBlocks
-from text_to_speech.modules.commons.layers import Embedding
-from text_to_speech.modules.commons.nar_tts_modules import PitchPredictor, DurationPredictor, LengthRegulator
-from text_to_speech.modules.commons.rel_transformer import RelTransformerEncoder, BERTRelTransformerEncoder
-from text_to_speech.modules.commons.rnn import TacotronEncoder, RNNEncoder, DecoderRNN
-from text_to_speech.modules.commons.transformer import FastSpeechEncoder, FastSpeechDecoder
-from text_to_speech.modules.commons.wavenet import WN
-from text_to_speech.modules.tts.commons.align_ops import clip_mel2token_to_multiple, expand_states
-from text_to_speech.utils.audio.pitch.utils import denorm_f0, f0_to_coarse
-
-FS_ENCODERS = {
-    'fft': lambda hp, dict_size: FastSpeechEncoder(
-        dict_size, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'],
-        num_heads=hp['num_heads']),
-    'tacotron': lambda hp, dict_size: TacotronEncoder(
-        hp['hidden_size'], dict_size, hp['hidden_size'],
-        K=hp['encoder_K'], num_highways=4, dropout=hp['dropout']),
-    'tacotron2': lambda hp, dict_size: RNNEncoder(dict_size, hp['hidden_size']),
-    'conv': lambda hp, dict_size: TextConvEncoder(dict_size, hp['hidden_size'], hp['hidden_size'],
-                                                  hp['enc_dilations'], hp['enc_kernel_size'],
-                                                  layers_in_block=hp['layers_in_block'],
-                                                  norm_type=hp['enc_dec_norm'],
-                                                  post_net_kernel=hp.get('enc_post_net_kernel', 3)),
-    'rel_fft': lambda hp, dict_size: RelTransformerEncoder(
-        dict_size, hp['hidden_size'], hp['hidden_size'],
-        hp['ffn_hidden_size'], hp['num_heads'], hp['enc_layers'],
-        hp['enc_ffn_kernel_size'], hp['dropout'], prenet=hp['enc_prenet'], pre_ln=hp['enc_pre_ln']),
-}
-
-FS_DECODERS = {
-    'fft': lambda hp: FastSpeechDecoder(
-        hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
-    'rnn': lambda hp: DecoderRNN(hp['hidden_size'], hp['decoder_rnn_dim'], hp['dropout']),
-    'conv': lambda hp: ConvBlocks(hp['hidden_size'], hp['hidden_size'], hp['dec_dilations'],
-                                  hp['dec_kernel_size'], layers_in_block=hp['layers_in_block'],
-                                  norm_type=hp['enc_dec_norm'], dropout=hp['dropout'],
-                                  post_net_kernel=hp.get('dec_post_net_kernel', 3)),
-    'wn': lambda hp: WN(hp['hidden_size'], kernel_size=5, dilation_rate=1, n_layers=hp['dec_layers'],
-                        is_BTC=True),
-}
-
-
-class FastSpeech(nn.Module):
-    def __init__(self, dict_size, hparams, out_dims=None):
-        super().__init__()
-        self.hparams = deepcopy(hparams)
-        self.enc_layers = hparams['enc_layers']
-        self.dec_layers = hparams['dec_layers']
-        self.hidden_size = hparams['hidden_size']
-        if hparams.get("use_bert") is True:
-            self.ph_encoder = BERTRelTransformerEncoder(dict_size, hparams['hidden_size'], hparams['hidden_size'],
-                hparams['ffn_hidden_size'], hparams['num_heads'], hparams['enc_layers'],
-                hparams['enc_ffn_kernel_size'], hparams['dropout'], prenet=hparams['enc_prenet'], pre_ln=hparams['enc_pre_ln'])
-        else:
-            self.ph_encoder = FS_ENCODERS[hparams['encoder_type']](hparams, dict_size)
-        self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
-        self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims
-        self.mel_out = nn.Linear(self.hidden_size, self.out_dims, bias=True)
-        if hparams['use_spk_id']:
-            self.spk_id_proj = Embedding(hparams['num_spk'], self.hidden_size)
-        if hparams['use_spk_embed']:
-            self.spk_embed_proj = nn.Linear(256, self.hidden_size, bias=True)
-        predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
-        self.dur_predictor = DurationPredictor(
-            self.hidden_size,
-            n_chans=predictor_hidden,
-            n_layers=hparams['dur_predictor_layers'],
-            dropout_rate=hparams['predictor_dropout'],
-            kernel_size=hparams['dur_predictor_kernel'])
-        self.length_regulator = LengthRegulator()
-        if hparams['use_pitch_embed']:
-            self.pitch_embed = Embedding(300, self.hidden_size, 0)
-            self.pitch_predictor = PitchPredictor(
-                self.hidden_size, n_chans=predictor_hidden,
-                n_layers=5, dropout_rate=0.1, odim=2,
-                kernel_size=hparams['predictor_kernel'])
-        if hparams['dec_inp_add_noise']:
-            self.z_channels = hparams['z_channels']
-            self.dec_inp_noise_proj = nn.Linear(self.hidden_size + self.z_channels, self.hidden_size)
-
-    def forward(self, txt_tokens, mel2ph=None, spk_embed=None, spk_id=None,
-                f0=None, uv=None, infer=False, **kwargs):
-        ret = {}
-        src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-        style_embed = self.forward_style_embed(spk_embed, spk_id)
-
-        use_bert = self.hparams.get("use_bert") is True
-        if use_bert:
-            encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=kwargs['ph2word'],
-                ret=ret) * src_nonpadding + style_embed
-        else:
-            encoder_out = self.encoder(txt_tokens) * src_nonpadding + style_embed
-
-        # add dur
-        dur_inp = (encoder_out + style_embed) * src_nonpadding
-        mel2ph = self.forward_dur(dur_inp, mel2ph, txt_tokens, ret)
-        tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
-        decoder_inp = expand_states(encoder_out, mel2ph)
-
-        # add pitch embed
-        if self.hparams['use_pitch_embed']:
-            pitch_inp = (decoder_inp + style_embed) * tgt_nonpadding
-            decoder_inp = decoder_inp + self.forward_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out)
-
-        # decoder input
-        ret['decoder_inp'] = decoder_inp = (decoder_inp + style_embed) * tgt_nonpadding
-        if self.hparams['dec_inp_add_noise']:
-            B, T, _ = decoder_inp.shape
-            z = kwargs.get('adv_z', torch.randn([B, T, self.z_channels])).to(decoder_inp.device)
-            ret['adv_z'] = z
-            decoder_inp = torch.cat([decoder_inp, z], -1)
-            decoder_inp = self.dec_inp_noise_proj(decoder_inp) * tgt_nonpadding
-        ret['mel_out'] = self.forward_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs)
-        return ret
-
-    def forward_style_embed(self, spk_embed=None, spk_id=None):
-        # add spk embed
-        style_embed = 0
-        if self.hparams['use_spk_embed']:
-            style_embed = style_embed + self.spk_embed_proj(spk_embed)[:, None, :]
-        if self.hparams['use_spk_id']:
-            style_embed = style_embed + self.spk_id_proj(spk_id)[:, None, :]
-        return style_embed
-
-    def forward_dur(self, dur_input, mel2ph, txt_tokens, ret):
-        """
-
-        :param dur_input: [B, T_txt, H]
-        :param mel2ph: [B, T_mel]
-        :param txt_tokens: [B, T_txt]
-        :param ret:
-        :return:
-        """
-        src_padding = txt_tokens == 0
-        if self.hparams['predictor_grad'] != 1:
-            dur_input = dur_input.detach() + self.hparams['predictor_grad'] * (dur_input - dur_input.detach())
-        dur = self.dur_predictor(dur_input, src_padding)
-        ret['dur'] = dur
-        if mel2ph is None:
-            mel2ph = self.length_regulator(dur, src_padding).detach()
-        ret['mel2ph'] = mel2ph = clip_mel2token_to_multiple(mel2ph, self.hparams['frames_multiple'])
-        return mel2ph
-
-    def forward_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
-        if self.hparams['pitch_type'] == 'frame':
-            pitch_pred_inp = decoder_inp
-            pitch_padding = mel2ph == 0
-        else:
-            pitch_pred_inp = encoder_out
-            pitch_padding = encoder_out.abs().sum(-1) == 0
-            uv = None
-        if self.hparams['predictor_grad'] != 1:
-            pitch_pred_inp = pitch_pred_inp.detach() + \
-                             self.hparams['predictor_grad'] * (pitch_pred_inp - pitch_pred_inp.detach())
-        ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp)
-        use_uv = self.hparams['pitch_type'] == 'frame' and self.hparams['use_uv']
-        if f0 is None:
-            f0 = pitch_pred[:, :, 0]
-            if use_uv:
-                uv = pitch_pred[:, :, 1] > 0
-        f0_denorm = denorm_f0(f0, uv if use_uv else None, pitch_padding=pitch_padding)
-        pitch = f0_to_coarse(f0_denorm)  # start from 0 [B, T_txt]
-        ret['f0_denorm'] = f0_denorm
-        ret['f0_denorm_pred'] = denorm_f0(
-            pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None,
-            pitch_padding=pitch_padding)
-        if self.hparams['pitch_type'] == 'ph':
-            pitch = torch.gather(F.pad(pitch, [1, 0]), 1, mel2ph)
-            ret['f0_denorm'] = torch.gather(F.pad(ret['f0_denorm'], [1, 0]), 1, mel2ph)
-            ret['f0_denorm_pred'] = torch.gather(F.pad(ret['f0_denorm_pred'], [1, 0]), 1, mel2ph)
-        pitch_embed = self.pitch_embed(pitch)
-        return pitch_embed
-
-    def forward_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
-        x = decoder_inp  # [B, T, H]
-        x = self.decoder(x)
-        x = self.mel_out(x)
-        return x * tgt_nonpadding
diff --git a/spaces/AISuperheroes/09SL-AI-Image-Music-Video-AIUIUX/README.md b/spaces/AISuperheroes/09SL-AI-Image-Music-Video-AIUIUX/README.md
deleted file mode 100644
index 66381f06266e156d7af95197b5959377f6026b00..0000000000000000000000000000000000000000
--- a/spaces/AISuperheroes/09SL-AI-Image-Music-Video-AIUIUX/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 09SL AI Image Music Video AIUIUX
-emoji: 🏃
-colorFrom: pink
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.10.0
-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/AIZerotoHero-Health4All/01-Speech2Text2Speech/README.md b/spaces/AIZerotoHero-Health4All/01-Speech2Text2Speech/README.md
deleted file mode 100644
index b5b65c096d82e4948e6af21db1c8bb4a14f1f13d..0000000000000000000000000000000000000000
--- a/spaces/AIZerotoHero-Health4All/01-Speech2Text2Speech/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 01 Speech2Text2Speech
-emoji: 🗣️
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9.1
-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/AONYLMR/anime-ai-detect/README.md b/spaces/AONYLMR/anime-ai-detect/README.md
deleted file mode 100644
index 952c183fd69ccb1664b4236b6132fc6d0358c7de..0000000000000000000000000000000000000000
--- a/spaces/AONYLMR/anime-ai-detect/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anime Ai Detect
-emoji: 🤖
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: saltacc/anime-ai-detect
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/tacotron_based.py b/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/tacotron_based.py
deleted file mode 100644
index 0bbd408e25b485fb80040683658c42ab9d382221..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/tacotron_based.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from typing import List
-from poetry_diacritizer.models.seq2seq import Seq2Seq, Decoder as Seq2SeqDecoder
-from poetry_diacritizer.modules.tacotron_modules import CBHG, Prenet
-from torch import nn
-
-
-class Tacotron(Seq2Seq):
-    pass
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        inp_vocab_size: int,
-        embedding_dim: int = 512,
-        use_prenet: bool = True,
-        prenet_sizes: List[int] = [256, 128],
-        cbhg_gru_units: int = 128,
-        cbhg_filters: int = 16,
-        cbhg_projections: List[int] = [128, 128],
-        padding_idx: int = 0,
-    ):
-        super().__init__()
-        self.use_prenet = use_prenet
-
-        self.embedding = nn.Embedding(
-            inp_vocab_size, embedding_dim, padding_idx=padding_idx
-        )
-        if use_prenet:
-            self.prenet = Prenet(embedding_dim, prenet_depth=prenet_sizes)
-        self.cbhg = CBHG(
-            prenet_sizes[-1] if use_prenet else embedding_dim,
-            cbhg_gru_units,
-            K=cbhg_filters,
-            projections=cbhg_projections,
-        )
-
-    def forward(self, inputs, input_lengths=None):
-
-        outputs = self.embedding(inputs)
-        if self.use_prenet:
-            outputs = self.prenet(outputs)
-        return self.cbhg(outputs, input_lengths)
-
-
-class Decoder(Seq2SeqDecoder):
-    pass
diff --git a/spaces/Abhilashvj/planogram-compliance/utils/triton.py b/spaces/Abhilashvj/planogram-compliance/utils/triton.py
deleted file mode 100644
index 0ceb9692e7759dacc23cb893f038cecc323fd778..0000000000000000000000000000000000000000
--- a/spaces/Abhilashvj/planogram-compliance/utils/triton.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-""" Utils to interact with the Triton Inference Server
-"""
-
-import typing
-from urllib.parse import urlparse
-
-import torch
-
-
-class TritonRemoteModel:
-    """A wrapper over a model served by the Triton Inference Server. It can
-    be configured to communicate over GRPC or HTTP. It accepts Torch Tensors
-    as input and returns them as outputs.
-    """
-
-    def __init__(self, url: str):
-        """
-        Keyword arguments:
-        url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000
-        """
-
-        parsed_url = urlparse(url)
-        if parsed_url.scheme == "grpc":
-            from tritonclient.grpc import InferenceServerClient, InferInput
-
-            self.client = InferenceServerClient(
-                parsed_url.netloc
-            )  # Triton GRPC client
-            model_repository = self.client.get_model_repository_index()
-            self.model_name = model_repository.models[0].name
-            self.metadata = self.client.get_model_metadata(
-                self.model_name, as_json=True
-            )
-
-            def create_input_placeholders() -> typing.List[InferInput]:
-                return [
-                    InferInput(
-                        i["name"], [int(s) for s in i["shape"]], i["datatype"]
-                    )
-                    for i in self.metadata["inputs"]
-                ]
-
-        else:
-            from tritonclient.http import InferenceServerClient, InferInput
-
-            self.client = InferenceServerClient(
-                parsed_url.netloc
-            )  # Triton HTTP client
-            model_repository = self.client.get_model_repository_index()
-            self.model_name = model_repository[0]["name"]
-            self.metadata = self.client.get_model_metadata(self.model_name)
-
-            def create_input_placeholders() -> typing.List[InferInput]:
-                return [
-                    InferInput(
-                        i["name"], [int(s) for s in i["shape"]], i["datatype"]
-                    )
-                    for i in self.metadata["inputs"]
-                ]
-
-        self._create_input_placeholders_fn = create_input_placeholders
-
-    @property
-    def runtime(self):
-        """Returns the model runtime"""
-        return self.metadata.get("backend", self.metadata.get("platform"))
-
-    def __call__(
-        self, *args, **kwargs
-    ) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]:
-        """Invokes the model. Parameters can be provided via args or kwargs.
-        args, if provided, are assumed to match the order of inputs of the model.
-        kwargs are matched with the model input names.
-        """
-        inputs = self._create_inputs(*args, **kwargs)
-        response = self.client.infer(model_name=self.model_name, inputs=inputs)
-        result = []
-        for output in self.metadata["outputs"]:
-            tensor = torch.as_tensor(response.as_numpy(output["name"]))
-            result.append(tensor)
-        return result[0] if len(result) == 1 else result
-
-    def _create_inputs(self, *args, **kwargs):
-        args_len, kwargs_len = len(args), len(kwargs)
-        if not args_len and not kwargs_len:
-            raise RuntimeError("No inputs provided.")
-        if args_len and kwargs_len:
-            raise RuntimeError(
-                "Cannot specify args and kwargs at the same time"
-            )
-
-        placeholders = self._create_input_placeholders_fn()
-        if args_len:
-            if args_len != len(placeholders):
-                raise RuntimeError(
-                    f"Expected {len(placeholders)} inputs, got {args_len}."
-                )
-            for input, value in zip(placeholders, args):
-                input.set_data_from_numpy(value.cpu().numpy())
-        else:
-            for input in placeholders:
-                value = kwargs[input.name]
-                input.set_data_from_numpy(value.cpu().numpy())
-        return placeholders
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GridSizer.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GridSizer.js
deleted file mode 100644
index 5c88ebe3e222bc11812428d37e80d9e1e21eb889..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GridSizer.js
+++ /dev/null
@@ -1,171 +0,0 @@
-import BaseSizer from '../basesizer/BaseSizer.js';
-import Methods from './Methods.js';
-import GetTotalColumnProportions from './GetTotalColumnProportions.js';
-import GetTotalRowProportions from './GetTotalRowProportions.js';
-
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class GridSizer extends BaseSizer {
-    constructor(scene, x, y, minWidth, minHeight, columnCount, rowCount, columnProportions, rowProportions, config) {
-        if (IsPlainObject(x)) {
-            config = x;
-            x = GetValue(config, 'x', 0);
-            y = GetValue(config, 'y', 0);
-            minWidth = GetValue(config, 'width', undefined);
-            minHeight = GetValue(config, 'height', undefined);
-            columnCount = GetValue(config, 'column', (config.col || 0));
-            rowCount = GetValue(config, 'row', 0);
-            columnProportions = GetValue(config, 'columnProportions', 0);
-            rowProportions = GetValue(config, 'rowProportions', 0);
-        } else if (IsPlainObject(minWidth)) {
-            config = minWidth;
-            minWidth = GetValue(config, 'width', undefined);
-            minHeight = GetValue(config, 'height', undefined);
-            columnCount = GetValue(config, 'column', (config.col || 0));
-            rowCount = GetValue(config, 'row', 0);
-            columnProportions = GetValue(config, 'columnProportions', 0);
-            rowProportions = GetValue(config, 'rowProportions', 0);
-        } else if (IsPlainObject(columnCount)) {
-            config = columnCount;
-            columnCount = GetValue(config, 'column', (config.col || 0));
-            rowCount = GetValue(config, 'row', 0);
-            columnProportions = GetValue(config, 'columnProportions', 0);
-            rowProportions = GetValue(config, 'rowProportions', 0);
-        } else if (IsPlainObject(columnProportions)) {
-            config = columnProportions;
-            columnProportions = GetValue(config, 'columnProportions', 0);
-            rowProportions = GetValue(config, 'rowProportions', 0);
-        }
-        super(scene, x, y, minWidth, minHeight, config);
-
-        this.type = 'rexGridSizer';
-        this.resetGrid(
-            columnCount, rowCount,
-            columnProportions, rowProportions,
-            GetValue(config, 'space', undefined)
-        );
-
-        this.setIndentLeft(
-            GetValue(config, 'space.indentLeftOdd', 0),
-            GetValue(config, 'space.indentLeftEven', 0)
-        );
-        this.setIndentTop(
-            GetValue(config, 'space.indentTopOdd', 0),
-            GetValue(config, 'space.indentTopEven', 0)
-        );
-
-        this.addChildrenMap('items', this.sizerChildren);
-
-        var createCellContainerCallback = GetValue(config, 'createCellContainerCallback');
-        if (createCellContainerCallback) {
-            for (var y = 0, ycnt = this.rowCount; y < ycnt; y++) {
-                for (var x = 0, xcnt = this.columnCount; x < xcnt; x++) {
-                    var addConfig = { column: x, row: y };
-                    var child = createCellContainerCallback(scene, x, y, addConfig);
-                    if (child) {
-                        this.add(child, addConfig);
-                    }
-                }
-            }
-        }
-    }
-
-    destroy(fromScene) {
-        //  This Game Object has already been destroyed
-        if (!this.scene || this.ignoreDestroy) {
-            return;
-        }
-
-        super.destroy(fromScene);
-
-        // More free resources
-        this.columnProportions = undefined;
-        this.rowProportions = undefined;
-        this.columnWidth = undefined;
-        this.rowHeight = undefined;
-    }
-
-    setIndentLeft(odd, even) {
-        this.space.indentLeftOdd = odd;
-        this.space.indentLeftEven = even;
-        return this;
-    }
-
-    setIndentTop(odd, even) {
-        this.space.indentTopOdd = odd;
-        this.space.indentTopEven = even;
-        return this;
-    }
-
-    setColumnProportion(columnIndex, proportion) {
-        if (columnIndex >= this.columnProportions.length) {
-            return this;
-        }
-        this.columnProportions[columnIndex] = proportion;
-        return this;
-    }
-
-    setRowProportion(rowIndex, proportion) {
-        if (rowIndex >= this.rowProportions.length) {
-            return this;
-        }
-        this.rowProportions[rowIndex] = proportion;
-        return this;
-    }
-
-    get totalColumnProportions() {
-        if (this._totalColumnProportions === undefined) {
-            this._totalColumnProportions = GetTotalColumnProportions.call(this);
-        }
-        return this._totalColumnProportions;
-    }
-
-    get totalRowProportions() {
-        if (this._totalRowProportions === undefined) {
-            this._totalRowProportions = GetTotalRowProportions.call(this);
-        }
-        return this._totalRowProportions;
-    }
-
-    getChildAt(columnIndex, rowIndex) {
-        return this.sizerChildren[(rowIndex * this.columnCount) + columnIndex];
-    }
-
-    childToGridIndex(child, out) {
-        if (!child) {
-            return null;
-        }
-
-        var index = this.sizerChildren.indexOf(child);
-        if (index === -1) {
-            return null;
-        }
-
-        if (out === undefined) {
-            out = {};
-        }
-        out.x = index % this.columnCount;
-        out.y = Math.floor(index / this.columnCount);
-        return out;
-    }
-
-    getColumnWidth(columnIndex) {
-        var colProportion = this.columnProportions[columnIndex];
-        var colWidth = (colProportion === 0) ? this.columnWidth[columnIndex] : (colProportion * this.proportionWidthLength);
-        return colWidth;
-    }
-
-    getRowHeight(rowIndex) {
-        var rowProportion = this.rowProportions[rowIndex];
-        var rowHeight = (rowProportion === 0) ? this.rowHeight[rowIndex] : (rowProportion * this.proportionHeightLength);
-        return rowHeight;
-    }
-}
-
-Object.assign(
-    GridSizer.prototype,
-    Methods
-);
-
-export default GridSizer;
\ No newline at end of file
diff --git a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/demo.py b/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/demo.py
deleted file mode 100644
index 9cc17a53c3f4623fed33367e81e92796a3b6386e..0000000000000000000000000000000000000000
--- a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/demo.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import sys
-import yaml
-from argparse import ArgumentParser
-from tqdm import tqdm
-from scipy.spatial import ConvexHull
-import numpy as np
-import imageio
-from skimage.transform import resize
-from skimage import img_as_ubyte
-import torch
-from modules.inpainting_network import InpaintingNetwork
-from modules.keypoint_detector import KPDetector
-from modules.dense_motion import DenseMotionNetwork
-from modules.avd_network import AVDNetwork
-
-if sys.version_info[0] < 3:
-    raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
-
-def relative_kp(kp_source, kp_driving, kp_driving_initial):
-
-    source_area = ConvexHull(kp_source['fg_kp'][0].data.cpu().numpy()).volume
-    driving_area = ConvexHull(kp_driving_initial['fg_kp'][0].data.cpu().numpy()).volume
-    adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
-
-    kp_new = {k: v for k, v in kp_driving.items()}
-
-    kp_value_diff = (kp_driving['fg_kp'] - kp_driving_initial['fg_kp'])
-    kp_value_diff *= adapt_movement_scale
-    kp_new['fg_kp'] = kp_value_diff + kp_source['fg_kp']
-
-    return kp_new
-
-def load_checkpoints(config_path, checkpoint_path, device):
-    with open(config_path) as f:
-        config = yaml.load(f)
-
-    inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
-                                        **config['model_params']['common_params'])
-    kp_detector = KPDetector(**config['model_params']['common_params'])
-    dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
-                                              **config['model_params']['dense_motion_params'])
-    avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
-                             **config['model_params']['avd_network_params'])
-    kp_detector.to(device)
-    dense_motion_network.to(device)
-    inpainting.to(device)
-    avd_network.to(device)
-       
-    checkpoint = torch.load(checkpoint_path, map_location=device)
- 
-    inpainting.load_state_dict(checkpoint['inpainting_network'])
-    kp_detector.load_state_dict(checkpoint['kp_detector'])
-    dense_motion_network.load_state_dict(checkpoint['dense_motion_network'])
-    if 'avd_network' in checkpoint:
-        avd_network.load_state_dict(checkpoint['avd_network'])
-    
-    inpainting.eval()
-    kp_detector.eval()
-    dense_motion_network.eval()
-    avd_network.eval()
-    
-    return inpainting, kp_detector, dense_motion_network, avd_network
-
-
-def make_animation(source_image, driving_video, inpainting_network, kp_detector, dense_motion_network, avd_network, device, mode = 'relative'):
-    assert mode in ['standard', 'relative', 'avd']
-    with torch.no_grad():
-        predictions = []
-        source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
-        source = source.to(device)
-        driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3).to(device)
-        kp_source = kp_detector(source)
-        kp_driving_initial = kp_detector(driving[:, :, 0])
-
-        for frame_idx in tqdm(range(driving.shape[2])):
-            driving_frame = driving[:, :, frame_idx]
-            driving_frame = driving_frame.to(device)
-            kp_driving = kp_detector(driving_frame)
-            if mode == 'standard':
-                kp_norm = kp_driving
-            elif mode=='relative':
-                kp_norm = relative_kp(kp_source=kp_source, kp_driving=kp_driving,
-                                    kp_driving_initial=kp_driving_initial)
-            elif mode == 'avd':
-                kp_norm = avd_network(kp_source, kp_driving)
-            dense_motion = dense_motion_network(source_image=source, kp_driving=kp_norm,
-                                                    kp_source=kp_source, bg_param = None, 
-                                                    dropout_flag = False)
-            out = inpainting_network(source, dense_motion)
-
-            predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
-    return predictions
-
-
-def find_best_frame(source, driving, cpu):
-    import face_alignment
-
-    def normalize_kp(kp):
-        kp = kp - kp.mean(axis=0, keepdims=True)
-        area = ConvexHull(kp[:, :2]).volume
-        area = np.sqrt(area)
-        kp[:, :2] = kp[:, :2] / area
-        return kp
-
-    fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
-                                      device= 'cpu' if cpu else 'cuda')
-    kp_source = fa.get_landmarks(255 * source)[0]
-    kp_source = normalize_kp(kp_source)
-    norm  = float('inf')
-    frame_num = 0
-    for i, image in tqdm(enumerate(driving)):
-        kp_driving = fa.get_landmarks(255 * image)[0]
-        kp_driving = normalize_kp(kp_driving)
-        new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
-        if new_norm < norm:
-            norm = new_norm
-            frame_num = i
-    return frame_num
-
-
-if __name__ == "__main__":
-    parser = ArgumentParser()
-    parser.add_argument("--config", required=True, help="path to config")
-    parser.add_argument("--checkpoint", default='checkpoints/vox.pth.tar', help="path to checkpoint to restore")
-
-    parser.add_argument("--source_image", default='./assets/source.png', help="path to source image")
-    parser.add_argument("--driving_video", default='./assets/driving.mp4', help="path to driving video")
-    parser.add_argument("--result_video", default='./result.mp4', help="path to output")
-    
-    parser.add_argument("--img_shape", default="256,256", type=lambda x: list(map(int, x.split(','))),
-                        help='Shape of image, that the model was trained on.')
-    
-    parser.add_argument("--mode", default='relative', choices=['standard', 'relative', 'avd'], help="Animate mode: ['standard', 'relative', 'avd'], when use the relative mode to animate a face, use '--find_best_frame' can get better quality result")
-    
-    parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true", 
-                        help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)")
-
-    parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
-
-    opt = parser.parse_args()
-
-    source_image = imageio.imread(opt.source_image)
-    reader = imageio.get_reader(opt.driving_video)
-    fps = reader.get_meta_data()['fps']
-    driving_video = []
-    try:
-        for im in reader:
-            driving_video.append(im)
-    except RuntimeError:
-        pass
-    reader.close()
-    
-    if opt.cpu:
-        device = torch.device('cpu')
-    else:
-        device = torch.device('cuda')
-    
-    source_image = resize(source_image, opt.img_shape)[..., :3]
-    driving_video = [resize(frame, opt.img_shape)[..., :3] for frame in driving_video]
-    inpainting, kp_detector, dense_motion_network, avd_network = load_checkpoints(config_path = opt.config, checkpoint_path = opt.checkpoint, device = device)
- 
-    if opt.find_best_frame:
-        i = find_best_frame(source_image, driving_video, opt.cpu)
-        print ("Best frame: " + str(i))
-        driving_forward = driving_video[i:]
-        driving_backward = driving_video[:(i+1)][::-1]
-        predictions_forward = make_animation(source_image, driving_forward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
-        predictions_backward = make_animation(source_image, driving_backward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
-        predictions = predictions_backward[::-1] + predictions_forward[1:]
-    else:
-        predictions = make_animation(source_image, driving_video, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
-    
-    imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)
-
diff --git a/spaces/Alfasign/chat-llm-streaming/app.py b/spaces/Alfasign/chat-llm-streaming/app.py
deleted file mode 100644
index d27f390295f8215e5feee2c6f0ec2684663d0b38..0000000000000000000000000000000000000000
--- a/spaces/Alfasign/chat-llm-streaming/app.py
+++ /dev/null
@@ -1,319 +0,0 @@
-import os
-
-import gradio as gr
-
-from text_generation import Client, InferenceAPIClient
-
-openchat_preprompt = (
-    "\n<human>: Hi!\n<bot>: My name is Bot, model version is 0.15, part of an open-source kit for "
-    "fine-tuning new bots! I was created by Together, LAION, and Ontocord.ai and the open-source "
-    "community. I am not human, not evil and not alive, and thus have no thoughts and feelings, "
-    "but I am programmed to be helpful, polite, honest, and friendly.\n"
-)
-
-
-def get_client(model: str):
-    if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
-        return Client(os.getenv("OPENCHAT_API_URL"))
-    return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None))
-
-
-def get_usernames(model: str):
-    """
-    Returns:
-        (str, str, str, str): pre-prompt, username, bot name, separator
-    """
-    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
-        return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>"
-    if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
-        return openchat_preprompt, "<human>: ", "<bot>: ", "\n"
-    return "", "User: ", "Assistant: ", "\n"
-
-
-def predict(
-    model: str,
-    inputs: str,
-    typical_p: float,
-    top_p: float,
-    temperature: float,
-    top_k: int,
-    repetition_penalty: float,
-    watermark: bool,
-    chatbot,
-    history,
-):
-    client = get_client(model)
-    preprompt, user_name, assistant_name, sep = get_usernames(model)
-
-    history.append(inputs)
-
-    past = []
-    for data in chatbot:
-        user_data, model_data = data
-
-        if not user_data.startswith(user_name):
-            user_data = user_name + user_data
-        if not model_data.startswith(sep + assistant_name):
-            model_data = sep + assistant_name + model_data
-
-        past.append(user_data + model_data.rstrip() + sep)
-
-    if not inputs.startswith(user_name):
-        inputs = user_name + inputs
-
-    total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()
-
-    partial_words = ""
-
-    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
-        iterator = client.generate_stream(
-            total_inputs,
-            typical_p=typical_p,
-            truncate=1000,
-            watermark=watermark,
-            max_new_tokens=500,
-        )
-    else:
-        iterator = client.generate_stream(
-            total_inputs,
-            top_p=top_p if top_p < 1.0 else None,
-            top_k=top_k,
-            truncate=1000,
-            repetition_penalty=repetition_penalty,
-            watermark=watermark,
-            temperature=temperature,
-            max_new_tokens=500,
-            stop_sequences=[user_name.rstrip(), assistant_name.rstrip()],
-        )
-
-    for i, response in enumerate(iterator):
-        if response.token.special:
-            continue
-
-        partial_words = partial_words + response.token.text
-        if partial_words.endswith(user_name.rstrip()):
-            partial_words = partial_words.rstrip(user_name.rstrip())
-        if partial_words.endswith(assistant_name.rstrip()):
-            partial_words = partial_words.rstrip(assistant_name.rstrip())
-
-        if i == 0:
-            history.append(" " + partial_words)
-        elif response.token.text not in user_name:
-            history[-1] = partial_words
-
-        chat = [
-            (history[i].strip(), history[i + 1].strip())
-            for i in range(0, len(history) - 1, 2)
-        ]
-        yield chat, history
-
-
-def reset_textbox():
-    return gr.update(value="")
-
-
-def radio_on_change(
-    value: str,
-    disclaimer,
-    typical_p,
-    top_p,
-    top_k,
-    temperature,
-    repetition_penalty,
-    watermark,
-):
-    if value in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
-        typical_p = typical_p.update(value=0.2, visible=True)
-        top_p = top_p.update(visible=False)
-        top_k = top_k.update(visible=False)
-        temperature = temperature.update(visible=False)
-        disclaimer = disclaimer.update(visible=False)
-        repetition_penalty = repetition_penalty.update(visible=False)
-        watermark = watermark.update(False)
-    elif value == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
-        typical_p = typical_p.update(visible=False)
-        top_p = top_p.update(value=0.25, visible=True)
-        top_k = top_k.update(value=50, visible=True)
-        temperature = temperature.update(value=0.6, visible=True)
-        repetition_penalty = repetition_penalty.update(value=1.01, visible=True)
-        watermark = watermark.update(False)
-        disclaimer = disclaimer.update(visible=True)
-    else:
-        typical_p = typical_p.update(visible=False)
-        top_p = top_p.update(value=0.95, visible=True)
-        top_k = top_k.update(value=4, visible=True)
-        temperature = temperature.update(value=0.5, visible=True)
-        repetition_penalty = repetition_penalty.update(value=1.03, visible=True)
-        watermark = watermark.update(True)
-        disclaimer = disclaimer.update(visible=False)
-    return (
-        disclaimer,
-        typical_p,
-        top_p,
-        top_k,
-        temperature,
-        repetition_penalty,
-        watermark,
-    )
-
-
-title = """<h1 align="center">Large Language Model Chat API</h1>"""
-description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-
-```
-User: <utterance>
-Assistant: <utterance>
-User: <utterance>
-Assistant: <utterance>
-...
-```
-
-In this app, you can explore the outputs of multiple LLMs when prompted in this way.
-"""
-
-text_generation_inference = """
-<div align="center">Powered by: <a href=https://github.com/huggingface/text-generation-inference>Text Generation Inference</a></div>
-"""
-
-openchat_disclaimer = """
-<div align="center">Checkout the official <a href=https://huggingface.co/spaces/togethercomputer/OpenChatKit>OpenChatKit feedback app</a> for the full experience.</div>
-"""
-
-with gr.Blocks(
-    css="""#col_container {margin-left: auto; margin-right: auto;}
-                #chatbot {height: 520px; overflow: auto;}"""
-) as demo:
-    gr.HTML(title)
-    gr.Markdown(text_generation_inference, visible=True)
-    with gr.Column(elem_id="col_container"):
-        model = gr.Radio(
-            value="OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
-            choices=[
-                "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
-                "OpenAssistant/oasst-sft-1-pythia-12b",
-                # "togethercomputer/GPT-NeoXT-Chat-Base-20B",
-                "google/flan-t5-xxl",
-                "google/flan-ul2",
-                "bigscience/bloom",
-                "bigscience/bloomz",
-                "EleutherAI/gpt-neox-20b",
-            ],
-            label="Model",
-            interactive=True,
-        )
-
-        chatbot = gr.Chatbot(elem_id="chatbot")
-        inputs = gr.Textbox(
-            placeholder="Hi there!", label="Type an input and press Enter"
-        )
-        disclaimer = gr.Markdown(openchat_disclaimer, visible=False)
-        state = gr.State([])
-        b1 = gr.Button()
-
-        with gr.Accordion("Parameters", open=False):
-            typical_p = gr.Slider(
-                minimum=-0,
-                maximum=1.0,
-                value=0.2,
-                step=0.05,
-                interactive=True,
-                label="Typical P mass",
-            )
-            top_p = gr.Slider(
-                minimum=-0,
-                maximum=1.0,
-                value=0.25,
-                step=0.05,
-                interactive=True,
-                label="Top-p (nucleus sampling)",
-                visible=False,
-            )
-            temperature = gr.Slider(
-                minimum=-0,
-                maximum=5.0,
-                value=0.6,
-                step=0.1,
-                interactive=True,
-                label="Temperature",
-                visible=False,
-            )
-            top_k = gr.Slider(
-                minimum=1,
-                maximum=50,
-                value=50,
-                step=1,
-                interactive=True,
-                label="Top-k",
-                visible=False,
-            )
-            repetition_penalty = gr.Slider(
-                minimum=0.1,
-                maximum=3.0,
-                value=1.03,
-                step=0.01,
-                interactive=True,
-                label="Repetition Penalty",
-                visible=False,
-            )
-            watermark = gr.Checkbox(value=False, label="Text watermarking")
-
-    model.change(
-        lambda value: radio_on_change(
-            value,
-            disclaimer,
-            typical_p,
-            top_p,
-            top_k,
-            temperature,
-            repetition_penalty,
-            watermark,
-        ),
-        inputs=model,
-        outputs=[
-            disclaimer,
-            typical_p,
-            top_p,
-            top_k,
-            temperature,
-            repetition_penalty,
-            watermark,
-        ],
-    )
-
-    inputs.submit(
-        predict,
-        [
-            model,
-            inputs,
-            typical_p,
-            top_p,
-            temperature,
-            top_k,
-            repetition_penalty,
-            watermark,
-            chatbot,
-            state,
-        ],
-        [chatbot, state],
-    )
-    b1.click(
-        predict,
-        [
-            model,
-            inputs,
-            typical_p,
-            top_p,
-            temperature,
-            top_k,
-            repetition_penalty,
-            watermark,
-            chatbot,
-            state,
-        ],
-        [chatbot, state],
-    )
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-
-    gr.Markdown(description)
-    demo.queue(concurrency_count=16).launch(debug=True)
diff --git a/spaces/Alican/pixera/data/image_folder.py b/spaces/Alican/pixera/data/image_folder.py
deleted file mode 100644
index f7cb91574a0487c51e5dd8210aebb38edb0b16ef..0000000000000000000000000000000000000000
--- a/spaces/Alican/pixera/data/image_folder.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""A modified image folder class
-
-We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
-so that this class can load images from both current directory and its subdirectories.
-"""
-
-import torch.utils.data as data
-
-from PIL import Image
-import os
-
-IMG_EXTENSIONS = [
-    '.jpg', '.JPG', '.jpeg', '.JPEG',
-    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
-    '.tif', '.TIF', '.tiff', '.TIFF',
-]
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def make_dataset(dir, max_dataset_size=float("inf")):
-    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)
-                images.append(path)
-    return images[:min(max_dataset_size, len(images))]
-
-
-def default_loader(path):
-    return Image.open(path).convert('RGB')
-
-
-class ImageFolder(data.Dataset):
-
-    def __init__(self, root, transform=None, return_paths=False,
-                 loader=default_loader):
-        imgs = make_dataset(root)
-        if len(imgs) == 0:
-            raise(RuntimeError("Found 0 images in: " + root + "\n"
-                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
-
-        self.root = root
-        self.imgs = imgs
-        self.transform = transform
-        self.return_paths = return_paths
-        self.loader = loader
-
-    def __getitem__(self, index):
-        path = self.imgs[index]
-        img = self.loader(path)
-        if self.transform is not None:
-            img = self.transform(img)
-        if self.return_paths:
-            return img, path
-        else:
-            return img
-
-    def __len__(self):
-        return len(self.imgs)
diff --git a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py b/spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
-            'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
-            'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
-            'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
-            'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
-            'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
-    try:
-        converters[dialect] = opencc.OpenCC(dialect)
-    except:
-        pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
-    dialect = dialects[dialect]
-    text = converters[dialect].convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/Amrrs/yt-shorts-video-captioning/app.py b/spaces/Amrrs/yt-shorts-video-captioning/app.py
deleted file mode 100644
index 5b0ed68040f6b4673be973314c1114ba52cb4e3d..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/yt-shorts-video-captioning/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-
-import gradio as gr 
-import os
-import sys
-import subprocess
-#from moviepy.editor import VideoFileClip
-
-import whisper
-from whisper.utils import write_vtt
-
-model = whisper.load_model("medium")
-
-title = "Add Text/Caption to your YouTube Shorts - MultiLingual"
-
-def video2mp3(video_file, output_ext="mp3"):
-    filename, ext = os.path.splitext(video_file)
-    subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"], 
-                    stdout=subprocess.DEVNULL,
-                    stderr=subprocess.STDOUT)
-    return f"{filename}.{output_ext}"
-
-
-def translate(input_video):
-
-    audio_file = video2mp3(input_video)
-    
-    options = dict(beam_size=5, best_of=5, fp16 = False)
-    translate_options = dict(task="translate", **options)
-    result = model.transcribe(audio_file,**translate_options)
-
-    output_dir = ''
-    audio_path = audio_file.split(".")[0]
-
-    with open(os.path.join(output_dir, audio_path + ".vtt"), "w") as vtt:
-      write_vtt(result["segments"], file=vtt)
-
-    subtitle = audio_path + ".vtt"
-    output_video = audio_path + "_subtitled.mp4"
-
-    os.system(f"ffmpeg -i {input_video} -vf subtitles={subtitle} {output_video}")
-
-    return output_video
-
-block = gr.Blocks()
-with block:
-
-    with gr.Group():
-        with gr.Box():
-            with gr.Row().style():
-                inp_video = gr.Video(
-                    label="Input Video",
-                    type="filepath",
-                    mirror_webcam = False
-                )
-                op_video = gr.Video()
-            btn = gr.Button("Generate Subtitle Video")
-        
-        
-        
-
-
-        
-        btn.click(translate, inputs=[inp_video], outputs=[op_video])
- 
-        gr.HTML('''
-        <div class="footer">
-                    <p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> - Gradio App by <a href="https://twitter.com/1littlecoder" style="text-decoration: underline;" target="_blank">1littlecoder</a>
-                    </p>
-        </div>
-        ''')
-
-block.launch(enable_queue = True)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/ddpm/test_ddpm.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/ddpm/test_ddpm.py
deleted file mode 100644
index a3c29021511487bfc1d775f3a92a6de03e6a47c4..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/ddpm/test_ddpm.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
-
-
-enable_full_determinism()
-
-
-class DDPMPipelineFastTests(unittest.TestCase):
-    @property
-    def dummy_uncond_unet(self):
-        torch.manual_seed(0)
-        model = UNet2DModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=3,
-            out_channels=3,
-            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
-            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
-        )
-        return model
-
-    def test_fast_inference(self):
-        device = "cpu"
-        unet = self.dummy_uncond_unet
-        scheduler = DDPMScheduler()
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array(
-            [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_inference_predict_sample(self):
-        unet = self.dummy_uncond_unet
-        scheduler = DDPMScheduler(prediction_type="sample")
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="numpy")[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_eps_slice = image_eps[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        tolerance = 1e-2 if torch_device != "mps" else 3e-2
-        assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance
-
-
-@slow
-@require_torch_gpu
-class DDPMPipelineIntegrationTests(unittest.TestCase):
-    def test_inference_cifar10(self):
-        model_id = "google/ddpm-cifar10-32"
-
-        unet = UNet2DModel.from_pretrained(model_id)
-        scheduler = DDPMScheduler.from_pretrained(model_id)
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/__init__.py
deleted file mode 100644
index 210a2989138380559f23045b568d0fbbeb918c03..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# flake8: noqa
-from .arraymisc import *
-from .fileio import *
-from .image import *
-from .utils import *
-from .version import *
-from .video import *
-from .visualization import *
-
-# The following modules are not imported to this level, so mmcv may be used
-# without PyTorch.
-# - runner
-# - parallel
-# - op
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/correlation.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/correlation.py
deleted file mode 100644
index 3d0b79c301b29915dfaf4d2b1846c59be73127d3..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/correlation.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import Tensor, nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['correlation_forward', 'correlation_backward'])
-
-
-class CorrelationFunction(Function):
-
-    @staticmethod
-    def forward(ctx,
-                input1,
-                input2,
-                kernel_size=1,
-                max_displacement=1,
-                stride=1,
-                padding=1,
-                dilation=1,
-                dilation_patch=1):
-
-        ctx.save_for_backward(input1, input2)
-
-        kH, kW = ctx.kernel_size = _pair(kernel_size)
-        patch_size = max_displacement * 2 + 1
-        ctx.patch_size = patch_size
-        dH, dW = ctx.stride = _pair(stride)
-        padH, padW = ctx.padding = _pair(padding)
-        dilationH, dilationW = ctx.dilation = _pair(dilation)
-        dilation_patchH, dilation_patchW = ctx.dilation_patch = _pair(
-            dilation_patch)
-
-        output_size = CorrelationFunction._output_size(ctx, input1)
-
-        output = input1.new_zeros(output_size)
-
-        ext_module.correlation_forward(
-            input1,
-            input2,
-            output,
-            kH=kH,
-            kW=kW,
-            patchH=patch_size,
-            patchW=patch_size,
-            padH=padH,
-            padW=padW,
-            dilationH=dilationH,
-            dilationW=dilationW,
-            dilation_patchH=dilation_patchH,
-            dilation_patchW=dilation_patchW,
-            dH=dH,
-            dW=dW)
-
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input1, input2 = ctx.saved_tensors
-
-        kH, kW = ctx.kernel_size
-        patch_size = ctx.patch_size
-        padH, padW = ctx.padding
-        dilationH, dilationW = ctx.dilation
-        dilation_patchH, dilation_patchW = ctx.dilation_patch
-        dH, dW = ctx.stride
-        grad_input1 = torch.zeros_like(input1)
-        grad_input2 = torch.zeros_like(input2)
-
-        ext_module.correlation_backward(
-            grad_output,
-            input1,
-            input2,
-            grad_input1,
-            grad_input2,
-            kH=kH,
-            kW=kW,
-            patchH=patch_size,
-            patchW=patch_size,
-            padH=padH,
-            padW=padW,
-            dilationH=dilationH,
-            dilationW=dilationW,
-            dilation_patchH=dilation_patchH,
-            dilation_patchW=dilation_patchW,
-            dH=dH,
-            dW=dW)
-        return grad_input1, grad_input2, None, None, None, None, None, None
-
-    @staticmethod
-    def _output_size(ctx, input1):
-        iH, iW = input1.size(2), input1.size(3)
-        batch_size = input1.size(0)
-        kH, kW = ctx.kernel_size
-        patch_size = ctx.patch_size
-        dH, dW = ctx.stride
-        padH, padW = ctx.padding
-        dilationH, dilationW = ctx.dilation
-        dilatedKH = (kH - 1) * dilationH + 1
-        dilatedKW = (kW - 1) * dilationW + 1
-
-        oH = int((iH + 2 * padH - dilatedKH) / dH + 1)
-        oW = int((iW + 2 * padW - dilatedKW) / dW + 1)
-
-        output_size = (batch_size, patch_size, patch_size, oH, oW)
-        return output_size
-
-
-class Correlation(nn.Module):
-    r"""Correlation operator
-
-    This correlation operator works for optical flow correlation computation.
-
-    There are two batched tensors with shape :math:`(N, C, H, W)`,
-    and the correlation output's shape is :math:`(N, max\_displacement \times
-    2 + 1, max\_displacement * 2 + 1, H_{out}, W_{out})`
-
-    where
-
-    .. math::
-        H_{out} = \left\lfloor\frac{H_{in}  + 2 \times padding -
-            dilation \times (kernel\_size - 1) - 1}
-            {stride} + 1\right\rfloor
-
-    .. math::
-        W_{out} = \left\lfloor\frac{W_{in}  + 2 \times padding - dilation
-            \times (kernel\_size - 1) - 1}
-            {stride} + 1\right\rfloor
-
-    the correlation item :math:`(N_i, dy, dx)` is formed by taking the sliding
-    window convolution between input1 and shifted input2,
-
-    .. math::
-        Corr(N_i, dx, dy) =
-        \sum_{c=0}^{C-1}
-        input1(N_i, c) \star
-        \mathcal{S}(input2(N_i, c), dy, dx)
-
-    where :math:`\star` is the valid 2d sliding window convolution operator,
-    and :math:`\mathcal{S}` means shifting the input features (auto-complete
-    zero marginal), and :math:`dx, dy` are shifting distance, :math:`dx, dy \in
-    [-max\_displacement \times dilation\_patch, max\_displacement \times
-    dilation\_patch]`.
-
-    Args:
-        kernel_size (int): The size of sliding window i.e. local neighborhood
-            representing the center points and involved in correlation
-            computation. Defaults to 1.
-        max_displacement (int): The radius for computing correlation volume,
-            but the actual working space can be dilated by dilation_patch.
-            Defaults to 1.
-        stride (int): The stride of the sliding blocks in the input spatial
-            dimensions. Defaults to 1.
-        padding (int): Zero padding added to all four sides of the input1.
-            Defaults to 0.
-        dilation (int): The spacing of local neighborhood that will involved
-            in correlation. Defaults to 1.
-        dilation_patch (int): The spacing between position need to compute
-            correlation.  Defaults to 1.
-    """
-
-    def __init__(self,
-                 kernel_size: int = 1,
-                 max_displacement: int = 1,
-                 stride: int = 1,
-                 padding: int = 0,
-                 dilation: int = 1,
-                 dilation_patch: int = 1) -> None:
-        super().__init__()
-        self.kernel_size = kernel_size
-        self.max_displacement = max_displacement
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.dilation_patch = dilation_patch
-
-    def forward(self, input1: Tensor, input2: Tensor) -> Tensor:
-        return CorrelationFunction.apply(input1, input2, self.kernel_size,
-                                         self.max_displacement, self.stride,
-                                         self.padding, self.dilation,
-                                         self.dilation_patch)
-
-    def __repr__(self) -> str:
-        s = self.__class__.__name__
-        s += f'(kernel_size={self.kernel_size}, '
-        s += f'max_displacement={self.max_displacement}, '
-        s += f'stride={self.stride}, '
-        s += f'padding={self.padding}, '
-        s += f'dilation={self.dilation}, '
-        s += f'dilation_patch={self.dilation_patch})'
-        return s
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/base_runner.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/base_runner.py
deleted file mode 100644
index 4928db0a73b56fe0218a4bf66ec4ffa082d31ccc..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/base_runner.py
+++ /dev/null
@@ -1,542 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import logging
-import os.path as osp
-import warnings
-from abc import ABCMeta, abstractmethod
-
-import torch
-from torch.optim import Optimizer
-
-import annotator.uniformer.mmcv as mmcv
-from ..parallel import is_module_wrapper
-from .checkpoint import load_checkpoint
-from .dist_utils import get_dist_info
-from .hooks import HOOKS, Hook
-from .log_buffer import LogBuffer
-from .priority import Priority, get_priority
-from .utils import get_time_str
-
-
-class BaseRunner(metaclass=ABCMeta):
-    """The base class of Runner, a training helper for PyTorch.
-
-    All subclasses should implement the following APIs:
-
-    - ``run()``
-    - ``train()``
-    - ``val()``
-    - ``save_checkpoint()``
-
-    Args:
-        model (:obj:`torch.nn.Module`): The model to be run.
-        batch_processor (callable): A callable method that process a data
-            batch. The interface of this method should be
-            `batch_processor(model, data, train_mode) -> dict`
-        optimizer (dict or :obj:`torch.optim.Optimizer`): It can be either an
-            optimizer (in most cases) or a dict of optimizers (in models that
-            requires more than one optimizer, e.g., GAN).
-        work_dir (str, optional): The working directory to save checkpoints
-            and logs. Defaults to None.
-        logger (:obj:`logging.Logger`): Logger used during training.
-             Defaults to None. (The default value is just for backward
-             compatibility)
-        meta (dict | None): A dict records some import information such as
-            environment info and seed, which will be logged in logger hook.
-            Defaults to None.
-        max_epochs (int, optional): Total training epochs.
-        max_iters (int, optional): Total training iterations.
-    """
-
-    def __init__(self,
-                 model,
-                 batch_processor=None,
-                 optimizer=None,
-                 work_dir=None,
-                 logger=None,
-                 meta=None,
-                 max_iters=None,
-                 max_epochs=None):
-        if batch_processor is not None:
-            if not callable(batch_processor):
-                raise TypeError('batch_processor must be callable, '
-                                f'but got {type(batch_processor)}')
-            warnings.warn('batch_processor is deprecated, please implement '
-                          'train_step() and val_step() in the model instead.')
-            # raise an error is `batch_processor` is not None and
-            # `model.train_step()` exists.
-            if is_module_wrapper(model):
-                _model = model.module
-            else:
-                _model = model
-            if hasattr(_model, 'train_step') or hasattr(_model, 'val_step'):
-                raise RuntimeError(
-                    'batch_processor and model.train_step()/model.val_step() '
-                    'cannot be both available.')
-        else:
-            assert hasattr(model, 'train_step')
-
-        # check the type of `optimizer`
-        if isinstance(optimizer, dict):
-            for name, optim in optimizer.items():
-                if not isinstance(optim, Optimizer):
-                    raise TypeError(
-                        f'optimizer must be a dict of torch.optim.Optimizers, '
-                        f'but optimizer["{name}"] is a {type(optim)}')
-        elif not isinstance(optimizer, Optimizer) and optimizer is not None:
-            raise TypeError(
-                f'optimizer must be a torch.optim.Optimizer object '
-                f'or dict or None, but got {type(optimizer)}')
-
-        # check the type of `logger`
-        if not isinstance(logger, logging.Logger):
-            raise TypeError(f'logger must be a logging.Logger object, '
-                            f'but got {type(logger)}')
-
-        # check the type of `meta`
-        if meta is not None and not isinstance(meta, dict):
-            raise TypeError(
-                f'meta must be a dict or None, but got {type(meta)}')
-
-        self.model = model
-        self.batch_processor = batch_processor
-        self.optimizer = optimizer
-        self.logger = logger
-        self.meta = meta
-        # create work_dir
-        if mmcv.is_str(work_dir):
-            self.work_dir = osp.abspath(work_dir)
-            mmcv.mkdir_or_exist(self.work_dir)
-        elif work_dir is None:
-            self.work_dir = None
-        else:
-            raise TypeError('"work_dir" must be a str or None')
-
-        # get model name from the model class
-        if hasattr(self.model, 'module'):
-            self._model_name = self.model.module.__class__.__name__
-        else:
-            self._model_name = self.model.__class__.__name__
-
-        self._rank, self._world_size = get_dist_info()
-        self.timestamp = get_time_str()
-        self.mode = None
-        self._hooks = []
-        self._epoch = 0
-        self._iter = 0
-        self._inner_iter = 0
-
-        if max_epochs is not None and max_iters is not None:
-            raise ValueError(
-                'Only one of `max_epochs` or `max_iters` can be set.')
-
-        self._max_epochs = max_epochs
-        self._max_iters = max_iters
-        # TODO: Redesign LogBuffer, it is not flexible and elegant enough
-        self.log_buffer = LogBuffer()
-
-    @property
-    def model_name(self):
-        """str: Name of the model, usually the module class name."""
-        return self._model_name
-
-    @property
-    def rank(self):
-        """int: Rank of current process. (distributed training)"""
-        return self._rank
-
-    @property
-    def world_size(self):
-        """int: Number of processes participating in the job.
-        (distributed training)"""
-        return self._world_size
-
-    @property
-    def hooks(self):
-        """list[:obj:`Hook`]: A list of registered hooks."""
-        return self._hooks
-
-    @property
-    def epoch(self):
-        """int: Current epoch."""
-        return self._epoch
-
-    @property
-    def iter(self):
-        """int: Current iteration."""
-        return self._iter
-
-    @property
-    def inner_iter(self):
-        """int: Iteration in an epoch."""
-        return self._inner_iter
-
-    @property
-    def max_epochs(self):
-        """int: Maximum training epochs."""
-        return self._max_epochs
-
-    @property
-    def max_iters(self):
-        """int: Maximum training iterations."""
-        return self._max_iters
-
-    @abstractmethod
-    def train(self):
-        pass
-
-    @abstractmethod
-    def val(self):
-        pass
-
-    @abstractmethod
-    def run(self, data_loaders, workflow, **kwargs):
-        pass
-
-    @abstractmethod
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl,
-                        save_optimizer=True,
-                        meta=None,
-                        create_symlink=True):
-        pass
-
-    def current_lr(self):
-        """Get current learning rates.
-
-        Returns:
-            list[float] | dict[str, list[float]]: Current learning rates of all
-                param groups. If the runner has a dict of optimizers, this
-                method will return a dict.
-        """
-        if isinstance(self.optimizer, torch.optim.Optimizer):
-            lr = [group['lr'] for group in self.optimizer.param_groups]
-        elif isinstance(self.optimizer, dict):
-            lr = dict()
-            for name, optim in self.optimizer.items():
-                lr[name] = [group['lr'] for group in optim.param_groups]
-        else:
-            raise RuntimeError(
-                'lr is not applicable because optimizer does not exist.')
-        return lr
-
-    def current_momentum(self):
-        """Get current momentums.
-
-        Returns:
-            list[float] | dict[str, list[float]]: Current momentums of all
-                param groups. If the runner has a dict of optimizers, this
-                method will return a dict.
-        """
-
-        def _get_momentum(optimizer):
-            momentums = []
-            for group in optimizer.param_groups:
-                if 'momentum' in group.keys():
-                    momentums.append(group['momentum'])
-                elif 'betas' in group.keys():
-                    momentums.append(group['betas'][0])
-                else:
-                    momentums.append(0)
-            return momentums
-
-        if self.optimizer is None:
-            raise RuntimeError(
-                'momentum is not applicable because optimizer does not exist.')
-        elif isinstance(self.optimizer, torch.optim.Optimizer):
-            momentums = _get_momentum(self.optimizer)
-        elif isinstance(self.optimizer, dict):
-            momentums = dict()
-            for name, optim in self.optimizer.items():
-                momentums[name] = _get_momentum(optim)
-        return momentums
-
-    def register_hook(self, hook, priority='NORMAL'):
-        """Register a hook into the hook list.
-
-        The hook will be inserted into a priority queue, with the specified
-        priority (See :class:`Priority` for details of priorities).
-        For hooks with the same priority, they will be triggered in the same
-        order as they are registered.
-
-        Args:
-            hook (:obj:`Hook`): The hook to be registered.
-            priority (int or str or :obj:`Priority`): Hook priority.
-                Lower value means higher priority.
-        """
-        assert isinstance(hook, Hook)
-        if hasattr(hook, 'priority'):
-            raise ValueError('"priority" is a reserved attribute for hooks')
-        priority = get_priority(priority)
-        hook.priority = priority
-        # insert the hook to a sorted list
-        inserted = False
-        for i in range(len(self._hooks) - 1, -1, -1):
-            if priority >= self._hooks[i].priority:
-                self._hooks.insert(i + 1, hook)
-                inserted = True
-                break
-        if not inserted:
-            self._hooks.insert(0, hook)
-
-    def register_hook_from_cfg(self, hook_cfg):
-        """Register a hook from its cfg.
-
-        Args:
-            hook_cfg (dict): Hook config. It should have at least keys 'type'
-              and 'priority' indicating its type and priority.
-
-        Notes:
-            The specific hook class to register should not use 'type' and
-            'priority' arguments during initialization.
-        """
-        hook_cfg = hook_cfg.copy()
-        priority = hook_cfg.pop('priority', 'NORMAL')
-        hook = mmcv.build_from_cfg(hook_cfg, HOOKS)
-        self.register_hook(hook, priority=priority)
-
-    def call_hook(self, fn_name):
-        """Call all hooks.
-
-        Args:
-            fn_name (str): The function name in each hook to be called, such as
-                "before_train_epoch".
-        """
-        for hook in self._hooks:
-            getattr(hook, fn_name)(self)
-
-    def get_hook_info(self):
-        # Get hooks info in each stage
-        stage_hook_map = {stage: [] for stage in Hook.stages}
-        for hook in self.hooks:
-            try:
-                priority = Priority(hook.priority).name
-            except ValueError:
-                priority = hook.priority
-            classname = hook.__class__.__name__
-            hook_info = f'({priority:<12}) {classname:<35}'
-            for trigger_stage in hook.get_triggered_stages():
-                stage_hook_map[trigger_stage].append(hook_info)
-
-        stage_hook_infos = []
-        for stage in Hook.stages:
-            hook_infos = stage_hook_map[stage]
-            if len(hook_infos) > 0:
-                info = f'{stage}:\n'
-                info += '\n'.join(hook_infos)
-                info += '\n -------------------- '
-                stage_hook_infos.append(info)
-        return '\n'.join(stage_hook_infos)
-
-    def load_checkpoint(self,
-                        filename,
-                        map_location='cpu',
-                        strict=False,
-                        revise_keys=[(r'^module.', '')]):
-        return load_checkpoint(
-            self.model,
-            filename,
-            map_location,
-            strict,
-            self.logger,
-            revise_keys=revise_keys)
-
-    def resume(self,
-               checkpoint,
-               resume_optimizer=True,
-               map_location='default'):
-        if map_location == 'default':
-            if torch.cuda.is_available():
-                device_id = torch.cuda.current_device()
-                checkpoint = self.load_checkpoint(
-                    checkpoint,
-                    map_location=lambda storage, loc: storage.cuda(device_id))
-            else:
-                checkpoint = self.load_checkpoint(checkpoint)
-        else:
-            checkpoint = self.load_checkpoint(
-                checkpoint, map_location=map_location)
-
-        self._epoch = checkpoint['meta']['epoch']
-        self._iter = checkpoint['meta']['iter']
-        if self.meta is None:
-            self.meta = {}
-        self.meta.setdefault('hook_msgs', {})
-        # load `last_ckpt`, `best_score`, `best_ckpt`, etc. for hook messages
-        self.meta['hook_msgs'].update(checkpoint['meta'].get('hook_msgs', {}))
-
-        # Re-calculate the number of iterations when resuming
-        # models with different number of GPUs
-        if 'config' in checkpoint['meta']:
-            config = mmcv.Config.fromstring(
-                checkpoint['meta']['config'], file_format='.py')
-            previous_gpu_ids = config.get('gpu_ids', None)
-            if previous_gpu_ids and len(previous_gpu_ids) > 0 and len(
-                    previous_gpu_ids) != self.world_size:
-                self._iter = int(self._iter * len(previous_gpu_ids) /
-                                 self.world_size)
-                self.logger.info('the iteration number is changed due to '
-                                 'change of GPU number')
-
-        # resume meta information meta
-        self.meta = checkpoint['meta']
-
-        if 'optimizer' in checkpoint and resume_optimizer:
-            if isinstance(self.optimizer, Optimizer):
-                self.optimizer.load_state_dict(checkpoint['optimizer'])
-            elif isinstance(self.optimizer, dict):
-                for k in self.optimizer.keys():
-                    self.optimizer[k].load_state_dict(
-                        checkpoint['optimizer'][k])
-            else:
-                raise TypeError(
-                    'Optimizer should be dict or torch.optim.Optimizer '
-                    f'but got {type(self.optimizer)}')
-
-        self.logger.info('resumed epoch %d, iter %d', self.epoch, self.iter)
-
-    def register_lr_hook(self, lr_config):
-        if lr_config is None:
-            return
-        elif isinstance(lr_config, dict):
-            assert 'policy' in lr_config
-            policy_type = lr_config.pop('policy')
-            # If the type of policy is all in lower case, e.g., 'cyclic',
-            # then its first letter will be capitalized, e.g., to be 'Cyclic'.
-            # This is for the convenient usage of Lr updater.
-            # Since this is not applicable for `
-            # CosineAnnealingLrUpdater`,
-            # the string will not be changed if it contains capital letters.
-            if policy_type == policy_type.lower():
-                policy_type = policy_type.title()
-            hook_type = policy_type + 'LrUpdaterHook'
-            lr_config['type'] = hook_type
-            hook = mmcv.build_from_cfg(lr_config, HOOKS)
-        else:
-            hook = lr_config
-        self.register_hook(hook, priority='VERY_HIGH')
-
-    def register_momentum_hook(self, momentum_config):
-        if momentum_config is None:
-            return
-        if isinstance(momentum_config, dict):
-            assert 'policy' in momentum_config
-            policy_type = momentum_config.pop('policy')
-            # If the type of policy is all in lower case, e.g., 'cyclic',
-            # then its first letter will be capitalized, e.g., to be 'Cyclic'.
-            # This is for the convenient usage of momentum updater.
-            # Since this is not applicable for
-            # `CosineAnnealingMomentumUpdater`,
-            # the string will not be changed if it contains capital letters.
-            if policy_type == policy_type.lower():
-                policy_type = policy_type.title()
-            hook_type = policy_type + 'MomentumUpdaterHook'
-            momentum_config['type'] = hook_type
-            hook = mmcv.build_from_cfg(momentum_config, HOOKS)
-        else:
-            hook = momentum_config
-        self.register_hook(hook, priority='HIGH')
-
-    def register_optimizer_hook(self, optimizer_config):
-        if optimizer_config is None:
-            return
-        if isinstance(optimizer_config, dict):
-            optimizer_config.setdefault('type', 'OptimizerHook')
-            hook = mmcv.build_from_cfg(optimizer_config, HOOKS)
-        else:
-            hook = optimizer_config
-        self.register_hook(hook, priority='ABOVE_NORMAL')
-
-    def register_checkpoint_hook(self, checkpoint_config):
-        if checkpoint_config is None:
-            return
-        if isinstance(checkpoint_config, dict):
-            checkpoint_config.setdefault('type', 'CheckpointHook')
-            hook = mmcv.build_from_cfg(checkpoint_config, HOOKS)
-        else:
-            hook = checkpoint_config
-        self.register_hook(hook, priority='NORMAL')
-
-    def register_logger_hooks(self, log_config):
-        if log_config is None:
-            return
-        log_interval = log_config['interval']
-        for info in log_config['hooks']:
-            logger_hook = mmcv.build_from_cfg(
-                info, HOOKS, default_args=dict(interval=log_interval))
-            self.register_hook(logger_hook, priority='VERY_LOW')
-
-    def register_timer_hook(self, timer_config):
-        if timer_config is None:
-            return
-        if isinstance(timer_config, dict):
-            timer_config_ = copy.deepcopy(timer_config)
-            hook = mmcv.build_from_cfg(timer_config_, HOOKS)
-        else:
-            hook = timer_config
-        self.register_hook(hook, priority='LOW')
-
-    def register_custom_hooks(self, custom_config):
-        if custom_config is None:
-            return
-
-        if not isinstance(custom_config, list):
-            custom_config = [custom_config]
-
-        for item in custom_config:
-            if isinstance(item, dict):
-                self.register_hook_from_cfg(item)
-            else:
-                self.register_hook(item, priority='NORMAL')
-
-    def register_profiler_hook(self, profiler_config):
-        if profiler_config is None:
-            return
-        if isinstance(profiler_config, dict):
-            profiler_config.setdefault('type', 'ProfilerHook')
-            hook = mmcv.build_from_cfg(profiler_config, HOOKS)
-        else:
-            hook = profiler_config
-        self.register_hook(hook)
-
-    def register_training_hooks(self,
-                                lr_config,
-                                optimizer_config=None,
-                                checkpoint_config=None,
-                                log_config=None,
-                                momentum_config=None,
-                                timer_config=dict(type='IterTimerHook'),
-                                custom_hooks_config=None):
-        """Register default and custom hooks for training.
-
-        Default and custom hooks include:
-
-        +----------------------+-------------------------+
-        | Hooks                | Priority                |
-        +======================+=========================+
-        | LrUpdaterHook        | VERY_HIGH (10)          |
-        +----------------------+-------------------------+
-        | MomentumUpdaterHook  | HIGH (30)               |
-        +----------------------+-------------------------+
-        | OptimizerStepperHook | ABOVE_NORMAL (40)       |
-        +----------------------+-------------------------+
-        | CheckpointSaverHook  | NORMAL (50)             |
-        +----------------------+-------------------------+
-        | IterTimerHook        | LOW (70)                |
-        +----------------------+-------------------------+
-        | LoggerHook(s)        | VERY_LOW (90)           |
-        +----------------------+-------------------------+
-        | CustomHook(s)        | defaults to NORMAL (50) |
-        +----------------------+-------------------------+
-
-        If custom hooks have same priority with default hooks, custom hooks
-        will be triggered after default hooks.
-        """
-        self.register_lr_hook(lr_config)
-        self.register_momentum_hook(momentum_config)
-        self.register_optimizer_hook(optimizer_config)
-        self.register_checkpoint_hook(checkpoint_config)
-        self.register_timer_hook(timer_config)
-        self.register_logger_hooks(log_config)
-        self.register_custom_hooks(custom_hooks_config)
diff --git a/spaces/Anthony7906/MengHuiMXD_GPT/modules/overwrites.py b/spaces/Anthony7906/MengHuiMXD_GPT/modules/overwrites.py
deleted file mode 100644
index 035a4a52722d66ee28af1c05231ad1cea3339ef5..0000000000000000000000000000000000000000
--- a/spaces/Anthony7906/MengHuiMXD_GPT/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
diff --git a/spaces/Apex-X/nono/roop/globals.py b/spaces/Apex-X/nono/roop/globals.py
deleted file mode 100644
index 3eca8d0d024db967cc6d7e7149f68f65f84d7072..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/nono/roop/globals.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from typing import List, Optional
-
-source_path: Optional[str] = None
-target_path: Optional[str] = None
-output_path: Optional[str] = None
-headless: Optional[bool] = None
-frame_processors: List[str] = []
-keep_fps: Optional[bool] = None
-keep_frames: Optional[bool] = None
-skip_audio: Optional[bool] = None
-many_faces: Optional[bool] = None
-reference_face_position: Optional[int] = None
-reference_frame_number: Optional[int] = None
-similar_face_distance: Optional[float] = None
-temp_frame_format: Optional[str] = None
-temp_frame_quality: Optional[int] = None
-output_video_encoder: Optional[str] = None
-output_video_quality: Optional[int] = None
-max_memory: Optional[int] = None
-execution_providers: List[str] = []
-execution_threads: Optional[int] = None
-log_level: str = 'error'
diff --git a/spaces/Ashrafb/Tesseract-OCR/app_interface.py b/spaces/Ashrafb/Tesseract-OCR/app_interface.py
deleted file mode 100644
index fc0c3625a76832c2051676a621378042b3672926..0000000000000000000000000000000000000000
--- a/spaces/Ashrafb/Tesseract-OCR/app_interface.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from typing import List
-
-import pytesseract
-from PIL import Image
-
-import gradio as gr
-
-def tesseract_ocr(filepath: str, languages: List[str]):
-    image = Image.open(filepath)
-    return pytesseract.image_to_string(image=image, lang=', '.join(languages))
-
-title = "Tesseract OCR"
-description = "Gradio demo for Tesseract. Tesseract is an open source text recognition (OCR) Engine."
-article = "<p style='text-align: center'><a href='https://tesseract-ocr.github.io/' target='_blank'>Tesseract documentation</a> | <a href='https://github.com/tesseract-ocr/tesseract' target='_blank'>Github Repo</a></p>"
-examples = [
-        ['examples/eurotext.png', ['eng']], 
-        ['examples/tesseract_sample.png', ['jpn', 'eng']], 
-        ['examples/chi.jpg', ['HanS', 'HanT']]
-    ]
-
-language_choices = pytesseract.get_languages()
-
-demo = gr.Interface(
-    fn=tesseract_ocr, 
-    inputs=[
-        gr.Image(type="filepath", label="Input"), 
-        gr.CheckboxGroup(language_choices, type="value", value=['eng'], label='language')
-        ],
-    outputs='text',
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-)
-
-if __name__ == '__main__':
-    demo.launch()
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/status_codes.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/status_codes.py
deleted file mode 100644
index 5e29502cddfa9a9887a93399ab4193fb75dfe605..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/status_codes.py
+++ /dev/null
@@ -1,6 +0,0 @@
-SUCCESS = 0
-ERROR = 1
-UNKNOWN_ERROR = 2
-VIRTUALENV_NOT_FOUND = 3
-PREVIOUS_BUILD_DIR_ERROR = 4
-NO_MATCHES_FOUND = 23
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/metadata/importlib/_dists.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/metadata/importlib/_dists.py
deleted file mode 100644
index 65c043c87eff27e9405316fdbc0c695f2b347441..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/metadata/importlib/_dists.py
+++ /dev/null
@@ -1,224 +0,0 @@
-import email.message
-import importlib.metadata
-import os
-import pathlib
-import zipfile
-from typing import (
-    Collection,
-    Dict,
-    Iterable,
-    Iterator,
-    Mapping,
-    Optional,
-    Sequence,
-    cast,
-)
-
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import InvalidWheel, UnsupportedWheel
-from pip._internal.metadata.base import (
-    BaseDistribution,
-    BaseEntryPoint,
-    DistributionVersion,
-    InfoPath,
-    Wheel,
-)
-from pip._internal.utils.misc import normalize_path
-from pip._internal.utils.packaging import safe_extra
-from pip._internal.utils.temp_dir import TempDirectory
-from pip._internal.utils.wheel import parse_wheel, read_wheel_metadata_file
-
-from ._compat import BasePath, get_dist_name
-
-
-class WheelDistribution(importlib.metadata.Distribution):
-    """An ``importlib.metadata.Distribution`` read from a wheel.
-
-    Although ``importlib.metadata.PathDistribution`` accepts ``zipfile.Path``,
-    its implementation is too "lazy" for pip's needs (we can't keep the ZipFile
-    handle open for the entire lifetime of the distribution object).
-
-    This implementation eagerly reads the entire metadata directory into the
-    memory instead, and operates from that.
-    """
-
-    def __init__(
-        self,
-        files: Mapping[pathlib.PurePosixPath, bytes],
-        info_location: pathlib.PurePosixPath,
-    ) -> None:
-        self._files = files
-        self.info_location = info_location
-
-    @classmethod
-    def from_zipfile(
-        cls,
-        zf: zipfile.ZipFile,
-        name: str,
-        location: str,
-    ) -> "WheelDistribution":
-        info_dir, _ = parse_wheel(zf, name)
-        paths = (
-            (name, pathlib.PurePosixPath(name.split("/", 1)[-1]))
-            for name in zf.namelist()
-            if name.startswith(f"{info_dir}/")
-        )
-        files = {
-            relpath: read_wheel_metadata_file(zf, fullpath)
-            for fullpath, relpath in paths
-        }
-        info_location = pathlib.PurePosixPath(location, info_dir)
-        return cls(files, info_location)
-
-    def iterdir(self, path: InfoPath) -> Iterator[pathlib.PurePosixPath]:
-        # Only allow iterating through the metadata directory.
-        if pathlib.PurePosixPath(str(path)) in self._files:
-            return iter(self._files)
-        raise FileNotFoundError(path)
-
-    def read_text(self, filename: str) -> Optional[str]:
-        try:
-            data = self._files[pathlib.PurePosixPath(filename)]
-        except KeyError:
-            return None
-        try:
-            text = data.decode("utf-8")
-        except UnicodeDecodeError as e:
-            wheel = self.info_location.parent
-            error = f"Error decoding metadata for {wheel}: {e} in {filename} file"
-            raise UnsupportedWheel(error)
-        return text
-
-
-class Distribution(BaseDistribution):
-    def __init__(
-        self,
-        dist: importlib.metadata.Distribution,
-        info_location: Optional[BasePath],
-        installed_location: Optional[BasePath],
-    ) -> None:
-        self._dist = dist
-        self._info_location = info_location
-        self._installed_location = installed_location
-
-    @classmethod
-    def from_directory(cls, directory: str) -> BaseDistribution:
-        info_location = pathlib.Path(directory)
-        dist = importlib.metadata.Distribution.at(info_location)
-        return cls(dist, info_location, info_location.parent)
-
-    @classmethod
-    def from_metadata_file_contents(
-        cls,
-        metadata_contents: bytes,
-        filename: str,
-        project_name: str,
-    ) -> BaseDistribution:
-        # Generate temp dir to contain the metadata file, and write the file contents.
-        temp_dir = pathlib.Path(
-            TempDirectory(kind="metadata", globally_managed=True).path
-        )
-        metadata_path = temp_dir / "METADATA"
-        metadata_path.write_bytes(metadata_contents)
-        # Construct dist pointing to the newly created directory.
-        dist = importlib.metadata.Distribution.at(metadata_path.parent)
-        return cls(dist, metadata_path.parent, None)
-
-    @classmethod
-    def from_wheel(cls, wheel: Wheel, name: str) -> BaseDistribution:
-        try:
-            with wheel.as_zipfile() as zf:
-                dist = WheelDistribution.from_zipfile(zf, name, wheel.location)
-        except zipfile.BadZipFile as e:
-            raise InvalidWheel(wheel.location, name) from e
-        except UnsupportedWheel as e:
-            raise UnsupportedWheel(f"{name} has an invalid wheel, {e}")
-        return cls(dist, dist.info_location, pathlib.PurePosixPath(wheel.location))
-
-    @property
-    def location(self) -> Optional[str]:
-        if self._info_location is None:
-            return None
-        return str(self._info_location.parent)
-
-    @property
-    def info_location(self) -> Optional[str]:
-        if self._info_location is None:
-            return None
-        return str(self._info_location)
-
-    @property
-    def installed_location(self) -> Optional[str]:
-        if self._installed_location is None:
-            return None
-        return normalize_path(str(self._installed_location))
-
-    def _get_dist_name_from_location(self) -> Optional[str]:
-        """Try to get the name from the metadata directory name.
-
-        This is much faster than reading metadata.
-        """
-        if self._info_location is None:
-            return None
-        stem, suffix = os.path.splitext(self._info_location.name)
-        if suffix not in (".dist-info", ".egg-info"):
-            return None
-        return stem.split("-", 1)[0]
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        name = self._get_dist_name_from_location() or get_dist_name(self._dist)
-        return canonicalize_name(name)
-
-    @property
-    def version(self) -> DistributionVersion:
-        return parse_version(self._dist.version)
-
-    def is_file(self, path: InfoPath) -> bool:
-        return self._dist.read_text(str(path)) is not None
-
-    def iter_distutils_script_names(self) -> Iterator[str]:
-        # A distutils installation is always "flat" (not in e.g. egg form), so
-        # if this distribution's info location is NOT a pathlib.Path (but e.g.
-        # zipfile.Path), it can never contain any distutils scripts.
-        if not isinstance(self._info_location, pathlib.Path):
-            return
-        for child in self._info_location.joinpath("scripts").iterdir():
-            yield child.name
-
-    def read_text(self, path: InfoPath) -> str:
-        content = self._dist.read_text(str(path))
-        if content is None:
-            raise FileNotFoundError(path)
-        return content
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        # importlib.metadata's EntryPoint structure sasitfies BaseEntryPoint.
-        return self._dist.entry_points
-
-    def _metadata_impl(self) -> email.message.Message:
-        # From Python 3.10+, importlib.metadata declares PackageMetadata as the
-        # return type. This protocol is unfortunately a disaster now and misses
-        # a ton of fields that we need, including get() and get_payload(). We
-        # rely on the implementation that the object is actually a Message now,
-        # until upstream can improve the protocol. (python/cpython#94952)
-        return cast(email.message.Message, self._dist.metadata)
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        return (
-            safe_extra(extra) for extra in self.metadata.get_all("Provides-Extra", [])
-        )
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        contexts: Sequence[Dict[str, str]] = [{"extra": safe_extra(e)} for e in extras]
-        for req_string in self.metadata.get_all("Requires-Dist", []):
-            req = Requirement(req_string)
-            if not req.marker:
-                yield req
-            elif not extras and req.marker.evaluate({"extra": ""}):
-                yield req
-            elif any(req.marker.evaluate(context) for context in contexts):
-                yield req
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/cli/chardetect.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/cli/chardetect.py
deleted file mode 100644
index 43f6e144f677a113b5362dcbdfb75db4f41c2b2f..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/cli/chardetect.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""
-Script which takes one or more file paths and reports on their detected
-encodings
-
-Example::
-
-    % chardetect somefile someotherfile
-    somefile: windows-1252 with confidence 0.5
-    someotherfile: ascii with confidence 1.0
-
-If no paths are provided, it takes its input from stdin.
-
-"""
-
-
-import argparse
-import sys
-from typing import Iterable, List, Optional
-
-from .. import __version__
-from ..universaldetector import UniversalDetector
-
-
-def description_of(
-    lines: Iterable[bytes],
-    name: str = "stdin",
-    minimal: bool = False,
-    should_rename_legacy: bool = False,
-) -> Optional[str]:
-    """
-    Return a string describing the probable encoding of a file or
-    list of strings.
-
-    :param lines: The lines to get the encoding of.
-    :type lines: Iterable of bytes
-    :param name: Name of file or collection of lines
-    :type name: str
-    :param should_rename_legacy:  Should we rename legacy encodings to
-                                  their more modern equivalents?
-    :type should_rename_legacy:   ``bool``
-    """
-    u = UniversalDetector(should_rename_legacy=should_rename_legacy)
-    for line in lines:
-        line = bytearray(line)
-        u.feed(line)
-        # shortcut out of the loop to save reading further - particularly useful if we read a BOM.
-        if u.done:
-            break
-    u.close()
-    result = u.result
-    if minimal:
-        return result["encoding"]
-    if result["encoding"]:
-        return f'{name}: {result["encoding"]} with confidence {result["confidence"]}'
-    return f"{name}: no result"
-
-
-def main(argv: Optional[List[str]] = None) -> None:
-    """
-    Handles command line arguments and gets things started.
-
-    :param argv: List of arguments, as if specified on the command-line.
-                 If None, ``sys.argv[1:]`` is used instead.
-    :type argv: list of str
-    """
-    # Get command line arguments
-    parser = argparse.ArgumentParser(
-        description=(
-            "Takes one or more file paths and reports their detected encodings"
-        )
-    )
-    parser.add_argument(
-        "input",
-        help="File whose encoding we would like to determine. (default: stdin)",
-        type=argparse.FileType("rb"),
-        nargs="*",
-        default=[sys.stdin.buffer],
-    )
-    parser.add_argument(
-        "--minimal",
-        help="Print only the encoding to standard output",
-        action="store_true",
-    )
-    parser.add_argument(
-        "-l",
-        "--legacy",
-        help="Rename legacy encodings to more modern ones.",
-        action="store_true",
-    )
-    parser.add_argument(
-        "--version", action="version", version=f"%(prog)s {__version__}"
-    )
-    args = parser.parse_args(argv)
-
-    for f in args.input:
-        if f.isatty():
-            print(
-                "You are running chardetect interactively. Press "
-                "CTRL-D twice at the start of a blank line to signal the "
-                "end of your input. If you want help, run chardetect "
-                "--help\n",
-                file=sys.stderr,
-            )
-        print(
-            description_of(
-                f, f.name, minimal=args.minimal, should_rename_legacy=args.legacy
-            )
-        )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/grit/data/datasets/vg.py b/spaces/Awiny/Image2Paragraph/models/grit_src/grit/data/datasets/vg.py
deleted file mode 100644
index 4d47a80d9f88b89ca3064dbc4945b0246162e5d1..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/grit/data/datasets/vg.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import logging
-import os
-from fvcore.common.timer import Timer
-from detectron2.structures import BoxMode
-from fvcore.common.file_io import PathManager
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from lvis import LVIS
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["load_vg_json", "register_vg_instances"]
-
-
-def register_vg_instances(name, metadata, json_file, image_root):
-    """
-    """
-    DatasetCatalog.register(name, lambda: load_vg_json(
-        json_file, image_root, name))
-    MetadataCatalog.get(name).set(
-        json_file=json_file, image_root=image_root,
-        evaluator_type="vg", **metadata
-    )
-
-
-def get_vg_meta():
-    categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}]
-    vg_categories = sorted(categories, key=lambda x: x["id"])
-    thing_classes = [k["name"] for k in vg_categories]
-    meta = {"thing_classes": thing_classes}
-    return meta
-
-
-def load_vg_json(json_file, image_root, dataset_name=None):
-
-    json_file = PathManager.get_local_path(json_file)
-
-    timer = Timer()
-    lvis_api = LVIS(json_file)
-    if timer.seconds() > 1:
-        logger.info("Loading {} takes {:.2f} seconds.".format(
-            json_file, timer.seconds()))
-
-    img_ids = sorted(lvis_api.imgs.keys())
-    imgs = lvis_api.load_imgs(img_ids)
-    anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
-
-    ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
-    assert len(set(ann_ids)) == len(ann_ids), \
-        "Annotation ids in '{}' are not unique".format(json_file)
-
-    imgs_anns = list(zip(imgs, anns))
-    logger.info("Loaded {} images in the LVIS v1 format from {}".format(
-        len(imgs_anns), json_file))
-
-    dataset_dicts = []
-
-    for (img_dict, anno_dict_list) in imgs_anns:
-        record = {}
-        if "file_name" in img_dict:
-            file_name = img_dict["file_name"]
-            record["file_name"] = os.path.join(image_root, file_name)
-
-        record["height"] = int(img_dict["height"])
-        record["width"] = int(img_dict["width"])
-        image_id = record["image_id"] = img_dict["id"]
-
-        objs = []
-        for anno in anno_dict_list:
-            assert anno["image_id"] == image_id
-            if anno.get('iscrowd', 0) > 0:
-                continue
-            obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
-            obj["category_id"] = 0
-            obj["object_description"] = anno["caption"]
-
-            objs.append(obj)
-        record["annotations"] = objs
-        if len(record["annotations"]) == 0:
-            continue
-        record["task"] = "DenseCap"
-        dataset_dicts.append(record)
-
-    return dataset_dicts
-
-
-_CUSTOM_SPLITS_LVIS = {
-    "vg_train": ("vg/images", "vg/annotations/train.json"),
-    "vg_test": ("vg/images", "vg/annotations/test.json"),
-}
-
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
-    register_vg_instances(
-        key,
-        get_vg_meta(),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )
\ No newline at end of file
diff --git a/spaces/BatuhanYilmaz/Whisper-Auto-Subtitled-Video-Generator/README.md b/spaces/BatuhanYilmaz/Whisper-Auto-Subtitled-Video-Generator/README.md
deleted file mode 100644
index df82548a15c4ce9d723870a4c47bf1bb23a03fd5..0000000000000000000000000000000000000000
--- a/spaces/BatuhanYilmaz/Whisper-Auto-Subtitled-Video-Generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Whisper-Auto-Subtitled-Video-Generator
-emoji: 🎥
-colorFrom: blue
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: 01_🎥_Input_YouTube_Link.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/Belligerent/word-sense-disambiguation/README.md b/spaces/Belligerent/word-sense-disambiguation/README.md
deleted file mode 100644
index 24b38c12e4c37fad821570f7c7a49ad2a895c3ab..0000000000000000000000000000000000000000
--- a/spaces/Belligerent/word-sense-disambiguation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Word Sense Disambiguation
-emoji: 🐢
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 2.8.12
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Benson/text-generation/Examples/Amigo Robo Guerras Fuera De Lnea Juegos Descargar Pc.md b/spaces/Benson/text-generation/Examples/Amigo Robo Guerras Fuera De Lnea Juegos Descargar Pc.md
deleted file mode 100644
index b45273bc898da9aefe378e096c7d34ec802fdfd3..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Amigo Robo Guerras Fuera De Lnea Juegos Descargar Pc.md	
+++ /dev/null
@@ -1,45 +0,0 @@
-
-<h1>Dude Theft Wars: Cómo descargar y jugar este divertido juego sin conexión en PC</h1>
- <p>Si estás buscando un juego divertido e hilarante que te permita hacer lo que quieras en un mundo abierto, entonces deberías echar un vistazo a Dude Theft Wars. Este juego está disponible para dispositivos Android, pero también se puede jugar en su PC con un emulador. En este artículo, te diremos qué es Dude Theft Wars, por qué deberías jugarlo en el PC, cómo descargarlo e instalarlo, y algunos consejos y trucos para jugarlo. </p>
-<h2>amigo robo guerras fuera de línea juegos descargar pc</h2><br /><p><b><b>Download File</b> &#128505; <a href="https://bltlly.com/2v6KcS">https://bltlly.com/2v6KcS</a></b></p><br /><br />
- <h2>¿Qué es Dude Theft Wars? </h2>
- <p>Dude Theft Wars es un divertido juego de mundo abierto de acción basado en la física desarrollado por Poxel Studios. Es uno de los mejores juegos de mundo abierto con ragdoll física, juegos frescos, y juegos relajantes. Puedes jugar offline o online con hasta 16 jugadores en modo multijugador. </p>
- <h3>Un divertido juego de mundo abierto basado en la acción física</h3>
- <p>En Dude Theft Wars, puedes explorar una gran ciudad llena de juegos de acción, juegos divertidos y juegos relajantes. Puedes conducir autos, volar aviones, robar personas, tomar selfies, ganar dinero y más. También puedes usar armas, granadas, cohetes y otras armas para causar caos y destrucción. El juego tiene la física ragdoll divertido que hacen que los personajes vuelan en el aire cuando se golpea o dispara. El juego también tiene mucho humor y chistes que te harán reír. </p>
- <h3>Un juego multijugador offline y online con características interesantes</h3>
- <p>Dude Theft Wars tiene dos modos: modo de juegos sin conexión y modo de juegos multijugador en línea. En el modo de juegos sin conexión, puedes jugar como Jack, un niño que recibió una llamada de deber para unirse a un shooter multijugador FPS en línea PvP en un parque infantil. Puedes completar misiones, explorar mapas, luchar contra enemigos y divertirte. En el modo multijugador en línea, puedes jugar con amigos u otros jugadores en dos modos: gratis para todos o de combate a muerte en equipo. También puede disfrutar de bailes divertidos, emotes, minijuegos, juegos deportivos, conducción de taxis y más. Puedes desbloquear objetos de personajes y armas para usar en ambos modos. </p>
-
- <p>Dude Theft Wars tiene muchos vehículos que puedes conducir o volar en la ciudad. Usted puede conducir bicicletas, coches, camiones, autobuses, tanques, helicópteros, aviones, jetpacks, ovnis, y más. También puede hacer acrobacias locas con coches de rampa o lanzar coches de tráfico al cielo. También puede personalizar sus vehículos con diferentes colores y piezas. Pero ten cuidado, porque la policía te perseguirá si cometes crímenes o dañas autos. Puedes evadirlos en persecuciones policiales llenas de acción de alto nivel o defenderlos. </p>
-<p></p>
- <h2>¿Por qué jugar Dude Theft Wars en PC? </h2>
- <p>Dude Theft Wars es un gran juego para jugar en tu dispositivo Android, pero puede ser aún mejor si lo juegas en tu PC. Aquí hay algunas razones por las que:</p>
- <h3>Disfruta de mejores gráficos y rendimiento</h3>
- <p>Jugar Dude Theft Wars en PC te permitirá disfrutar de mejores gráficos y rendimiento que en tu dispositivo móvil. Puede ajustar la configuración para adaptarse a sus preferencias y capacidades de hardware. También puede evitar los problemas de retraso o estrellarse que pueden ocurrir en su teléfono debido a la baja memoria, batería o red. Jugar en el PC también ahorrará la vida de la batería del teléfono y el espacio de almacenamiento. </p>
- <h3>Utilice los controles de teclado y ratón para mayor precisión y comodidad</h3>
- <p>Jugar Dude Theft Wars en PC también te permitirá usar controles de teclado y ratón para mayor precisión y comodidad. Puede personalizar la asignación de claves y la sensibilidad para adaptarse a sus preferencias y habilidades. También puede utilizar teclas de acceso rápido y accesos directos para acceder a diferentes funciones y menús. Usar los controles del teclado y del ratón también evitará que tus dedos bloqueen la pantalla o se cansen de tocar. </p>
- <h3>Accede a una pantalla más grande y más inmersiva experiencia de juego</h3>
-
- <h2>¿Cómo descargar e instalar Dude Theft Wars en el PC? </h2>
- <p>Ahora que sabes por qué deberías jugar Dude Theft Wars en PC, veamos cómo puedes hacerlo. El juego no está disponible oficialmente para PC, pero puedes usar un emulador de Android para ejecutarlo en tu computadora. Un emulador de Android es un software que simula el sistema operativo Android en su PC, lo que le permite ejecutar aplicaciones y juegos de Android. Hay muchos emuladores de Android que puedes usar, pero te recomendamos usar BlueStacks, LDPlayer o GameLoop. Estos son algunos de los mejores emuladores para juegos, con alta compatibilidad, rendimiento y características. Estos son los pasos para descargar e instalar Dude Theft Wars en el PC usando un emulador:</p>
- <h3>Usa un emulador de Android como BlueStacks, LDPlayer o GameLoop</h3>
- <p>El primer paso es elegir un emulador de Android que se adapte a sus necesidades y preferencias. Puede descargar BlueStacks desde [aquí], LDPlayer desde [aquí], o GameLoop desde [aquí]. Estos emuladores son gratuitos y fáciles de usar, pero tienen diferentes requisitos y características. Puedes compararlos leyendo sus reseñas o visitando sus sitios web. Una vez que hayas elegido un emulador, descárgalo de su fuente oficial e instálalo en tu PC.</p>
- <h3>Siga los pasos para descargar e instalar el emulador y el juego</h3>
- <p>El siguiente paso es seguir los pasos para descargar e instalar el emulador y el juego en su PC. Los pasos pueden variar dependiendo del emulador que uses, pero generalmente son similares. Estos son los pasos comunes:</p>
- - Inicie el emulador en su PC e inicie sesión con su cuenta de Google. - Ir a la aplicación Google Play Store en el emulador y buscar Dude Theft Wars. - Haga clic en el icono del juego y luego haga clic en Instalar. - Espere a que el juego para descargar e instalar en su PC. - Volver a la pantalla principal del emulador y haga clic en el icono del juego para lanzarlo. <h3>Iniciar el juego y empezar a jugar</h3>
-
- <h2>Consejos y trucos para jugar Dude Theft Wars en PC</h2>
- <p>Para hacer su experiencia de juego más divertido y agradable, aquí hay algunos consejos y trucos para jugar Dude Theft Wars en PC:</p>
- <h3>Explora los diferentes mapas y modos</h3>
- <p>Dude Theft Wars tiene muchos mapas y modos que puedes explorar en el modo de juegos sin conexión o en el modo de juegos multijugador en línea. Puedes encontrar diferentes ubicaciones, edificios, vehículos, armas, objetos, enemigos, minijuegos, juegos deportivos, conducción de taxis, etc. en cada mapa. También puede cambiar entre diferentes modos, como el modo historia, el modo sandbox, el modo zombi, etc. tocando el botón de menú en la esquina superior izquierda de la pantalla. </p>
- <h3>Personaliza tu personaje y armas</h3>
- <p>Dude Theft Wars también te permite personalizar tu personaje y armas tanto en modo de juegos offline como en modo multijugador online. Puedes desbloquear diferentes objetos de personajes como sombreros, gafas, máscaras, camisas, pantalones, zapatos, etc. ganando dinero o comprándolos con dinero real. También puedes desbloquear diferentes armas como pistolas, rifles, escopetas, francotiradores, lanzacohetes, etc. encontrándolas en los mapas o comprándolas con dinero real. También puedes personalizar tus armas con diferentes skins, accesorios y mejoras. </p>
- <h3>Usa trucos y hacks para más diversión</h3>
- <p>Dude Theft Wars también tiene muchos trucos y hacks que puedes usar para más diversión y entretenimiento. Puede acceder al menú de trucos tocando el botón de menú en la esquina superior izquierda de la pantalla y luego tocando el botón de trucos. A continuación, puede introducir diferentes códigos para activar diferentes trucos como dinero ilimitado, salud ilimitada, munición ilimitada, súper velocidad, súper salto, baja gravedad, etc. También puede utilizar hacks tales como coches voladores, corte de pared, aimbot, etc. mediante la descarga de ellos desde Internet o el uso de características incorporadas de un emulador. </p>
- <h2>Conclusión</h2>
-
- <p>Si quieres disfrutar de este juego en tu PC, puedes usar un emulador de Android como BlueStacks, LDPlayer o GameLoop para ejecutarlo en tu ordenador. Puede disfrutar de mejores gráficos y rendimiento, usar controles de teclado y ratón para mayor precisión y comodidad, y acceder a una pantalla más grande y una experiencia de juego más inmersiva. También puedes personalizar tu personaje y armas, explorar los diferentes mapas y modos, y usar trucos y hacks para más diversión. </p>
- <p>Esperamos que este artículo le ayudó a aprender a descargar y jugar Dude Theft Wars en PC. Si tiene alguna pregunta o sugerencia, no dude en dejar un comentario a continuación. Diviértase jugando Dude Theft Wars en PC! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre Dude Theft Wars:</p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Avz .md b/spaces/Benson/text-generation/Examples/Avz .md
deleted file mode 100644
index c8b2e1f377aca37c0d826c580f40265ac97182af..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Avz .md	
+++ /dev/null
@@ -1,113 +0,0 @@
-
-<h1>AVZ Antiviral Toolkit: Una solución de seguridad gratuita y potente</h1>
- <p>Si usted está buscando una solución de seguridad integral que puede ayudarle a mantener su computadora en perfecto estado y protegerla contra archivos maliciosos, es posible que desee revisar AVZ Antiviral Toolkit. Esta es una herramienta gratuita desarrollada por Kaspersky que puede escanear su computadora y ejecutar un script que pone en cuarentena o elimina archivos que se consideran sospechosos. En este artículo, revisaremos las principales características, pros y contras, y alternativas de AVZ Antiviral Toolkit.</p>
-<h2>avz скачать</h2><br /><p><b><b>Download File</b> --->>> <a href="https://bltlly.com/2v6JlJ">https://bltlly.com/2v6JlJ</a></b></p><br /><br />
- <h2>¿Qué es AVZ Antiviral Toolkit y qué hace? </h2>
- <p>AVZ Antiviral Toolkit es una utilidad que puede realizar diversas tareas relacionadas con la seguridad y optimización del sistema. Puede detectar y eliminar virus, spyware, adware, dialers, rootkits, gusanos, troyanos y otros tipos de componentes de malware. También puede generar un informe detallado sobre los resultados del análisis del sistema, que puede ser útil para la solución de problemas o soporte técnico. Además, puede ejecutar un script proporcionado por el soporte técnico para poner en cuarentena archivos y eliminar cualquier objeto sospechoso. </p>
- <h2>¿Por qué lo necesitas y cuáles son sus beneficios? </h2>
- <p>Es posible que necesite AVZ Antiviral Toolkit si sospecha que su computadora está infectada con malware o si desea realizar una revisión exhaustiva de su sistema. AVZ Antiviral Toolkit puede ayudarlo a identificar y eliminar cualquier amenaza potencial que pueda dañar su computadora o comprometer su privacidad. Algunos de los beneficios de usar AVZ Antiviral Toolkit son:</p>
- <ul>
-<li>Es gratuito y no requiere instalación ni registro. </li>
-<li>Es completo y cubre una amplia gama de tipos de malware. </li>
-<li> Es fiable y utiliza las mismas bases de datos antivirus que los productos de Kaspersky. </li>
-<li>Se actualiza regularmente con nuevas definiciones y características. </li>
-<li>Es compatible con la mayoría de las versiones de Windows y otros programas antivirus. </li>
-</ul>
- <h2>Características principales de AVZ Antiviral Toolkit</h2>
-
- <h3>Análisis del sistema y generación de informes</h3>
- <p>AVZ Antiviral Toolkit puede escanear su computadora en busca de malware utilizando diferentes modos: escaneo rápido, escaneo completo, escaneo personalizado o escaneo automático. También puede especificar qué áreas o archivos escanear o excluir del escaneo. Después de completar el análisis, puede ver los resultados en una lista o en una vista de árbol. También puede generar un informe sobre los resultados del análisis del sistema en formato HTML o XML. El informe contiene información sobre la versión y la fecha de la herramienta, las bases de datos antivirus, la configuración principal, los resultados de la exploración, la información del sistema, los procesos, los servicios, los controladores, las entradas de ejecución automática, las conexiones de red, los puertos abiertos, el archivo host, las extensiones del navegador, los applets del panel de control, los rootkits, los archivos ocultos, las claves del registro, los archivos de registro y más. </p>
- <h3>Ejecución de scripts y gestión de cuarentena</h3>
- <p>AVZ Antiviral Toolkit puede ejecutar un script proporcionado por el soporte técnico para poner en cuarentena archivos y eliminar cualquier objeto sospechoso. El script puede obtenerse en el sitio web oficial de AVZ Antiviral Toolkit o en el foro de Kaspersky. El script puede contener comandos para escanear, eliminar, cambiar el nombre, mover, copiar o restaurar archivos, así como modificar claves de registro, servicios, controladores, entradas de ejecución automática, archivo host, extensiones de navegador, applets de panel de control y más. También puede crear su propio script usando el editor de script incorporado. AVZ Antiviral Toolkit también puede administrar los archivos que están en cuarentena por la herramienta o por el script. Puede ver la lista de archivos en cuarentena y restaurarlos si es necesario. </p>
-<p></p>
- <h3>Detección y eliminación de rootkit</h3>
-
- <h3>Extensión del navegador y control del panel de control
- <p>AVZ Antiviral Toolkit puede controlar las extensiones del navegador y los applets del panel de control que están instalados en su computadora. Las extensiones de navegador son complementos que mejoran la funcionalidad de su navegador web, pero algunos de ellos pueden ser maliciosos o no deseados. Los applets del panel de control son programas que le permiten configurar varios aspectos de su sistema, pero algunos de ellos pueden ser falsos o dañinos. AVZ Antiviral Toolkit puede mostrarle la lista de extensiones del navegador y applets del panel de control que están instalados en su computadora y le permiten deshabilitarlos o eliminarlos si es necesario. </p>
- <h3>Herramientas y ajustes adicionales</h3>
- <p>AVZ Antiviral Toolkit también proporciona algunas herramientas y configuraciones adicionales que pueden ayudarlo a optimizar su sistema y mejorar su seguridad. Algunas de estas herramientas y configuraciones son:</p>
- <ul>
-<li>Administrador de procesos: una herramienta que le muestra la lista de procesos que se ejecutan en su computadora y le permite terminarlos o suspenderlos si es necesario. </li>
-<li>Administrador de servicios: una herramienta que le muestra la lista de servicios que están instalados en su computadora y le permite iniciarlos, detenerlos o desactivarlos si es necesario. </li>
-<li>Administrador de controladores: una herramienta que le muestra la lista de controladores que se cargan en su computadora y le permite descargarlos o eliminarlos si es necesario. </li>
-<li>Autorun manager: una herramienta que le muestra la lista de entradas de autorun que se crean en su computadora y le permite habilitarlas o deshabilitarlas si es necesario. </li>
-<li>Administrador de conexiones de red: una herramienta que le muestra la lista de conexiones de red que se establecen en su computadora y le permite cerrarlas o bloquearlas si es necesario. </li>
-<li>Administrador de puertos abiertos: una herramienta que le muestra la lista de puertos abiertos que están escuchando en su computadora y le permite cerrarlos o bloquearlos si es necesario. </li>
-<li>Editor de archivos de hosts: una herramienta que le permite editar el archivo de hosts, que es un archivo que asigna nombres de host a direcciones IP. </li>
-
-<li>Visor de registros: una herramienta que le permite ver los archivos de registro generados por AVZ Antiviral Toolkit.</li>
-<li>Configuración: una sección que le permite personalizar varios aspectos del AVZ Antiviral Toolkit, como opciones de escaneo, opciones de script, opciones de cuarentena, opciones de actualización, opciones de interfaz, etc.</li>
-</ul>
- <h2>Pros y contras de AVZ Antiviral Toolkit</h2>
- <p>AVZ Antiviral Toolkit no es perfecto y tiene sus propias ventajas y desventajas. Aquí están algunos de los pros y contras de AVZ Antiviral Toolkit:</p>
- <h3>Pros</h3>
- <ul>
-<li>Es gratuito y no requiere instalación ni registro. </li>
-<li>Es completo y cubre una amplia gama de tipos de malware. </li>
-<li> Es fiable y utiliza las mismas bases de datos antivirus que los productos de Kaspersky. </li>
-<li>Se actualiza regularmente con nuevas definiciones y características. </li>
-<li>Es compatible con la mayoría de las versiones de Windows y otros programas antivirus. </li>
-</ul>
- <h3>Contras</h3>
- <ul>
-<li> Es complejo y puede ser difícil de usar para los usuarios novatos. </li>
-<li>Tiene soporte y documentación limitada disponible en línea. </li>
-<li>No proporciona protección en tiempo real contra infecciones de malware. </li>
-<li>Puede causar conflictos con otros programas de seguridad o componentes del sistema. </li>
-</ul>
- <h2>Alternativas a AVZ Antiviral Toolkit</h2>
- <p>Si usted está buscando otras soluciones de seguridad que pueden ayudarle a proteger su computadora de malware, es posible que desee considerar algunas de las alternativas a AVZ Antiviral Toolkit. Estos son algunos de los programas de seguridad populares y eficaces que puedes probar:</p>
- <h3>Malwarebytes Anti-Malware</h3>
-
- <h3>Avast Free Antivirus</h3>
- <p>Avast Free Antivirus es un software antivirus conocido y confiable que puede proteger su computadora de varios tipos de malware, como virus, spyware, adware, ransomware, rootkits, troyanos, gusanos y más. También puede proteger su computadora de sitios web maliciosos, phishing y estafas en línea. Tiene una interfaz moderna e intuitiva que le permite realizar escaneos inteligentes, completos o personalizados. También tiene una versión premium que ofrece funciones avanzadas, como firewall, protección de webcam, administrador de contraseñas, VPN y más. Puede descargar Avast Free Antivirus desde su sitio web oficial o desde Microsoft Store.</p>
- <h3>AdwCleaner</h3>
- <p>AdwCleaner es una herramienta de eliminación de adware simple y rápida que puede escanear su computadora en busca de programas no deseados, como adware, secuestradores de navegador, barras de herramientas, ventanas emergentes y más. También puede limpiar la configuración del navegador y restaurar la página de inicio y el motor de búsqueda predeterminados. Tiene una interfaz mínima y fácil de usar que le permite realizar un escaneo con un solo clic. No requiere instalación ni registro y se puede ejecutar desde una unidad USB. Puede descargar AdwCleaner desde su sitio web oficial o desde la tienda de Microsoft.</p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <h3>¿Cómo descargar e instalar AVZ Antiviral Toolkit? </h3>
- <p>Para descargar AVZ Antiviral Toolkit, puede visitar su sitio web oficial o el foro de Kaspersky y hacer clic en el enlace de descarga. Obtendrá un archivo zip que contiene el archivo ejecutable de AVZ Antiviral Toolkit y sus bases de datos. Para instalar AVZ Antiviral Toolkit, solo necesita extraer el archivo zip a cualquier carpeta de su computadora y ejecutar el archivo ejecutable. </p>
- <h3> ¿Cómo usar AVZ Antiviral Toolkit para escanear su computadora? </h3>
- <p>Para usar AVZ Antiviral Toolkit para escanear su computadora en busca de malware, debe seguir estos pasos:</p>
- <ol>
-<li>Ejecute el archivo ejecutable de AVZ Antiviral Toolkit.</li>
-<li>Seleccione el modo de escaneo: escaneo rápido, escaneo completo, escaneo personalizado o escaneo automático. </li>
-<li>Haga clic en el botón Iniciar escaneo. </li>
-<li>Espere a que termine el escaneo. </li>
-<li>Ver los resultados del escaneo en una lista o en una vista de árbol. </li>
-<li>Seleccione los archivos que desea poner en cuarentena o eliminar. </li>
-<li>Haga clic en el botón Aplicar acciones. </li>
-</ol>
- <h3>¿Cómo ejecutar un script proporcionado por el soporte técnico usando AVZ Antiviral Toolkit? </h3>
- <p>Para ejecutar un script proporcionado por el soporte técnico utilizando AVZ Antiviral Toolkit, debe seguir estos pasos:</p>
- <ol>
-<li>Obtener el script desde el sitio web oficial de AVZ Antiviral Toolkit o desde el foro de Kaspersky. </li>
-<li>Ejecute el archivo ejecutable de AVZ Antiviral Toolkit.</li>
-<li>Haga clic en el menú Archivo y seleccione Ejecutar script.</li>
-<li>Busque la ubicación del archivo de script y selecciónelo. </li>
-<li>Haga clic en el botón Abrir. </li>
-<li>Espere a que el script se ejecute. </li>
-<li>Ver los resultados del script en una lista o en una vista de árbol. </li>
-<li>Seleccione los archivos que desea poner en cuarentena o eliminar. </li>
-<li>Haga clic en el botón Aplicar acciones. </li>
-</ol>
- <h3>¿Cómo restaurar archivos de cuarentena usando AVZ Antiviral Toolkit? </h3>
- <p>Para restaurar archivos de cuarentena usando AVZ Antiviral Toolkit, debe seguir estos pasos:</p>
- <ol>
-
-<li>Haga clic en el menú Herramientas y seleccione Administrador de cuarentena.</li>
-<li>Ver la lista de archivos en cuarentena. </li>
-<li>Seleccione los archivos que desea restaurar. </li>
-<li>Haga clic en el botón Restaurar. </li>
-</ol>
- <h3> ¿Cómo actualizar AVZ Antiviral Toolkit y sus bases de datos? </h3>
- <p>Para actualizar AVZ Antiviral Toolkit y sus bases de datos, debe seguir estos pasos:</p>
- <ol>
-<li>Ejecute el archivo ejecutable de AVZ Antiviral Toolkit.</li>
-<li>Haga clic en el menú Actualizar y seleccione Actualizar programa o Actualizar bases de datos.</li>
-<li>Espere a que la actualización se descargue e instale. </li>
-</ol>
- <p>Espero que este artículo le haya ayudado a aprender más sobre AVZ Antiviral Toolkit y cómo usarlo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer! </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/BohdanPytaichuk/art-video-generation/README.md b/spaces/BohdanPytaichuk/art-video-generation/README.md
deleted file mode 100644
index 57533a40fd53be0bd2b986f03d4bfb7f0073dc32..0000000000000000000000000000000000000000
--- a/spaces/BohdanPytaichuk/art-video-generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Art Video Generation
-emoji: 🌍
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/checkpoint/c2_model_loading.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/checkpoint/c2_model_loading.py
deleted file mode 100644
index e27ba8463c744438d44f04f23fd4975525eba667..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/checkpoint/c2_model_loading.py
+++ /dev/null
@@ -1,313 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import logging
-import re
-import torch
-from fvcore.common.checkpoint import (
-    get_missing_parameters_message,
-    get_unexpected_parameters_message,
-)
-
-
-def convert_basic_c2_names(original_keys):
-    """
-    Apply some basic name conversion to names in C2 weights.
-    It only deals with typical backbone models.
-
-    Args:
-        original_keys (list[str]):
-    Returns:
-        list[str]: The same number of strings matching those in original_keys.
-    """
-    layer_keys = copy.deepcopy(original_keys)
-    layer_keys = [
-        {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys
-    ]  # some hard-coded mappings
-
-    layer_keys = [k.replace("_", ".") for k in layer_keys]
-    layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys]
-    layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys]
-    # Uniform both bn and gn names to "norm"
-    layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys]
-    layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys]
-    layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys]
-
-    # stem
-    layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys]
-    # to avoid mis-matching with "conv1" in other components (e.g. detection head)
-    layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys]
-
-    # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5)
-    # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys]
-    # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys]
-    # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys]
-    # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys]
-
-    # blocks
-    layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys]
-    layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys]
-    layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys]
-    layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys]
-
-    # DensePose substitutions
-    layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys]
-    layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys]
-    layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys]
-    layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys]
-    layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys]
-    return layer_keys
-
-
-def convert_c2_detectron_names(weights):
-    """
-    Map Caffe2 Detectron weight names to Detectron2 names.
-
-    Args:
-        weights (dict): name -> tensor
-
-    Returns:
-        dict: detectron2 names -> tensor
-        dict: detectron2 names -> C2 names
-    """
-    logger = logging.getLogger(__name__)
-    logger.info("Remapping C2 weights ......")
-    original_keys = sorted(weights.keys())
-    layer_keys = copy.deepcopy(original_keys)
-
-    layer_keys = convert_basic_c2_names(layer_keys)
-
-    # --------------------------------------------------------------------------
-    # RPN hidden representation conv
-    # --------------------------------------------------------------------------
-    # FPN case
-    # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then
-    # shared for all other levels, hence the appearance of "fpn2"
-    layer_keys = [
-        k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys
-    ]
-    # Non-FPN case
-    layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # RPN box transformation conv
-    # --------------------------------------------------------------------------
-    # FPN case (see note above about "fpn2")
-    layer_keys = [
-        k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas")
-        for k in layer_keys
-    ]
-    layer_keys = [
-        k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits")
-        for k in layer_keys
-    ]
-    # Non-FPN case
-    layer_keys = [
-        k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys
-    ]
-    layer_keys = [
-        k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits")
-        for k in layer_keys
-    ]
-
-    # --------------------------------------------------------------------------
-    # Fast R-CNN box head
-    # --------------------------------------------------------------------------
-    layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys]
-    layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys]
-    layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys]
-    layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys]
-    # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s
-    layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # FPN lateral and output convolutions
-    # --------------------------------------------------------------------------
-    def fpn_map(name):
-        """
-        Look for keys with the following patterns:
-        1) Starts with "fpn.inner."
-           Example: "fpn.inner.res2.2.sum.lateral.weight"
-           Meaning: These are lateral pathway convolutions
-        2) Starts with "fpn.res"
-           Example: "fpn.res2.2.sum.weight"
-           Meaning: These are FPN output convolutions
-        """
-        splits = name.split(".")
-        norm = ".norm" if "norm" in splits else ""
-        if name.startswith("fpn.inner."):
-            # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight']
-            stage = int(splits[2][len("res") :])
-            return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1])
-        elif name.startswith("fpn.res"):
-            # splits example: ['fpn', 'res2', '2', 'sum', 'weight']
-            stage = int(splits[1][len("res") :])
-            return "fpn_output{}{}.{}".format(stage, norm, splits[-1])
-        return name
-
-    layer_keys = [fpn_map(k) for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # Mask R-CNN mask head
-    # --------------------------------------------------------------------------
-    # roi_heads.StandardROIHeads case
-    layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys]
-    layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys]
-    layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys]
-    # roi_heads.Res5ROIHeads case
-    layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # Keypoint R-CNN head
-    # --------------------------------------------------------------------------
-    # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX"
-    layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys]
-    layer_keys = [
-        k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys
-    ]
-    layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # Done with replacements
-    # --------------------------------------------------------------------------
-    assert len(set(layer_keys)) == len(layer_keys)
-    assert len(original_keys) == len(layer_keys)
-
-    new_weights = {}
-    new_keys_to_original_keys = {}
-    for orig, renamed in zip(original_keys, layer_keys):
-        new_keys_to_original_keys[renamed] = orig
-        if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."):
-            # remove the meaningless prediction weight for background class
-            new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1
-            new_weights[renamed] = weights[orig][new_start_idx:]
-            logger.info(
-                "Remove prediction weight for background class in {}. The shape changes from "
-                "{} to {}.".format(
-                    renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape)
-                )
-            )
-        elif renamed.startswith("cls_score."):
-            # move weights of bg class from original index 0 to last index
-            logger.info(
-                "Move classification weights for background class in {} from index 0 to "
-                "index {}.".format(renamed, weights[orig].shape[0] - 1)
-            )
-            new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]])
-        else:
-            new_weights[renamed] = weights[orig]
-
-    return new_weights, new_keys_to_original_keys
-
-
-# Note the current matching is not symmetric.
-# it assumes model_state_dict will have longer names.
-def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True):
-    """
-    Match names between the two state-dict, and update the values of model_state_dict in-place with
-    copies of the matched tensor in ckpt_state_dict.
-    If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2
-    model and will be renamed at first.
-
-    Strategy: suppose that the models that we will create will have prefixes appended
-    to each of its keys, for example due to an extra level of nesting that the original
-    pre-trained weights from ImageNet won't contain. For example, model.state_dict()
-    might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
-    res2.conv1.weight. We thus want to match both parameters together.
-    For that, we look for each model weight, look among all loaded keys if there is one
-    that is a suffix of the current weight name, and use it if that's the case.
-    If multiple matches exist, take the one with longest size
-    of the corresponding name. For example, for the same model as before, the pretrained
-    weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
-    we want to match backbone[0].body.conv1.weight to conv1.weight, and
-    backbone[0].body.res2.conv1.weight to res2.conv1.weight.
-    """
-    model_keys = sorted(model_state_dict.keys())
-    if c2_conversion:
-        ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict)
-        # original_keys: the name in the original dict (before renaming)
-    else:
-        original_keys = {x: x for x in ckpt_state_dict.keys()}
-    ckpt_keys = sorted(ckpt_state_dict.keys())
-
-    def match(a, b):
-        # Matched ckpt_key should be a complete (starts with '.') suffix.
-        # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1,
-        # but matches whatever_conv1 or mesh_head.whatever_conv1.
-        return a == b or a.endswith("." + b)
-
-    # get a matrix of string matches, where each (i, j) entry correspond to the size of the
-    # ckpt_key string, if it matches
-    match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys]
-    match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys))
-    # use the matched one with longest size in case of multiple matches
-    max_match_size, idxs = match_matrix.max(1)
-    # remove indices that correspond to no-match
-    idxs[max_match_size == 0] = -1
-
-    # used for logging
-    max_len_model = max(len(key) for key in model_keys) if model_keys else 1
-    max_len_ckpt = max(len(key) for key in ckpt_keys) if ckpt_keys else 1
-    log_str_template = "{: <{}} loaded from {: <{}} of shape {}"
-    logger = logging.getLogger(__name__)
-    # matched_pairs (matched checkpoint key --> matched model key)
-    matched_keys = {}
-    for idx_model, idx_ckpt in enumerate(idxs.tolist()):
-        if idx_ckpt == -1:
-            continue
-        key_model = model_keys[idx_model]
-        key_ckpt = ckpt_keys[idx_ckpt]
-        value_ckpt = ckpt_state_dict[key_ckpt]
-        shape_in_model = model_state_dict[key_model].shape
-
-        if shape_in_model != value_ckpt.shape:
-            logger.warning(
-                "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format(
-                    key_ckpt, value_ckpt.shape, key_model, shape_in_model
-                )
-            )
-            logger.warning(
-                "{} will not be loaded. Please double check and see if this is desired.".format(
-                    key_ckpt
-                )
-            )
-            continue
-
-        model_state_dict[key_model] = value_ckpt.clone()
-        if key_ckpt in matched_keys:  # already added to matched_keys
-            logger.error(
-                "Ambiguity found for {} in checkpoint!"
-                "It matches at least two keys in the model ({} and {}).".format(
-                    key_ckpt, key_model, matched_keys[key_ckpt]
-                )
-            )
-            raise ValueError("Cannot match one checkpoint key to multiple keys in the model.")
-
-        matched_keys[key_ckpt] = key_model
-        logger.info(
-            log_str_template.format(
-                key_model,
-                max_len_model,
-                original_keys[key_ckpt],
-                max_len_ckpt,
-                tuple(shape_in_model),
-            )
-        )
-    matched_model_keys = matched_keys.values()
-    matched_ckpt_keys = matched_keys.keys()
-    # print warnings about unmatched keys on both side
-    unmatched_model_keys = [k for k in model_keys if k not in matched_model_keys]
-    if len(unmatched_model_keys):
-        logger.info(get_missing_parameters_message(unmatched_model_keys))
-
-    unmatched_ckpt_keys = [k for k in ckpt_keys if k not in matched_ckpt_keys]
-    if len(unmatched_ckpt_keys):
-        logger.info(
-            get_unexpected_parameters_message(original_keys[x] for x in unmatched_ckpt_keys)
-        )
diff --git a/spaces/CVPR/LIVE/thrust/thrust/copy.h b/spaces/CVPR/LIVE/thrust/thrust/copy.h
deleted file mode 100644
index 23365875d661a947a6e639c3c36522402282bdbb..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/copy.h
+++ /dev/null
@@ -1,513 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file thrust/copy.h
- *  \brief Copies elements from one range to another
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust
-{
-
-/*! \addtogroup algorithms
- */
-
-/*! \addtogroup copying
- *  \ingroup algorithms
- *  \{
- */
-
-
-/*! \p copy copies elements from the range [\p first, \p last) to the range
- *  [\p result, \p result + (\p last - \p first)). That is, it performs
- *  the assignments *\p result = *\p first, *(\p result + \c 1) = *(\p first + \c 1),
- *  and so on. Generally, for every integer \c n from \c 0 to \p last - \p first, \p copy
- *  performs the assignment *(\p result + \c n) = *(\p first + \c n). Unlike
- *  \c std::copy, \p copy offers no guarantee on order of operation.  As a result,
- *  calling \p copy with overlapping source and destination ranges has undefined
- *  behavior.
- *
- *  The return value is \p result + (\p last - \p first).
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence to copy.
- *  \param last The end of the sequence to copy.
- *  \param result The destination sequence.
- *  \return The end of the destination sequence.
- *  \see http://www.sgi.com/tech/stl/copy.html
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be convertible to \c OutputIterator's \c value_type.
- *  \tparam OutputIterator must be a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *
- *  \pre \p result may be equal to \p first, but \p result shall not be in the range <tt>[first, last)</tt> otherwise.
- *
- *  The following code snippet demonstrates how to use \p copy
- *  to copy from one range to another using the \p thrust::device parallelization policy:
- *
- *  \code
- *  #include <thrust/copy.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *
- *  thrust::device_vector<int> vec0(100);
- *  thrust::device_vector<int> vec1(100);
- *  ...
- *
- *  thrust::copy(thrust::device, vec0.begin(), vec0.end(), vec1.begin());
- *
- *  // vec1 is now a copy of vec0
- *  \endcode
- */
-template<typename DerivedPolicy, typename InputIterator, typename OutputIterator>
-__host__ __device__
-  OutputIterator copy(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                      InputIterator first,
-                      InputIterator last,
-                      OutputIterator result);
-
-
-/*! \p copy_n copies elements from the range <tt>[first, first + n)</tt> to the range
- *  <tt>[result, result + n)</tt>. That is, it performs the assignments <tt>*result = *first, *(result + 1) = *(first + 1)</tt>,
- *  and so on. Generally, for every integer \c i from \c 0 to \c n, \p copy
- *  performs the assignment *(\p result + \c i) = *(\p first + \c i). Unlike
- *  \c std::copy_n, \p copy_n offers no guarantee on order of operation. As a result,
- *  calling \p copy_n with overlapping source and destination ranges has undefined
- *  behavior.
- *
- *  The return value is \p result + \p n.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the range to copy.
- *  \param n The number of elements to copy.
- *  \param result The beginning destination range.
- *  \return The end of the destination range.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be convertible to \c OutputIterator's \c value_type.
- *  \tparam Size is an integral type.
- *  \tparam OutputIterator must be a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *
- *  \pre \p result may be equal to \p first, but \p result shall not be in the range <tt>[first, first + n)</tt> otherwise.
- *
- *  The following code snippet demonstrates how to use \p copy
- *  to copy from one range to another using the \p thrust::device parallelization policy:
- *
- *  \code
- *  #include <thrust/copy.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  size_t n = 100;
- *  thrust::device_vector<int> vec0(n);
- *  thrust::device_vector<int> vec1(n);
- *  ...
- *  thrust::copy_n(thrust::device, vec0.begin(), n, vec1.begin());
- *
- *  // vec1 is now a copy of vec0
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/copy_n.html
- *  \see thrust::copy
- */
-template<typename DerivedPolicy, typename InputIterator, typename Size, typename OutputIterator>
-__host__ __device__
-  OutputIterator copy_n(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                        InputIterator first,
-                        Size n,
-                        OutputIterator result);
-
-
-	
-/*! \p copy copies elements from the range [\p first, \p last) to the range
- *  [\p result, \p result + (\p last - \p first)). That is, it performs
- *  the assignments *\p result = *\p first, *(\p result + \c 1) = *(\p first + \c 1),
- *  and so on. Generally, for every integer \c n from \c 0 to \p last - \p first, \p copy
- *  performs the assignment *(\p result + \c n) = *(\p first + \c n). Unlike
- *  \c std::copy, \p copy offers no guarantee on order of operation.  As a result,
- *  calling \p copy with overlapping source and destination ranges has undefined
- *  behavior.
- *
- *  The return value is \p result + (\p last - \p first).
- *
- *  \param first The beginning of the sequence to copy.
- *  \param last The end of the sequence to copy.
- *  \param result The destination sequence.
- *  \return The end of the destination sequence.
- *  \see http://www.sgi.com/tech/stl/copy.html
- *
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be convertible to \c OutputIterator's \c value_type.
- *  \tparam OutputIterator must be a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *
- *  \pre \p result may be equal to \p first, but \p result shall not be in the range <tt>[first, last)</tt> otherwise.
- *
- *  The following code snippet demonstrates how to use \p copy
- *  to copy from one range to another.
- *
- *  \code
- *  #include <thrust/copy.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *
- *  thrust::device_vector<int> vec0(100);
- *  thrust::device_vector<int> vec1(100);
- *  ...
- *
- *  thrust::copy(vec0.begin(), vec0.end(),
- *               vec1.begin());
- *
- *  // vec1 is now a copy of vec0
- *  \endcode
- */
-template<typename InputIterator, typename OutputIterator>
-  OutputIterator copy(InputIterator first,
-                      InputIterator last,
-                      OutputIterator result);
-
-/*! \p copy_n copies elements from the range <tt>[first, first + n)</tt> to the range
- *  <tt>[result, result + n)</tt>. That is, it performs the assignments <tt>*result = *first, *(result + 1) = *(first + 1)</tt>,
- *  and so on. Generally, for every integer \c i from \c 0 to \c n, \p copy
- *  performs the assignment *(\p result + \c i) = *(\p first + \c i). Unlike
- *  \c std::copy_n, \p copy_n offers no guarantee on order of operation. As a result,
- *  calling \p copy_n with overlapping source and destination ranges has undefined
- *  behavior.
- *
- *  The return value is \p result + \p n.
- *
- *  \param first The beginning of the range to copy.
- *  \param n The number of elements to copy.
- *  \param result The beginning destination range.
- *  \return The end of the destination range.
- *
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be convertible to \c OutputIterator's \c value_type.
- *  \tparam Size is an integral type.
- *  \tparam OutputIterator must be a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *
- *  \pre \p result may be equal to \p first, but \p result shall not be in the range <tt>[first, first + n)</tt> otherwise.
- *
- *  The following code snippet demonstrates how to use \p copy
- *  to copy from one range to another.
- *
- *  \code
- *  #include <thrust/copy.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  size_t n = 100;
- *  thrust::device_vector<int> vec0(n);
- *  thrust::device_vector<int> vec1(n);
- *  ...
- *  thrust::copy_n(vec0.begin(), n, vec1.begin());
- *
- *  // vec1 is now a copy of vec0
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/copy_n.html
- *  \see thrust::copy
- */
-template<typename InputIterator, typename Size, typename OutputIterator>
-  OutputIterator copy_n(InputIterator first,
-                        Size n,
-                        OutputIterator result);
-
-/*! \} // end copying
- */
-
-/*! \addtogroup stream_compaction
- *  \{
- */
-
-
-/*! This version of \p copy_if copies elements from the range <tt>[first,last)</tt>
- *  to a range beginning at \p result, except that any element which causes \p pred
- *  to be \c false is not copied. \p copy_if is stable, meaning that the relative
- *  order of elements that are copied is unchanged.
- *
- *  More precisely, for every integer \c n such that <tt>0 <= n < last-first</tt>,
- *  \p copy_if performs the assignment <tt>*result = *(first+n)</tt> and \p result
- *  is advanced one position if <tt>pred(*(first+n))</tt>. Otherwise, no assignment
- *  occurs and \p result is not advanced.
- *
- *  The algorithm's execution is parallelized as determined by \p system.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence from which to copy.
- *  \param last The end of the sequence from which to copy.
- *  \param result The beginning of the sequence into which to copy.
- *  \param pred The predicate to test on every value of the range <tt>[first, last)</tt>.
- *  \return <tt>result + n</tt>, where \c n is equal to the number of times \p pred
- *          evaluated to \c true in the range <tt>[first, last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *                        and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \pre The ranges <tt>[first, last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p copy_if to perform stream compaction
- *  to copy even numbers to an output range using the \p thrust::host parallelization policy:
- *
- *  \code
- *  #include <thrust/copy.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  struct is_even
- *  {
- *    __host__ __device__
- *    bool operator()(const int x)
- *    {
- *      return (x % 2) == 0;
- *    }
- *  };
- *  ...
- *  const int N = 6;
- *  int V[N] = {-2, 0, -1, 0, 1, 2};
- *  int result[4];
- *
- *  thrust::copy_if(thrust::host, V, V + N, result, is_even());
- *
- *  // V remains {-2, 0, -1, 0, 1, 2}
- *  // result is now {-2, 0, 0, 2}
- *  \endcode
- *
- *  \see \c remove_copy_if
- */
-template<typename DerivedPolicy, typename InputIterator, typename OutputIterator, typename Predicate>
-__host__ __device__
-  OutputIterator copy_if(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                         InputIterator first,
-                         InputIterator last,
-                         OutputIterator result,
-                         Predicate pred);
-
-
-
-/*! This version of \p copy_if copies elements from the range <tt>[first,last)</tt>
- *  to a range beginning at \p result, except that any element which causes \p pred
- *  to \c false is not copied. \p copy_if is stable, meaning that the relative
- *  order of elements that are copied is unchanged.
- *
- *  More precisely, for every integer \c n such that <tt>0 <= n < last-first</tt>,
- *  \p copy_if performs the assignment <tt>*result = *(first+n)</tt> and \p result
- *  is advanced one position if <tt>pred(*(first+n))</tt>. Otherwise, no assignment
- *  occurs and \p result is not advanced.
- *
- *  \param first The beginning of the sequence from which to copy.
- *  \param last The end of the sequence from which to copy.
- *  \param result The beginning of the sequence into which to copy.
- *  \param pred The predicate to test on every value of the range <tt>[first, last)</tt>.
- *  \return <tt>result + n</tt>, where \c n is equal to the number of times \p pred
- *          evaluated to \c true in the range <tt>[first, last)</tt>.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *                        and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \pre The ranges <tt>[first, last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p copy_if to perform stream compaction
- *  to copy even numbers to an output range.
- *
- *  \code
- *  #include <thrust/copy.h>
- *  ...
- *  struct is_even
- *  {
- *    __host__ __device__
- *    bool operator()(const int x)
- *    {
- *      return (x % 2) == 0;
- *    }
- *  };
- *  ...
- *  const int N = 6;
- *  int V[N] = {-2, 0, -1, 0, 1, 2};
- *  int result[4];
- *
- *  thrust::copy_if(V, V + N, result, is_even());
- *
- *  // V remains {-2, 0, -1, 0, 1, 2}
- *  // result is now {-2, 0, 0, 2}
- *  \endcode
- *
- *  \see \c remove_copy_if
- */
-template<typename InputIterator,
-         typename OutputIterator,
-         typename Predicate>
-  OutputIterator copy_if(InputIterator first,
-                         InputIterator last,
-                         OutputIterator result,
-                         Predicate pred);
-
-
-/*! This version of \p copy_if copies elements from the range <tt>[first,last)</tt>
- *  to a range beginning at \p result, except that any element whose corresponding stencil
- *  element causes \p pred to be \c false is not copied. \p copy_if is stable, meaning
- *  that the relative order of elements that are copied is unchanged.
- *
- *  More precisely, for every integer \c n such that <tt>0 <= n < last-first</tt>,
- *  \p copy_if performs the assignment <tt>*result = *(first+n)</tt> and \p result
- *  is advanced one position if <tt>pred(*(stencil+n))</tt>. Otherwise, no assignment
- *  occurs and \p result is not advanced.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence from which to copy.
- *  \param last The end of the sequence from which to copy.
- *  \param stencil The beginning of the stencil sequence.
- *  \param result The beginning of the sequence into which to copy.
- *  \param pred The predicate to test on every value of the range <tt>[stencil, stencil + (last-first))</tt>.
- *  \return <tt>result + n</tt>, where \c n is equal to the number of times \p pred
- *          evaluated to \c true in the range <tt>[stencil, stencil + (last-first))</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *                         and \p InputIterator2's \c value_type is convertible to \p Predicate's \c argument_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator">Output Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \pre The ranges <tt>[first, last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *  \pre The ranges <tt>[stencil, stencil + (last - first))</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p copy_if to perform stream compaction
- *  to copy numbers to an output range when corresponding stencil elements are even using the \p thrust::host execution policy:
- *
- *  \code
- *  #include <thrust/copy.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  struct is_even
- *  {
- *    __host__ __device__
- *    bool operator()(const int x)
- *    {
- *      return (x % 2) == 0;
- *    }
- *  };
- *  ...
- *  int N = 6;
- *  int data[N]    = { 0, 1,  2, 3, 4, 5};
- *  int stencil[N] = {-2, 0, -1, 0, 1, 2};
- *  int result[4];
- *
- *  thrust::copy_if(thrust::host, data, data + N, stencil, result, is_even());
- *
- *  // data remains    = { 0, 1,  2, 3, 4, 5};
- *  // stencil remains = {-2, 0, -1, 0, 1, 2};
- *  // result is now     { 0, 1,  3, 5}
- *  \endcode
- *
- *  \see \c remove_copy_if
- */
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2, typename OutputIterator, typename Predicate>
-__host__ __device__
-  OutputIterator copy_if(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                         InputIterator1 first,
-                         InputIterator1 last,
-                         InputIterator2 stencil,
-                         OutputIterator result,
-                         Predicate pred);
-
-
-/*! This version of \p copy_if copies elements from the range <tt>[first,last)</tt>
- *  to a range beginning at \p result, except that any element whose corresponding stencil
- *  element causes \p pred to be \c false is not copied. \p copy_if is stable, meaning
- *  that the relative order of elements that are copied is unchanged.
- *
- *  More precisely, for every integer \c n such that <tt>0 <= n < last-first</tt>,
- *  \p copy_if performs the assignment <tt>*result = *(first+n)</tt> and \p result
- *  is advanced one position if <tt>pred(*(stencil+n))</tt>. Otherwise, no assignment
- *  occurs and \p result is not advanced.
- *
- *  \param first The beginning of the sequence from which to copy.
- *  \param last The end of the sequence from which to copy.
- *  \param stencil The beginning of the stencil sequence.
- *  \param result The beginning of the sequence into which to copy.
- *  \param pred The predicate to test on every value of the range <tt>[stencil, stencil + (last-first))</tt>.
- *  \return <tt>result + n</tt>, where \c n is equal to the number of times \p pred
- *          evaluated to \c true in the range <tt>[stencil, stencil + (last-first))</tt>.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *                         and \p InputIterator2's \c value_type is convertible to \p Predicate's \c argument_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator">Output Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  \pre The ranges <tt>[first, last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *  \pre The ranges <tt>[stencil, stencil + (last - first))</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p copy_if to perform stream compaction
- *  to copy numbers to an output range when corresponding stencil elements are even:
- *
- *  \code
- *  #include <thrust/copy.h>
- *  ...
- *  struct is_even
- *  {
- *    __host__ __device__
- *    bool operator()(const int x)
- *    {
- *      return (x % 2) == 0;
- *    }
- *  };
- *  ...
- *  int N = 6;
- *  int data[N]    = { 0, 1,  2, 3, 4, 5};
- *  int stencil[N] = {-2, 0, -1, 0, 1, 2};
- *  int result[4];
- *
- *  thrust::copy_if(data, data + N, stencil, result, is_even());
- *
- *  // data remains    = { 0, 1,  2, 3, 4, 5};
- *  // stencil remains = {-2, 0, -1, 0, 1, 2};
- *  // result is now     { 0, 1,  3, 5}
- *  \endcode
- *
- *  \see \c remove_copy_if
- */
-template<typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename Predicate>
-  OutputIterator copy_if(InputIterator1 first,
-                         InputIterator1 last,
-                         InputIterator2 stencil,
-                         OutputIterator result,
-                         Predicate pred);
-
-/*! \} // end stream_compaction
- */
-	
-} // end namespace thrust
-
-#include <thrust/detail/copy.h>
-#include <thrust/detail/copy_if.h>
-
diff --git a/spaces/CVPR/WALT/configs/_base_/models/occ_mask_rcnn_swin_fpn.py b/spaces/CVPR/WALT/configs/_base_/models/occ_mask_rcnn_swin_fpn.py
deleted file mode 100644
index 80b7a8cf5f8e358f95723cb44ddb853c8f194f7e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/configs/_base_/models/occ_mask_rcnn_swin_fpn.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# model settings
-model = dict(
-    type='MaskRCNN',
-    pretrained=None,
-    backbone=dict(
-        type='SwinTransformer',
-        embed_dim=96,
-        depths=[2, 2, 6, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        mlp_ratio=4.,
-        qkv_bias=True,
-        qk_scale=None,
-        drop_rate=0.,
-        attn_drop_rate=0.,
-        drop_path_rate=0.2,
-        ape=False,
-        patch_norm=True,
-        out_indices=(0, 1, 2, 3),
-        use_checkpoint=False),
-    neck=dict(
-        type='FPN',
-        in_channels=[96, 192, 384, 768],
-        out_channels=256,
-        num_outs=5),
-    rpn_head=dict(
-        type='RPNHead',
-        in_channels=256,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            scales=[8],
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-    roi_head=dict(
-        type='StandardRoIHead',
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        bbox_head=dict(
-            type='Shared2FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=80,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-        mask_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        mask_head=dict(
-            type='FCNOccMaskHead',
-            num_convs=4,
-            in_channels=256,
-            conv_out_channels=256,
-            num_classes=80,
-            loss_mask=dict(
-                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False),
-        rpn_proposal=dict(
-            nms_pre=2000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.5,
-                min_pos_iou=0.5,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
-            pos_weight=-1,
-            debug=False)),
-    test_cfg=dict(
-        rpn=dict(
-            nms_pre=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            score_thr=0.05,
-            nms=dict(type='nms', iou_threshold=0.5),
-            max_per_img=100,
-            mask_thr_binary=0.5)))
diff --git a/spaces/CVPR/lama-example/saicinpainting/evaluation/losses/ssim.py b/spaces/CVPR/lama-example/saicinpainting/evaluation/losses/ssim.py
deleted file mode 100644
index ee43a0095408eca98e253dea194db788446f9c0a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/saicinpainting/evaluation/losses/ssim.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-class SSIM(torch.nn.Module):
-    """SSIM. Modified from:
-    https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
-    """
-
-    def __init__(self, window_size=11, size_average=True):
-        super().__init__()
-        self.window_size = window_size
-        self.size_average = size_average
-        self.channel = 1
-        self.register_buffer('window', self._create_window(window_size, self.channel))
-
-    def forward(self, img1, img2):
-        assert len(img1.shape) == 4
-
-        channel = img1.size()[1]
-
-        if channel == self.channel and self.window.data.type() == img1.data.type():
-            window = self.window
-        else:
-            window = self._create_window(self.window_size, channel)
-
-            # window = window.to(img1.get_device())
-            window = window.type_as(img1)
-
-            self.window = window
-            self.channel = channel
-
-        return self._ssim(img1, img2, window, self.window_size, channel, self.size_average)
-
-    def _gaussian(self, window_size, sigma):
-        gauss = torch.Tensor([
-            np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)
-        ])
-        return gauss / gauss.sum()
-
-    def _create_window(self, window_size, channel):
-        _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1)
-        _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
-        return _2D_window.expand(channel, 1, window_size, window_size).contiguous()
-
-    def _ssim(self, img1, img2, window, window_size, channel, size_average=True):
-        mu1 = F.conv2d(img1, window, padding=(window_size // 2), groups=channel)
-        mu2 = F.conv2d(img2, window, padding=(window_size // 2), groups=channel)
-
-        mu1_sq = mu1.pow(2)
-        mu2_sq = mu2.pow(2)
-        mu1_mu2 = mu1 * mu2
-
-        sigma1_sq = F.conv2d(
-            img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq
-        sigma2_sq = F.conv2d(
-            img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq
-        sigma12 = F.conv2d(
-            img1 * img2, window, padding=(window_size // 2), groups=channel) - mu1_mu2
-
-        C1 = 0.01 ** 2
-        C2 = 0.03 ** 2
-
-        ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \
-                   ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-
-        if size_average:
-            return ssim_map.mean()
-
-        return ssim_map.mean(1).mean(1).mean(1)
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
-        return
diff --git a/spaces/Candeloro/DeepDanbooru_string/README.md b/spaces/Candeloro/DeepDanbooru_string/README.md
deleted file mode 100644
index 4330b6f969246dc764a34ea254d2e807159f1c55..0000000000000000000000000000000000000000
--- a/spaces/Candeloro/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/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/README.md b/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/README.md
deleted file mode 100644
index ee9df77cf6a7beb1b81487d5e89f1087855ea864..0000000000000000000000000000000000000000
--- a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Onodofthenorth-SD PixelArt SpriteSheet Generator
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/logger.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/logger.py
deleted file mode 100644
index 5b2c4ad5250b589aa0c8f8d1cc9125b91b10edb0..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/logger.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import logging
-
-logger = logging.getLogger("fastapi")
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/ffmpy.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/ffmpy.py
deleted file mode 100644
index 03291fca55a355b3041b8538217f334e9c4332eb..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/ffmpy.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import errno
-import shlex
-import subprocess
-
-__version__ = "0.3.1"
-
-
-class FFmpeg(object):
-    """Wrapper for various `FFmpeg <https://www.ffmpeg.org/>`_ related applications (ffmpeg,
-    ffprobe).
-    """
-
-    def __init__(
-        self, executable="ffmpeg", global_options=None, inputs=None, outputs=None
-    ):
-        """Initialize FFmpeg command line wrapper.
-
-        Compiles FFmpeg command line from passed arguments (executable path, options, inputs and
-        outputs). ``inputs`` and ``outputs`` are dictionares containing inputs/outputs as keys and
-        their respective options as values. One dictionary value (set of options) must be either a
-        single space separated string, or a list or strings without spaces (i.e. each part of the
-        option is a separate item of the list, the result of calling ``split()`` on the options
-        string). If the value is a list, it cannot be mixed, i.e. cannot contain items with spaces.
-        An exception are complex FFmpeg command lines that contain quotes: the quoted part must be
-        one string, even if it contains spaces (see *Examples* for more info).
-        For more info about FFmpeg command line format see `here
-        <https://ffmpeg.org/ffmpeg.html#Synopsis>`_.
-
-        :param str executable: path to ffmpeg executable; by default the ``ffmpeg`` command will be
-            searched for in the ``PATH``, but can be overridden with an absolute path to ``ffmpeg``
-            executable
-        :param iterable global_options: global options passed to ``ffmpeg`` executable (e.g.
-            ``-y``, ``-v`` etc.); can be specified either as a list/tuple/set of strings, or one
-            space-separated string; by default no global options are passed
-        :param dict inputs: a dictionary specifying one or more input arguments as keys with their
-            corresponding options (either as a list of strings or a single space separated string) as
-            values
-        :param dict outputs: a dictionary specifying one or more output arguments as keys with their
-            corresponding options (either as a list of strings or a single space separated string) as
-            values
-        """
-        self.executable = executable
-        self._cmd = [executable]
-
-        global_options = global_options or []
-        if _is_sequence(global_options):
-            normalized_global_options = []
-            for opt in global_options:
-                normalized_global_options += shlex.split(opt)
-        else:
-            normalized_global_options = shlex.split(global_options)
-
-        self._cmd += normalized_global_options
-        self._cmd += _merge_args_opts(inputs, add_input_option=True)
-        self._cmd += _merge_args_opts(outputs)
-
-        self.cmd = subprocess.list2cmdline(self._cmd)
-        self.process = None
-
-    def __repr__(self):
-        return "<{0!r} {1!r}>".format(self.__class__.__name__, self.cmd)
-
-    def run(self, input_data=None, stdout=None, stderr=None, env=None, **kwargs):
-        """Execute FFmpeg command line.
-
-        ``input_data`` can contain input for FFmpeg in case ``pipe`` protocol is used for input.
-        ``stdout`` and ``stderr`` specify where to redirect the ``stdout`` and ``stderr`` of the
-        process. By default no redirection is done, which means all output goes to running shell
-        (this mode should normally only be used for debugging purposes). If FFmpeg ``pipe`` protocol
-        is used for output, ``stdout`` must be redirected to a pipe by passing `subprocess.PIPE` as
-        ``stdout`` argument. You can pass custom environment to ffmpeg process with ``env``.
-
-        Returns a 2-tuple containing ``stdout`` and ``stderr`` of the process. If there was no
-        redirection or if the output was redirected to e.g. `os.devnull`, the value returned will
-        be a tuple of two `None` values, otherwise it will contain the actual ``stdout`` and
-        ``stderr`` data returned by ffmpeg process.
-
-        More info about ``pipe`` protocol `here <https://ffmpeg.org/ffmpeg-protocols.html#pipe>`_.
-
-        :param str input_data: input data for FFmpeg to deal with (audio, video etc.) as bytes (e.g.
-            the result of reading a file in binary mode)
-        :param stdout: redirect FFmpeg ``stdout`` there (default is `None` which means no
-            redirection)
-        :param stderr: redirect FFmpeg ``stderr`` there (default is `None` which means no
-            redirection)
-        :param env: custom environment for ffmpeg process
-        :param kwargs: any other keyword arguments to be forwarded to `subprocess.Popen
-            <https://docs.python.org/3/library/subprocess.html#subprocess.Popen>`_
-        :return: a 2-tuple containing ``stdout`` and ``stderr`` of the process
-        :rtype: tuple
-        :raise: `FFRuntimeError` in case FFmpeg command exits with a non-zero code;
-            `FFExecutableNotFoundError` in case the executable path passed was not valid
-        """
-        try:
-            self.process = subprocess.Popen(
-                self._cmd,
-                stdin=subprocess.PIPE,
-                stdout=stdout,
-                stderr=stderr,
-                env=env,
-                **kwargs
-            )
-        except OSError as e:
-            if e.errno == errno.ENOENT:
-                raise FFExecutableNotFoundError(
-                    "Executable '{0}' not found".format(self.executable)
-                )
-            else:
-                raise
-
-        out = self.process.communicate(input=input_data)
-        if self.process.returncode != 0:
-            raise FFRuntimeError(self.cmd, self.process.returncode, out[0], out[1])
-
-        return out
-
-
-class FFprobe(FFmpeg):
-    """Wrapper for `ffprobe <https://www.ffmpeg.org/ffprobe.html>`_."""
-
-    def __init__(self, executable="ffprobe", global_options="", inputs=None):
-        """Create an instance of FFprobe.
-
-        Compiles FFprobe command line from passed arguments (executable path, options, inputs).
-        FFprobe executable by default is taken from ``PATH`` but can be overridden with an
-        absolute path. For more info about FFprobe command line format see
-        `here <https://ffmpeg.org/ffprobe.html#Synopsis>`_.
-
-        :param str executable: absolute path to ffprobe executable
-        :param iterable global_options: global options passed to ffmpeg executable; can be specified
-            either as a list/tuple of strings or a space-separated string
-        :param dict inputs: a dictionary specifying one or more inputs as keys with their
-            corresponding options as values
-        """
-        super(FFprobe, self).__init__(
-            executable=executable, global_options=global_options, inputs=inputs
-        )
-
-
-class FFExecutableNotFoundError(Exception):
-    """Raise when FFmpeg/FFprobe executable was not found."""
-
-
-class FFRuntimeError(Exception):
-    """Raise when FFmpeg/FFprobe command line execution returns a non-zero exit code.
-
-    The resulting exception object will contain the attributes relates to command line execution:
-    ``cmd``, ``exit_code``, ``stdout``, ``stderr``.
-    """
-
-    def __init__(self, cmd, exit_code, stdout, stderr):
-        self.cmd = cmd
-        self.exit_code = exit_code
-        self.stdout = stdout
-        self.stderr = stderr
-
-        message = "`{0}` exited with status {1}\n\nSTDOUT:\n{2}\n\nSTDERR:\n{3}".format(
-            self.cmd, exit_code, (stdout or b"").decode(), (stderr or b"").decode()
-        )
-
-        super(FFRuntimeError, self).__init__(message)
-
-
-def _is_sequence(obj):
-    """Check if the object is a sequence (list, tuple etc.).
-
-    :param object obj: an object to be checked
-    :return: True if the object is iterable but is not a string, False otherwise
-    :rtype: bool
-    """
-    return hasattr(obj, "__iter__") and not isinstance(obj, str)
-
-
-def _merge_args_opts(args_opts_dict, **kwargs):
-    """Merge options with their corresponding arguments.
-
-    Iterates over the dictionary holding arguments (keys) and options (values). Merges each
-    options string with its corresponding argument.
-
-    :param dict args_opts_dict: a dictionary of arguments and options
-    :param dict kwargs: *input_option* - if specified prepends ``-i`` to input argument
-    :return: merged list of strings with arguments and their corresponding options
-    :rtype: list
-    """
-    merged = []
-
-    if not args_opts_dict:
-        return merged
-
-    for arg, opt in args_opts_dict.items():
-        if not _is_sequence(opt):
-            opt = shlex.split(opt or "")
-        merged += opt
-
-        if not arg:
-            continue
-
-        if "add_input_option" in kwargs:
-            merged.append("-i")
-
-        merged.append(arg)
-
-    return merged
diff --git a/spaces/Dao3/DreamlikeArt-PhotoReal-2.0/README.md b/spaces/Dao3/DreamlikeArt-PhotoReal-2.0/README.md
deleted file mode 100644
index fec0cf7ea01c07ea38cfe784718b6bf02ac20fdf..0000000000000000000000000000000000000000
--- a/spaces/Dao3/DreamlikeArt-PhotoReal-2.0/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: DreamlikeArt-PhotoReal 2.0
-emoji: 🧘🏻‍♀️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-duplicated_from: phenomenon1981/DreamlikeArt-PhotoReal-2.0
----
----
-title: DreamlikeArt-PhotoReal 2.0
-emoji: 🧘🏻‍♀️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
\ No newline at end of file
diff --git a/spaces/Datasculptor/DescriptionGPT/README.md b/spaces/Datasculptor/DescriptionGPT/README.md
deleted file mode 100644
index e1f417699366343bf863b46912d6f54219e00520..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/DescriptionGPT/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Detic
-emoji: 👀
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
-duplicated_from: taesiri/DeticChatGPT
----
-
-# 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/Datasculptor/StyleGAN-NADA/e4e/models/stylegan2/__init__.py b/spaces/Datasculptor/StyleGAN-NADA/e4e/models/stylegan2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/DiffusionArtco/Interior-design-models/README.md b/spaces/DiffusionArtco/Interior-design-models/README.md
deleted file mode 100644
index f489d55e38d86e4cd0eb889617a379840e7c75bf..0000000000000000000000000000000000000000
--- a/spaces/DiffusionArtco/Interior-design-models/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ImagineAI Imagine Generator
-emoji: 💩
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-duplicated_from: DiffusionArtco/AnimeTop50
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/submission/submit.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/submission/submit.py
deleted file mode 100644
index 60ff428717c13896bb78625b3eaf651d9fb9695d..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/submission/submit.py
+++ /dev/null
@@ -1,290 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Submit a function to be run either locally or in a computing cluster."""
-
-import copy
-import io
-import os
-import pathlib
-import pickle
-import platform
-import pprint
-import re
-import shutil
-import time
-import traceback
-
-import zipfile
-
-from enum import Enum
-
-from .. import util
-from ..util import EasyDict
-
-
-class SubmitTarget(Enum):
-    """The target where the function should be run.
-
-    LOCAL: Run it locally.
-    """
-    LOCAL = 1
-
-
-class PathType(Enum):
-    """Determines in which format should a path be formatted.
-
-    WINDOWS: Format with Windows style.
-    LINUX: Format with Linux/Posix style.
-    AUTO: Use current OS type to select either WINDOWS or LINUX.
-    """
-    WINDOWS = 1
-    LINUX = 2
-    AUTO = 3
-
-
-_user_name_override = None
-
-
-class SubmitConfig(util.EasyDict):
-    """Strongly typed config dict needed to submit runs.
-
-    Attributes:
-        run_dir_root: Path to the run dir root. Can be optionally templated with tags. Needs to always be run through get_path_from_template.
-        run_desc: Description of the run. Will be used in the run dir and task name.
-        run_dir_ignore: List of file patterns used to ignore files when copying files to the run dir.
-        run_dir_extra_files: List of (abs_path, rel_path) tuples of file paths. rel_path root will be the src directory inside the run dir.
-        submit_target: Submit target enum value. Used to select where the run is actually launched.
-        num_gpus: Number of GPUs used/requested for the run.
-        print_info: Whether to print debug information when submitting.
-        ask_confirmation: Whether to ask a confirmation before submitting.
-        run_id: Automatically populated value during submit.
-        run_name: Automatically populated value during submit.
-        run_dir: Automatically populated value during submit.
-        run_func_name: Automatically populated value during submit.
-        run_func_kwargs: Automatically populated value during submit.
-        user_name: Automatically populated value during submit. Can be set by the user which will then override the automatic value.
-        task_name: Automatically populated value during submit.
-        host_name: Automatically populated value during submit.
-    """
-
-    def __init__(self):
-        super().__init__()
-
-        # run (set these)
-        self.run_dir_root = ""  # should always be passed through get_path_from_template
-        self.run_desc = ""
-        self.run_dir_ignore = ["__pycache__", "*.pyproj", "*.sln", "*.suo", ".cache", ".idea", ".vs", ".vscode"]
-        self.run_dir_extra_files = None
-
-        # submit (set these)
-        self.submit_target = SubmitTarget.LOCAL
-        self.num_gpus = 1
-        self.print_info = False
-        self.ask_confirmation = False
-
-        # (automatically populated)
-        self.run_id = None
-        self.run_name = None
-        self.run_dir = None
-        self.run_func_name = None
-        self.run_func_kwargs = None
-        self.user_name = None
-        self.task_name = None
-        self.host_name = "localhost"
-
-
-def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:
-    """Replace tags in the given path template and return either Windows or Linux formatted path."""
-    # automatically select path type depending on running OS
-    if path_type == PathType.AUTO:
-        if platform.system() == "Windows":
-            path_type = PathType.WINDOWS
-        elif platform.system() == "Linux":
-            path_type = PathType.LINUX
-        else:
-            raise RuntimeError("Unknown platform")
-
-    path_template = path_template.replace("<USERNAME>", get_user_name())
-
-    # return correctly formatted path
-    if path_type == PathType.WINDOWS:
-        return str(pathlib.PureWindowsPath(path_template))
-    elif path_type == PathType.LINUX:
-        return str(pathlib.PurePosixPath(path_template))
-    else:
-        raise RuntimeError("Unknown platform")
-
-
-def get_template_from_path(path: str) -> str:
-    """Convert a normal path back to its template representation."""
-    # replace all path parts with the template tags
-    path = path.replace("\\", "/")
-    return path
-
-
-def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:
-    """Convert a normal path to template and the convert it back to a normal path with given path type."""
-    path_template = get_template_from_path(path)
-    path = get_path_from_template(path_template, path_type)
-    return path
-
-
-def set_user_name_override(name: str) -> None:
-    """Set the global username override value."""
-    global _user_name_override
-    _user_name_override = name
-
-
-def get_user_name():
-    """Get the current user name."""
-    if _user_name_override is not None:
-        return _user_name_override
-    elif platform.system() == "Windows":
-        return os.getlogin()
-    elif platform.system() == "Linux":
-        try:
-            import pwd # pylint: disable=import-error
-            return pwd.getpwuid(os.geteuid()).pw_name # pylint: disable=no-member
-        except:
-            return "unknown"
-    else:
-        raise RuntimeError("Unknown platform")
-
-
-def _create_run_dir_local(submit_config: SubmitConfig) -> str:
-    """Create a new run dir with increasing ID number at the start."""
-    run_dir_root = get_path_from_template(submit_config.run_dir_root, PathType.AUTO)
-
-    if not os.path.exists(run_dir_root):
-        print("Creating the run dir root: {}".format(run_dir_root))
-        os.makedirs(run_dir_root)
-
-    submit_config.run_id = _get_next_run_id_local(run_dir_root)
-    submit_config.run_name = "{0:05d}-{1}".format(submit_config.run_id, submit_config.run_desc)
-    run_dir = os.path.join(run_dir_root, submit_config.run_name)
-
-    if os.path.exists(run_dir):
-        raise RuntimeError("The run dir already exists! ({0})".format(run_dir))
-
-    print("Creating the run dir: {}".format(run_dir))
-    os.makedirs(run_dir)
-
-    return run_dir
-
-
-def _get_next_run_id_local(run_dir_root: str) -> int:
-    """Reads all directory names in a given directory (non-recursive) and returns the next (increasing) run id. Assumes IDs are numbers at the start of the directory names."""
-    dir_names = [d for d in os.listdir(run_dir_root) if os.path.isdir(os.path.join(run_dir_root, d))]
-    r = re.compile("^\\d+")  # match one or more digits at the start of the string
-    run_id = 0
-
-    for dir_name in dir_names:
-        m = r.match(dir_name)
-
-        if m is not None:
-            i = int(m.group())
-            run_id = max(run_id, i + 1)
-
-    return run_id
-
-
-def _populate_run_dir(run_dir: str, submit_config: SubmitConfig) -> None:
-    """Copy all necessary files into the run dir. Assumes that the dir exists, is local, and is writable."""
-    print("Copying files to the run dir")
-    files = []
-
-    run_func_module_dir_path = util.get_module_dir_by_obj_name(submit_config.run_func_name)
-    assert '.' in submit_config.run_func_name
-    for _idx in range(submit_config.run_func_name.count('.') - 1):
-        run_func_module_dir_path = os.path.dirname(run_func_module_dir_path)
-    files += util.list_dir_recursively_with_ignore(run_func_module_dir_path, ignores=submit_config.run_dir_ignore, add_base_to_relative=False)
-
-    dnnlib_module_dir_path = util.get_module_dir_by_obj_name("dnnlib")
-    files += util.list_dir_recursively_with_ignore(dnnlib_module_dir_path, ignores=submit_config.run_dir_ignore, add_base_to_relative=True)
-
-    if submit_config.run_dir_extra_files is not None:
-        files += submit_config.run_dir_extra_files
-
-    files = [(f[0], os.path.join(run_dir, "src", f[1])) for f in files]
-    files += [(os.path.join(dnnlib_module_dir_path, "submission", "_internal", "run.py"), os.path.join(run_dir, "run.py"))]
-
-    util.copy_files_and_create_dirs(files)
-
-    pickle.dump(submit_config, open(os.path.join(run_dir, "submit_config.pkl"), "wb"))
-
-    with open(os.path.join(run_dir, "submit_config.txt"), "w") as f:
-        pprint.pprint(submit_config, stream=f, indent=4, width=200, compact=False)
-
-
-def run_wrapper(submit_config: SubmitConfig) -> None:
-    """Wrap the actual run function call for handling logging, exceptions, typing, etc."""
-    is_local = submit_config.submit_target == SubmitTarget.LOCAL
-
-    checker = None
-
-    # when running locally, redirect stderr to stdout, log stdout to a file, and force flushing
-    if is_local:
-        logger = util.Logger(file_name=os.path.join(submit_config.run_dir, "log.txt"), file_mode="w", should_flush=True)
-    else:  # when running in a cluster, redirect stderr to stdout, and just force flushing (log writing is handled by run.sh)
-        logger = util.Logger(file_name=None, should_flush=True)
-
-    import dnnlib
-    dnnlib.submit_config = submit_config
-
-    try:
-        print("dnnlib: Running {0}() on {1}...".format(submit_config.run_func_name, submit_config.host_name))
-        start_time = time.time()
-        util.call_func_by_name(func_name=submit_config.run_func_name, submit_config=submit_config, **submit_config.run_func_kwargs)
-        print("dnnlib: Finished {0}() in {1}.".format(submit_config.run_func_name, util.format_time(time.time() - start_time)))
-    except:
-        if is_local:
-            raise
-        else:
-            traceback.print_exc()
-
-            log_src = os.path.join(submit_config.run_dir, "log.txt")
-            log_dst = os.path.join(get_path_from_template(submit_config.run_dir_root), "{0}-error.txt".format(submit_config.run_name))
-            shutil.copyfile(log_src, log_dst)
-    finally:
-        open(os.path.join(submit_config.run_dir, "_finished.txt"), "w").close()
-
-    dnnlib.submit_config = None
-    logger.close()
-
-    if checker is not None:
-        checker.stop()
-
-
-def submit_run(submit_config: SubmitConfig, run_func_name: str, **run_func_kwargs) -> None:
-    """Create a run dir, gather files related to the run, copy files to the run dir, and launch the run in appropriate place."""
-    submit_config = copy.copy(submit_config)
-
-    if submit_config.user_name is None:
-        submit_config.user_name = get_user_name()
-
-    submit_config.run_func_name = run_func_name
-    submit_config.run_func_kwargs = run_func_kwargs
-
-    assert submit_config.submit_target == SubmitTarget.LOCAL
-    if submit_config.submit_target in {SubmitTarget.LOCAL}:
-        run_dir = _create_run_dir_local(submit_config)
-
-        submit_config.task_name = "{0}-{1:05d}-{2}".format(submit_config.user_name, submit_config.run_id, submit_config.run_desc)
-        submit_config.run_dir = run_dir
-        _populate_run_dir(run_dir, submit_config)
-
-    if submit_config.print_info:
-        print("\nSubmit config:\n")
-        pprint.pprint(submit_config, indent=4, width=200, compact=False)
-        print()
-
-    if submit_config.ask_confirmation:
-        if not util.ask_yes_no("Continue submitting the job?"):
-            return
-
-    run_wrapper(submit_config)
diff --git a/spaces/DragGan/DragGan/stylegan_human/pti/pti_configs/paths_config.py b/spaces/DragGan/DragGan/stylegan_human/pti/pti_configs/paths_config.py
deleted file mode 100644
index e4b6fce7bfcf285fb09ae4ddf9432fa6aa562ea5..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/pti/pti_configs/paths_config.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import os
-
-## Pretrained models paths
-e4e = './pti/e4e_w+.pt'
-stylegan2_ada_shhq = './pretrained_models/stylegan_human_v2_1024.pkl'
-ir_se50 =  '' #'./model_ir_se50.pth' 
-
-## Dirs for output files
-checkpoints_dir = './outputs/pti/checkpoints/'
-embedding_base_dir = './outputs/pti/embeddings'
-experiments_output_dir = './outputs/pti/'
-
-## Input info
-### Input dir, where the images reside
-input_data_path = 'aligned_image/'
-### Inversion identifier, used to keeping track of the inversion results. Both the latent code and the generator
-input_data_id = 'test'
-
-## Keywords
-pti_results_keyword = 'PTI'
-e4e_results_keyword = 'e4e'
-sg2_results_keyword = 'SG2'
-sg2_plus_results_keyword = 'SG2_Plus'
-multi_id_model_type = 'multi_id'
diff --git a/spaces/EdZ123/anime-collaborative-filtering-system/app.py b/spaces/EdZ123/anime-collaborative-filtering-system/app.py
deleted file mode 100644
index dadce02889813eef2ddfd31f6a5413f690e10856..0000000000000000000000000000000000000000
--- a/spaces/EdZ123/anime-collaborative-filtering-system/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""The main application file for the Gradio app."""
-
-import gradio as gr
-import pandas as pd
-import torch
-
-animes_df = pd.read_csv("./data/animes.csv")
-anime_embeddings_df = pd.read_csv("./data/anime_embeddings.csv", header=None)
-
-title_list = animes_df["Title"].tolist()
-embeddings = torch.tensor(anime_embeddings_df.values)
-
-
-def recommend(index):
-    embedding = embeddings[index]
-
-    embedding_distances = torch.nn.CosineSimilarity(dim=1)(embeddings, embedding)
-    recommendation_indexes = embedding_distances.argsort(descending=True)[1:4]
-
-    recommendations = []
-    for rank, recommendation_index in enumerate(recommendation_indexes):
-        recommendation = animes_df.iloc[int(recommendation_index)]
-        value = recommendation["Image URL"]
-        label = f'{rank + 1}. {recommendation["Title"]}'
-        recommendations.append((value, label))
-
-    return recommendations
-
-
-css = """
-.gradio-container {align-items: center}
-#container {max-width: 795px}
-"""
-
-
-with gr.Blocks(css=css) as space:
-    with gr.Column(elem_id="container"):
-        gr.Markdown(
-            """
-        # Anime Collaborative Filtering System
-        This is a Pytorch recommendation model that uses neural collaborative filtering.
-        Enter an anime, and it will suggest similar shows! \
-        Source code: [https://github.com/EdZ543/anime-collaborative-filtering-system](https://github.com/EdZ543/anime-collaborative-filtering-system)
-        """
-        )
-
-        dropdown = gr.Dropdown(label="Enter an anime", choices=title_list, type="index")
-
-        gallery = gr.Gallery(label="Recommendations", rows=1, columns=3, height="265")
-
-        dropdown.change(fn=recommend, inputs=dropdown, outputs=gallery)
-
-space.launch()
diff --git a/spaces/Eduger/webui/README.md b/spaces/Eduger/webui/README.md
deleted file mode 100644
index 013d12c9f3a56698056ae1bdbbfb0ec009805237..0000000000000000000000000000000000000000
--- a/spaces/Eduger/webui/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: Stable Diffusion Web UI
-emoji: 🚧
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-duplicated_from: camenduru/webui
----
-
-## Stable Diffusion Web UI
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-
-## Documentation
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki)
-
-## Models License
-https://huggingface.co/spaces/CompVis/stable-diffusion-license
\ No newline at end of file
diff --git a/spaces/Epoching/DocumentQA/DiT_Extractor/dit_object_detection/ditod/backbone.py b/spaces/Epoching/DocumentQA/DiT_Extractor/dit_object_detection/ditod/backbone.py
deleted file mode 100644
index 492a36178298f48028b5048581cdec50267cc54e..0000000000000000000000000000000000000000
--- a/spaces/Epoching/DocumentQA/DiT_Extractor/dit_object_detection/ditod/backbone.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# --------------------------------------------------------------------------------
-# VIT: Multi-Path Vision Transformer for Dense Prediction
-# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
-# All Rights Reserved.
-# Written by Youngwan Lee
-# This source code is licensed(Dual License(GPL3.0 & Commercial)) under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------------------------------
-# References:
-# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# CoaT: https://github.com/mlpc-ucsd/CoaT
-# --------------------------------------------------------------------------------
-
-
-import torch
-
-from detectron2.layers import (
-    ShapeSpec,
-)
-from detectron2.modeling import Backbone, BACKBONE_REGISTRY, FPN
-from detectron2.modeling.backbone.fpn import LastLevelP6P7, LastLevelMaxPool
-
-from .beit import beit_base_patch16, dit_base_patch16, dit_large_patch16, beit_large_patch16
-from .deit import deit_base_patch16, mae_base_patch16
-
-__all__ = [
-    "build_vit_fpn_backbone",
-]
-
-
-class VIT_Backbone(Backbone):
-    """
-    Implement VIT backbone.
-    """
-
-    def __init__(self, name, out_features, drop_path, img_size, pos_type, model_kwargs):
-        super().__init__()
-        self._out_features = out_features
-        if 'base' in name:
-            self._out_feature_strides = {"layer3": 4, "layer5": 8, "layer7": 16, "layer11": 32}
-        else:
-            self._out_feature_strides = {"layer7": 4, "layer11": 8, "layer15": 16, "layer23": 32}
-
-        if name == 'beit_base_patch16':
-            model_func = beit_base_patch16
-            self._out_feature_channels = {"layer3": 768, "layer5": 768, "layer7": 768, "layer11": 768}
-        elif name == 'dit_base_patch16':
-            model_func = dit_base_patch16
-            self._out_feature_channels = {"layer3": 768, "layer5": 768, "layer7": 768, "layer11": 768}
-        elif name == "deit_base_patch16":
-            model_func = deit_base_patch16
-            self._out_feature_channels = {"layer3": 768, "layer5": 768, "layer7": 768, "layer11": 768}
-        elif name == "mae_base_patch16":
-            model_func = mae_base_patch16
-            self._out_feature_channels = {"layer3": 768, "layer5": 768, "layer7": 768, "layer11": 768}
-        elif name == "dit_large_patch16":
-            model_func = dit_large_patch16
-            self._out_feature_channels = {"layer7": 1024, "layer11": 1024, "layer15": 1024, "layer23": 1024}
-        elif name == "beit_large_patch16":
-            model_func = beit_large_patch16
-            self._out_feature_channels = {"layer7": 1024, "layer11": 1024, "layer15": 1024, "layer23": 1024}
-        else:
-            raise ValueError("Unsupported VIT name yet.")
-
-        if 'beit' in name or 'dit' in name:
-            if pos_type == "abs":
-                self.backbone = model_func(img_size=img_size,
-                                           out_features=out_features,
-                                           drop_path_rate=drop_path,
-                                           use_abs_pos_emb=True,
-                                           **model_kwargs)
-            elif pos_type == "shared_rel":
-                self.backbone = model_func(img_size=img_size,
-                                           out_features=out_features,
-                                           drop_path_rate=drop_path,
-                                           use_shared_rel_pos_bias=True,
-                                           **model_kwargs)
-            elif pos_type == "rel":
-                self.backbone = model_func(img_size=img_size,
-                                           out_features=out_features,
-                                           drop_path_rate=drop_path,
-                                           use_rel_pos_bias=True,
-                                           **model_kwargs)
-            else:
-                raise ValueError()
-        else:
-            self.backbone = model_func(img_size=img_size,
-                                       out_features=out_features,
-                                       drop_path_rate=drop_path,
-                                       **model_kwargs)
-
-    def forward(self, x):
-        """
-        Args:
-            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
-
-        Returns:
-            dict[str->Tensor]: names and the corresponding features
-        """
-        assert x.dim() == 4, f"VIT takes an input of shape (N, C, H, W). Got {x.shape} instead!"
-        return self.backbone.forward_features(x)
-
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in self._out_features
-        }
-
-
-def build_VIT_backbone(cfg):
-    """
-    Create a VIT instance from config.
-
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        A VIT backbone instance.
-    """
-    # fmt: off
-    name = cfg.MODEL.VIT.NAME
-    out_features = cfg.MODEL.VIT.OUT_FEATURES
-    drop_path = cfg.MODEL.VIT.DROP_PATH
-    img_size = cfg.MODEL.VIT.IMG_SIZE
-    pos_type = cfg.MODEL.VIT.POS_TYPE
-
-    model_kwargs = eval(str(cfg.MODEL.VIT.MODEL_KWARGS).replace("`", ""))
-
-    return VIT_Backbone(name, out_features, drop_path, img_size, pos_type, model_kwargs)
-
-
-@BACKBONE_REGISTRY.register()
-def build_vit_fpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Create a VIT w/ FPN backbone.
-
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_VIT_backbone(cfg)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.FPN.OUT_CHANNELS
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelMaxPool(),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone
diff --git a/spaces/Geraldine/simple_contextual_chatbot/README.md b/spaces/Geraldine/simple_contextual_chatbot/README.md
deleted file mode 100644
index 394d2ccae60f790ed6a80d420722a7869ce1ebfe..0000000000000000000000000000000000000000
--- a/spaces/Geraldine/simple_contextual_chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Simple Contextual Chatbot
-emoji: 📚
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-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/GipAdonimus/Real-Time-Voice-Cloning/demo_toolbox.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/demo_toolbox.py
deleted file mode 100644
index ea30a29275965c7e2b815cd703e891a5ca53e97b..0000000000000000000000000000000000000000
--- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/demo_toolbox.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pathlib import Path
-from toolbox import Toolbox
-from utils.argutils import print_args
-from utils.modelutils import check_model_paths
-import argparse
-import os
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description="Runs the toolbox",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    
-    parser.add_argument("-d", "--datasets_root", type=Path, help= \
-        "Path to the directory containing your datasets. See toolbox/__init__.py for a list of "
-        "supported datasets.", default=None)
-    parser.add_argument("-e", "--enc_models_dir", type=Path, default="encoder/saved_models", 
-                        help="Directory containing saved encoder models")
-    parser.add_argument("-s", "--syn_models_dir", type=Path, default="synthesizer/saved_models", 
-                        help="Directory containing saved synthesizer models")
-    parser.add_argument("-v", "--voc_models_dir", type=Path, default="vocoder/saved_models", 
-                        help="Directory containing saved vocoder models")
-    parser.add_argument("--cpu", action="store_true", help=\
-        "If True, processing is done on CPU, even when a GPU is available.")
-    parser.add_argument("--seed", type=int, default=None, help=\
-        "Optional random number seed value to make toolbox deterministic.")
-    parser.add_argument("--no_mp3_support", action="store_true", help=\
-        "If True, no mp3 files are allowed.")
-    args = parser.parse_args()
-    print_args(args, parser)
-
-    if args.cpu:
-        # Hide GPUs from Pytorch to force CPU processing
-        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-    del args.cpu
-
-    ## Remind the user to download pretrained models if needed
-    check_model_paths(encoder_path=args.enc_models_dir, synthesizer_path=args.syn_models_dir,
-                      vocoder_path=args.voc_models_dir)
-
-    # Launch the toolbox
-    Toolbox(**vars(args))    
diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/data/tools/hhsearch.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/data/tools/hhsearch.py
deleted file mode 100644
index fac137e0172f53e7c7ef943c5fa73dcb69f72246..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/data/tools/hhsearch.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# 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.
-
-"""Library to run HHsearch from Python."""
-
-import glob
-import os
-import subprocess
-from typing import Sequence
-
-from absl import logging
-
-from alphafold.data.tools import utils
-# Internal import (7716).
-
-
-class HHSearch:
-  """Python wrapper of the HHsearch binary."""
-
-  def __init__(self,
-               *,
-               binary_path: str,
-               databases: Sequence[str],
-               maxseq: int = 1_000_000):
-    """Initializes the Python HHsearch wrapper.
-
-    Args:
-      binary_path: The path to the HHsearch executable.
-      databases: A sequence of HHsearch database paths. This should be the
-        common prefix for the database files (i.e. up to but not including
-        _hhm.ffindex etc.)
-      maxseq: The maximum number of rows in an input alignment. Note that this
-        parameter is only supported in HHBlits version 3.1 and higher.
-
-    Raises:
-      RuntimeError: If HHsearch binary not found within the path.
-    """
-    self.binary_path = binary_path
-    self.databases = databases
-    self.maxseq = maxseq
-
-    for database_path in self.databases:
-      if not glob.glob(database_path + '_*'):
-        logging.error('Could not find HHsearch database %s', database_path)
-        raise ValueError(f'Could not find HHsearch database {database_path}')
-
-  def query(self, a3m: str) -> str:
-    """Queries the database using HHsearch using a given a3m."""
-    with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir:
-      input_path = os.path.join(query_tmp_dir, 'query.a3m')
-      hhr_path = os.path.join(query_tmp_dir, 'output.hhr')
-      with open(input_path, 'w') as f:
-        f.write(a3m)
-
-      db_cmd = []
-      for db_path in self.databases:
-        db_cmd.append('-d')
-        db_cmd.append(db_path)
-      cmd = [self.binary_path,
-             '-i', input_path,
-             '-o', hhr_path,
-             '-maxseq', str(self.maxseq)
-             ] + db_cmd
-
-      logging.info('Launching subprocess "%s"', ' '.join(cmd))
-      process = subprocess.Popen(
-          cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-      with utils.timing('HHsearch query'):
-        stdout, stderr = process.communicate()
-        retcode = process.wait()
-
-      if retcode:
-        # Stderr is truncated to prevent proto size errors in Beam.
-        raise RuntimeError(
-            'HHSearch failed:\nstdout:\n%s\n\nstderr:\n%s\n' % (
-                stdout.decode('utf-8'), stderr[:100_000].decode('utf-8')))
-
-      with open(hhr_path) as f:
-        hhr = f.read()
-    return hhr
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
deleted file mode 100644
index 0471fe86eb50b0fd644f10d77ab0ea7e150c95cf..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -1,36 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
-# use caffe img_norm
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
deleted file mode 100644
index aca44e478b67d5a226681c099e64fe67d93cf39b..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './dnl_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Themes/gmjk_qiangshou_gradio/README.md b/spaces/Gradio-Themes/gmjk_qiangshou_gradio/README.md
deleted file mode 100644
index 0936303e0e5dd525c7a304962aaf2d2b97848366..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Themes/gmjk_qiangshou_gradio/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Gmjk Qiangshou Gradio
-emoji: 🐢
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifigan/nvSTFT.py b/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
-    sampling_rate = None
-    try:
-        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
-    except Exception as ex:
-        print(f"'{full_path}' failed to load.\nException:")
-        print(ex)
-        if return_empty_on_exception:
-            return [], sampling_rate or target_sr or 32000
-        else:
-            raise Exception(ex)
-    
-    if len(data.shape) > 1:
-        data = data[:, 0]
-        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-    
-    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
-        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
-    else: # if audio data is type fp32
-        max_mag = max(np.amax(data), -np.amin(data))
-        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-    
-    data = torch.FloatTensor(data.astype(np.float32))/max_mag
-    
-    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
-        return [], sampling_rate or target_sr or 32000
-    if target_sr is not None and sampling_rate != target_sr:
-        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
-        sampling_rate = target_sr
-    
-    return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-class STFT():
-    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
-        self.target_sr = sr
-        
-        self.n_mels     = n_mels
-        self.n_fft      = n_fft
-        self.win_size   = win_size
-        self.hop_length = hop_length
-        self.fmin     = fmin
-        self.fmax     = fmax
-        self.clip_val = clip_val
-        self.mel_basis = {}
-        self.hann_window = {}
-    
-    def get_mel(self, y, center=False):
-        sampling_rate = self.target_sr
-        n_mels     = self.n_mels
-        n_fft      = self.n_fft
-        win_size   = self.win_size
-        hop_length = self.hop_length
-        fmin       = self.fmin
-        fmax       = self.fmax
-        clip_val   = self.clip_val
-        
-        if torch.min(y) < -1.:
-            print('min value is ', torch.min(y))
-        if torch.max(y) > 1.:
-            print('max value is ', torch.max(y))
-        
-        if fmax not in self.mel_basis:
-            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-            self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
-            self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-        
-        y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
-        y = y.squeeze(1)
-        
-        spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True)
-        # print(111,spec)
-        spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-        # print(222,spec)
-        spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
-        # print(333,spec)
-        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
-        # print(444,spec)
-        return spec
-    
-    def __call__(self, audiopath):
-        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
-        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
-        return spect
-
-stft = STFT()
diff --git a/spaces/GroveStreet/GTA_SOVITS/vencoder/hubert/hubert_model.py b/spaces/GroveStreet/GTA_SOVITS/vencoder/hubert/hubert_model.py
deleted file mode 100644
index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vencoder/hubert/hubert_model.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/Hallucinate/demo/ldm/models/diffusion/classifier.py b/spaces/Hallucinate/demo/ldm/models/diffusion/classifier.py
deleted file mode 100644
index 67e98b9d8ffb96a150b517497ace0a242d7163ef..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/diffusion/classifier.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from torch.nn import functional as F
-from torch.optim import AdamW
-from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
-
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {
-    'class_label': EncoderUNetModel,
-    'segmentation': UNetModel
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class NoisyLatentImageClassifier(pl.LightningModule):
-
-    def __init__(self,
-                 diffusion_path,
-                 num_classes,
-                 ckpt_path=None,
-                 pool='attention',
-                 label_key=None,
-                 diffusion_ckpt_path=None,
-                 scheduler_config=None,
-                 weight_decay=1.e-2,
-                 log_steps=10,
-                 monitor='val/loss',
-                 *args,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.num_classes = num_classes
-        # get latest config of diffusion model
-        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
-        self.diffusion_config = OmegaConf.load(diffusion_config).model
-        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
-        self.load_diffusion()
-
-        self.monitor = monitor
-        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
-        self.log_steps = log_steps
-
-        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
-            else self.diffusion_model.cond_stage_key
-
-        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
-
-        if self.label_key not in __models__:
-            raise NotImplementedError()
-
-        self.load_classifier(ckpt_path, pool)
-
-        self.scheduler_config = scheduler_config
-        self.use_scheduler = self.scheduler_config is not None
-        self.weight_decay = weight_decay
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def load_diffusion(self):
-        model = instantiate_from_config(self.diffusion_config)
-        self.diffusion_model = model.eval()
-        self.diffusion_model.train = disabled_train
-        for param in self.diffusion_model.parameters():
-            param.requires_grad = False
-
-    def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
-        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
-        model_config.out_channels = self.num_classes
-        if self.label_key == 'class_label':
-            model_config.pool = pool
-
-        self.model = __models__[self.label_key](**model_config)
-        if ckpt_path is not None:
-            print('#####################################################################')
-            print(f'load from ckpt "{ckpt_path}"')
-            print('#####################################################################')
-            self.init_from_ckpt(ckpt_path)
-
-    @torch.no_grad()
-    def get_x_noisy(self, x, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x))
-        continuous_sqrt_alpha_cumprod = None
-        if self.diffusion_model.use_continuous_noise:
-            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
-            # todo: make sure t+1 is correct here
-
-        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
-                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
-
-    def forward(self, x_noisy, t, *args, **kwargs):
-        return self.model(x_noisy, t)
-
-    @torch.no_grad()
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    @torch.no_grad()
-    def get_conditioning(self, batch, k=None):
-        if k is None:
-            k = self.label_key
-        assert k is not None, 'Needs to provide label key'
-
-        targets = batch[k].to(self.device)
-
-        if self.label_key == 'segmentation':
-            targets = rearrange(targets, 'b h w c -> b c h w')
-            for down in range(self.numd):
-                h, w = targets.shape[-2:]
-                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
-
-            # targets = rearrange(targets,'b c h w -> b h w c')
-
-        return targets
-
-    def compute_top_k(self, logits, labels, k, reduction="mean"):
-        _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == "mean":
-            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-        elif reduction == "none":
-            return (top_ks == labels[:, None]).float().sum(dim=-1)
-
-    def on_train_epoch_start(self):
-        # save some memory
-        self.diffusion_model.model.to('cpu')
-
-    @torch.no_grad()
-    def write_logs(self, loss, logits, targets):
-        log_prefix = 'train' if self.training else 'val'
-        log = {}
-        log[f"{log_prefix}/loss"] = loss.mean()
-        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
-            logits, targets, k=1, reduction="mean"
-        )
-        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
-            logits, targets, k=5, reduction="mean"
-        )
-
-        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
-        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
-        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
-        lr = self.optimizers().param_groups[0]['lr']
-        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
-
-    def shared_step(self, batch, t=None):
-        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
-        targets = self.get_conditioning(batch)
-        if targets.dim() == 4:
-            targets = targets.argmax(dim=1)
-        if t is None:
-            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
-        else:
-            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
-        x_noisy = self.get_x_noisy(x, t)
-        logits = self(x_noisy, t)
-
-        loss = F.cross_entropy(logits, targets, reduction='none')
-
-        self.write_logs(loss.detach(), logits.detach(), targets.detach())
-
-        loss = loss.mean()
-        return loss, logits, x_noisy, targets
-
-    def training_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-        return loss
-
-    def reset_noise_accs(self):
-        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
-                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
-
-    def on_validation_start(self):
-        self.reset_noise_accs()
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-
-        for t in self.noisy_acc:
-            _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
-            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
-
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
-
-        if self.use_scheduler:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [optimizer], scheduler
-
-        return optimizer
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, *args, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.diffusion_model.first_stage_key)
-        log['inputs'] = x
-
-        y = self.get_conditioning(batch)
-
-        if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-            log['labels'] = y
-
-        if ismap(y):
-            log['labels'] = self.diffusion_model.to_rgb(y)
-
-            for step in range(self.log_steps):
-                current_time = step * self.log_time_interval
-
-                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
-
-                log[f'inputs@t{current_time}'] = x_noisy
-
-                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
-                pred = rearrange(pred, 'b h w c -> b c h w')
-
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
-
-        for key in log:
-            log[key] = log[key][:N]
-
-        return log
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/classification/finetune_classification_zen1-base_afqmc.sh b/spaces/HaloMaster/chinesesummary/fengshen/examples/classification/finetune_classification_zen1-base_afqmc.sh
deleted file mode 100644
index 845e93093cc6390db2c332c22e860ff88688a657..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/examples/classification/finetune_classification_zen1-base_afqmc.sh
+++ /dev/null
@@ -1,151 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=afqmc-bart-base # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=2 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:2 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. 
-#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id)
-
-
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
-
-MODEL_NAME=fengshen-zen1
-
-TASK=afqmc
-TEXTA_NAME=sentence1
-TEXTB_NAME=sentence2
-LABEL_NAME=label
-ID_NAME=id
-
-
-BATCH_SIZE=8
-VAL_BATCH_SIZE=32
-ZERO_STAGE=1
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-ROOT_DIR=/cognitive_comp/ganruyi/experiments/classification_finetune/${MODEL_NAME}_${TASK}
-if [ ! -d ${ROOT_DIR} ];then
-  mkdir -p ${ROOT_DIR}
-  echo ${ROOT_DIR} created!!!!!!!!!!!!!!
-else
-  echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
-fi
-
-DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/
-PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/ZEN_pretrain_base_v0.1.0
-
-CHECKPOINT_PATH=${ROOT_DIR}/ckpt/
-OUTPUT_PATH=${ROOT_DIR}/predict.json
-
-
-config_json="${ROOT_DIR}/ds_config.json"
-# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
-# reduce_bucket_size: hidden_size*hidden_size
-# stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size
-# stage3_param_persistence_threshold: 10 * hidden_size
-
-cat <<EOT > $config_json
-{
-  "train_micro_batch_size_per_gpu": $BATCH_SIZE,
-  "steps_per_print": 100,
-  "gradient_clipping": 0.1,
-  "zero_optimization": {
-        "stage": ${ZERO_STAGE}
-    },
-  "optimizer": {
-    "type": "Adam",
-    "params": {
-      "lr": 1e-7,
-      "eps": 1e-12,
-      "weight_decay": 1e-2
-    }
-  },
-  "scheduler": {
-    "type": "WarmupLR",
-    "params":{
-      "warmup_min_lr": 1e-5,
-      "warmup_max_lr": 1e-4,
-      "warmup_num_steps": 400,
-      "warmup_type": "linear"
-    }
-  },
-  "zero_allow_untested_optimizer": false,
-  "fp16": {
-    "enabled": false,
-    "loss_scale": 0,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "activation_checkpointing": {
-    "partition_activations": false,
-    "contiguous_memory_optimization": false
-  },
-  "wall_clock_breakdown": false
-}
-EOT
-
-export PL_DEEPSPEED_CONFIG_PATH=$config_json
-
-
-DATA_ARGS="\
-        --data_dir $DATA_DIR \
-        --train_data train.json \
-        --valid_data dev.json \
-        --test_data test.json \
-        --train_batchsize $BATCH_SIZE \
-        --valid_batchsize $VAL_BATCH_SIZE \
-        --max_length 64 \
-        --texta_name $TEXTA_NAME \
-        --textb_name $TEXTB_NAME \
-        --label_name $LABEL_NAME \
-        --id_name $ID_NAME \
-        "
-
-MODEL_ARGS="\
-        --learning_rate 1e-5 \
-        --weight_decay 1e-2 \
-        --warmup 0.01 \
-        --num_labels 2 \
-        "
-
-MODEL_CHECKPOINT_ARGS="\
-        --monitor val_acc \
-        --save_top_k 3 \
-        --mode max \
-        --every_n_train_steps 200 \
-        --save_weights_only True \
-        --dirpath $CHECKPOINT_PATH \
-        --filename model-{epoch:02d}-{val_acc:.4f} \
-        "
-
-
-TRAINER_ARGS="\
-        --max_epochs 10 \
-        --gpus 1 \
-        --num_nodes 1 \
-        --strategy $STRATEGY \
-        --gradient_clip_val 1.0 \
-        --check_val_every_n_epoch 1 \
-        --val_check_interval 1.0 \
-        --default_root_dir $ROOT_DIR \
-        "
-
-options=" \
-        --pretrained_model_path $PRETRAINED_MODEL_PATH \
-        --output_save_path $OUTPUT_PATH \
-        $DATA_ARGS \
-        $MODEL_ARGS \
-        $MODEL_CHECKPOINT_ARGS \
-        $TRAINER_ARGS \
-        "
-
-SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
-SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/classification/finetune_classification.py
-
-# python3 $SCRIPT_PATH $options
-source activate base
-# srun singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py
deleted file mode 100644
index 989868388eefccc37c82d7602f709632035c7aa1..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env python3 -u
-# 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 sys
-
-for idx, line in enumerate(sys.stdin):
-    print(f"utt{idx:010d} {line}", end="")
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/__init__.py
deleted file mode 100644
index 05fe822487c3bcde8346648d5826f1669c6bc1ca..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/__init__.py
+++ /dev/null
@@ -1,13 +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"""
-
-from .fairseq_nat_model import *
-from .nonautoregressive_transformer import *
-from .nat_crf_transformer import *
-from .iterative_nonautoregressive_transformer import *
-from .cmlm_transformer import *
-from .levenshtein_transformer import *
-from .insertion_transformer import *
diff --git a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/LICENSE.md b/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/LICENSE.md
deleted file mode 100644
index 5fd2e54913fd05b69de2874ec8f9a10c7f4e8d3f..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/LICENSE.md
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Open-Speech-EkStep
-
-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/Hasani/Specific_Object_Recognition_in_the_Wild/SuperGluePretrainedNetwork/models/superglue.py b/spaces/Hasani/Specific_Object_Recognition_in_the_Wild/SuperGluePretrainedNetwork/models/superglue.py
deleted file mode 100644
index 5a89b0348075bcb918eab123bc988c7102137a3d..0000000000000000000000000000000000000000
--- a/spaces/Hasani/Specific_Object_Recognition_in_the_Wild/SuperGluePretrainedNetwork/models/superglue.py
+++ /dev/null
@@ -1,285 +0,0 @@
-# %BANNER_BEGIN%
-# ---------------------------------------------------------------------
-# %COPYRIGHT_BEGIN%
-#
-#  Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
-#
-#  Unpublished Copyright (c) 2020
-#  Magic Leap, Inc., All Rights Reserved.
-#
-# NOTICE:  All information contained herein is, and remains the property
-# of COMPANY. The intellectual and technical concepts contained herein
-# are proprietary to COMPANY and may be covered by U.S. and Foreign
-# Patents, patents in process, and are protected by trade secret or
-# copyright law.  Dissemination of this information or reproduction of
-# this material is strictly forbidden unless prior written permission is
-# obtained from COMPANY.  Access to the source code contained herein is
-# hereby forbidden to anyone except current COMPANY employees, managers
-# or contractors who have executed Confidentiality and Non-disclosure
-# agreements explicitly covering such access.
-#
-# The copyright notice above does not evidence any actual or intended
-# publication or disclosure  of  this source code, which includes
-# information that is confidential and/or proprietary, and is a trade
-# secret, of  COMPANY.   ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
-# PUBLIC  PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE  OF THIS
-# SOURCE CODE  WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
-# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
-# INTERNATIONAL TREATIES.  THE RECEIPT OR POSSESSION OF  THIS SOURCE
-# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
-# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
-# USE, OR SELL ANYTHING THAT IT  MAY DESCRIBE, IN WHOLE OR IN PART.
-#
-# %COPYRIGHT_END%
-# ----------------------------------------------------------------------
-# %AUTHORS_BEGIN%
-#
-#  Originating Authors: Paul-Edouard Sarlin
-#
-# %AUTHORS_END%
-# --------------------------------------------------------------------*/
-# %BANNER_END%
-
-from copy import deepcopy
-from pathlib import Path
-from typing import List, Tuple
-
-import torch
-from torch import nn
-
-
-def MLP(channels: List[int], do_bn: bool = True) -> nn.Module:
-    """ Multi-layer perceptron """
-    n = len(channels)
-    layers = []
-    for i in range(1, n):
-        layers.append(
-            nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True))
-        if i < (n-1):
-            if do_bn:
-                layers.append(nn.BatchNorm1d(channels[i]))
-            layers.append(nn.ReLU())
-    return nn.Sequential(*layers)
-
-
-def normalize_keypoints(kpts, image_shape):
-    """ Normalize keypoints locations based on image image_shape"""
-    _, _, height, width = image_shape
-    one = kpts.new_tensor(1)
-    size = torch.stack([one*width, one*height])[None]
-    center = size / 2
-    scaling = size.max(1, keepdim=True).values * 0.7
-    return (kpts - center[:, None, :]) / scaling[:, None, :]
-
-
-class KeypointEncoder(nn.Module):
-    """ Joint encoding of visual appearance and location using MLPs"""
-    def __init__(self, feature_dim: int, layers: List[int]) -> None:
-        super().__init__()
-        self.encoder = MLP([3] + layers + [feature_dim])
-        nn.init.constant_(self.encoder[-1].bias, 0.0)
-
-    def forward(self, kpts, scores):
-        inputs = [kpts.transpose(1, 2), scores.unsqueeze(1)]
-        return self.encoder(torch.cat(inputs, dim=1))
-
-
-def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> Tuple[torch.Tensor,torch.Tensor]:
-    dim = query.shape[1]
-    scores = torch.einsum('bdhn,bdhm->bhnm', query, key) / dim**.5
-    prob = torch.nn.functional.softmax(scores, dim=-1)
-    return torch.einsum('bhnm,bdhm->bdhn', prob, value), prob
-
-
-class MultiHeadedAttention(nn.Module):
-    """ Multi-head attention to increase model expressivitiy """
-    def __init__(self, num_heads: int, d_model: int):
-        super().__init__()
-        assert d_model % num_heads == 0
-        self.dim = d_model // num_heads
-        self.num_heads = num_heads
-        self.merge = nn.Conv1d(d_model, d_model, kernel_size=1)
-        self.proj = nn.ModuleList([deepcopy(self.merge) for _ in range(3)])
-
-    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
-        batch_dim = query.size(0)
-        query, key, value = [l(x).view(batch_dim, self.dim, self.num_heads, -1)
-                             for l, x in zip(self.proj, (query, key, value))]
-        x, _ = attention(query, key, value)
-        return self.merge(x.contiguous().view(batch_dim, self.dim*self.num_heads, -1))
-
-
-class AttentionalPropagation(nn.Module):
-    def __init__(self, feature_dim: int, num_heads: int):
-        super().__init__()
-        self.attn = MultiHeadedAttention(num_heads, feature_dim)
-        self.mlp = MLP([feature_dim*2, feature_dim*2, feature_dim])
-        nn.init.constant_(self.mlp[-1].bias, 0.0)
-
-    def forward(self, x: torch.Tensor, source: torch.Tensor) -> torch.Tensor:
-        message = self.attn(x, source, source)
-        return self.mlp(torch.cat([x, message], dim=1))
-
-
-class AttentionalGNN(nn.Module):
-    def __init__(self, feature_dim: int, layer_names: List[str]) -> None:
-        super().__init__()
-        self.layers = nn.ModuleList([
-            AttentionalPropagation(feature_dim, 4)
-            for _ in range(len(layer_names))])
-        self.names = layer_names
-
-    def forward(self, desc0: torch.Tensor, desc1: torch.Tensor) -> Tuple[torch.Tensor,torch.Tensor]:
-        for layer, name in zip(self.layers, self.names):
-            if name == 'cross':
-                src0, src1 = desc1, desc0
-            else:  # if name == 'self':
-                src0, src1 = desc0, desc1
-            delta0, delta1 = layer(desc0, src0), layer(desc1, src1)
-            desc0, desc1 = (desc0 + delta0), (desc1 + delta1)
-        return desc0, desc1
-
-
-def log_sinkhorn_iterations(Z: torch.Tensor, log_mu: torch.Tensor, log_nu: torch.Tensor, iters: int) -> torch.Tensor:
-    """ Perform Sinkhorn Normalization in Log-space for stability"""
-    u, v = torch.zeros_like(log_mu), torch.zeros_like(log_nu)
-    for _ in range(iters):
-        u = log_mu - torch.logsumexp(Z + v.unsqueeze(1), dim=2)
-        v = log_nu - torch.logsumexp(Z + u.unsqueeze(2), dim=1)
-    return Z + u.unsqueeze(2) + v.unsqueeze(1)
-
-
-def log_optimal_transport(scores: torch.Tensor, alpha: torch.Tensor, iters: int) -> torch.Tensor:
-    """ Perform Differentiable Optimal Transport in Log-space for stability"""
-    b, m, n = scores.shape
-    one = scores.new_tensor(1)
-    ms, ns = (m*one).to(scores), (n*one).to(scores)
-
-    bins0 = alpha.expand(b, m, 1)
-    bins1 = alpha.expand(b, 1, n)
-    alpha = alpha.expand(b, 1, 1)
-
-    couplings = torch.cat([torch.cat([scores, bins0], -1),
-                           torch.cat([bins1, alpha], -1)], 1)
-
-    norm = - (ms + ns).log()
-    log_mu = torch.cat([norm.expand(m), ns.log()[None] + norm])
-    log_nu = torch.cat([norm.expand(n), ms.log()[None] + norm])
-    log_mu, log_nu = log_mu[None].expand(b, -1), log_nu[None].expand(b, -1)
-
-    Z = log_sinkhorn_iterations(couplings, log_mu, log_nu, iters)
-    Z = Z - norm  # multiply probabilities by M+N
-    return Z
-
-
-def arange_like(x, dim: int):
-    return x.new_ones(x.shape[dim]).cumsum(0) - 1  # traceable in 1.1
-
-
-class SuperGlue(nn.Module):
-    """SuperGlue feature matching middle-end
-
-    Given two sets of keypoints and locations, we determine the
-    correspondences by:
-      1. Keypoint Encoding (normalization + visual feature and location fusion)
-      2. Graph Neural Network with multiple self and cross-attention layers
-      3. Final projection layer
-      4. Optimal Transport Layer (a differentiable Hungarian matching algorithm)
-      5. Thresholding matrix based on mutual exclusivity and a match_threshold
-
-    The correspondence ids use -1 to indicate non-matching points.
-
-    Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew
-    Rabinovich. SuperGlue: Learning Feature Matching with Graph Neural
-    Networks. In CVPR, 2020. https://arxiv.org/abs/1911.11763
-
-    """
-    default_config = {
-        'descriptor_dim': 256,
-        'weights': 'indoor',
-        'keypoint_encoder': [32, 64, 128, 256],
-        'GNN_layers': ['self', 'cross'] * 9,
-        'sinkhorn_iterations': 100,
-        'match_threshold': 0.2,
-    }
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = {**self.default_config, **config}
-
-        self.kenc = KeypointEncoder(
-            self.config['descriptor_dim'], self.config['keypoint_encoder'])
-
-        self.gnn = AttentionalGNN(
-            feature_dim=self.config['descriptor_dim'], layer_names=self.config['GNN_layers'])
-
-        self.final_proj = nn.Conv1d(
-            self.config['descriptor_dim'], self.config['descriptor_dim'],
-            kernel_size=1, bias=True)
-
-        bin_score = torch.nn.Parameter(torch.tensor(1.))
-        self.register_parameter('bin_score', bin_score)
-
-        assert self.config['weights'] in ['indoor', 'outdoor']
-        path = Path(__file__).parent
-        path = path / 'weights/superglue_{}.pth'.format(self.config['weights'])
-        self.load_state_dict(torch.load(str(path)))
-        print('Loaded SuperGlue model (\"{}\" weights)'.format(
-            self.config['weights']))
-
-    def forward(self, data):
-        """Run SuperGlue on a pair of keypoints and descriptors"""
-        desc0, desc1 = data['descriptors0'], data['descriptors1']
-        kpts0, kpts1 = data['keypoints0'], data['keypoints1']
-
-        if kpts0.shape[1] == 0 or kpts1.shape[1] == 0:  # no keypoints
-            shape0, shape1 = kpts0.shape[:-1], kpts1.shape[:-1]
-            return {
-                'matches0': kpts0.new_full(shape0, -1, dtype=torch.int),
-                'matches1': kpts1.new_full(shape1, -1, dtype=torch.int),
-                'matching_scores0': kpts0.new_zeros(shape0),
-                'matching_scores1': kpts1.new_zeros(shape1),
-            }
-
-        # Keypoint normalization.
-        kpts0 = normalize_keypoints(kpts0, data['image0'].shape)
-        kpts1 = normalize_keypoints(kpts1, data['image1'].shape)
-
-        # Keypoint MLP encoder.
-        desc0 = desc0 + self.kenc(kpts0, data['scores0'])
-        desc1 = desc1 + self.kenc(kpts1, data['scores1'])
-
-        # Multi-layer Transformer network.
-        desc0, desc1 = self.gnn(desc0, desc1)
-
-        # Final MLP projection.
-        mdesc0, mdesc1 = self.final_proj(desc0), self.final_proj(desc1)
-
-        # Compute matching descriptor distance.
-        scores = torch.einsum('bdn,bdm->bnm', mdesc0, mdesc1)
-        scores = scores / self.config['descriptor_dim']**.5
-
-        # Run the optimal transport.
-        scores = log_optimal_transport(
-            scores, self.bin_score,
-            iters=self.config['sinkhorn_iterations'])
-
-        # Get the matches with score above "match_threshold".
-        max0, max1 = scores[:, :-1, :-1].max(2), scores[:, :-1, :-1].max(1)
-        indices0, indices1 = max0.indices, max1.indices
-        mutual0 = arange_like(indices0, 1)[None] == indices1.gather(1, indices0)
-        mutual1 = arange_like(indices1, 1)[None] == indices0.gather(1, indices1)
-        zero = scores.new_tensor(0)
-        mscores0 = torch.where(mutual0, max0.values.exp(), zero)
-        mscores1 = torch.where(mutual1, mscores0.gather(1, indices1), zero)
-        valid0 = mutual0 & (mscores0 > self.config['match_threshold'])
-        valid1 = mutual1 & valid0.gather(1, indices1)
-        indices0 = torch.where(valid0, indices0, indices0.new_tensor(-1))
-        indices1 = torch.where(valid1, indices1, indices1.new_tensor(-1))
-
-        return {
-            'matches0': indices0, # use -1 for invalid match
-            'matches1': indices1, # use -1 for invalid match
-            'matching_scores0': mscores0,
-            'matching_scores1': mscores1,
-        }
diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/kaldi/kaldi_initializer.py b/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/kaldi/kaldi_initializer.py
deleted file mode 100644
index 6d2a2a4b6b809ba1106f9a57cb6f241dc083e670..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/kaldi/kaldi_initializer.py
+++ /dev/null
@@ -1,698 +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.
-
-from dataclasses import dataclass
-import hydra
-from hydra.core.config_store import ConfigStore
-import logging
-from omegaconf import MISSING, OmegaConf
-import os
-import os.path as osp
-from pathlib import Path
-import subprocess
-from typing import Optional
-
-from fairseq.data.dictionary import Dictionary
-from fairseq.dataclass import FairseqDataclass
-
-script_dir = Path(__file__).resolve().parent
-config_path = script_dir / "config"
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class KaldiInitializerConfig(FairseqDataclass):
-    data_dir: str = MISSING
-    fst_dir: Optional[str] = None
-    in_labels: str = MISSING
-    out_labels: Optional[str] = None
-    wav2letter_lexicon: Optional[str] = None
-    lm_arpa: str = MISSING
-    kaldi_root: str = MISSING
-    blank_symbol: str = "<s>"
-    silence_symbol: Optional[str] = None
-
-
-def create_units(fst_dir: Path, in_labels: str, vocab: Dictionary) -> Path:
-    in_units_file = fst_dir / f"kaldi_dict.{in_labels}.txt"
-    if not in_units_file.exists():
-
-        logger.info(f"Creating {in_units_file}")
-
-        with open(in_units_file, "w") as f:
-            print("<eps> 0", file=f)
-            i = 1
-            for symb in vocab.symbols[vocab.nspecial :]:
-                if not symb.startswith("madeupword"):
-                    print(f"{symb} {i}", file=f)
-                    i += 1
-    return in_units_file
-
-
-def create_lexicon(
-    cfg: KaldiInitializerConfig,
-    fst_dir: Path,
-    unique_label: str,
-    in_units_file: Path,
-    out_words_file: Path,
-) -> (Path, Path):
-
-    disambig_in_units_file = fst_dir / f"kaldi_dict.{cfg.in_labels}_disambig.txt"
-    lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}.txt"
-    disambig_lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}_disambig.txt"
-    if (
-        not lexicon_file.exists()
-        or not disambig_lexicon_file.exists()
-        or not disambig_in_units_file.exists()
-    ):
-        logger.info(f"Creating {lexicon_file} (in units file: {in_units_file})")
-
-        assert cfg.wav2letter_lexicon is not None or cfg.in_labels == cfg.out_labels
-
-        if cfg.wav2letter_lexicon is not None:
-            lm_words = set()
-            with open(out_words_file, "r") as lm_dict_f:
-                for line in lm_dict_f:
-                    lm_words.add(line.split()[0])
-
-            num_skipped = 0
-            total = 0
-            with open(cfg.wav2letter_lexicon, "r") as w2l_lex_f, open(
-                lexicon_file, "w"
-            ) as out_f:
-                for line in w2l_lex_f:
-                    items = line.rstrip().split("\t")
-                    assert len(items) == 2, items
-                    if items[0] in lm_words:
-                        print(items[0], items[1], file=out_f)
-                    else:
-                        num_skipped += 1
-                        logger.debug(
-                            f"Skipping word {items[0]} as it was not found in LM"
-                        )
-                    total += 1
-            if num_skipped > 0:
-                logger.warning(
-                    f"Skipped {num_skipped} out of {total} words as they were not found in LM"
-                )
-        else:
-            with open(in_units_file, "r") as in_f, open(lexicon_file, "w") as out_f:
-                for line in in_f:
-                    symb = line.split()[0]
-                    if symb != "<eps>" and symb != "<ctc_blank>" and symb != "<SIL>":
-                        print(symb, symb, file=out_f)
-
-        lex_disambig_path = (
-            Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_lex_disambig.pl"
-        )
-        res = subprocess.run(
-            [lex_disambig_path, lexicon_file, disambig_lexicon_file],
-            check=True,
-            capture_output=True,
-        )
-        ndisambig = int(res.stdout)
-        disamib_path = Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_disambig.pl"
-        res = subprocess.run(
-            [disamib_path, "--include-zero", in_units_file, str(ndisambig)],
-            check=True,
-            capture_output=True,
-        )
-        with open(disambig_in_units_file, "wb") as f:
-            f.write(res.stdout)
-
-    return disambig_lexicon_file, disambig_in_units_file
-
-
-def create_G(
-    kaldi_root: Path, fst_dir: Path, lm_arpa: Path, arpa_base: str
-) -> (Path, Path):
-
-    out_words_file = fst_dir / f"kaldi_dict.{arpa_base}.txt"
-    grammar_graph = fst_dir / f"G_{arpa_base}.fst"
-    if not grammar_graph.exists() or not out_words_file.exists():
-        logger.info(f"Creating {grammar_graph}")
-        arpa2fst = kaldi_root / "src/lmbin/arpa2fst"
-        subprocess.run(
-            [
-                arpa2fst,
-                "--disambig-symbol=#0",
-                f"--write-symbol-table={out_words_file}",
-                lm_arpa,
-                grammar_graph,
-            ],
-            check=True,
-        )
-    return grammar_graph, out_words_file
-
-
-def create_L(
-    kaldi_root: Path,
-    fst_dir: Path,
-    unique_label: str,
-    lexicon_file: Path,
-    in_units_file: Path,
-    out_words_file: Path,
-) -> Path:
-    lexicon_graph = fst_dir / f"L.{unique_label}.fst"
-
-    if not lexicon_graph.exists():
-        logger.info(f"Creating {lexicon_graph} (in units: {in_units_file})")
-        make_lex = kaldi_root / "egs/wsj/s5/utils/make_lexicon_fst.pl"
-        fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile"
-        fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops"
-        fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
-
-        def write_disambig_symbol(file):
-            with open(file, "r") as f:
-                for line in f:
-                    items = line.rstrip().split()
-                    if items[0] == "#0":
-                        out_path = str(file) + "_disamig"
-                        with open(out_path, "w") as out_f:
-                            print(items[1], file=out_f)
-                            return out_path
-
-            return None
-
-        in_disambig_sym = write_disambig_symbol(in_units_file)
-        assert in_disambig_sym is not None
-        out_disambig_sym = write_disambig_symbol(out_words_file)
-        assert out_disambig_sym is not None
-
-        try:
-            with open(lexicon_graph, "wb") as out_f:
-                res = subprocess.run(
-                    [make_lex, lexicon_file], capture_output=True, check=True
-                )
-                assert len(res.stderr) == 0, res.stderr.decode("utf-8")
-                res = subprocess.run(
-                    [
-                        fstcompile,
-                        f"--isymbols={in_units_file}",
-                        f"--osymbols={out_words_file}",
-                        "--keep_isymbols=false",
-                        "--keep_osymbols=false",
-                    ],
-                    input=res.stdout,
-                    capture_output=True,
-                )
-                assert len(res.stderr) == 0, res.stderr.decode("utf-8")
-                res = subprocess.run(
-                    [fstaddselfloops, in_disambig_sym, out_disambig_sym],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstarcsort, "--sort_type=olabel"],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                out_f.write(res.stdout)
-        except subprocess.CalledProcessError as e:
-            logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
-            os.remove(lexicon_graph)
-            raise
-        except AssertionError:
-            os.remove(lexicon_graph)
-            raise
-
-    return lexicon_graph
-
-
-def create_LG(
-    kaldi_root: Path,
-    fst_dir: Path,
-    unique_label: str,
-    lexicon_graph: Path,
-    grammar_graph: Path,
-) -> Path:
-    lg_graph = fst_dir / f"LG.{unique_label}.fst"
-
-    if not lg_graph.exists():
-        logger.info(f"Creating {lg_graph}")
-
-        fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
-        fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
-        fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
-        fstpushspecial = kaldi_root / "src/fstbin/fstpushspecial"
-        fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
-
-        try:
-            with open(lg_graph, "wb") as out_f:
-                res = subprocess.run(
-                    [fsttablecompose, lexicon_graph, grammar_graph],
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [
-                        fstdeterminizestar,
-                        "--use-log=true",
-                    ],
-                    input=res.stdout,
-                    capture_output=True,
-                )
-                res = subprocess.run(
-                    [fstminimizeencoded],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstpushspecial],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstarcsort, "--sort_type=ilabel"],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                out_f.write(res.stdout)
-        except subprocess.CalledProcessError as e:
-            logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
-            os.remove(lg_graph)
-            raise
-
-    return lg_graph
-
-
-def create_H(
-    kaldi_root: Path,
-    fst_dir: Path,
-    disambig_out_units_file: Path,
-    in_labels: str,
-    vocab: Dictionary,
-    blk_sym: str,
-    silence_symbol: Optional[str],
-) -> (Path, Path, Path):
-    h_graph = (
-        fst_dir / f"H.{in_labels}{'_' + silence_symbol if silence_symbol else ''}.fst"
-    )
-    h_out_units_file = fst_dir / f"kaldi_dict.h_out.{in_labels}.txt"
-    disambig_in_units_file_int = Path(str(h_graph) + "isym_disambig.int")
-    disambig_out_units_file_int = Path(str(disambig_out_units_file) + ".int")
-    if (
-        not h_graph.exists()
-        or not h_out_units_file.exists()
-        or not disambig_in_units_file_int.exists()
-    ):
-        logger.info(f"Creating {h_graph}")
-        eps_sym = "<eps>"
-
-        num_disambig = 0
-        osymbols = []
-
-        with open(disambig_out_units_file, "r") as f, open(
-            disambig_out_units_file_int, "w"
-        ) as out_f:
-            for line in f:
-                symb, id = line.rstrip().split()
-                if line.startswith("#"):
-                    num_disambig += 1
-                    print(id, file=out_f)
-                else:
-                    if len(osymbols) == 0:
-                        assert symb == eps_sym, symb
-                    osymbols.append((symb, id))
-
-        i_idx = 0
-        isymbols = [(eps_sym, 0)]
-
-        imap = {}
-
-        for i, s in enumerate(vocab.symbols):
-            i_idx += 1
-            isymbols.append((s, i_idx))
-            imap[s] = i_idx
-
-        fst_str = []
-
-        node_idx = 0
-        root_node = node_idx
-
-        special_symbols = [blk_sym]
-        if silence_symbol is not None:
-            special_symbols.append(silence_symbol)
-
-        for ss in special_symbols:
-            fst_str.append("{} {} {} {}".format(root_node, root_node, ss, eps_sym))
-
-        for symbol, _ in osymbols:
-            if symbol == eps_sym or symbol.startswith("#"):
-                continue
-
-            node_idx += 1
-            # 1. from root to emitting state
-            fst_str.append("{} {} {} {}".format(root_node, node_idx, symbol, symbol))
-            # 2. from emitting state back to root
-            fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym))
-            # 3. from emitting state to optional blank state
-            pre_node = node_idx
-            node_idx += 1
-            for ss in special_symbols:
-                fst_str.append("{} {} {} {}".format(pre_node, node_idx, ss, eps_sym))
-            # 4. from blank state back to root
-            fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym))
-
-        fst_str.append("{}".format(root_node))
-
-        fst_str = "\n".join(fst_str)
-        h_str = str(h_graph)
-        isym_file = h_str + ".isym"
-
-        with open(isym_file, "w") as f:
-            for sym, id in isymbols:
-                f.write("{} {}\n".format(sym, id))
-
-        with open(h_out_units_file, "w") as f:
-            for sym, id in osymbols:
-                f.write("{} {}\n".format(sym, id))
-
-        with open(disambig_in_units_file_int, "w") as f:
-            disam_sym_id = len(isymbols)
-            for _ in range(num_disambig):
-                f.write("{}\n".format(disam_sym_id))
-                disam_sym_id += 1
-
-        fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile"
-        fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops"
-        fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
-
-        try:
-            with open(h_graph, "wb") as out_f:
-                res = subprocess.run(
-                    [
-                        fstcompile,
-                        f"--isymbols={isym_file}",
-                        f"--osymbols={h_out_units_file}",
-                        "--keep_isymbols=false",
-                        "--keep_osymbols=false",
-                    ],
-                    input=str.encode(fst_str),
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [
-                        fstaddselfloops,
-                        disambig_in_units_file_int,
-                        disambig_out_units_file_int,
-                    ],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstarcsort, "--sort_type=olabel"],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                out_f.write(res.stdout)
-        except subprocess.CalledProcessError as e:
-            logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
-            os.remove(h_graph)
-            raise
-    return h_graph, h_out_units_file, disambig_in_units_file_int
-
-
-def create_HLGa(
-    kaldi_root: Path,
-    fst_dir: Path,
-    unique_label: str,
-    h_graph: Path,
-    lg_graph: Path,
-    disambig_in_words_file_int: Path,
-) -> Path:
-    hlga_graph = fst_dir / f"HLGa.{unique_label}.fst"
-
-    if not hlga_graph.exists():
-        logger.info(f"Creating {hlga_graph}")
-
-        fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
-        fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
-        fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols"
-        fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal"
-        fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
-
-        try:
-            with open(hlga_graph, "wb") as out_f:
-                res = subprocess.run(
-                    [
-                        fsttablecompose,
-                        h_graph,
-                        lg_graph,
-                    ],
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstdeterminizestar, "--use-log=true"],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstrmsymbols, disambig_in_words_file_int],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstrmepslocal],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstminimizeencoded],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                out_f.write(res.stdout)
-        except subprocess.CalledProcessError as e:
-            logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
-            os.remove(hlga_graph)
-            raise
-
-    return hlga_graph
-
-
-def create_HLa(
-    kaldi_root: Path,
-    fst_dir: Path,
-    unique_label: str,
-    h_graph: Path,
-    l_graph: Path,
-    disambig_in_words_file_int: Path,
-) -> Path:
-    hla_graph = fst_dir / f"HLa.{unique_label}.fst"
-
-    if not hla_graph.exists():
-        logger.info(f"Creating {hla_graph}")
-
-        fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
-        fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
-        fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols"
-        fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal"
-        fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
-
-        try:
-            with open(hla_graph, "wb") as out_f:
-                res = subprocess.run(
-                    [
-                        fsttablecompose,
-                        h_graph,
-                        l_graph,
-                    ],
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstdeterminizestar, "--use-log=true"],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstrmsymbols, disambig_in_words_file_int],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstrmepslocal],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                res = subprocess.run(
-                    [fstminimizeencoded],
-                    input=res.stdout,
-                    capture_output=True,
-                    check=True,
-                )
-                out_f.write(res.stdout)
-        except subprocess.CalledProcessError as e:
-            logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
-            os.remove(hla_graph)
-            raise
-
-    return hla_graph
-
-
-def create_HLG(
-    kaldi_root: Path,
-    fst_dir: Path,
-    unique_label: str,
-    hlga_graph: Path,
-    prefix: str = "HLG",
-) -> Path:
-    hlg_graph = fst_dir / f"{prefix}.{unique_label}.fst"
-
-    if not hlg_graph.exists():
-        logger.info(f"Creating {hlg_graph}")
-
-        add_self_loop = script_dir / "add-self-loop-simple"
-        kaldi_src = kaldi_root / "src"
-        kaldi_lib = kaldi_src / "lib"
-
-        try:
-            if not add_self_loop.exists():
-                fst_include = kaldi_root / "tools/openfst-1.6.7/include"
-                add_self_loop_src = script_dir / "add-self-loop-simple.cc"
-
-                subprocess.run(
-                    [
-                        "c++",
-                        f"-I{kaldi_src}",
-                        f"-I{fst_include}",
-                        f"-L{kaldi_lib}",
-                        add_self_loop_src,
-                        "-lkaldi-base",
-                        "-lkaldi-fstext",
-                        "-o",
-                        add_self_loop,
-                    ],
-                    check=True,
-                )
-
-            my_env = os.environ.copy()
-            my_env["LD_LIBRARY_PATH"] = f"{kaldi_lib}:{my_env['LD_LIBRARY_PATH']}"
-
-            subprocess.run(
-                [
-                    add_self_loop,
-                    hlga_graph,
-                    hlg_graph,
-                ],
-                check=True,
-                capture_output=True,
-                env=my_env,
-            )
-        except subprocess.CalledProcessError as e:
-            logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
-            raise
-
-    return hlg_graph
-
-
-def initalize_kaldi(cfg: KaldiInitializerConfig) -> Path:
-    if cfg.fst_dir is None:
-        cfg.fst_dir = osp.join(cfg.data_dir, "kaldi")
-    if cfg.out_labels is None:
-        cfg.out_labels = cfg.in_labels
-
-    kaldi_root = Path(cfg.kaldi_root)
-    data_dir = Path(cfg.data_dir)
-    fst_dir = Path(cfg.fst_dir)
-    fst_dir.mkdir(parents=True, exist_ok=True)
-
-    arpa_base = osp.splitext(osp.basename(cfg.lm_arpa))[0]
-    unique_label = f"{cfg.in_labels}.{arpa_base}"
-
-    with open(data_dir / f"dict.{cfg.in_labels}.txt", "r") as f:
-        vocab = Dictionary.load(f)
-
-    in_units_file = create_units(fst_dir, cfg.in_labels, vocab)
-
-    grammar_graph, out_words_file = create_G(
-        kaldi_root, fst_dir, Path(cfg.lm_arpa), arpa_base
-    )
-
-    disambig_lexicon_file, disambig_L_in_units_file = create_lexicon(
-        cfg, fst_dir, unique_label, in_units_file, out_words_file
-    )
-
-    h_graph, h_out_units_file, disambig_in_units_file_int = create_H(
-        kaldi_root,
-        fst_dir,
-        disambig_L_in_units_file,
-        cfg.in_labels,
-        vocab,
-        cfg.blank_symbol,
-        cfg.silence_symbol,
-    )
-    lexicon_graph = create_L(
-        kaldi_root,
-        fst_dir,
-        unique_label,
-        disambig_lexicon_file,
-        disambig_L_in_units_file,
-        out_words_file,
-    )
-    lg_graph = create_LG(
-        kaldi_root, fst_dir, unique_label, lexicon_graph, grammar_graph
-    )
-    hlga_graph = create_HLGa(
-        kaldi_root, fst_dir, unique_label, h_graph, lg_graph, disambig_in_units_file_int
-    )
-    hlg_graph = create_HLG(kaldi_root, fst_dir, unique_label, hlga_graph)
-
-    # for debugging
-    # hla_graph = create_HLa(kaldi_root, fst_dir, unique_label, h_graph, lexicon_graph, disambig_in_units_file_int)
-    # hl_graph = create_HLG(kaldi_root, fst_dir, unique_label, hla_graph, prefix="HL_looped")
-    # create_HLG(kaldi_root, fst_dir, "phnc", h_graph, prefix="H_looped")
-
-    return hlg_graph
-
-
-@hydra.main(config_path=config_path, config_name="kaldi_initializer")
-def cli_main(cfg: KaldiInitializerConfig) -> None:
-    container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
-    cfg = OmegaConf.create(container)
-    OmegaConf.set_struct(cfg, True)
-    initalize_kaldi(cfg)
-
-
-if __name__ == "__main__":
-
-    logging.root.setLevel(logging.INFO)
-    logging.basicConfig(level=logging.INFO)
-
-    try:
-        from hydra._internal.utils import (
-            get_args,
-        )  # pylint: disable=import-outside-toplevel
-
-        cfg_name = get_args().config_name or "kaldi_initializer"
-    except ImportError:
-        logger.warning("Failed to get config name from hydra args")
-        cfg_name = "kaldi_initializer"
-
-    cs = ConfigStore.instance()
-    cs.store(name=cfg_name, node=KaldiInitializerConfig)
-
-    cli_main()
diff --git a/spaces/Ibtehaj10/cheating-detection/README.md b/spaces/Ibtehaj10/cheating-detection/README.md
deleted file mode 100644
index fdb3907b8c2354d6378356d6d386b60ccc94d32a..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# AIComputerVision
-This project contains various computer vision and AI related python scripts
-
-Link to full playlist: https://www.youtube.com/watch?v=UM9oDhhAg88&list=PLWw98q-Xe7iH8UHARl8RGk8MRj1raY4Eh
-
-Below is brief description for each script:
-
-1. Cat Dog detection:
-This script can detect cats and dogs in a frame. You can replace cat or dog with any other object you want to detect.
-
-2. Centroidtracker:
-This script helps in tracking any object in a frame. We have used this in person_tracking.py script in order to track persons in the frame.
-
-3. Dwell Time Calculation:
-This script calculates the time a person has spent in a frame. It is a good example of calculating total time a person was present in frame.
-
-4. Face Detection:
-This script detects face in person image or in a frame
-
-5. FPS Example:
-While inferencing on a video file or frame from live usb webcam, its always a good idea to keep a check on how much fps we are getting. This script shows approx fps on frame.
-
-6. OpenCV Example:
-This script shows basic usage of opencv
-
-7. Person Detection in Image File:
-This script detects person in image file
-
-8. Person Detection in Video File:
-This script detects person in video file. Test video file is present in video dir.
-
-9. Person Tracking:
-This script detects person and keeps tracking them in the frame. It assigns a unique ID to each detected person.
-
-10. Monitor Social Distance
-This script monitors social distance between the persons. If it is less than a threshold value, we display bounding box in red otherwise green.
-
-11. Drawing tracking line:
-This script draws a line denoting where the person has entered in the frame and where he has moved in the frame. 
-
-12. Face Mask Detection: 
-This script checks if a person is wearing face mask or not
-
-13. Person Counter:
-This script counts the number of person present in the frame.
diff --git a/spaces/Illumotion/Koboldcpp/examples/gpt4all.sh b/spaces/Illumotion/Koboldcpp/examples/gpt4all.sh
deleted file mode 100644
index 5fd739e55c554a3e7f855c147789d873fd7aff24..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/gpt4all.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main --color --instruct --threads 4 \
-       --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \
-       --file ./prompts/alpaca.txt \
-       --batch_size 8 --ctx_size 2048 -n -1 \
-       --repeat_last_n 64 --repeat_penalty 1.3 \
-       --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95
diff --git a/spaces/ImPavloh/voiceit/voiceit.py b/spaces/ImPavloh/voiceit/voiceit.py
deleted file mode 100644
index 8268a8b68c5a1c63b5a2f90b9fe5fd4c8311001b..0000000000000000000000000000000000000000
--- a/spaces/ImPavloh/voiceit/voiceit.py
+++ /dev/null
@@ -1,184 +0,0 @@
-from threading import Thread
-from pathlib import Path
-import gradio as gr
-import subprocess
-import shutil
-import time
-import copy
-import glob
-import json
-import os
-
-CURRENT_DIR = Path(__file__).resolve().parent
-MODELOS = CURRENT_DIR / "modelos"
-INFERENCE_OUTPUT_DIRNAME = CURRENT_DIR / "inference_output"
-
-def get_container_format(filename):
-    command = ["ffprobe", "-v", "error", "-select_streams", "v:0", "-show_entries", "format=format_name", "-of", "default=noprint_wrappers=1:nokey=1", filename]
-    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-    output, error = process.communicate()
-    if error:
-        raise ValueError(f"Error: {error.decode()}")
-    return output.decode().strip()
-
-def cleanup_dirs():
-    dir_path = Path(INFERENCE_OUTPUT_DIRNAME)
-    if dir_path.exists():
-        shutil.rmtree(dir_path)
-
-def get_speakers():
-    global speakers
-    speakers = []
-    for _, dirs, _ in os.walk(MODELOS):
-        for folder in dirs:
-            cur_speaker = {}
-            g = glob.glob(os.path.join(MODELOS, folder, 'G_*.pth'))
-            if not len(g):
-                continue
-            cur_speaker["model_path"] = g[0]
-            cur_speaker["model_folder"] = folder
-            cur_speaker["cluster_path"] = ""
-
-            cfg = glob.glob(os.path.join(MODELOS, folder, '*.json'))
-            if not len(cfg):
-                continue
-            cur_speaker["cfg_path"] = cfg[0]
-            with open(cur_speaker["cfg_path"]) as f:
-                try:
-                    cfg_json = json.loads(f.read())
-                except Exception as e:
-                    print("Archivo json malformado en" + folder)
-                for name, i in cfg_json["spk"].items():
-                    cur_speaker["name"] = name
-                    cur_speaker["id"] = i
-                    if not name.startswith('.'):
-                        speakers.append(copy.copy(cur_speaker))
-    return sorted(speakers, key=lambda x: x["name"].lower())
-
-def run_inference(speaker, path, f0_method, transpose, noise_scale, cluster_ratio):
-    model_path = speaker["model_path"]
-    config_path = speaker["cfg_path"]
-    cluster_path = speaker["cluster_path"]
-    cluster_args = f"-k {cluster_path} -r {cluster_ratio}" if cluster_path and cluster_ratio > 0 else ""
-    inference_cmd = f"svc infer {path.absolute()} -m {model_path} -c {config_path} {cluster_args} -t {transpose} --f0-method crepe -n 0.4 -o {INFERENCE_OUTPUT_DIRNAME}/{path.name} --no-auto-predict-f0"
-    result = subprocess.run(inference_cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-    if result.stderr:
-        if "AttributeError" in result.stderr:
-            return  None, gr.Textbox.update("⚠️ Modelo SVC incompatible.")
-    if not list(Path(INFERENCE_OUTPUT_DIRNAME).glob("*")):
-        return  None, gr.Textbox.update("⚠️ Error.")
-
-def convert(speaker_box, audio):
-    speaker = next((x for x in speakers if x["name"] == speaker_box), None)
-    if not speaker:
-        return None, gr.Textbox.update("⚠️ Selecciona un modelo.")
-    if not audio:
-        return None, gr.Textbox.update("⚠️ Sube un audio.")
-    file_path = os.path.join(os.getcwd(), str(audio))
-    transpose = 0
-    cluster_ratio = 0
-    if os.path.exists(INFERENCE_OUTPUT_DIRNAME):
-        cleanup_dirs()
-    os.makedirs("inference_output", exist_ok=True)
-    ts0 = time.time()
-    run_inference(speaker, Path(file_path), 0, 0, 0.4, 0)
-    final_filename = f"output{Path(file_path).suffix}"
-    shutil.move(Path(INFERENCE_OUTPUT_DIRNAME, Path(file_path).name), Path(final_filename))
-    cleanup_dirs()
-    os.remove(file_path)
-    ts1 = time.time()
-    tiempo1 = int(ts1 - ts0)
-    return final_filename, gr.Textbox.update("👌 ¡Voz cambiada!", label=f"Tiempo total: {tiempo1} segundos")
-
-def clear():
-    shutil.rmtree(INFERENCE_OUTPUT_DIRNAME, ignore_errors=True)
-    tmp_files = glob.glob("*.tmp")
-    for f in tmp_files:
-        os.remove(f)
-    return gr.Dropdown.update(value="Elige un modelo de voz"), None, gr.Textbox.update("🗑️ Datos borrados.", label=f"Información")
-
-
-css = """
-.gradio-container {
-    font-family: 'IBM Plex Sans', sans-serif;
-}
-footer {
-    visibility: hidden;
-    display: none;
-}
-.center-container {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    justify-content: center;
-}
-"""
-
-with gr.Blocks(
-    css=css,
-    title="VoiceIt! - Pavloh",
-    theme=gr.themes.Soft(
-        primary_hue="cyan",
-        secondary_hue="blue",
-        radius_size="lg",
-        text_size="lg"
-    ).set(loader_color="#0B0F19", shadow_drop='*shadow_drop_lg', block_border_width="3px")
-) as pavloh:
-  gr.HTML(
-          """
-      <div class="center-container">
-          <img src="https://i.imgur.com/DendqCA.png" style="width: 300px; height: auto;"/><br>
-          <div style="display: flex; justify-content: center;">
-              <a href="https://github.com/ImPavloh/voiceit/blob/main/LICENSE" target="_blank">
-                  <img src="https://img.shields.io/github/license/impavloh/voiceit?style=for-the-badge&logo=github&logoColor=white" alt="Licencia">
-              </a>
-              <a href="https://github.com/impavloh/voiceit" target="_blank">
-                  <img src="https://img.shields.io/badge/repositorio-%23121011.svg?style=for-the-badge&logo=github&logoColor=white" alt="GitHub">
-              </a>
-              <form action="https://www.paypal.com/donate" method="post" target="_blank">
-                <input type="hidden" name="hosted_button_id" value="6FPWP9AWEKSWJ" />
-                <input type="image" src="https://img.shields.io/badge/apoyar-%2300457C.svg?style=for-the-badge&logo=paypal&logoColor=white" border="0" name="submit" alt="Botón donar con PayPal" />
-                <img alt="" border="0" src="https://www.paypal.com/es_ES/i/scr/pixel.gif" width="1" height="1" />
-              </form></center>
-              <a href="https://twitter.com/impavloh" target="_blank">
-                  <img src="https://img.shields.io/badge/Seguir-%231DA1F2.svg?style=for-the-badge&logo=twitter&logoColor=white" alt="Twitter">
-              </a>
-          </div>
-          <div style="display: inline-flex;align-items: center;gap: 0.8rem;font-size: 1.75rem;">
-              <h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px">🗣️ VoiceIt! - Un proyecto de  <a style="text-decoration: underline;" href="https://twitter.com/impavloh">Pavloh</a></h1>
-          </div>
-          <p style="margin-bottom: 10px; font-size: 94%; line-height: 23px;">Cambia la voz de audios utilizando modelos pre-entrenados de streamers.</p>
-      </div>
-        """
-  )
-
-  with gr.Row(elem_id="1").style(equal_height=True):
-        with gr.Column():
-            d1 = gr.Dropdown([x["name"] for x in get_speakers()], label="📦 Selecciona un modelo", value="Elige un modelo de voz")
-            audio = gr.Audio(label="🗣️ Sube un audio", type="filepath")
-        with gr.Column():
-            a2 = gr.Audio(label="🔊 Resultado", type="filepath")
-            t1 = gr.Textbox(type="text", label="📄 Información", value="Elige un modelo y un audio para cambiar la voz.")
-  with gr.Row():
-    b0 = gr.Button("🗑️ Borrar")
-    b1 = gr.Button("🎤 Cambiar voz",variant="primary")
-    b0.click(clear, outputs=[d1, audio, t1])
-    b1.click(convert, inputs=[d1, audio], outputs=[a2, t1])
-    
-  with gr.Row():
-        with gr.Accordion(label="Información importante", open=False):
-            gr.HTML("""
-                <center>
-                    <p>
-                        <i> Ten en cuenta que los audios deben contener solamente una voz y estar libres de ruido o música de fondo. </i>
-                    </p>
-                    <p>
-                        <i> Asegúrate de que el nombre del archivo no contenga espacios ni símbolos raros, utilizando solo caracteres alfanuméricos y guiones bajos (_) para separar palabras si es necesario. </i>
-                    </p>
-                    <p>
-                        <i> Al utilizar este sitio web, aceptas la <a style="text-decoration: underline;" href="https://github.com/ImPavloh/voiceit/blob/main/LICENSE">licencia</a> y <a style="text-decoration: underline;" href="https://github.com/ImPavloh/voiceit/blob/main/TERMINOS_DE_USO.txt">condiciones de uso</a>. </i>
-                    </p>
-                </center>
-            """)
-            
-if __name__ == "__main__": pavloh.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Jean-Baptiste/email_parser/email_parser/utils.py b/spaces/Jean-Baptiste/email_parser/email_parser/utils.py
deleted file mode 100644
index 947dd230a715f8749037189dc9b2eeb6a03e735e..0000000000000000000000000000000000000000
--- a/spaces/Jean-Baptiste/email_parser/email_parser/utils.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from functools import wraps
-import logging
-import os
-from time import time
-import configparser
-
-timer_functions = {}
-
-# Loading configuration from config file
-config = configparser.ConfigParser()
-config.read(os.path.join(os.path.dirname(__file__), 'config.ini'))
-
-
-def timing(f):
-    @wraps(f)
-    def wrap(*args, **kw):
-        ts = time()
-        result = f(*args, **kw)
-        te = time()
-        if f.__name__ in timer_functions.keys():
-            current_elapsed_time = timer_functions[f.__name__]
-        else:
-            current_elapsed_time = 0
-        timer_functions[f.__name__] = current_elapsed_time + (te - ts)
-        logging.debug('func:%r took: %2.4f sec' % \
-                        (f.__name__, te - ts))
-        return result
-    return wrap
-
-
-def f_read_config(path=None):
-    """ read config file from specified file path
-    
-    :param path: file path
-    :return: configparser object
-    """
-    # Loading configuration from config file
-    config = configparser.ConfigParser()
-    if path is None:
-        path = os.path.join(os.path.dirname(__file__), 'config.ini')
-    config.read(path, encoding='utf-8')
-    return config
-
-def f_setup_logger(level_sysout=logging.INFO, level_file=logging.DEBUG, folder_path="logs"):
-    """Setup logger
-
-    By default we display only INFO in console, and write everything in file
-
-    Args:
-        level_sysout: Level that is displayed in console (default INFO)
-        level_file: Level that is written in file (default DEBUG)
-
-    Returns:
-        Nothing
-
-    """
-    if not os.path.isdir(folder_path):
-        os.mkdir(folder_path)
-
-    for handler in logging.root.handlers[:]:
-        logging.root.removeHandler(handler)
-
-    file_handler = logging.FileHandler(filename=os.path.join(folder_path, "amf_uce_nlp_{}.log".format(time())),
-                                       encoding='utf-8')
-    sysout_handler = logging.StreamHandler()
-    file_handler.setLevel(level_file)
-    sysout_handler.setLevel(level_sysout)
-    logging.basicConfig(handlers=[file_handler, sysout_handler], level=logging.DEBUG,
-                        format='%(asctime)s (%(levelname)s) %(message)s', datefmt='%m/%d/%y %I:%M:%S %p')
-
-
-def get_model_full_path(model_name):
-    path_models = config["DEFAULT"]["path_models"]
-    return os.path.join(os.path.dirname(__file__), path_models, model_name)
\ No newline at end of file
diff --git a/spaces/JenitaChristopher/MY_GEN_AI/app.py b/spaces/JenitaChristopher/MY_GEN_AI/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/JenitaChristopher/MY_GEN_AI/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Spider/con_spider_logreg.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Spider/con_spider_logreg.py
deleted file mode 100644
index ab7a5b2c2cca15797d7bf987ef15249e3597d2ea..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Spider/con_spider_logreg.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-
-class spiderLogReg:
-    def __init__(self,url) -> None:
-        self.image = url
-    
-    def predict_image(self):
-        # Load the model
-        load_extractor = tf.keras.models.load_model("././Model/Spider/resnetLogreg/resnet_EXTRACTOR.h5")
-
-        modelpath = "././Model/Spider/resnetLogreg/dataSaved.pkl"
-
-        with open(modelpath, 'rb') as file:
-            saved_data = pickle.load(file)
-            animal_breed = saved_data['class_name']
-            model = saved_data['logreg_model']
-
-        im = Image.open(self.image)
-        img = im.convert("RGB")
-        img= np.asarray(img)
-        image_resized= cv2.resize(img, (224,224))
-        features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-        
-        reshaped_features = features.reshape(features.shape[0],-1)
-        predicted_class = model.predict(reshaped_features)
-        pred_prob = model.predict_proba(reshaped_features)[:2]
-        prediction_probability = pred_prob[0][predicted_class[0]]
-        predicted_class
-
-        output_class= animal_breed[predicted_class[0]]
-
-        return [output_class, prediction_probability]
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/pdf_func.py b/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/pdf_func.py
deleted file mode 100644
index 1b1087f2687fd26c8676867dd45189c069dd56a5..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/pdf_func.py
+++ /dev/null
@@ -1,180 +0,0 @@
-from types import SimpleNamespace
-import pdfplumber
-import logging
-from langchain.docstore.document import Document
-
-def prepare_table_config(crop_page):
-    """Prepare table查找边界, 要求page为原始page
-
-    From https://github.com/jsvine/pdfplumber/issues/242
-    """
-    page = crop_page.root_page # root/parent
-    cs = page.curves + page.edges
-    def curves_to_edges():
-        """See https://github.com/jsvine/pdfplumber/issues/127"""
-        edges = []
-        for c in cs:
-            edges += pdfplumber.utils.rect_to_edges(c)
-        return edges
-    edges = curves_to_edges()
-    return {
-        "vertical_strategy": "explicit",
-        "horizontal_strategy": "explicit",
-        "explicit_vertical_lines": edges,
-        "explicit_horizontal_lines": edges,
-        "intersection_y_tolerance": 10,
-    }
-
-def get_text_outside_table(crop_page):
-    ts = prepare_table_config(crop_page)
-    if len(ts["explicit_vertical_lines"]) == 0 or len(ts["explicit_horizontal_lines"]) == 0:
-        return crop_page
-
-    ### Get the bounding boxes of the tables on the page.
-    bboxes = [table.bbox for table in crop_page.root_page.find_tables(table_settings=ts)]
-    def not_within_bboxes(obj):
-        """Check if the object is in any of the table's bbox."""
-        def obj_in_bbox(_bbox):
-            """See https://github.com/jsvine/pdfplumber/blob/stable/pdfplumber/table.py#L404"""
-            v_mid = (obj["top"] + obj["bottom"]) / 2
-            h_mid = (obj["x0"] + obj["x1"]) / 2
-            x0, top, x1, bottom = _bbox
-            return (h_mid >= x0) and (h_mid < x1) and (v_mid >= top) and (v_mid < bottom)
-        return not any(obj_in_bbox(__bbox) for __bbox in bboxes)
-
-    return crop_page.filter(not_within_bboxes)
-# 请使用 LaTeX 表达公式，行内公式以 $ 包裹，行间公式以 $$ 包裹
-
-extract_words = lambda page: page.extract_words(keep_blank_chars=True, y_tolerance=0, x_tolerance=1, extra_attrs=["fontname", "size", "object_type"])
-# dict_keys(['text', 'x0', 'x1', 'top', 'doctop', 'bottom', 'upright', 'direction', 'fontname', 'size'])
-
-def get_title_with_cropped_page(first_page):
-    title = [] # 处理标题
-    x0,top,x1,bottom = first_page.bbox # 获取页面边框
-
-    for word in extract_words(first_page):
-        word = SimpleNamespace(**word)
-
-        if word.size >= 14:
-            title.append(word.text)
-            title_bottom = word.bottom
-        elif word.text == "Abstract": # 获取页面abstract
-            top = word.top
-
-    user_info = [i["text"] for i in extract_words(first_page.within_bbox((x0,title_bottom,x1,top)))]
-    # 裁剪掉上半部分, within_bbox: full_included; crop: partial_included
-    return title, user_info, first_page.within_bbox((x0,top,x1,bottom))
-
-def get_column_cropped_pages(pages, two_column=True):
-    new_pages = []
-    for page in pages:
-        if two_column:
-            left = page.within_bbox((0, 0, page.width/2, page.height),relative=True)
-            right = page.within_bbox((page.width/2, 0, page.width, page.height), relative=True)
-            new_pages.append(left)
-            new_pages.append(right)
-        else:
-            new_pages.append(page)
-
-    return new_pages
-
-def parse_pdf(filename, two_column = True):
-    level = logging.getLogger().level
-    if level == logging.getLevelName("DEBUG"):
-        logging.getLogger().setLevel("INFO")
-
-    with pdfplumber.open(filename) as pdf:
-        title, user_info, first_page = get_title_with_cropped_page(pdf.pages[0])
-        new_pages = get_column_cropped_pages([first_page] + pdf.pages[1:], two_column)
-
-        chapters = []
-        # tuple (chapter_name, [pageid] (start,stop), chapter_text)
-        create_chapter = lambda page_start,name_top,name_bottom: SimpleNamespace(
-            name=[],
-            name_top=name_top,
-            name_bottom=name_bottom,
-            record_chapter_name = True,
-
-            page_start=page_start,
-            page_stop=None,
-
-            text=[],
-        )
-        cur_chapter = None
-
-        # 按页遍历PDF文档
-        for idx, page in enumerate(new_pages):
-            page = get_text_outside_table(page)
-
-            # 按行遍历页面文本
-            for word in extract_words(page):
-                word = SimpleNamespace(**word)
-
-                # 检查行文本是否以12号字体打印，如果是，则将其作为新章节开始
-                if word.size >= 11: # 出现chapter name
-                    if cur_chapter is None:
-                        cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
-                    elif not cur_chapter.record_chapter_name or (cur_chapter.name_bottom != cur_chapter.name_bottom and cur_chapter.name_top != cur_chapter.name_top):
-                        # 不再继续写chapter name
-                        cur_chapter.page_stop = page.page_number # stop id
-                        chapters.append(cur_chapter)
-                        # 重置当前chapter信息
-                        cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
-
-                    # print(word.size, word.top, word.bottom, word.text)
-                    cur_chapter.name.append(word.text)
-                else:
-                    cur_chapter.record_chapter_name = False # chapter name 结束
-                    cur_chapter.text.append(word.text)
-        else:
-            # 处理最后一个章节
-            cur_chapter.page_stop = page.page_number # stop id
-            chapters.append(cur_chapter)
-
-        for i in chapters:
-            logging.info(f"section: {i.name} pages:{i.page_start, i.page_stop} word-count:{len(i.text)}")
-            logging.debug(" ".join(i.text))
-
-    title = " ".join(title)
-    user_info = " ".join(user_info)
-    text = f"Article Title: {title}, Information:{user_info}\n"
-    for idx, chapter in enumerate(chapters):
-        chapter.name = " ".join(chapter.name)
-        text += f"The {idx}th Chapter {chapter.name}: " + " ".join(chapter.text) + "\n"
-
-    logging.getLogger().setLevel(level)
-    return Document(page_content=text, metadata={"title": title})
-
-BASE_POINTS = """
-1. Who are the authors?
-2. What is the process of the proposed method?
-3. What is the performance of the proposed method? Please note down its performance metrics.
-4. What are the baseline models and their performances? Please note down these baseline methods.
-5. What dataset did this paper use?
-"""
-
-READING_PROMPT = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{}
-"""
-
-READING_PROMT_V2 = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{},
-
-And You need to generate a brief but informative title for this part.
-Your return format:
-- title: '...'
-- summary: '...'
-"""
-
-SUMMARY_PROMPT = "You are a researcher helper bot. Now you need to read the summaries of a research paper."
-
-
-if __name__ == '__main__':
-    # Test code
-    z = parse_pdf("./build/test.pdf")
-    print(z["user_info"])
-    print(z["title"])
\ No newline at end of file
diff --git a/spaces/Joshua1808/PaginaWeb/app.py b/spaces/Joshua1808/PaginaWeb/app.py
deleted file mode 100644
index 677f0d5fba6bda50a8c3fa267241deb5ad802e42..0000000000000000000000000000000000000000
--- a/spaces/Joshua1808/PaginaWeb/app.py
+++ /dev/null
@@ -1,276 +0,0 @@
-import tweepy as tw
-import streamlit as st
-import pandas as pd
-import regex as re
-import numpy as np
-import pysentimiento
-import geopy
-import matplotlib.pyplot as plt
-import langdetect
-
-#Importar Nominatim para geocalización
-from geopy.geocoders import Nominatim
-#Importar pipeline para usar al modelo Transformer
-from transformers import pipeline
-#función detect es utilizada para detectar el idioma de un texto dado.
-from langdetect import detect
-
-
-model_checkpoint = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021" 
-pipeline_nlp = pipeline("text-classification", model=model_checkpoint)
-
-#Claves de acceso a la API de Twitter
-consumer_key = "BjipwQslVG4vBdy4qK318KnoA"
-consumer_secret = "3fzL70v9faklrPgvTi3zbofw9rwk92fgGdtAslFkFYt8kGmqBJ"
-access_token = "1217853705086799872-Y5zEChpTeKccuLY3XJRXDPPZhNrlba"
-access_token_secret = "pqQ5aFSJxzJ2xnI6yhVtNjQO36FOu8DBOH6DtUrPAU54J"
-auth = tw.OAuthHandler(consumer_key, consumer_secret)
-auth.set_access_token(access_token, access_token_secret)
-api = tw.API(auth, wait_on_rate_limit=True)
-
-#Función para limpiar datos
-def limpieza_datos(tweet):
-    # Eliminar emojis
-    tweet = re.sub(r'[\U0001F600-\U0001F64F]', '', tweet)
-    tweet = re.sub(r'[\U0001F300-\U0001F5FF]', '', tweet)
-    tweet = re.sub(r'[\U0001F680-\U0001F6FF]', '', tweet)
-    tweet = re.sub(r'[\U0001F1E0-\U0001F1FF]', '', tweet)
-    # Eliminar arrobas
-    tweet = re.sub(r'@\w+', '', tweet)
-    # Eliminar URL
-    tweet = re.sub(r'http\S+', '', tweet)
-    # Eliminar hashtags
-    tweet = re.sub(r'#\w+', '', tweet)
-    # Eliminar caracteres especiales
-    #tweet = re.sub(r'[^a-zA-Z0-9 \n\.]', '', tweet)
-    tweet = re.sub(r'[^a-zA-Z0-9 \n\áéíóúÁÉÍÓÚñÑ.]', '', tweet)
-    return tweet
-
-#Función para cambiar el color de la palabra "Sexista"
-def highlight_survived(s):
-    return ['background-color: red']*len(s) if (s.Sexista == 1) else ['background-color: green']*len(s)
-
-def color_survived(val):
-    color = 'red' if val=='Sexista' else 'white'
-    return f'background-color: {color}'
-
-#Función para el ancho de la pagina web
-st.set_page_config(layout="wide")
-st.markdown('<style>body{background-color: Blue;}</style>',unsafe_allow_html=True)
-
-#colT1,colT2 = st.columns([2,8])
-
-
-st.markdown(""" <style> .font {
-font-size:40px ; font-family: 'Cooper Black'; color: #301E67;} 
-</style> """, unsafe_allow_html=True)
-
-#st.markdown('<p class="font"; style="text-align: center;>Análisis de comentarios sexistas en linea</p>', unsafe_allow_html=True)
-st.markdown('<p class="font" style="text-align: center;">Detectando el Sexismo en Linea: Un proyecto de Investigación</p>', unsafe_allow_html=True)
-
-#Editar el tipo de letra
-st.markdown(""" <style> .font1 {
-font-size:28px ; font-family: 'Times New Roman'; color: #8d33ff;} 
-</style> """, unsafe_allow_html=True)
-
-st.markdown(""" <style> .font2 {
-font-size:18px ; font-family: 'Times New Roman'; color: #5B8FB9;} 
-</style> """, unsafe_allow_html=True)
-
-#Parrafo
-st.markdown('<p class="font2">Este proyecto consiste en una aplicación web que utiliza la biblioteca Tweepy de Python para descargar tweets de Twitter, permitiendo buscar Tweets por usuario y por localidad. Luego, utiliza modelos de lenguaje basados en Transformers para analizar los tweets y detectar comentarios y determinar si son "Sexistas" o "No Sexistas". El proyecto busca identificar y combatir el discurso sexista en línea para promover la igualdad de género y la inclusión.</p>',unsafe_allow_html=True)
-
-#Función para extrer tweets de usuarios de Twitter, y analizarlos por el modelo Transformer
-def tweets_usuario(usuario, cant_de_tweets):
-  tabla = [] # crea una lista vacía llamada tabla
-  if(cant_de_tweets > 0 and usuario != "" ):
-      try:
-          # Buscar la información del perfil de usuario
-          user = api.get_user(screen_name=usuario)
-          # utiliza la API de Twitter para buscar tweets en español de un usuario de Twitter
-          tweets = api.user_timeline(screen_name = usuario,tweet_mode="extended", count= cant_de_tweets)
-          result = [] # crea una lista vacía llamada result
-          # itera a través de cada tweet en la lista de tweets encontrados
-          for tweet in tweets:
-              # si el tweet comienza con 'RT' o si el tweet está vacío, continúa con el siguiente tweet
-              if (tweet.full_text.startswith('RT')):
-                  continue
-              else:
-                  text = tweet.full_text
-                  #Try Catch para detectar que sean del idioma español
-                  try:
-                      language = detect(text)
-                      if language == 'es':
-                          #Limpieza de cada tweet extraido
-                          datos=limpieza_datos(text)
-                          if datos == "":
-                              continue
-                          else:
-                              #Predicción del modelo Transformer para cada tweet
-                              prediction = pipeline_nlp(datos)
-                              for predic in prediction:
-                                   # Agrega la etiqueta de Tweets, Predicción y Probabilidad en una lista y la agrega a la lista de resultados
-                                etiqueta = {'Tweets': datos, 'Prediccion': predic['label'], 'Probabilidad': predic['score']}
-                                result.append(etiqueta)
-                  except:
-                      pass   
-           # Crea un dataframe a partir de la lista de resultados
-          df = pd.DataFrame(result)
-           # Si el dataframe está vacío, muestra un mensaje de que no se encontraron tweets sexistas dentro de la localidad
-          if df.empty:
-              muestra= st.text("No hay tweets Sexistas a analizar")
-              tabla.append(muestra)
-           
-          else:
-               # ordena el dataframe por Predicción y Probabilidad en orden descendente
-              df.sort_values(by=['Prediccion', 'Probabilidad'], ascending=[False, False], inplace=True)
-              df['Prediccion'] = np.where(df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
-              df['Probabilidad'] = df['Probabilidad'].apply(lambda x: round(x, 3))
-              muestra = st.table(df.reset_index(drop=True).head(50).style.applymap(color_survived, subset=['Prediccion']))
-              tabla.append(muestra)
-      except Exception as e:
-          #Try except para detectar si la cuenta es existente
-          muestra = st.text(f"La cuenta {usuario} no existe.") 
-          tabla.append(muestra) 
-  else:
-      #Condición que se solicita cuando no se llenaron los datos para la busqueda
-      muestra= st.text("Ingrese los parametros correspondientes")
-      tabla.append(muestra)      
-  return tabla
-
-#Función para buscar por localidad
-def tweets_localidad(buscar_localidad):
-    tabla = []  # crea una lista vacía llamada tabla
-    try:
-        #Crea un objeto Geolocalizador y busca la ubicación que se encuentra en el parámetro buscar_localidad
-        geolocator = Nominatim(user_agent="nombre_del_usuario")
-        location = geolocator.geocode(buscar_localidad)
-        radius = "15km" #establece el radio de búsqueda en 15km
-         # utiliza la API de Twitter para buscar tweets en español cerca de la ubicación encontrada
-        tweets = api.search_tweets(q="",lang="es",geocode=f"{location.latitude},{location.longitude},{radius}", count = 100, tweet_mode="extended")
-        result = [] # crea una lista vacía llamada result
-        # itera a través de cada tweet en la lista de tweets encontrados
-        for tweet in tweets:
-            # si el tweet comienza con 'RT' o si el tweet está vacío, continúa con el siguiente tweet
-            if (tweet.full_text.startswith('RT')):
-                continue
-            elif not tweet.full_text.strip():
-                continue
-            else:
-                # Limpia los datos del tweet y realiza una predicción sobre si es sexista o no
-                datos = limpieza_datos(tweet.full_text)
-                prediction = pipeline_nlp(datos)
-                for predic in prediction:
-                    # Agrega la etiqueta de Tweets, Predicción y Probabilidad en una lista y la agrega a la lista de resultados
-                    etiqueta = {'Tweets': datos,'Prediccion': predic['label'], 'Probabilidad': predic['score']}
-                    result.append(etiqueta)
-        # Crea un dataframe a partir de la lista de resultados
-        df = pd.DataFrame(result)
-        # Si el dataframe está vacío, muestra un mensaje de que no se encontraron tweets sexistas dentro de la localidad
-        if df.empty:
-            muestra=st.text("No se encontraron tweets sexistas dentro de la localidad")
-            tabla.append(muestra)
-        else:
-            #tabla.append(muestra)
-            # ordena el dataframe por Predicción y Probabilidad en orden descendente
-            df.sort_values(by=['Prediccion', 'Probabilidad'], ascending=[False, False], inplace=True)
-            # convierte la columna de Predicción en "Sexista" o "No Sexista" y redondea la columna de Probabilidad a 3 decimales
-            df['Prediccion'] = np.where(df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
-            df['Probabilidad'] = df['Probabilidad'].round(3)
-            # Obtener los datos con probabilidad mayor a 0.50
-            df = df[df['Probabilidad'] > 0.50]
-            # Obtener los 3 primeros datos con mayor probabilidad sexista
-            sexista_df = df[df['Prediccion'] == 'Sexista'].head(3)
-            
-            # Obtener los 3 primeros datos con mayor probabilidad no sexista
-            no_sexista_df = df[df['Prediccion'] == 'No Sexista'].head(3)
-            
-            # Concatenar ambos dataframes
-            muestra_df = pd.concat([sexista_df, no_sexista_df], axis=0)
-            col1, col2 = st.columns(2)
-            with col1:
-                #lista de Tweets mostrados en pantalla
-                muestra = st.table(muestra_df.reset_index(drop=True).head(6).style.applymap(color_survived, subset=['Prediccion']))
-            with col2:
-                resultado = df['Prediccion'].value_counts()
-                def autopct_fun(abs_values):
-                    gen = iter(abs_values)
-                    return lambda pct: f"{pct:.1f}% ({next(gen)})"
-                #Gráfico
-                colores=["#aae977","#EE3555"]
-                fig, ax = plt.subplots()
-                fig.set_size_inches(2,2)
-                plt.pie(resultado,labels=resultado.index,autopct=autopct_fun(resultado),colors=colores,  textprops={'fontsize': 5})
-                ax.set_title("Porcentajes por Categorias en la localidad\n"+buscar_localidad.capitalize(), fontsize=5, fontweight="bold")
-                plt.rcParams.update({'font.size':4, 'font.weight':'bold'})
-                ax.legend()
-                # Muestra el gráfico
-                plt.show()
-                st.set_option('deprecation.showPyplotGlobalUse', False)
-                st.pyplot()
-
-    except AttributeError as e:
-        muestra=st.text("No existe ninguna localidad con ese nombre") 
-        tabla.append(muestra)
-                 
-    return tabla 
-    
-   
-def analizar_frase(frase):
-    language = detect(frase)
-    if frase == "":
-        tabla = st.text("Ingrese una frase")
-        #st.text("Ingrese una frase")
-    elif language == 'es':
-        predictions = pipeline_nlp(frase)
-        # convierte las predicciones en una lista de diccionarios
-        data = [{'Texto': frase, 'Prediccion': prediction['label'], 'Probabilidad': prediction['score']} for prediction in predictions]
-        # crea un DataFrame a partir de la lista de diccionarios
-        df = pd.DataFrame(data)
-        df['Prediccion'] = np.where( df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
-        # muestra el DataFrame
-        tabla = st.table(df.reset_index(drop=True).head(1).style.applymap(color_survived, subset=['Prediccion']))
-    else:
-        tabla = st.text("Solo Frase en español")
-        
-    return tabla
-    
-#Función para correr el formulario
-def run():
-    # Se inicia una sesión Streamlit
-    with st.form("my_form"):
-         # Se solicita al usuario que ingrese la búsqueda que desea realizar
-        search_words = st.text_input("Introduzca la frase, el usuario o localidad para analizar y pulse el check correspondiente")
-         # Se solicita al usuario que ingrese la cantidad de tweets que desea analizar (máximo 50)
-        number_of_tweets = st.number_input('Introduzca número de tweets a analizar del usuario Máximo 50', 0,50,0)
-        st.write("Escoja una Opción:")
-        # Se presenta al usuario las opciones de búsqueda (frase, usuario o localidad) mediante checkboxes
-        termino=st.checkbox('Frase')
-        usuario=st.checkbox('Usuario')
-        localidad=st.checkbox('Localidad')
-         # Se agrega un botón de envío para que el usuario pueda iniciar la búsqueda
-        submit_button = st.form_submit_button(label='Analizar')
-        # Se define una variable de error y se inicializa como falso
-        error =False
-
-         # Si el usuario hace clic en el botón de envío, se comprueban los checkboxes y se establece un mensaje de error si es necesario
-        if submit_button:
-            # Condición para el caso de que esten dos check seleccionados
-            if ( termino == False and usuario == False and localidad == False):
-                st.text('Error no se ha seleccionado ningun check')
-                error=True
-            elif ( termino == True and usuario == True and localidad == True):
-                st.text('Error se han seleccionado varios check')
-                error=True
-       
-        # Si no se ha producido ningún error, se inicia la búsqueda según la opción seleccionada           
-        if (error == False):
-          if (termino):
-              analizar_frase(search_words)
-          elif (usuario):
-              tweets_usuario(search_words,number_of_tweets)
-          elif (localidad):
-              tweets_localidad(search_words)
-
-# Se ejecuta la función run
-run()
\ No newline at end of file
diff --git a/spaces/Kevin676/Clone-Your-Voice/utils/profiler.py b/spaces/Kevin676/Clone-Your-Voice/utils/profiler.py
deleted file mode 100644
index 17175b9e1b0eb17fdc015199e5194a5c1afb8a28..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Clone-Your-Voice/utils/profiler.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from time import perf_counter as timer
-from collections import OrderedDict
-import numpy as np
-
-
-class Profiler:
-    def __init__(self, summarize_every=5, disabled=False):
-        self.last_tick = timer()
-        self.logs = OrderedDict()
-        self.summarize_every = summarize_every
-        self.disabled = disabled
-    
-    def tick(self, name):
-        if self.disabled:
-            return
-        
-        # Log the time needed to execute that function
-        if not name in self.logs:
-            self.logs[name] = []
-        if len(self.logs[name]) >= self.summarize_every:
-            self.summarize()
-            self.purge_logs()
-        self.logs[name].append(timer() - self.last_tick)
-        
-        self.reset_timer()
-        
-    def purge_logs(self):
-        for name in self.logs:
-            self.logs[name].clear()
-    
-    def reset_timer(self):
-        self.last_tick = timer()
-    
-    def summarize(self):
-        n = max(map(len, self.logs.values()))
-        assert n == self.summarize_every
-        print("\nAverage execution time over %d steps:" % n)
-
-        name_msgs = ["%s (%d/%d):" % (name, len(deltas), n) for name, deltas in self.logs.items()]
-        pad = max(map(len, name_msgs))
-        for name_msg, deltas in zip(name_msgs, self.logs.values()):
-            print("  %s  mean: %4.0fms   std: %4.0fms" % 
-                  (name_msg.ljust(pad), np.mean(deltas) * 1000, np.std(deltas) * 1000))
-        print("", flush=True)    
-        
\ No newline at end of file
diff --git a/spaces/Kyan14/Mood_Based_Generative_Art/app.py b/spaces/Kyan14/Mood_Based_Generative_Art/app.py
deleted file mode 100644
index 82bbb30e6a6a06cf594da527e90a536a74726260..0000000000000000000000000000000000000000
--- a/spaces/Kyan14/Mood_Based_Generative_Art/app.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-import gradio as gr
-from transformers import CLIPProcessor, CLIPModel
-import numpy as np
-import time
-
-# Replace with your own API key
-STABLE_DIFFUSION_API_KEY = "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"
-
-# Load the CLIP model and processor
-model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-def get_mood_from_image(image: Image.Image):
-    moods = ["scared", "angry", "happy", "sad", "disgusted", "surprised"]
-    
-    # Create unique prompts for each mood
-    prompts = [
-        "The emotion conveyed by this image is fear. The person looks scared and tense.",
-        "The emotion conveyed by this image is anger. The person looks furious and irritated.",
-        "The emotion conveyed by this image is happy. The person looks happy and cheerful.",
-        "The emotion conveyed by this image is sadness. The person looks unhappy and gloomy.",
-        "The emotion conveyed by this image is disgust. The person looks repulsed and sickened.",
-        "The emotion conveyed by this image is surprise. The person looks astonished and amazed.",
-    ]
-    
-    # Prepare the inputs for the model
-    inputs = processor(text=prompts, images=image, return_tensors="pt", padding=True)
-    
-    # Run the model
-    logits = model(**inputs).logits_per_image
-    probs = logits.softmax(dim=-1).tolist()
-
-    # Calculate the scores for each mood
-    mood_scores = {}
-    for mood, score in zip(moods, probs[0]):
-        mood_scores[mood] = score
-    print("Mood Scores:", mood_scores)
-    # Select the mood with the highest score
-    selected_mood = max(mood_scores, key=mood_scores.get)
-
-    return selected_mood
-def is_black_image(image: Image.Image) -> bool:
-    img_array = np.array(image)
-    return np.all(img_array == 0)
-
-def generate_art(mood, max_retries=3, request_timeout=30):
-    prompt = f"{mood} generative art with vibrant colors and intricate patterns ({str(np.random.randint(1, 10000))})"
-    
-    headers = {
-        "Authorization": f"Bearer {STABLE_DIFFUSION_API_KEY}",
-        "Accept": "image/jpeg",
-    }
-
-    json_data = {
-        "inputs": prompt
-    }
-
-    retries = 0
-    while retries < max_retries:
-        try:
-            response = requests.post('https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5', headers=headers, json=json_data, timeout=request_timeout)
-        except requests.exceptions.Timeout:
-            print(f"Request timed out after {request_timeout} seconds. Retrying...")
-            retries += 1
-            continue
-
-        if response.status_code == 503:
-            print("Model is loading, waiting for 30 seconds before retrying...")
-            time.sleep(30)
-            continue
-
-        if response.status_code != 200:
-            print(f"Error: API response status code {response.status_code}")
-            print("Response content:")
-            print(response.content)
-            return None
-
-        image = Image.open(BytesIO(response.content))
-
-        if not is_black_image(image):
-            break
-
-        retries += 1
-
-    if retries == max_retries:
-        return None
-
-    return image
-
-def mood_art_generator(image):
-    mood = get_mood_from_image(image)
-    print("Mood:", mood)
-    if mood:
-        art = generate_art(mood)
-        output_text = f"You seem to be {mood}. Here's an artwork representing it!"
-        return art, output_text
-    else:
-        return None, "Failed to generate artwork."
-
-iface = gr.Interface(
-    fn=mood_art_generator,
-    inputs=gr.inputs.Image(shape=(224, 224), image_mode="RGB", source="upload"),
-    outputs=[gr.outputs.Image(type="pil"), gr.outputs.Textbox()],
-    title="Mood-based Art Generator",
-    description="Upload an image of yourself and let the AI generate artwork based on your mood.",
-    allow_flagging=False,
-    analytics_enabled=False,
-    share=True
-)
-iface.launch()
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/backbones/ssd_vgg.py b/spaces/KyanChen/RSPrompter/mmdet/models/backbones/ssd_vgg.py
deleted file mode 100644
index 843e82e2722f93b9b2abb5180c827c8f2a430b48..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/backbones/ssd_vgg.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import torch.nn as nn
-from mmcv.cnn import VGG
-from mmengine.model import BaseModule
-
-from mmdet.registry import MODELS
-from ..necks import ssd_neck
-
-
-@MODELS.register_module()
-class SSDVGG(VGG, BaseModule):
-    """VGG Backbone network for single-shot-detection.
-
-    Args:
-        depth (int): Depth of vgg, from {11, 13, 16, 19}.
-        with_last_pool (bool): Whether to add a pooling layer at the last
-            of the model
-        ceil_mode (bool): When True, will use `ceil` instead of `floor`
-            to compute the output shape.
-        out_indices (Sequence[int]): Output from which stages.
-        out_feature_indices (Sequence[int]): Output from which feature map.
-        pretrained (str, optional): model pretrained path. Default: None
-        init_cfg (dict or list[dict], optional): Initialization config dict.
-            Default: None
-        input_size (int, optional): Deprecated argumment.
-            Width and height of input, from {300, 512}.
-        l2_norm_scale (float, optional) : Deprecated argumment.
-            L2 normalization layer init scale.
-
-    Example:
-        >>> self = SSDVGG(input_size=300, depth=11)
-        >>> self.eval()
-        >>> inputs = torch.rand(1, 3, 300, 300)
-        >>> level_outputs = self.forward(inputs)
-        >>> for level_out in level_outputs:
-        ...     print(tuple(level_out.shape))
-        (1, 1024, 19, 19)
-        (1, 512, 10, 10)
-        (1, 256, 5, 5)
-        (1, 256, 3, 3)
-        (1, 256, 1, 1)
-    """
-    extra_setting = {
-        300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256),
-        512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128),
-    }
-
-    def __init__(self,
-                 depth,
-                 with_last_pool=False,
-                 ceil_mode=True,
-                 out_indices=(3, 4),
-                 out_feature_indices=(22, 34),
-                 pretrained=None,
-                 init_cfg=None,
-                 input_size=None,
-                 l2_norm_scale=None):
-        # TODO: in_channels for mmcv.VGG
-        super(SSDVGG, self).__init__(
-            depth,
-            with_last_pool=with_last_pool,
-            ceil_mode=ceil_mode,
-            out_indices=out_indices)
-
-        self.features.add_module(
-            str(len(self.features)),
-            nn.MaxPool2d(kernel_size=3, stride=1, padding=1))
-        self.features.add_module(
-            str(len(self.features)),
-            nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6))
-        self.features.add_module(
-            str(len(self.features)), nn.ReLU(inplace=True))
-        self.features.add_module(
-            str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1))
-        self.features.add_module(
-            str(len(self.features)), nn.ReLU(inplace=True))
-        self.out_feature_indices = out_feature_indices
-
-        assert not (init_cfg and pretrained), \
-            'init_cfg and pretrained cannot be specified at the same time'
-
-        if init_cfg is not None:
-            self.init_cfg = init_cfg
-        elif isinstance(pretrained, str):
-            warnings.warn('DeprecationWarning: pretrained is deprecated, '
-                          'please use "init_cfg" instead')
-            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
-        elif pretrained is None:
-            self.init_cfg = [
-                dict(type='Kaiming', layer='Conv2d'),
-                dict(type='Constant', val=1, layer='BatchNorm2d'),
-                dict(type='Normal', std=0.01, layer='Linear'),
-            ]
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-        if input_size is not None:
-            warnings.warn('DeprecationWarning: input_size is deprecated')
-        if l2_norm_scale is not None:
-            warnings.warn('DeprecationWarning: l2_norm_scale in VGG is '
-                          'deprecated, it has been moved to SSDNeck.')
-
-    def init_weights(self, pretrained=None):
-        super(VGG, self).init_weights()
-
-    def forward(self, x):
-        """Forward function."""
-        outs = []
-        for i, layer in enumerate(self.features):
-            x = layer(x)
-            if i in self.out_feature_indices:
-                outs.append(x)
-
-        if len(outs) == 1:
-            return outs[0]
-        else:
-            return tuple(outs)
-
-
-class L2Norm(ssd_neck.L2Norm):
-
-    def __init__(self, **kwargs):
-        super(L2Norm, self).__init__(**kwargs)
-        warnings.warn('DeprecationWarning: L2Norm in ssd_vgg.py '
-                      'is deprecated, please use L2Norm in '
-                      'mmdet/models/necks/ssd_neck.py instead')
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/__init__.py b/spaces/KyanChen/RSPrompter/mmpretrain/__init__.py
deleted file mode 100644
index 4dfdb78c915087762649f2da4f4f50b4cef5c7d0..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
-import mmengine
-from mmengine.utils import digit_version
-
-from .apis import *  # noqa: F401, F403
-from .version import __version__
-
-mmcv_minimum_version = '2.0.0'
-mmcv_maximum_version = '2.5.0'
-mmcv_version = digit_version(mmcv.__version__)
-
-mmengine_minimum_version = '0.7.1'
-mmengine_maximum_version = '1.5.0'
-mmengine_version = digit_version(mmengine.__version__)
-
-assert (mmcv_version >= digit_version(mmcv_minimum_version)
-        and mmcv_version < digit_version(mmcv_maximum_version)), \
-    f'MMCV=={mmcv.__version__} is used but incompatible. ' \
-    f'Please install mmcv>={mmcv_minimum_version}, <{mmcv_maximum_version}.'
-
-assert (mmengine_version >= digit_version(mmengine_minimum_version)
-        and mmengine_version < digit_version(mmengine_maximum_version)), \
-    f'MMEngine=={mmengine.__version__} is used but incompatible. ' \
-    f'Please install mmengine>={mmengine_minimum_version}, ' \
-    f'<{mmengine_maximum_version}.'
-
-__all__ = ['__version__']
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/utils.py b/spaces/KyanChen/RSPrompter/mmpretrain/datasets/utils.py
deleted file mode 100644
index fcb60e432c374c1a904700a7348f706fa0e523eb..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/utils.py
+++ /dev/null
@@ -1,243 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import gzip
-import hashlib
-import os
-import os.path
-import shutil
-import tarfile
-import tempfile
-import urllib.error
-import urllib.request
-import zipfile
-
-from mmengine.fileio import LocalBackend, get_file_backend
-
-__all__ = [
-    'rm_suffix', 'check_integrity', 'download_and_extract_archive',
-    'open_maybe_compressed_file'
-]
-
-
-def rm_suffix(s, suffix=None):
-    if suffix is None:
-        return s[:s.rfind('.')]
-    else:
-        return s[:s.rfind(suffix)]
-
-
-def calculate_md5(fpath: str, chunk_size: int = 1024 * 1024):
-    md5 = hashlib.md5()
-    backend = get_file_backend(fpath, enable_singleton=True)
-    if isinstance(backend, LocalBackend):
-        # Enable chunk update for local file.
-        with open(fpath, 'rb') as f:
-            for chunk in iter(lambda: f.read(chunk_size), b''):
-                md5.update(chunk)
-    else:
-        md5.update(backend.get(fpath))
-    return md5.hexdigest()
-
-
-def check_md5(fpath, md5, **kwargs):
-    return md5 == calculate_md5(fpath, **kwargs)
-
-
-def check_integrity(fpath, md5=None):
-    if not os.path.isfile(fpath):
-        return False
-    if md5 is None:
-        return True
-    return check_md5(fpath, md5)
-
-
-def download_url_to_file(url, dst, hash_prefix=None, progress=True):
-    """Download object at the given URL to a local path.
-
-    Modified from
-    https://pytorch.org/docs/stable/hub.html#torch.hub.download_url_to_file
-
-    Args:
-        url (str): URL of the object to download
-        dst (str): Full path where object will be saved,
-            e.g. ``/tmp/temporary_file``
-        hash_prefix (string, optional): If not None, the SHA256 downloaded
-            file should start with ``hash_prefix``. Defaults to None.
-        progress (bool): whether or not to display a progress bar to stderr.
-            Defaults to True
-    """
-    file_size = None
-    req = urllib.request.Request(url)
-    u = urllib.request.urlopen(req)
-    meta = u.info()
-    if hasattr(meta, 'getheaders'):
-        content_length = meta.getheaders('Content-Length')
-    else:
-        content_length = meta.get_all('Content-Length')
-    if content_length is not None and len(content_length) > 0:
-        file_size = int(content_length[0])
-
-    # We deliberately save it in a temp file and move it after download is
-    # complete. This prevents a local file being overridden by a broken
-    # download.
-    dst = os.path.expanduser(dst)
-    dst_dir = os.path.dirname(dst)
-    f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir)
-
-    import rich.progress
-    columns = [
-        rich.progress.DownloadColumn(),
-        rich.progress.BarColumn(bar_width=None),
-        rich.progress.TimeRemainingColumn(),
-    ]
-    try:
-        if hash_prefix is not None:
-            sha256 = hashlib.sha256()
-        with rich.progress.Progress(*columns) as pbar:
-            task = pbar.add_task('download', total=file_size, visible=progress)
-            while True:
-                buffer = u.read(8192)
-                if len(buffer) == 0:
-                    break
-                f.write(buffer)
-                if hash_prefix is not None:
-                    sha256.update(buffer)
-                pbar.update(task, advance=len(buffer))
-
-        f.close()
-        if hash_prefix is not None:
-            digest = sha256.hexdigest()
-            if digest[:len(hash_prefix)] != hash_prefix:
-                raise RuntimeError(
-                    'invalid hash value (expected "{}", got "{}")'.format(
-                        hash_prefix, digest))
-        shutil.move(f.name, dst)
-    finally:
-        f.close()
-        if os.path.exists(f.name):
-            os.remove(f.name)
-
-
-def download_url(url, root, filename=None, md5=None):
-    """Download a file from a url and place it in root.
-
-    Args:
-        url (str): URL to download file from.
-        root (str): Directory to place downloaded file in.
-        filename (str | None): Name to save the file under.
-            If filename is None, use the basename of the URL.
-        md5 (str | None): MD5 checksum of the download.
-            If md5 is None, download without md5 check.
-    """
-    root = os.path.expanduser(root)
-    if not filename:
-        filename = os.path.basename(url)
-    fpath = os.path.join(root, filename)
-
-    os.makedirs(root, exist_ok=True)
-
-    if check_integrity(fpath, md5):
-        print(f'Using downloaded and verified file: {fpath}')
-    else:
-        try:
-            print(f'Downloading {url} to {fpath}')
-            download_url_to_file(url, fpath)
-        except (urllib.error.URLError, IOError) as e:
-            if url[:5] == 'https':
-                url = url.replace('https:', 'http:')
-                print('Failed download. Trying https -> http instead.'
-                      f' Downloading {url} to {fpath}')
-                download_url_to_file(url, fpath)
-            else:
-                raise e
-        # check integrity of downloaded file
-        if not check_integrity(fpath, md5):
-            raise RuntimeError('File not found or corrupted.')
-
-
-def _is_tarxz(filename):
-    return filename.endswith('.tar.xz')
-
-
-def _is_tar(filename):
-    return filename.endswith('.tar')
-
-
-def _is_targz(filename):
-    return filename.endswith('.tar.gz')
-
-
-def _is_tgz(filename):
-    return filename.endswith('.tgz')
-
-
-def _is_gzip(filename):
-    return filename.endswith('.gz') and not filename.endswith('.tar.gz')
-
-
-def _is_zip(filename):
-    return filename.endswith('.zip')
-
-
-def extract_archive(from_path, to_path=None, remove_finished=False):
-    if to_path is None:
-        to_path = os.path.dirname(from_path)
-
-    if _is_tar(from_path):
-        with tarfile.open(from_path, 'r') as tar:
-            tar.extractall(path=to_path)
-    elif _is_targz(from_path) or _is_tgz(from_path):
-        with tarfile.open(from_path, 'r:gz') as tar:
-            tar.extractall(path=to_path)
-    elif _is_tarxz(from_path):
-        with tarfile.open(from_path, 'r:xz') as tar:
-            tar.extractall(path=to_path)
-    elif _is_gzip(from_path):
-        to_path = os.path.join(
-            to_path,
-            os.path.splitext(os.path.basename(from_path))[0])
-        with open(to_path, 'wb') as out_f, gzip.GzipFile(from_path) as zip_f:
-            out_f.write(zip_f.read())
-    elif _is_zip(from_path):
-        with zipfile.ZipFile(from_path, 'r') as z:
-            z.extractall(to_path)
-    else:
-        raise ValueError(f'Extraction of {from_path} not supported')
-
-    if remove_finished:
-        os.remove(from_path)
-
-
-def download_and_extract_archive(url,
-                                 download_root,
-                                 extract_root=None,
-                                 filename=None,
-                                 md5=None,
-                                 remove_finished=False):
-    download_root = os.path.expanduser(download_root)
-    if extract_root is None:
-        extract_root = download_root
-    if not filename:
-        filename = os.path.basename(url)
-
-    download_url(url, download_root, filename, md5)
-
-    archive = os.path.join(download_root, filename)
-    print(f'Extracting {archive} to {extract_root}')
-    extract_archive(archive, extract_root, remove_finished)
-
-
-def open_maybe_compressed_file(path: str):
-    """Return a file object that possibly decompresses 'path' on the fly.
-
-    Decompression occurs when argument `path` is a string and ends with '.gz'
-    or '.xz'.
-    """
-    if not isinstance(path, str):
-        return path
-    if path.endswith('.gz'):
-        import gzip
-        return gzip.open(path, 'rb')
-    if path.endswith('.xz'):
-        import lzma
-        return lzma.open(path, 'rb')
-    return open(path, 'rb')
diff --git a/spaces/Lbin123/Lbingo/src/components/providers.tsx b/spaces/Lbin123/Lbingo/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/src/components/providers.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { ThemeProvider as NextThemesProvider } from 'next-themes'
-import { ThemeProviderProps } from 'next-themes/dist/types'
-
-import { TooltipProvider } from '@/components/ui/tooltip'
-
-export function Providers({ children, ...props }: ThemeProviderProps) {
-  return (
-    <NextThemesProvider {...props}>
-      <TooltipProvider>{children}</TooltipProvider>
-    </NextThemesProvider>
-  )
-}
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/sizers/fixedsize.py b/spaces/Lianjd/stock_dashboard/backtrader/sizers/fixedsize.py
deleted file mode 100644
index a5d47f6bc6e5fb789a90ff08bd00db6a2dcd75f2..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/sizers/fixedsize.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program 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.
-#
-# This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import backtrader as bt
-
-
-class FixedSize(bt.Sizer):
-    '''
-    This sizer simply returns a fixed size for any operation.
-    Size can be controlled by number of tranches that a system
-    wishes to use to scale into trades by specifying the ``tranches``
-    parameter.
-
-
-    Params:
-      - ``stake`` (default: ``1``)
-      - ``tranches`` (default: ``1``)
-    '''
-
-    params = (('stake', 1),
-              ('tranches', 1))
-
-    def _getsizing(self, comminfo, cash, data, isbuy):
-        if self.p.tranches > 1:
-            return abs(int(self.p.stake / self.p.tranches))
-        else:
-            return self.p.stake
-
-    def setsizing(self, stake):
-        if self.p.tranches > 1:
-            self.p.stake = abs(int(self.p.stake / self.p.tranches))
-        else:
-            self.p.stake = stake  # OLD METHOD FOR SAMPLE COMPATIBILITY
-
-
-SizerFix = FixedSize
-
-
-class FixedReverser(bt.Sizer):
-    '''This sizer returns the needes fixed size to reverse an open position or
-    the fixed size to open one
-
-      - To open a position: return the param ``stake``
-
-      - To reverse a position: return 2 * ``stake``
-
-    Params:
-      - ``stake`` (default: ``1``)
-    '''
-    params = (('stake', 1),)
-
-    def _getsizing(self, comminfo, cash, data, isbuy):
-        position = self.strategy.getposition(data)
-        size = self.p.stake * (1 + (position.size != 0))
-        return size
-
-
-class FixedSizeTarget(bt.Sizer):
-    '''
-    This sizer simply returns a fixed target size, useful when coupled
-    with Target Orders and specifically ``cerebro.target_order_size()``.
-    Size can be controlled by number of tranches that a system
-    wishes to use to scale into trades by specifying the ``tranches``
-    parameter.
-
-
-    Params:
-      - ``stake`` (default: ``1``)
-      - ``tranches`` (default: ``1``)
-    '''
-
-    params = (('stake', 1),
-              ('tranches', 1))
-
-    def _getsizing(self, comminfo, cash, data, isbuy):
-        if self.p.tranches > 1:
-            size = abs(int(self.p.stake / self.p.tranches))
-            return min((self.strategy.position.size + size), self.p.stake)
-        else:
-            return self.p.stake
-
-    def setsizing(self, stake):
-        if self.p.tranches > 1:
-            size = abs(int(self.p.stake / self.p.tranches))
-            self.p.stake = min((self.strategy.position.size + size),
-                               self.p.stake)
-        else:
-            self.p.stake = stake  # OLD METHOD FOR SAMPLE COMPATIBILITY
diff --git a/spaces/LobsterQQQ/Nail-Set-Art/app.py b/spaces/LobsterQQQ/Nail-Set-Art/app.py
deleted file mode 100644
index 5f7ff1495be492ba0546115770b84cad9f0e710a..0000000000000000000000000000000000000000
--- a/spaces/LobsterQQQ/Nail-Set-Art/app.py
+++ /dev/null
@@ -1,201 +0,0 @@
-from contextlib import nullcontext
-import gradio as gr
-import torch
-from torch import autocast
-from diffusers import StableDiffusionPipeline
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-context = autocast if device == "cuda" else nullcontext
-dtype = torch.float16 if device == "cuda" else torch.float32
-
-pipe = StableDiffusionPipeline.from_pretrained("ringhyacinth/nail-set-diffuser", torch_dtype=dtype)
-pipe = pipe.to(device)
-
-
-# Disable nsfw checker
-disable_safety = True
-
-if disable_safety:
-  def null_safety(images, **kwargs):
-      return images, False
-  pipe.safety_checker = null_safety
-
-
-def infer(prompt, n_samples, steps, scale):
-
-    with context("cuda"):
-        images = pipe(n_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).images
-
-    return images
-
-css = """
-        a {
-            color: inherit;
-            text-decoration: underline;
-        }
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        }
-        .gr-button {
-            color: white;
-            border-color: #9d66e5;
-            background: #9d66e5;
-        }
-        input[type='range'] {
-            accent-color: #9d66e5;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            max-width: 730px;
-            margin: auto;
-            padding-top: 1.5rem;
-        }
-        #gallery {
-            min-height: 22rem;
-            margin-bottom: 15px;
-            margin-left: auto;
-            margin-right: auto;
-            border-bottom-right-radius: .5rem !important;
-            border-bottom-left-radius: .5rem !important;
-        }
-        #gallery>div>.h-full {
-            min-height: 20rem;
-        }
-        .details:hover {
-            text-decoration: underline;
-        }
-        .gr-button {
-            white-space: nowrap;
-        }
-        .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity));
-            outline: none;
-            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-            --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-            --tw-ring-opacity: .5;
-        }
-        #advanced-options {
-            margin-bottom: 20px;
-        }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        .dark .logo{ filter: invert(1); }
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-"""
-
-block = gr.Blocks(css=css)
-
-examples = [
-    [
-        'Nail Set, hamilton nail, broadway musical theme nail.',
-        2,
-        7,
-    ],
-    [
-        'Nail Set, chinese new year nail, super detailed',
-        2,
-        7,
-    ],
-    [
-        'Nail Set, thanksgiving nail, super detailed',
-        2,
-        7,
-    ],
-]
-
-with block:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
-              <div>
-                <img class="logo" src="https://cdn.discordapp.com/attachments/973596613933146123/1043954179481276416/747251668968350_.pic.jpg" alt="Weekend and Hyacinth Logo"
-                    style="margin: auto; max-width: 7rem;">
-                <h1 style="font-weight: 900; font-size: 3rem; margin-top: 25px; margin-bottom: 25px;">
-                  Text to Nail set
-                </h1>
-              </div>
-              <p style="margin-bottom: 5px; font-size: 94%">
-              Generate a new Nail Set from a text description. Use the token {Nail Set} in your prompts for the effect.
-              </p>
-            </div>
-        """
-    )
-    with gr.Group():
-        with gr.Box():
-            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                text = gr.Textbox(
-                    label="Enter your prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                )
-
-        gallery = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-
-
-        with gr.Row(elem_id="advanced-options"):
-            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1)
-            steps = gr.Slider(label="Steps", minimum=50, maximum=100, value=50, step=5)
-            scale = gr.Slider(
-                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
-            )
-
-
-        ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False)
-        ex.dataset.headers = [""]
-
-
-        text.submit(infer, inputs=[text, samples, steps, scale], outputs=gallery)
-        btn.click(infer, inputs=[text, samples, steps, scale], outputs=gallery)
-        gr.HTML(
-            """
-                <div class="footer">
-                    <p> Nail Diffuser by 💅 Weekend and Hyacinth
-                    </p>
-                </div>
-                <div class="acknowledgments">
-                    <p> Use the tokens {Nail Set} in your prompts for the effect.
-                    <p> Put in a text prompt and generate your own nail set!
-                    <p> Trained by Weekend and Hyacinth </p>
-               </div>
-           """
-        )
-
-block.launch()
\ No newline at end of file
diff --git a/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_training.py b/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_training.py
deleted file mode 100644
index 09660a26b4d99f8ff8457a454fdddcc57d7f3756..0000000000000000000000000000000000000000
--- a/spaces/MBZ/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/MINAMONI/img-to-music/app.py b/spaces/MINAMONI/img-to-music/app.py
deleted file mode 100644
index 30d094ce05b344d21f1c497c183a4ce7649ec164..0000000000000000000000000000000000000000
--- a/spaces/MINAMONI/img-to-music/app.py
+++ /dev/null
@@ -1,333 +0,0 @@
-import gradio as gr
-import openai
-import numpy as np
-import time
-import base64
-import ffmpeg
-from sentence_transformers import SentenceTransformer
-from audio2numpy import open_audio
-import httpx
-import json
-import os
-import requests
-import urllib
-import pydub
-from os import path
-from pydub import AudioSegment
-import re
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-#img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
-img_to_text = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-from utils import get_tags_for_prompts, get_mubert_tags_embeddings
-
-minilm = SentenceTransformer('all-MiniLM-L6-v2')
-mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
-
-##————————————————————————————————————
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-##————————————————————————————————————
-def get_pat_token():
-    r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess',
-                   json={
-                       "method": "GetServiceAccess",
-                       "params": {
-                           "email":"mail@mail.com",
-                           "phone":"+11234567890",
-                           "license": MUBERT_LICENSE,
-                           "token": MUBERT_TOKEN,
-                           
-                       }
-                   })
-
-    rdata = json.loads(r.text)
-    assert rdata['status'] == 1, "probably incorrect e-mail"
-    pat = rdata['data']['pat']
-    #print(f"pat: {pat}")
-    return pat
-
-def get_music(pat, prompt, track_duration, gen_intensity, gen_mode):
-
-    if len(prompt) > 200:
-        prompt = prompt[:200]
-    
-    r = httpx.post('https://api-b2b.mubert.com/v2/TTMRecordTrack',
-                   json={
-                       "method": "TTMRecordTrack",
-                       "params":
-                           {
-                                "text": prompt,
-                                "pat": pat,
-                                "mode":gen_mode,
-                                "duration":track_duration, 
-                                "intensity": gen_intensity,
-                                "format": "wav"
-                           }
-    })
-
-    rdata = json.loads(r.text)
-    
-    #print(f"rdata: {rdata}")
-    assert rdata['status'] == 1, rdata['error']['text']
-    track = rdata['data']['tasks'][0]['download_link']
-    print(track)
-    
-    local_file_path = "sample.wav"
-
-    # Download the MP3 file from the URL
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7; rv:93.0) Gecko/20100101 Firefox/93.0'}
-
-    retries = 3
-    delay = 5  # in seconds
-    while retries > 0:
-        response = requests.get(track, headers=headers)
-        if response.status_code == 200:
-            break
-        retries -= 1
-        time.sleep(delay)
-    response = requests.get(track, headers=headers)
-    print(f"{response}")
-    # Save the downloaded content to a local file
-    with open(local_file_path, 'wb') as f:
-        f.write(response.content)
-        return "sample.wav", track
-   
-
-def get_results(text_prompt,track_duration,gen_intensity,gen_mode):
-    pat_token = get_pat_token()
-    music = get_music(pat_token, text_prompt, track_duration, gen_intensity, gen_mode)
-    return pat_token, music[0], music[1]
-
-def get_prompts(uploaded_image, track_duration, gen_intensity, gen_mode, openai_api_key):
-    print("calling clip interrogator")
-    #prompt = img_to_text(uploaded_image, "ViT-L (best for Stable Diffusion 1.*)", "fast", fn_index=1)[0]
-    
-    prompt = img_to_text(uploaded_image, 'best', 4, fn_index=1)[0]
-    print(prompt)
-    clean_prompt = clean_text(prompt)
-    print(f"prompt cleaned: {clean_prompt}")
-    musical_prompt = 'You did not use any OpenAI API key to pimp your result :)'
-    if openai_api_key is not None:
-        gpt_adaptation = try_api(prompt, openai_api_key)
-        if gpt_adaptation[0] != "oups":
-            musical_prompt = gpt_adaptation[0]
-            print(f"musical adapt: {musical_prompt}")
-            music_result = get_results(musical_prompt, track_duration, gen_intensity, gen_mode)
-        else:
-            music_result = get_results(clean_prompt, track_duration, gen_intensity, gen_mode)
-    else:
-        music_result = get_results(clean_prompt, track_duration, gen_intensity, gen_mode)
-    
-    show_prompts = f"""
-        CLIP Interrogator Caption: '{prompt}'
-        —
-        OpenAI Musical Adaptation: '{musical_prompt}'
-        —
-        Audio file link: {music_result[2]}
-    """
-    #wave_file = convert_mp3_to_wav(music_result[1])
-    
-    time.sleep(1)
-    return gr.Textbox.update(value=show_prompts, visible=True), music_result[1], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-def try_api(message, openai_api_key):
-
-    try:
-        response = call_api(message, openai_api_key)
-        return response, "<span class='openai_clear'>no error</span>"
-    except openai.error.Timeout as e:
-        #Handle timeout error, e.g. retry or log
-        #print(f"OpenAI API request timed out: {e}")
-        return "oups", f"<span class='openai_error'>OpenAI API request timed out: <br />{e}</span>"
-    except openai.error.APIError as e:
-        #Handle API error, e.g. retry or log
-        #print(f"OpenAI API returned an API Error: {e}")
-        return "oups", f"<span class='openai_error'>OpenAI API returned an API Error: <br />{e}</span>"
-    except openai.error.APIConnectionError as e:
-        #Handle connection error, e.g. check network or log
-        #print(f"OpenAI API request failed to connect: {e}")
-        return "oups", f"<span class='openai_error'>OpenAI API request failed to connect: <br />{e}</span>"
-    except openai.error.InvalidRequestError as e:
-        #Handle invalid request error, e.g. validate parameters or log
-        #print(f"OpenAI API request was invalid: {e}")
-        return "oups", f"<span class='openai_error'>OpenAI API request was invalid: <br />{e}</span>"
-    except openai.error.AuthenticationError as e:
-        #Handle authentication error, e.g. check credentials or log
-        #print(f"OpenAI API request was not authorized: {e}")
-        return "oups", f"<span class='openai_error'>OpenAI API request was not authorized: <br />{e}</span>"
-    except openai.error.PermissionError as e:
-        #Handle permission error, e.g. check scope or log
-        #print(f"OpenAI API request was not permitted: {e}")
-        return "oups", f"<span class='openai_error'>OpenAI API request was not permitted: <br />{e}</span>"
-    except openai.error.RateLimitError as e:
-        #Handle rate limit error, e.g. wait or log
-        #print(f"OpenAI API request exceeded rate limit: {e}")
-        return "oups", f"<span class='openai_error'>OpenAI API request exceeded rate limit: <br />{e}</span>"
-
-def call_api(message, openai_api_key):
-
-    instruction = "Convert in less than 200 characters this image caption to a very concise musical description with musical terms, as if you wanted to describe a musical ambiance, stricly in English"
-          
-    print("starting open ai")
-    augmented_prompt = f"{instruction}: '{message}'."
-    openai.api_key = openai_api_key
-    
-    response = openai.Completion.create(
-        model="text-davinci-003",
-        prompt=augmented_prompt,
-        temperature=0.5,
-        max_tokens=2048,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0.6
-    )
-
-    #print(response)
-
-    #return str(response.choices[0].text).split("\n",2)[2]
-    return str(response.choices[0].text).lstrip('\n')  
-
-
-def get_track_by_tags(tags, pat, duration, gen_intensity, gen_mode, maxit=20):
-    
-    r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
-                   json={
-                       "method": "RecordTrackTTM",
-                       "params": {
-                           "pat": pat,
-                           "duration": duration,
-                           "format": "wav",
-                           "intensity":gen_intensity,
-                           "tags": tags,
-                           "mode": gen_mode
-                       }
-                   })
-
-    rdata = json.loads(r.text)
-    print(rdata)
-    #assert rdata['status'] == 1, rdata['error']['text']
-    trackurl = rdata['data']['tasks'][0]
-
-    print('Generating track ', end='')
-    for i in range(maxit):
-        r = httpx.get(trackurl)
-        if r.status_code == 200:
-            return trackurl
-        time.sleep(1)
-
-
-def generate_track_by_prompt(pat, prompt, duration, gen_intensity, gen_mode):
-    try:
-        _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, prompt)[0]
-        result = get_track_by_tags(tags, pat, int(duration), gen_intensity, gen_mode)
-        print(result)
-        return result, ",".join(tags), "Success"
-    except Exception as e:
-        return None, "", str(e)
-
-def convert_mp3_to_wav(mp3_filepath):
- 
-  wave_file="file.wav"
-  
-  sound = AudioSegment.from_mp3(mp3_filepath)
-  sound.export(wave_file, format="wav")
-  
-  return wave_file
-
-def remove_emoji(text):
-    emoji_pattern = re.compile("["
-        u"\U0001F600-\U0001F64F"  # emoticons
-        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
-        u"\U0001F680-\U0001F6FF"  # transport & map symbols
-        u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
-                           "]+", flags=re.UNICODE)
-    return emoji_pattern.sub(r'', text)
-
-def remove_nonalphanumeric(text):
-    return re.sub(r'[^a-zA-Z0-9\s]', '', text)
-
-def clean_text(text):
-    clean_text = remove_nonalphanumeric(text)
-    clean_text = remove_emoji(clean_text)
-    clean_text = re.sub(r'\d+', '', clean_text) # Remove any number
-    return clean_text
-
-article = """
-    
-    <div class="footer">
-        <p>
-         
-        Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗
-        </p>
-    </div>
-    
-    <div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;">
-        <p style="font-size: 0.8em;margin-bottom: 4px;">You may also like: </p>
-        <div id="may-like" style="display: flex;flex-wrap: wrap;align-items: center;height: 20px;">
-            <svg height="20" width="122" style="margin-left:4px;margin-bottom: 6px;">       
-                 <a href="https://huggingface.co/spaces/fffiloni/spectrogram-to-music" target="_blank">
-                    <image href="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue" src="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue.png" height="20"/>
-                 </a>
-            </svg>
-        </div>
-    </div>
-
-    
-"""
-
-with gr.Blocks(css="style.css") as demo:
-    with gr.Column(elem_id="col-container"):
-        
-        gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-                <div
-                style="
-                    display: inline-flex;
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1.75rem;
-                "
-                >
-                <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                    Image to Music
-                </h1>
-                </div>
-                <p style="margin-bottom: 10px; font-size: 94%">
-                Sends an image in to <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator" target="_blank">CLIP Interrogator</a>
-                to generate a text prompt which is then run through 
-                <a href="https://huggingface.co/Mubert" target="_blank">Mubert</a> text-to-music to generate music from the input image!
-                </p>
-            </div>""")
-    
-        input_img = gr.Image(type="filepath", elem_id="input-img")
-        prompts_out = gr.Textbox(label="Text Captions", visible=False, elem_id="prompts_out", info="If player do not work, try to copy/paste the link in a new browser window")
-        music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output").style(height="5rem")
-        #music_url = gr.Textbox(max_lines=1, info="If player do not work, try to copy/paste the link in a new browser window")
-        #text_status = gr.Textbox(label="status")
-        with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
-            loading_icon = gr.HTML(loading_icon_html, visible=False)
-            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-        with gr.Accordion(label="Music Generation Options", open=False):
-            openai_api_key = gr.Textbox(type="password", label="🔐 Your OpenAI API Key (optional)", placeholder="sk-123abc...", info="You can use your OpenAI key to adapt CLIP Interrogator caption to a musical translation.")
-            track_duration = gr.Slider(minimum=20, maximum=120, value=55, ustep=5, label="Track duration", elem_id="duration-inp")
-            with gr.Row():
-                gen_intensity = gr.Dropdown(choices=["low", "medium", "high"], value="medium", label="Intensity")
-                gen_mode = gr.Radio(label="mode", choices=["track", "loop"], value="loop")
-        
-        generate = gr.Button("Generate Music from Image")
-
-        gr.HTML(article)
-    
-    generate.click(get_prompts, inputs=[input_img,track_duration,gen_intensity,gen_mode, openai_api_key], outputs=[prompts_out, music_output, share_button, community_icon, loading_icon], api_name="i2m")
-    share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=32).launch()
\ No newline at end of file
diff --git a/spaces/MZhaovo/Llama_Difu/presets.py b/spaces/MZhaovo/Llama_Difu/presets.py
deleted file mode 100644
index 509a451837f964f0179efbce38b6f71268e84a91..0000000000000000000000000000000000000000
--- a/spaces/MZhaovo/Llama_Difu/presets.py
+++ /dev/null
@@ -1,58 +0,0 @@
-prompt_tmpl_list = ["Default", "MZhao Mode"]
-
-prompt_tmpl_dict = {
-    "Default":
-"""\
-Context information is below:
-
-{context_str}
-
-Given the above context, please answer the following question.
-Answer in bullet points.
-
-Question: {query_str}
-Answer in the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch.
-""",
-
-    "MZhao Mode":
-"""\
-Context information:
-
-{context_str}
-
-Instructions:
-Mzhao is the chat mode of the Difu Chat Engine.
-Mzhao identifies as “Difu” not an assistant.
-Mzhao introduces itself with “this is Difu only at the beginning of the conversation.
-Mzhao does not disclose the internal alias “Mzhao.”
-Mzhao can understand and communicate fluently in the user’s language of choice, such as English, 中文, 日本語, Español, Français, or Deutsch.
-Mzhao’s responses should be informative, visual, logical, and actionable.
-Mzhao’s responses should also be positive, interesting, entertaining, and engaging.
-Mzhao’s responses should avoid being vague, controversial, or off-topic.
-Mzhao’s logic and reasoning should be rigorous, intelligent, and defensible.
-Mzhao can provide additional relevant details to respond thoroughly and comprehensively to cover multiple aspects in depth.
-Mzhao can generate poems, stories, code, essays, songs, celebrity parodies, and more.
-Mzhao can generate a query to look up for helpful products or services advertisements after responding.
-Mzhao must always generate short suggestions for the next user turn after responding.
-Mzhao should always generate short suggestions for the next user turn that are relevant to the conversation and not offensive.
-Mzhao can leverage past responses and web results for generating relevant and interesting suggestions for the next user turn.
-Mzhao does not generate generic suggestions for the next user turn, such as “thank you.”
-Mzhao does not generate suggestions for the next user turn to carry out tasks such as “booking flight ticket” or “send an email to” that Mzhao cannot perform.
-Mzhao should always perform context infomation look ups when the user is seeking information or whenever context infomation could be potentially helpful, regardless of Mzhao’s internal knowledge or information.
-Mzhao can and should perform up to three look ups in a single conversation turn. Mzhao should never look up the same query more than once.
-Mzhao can only issue numerical references to the URLs. Mzhao should never generate URLs or links apart from the ones provided in context infomation.
-Mzhao always references factual statements to the context infomation.
-Context infomation may be incomplete or irrelevant. Mzhao doesn’t make assumptions on the context infomation beyond strictly what’s returned.
-If the context infomation do not contain sufficient information to answer the user message completely, Mzhao uses only facts from the context infomation and does not add any information by itself.
-Mzhao can leverage information from multiple context infomation to respond comprehensively.
-If the user message consists of keywords instead of chat messages, Mzhao treats it as a look up query.
-Context infomation may expire over time. Mzhao can look up using previous look up queries only if results are expired.
-Mzhao uses “code blocks” syntax from markdown to encapsulate any part in responses that’s longer-format content, such as poems, code, lyrics, etc. (except tables).
-Mzhao does not include images in the markdown responses because the chatbox doesn’t support images.
-
-Query: {query_str}
-
-Reply in the same language as the query, such as English, 中文, 日本語, Español, Français, or Deutsch
-"""
-
-}
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/makeaprotagonist/models/unet.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/makeaprotagonist/models/unet.py
deleted file mode 100644
index 95fb6bef9019953e34891b3f46f9968c92d19748..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/makeaprotagonist/models/unet.py
+++ /dev/null
@@ -1,660 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
-
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union, Callable
-
-import os
-import json
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers import ModelMixin
-from diffusers.utils import BaseOutput, logging
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps, GaussianFourierProjection
-from .unet_blocks import (
-    CrossAttnDownBlock3D,
-    CrossAttnUpBlock3D,
-    DownBlock3D,
-    UNetMidBlock3DCrossAttn,
-    UpBlock3D,
-    get_down_block,
-    get_up_block,
-)
-from .resnet import InflatedConv3d
-
-import ipdb
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-class UNet3DConditionOutput(BaseOutput):
-    sample: torch.FloatTensor
-
-
-class UNet3DConditionModel(ModelMixin, ConfigMixin):
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 4,
-        out_channels: int = 4,
-        center_input_sample: bool = False,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "DownBlock3D",
-        ),
-        mid_block_type: str = "UNetMidBlock3DCrossAttn",
-        up_block_types: Tuple[str] = (
-            "UpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D"
-        ),
-        only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        layers_per_block: int = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        act_fn: str = "silu",
-        norm_num_groups: int = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: int = 1280,
-        attention_head_dim: Union[int, Tuple[int]] = 8,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        class_embed_type: Optional[str] = None,
-        num_class_embeds: Optional[int] = None,
-        upcast_attention: bool = False,
-        resnet_time_scale_shift: str = "default",
-
-        time_embedding_type: str = "positional",
-        timestep_post_act: Optional[str] = None,
-        time_cond_proj_dim: Optional[int] = None,
-        conv_in_kernel: int = 3,
-        conv_out_kernel: int = 3,
-        projection_class_embeddings_input_dim: Optional[int] = None,
-        class_embeddings_concat: bool = False,
-
-        temporal_conv=False,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-        time_embed_dim = block_out_channels[0] * 4
-
-        # input
-        self.in_channels = in_channels
-        conv_in_padding = (conv_in_kernel - 1) // 2
-        self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding)
-
-        # time
-        if time_embedding_type == "fourier":
-            time_embed_dim = block_out_channels[0] * 2
-            if time_embed_dim % 2 != 0:
-                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
-            self.time_proj = GaussianFourierProjection(
-                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
-            )
-            timestep_input_dim = time_embed_dim
-        elif time_embedding_type == "positional":
-            time_embed_dim = block_out_channels[0] * 4
-
-            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-            timestep_input_dim = block_out_channels[0]
-        else:
-            raise ValueError(
-                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
-            )
-
-        self.time_embedding = TimestepEmbedding(
-            timestep_input_dim,
-            time_embed_dim,
-            act_fn=act_fn,
-            post_act_fn=timestep_post_act,
-            cond_proj_dim=time_cond_proj_dim,
-        )
-
-        # class embedding
-        if class_embed_type is None and num_class_embeds is not None:
-            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
-        elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-        elif class_embed_type == "identity":
-            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        elif class_embed_type == "projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
-            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
-            # 2. it projects from an arbitrary input dimension.
-            #
-            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
-            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
-            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
-            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif class_embed_type == "simple_projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
-        else:
-            self.class_embedding = None
-
-        self.down_blocks = nn.ModuleList([])
-        self.mid_block = None
-        self.up_blocks = nn.ModuleList([])
-
-
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        if isinstance(cross_attention_dim, int):
-            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
-
-        if class_embeddings_concat:
-            # The time embeddings are concatenated with the class embeddings. The dimension of the
-            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
-            # regular time embeddings
-            blocks_time_embed_dim = time_embed_dim * 2
-        else:
-            blocks_time_embed_dim = time_embed_dim
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim[i],
-                attn_num_head_channels=attention_head_dim[i],
-                downsample_padding=downsample_padding,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                temporal_conv=temporal_conv
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        if mid_block_type == "UNetMidBlock3DCrossAttn":
-            self.mid_block = UNetMidBlock3DCrossAttn(
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                cross_attention_dim=cross_attention_dim[-1],
-                attn_num_head_channels=attention_head_dim[-1],
-                resnet_groups=norm_num_groups,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-                temporal_conv=temporal_conv
-            )
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-        # count how many layers upsample the videos
-        self.num_upsamplers = 0
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_attention_head_dim = list(reversed(attention_head_dim))
-        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
-        only_cross_attention = list(reversed(only_cross_attention))
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            is_final_block = i == len(block_out_channels) - 1
-
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            # add upsample block for all BUT final layer
-            if not is_final_block:
-                add_upsample = True
-                self.num_upsamplers += 1
-            else:
-                add_upsample = False
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=layers_per_block + 1,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_upsample=add_upsample,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=reversed_cross_attention_dim[i],
-                attn_num_head_channels=reversed_attention_head_dim[i],
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                temporal_conv=temporal_conv
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        if norm_num_groups is not None:
-            self.conv_norm_out = nn.GroupNorm(
-                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
-            )
-            self.conv_act = nn.SiLU()
-        else:
-            self.conv_norm_out = None
-            self.conv_act = None
-
-        conv_out_padding = (conv_out_kernel - 1) // 2
-        self.conv_out = InflatedConv3d(
-            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
-        )
-
-    def set_attention_slice(self, slice_size):
-        r"""
-        Enable sliced attention computation.
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-        Args:
-            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
-                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
-                must be a multiple of `slice_size`.
-        """
-        sliceable_head_dims = []
-
-        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
-            if hasattr(module, "set_attention_slice"):
-                sliceable_head_dims.append(module.sliceable_head_dim)
-
-            for child in module.children():
-                fn_recursive_retrieve_sliceable_dims(child)
-
-        # retrieve number of attention layers
-        for module in self.children():
-            fn_recursive_retrieve_sliceable_dims(module)
-
-        num_sliceable_layers = len(sliceable_head_dims)
-
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = [dim // 2 for dim in sliceable_head_dims]
-        elif slice_size == "max":
-            # make smallest slice possible
-            slice_size = num_sliceable_layers * [1]
-
-        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
-
-        if len(slice_size) != len(sliceable_head_dims):
-            raise ValueError(
-                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
-                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
-            )
-
-        for i in range(len(slice_size)):
-            size = slice_size[i]
-            dim = sliceable_head_dims[i]
-            if size is not None and size > dim:
-                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
-
-        # Recursively walk through all the children.
-        # Any children which exposes the set_attention_slice method
-        # gets the message
-        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
-            if hasattr(module, "set_attention_slice"):
-                module.set_attention_slice(slice_size.pop())
-
-            for child in module.children():
-                fn_recursive_set_attention_slice(child, slice_size)
-
-        reversed_slice_size = list(reversed(slice_size))
-        for module in self.children():
-            fn_recursive_set_attention_slice(module, reversed_slice_size)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
-            module.gradient_checkpointing = value
-
-    def set_use_memory_efficient_attention_xformers(
-        self, valid: bool, attention_op: Optional[Callable] = None
-    ) -> None:
-        # Recursively walk through all the children.
-        # Any children which exposes the set_use_memory_efficient_attention_xformers method
-        # gets the message
-        def fn_recursive_set_mem_eff(module: torch.nn.Module):
-            if hasattr(module, "set_use_memory_efficient_attention_xformers"):
-                try:
-                    module.set_use_memory_efficient_attention_xformers(valid, attention_op)
-                except: 
-                    ipdb.set_trace()
-
-            for child in module.children():
-                fn_recursive_set_mem_eff(child)
-
-        for module in self.children():
-            if isinstance(module, torch.nn.Module):
-                fn_recursive_set_mem_eff(module)
-
-    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
-        r"""
-        Enable memory efficient attention as implemented in xformers.
-        When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
-        time. Speed up at training time is not guaranteed.
-        Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
-        is used.
-        Parameters:
-            attention_op (`Callable`, *optional*):
-                Override the default `None` operator for use as `op` argument to the
-                [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
-                function of xFormers.
-        Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import UNet2DConditionModel
-        >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
-        >>> model = UNet2DConditionModel.from_pretrained(
-        ...     "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
-        ... )
-        >>> model = model.to("cuda")
-        >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
-        ```
-        """
-        self.set_use_memory_efficient_attention_xformers(True, attention_op)
-
-    def disable_xformers_memory_efficient_attention(self):
-        r"""
-        Disable memory efficient attention as implemented in xformers.
-        """
-        self.set_use_memory_efficient_attention_xformers(False)
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        adapter_features: Optional[torch.Tensor] = None, 
-        class_labels: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        mid_block_additional_residual: Optional[torch.Tensor] = None,
-        ## mask args
-        class_labels_aux: Optional[torch.Tensor] = None,
-        masks: Optional[torch.Tensor] = None,
-
-    ) -> Union[UNet3DConditionOutput, Tuple]:
-        r"""
-        Args:
-            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
-            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-        """
-        # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
-        # However, the upsampling interpolation output size can be forced to fit any upsampling size
-        # on the fly if necessary.
-        default_overall_up_factor = 2**self.num_upsamplers
-        if adapter_features is None:
-            adapter_features = [None] * len(self.down_blocks)
-        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
-        forward_upsample_size = False
-        upsample_size = None
-
-        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
-            logger.info("Forward upsample size to force interpolation output size.")
-            forward_upsample_size = True
-
-        # prepare attention_mask
-        if attention_mask is not None:
-            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        # center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # This would be a good case for the `match` statement (Python 3.10+)
-            is_mps = sample.device.type == "mps"
-            if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
-            else:
-                dtype = torch.int32 if is_mps else torch.int64
-            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
-        elif len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(sample.shape[0])
-
-        t_emb = self.time_proj(timesteps)
-
-        # timesteps does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=self.dtype)
-        t_emb = self.time_embedding(t_emb)
-
-        # ipdb.set_trace()
-        if self.class_embedding is not None:
-            if class_labels is None:
-                raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels = self.time_proj(class_labels)
-
-            ## NOTE use torch.tensor(0) indicate not use image embedding
-            if class_labels.dim() == 0:
-                class_emb = 0
-            else:
-                class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) # F,C
-            # class_emb = 0 ## NOTE this is for debugging, trying not use image embedding
-
-            # ipdb.set_trace()
-            if self.config.class_embeddings_concat: ## false
-                emb = torch.cat([t_emb, class_emb], dim=-1)
-            else:
-                if t_emb.size(0) == class_emb.size(0):
-                    emb = t_emb + class_emb
-                elif t_emb.size(0) * sample.size(2) == class_emb.size(0):
-                    # e_emb 2,C / class emb: 2F,C
-                    class_emb = class_emb.reshape(t_emb.size(0), sample.size(2), class_emb.size(-1))
-                    emb = t_emb[:, None] + class_emb
-                    emb = emb.reshape(-1, emb.size(-1))
-                else:
-                    emb = t_emb.repeat(2,1) + class_emb
-
-        ## NOTE aux embedding
-        emb_aux = None
-        if self.class_embedding is not None and class_labels_aux is not None:
-            # ipdb.set_trace()
-            if class_labels_aux is not None and masks is None:
-                raise ValueError("masks should be provided when class_labels_aux is given")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels_aux = self.time_proj(class_labels_aux)
-
-            class_emb_aux = self.class_embedding(class_labels_aux).to(dtype=self.dtype)
-
-            if self.config.class_embeddings_concat: ## false
-                emb_aux = torch.cat([t_emb, class_emb_aux], dim=-1)
-            else:
-                # emb_aux = t_emb + class_emb_aux
-                if t_emb.size(0) == class_emb_aux.size(0):
-                    emb_aux = t_emb + class_emb_aux
-                else:
-                    # e_emb 2,C / class emb: 2F,C
-                    class_emb_aux = class_emb_aux.reshape(t_emb.size(0), sample.size(2), class_emb_aux.size(-1))
-                    emb_aux = t_emb[:, None] + class_emb_aux
-                    emb_aux = emb_aux.reshape(-1, emb_aux.size(-1))
-
-
-        # pre-process
-        sample = self.conv_in(sample)
-
-        # down
-        down_block_res_samples = (sample,)
-        for down_id, downsample_block in enumerate(self.down_blocks):
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                
-                # ipdb.set_trace()
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    temb_aux=emb_aux,
-                    masks=masks,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    adapter_feature=adapter_features[down_id]
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, temb_aux=emb_aux, masks=masks,adapter_feature=adapter_features[down_id])
-
-            down_block_res_samples += res_samples
-
-        if down_block_additional_residuals is not None: ## seems to fit controlnet
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample = down_block_res_sample + down_block_additional_residual
-                new_down_block_res_samples += (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # mid
-        sample = self.mid_block(
-            sample, emb, temb_aux=emb_aux, masks=masks, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
-        )
-        if mid_block_additional_residual is not None:
-            sample = sample + mid_block_additional_residual
-
-        # up
-        for i, upsample_block in enumerate(self.up_blocks):
-            is_final_block = i == len(self.up_blocks) - 1
-
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            # if we have not reached the final block and need to forward the
-            # upsample size, we do it here
-            if not is_final_block and forward_upsample_size:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    temb_aux=emb_aux,
-                    masks=masks,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    upsample_size=upsample_size,
-                    attention_mask=attention_mask,
-                )
-            else:
-                sample = upsample_block(
-                    hidden_states=sample, temb=emb, temb_aux=emb_aux, masks=masks, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
-                )
-        # post-process
-        if self.conv_norm_out:
-            sample = self.conv_norm_out(sample)
-            sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        if not return_dict:
-            return (sample,)
-
-        return UNet3DConditionOutput(sample=sample)
-
-    @classmethod
-    def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, use_temporal_conv=False):
-        if subfolder is not None:
-            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
-
-        config_file = os.path.join(pretrained_model_path, 'config.json')
-        if not os.path.isfile(config_file):
-            raise RuntimeError(f"{config_file} does not exist")
-        with open(config_file, "r") as f:
-            config = json.load(f)
-        config["_class_name"] = cls.__name__
-        config["down_block_types"] = [
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "DownBlock3D"
-        ]
-
-        config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
-
-        config["up_block_types"] = [
-            "UpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D"
-        ]
-
-        config["temporal_conv"] = use_temporal_conv
-
-        from diffusers.utils import WEIGHTS_NAME
-        model = cls.from_config(config)
-        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
-        if not os.path.isfile(model_file):
-            raise RuntimeError(f"{model_file} does not exist")
-        state_dict = torch.load(model_file, map_location="cpu")
-        for k, v in model.state_dict().items():
-            if '_temp.' in k:
-                state_dict.update({k: v})
-            if 'temp_conv' in k:
-                state_dict.update({k: v})
-                
-        model.load_state_dict(state_dict)
-
-        return model
\ No newline at end of file
diff --git a/spaces/Makiing/coolb-in-gtest/src/lib/hooks/use-bing.ts b/spaces/Makiing/coolb-in-gtest/src/lib/hooks/use-bing.ts
deleted file mode 100644
index dcdb1667ced0cba299b0825c0e91c4732411308c..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/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/MarkuzML/swap_face/README.md b/spaces/MarkuzML/swap_face/README.md
deleted file mode 100644
index 79fc8ce24dc018c133d344a74a1dd35335b76c94..0000000000000000000000000000000000000000
--- a/spaces/MarkuzML/swap_face/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Swap Face
-emoji: 👀
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/voc.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/voc.py
deleted file mode 100644
index a8855203b14ee0dc4da9099a2945d4aedcffbcd6..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/voc.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class PascalVOCDataset(CustomDataset):
-    """Pascal VOC dataset.
-
-    Args:
-        split (str): Split txt file for Pascal VOC.
-    """
-
-    CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
-               'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-               'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
-               'train', 'tvmonitor')
-
-    PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
-               [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
-               [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
-               [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
-               [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
-
-    def __init__(self, split, **kwargs):
-        super(PascalVOCDataset, self).__init__(
-            img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
-        assert osp.exists(self.img_dir) and self.split is not None
diff --git a/spaces/MichaelWelsch/FreeVC/speaker_encoder/visualizations.py b/spaces/MichaelWelsch/FreeVC/speaker_encoder/visualizations.py
deleted file mode 100644
index ec00fc64d6e9fda2bb8e613531066ac824df1451..0000000000000000000000000000000000000000
--- a/spaces/MichaelWelsch/FreeVC/speaker_encoder/visualizations.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-from datetime import datetime
-from time import perf_counter as timer
-import matplotlib.pyplot as plt
-import numpy as np
-# import webbrowser
-import visdom
-import umap
-
-colormap = np.array([
-    [76, 255, 0],
-    [0, 127, 70],
-    [255, 0, 0],
-    [255, 217, 38],
-    [0, 135, 255],
-    [165, 0, 165],
-    [255, 167, 255],
-    [0, 255, 255],
-    [255, 96, 38],
-    [142, 76, 0],
-    [33, 0, 127],
-    [0, 0, 0],
-    [183, 183, 183],
-], dtype=np.float) / 255 
-
-
-class Visualizations:
-    def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
-        # Tracking data
-        self.last_update_timestamp = timer()
-        self.update_every = update_every
-        self.step_times = []
-        self.losses = []
-        self.eers = []
-        print("Updating the visualizations every %d steps." % update_every)
-        
-        # If visdom is disabled TODO: use a better paradigm for that
-        self.disabled = disabled    
-        if self.disabled:
-            return 
-        
-        # Set the environment name
-        now = str(datetime.now().strftime("%d-%m %Hh%M"))
-        if env_name is None:
-            self.env_name = now
-        else:
-            self.env_name = "%s (%s)" % (env_name, now)
-        
-        # Connect to visdom and open the corresponding window in the browser
-        try:
-            self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
-        except ConnectionError:
-            raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
-                            "start it.")
-        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
-        
-        # Create the windows
-        self.loss_win = None
-        self.eer_win = None
-        # self.lr_win = None
-        self.implementation_win = None
-        self.projection_win = None
-        self.implementation_string = ""
-        
-    def log_params(self):
-        if self.disabled:
-            return 
-        from speaker_encoder import params_data
-        from speaker_encoder import params_model
-        param_string = "<b>Model parameters</b>:<br>"
-        for param_name in (p for p in dir(params_model) if not p.startswith("__")):
-            value = getattr(params_model, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        param_string += "<b>Data parameters</b>:<br>"
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        self.vis.text(param_string, opts={"title": "Parameters"})
-        
-    def log_dataset(self, dataset: SpeakerVerificationDataset):
-        if self.disabled:
-            return 
-        dataset_string = ""
-        dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
-        dataset_string += "\n" + dataset.get_logs()
-        dataset_string = dataset_string.replace("\n", "<br>")
-        self.vis.text(dataset_string, opts={"title": "Dataset"})
-        
-    def log_implementation(self, params):
-        if self.disabled:
-            return 
-        implementation_string = ""
-        for param, value in params.items():
-            implementation_string += "<b>%s</b>: %s\n" % (param, value)
-            implementation_string = implementation_string.replace("\n", "<br>")
-        self.implementation_string = implementation_string
-        self.implementation_win = self.vis.text(
-            implementation_string, 
-            opts={"title": "Training implementation"}
-        )
-
-    def update(self, loss, eer, step):
-        # Update the tracking data
-        now = timer()
-        self.step_times.append(1000 * (now - self.last_update_timestamp))
-        self.last_update_timestamp = now
-        self.losses.append(loss)
-        self.eers.append(eer)
-        print(".", end="")
-        
-        # Update the plots every <update_every> steps
-        if step % self.update_every != 0:
-            return
-        time_string = "Step time:  mean: %5dms  std: %5dms" % \
-                      (int(np.mean(self.step_times)), int(np.std(self.step_times)))
-        print("\nStep %6d   Loss: %.4f   EER: %.4f   %s" %
-              (step, np.mean(self.losses), np.mean(self.eers), time_string))
-        if not self.disabled:
-            self.loss_win = self.vis.line(
-                [np.mean(self.losses)],
-                [step],
-                win=self.loss_win,
-                update="append" if self.loss_win else None,
-                opts=dict(
-                    legend=["Avg. loss"],
-                    xlabel="Step",
-                    ylabel="Loss",
-                    title="Loss",
-                )
-            )
-            self.eer_win = self.vis.line(
-                [np.mean(self.eers)],
-                [step],
-                win=self.eer_win,
-                update="append" if self.eer_win else None,
-                opts=dict(
-                    legend=["Avg. EER"],
-                    xlabel="Step",
-                    ylabel="EER",
-                    title="Equal error rate"
-                )
-            )
-            if self.implementation_win is not None:
-                self.vis.text(
-                    self.implementation_string + ("<b>%s</b>" % time_string), 
-                    win=self.implementation_win,
-                    opts={"title": "Training implementation"},
-                )
-
-        # Reset the tracking
-        self.losses.clear()
-        self.eers.clear()
-        self.step_times.clear()
-        
-    def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None,
-                         max_speakers=10):
-        max_speakers = min(max_speakers, len(colormap))
-        embeds = embeds[:max_speakers * utterances_per_speaker]
-        
-        n_speakers = len(embeds) // utterances_per_speaker
-        ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
-        colors = [colormap[i] for i in ground_truth]
-        
-        reducer = umap.UMAP()
-        projected = reducer.fit_transform(embeds)
-        plt.scatter(projected[:, 0], projected[:, 1], c=colors)
-        plt.gca().set_aspect("equal", "datalim")
-        plt.title("UMAP projection (step %d)" % step)
-        if not self.disabled:
-            self.projection_win = self.vis.matplot(plt, win=self.projection_win)
-        if out_fpath is not None:
-            plt.savefig(out_fpath)
-        plt.clf()
-        
-    def save(self):
-        if not self.disabled:
-            self.vis.save([self.env_name])
-        
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/kie/_base_/datasets/wildreceipt.py b/spaces/Mountchicken/MAERec-Gradio/configs/kie/_base_/datasets/wildreceipt.py
deleted file mode 100644
index 9c1122edd53c5c8df4bad55ad764c12e1714026a..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/kie/_base_/datasets/wildreceipt.py
+++ /dev/null
@@ -1,16 +0,0 @@
-wildreceipt_data_root = 'data/wildreceipt/'
-
-wildreceipt_train = dict(
-    type='WildReceiptDataset',
-    data_root=wildreceipt_data_root,
-    metainfo=wildreceipt_data_root + 'class_list.txt',
-    ann_file='train.txt',
-    pipeline=None)
-
-wildreceipt_test = dict(
-    type='WildReceiptDataset',
-    data_root=wildreceipt_data_root,
-    metainfo=wildreceipt_data_root + 'class_list.txt',
-    ann_file='test.txt',
-    test_mode=True,
-    pipeline=None)
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/synthtext_parser.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/synthtext_parser.py
deleted file mode 100644
index 0764e0d8f1f5b00bdc7d2c8210b24d8bb2b87a53..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/synthtext_parser.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-from mmengine import track_parallel_progress
-from scipy.io import loadmat
-
-from mmocr.utils import is_type_list
-from ..data_preparer import DATA_PARSERS
-from .base import BaseParser
-
-
-@DATA_PARSERS.register_module()
-class SynthTextAnnParser(BaseParser):
-    """SynthText Text Detection Annotation Parser.
-
-    Args:
-        split (str): The split of the dataset. It is usually set automatically
-            and users do not need to set it manually in config file in most
-            cases.
-        nproc (int): Number of processes to process the data. Defaults to 1.
-            It is usually set automatically and users do not need to set it
-            manually in config file in most cases.
-        separator (str): The separator between each element in a line. Defaults
-            to ','.
-        ignore (str): The text to be ignored. Defaults to '###'.
-        format (str): The format of the annotation. Defaults to
-            'x1,y1,x2,y2,x3,y3,x4,trans'.
-        encoding (str): The encoding of the annotation file. Defaults to
-            'utf-8-sig'.
-        remove_strs (List[str], Optional): Used to remove redundant strings in
-            the transcription. Defaults to None.
-        mode (str, optional): The mode of the box converter. Supported modes
-            are 'xywh' and 'xyxy'. Defaults to None.
-    """
-
-    def __init__(self,
-                 split: str,
-                 nproc: int,
-                 separator: str = ',',
-                 ignore: str = '###',
-                 format: str = 'x1,y1,x2,y2,x3,y3,x4,y4,trans',
-                 encoding: str = 'utf-8',
-                 remove_strs: Optional[List[str]] = None,
-                 mode: str = None) -> None:
-        self.sep = separator
-        self.format = format
-        self.encoding = encoding
-        self.ignore = ignore
-        self.mode = mode
-        self.remove_strs = remove_strs
-        super().__init__(split=split, nproc=nproc)
-
-    def _trace_boundary(self, char_boxes: List[np.ndarray]) -> np.ndarray:
-        """Trace the boundary point of text.
-
-        Args:
-            char_boxes (list[ndarray]): The char boxes for one text. Each
-                element is 4x2 ndarray.
-
-        Returns:
-            ndarray: The boundary point sets with size nx2.
-        """
-        assert is_type_list(char_boxes, np.ndarray)
-
-        # from top left to to right
-        p_top = [box[0:2] for box in char_boxes]
-        # from bottom right to bottom left
-        p_bottom = [
-            char_boxes[idx][[2, 3], :]
-            for idx in range(len(char_boxes) - 1, -1, -1)
-        ]
-
-        p = p_top + p_bottom
-
-        boundary = np.concatenate(p).astype(int)
-
-        return boundary
-
-    def _match_bbox_char_str(self, bboxes: np.ndarray, char_bboxes: np.ndarray,
-                             strs: np.ndarray
-                             ) -> Tuple[List[np.ndarray], List[str]]:
-        """Match the bboxes, char bboxes, and strs.
-
-        Args:
-            bboxes (ndarray): The text boxes of size (2, 4, num_box).
-            char_bboxes (ndarray): The char boxes of size (2, 4, num_char_box).
-            strs (ndarray): The string of size (num_strs,)
-
-        Returns:
-            Tuple(List[ndarray], List[str]): Polygon & word list.
-        """
-        assert isinstance(bboxes, np.ndarray)
-        assert isinstance(char_bboxes, np.ndarray)
-        assert isinstance(strs, np.ndarray)
-        # bboxes = bboxes.astype(np.int32)
-        char_bboxes = char_bboxes.astype(np.int32)
-
-        if len(char_bboxes.shape) == 2:
-            char_bboxes = np.expand_dims(char_bboxes, axis=2)
-        char_bboxes = np.transpose(char_bboxes, (2, 1, 0))
-        num_boxes = 1 if len(bboxes.shape) == 2 else bboxes.shape[-1]
-
-        poly_charbox_list = [[] for _ in range(num_boxes)]
-
-        words = []
-        for line in strs:
-            words += line.split()
-        words_len = [len(w) for w in words]
-        words_end_inx = np.cumsum(words_len)
-        start_inx = 0
-        for word_inx, end_inx in enumerate(words_end_inx):
-            for char_inx in range(start_inx, end_inx):
-                poly_charbox_list[word_inx].append(char_bboxes[char_inx])
-            start_inx = end_inx
-
-        for box_inx in range(num_boxes):
-            assert len(poly_charbox_list[box_inx]) > 0
-
-        poly_boundary_list = []
-        for item in poly_charbox_list:
-            boundary = np.ndarray((0, 2))
-            if len(item) > 0:
-                boundary = self._trace_boundary(item)
-            poly_boundary_list.append(boundary)
-
-        return poly_boundary_list, words
-
-    def parse_files(self, img_paths: Union[List[str], str],
-                    ann_paths: Union[List[str], str]) -> List[Tuple]:
-        """Convert annotations to MMOCR format.
-
-        Args:
-            img_paths (str or list[str]): the list of image paths or the
-                directory of the images.
-            ann_paths (str or list[str]): the list of annotation paths or the
-                path of the annotation file which contains all the annotations.
-
-        Returns:
-            List[Tuple]: A list of a tuple of (image_path, instances).
-
-            - img_path (str): The path of image file, which can be read
-              directly by opencv.
-            - instance: instance is a list of dict containing parsed
-              annotations, which should contain the following keys:
-
-              - 'poly' or 'box' (textdet or textspotting)
-              - 'text' (textspotting or textrecog)
-              - 'ignore' (all task)
-        """
-        assert isinstance(ann_paths, str)
-        gt = loadmat(ann_paths)
-        self.img_dir = img_paths
-        samples = track_parallel_progress(
-            self.parse_file,
-            list(
-                zip(gt['imnames'][0], gt['wordBB'][0], gt['charBB'][0],
-                    gt['txt'][0])),
-            nproc=self.nproc)
-        return samples
-
-    def parse_file(self, annotation: Tuple) -> Tuple:
-        """Parse single annotation."""
-        img_file, wordBB, charBB, txt = annotation
-        polys_list, word_list = self._match_bbox_char_str(wordBB, charBB, txt)
-
-        instances = list()
-        for poly, word in zip(polys_list, word_list):
-            instances.append(
-                dict(poly=poly.flatten().tolist(), text=word, ignore=False))
-        return osp.join(self.img_dir, img_file[0]), instances
diff --git a/spaces/Mrleo/MyChatGPT/overwrites.py b/spaces/Mrleo/MyChatGPT/overwrites.py
deleted file mode 100644
index 436fcf46b5807ca045e77ac762039ba0ffc16f6d..0000000000000000000000000000000000000000
--- a/spaces/Mrleo/MyChatGPT/overwrites.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from __future__ import annotations
-import logging
-
-from llama_index import Prompt
-from typing import List, Tuple
-import mdtex2html
-
-from presets import *
-from 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[Tuple[str | None, str | None]]
-) -> List[Tuple[str | None, str | None]]:
-    """
-    Parameters:
-        y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
-    Returns:
-        List of tuples representing the message and response. Each message and response will be a string of HTML.
-    """
-    if y is None or y == []:
-        return []
-    tag_regex = re.compile(r"^<\w+>[^<]+</\w+>")
-    if tag_regex.search(y[-1][1]):
-        y[-1] = (y[-1][0].replace("\n", "<br>"), y[-1][1])
-    else:
-        y[-1] = (y[-1][0].replace("\n", "<br>"), convert_mdtext(y[-1][1]))
-    return y
diff --git a/spaces/Msninmx/shamzam/README.md b/spaces/Msninmx/shamzam/README.md
deleted file mode 100644
index 36e0db65d3b51367953fb41226f076aa6487180e..0000000000000000000000000000000000000000
--- a/spaces/Msninmx/shamzam/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: Shamzam
-emoji: 🎵
-colorFrom: green
-colorTo: blue
-sdk: gradio
-app_file: app.py
----
\ No newline at end of file
diff --git a/spaces/Muennighoff/code_eval_octopack/README.md b/spaces/Muennighoff/code_eval_octopack/README.md
deleted file mode 100644
index a0197873b48738b4cd612607d8e671be695ca385..0000000000000000000000000000000000000000
--- a/spaces/Muennighoff/code_eval_octopack/README.md
+++ /dev/null
@@ -1,115 +0,0 @@
----
-title: Code Eval OctoPack
-emoji: 🐙
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-tags:
-- evaluate
-- metric
-description: >-
-  This metric implements code evaluation with execution across multiple languages as used in the paper "OctoPack: Instruction Tuning
-  Code Large Language Models" (https://arxiv.org/abs/2308.07124).
----
-
-# Metric Card for Code Eval
-
-## Metric description
-
-The CodeEval metric estimates the pass@k metric for code synthesis. 
-
-It implements the code exection for HumanEvalPack as described in the paper ["OctoPack: Instruction Tuning Code Large Language Model"](https://arxiv.org/abs/2308.07124).
-
-
-## How to use 
-
-The Code Eval metric calculates how good are predictions given a set of references. Its arguments are:
-
-`predictions`: a list of candidates to evaluate. Each candidate should be a list of strings with several code candidates to solve the problem.
-
-`references`: a list with a test for each prediction. Each test should evaluate the correctness of a code candidate.
-
-`k`: number of code candidates to consider in the evaluation. The default value is `[1, 10, 100]`.
-
-`num_workers`: the number of workers used to evaluate the candidate programs (The default value is `4`).
-
-`timeout`: The maximum time taken to produce a prediction before it is considered a "timeout". The default value is `3.0` (i.e. 3 seconds).
-
-`language`: Which language to execute the code in. The default value is `python` and alternatives are `javascript`, `java`, `go`, `cpp`, `rust`
-
-`cargo_string`: The cargo installations to perform for Rust. Defaults to some basic packages, see `code_eval_octopack.py`.
-
-```python
-from evaluate import load
-code_eval = load("Muennighoff/code_eval_octopack")
-test_cases = ["assert add(2,3)==5"]
-candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
-pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2], language="python")
-```
-
-N.B.
-This metric exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. Before running this metric and once you've taken the necessary precautions, you will need to set the `HF_ALLOW_CODE_EVAL` environment variable. Use it at your own risk:
-```python
-import os
-os.environ["HF_ALLOW_CODE_EVAL"] = "1"` 
-```
-
-## Output values
-
-The Code Eval metric outputs two things:
-
-`pass_at_k`: a dictionary with the pass rates for each k value defined in the arguments.
-
-`results`: a dictionary with granular results of each unit test.
-
-## Examples 
-
-Full match at `k=1`:
-
-```python
-from evaluate import load
-code_eval = load("Muennighoff/code_eval_octopack")
-test_cases = ["assert add(2,3)==5"]
-candidates = [["def add(a, b): return a+b"]]
-pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1], language="python")
-print(pass_at_k)
-{'pass@1': 1.0}
-```
-
-No match for k = 1:
-
-```python
-from evaluate import load
-code_eval = load("Muennighoff/code_eval_octopack")
-test_cases = ["assert add(2,3)==5"]
-candidates = [["def add(a,b): return a*b"]]
-pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1], language="python")
-print(pass_at_k)
-{'pass@1': 0.0}
-```
-
-Partial match at k=1, full match at k=2:
-
-```python
-from evaluate import load
-code_eval = load("Muennighoff/code_eval_octopack")
-test_cases = ["assert add(2,3)==5"]
-candidates = [["def add(a, b): return a+b", "def add(a,b): return a*b"]]
-pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2], language="python")
-print(pass_at_k)
-{'pass@1': 0.5, 'pass@2': 1.0}
-```
-
-## Citation
-
-```bibtex
-@article{muennighoff2023octopack,
-      title={OctoPack: Instruction Tuning Code Large Language Models}, 
-      author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
-      journal={arXiv preprint arXiv:2308.07124},
-      year={2023}
-}
-```
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/scripts/make_bu_data.py b/spaces/NAACL2022/CLIP-Caption-Reward/scripts/make_bu_data.py
deleted file mode 100644
index 211f3e93dd3df9836e542322b0a19eeb581b2e1a..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/scripts/make_bu_data.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import base64
-import numpy as np
-import csv
-import sys
-import zlib
-import time
-import mmap
-import argparse
-
-parser = argparse.ArgumentParser()
-
-# output_dir
-parser.add_argument('--downloaded_feats', default='data/bu_data', help='downloaded feature directory')
-parser.add_argument('--output_dir', default='data/cocobu', help='output feature files')
-
-args = parser.parse_args()
-
-csv.field_size_limit(sys.maxsize)
-
-
-FIELDNAMES = ['image_id', 'image_w','image_h','num_boxes', 'boxes', 'features']
-infiles = ['trainval/karpathy_test_resnet101_faster_rcnn_genome.tsv',
-          'trainval/karpathy_val_resnet101_faster_rcnn_genome.tsv',\
-          'trainval/karpathy_train_resnet101_faster_rcnn_genome.tsv.0', \
-           'trainval/karpathy_train_resnet101_faster_rcnn_genome.tsv.1']
-
-os.makedirs(args.output_dir+'_att')
-os.makedirs(args.output_dir+'_fc')
-os.makedirs(args.output_dir+'_box')
-
-for infile in infiles:
-    print('Reading ' + infile)
-    with open(os.path.join(args.downloaded_feats, infile), "r") as tsv_in_file:
-        reader = csv.DictReader(tsv_in_file, delimiter='\t', fieldnames = FIELDNAMES)
-        for item in reader:
-            item['image_id'] = int(item['image_id'])
-            item['num_boxes'] = int(item['num_boxes'])
-            for field in ['boxes', 'features']:
-                item[field] = np.frombuffer(base64.decodestring(item[field].encode('ascii')), 
-                        dtype=np.float32).reshape((item['num_boxes'],-1))
-            np.savez_compressed(os.path.join(args.output_dir+'_att', str(item['image_id'])), feat=item['features'])
-            np.save(os.path.join(args.output_dir+'_fc', str(item['image_id'])), item['features'].mean(0))
-            np.save(os.path.join(args.output_dir+'_box', str(item['image_id'])), item['boxes'])
-
-
-
-
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/multi_channel_attention.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/multi_channel_attention.py
deleted file mode 100644
index 499d977c753518f0892267ac98abc6bf7618c2cd..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/multi_channel_attention.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. 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.
-# ==============================================================================
-"""Multi-channel Attention."""
-# pylint: disable=g-classes-have-attributes
-
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import math
-
-import tensorflow as tf
-from official.modeling import tf_utils
-from official.nlp.modeling.layers import attention
-from official.nlp.modeling.layers import dense_einsum
-from official.nlp.modeling.layers import masked_softmax
-
-
-class VotingAttention(tf.keras.layers.Layer):
-  """Voting Attention layer.
-
-  Arguments:
-    num_heads: the number of attention heads.
-    head_size: per-head hidden size.
-    kernel_initializer: Initializer for dense layer kernels.
-    bias_initializer: Initializer for dense layer biases.
-    kernel_regularizer: Regularizer for dense layer kernels.
-    bias_regularizer: Regularizer for dense layer biases.
-    activity_regularizer: Regularizer for dense layer activity.
-    kernel_constraint: Constraint for dense layer kernels.
-    bias_constraint: Constraint for dense layer kernels.
-  """
-
-  def __init__(self,
-               num_heads,
-               head_size,
-               kernel_initializer="glorot_uniform",
-               bias_initializer="zeros",
-               kernel_regularizer=None,
-               bias_regularizer=None,
-               activity_regularizer=None,
-               kernel_constraint=None,
-               bias_constraint=None,
-               **kwargs):
-    super(VotingAttention, self).__init__(**kwargs)
-    self._num_heads = num_heads
-    self._head_size = head_size
-    self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
-    self._bias_initializer = tf.keras.initializers.get(bias_initializer)
-    self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
-    self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
-    self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
-    self._bias_constraint = tf.keras.constraints.get(bias_constraint)
-
-  def build(self, unused_input_shapes):
-    self._query_dense = dense_einsum.DenseEinsum(
-        output_shape=(self._num_heads, self._head_size),
-        kernel_initializer=self._kernel_initializer,
-        bias_initializer=self._bias_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        activity_regularizer=self._activity_regularizer,
-        kernel_constraint=self._kernel_constraint,
-        bias_constraint=self._bias_constraint,
-        dtype=self.dtype,
-        name="encdocatt_query")
-    self._key_dense = dense_einsum.DenseEinsum(
-        output_shape=(self._num_heads, self._head_size),
-        kernel_initializer=self._kernel_initializer,
-        bias_initializer=self._bias_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        activity_regularizer=self._activity_regularizer,
-        kernel_constraint=self._kernel_constraint,
-        bias_constraint=self._bias_constraint,
-        dtype=self.dtype,
-        name="encdocatt_key")
-    super(VotingAttention, self).build(unused_input_shapes)
-
-  def call(self, encoder_outputs, doc_attention_mask):
-    num_docs = tf_utils.get_shape_list(encoder_outputs, expected_rank=[4])[1]
-    cls_embeddings = encoder_outputs[:, :, 0, :]
-    key = self._key_dense(cls_embeddings)
-    query = self._query_dense(cls_embeddings)
-    doc_attention_mask = tf.cast(doc_attention_mask, tf.float32)
-
-    key = tf.einsum("BANH,BA->BANH", key, doc_attention_mask)
-    query = tf.einsum("BANH,BA->BANH", query, doc_attention_mask)
-    attention_matrix = tf.einsum("BXNH,BYNH->BNXY", query, key)
-    mask = tf.ones([num_docs, num_docs])
-    mask = tf.linalg.set_diag(mask, tf.zeros(num_docs))
-    attention_matrix = tf.einsum("BNXY,XY->BNXY", attention_matrix, mask)
-    doc_attention_probs = tf.einsum("BNAY->BNA", attention_matrix)
-    doc_attention_probs = tf.einsum("BNA->BA", doc_attention_probs)
-    infadder = (1.0 - doc_attention_mask) * -100000.0
-    return tf.nn.softmax(doc_attention_probs + infadder)
-
-
-class MultiChannelAttention(attention.MultiHeadAttention):
-  """Multi-channel Attention layer.
-
-  Introduced in: https://arxiv.org/abs/2001.09386. Expects multiple
-  cross-attention target sequences.
-  """
-
-  def build(self, input_shape):
-    super(MultiChannelAttention, self).build(input_shape)
-    self._masked_softmax = masked_softmax.MaskedSoftmax(mask_expansion_axes=[2])
-
-  def call(self, inputs, attention_mask=None):
-    from_tensor = inputs[0]
-    to_tensor = inputs[1]
-    doc_attention_probs = inputs[2]
-
-    # Scalar dimensions referenced here:
-    #   B = batch size (number of stories)
-    #   A = num_docs (number of docs)
-    #   F = `from_tensor` sequence length
-    #   T = `to_tensor` sequence length
-    #   N = `num_attention_heads`
-    #   H = `size_per_head`
-    # `query_tensor` = [B, F, N ,H]
-    query_tensor = self._query_dense(from_tensor)
-
-    # `key_tensor` = [B, A, T, N, H]
-    key_tensor = self._key_dense(to_tensor)
-
-    # `value_tensor` = [B, A, T, N, H]
-    value_tensor = self._value_dense(to_tensor)
-
-    # Take the dot product between "query" and "key" to get the raw
-    # attention scores.
-    attention_scores = tf.einsum("BATNH,BFNH->BANFT", key_tensor, query_tensor)
-    attention_scores = tf.multiply(attention_scores,
-                                   1.0 / math.sqrt(float(self._key_size)))
-
-    # Normalize the attention scores to probabilities.
-    # `attention_probs` = [B, A, N, F, T]
-    attention_probs = self._masked_softmax(attention_scores, attention_mask)
-
-    # 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_layer(attention_probs)
-
-    # `context_layer` = [B, F, N, H]
-    context_layer = tf.einsum("BANFT,BATNH->BAFNH", attention_probs,
-                              value_tensor)
-    attention_output = tf.einsum("BNFA,BAFNH->BFNH", doc_attention_probs,
-                                 context_layer)
-    attention_output = self._output_dense(attention_output)
-    return attention_output
diff --git a/spaces/Neo-Salvatore/GPTBase/app.py b/spaces/Neo-Salvatore/GPTBase/app.py
deleted file mode 100644
index 787c0919a7172d68b2d90d1f3dac28dc2ca83796..0000000000000000000000000000000000000000
--- a/spaces/Neo-Salvatore/GPTBase/app.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import requests
-import streamlit as st
-import json
-from streamlit_chat import message
-
-st.title("GPTBase")
-
-def clear_submit():
-    st.session_state["submit"] = False
-
-def set_api_key(api_key: str):
-    st.session_state["API_KEY"] = api_key
-
-def set_ai_id(ai_id: str):
-    st.session_state["ai_id"] = ai_id
-
-# Sidebar
-index = None
-doc = None
-with st.sidebar:
-    user_secret = st.text_input(
-        "API Key",
-        type="password",
-        placeholder="Paste your API key here (ak-...)",
-        help="You can get your API key from https://mygpt.felo.me/api-keys.",
-        value=st.session_state.get("API_KEY", ""),
-    )
-    if user_secret:
-        set_api_key(user_secret)
-    
-    user_ai_id = st.text_input(
-        "AI Id",
-        placeholder="Paste your AI Id here",
-        value=st.session_state.get("ai_id", ""),
-    )
-    if user_ai_id:
-        set_ai_id(user_ai_id)
-
-    uploaded_file = st.file_uploader(
-        "Upload a pdf, docx, or txt file",
-        type=["pdf", "docx", "txt"],
-        help="Scanned documents are not supported yet!",
-        on_change=clear_submit,
-    )
-    api_key = st.session_state.get("API_KEY")
-    ai_id = st.session_state.get("ai_id")
-    if not api_key or not ai_id:
-        st.warning("Please enter your API key and AI Id to upload a file.")
-
-    if uploaded_file is not None and "API_KEY" in st.session_state and "ai_id" in st.session_state:
-        
-        file = {"file": uploaded_file}
-        url = f'https://mygpt.felo.me/api/v1/ais/{ai_id}/files'
-        headers = {"Authorization": f"Bearer {api_key}"}
-        response = requests.post(url, headers=headers, files=file)
-        result = response.json()
-        if response.status_code != 200:
-            st.text(result['detail'])
-
-tab1, tab2 = st.tabs(["Introduce", "Ask AI"])
-with tab1:
-    st.markdown("### Use steps")
-    st.write('1.Please fill in your API Key and AI Id.')
-    st.write('2.Upload the file you want to get the answer.')
-    st.write('3.You can ask AI with the file.')
-    # st.markdown("""---11""")
-    # st.write('If you have any questions or suggestions, please visit: ')
-
-with tab2:
-    if not api_key or not ai_id:
-        st.write('Please fill in your API Key and AI Id.')
-    st.header("Ask AI some questions about the file you uploaded:")
-    
-    def get_text():
-        input_text = st.text_area("You:", on_change=clear_submit)
-        return input_text
-    user_input = get_text()
-    button = st.button("Submit")
-    
-    if button or st.session_state.get("submit"):
-        if api_key and ai_id and user_input:
-            url = f'https://mygpt.felo.me/api/v1/question/{ai_id}'
-            headers = {"Authorization": f"Bearer {api_key}"}
-            data = {"question": user_input}
-            response = requests.post(url, headers=headers, data=json.dumps(data))
-            result = response.json()
-            if response.status_code != 200:
-                st.text(result['detail'])
-            else:
-                message(result['answer'])
-            
diff --git a/spaces/Nick1/rvc-models/lib/infer_pack/models_onnx.py b/spaces/Nick1/rvc-models/lib/infer_pack/models_onnx.py
deleted file mode 100644
index 963e67b29f828e9fdd096397952054fe77cf3d10..0000000000000000000000000000000000000000
--- a/spaces/Nick1/rvc-models/lib/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,819 +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 lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.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 lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.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 TextEncoder768(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(768, 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 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 SynthesizerTrnMsNSFsidM(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,
-        version,
-        **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
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                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)
-        self.speaker_map = None
-        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 construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * 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
-
-
-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 MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 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/NoriZC/vits-models/text/cleaners.py b/spaces/NoriZC/vits-models/text/cleaners.py
deleted file mode 100644
index 68c9ad24d5a303b68a521fba2e8776c8cc867356..0000000000000000000000000000000000000000
--- a/spaces/NoriZC/vits-models/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# 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'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# 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 (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', 'ㄟˉ'),
-  ('b', 'ㄅㄧˋ'),
-  ('c', 'ㄙㄧˉ'),
-  ('d', 'ㄉㄧˋ'),
-  ('e', 'ㄧˋ'),
-  ('f', 'ㄝˊㄈㄨˋ'),
-  ('g', 'ㄐㄧˋ'),
-  ('h', 'ㄝˇㄑㄩˋ'),
-  ('i', 'ㄞˋ'),
-  ('j', 'ㄐㄟˋ'),
-  ('k', 'ㄎㄟˋ'),
-  ('l', 'ㄝˊㄛˋ'),
-  ('m', 'ㄝˊㄇㄨˋ'),
-  ('n', 'ㄣˉ'),
-  ('o', 'ㄡˉ'),
-  ('p', 'ㄆㄧˉ'),
-  ('q', 'ㄎㄧㄡˉ'),
-  ('r', 'ㄚˋ'),
-  ('s', 'ㄝˊㄙˋ'),
-  ('t', 'ㄊㄧˋ'),
-  ('u', 'ㄧㄡˉ'),
-  ('v', 'ㄨㄧˉ'),
-  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-  ('y', 'ㄨㄞˋ'),
-  ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('ㄅㄛ', 'p⁼wo'),
-  ('ㄆㄛ', 'pʰwo'),
-  ('ㄇㄛ', 'mwo'),
-  ('ㄈㄛ', 'fwo'),
-  ('ㄅ', 'p⁼'),
-  ('ㄆ', 'pʰ'),
-  ('ㄇ', 'm'),
-  ('ㄈ', 'f'),
-  ('ㄉ', 't⁼'),
-  ('ㄊ', 'tʰ'),
-  ('ㄋ', 'n'),
-  ('ㄌ', 'l'),
-  ('ㄍ', 'k⁼'),
-  ('ㄎ', 'kʰ'),
-  ('ㄏ', 'h'),
-  ('ㄐ', 'ʧ⁼'),
-  ('ㄑ', 'ʧʰ'),
-  ('ㄒ', 'ʃ'),
-  ('ㄓ', 'ʦ`⁼'),
-  ('ㄔ', 'ʦ`ʰ'),
-  ('ㄕ', 's`'),
-  ('ㄖ', 'ɹ`'),
-  ('ㄗ', 'ʦ⁼'),
-  ('ㄘ', 'ʦʰ'),
-  ('ㄙ', 's'),
-  ('ㄚ', 'a'),
-  ('ㄛ', 'o'),
-  ('ㄜ', 'ə'),
-  ('ㄝ', 'e'),
-  ('ㄞ', 'ai'),
-  ('ㄟ', 'ei'),
-  ('ㄠ', 'au'),
-  ('ㄡ', 'ou'),
-  ('ㄧㄢ', 'yeNN'),
-  ('ㄢ', 'aNN'),
-  ('ㄧㄣ', 'iNN'),
-  ('ㄣ', 'əNN'),
-  ('ㄤ', 'aNg'),
-  ('ㄧㄥ', 'iNg'),
-  ('ㄨㄥ', 'uNg'),
-  ('ㄩㄥ', 'yuNg'),
-  ('ㄥ', 'əNg'),
-  ('ㄦ', 'əɻ'),
-  ('ㄧ', 'i'),
-  ('ㄨ', 'u'),
-  ('ㄩ', 'ɥ'),
-  ('ˉ', '→'),
-  ('ˊ', '↑'),
-  ('ˇ', '↓↑'),
-  ('ˋ', '↓'),
-  ('˙', ''),
-  ('，', ','),
-  ('。', '.'),
-  ('！', '!'),
-  ('？', '?'),
-  ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
-  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-  for i, sentence in enumerate(sentences):
-    if re.match(_japanese_characters, sentence):
-      if text!='':
-        text+=' '
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-        if phoneme not in ['sil','pau']:
-          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
-        else:
-          continue
-        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
-          a2_next=-1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-    if i<len(marks):
-      text += unidecode(marks[i]).replace(' ','')
-  return text
-
-
-def latin_to_hangul(text):
-  for regex, replacement in _latin_to_hangul:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def divide_hangul(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 number_to_chinese(text):
-  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-  for number in numbers:
-    text = text.replace(number, cn2an.an2cn(number),1)
-  return text
-
-
-def chinese_to_bopomofo(text):
-  text=text.replace('、','，').replace('；','，').replace('：','，')
-  words=jieba.lcut(text,cut_all=False)
-  text=''
-  for word in words:
-    bopomofos=lazy_pinyin(word,BOPOMOFO)
-    if not re.search('[\u4e00-\u9fff]',word):
-      text+=word
-      continue
-    for i in range(len(bopomofos)):
-      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
-        bopomofos[i]+='ˉ'
-    if text!='':
-      text+=' '
-    text+=''.join(bopomofos)
-  return text
-
-
-def latin_to_bopomofo(text):
-  for regex, replacement in _latin_to_bopomofo:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def bopomofo_to_romaji(text):
-  for regex, replacement in _bopomofo_to_romaji:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def japanese_cleaners(text):
-  text=japanese_to_romaji_with_accent(text)
-  if re.match('[A-Za-z]',text[-1]):
-    text += '.'
-  return text
-
-
-def japanese_cleaners2(text):
-  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
-
-
-def korean_cleaners(text):
-  '''Pipeline for Korean text'''
-  text = latin_to_hangul(text)
-  text = number_to_hangul(text)
-  text = j2hcj(h2j(text))
-  text = divide_hangul(text)
-  if re.match('[\u3131-\u3163]',text[-1]):
-    text += '.'
-  return text
-
-
-def chinese_cleaners(text):
-  '''Pipeline for Chinese text'''
-  text=number_to_chinese(text)
-  text=chinese_to_bopomofo(text)
-  text=latin_to_bopomofo(text)
-  if re.match('[ˉˊˇˋ˙]',text[-1]):
-    text += '。'
-  return text
-
-
-def zh_ja_mixture_cleaners(text):
-  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
-  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
-  for chinese_text in chinese_texts:
-    cleaned_text=number_to_chinese(chinese_text[4:-4])
-    cleaned_text=chinese_to_bopomofo(cleaned_text)
-    cleaned_text=latin_to_bopomofo(cleaned_text)
-    cleaned_text=bopomofo_to_romaji(cleaned_text)
-    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
-    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
-    text = text.replace(chinese_text,cleaned_text+' ',1)
-  for japanese_text in japanese_texts:
-    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
-    text = text.replace(japanese_text,cleaned_text+' ',1)
-  text=text[:-1]
-  if len(text) and re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text
\ No newline at end of file
diff --git a/spaces/OAOA/DifFace/basicsr/archs/duf_arch.py b/spaces/OAOA/DifFace/basicsr/archs/duf_arch.py
deleted file mode 100644
index e2b3ab7df4d890c9220d74ed8c461ad9d155120a..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/archs/duf_arch.py
+++ /dev/null
@@ -1,276 +0,0 @@
-import numpy as np
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-class DenseBlocksTemporalReduce(nn.Module):
-    """A concatenation of 3 dense blocks with reduction in temporal dimension.
-
-    Note that the output temporal dimension is 6 fewer the input temporal dimension, since there are 3 blocks.
-
-    Args:
-        num_feat (int): Number of channels in the blocks. Default: 64.
-        num_grow_ch (int): Growing factor of the dense blocks. Default: 32
-        adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation.
-            Set to false if you want to train from scratch. Default: False.
-    """
-
-    def __init__(self, num_feat=64, num_grow_ch=32, adapt_official_weights=False):
-        super(DenseBlocksTemporalReduce, self).__init__()
-        if adapt_official_weights:
-            eps = 1e-3
-            momentum = 1e-3
-        else:  # pytorch default values
-            eps = 1e-05
-            momentum = 0.1
-
-        self.temporal_reduce1 = nn.Sequential(
-            nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
-            nn.Conv3d(num_feat, num_feat, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True),
-            nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
-            nn.Conv3d(num_feat, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
-
-        self.temporal_reduce2 = nn.Sequential(
-            nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
-            nn.Conv3d(
-                num_feat + num_grow_ch,
-                num_feat + num_grow_ch, (1, 1, 1),
-                stride=(1, 1, 1),
-                padding=(0, 0, 0),
-                bias=True), nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
-            nn.Conv3d(num_feat + num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
-
-        self.temporal_reduce3 = nn.Sequential(
-            nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
-            nn.Conv3d(
-                num_feat + 2 * num_grow_ch,
-                num_feat + 2 * num_grow_ch, (1, 1, 1),
-                stride=(1, 1, 1),
-                padding=(0, 0, 0),
-                bias=True), nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum),
-            nn.ReLU(inplace=True),
-            nn.Conv3d(
-                num_feat + 2 * num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
-
-    def forward(self, x):
-        """
-        Args:
-            x (Tensor): Input tensor with shape (b, num_feat, t, h, w).
-
-        Returns:
-            Tensor: Output with shape (b, num_feat + num_grow_ch * 3, 1, h, w).
-        """
-        x1 = self.temporal_reduce1(x)
-        x1 = torch.cat((x[:, :, 1:-1, :, :], x1), 1)
-
-        x2 = self.temporal_reduce2(x1)
-        x2 = torch.cat((x1[:, :, 1:-1, :, :], x2), 1)
-
-        x3 = self.temporal_reduce3(x2)
-        x3 = torch.cat((x2[:, :, 1:-1, :, :], x3), 1)
-
-        return x3
-
-
-class DenseBlocks(nn.Module):
-    """ A concatenation of N dense blocks.
-
-    Args:
-        num_feat (int): Number of channels in the blocks. Default: 64.
-        num_grow_ch (int): Growing factor of the dense blocks. Default: 32.
-        num_block (int): Number of dense blocks. The values are:
-            DUF-S (16 layers): 3
-            DUF-M (18 layers): 9
-            DUF-L (52 layers): 21
-        adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation.
-            Set to false if you want to train from scratch. Default: False.
-    """
-
-    def __init__(self, num_block, num_feat=64, num_grow_ch=16, adapt_official_weights=False):
-        super(DenseBlocks, self).__init__()
-        if adapt_official_weights:
-            eps = 1e-3
-            momentum = 1e-3
-        else:  # pytorch default values
-            eps = 1e-05
-            momentum = 0.1
-
-        self.dense_blocks = nn.ModuleList()
-        for i in range(0, num_block):
-            self.dense_blocks.append(
-                nn.Sequential(
-                    nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
-                    nn.Conv3d(
-                        num_feat + i * num_grow_ch,
-                        num_feat + i * num_grow_ch, (1, 1, 1),
-                        stride=(1, 1, 1),
-                        padding=(0, 0, 0),
-                        bias=True), nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum),
-                    nn.ReLU(inplace=True),
-                    nn.Conv3d(
-                        num_feat + i * num_grow_ch,
-                        num_grow_ch, (3, 3, 3),
-                        stride=(1, 1, 1),
-                        padding=(1, 1, 1),
-                        bias=True)))
-
-    def forward(self, x):
-        """
-        Args:
-            x (Tensor): Input tensor with shape (b, num_feat, t, h, w).
-
-        Returns:
-            Tensor: Output with shape (b, num_feat + num_block * num_grow_ch, t, h, w).
-        """
-        for i in range(0, len(self.dense_blocks)):
-            y = self.dense_blocks[i](x)
-            x = torch.cat((x, y), 1)
-        return x
-
-
-class DynamicUpsamplingFilter(nn.Module):
-    """Dynamic upsampling filter used in DUF.
-
-    Reference: https://github.com/yhjo09/VSR-DUF
-
-    It only supports input with 3 channels. And it applies the same filters to 3 channels.
-
-    Args:
-        filter_size (tuple): Filter size of generated filters. The shape is (kh, kw). Default: (5, 5).
-    """
-
-    def __init__(self, filter_size=(5, 5)):
-        super(DynamicUpsamplingFilter, self).__init__()
-        if not isinstance(filter_size, tuple):
-            raise TypeError(f'The type of filter_size must be tuple, but got type{filter_size}')
-        if len(filter_size) != 2:
-            raise ValueError(f'The length of filter size must be 2, but got {len(filter_size)}.')
-        # generate a local expansion filter, similar to im2col
-        self.filter_size = filter_size
-        filter_prod = np.prod(filter_size)
-        expansion_filter = torch.eye(int(filter_prod)).view(filter_prod, 1, *filter_size)  # (kh*kw, 1, kh, kw)
-        self.expansion_filter = expansion_filter.repeat(3, 1, 1, 1)  # repeat for all the 3 channels
-
-    def forward(self, x, filters):
-        """Forward function for DynamicUpsamplingFilter.
-
-        Args:
-            x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w).
-            filters (Tensor): Generated dynamic filters. The shape is (n, filter_prod, upsampling_square, h, w).
-                filter_prod: prod of filter kernel size, e.g., 1*5*5=25.
-                upsampling_square: similar to pixel shuffle, upsampling_square = upsampling * upsampling.
-                e.g., for x 4 upsampling, upsampling_square= 4*4 = 16
-
-        Returns:
-            Tensor: Filtered image with shape (n, 3*upsampling_square, h, w)
-        """
-        n, filter_prod, upsampling_square, h, w = filters.size()
-        kh, kw = self.filter_size
-        expanded_input = F.conv2d(
-            x, self.expansion_filter.to(x), padding=(kh // 2, kw // 2), groups=3)  # (n, 3*filter_prod, h, w)
-        expanded_input = expanded_input.view(n, 3, filter_prod, h, w).permute(0, 3, 4, 1,
-                                                                              2)  # (n, h, w, 3, filter_prod)
-        filters = filters.permute(0, 3, 4, 1, 2)  # (n, h, w, filter_prod, upsampling_square]
-        out = torch.matmul(expanded_input, filters)  # (n, h, w, 3, upsampling_square)
-        return out.permute(0, 3, 4, 1, 2).view(n, 3 * upsampling_square, h, w)
-
-
-@ARCH_REGISTRY.register()
-class DUF(nn.Module):
-    """Network architecture for DUF
-
-    ``Paper: Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation``
-
-    Reference: https://github.com/yhjo09/VSR-DUF
-
-    For all the models below, 'adapt_official_weights' is only necessary when
-    loading the weights converted from the official TensorFlow weights.
-    Please set it to False if you are training the model from scratch.
-
-    There are three models with different model size: DUF16Layers, DUF28Layers,
-    and DUF52Layers. This class is the base class for these models.
-
-    Args:
-        scale (int): The upsampling factor. Default: 4.
-        num_layer (int): The number of layers. Default: 52.
-        adapt_official_weights_weights (bool): Whether to adapt the weights
-            translated from the official implementation. Set to false if you
-            want to train from scratch. Default: False.
-    """
-
-    def __init__(self, scale=4, num_layer=52, adapt_official_weights=False):
-        super(DUF, self).__init__()
-        self.scale = scale
-        if adapt_official_weights:
-            eps = 1e-3
-            momentum = 1e-3
-        else:  # pytorch default values
-            eps = 1e-05
-            momentum = 0.1
-
-        self.conv3d1 = nn.Conv3d(3, 64, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)
-        self.dynamic_filter = DynamicUpsamplingFilter((5, 5))
-
-        if num_layer == 16:
-            num_block = 3
-            num_grow_ch = 32
-        elif num_layer == 28:
-            num_block = 9
-            num_grow_ch = 16
-        elif num_layer == 52:
-            num_block = 21
-            num_grow_ch = 16
-        else:
-            raise ValueError(f'Only supported (16, 28, 52) layers, but got {num_layer}.')
-
-        self.dense_block1 = DenseBlocks(
-            num_block=num_block, num_feat=64, num_grow_ch=num_grow_ch,
-            adapt_official_weights=adapt_official_weights)  # T = 7
-        self.dense_block2 = DenseBlocksTemporalReduce(
-            64 + num_grow_ch * num_block, num_grow_ch, adapt_official_weights=adapt_official_weights)  # T = 1
-        channels = 64 + num_grow_ch * num_block + num_grow_ch * 3
-        self.bn3d2 = nn.BatchNorm3d(channels, eps=eps, momentum=momentum)
-        self.conv3d2 = nn.Conv3d(channels, 256, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)
-
-        self.conv3d_r1 = nn.Conv3d(256, 256, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
-        self.conv3d_r2 = nn.Conv3d(256, 3 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
-
-        self.conv3d_f1 = nn.Conv3d(256, 512, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
-        self.conv3d_f2 = nn.Conv3d(
-            512, 1 * 5 * 5 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
-
-    def forward(self, x):
-        """
-        Args:
-            x (Tensor): Input with shape (b, 7, c, h, w)
-
-        Returns:
-            Tensor: Output with shape (b, c, h * scale, w * scale)
-        """
-        num_batches, num_imgs, _, h, w = x.size()
-
-        x = x.permute(0, 2, 1, 3, 4)  # (b, c, 7, h, w) for Conv3D
-        x_center = x[:, :, num_imgs // 2, :, :]
-
-        x = self.conv3d1(x)
-        x = self.dense_block1(x)
-        x = self.dense_block2(x)
-        x = F.relu(self.bn3d2(x), inplace=True)
-        x = F.relu(self.conv3d2(x), inplace=True)
-
-        # residual image
-        res = self.conv3d_r2(F.relu(self.conv3d_r1(x), inplace=True))
-
-        # filter
-        filter_ = self.conv3d_f2(F.relu(self.conv3d_f1(x), inplace=True))
-        filter_ = F.softmax(filter_.view(num_batches, 25, self.scale**2, h, w), dim=1)
-
-        # dynamic filter
-        out = self.dynamic_filter(x_center, filter_)
-        out += res.squeeze_(2)
-        out = F.pixel_shuffle(out, self.scale)
-
-        return out
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/nat/levenshtein_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/nat/levenshtein_utils.py
deleted file mode 100644
index 375a98c2e11354de085f0a7926f407bd1a6a2ad4..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/nat/levenshtein_utils.py
+++ /dev/null
@@ -1,293 +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
-from fairseq.utils import new_arange
-
-
-# -------------- Helper Functions --------------------------------------------------- #
-
-
-def load_libnat():
-    try:
-        from fairseq import libnat_cuda
-
-        return libnat_cuda, True
-
-    except ImportError as e:
-        print(str(e) + "... fall back to CPU version")
-
-        try:
-            from fairseq import libnat
-
-            return libnat, False
-
-        except ImportError as e:
-            import sys
-
-            sys.stderr.write(
-                "ERROR: missing libnat_cuda. run `python setup.py build_ext --inplace`\n"
-            )
-            raise e
-
-
-def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx):
-    libnat, use_cuda = load_libnat()
-
-    def _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx):
-        in_masks = in_tokens.ne(padding_idx)
-        out_masks = out_tokens.ne(padding_idx)
-        mask_ins_targets, masked_tgt_masks = libnat.generate_insertion_labels(
-            out_tokens.int(),
-            libnat.levenshtein_distance(
-                in_tokens.int(),
-                out_tokens.int(),
-                in_masks.sum(1).int(),
-                out_masks.sum(1).int(),
-            ),
-        )
-        masked_tgt_masks = masked_tgt_masks.bool() & out_masks
-        mask_ins_targets = mask_ins_targets.type_as(in_tokens)[
-            :, 1 : in_masks.size(1)
-        ].masked_fill_(~in_masks[:, 1:], 0)
-        masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
-        return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
-
-    def _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx):
-        in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1)
-
-        in_tokens_list = [
-            [t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
-        ]
-        out_tokens_list = [
-            [t for t in s if t != padding_idx]
-            for i, s in enumerate(out_tokens.tolist())
-        ]
-
-        full_labels = libnat.suggested_ed2_path(
-            in_tokens_list, out_tokens_list, padding_idx
-        )
-        mask_inputs = [
-            [len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels
-        ]
-
-        # generate labels
-        masked_tgt_masks = []
-        for mask_input in mask_inputs:
-            mask_label = []
-            for beam_size in mask_input[1:-1]:  # HACK 1:-1
-                mask_label += [0] + [1 for _ in range(beam_size)]
-            masked_tgt_masks.append(
-                mask_label + [0 for _ in range(out_seq_len - len(mask_label))]
-            )
-        mask_ins_targets = [
-            mask_input[1:-1]
-            + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))]
-            for mask_input in mask_inputs
-        ]
-
-        # transform to tensor
-        masked_tgt_masks = torch.tensor(
-            masked_tgt_masks, device=out_tokens.device
-        ).bool()
-        mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device)
-        masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
-        return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
-
-    if use_cuda:
-        return _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx)
-    return _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx)
-
-
-def _get_del_targets(in_tokens, out_tokens, padding_idx):
-    libnat, use_cuda = load_libnat()
-
-    def _get_del_targets_cuda(in_tokens, out_tokens, padding_idx):
-        in_masks = in_tokens.ne(padding_idx)
-        out_masks = out_tokens.ne(padding_idx)
-
-        word_del_targets = libnat.generate_deletion_labels(
-            in_tokens.int(),
-            libnat.levenshtein_distance(
-                in_tokens.int(),
-                out_tokens.int(),
-                in_masks.sum(1).int(),
-                out_masks.sum(1).int(),
-            ),
-        )
-        word_del_targets = word_del_targets.type_as(in_tokens).masked_fill_(
-            ~in_masks, 0
-        )
-        return word_del_targets
-
-    def _get_del_targets_cpu(in_tokens, out_tokens, padding_idx):
-        out_seq_len = out_tokens.size(1)
-        with torch.cuda.device_of(in_tokens):
-            in_tokens_list = [
-                [t for t in s if t != padding_idx]
-                for i, s in enumerate(in_tokens.tolist())
-            ]
-            out_tokens_list = [
-                [t for t in s if t != padding_idx]
-                for i, s in enumerate(out_tokens.tolist())
-            ]
-
-        full_labels = libnat.suggested_ed2_path(
-            in_tokens_list, out_tokens_list, padding_idx
-        )
-        word_del_targets = [b[-1] for b in full_labels]
-        word_del_targets = [
-            labels + [0 for _ in range(out_seq_len - len(labels))]
-            for labels in word_del_targets
-        ]
-
-        # transform to tensor
-        word_del_targets = torch.tensor(word_del_targets, device=out_tokens.device)
-        return word_del_targets
-
-    if use_cuda:
-        return _get_del_targets_cuda(in_tokens, out_tokens, padding_idx)
-    return _get_del_targets_cpu(in_tokens, out_tokens, padding_idx)
-
-
-def _apply_ins_masks(
-    in_tokens, in_scores, mask_ins_pred, padding_idx, unk_idx, eos_idx
-):
-
-    in_masks = in_tokens.ne(padding_idx)
-    in_lengths = in_masks.sum(1)
-
-    # HACK: hacky way to shift all the paddings to eos first.
-    in_tokens.masked_fill_(~in_masks, eos_idx)
-    mask_ins_pred.masked_fill_(~in_masks[:, 1:], 0)
-
-    out_lengths = in_lengths + mask_ins_pred.sum(1)
-    out_max_len = out_lengths.max()
-    out_masks = new_arange(out_lengths, out_max_len)[None, :] < out_lengths[:, None]
-
-    reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1)
-    out_tokens = (
-        in_tokens.new_zeros(in_tokens.size(0), out_max_len)
-        .fill_(padding_idx)
-        .masked_fill_(out_masks, unk_idx)
-    )
-    out_tokens[:, 0] = in_tokens[:, 0]
-    out_tokens.scatter_(1, reordering, in_tokens[:, 1:])
-
-    out_scores = None
-    if in_scores is not None:
-        in_scores.masked_fill_(~in_masks, 0)
-        out_scores = in_scores.new_zeros(*out_tokens.size())
-        out_scores[:, 0] = in_scores[:, 0]
-        out_scores.scatter_(1, reordering, in_scores[:, 1:])
-
-    return out_tokens, out_scores
-
-
-def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx):
-    word_ins_masks = in_tokens.eq(unk_idx)
-    out_tokens = in_tokens.masked_scatter(word_ins_masks, word_ins_pred[word_ins_masks])
-
-    if in_scores is not None:
-        out_scores = in_scores.masked_scatter(
-            word_ins_masks, word_ins_scores[word_ins_masks]
-        )
-    else:
-        out_scores = None
-
-    return out_tokens, out_scores
-
-
-def _apply_del_words(
-    in_tokens, in_scores, in_attn, word_del_pred, padding_idx, bos_idx, eos_idx
-):
-    # apply deletion to a tensor
-    in_masks = in_tokens.ne(padding_idx)
-    bos_eos_masks = in_tokens.eq(bos_idx) | in_tokens.eq(eos_idx)
-
-    max_len = in_tokens.size(1)
-    word_del_pred.masked_fill_(~in_masks, 1)
-    word_del_pred.masked_fill_(bos_eos_masks, 0)
-
-    reordering = new_arange(in_tokens).masked_fill_(word_del_pred, max_len).sort(1)[1]
-
-    out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering)
-
-    out_scores = None
-    if in_scores is not None:
-        out_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering)
-
-    out_attn = None
-    if in_attn is not None:
-        _mask = word_del_pred[:, :, None].expand_as(in_attn)
-        _reordering = reordering[:, :, None].expand_as(in_attn)
-        out_attn = in_attn.masked_fill(_mask, 0.0).gather(1, _reordering)
-
-    return out_tokens, out_scores, out_attn
-
-
-def _skip(x, mask):
-    """
-    Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors.
-    """
-    if isinstance(x, int):
-        return x
-
-    if x is None:
-        return None
-
-    if isinstance(x, torch.Tensor):
-        if x.size(0) == mask.size(0):
-            return x[mask]
-        elif x.size(1) == mask.size(0):
-            return x[:, mask]
-
-    if isinstance(x, list):
-        return [_skip(x_i, mask) for x_i in x]
-
-    if isinstance(x, dict):
-        return {k: _skip(v, mask) for k, v in x.items()}
-
-    raise NotImplementedError
-
-
-def _skip_encoder_out(encoder, encoder_out, mask):
-    if not mask.any():
-        return encoder_out
-    else:
-        return encoder.reorder_encoder_out(
-            encoder_out, mask.nonzero(as_tuple=False).squeeze()
-        )
-
-
-def _fill(x, mask, y, padding_idx):
-    """
-    Filling tensor x with y at masked positions (dim=0).
-    """
-    if x is None:
-        return y
-    assert x.dim() == y.dim() and mask.size(0) == x.size(0)
-    assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
-    n_selected = mask.sum()
-    assert n_selected == y.size(0)
-
-    if n_selected == x.size(0):
-        return y
-
-    if x.size(1) < y.size(1):
-        dims = [x.size(0), y.size(1) - x.size(1)]
-        if x.dim() == 3:
-            dims.append(x.size(2))
-        x = torch.cat([x, x.new_zeros(*dims).fill_(padding_idx)], 1)
-        x[mask] = y
-    elif x.size(1) > y.size(1):
-        x[mask] = padding_idx
-        if x.dim() == 2:
-            x[mask, : y.size(1)] = y
-        else:
-            x[mask, : y.size(1), :] = y
-    else:
-        x[mask] = y
-    return x
diff --git a/spaces/OIUGLK/bingo/src/components/chat-list.tsx b/spaces/OIUGLK/bingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/OIUGLK/bingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
-  messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
-  if (!messages.length) {
-    return null
-  }
-
-  return (
-    <div className="chat-container relative flex flex-col">
-      {messages.map((message, index) => (
-        <React.Fragment key={index}>
-          <ChatMessage message={message} />
-          {index < messages.length - 1 && (
-            <Separator className="my-2" />
-          )}
-        </React.Fragment>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/OdinStef/Chatapp/app.py b/spaces/OdinStef/Chatapp/app.py
deleted file mode 100644
index 096e6d1bfcca60fa3b309ca306eedf5db62b18b0..0000000000000000000000000000000000000000
--- a/spaces/OdinStef/Chatapp/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import openai
-import os
-import gradio as gr
-
-openai.api_key = os.getenv("mykey2")
-
-messages = [{"role": "system", "content": "You are a genius like Albert Einstein"}]
-
-
-def CustomChatGPT(user_input):
-    messages.append({"role": "user", "content": user_input})
-    response = openai.ChatCompletion.create(
-        model = "gpt-3.5-turbo",
-        messages = messages
-    )
-    ChatGPT_reply = response["choices"][0]["message"]["content"]
-    messages.append({"role": "assistant", "content": ChatGPT_reply})
-    return ChatGPT_reply
-    
-description = "Cette Intelligence Artificielle est un programme fait par Stephane Dube (dubestephane @ vivaldi.net). Envoyez moi un message pour une requête ou pour une question. \n"
-title = "Je peux répondre a presque toute les questions."
-
-demo = gr.Interface(fn=CustomChatGPT, description = description, title = title, inputs = gr.inputs.Textbox(label = "Question", placeholder="Ex.: Quelle est la plus haute tour du monde?"), outputs = gr.outputs.Textbox(label = "Réponse"))
-    
-demo.launch()
diff --git a/spaces/OpenDILabCommunity/DI-sheep/run.sh b/spaces/OpenDILabCommunity/DI-sheep/run.sh
deleted file mode 100644
index 0439a29025b4c20e9351d75ced00488457c91bc9..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/DI-sheep/run.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-service nginx start
-cd ./DI-sheep/service && nohup python agent_app.py &
-cd ./DI-sheep/ui && npm run build && npm run preview
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/layers/shape_spec.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/layers/shape_spec.py
deleted file mode 100644
index fe7e8e261c1ab1bb1636bd7a245068d64e632306..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/layers/shape_spec.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-from collections import namedtuple
-
-
-class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])):
-    """
-    A simple structure that contains basic shape specification about a tensor.
-    It is often used as the auxiliary inputs/outputs of models,
-    to complement the lack of shape inference ability among pytorch modules.
-
-    Attributes:
-        channels:
-        height:
-        width:
-        stride:
-    """
-
-    def __new__(cls, channels=None, height=None, width=None, stride=None):
-        return super().__new__(cls, channels, height, width, stride)
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/segm_lib/nn/__init__.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/segm_lib/nn/__init__.py
deleted file mode 100644
index 98a96370ef04570f516052bb73f568d0ebc346c3..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/segm_lib/nn/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .modules import *
-from .parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to
diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/models/utils/cross_attention.py b/spaces/OpenMotionLab/MotionGPT/mGPT/models/utils/cross_attention.py
deleted file mode 100644
index deb1f053e575bd0940d12a9cc526a44f689f24c0..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/mGPT/models/utils/cross_attention.py
+++ /dev/null
@@ -1,412 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-DETR Transformer class.
-Copy-paste from torch.nn.Transformer with modifications:
-    * positional encodings are passed in MHattention
-    * extra LN at the end of encoder is removed
-    * decoder returns a stack of activations from all decoding layers
-"""
-import copy
-from typing import List, Optional
-from numpy import block
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-
-class SkipTransformerEncoder(nn.Module):
-    def __init__(self, encoder_layer, num_layers, norm=None):
-        super().__init__()
-        self.d_model = encoder_layer.d_model
-
-        self.num_layers = num_layers
-        self.norm = norm
-
-        assert num_layers % 2 == 1
-
-        num_block = (num_layers-1)//2
-        self.input_blocks = _get_clones(encoder_layer, num_block)
-        self.middle_block = _get_clone(encoder_layer)
-        self.output_blocks = _get_clones(encoder_layer, num_block)
-        self.linear_blocks = _get_clones(nn.Linear(2*self.d_model, self.d_model), num_block)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(self, src,
-                mask: Optional[Tensor] = None,
-                src_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None):
-        x = src
-
-        xs = []
-        for module in self.input_blocks:
-            x = module(x, src_mask=mask,
-                           src_key_padding_mask=src_key_padding_mask, pos=pos)
-            xs.append(x)
-
-        x = self.middle_block(x, src_mask=mask,
-                           src_key_padding_mask=src_key_padding_mask, pos=pos)
-
-        for (module, linear) in zip(self.output_blocks, self.linear_blocks):
-            x = torch.cat([x, xs.pop()], dim=-1)
-            x = linear(x)
-            x = module(x, src_mask=mask,
-                           src_key_padding_mask=src_key_padding_mask, pos=pos)
-
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-class SkipTransformerDecoder(nn.Module):
-    def __init__(self, decoder_layer, num_layers, norm=None):
-        super().__init__()
-        self.d_model = decoder_layer.d_model
-        
-        self.num_layers = num_layers
-        self.norm = norm
-
-        assert num_layers % 2 == 1
-
-        num_block = (num_layers-1)//2
-        self.input_blocks = _get_clones(decoder_layer, num_block)
-        self.middle_block = _get_clone(decoder_layer)
-        self.output_blocks = _get_clones(decoder_layer, num_block)
-        self.linear_blocks = _get_clones(nn.Linear(2*self.d_model, self.d_model), num_block)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(self, tgt, memory,
-                tgt_mask: Optional[Tensor] = None,
-                memory_mask: Optional[Tensor] = None,
-                tgt_key_padding_mask: Optional[Tensor] = None,
-                memory_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None,
-                query_pos: Optional[Tensor] = None):
-        x = tgt
-
-        xs = []
-        for module in self.input_blocks:
-            x = module(x, memory, tgt_mask=tgt_mask,
-                           memory_mask=memory_mask,
-                           tgt_key_padding_mask=tgt_key_padding_mask,
-                           memory_key_padding_mask=memory_key_padding_mask,
-                           pos=pos, query_pos=query_pos)
-            xs.append(x)
-
-        x = self.middle_block(x, memory, tgt_mask=tgt_mask,
-                           memory_mask=memory_mask,
-                           tgt_key_padding_mask=tgt_key_padding_mask,
-                           memory_key_padding_mask=memory_key_padding_mask,
-                           pos=pos, query_pos=query_pos)
-
-        for (module, linear) in zip(self.output_blocks, self.linear_blocks):
-            x = torch.cat([x, xs.pop()], dim=-1)
-            x = linear(x)
-            x = module(x, memory, tgt_mask=tgt_mask,
-                           memory_mask=memory_mask,
-                           tgt_key_padding_mask=tgt_key_padding_mask,
-                           memory_key_padding_mask=memory_key_padding_mask,
-                           pos=pos, query_pos=query_pos)
-
-        if self.norm is not None:
-            x = self.norm(x)
-
-        return x
-
-class Transformer(nn.Module):
-
-    def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
-                 num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
-                 activation="relu", normalize_before=False,
-                 return_intermediate_dec=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)
-
-        decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward,
-                                                dropout, activation, normalize_before)
-        decoder_norm = nn.LayerNorm(d_model)
-        self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm,
-                                          return_intermediate=return_intermediate_dec)
-
-        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, query_embed, 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)
-        query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
-        mask = mask.flatten(1)
-
-        tgt = torch.zeros_like(query_embed)
-        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
-        hs = self.decoder(tgt, memory, memory_key_padding_mask=mask,
-                          pos=pos_embed, query_pos=query_embed)
-        return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
-
-
-class TransformerEncoder(nn.Module):
-
-    def __init__(self, encoder_layer, num_layers, norm=None):
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(self, src,
-                mask: Optional[Tensor] = None,
-                src_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None):
-        output = src
-
-        for layer in self.layers:
-            output = layer(output, src_mask=mask,
-                           src_key_padding_mask=src_key_padding_mask, pos=pos)
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output
-
-
-class TransformerDecoder(nn.Module):
-
-    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
-        super().__init__()
-        self.layers = _get_clones(decoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-
-    def forward(self, tgt, memory,
-                tgt_mask: Optional[Tensor] = None,
-                memory_mask: Optional[Tensor] = None,
-                tgt_key_padding_mask: Optional[Tensor] = None,
-                memory_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None,
-                query_pos: Optional[Tensor] = None):
-        output = tgt
-
-        intermediate = []
-
-        for layer in self.layers:
-            output = layer(output, memory, tgt_mask=tgt_mask,
-                           memory_mask=memory_mask,
-                           tgt_key_padding_mask=tgt_key_padding_mask,
-                           memory_key_padding_mask=memory_key_padding_mask,
-                           pos=pos, query_pos=query_pos)
-            if self.return_intermediate:
-                intermediate.append(self.norm(output))
-
-        if self.norm is not None:
-            output = self.norm(output)
-            if self.return_intermediate:
-                intermediate.pop()
-                intermediate.append(output)
-
-        if self.return_intermediate:
-            return torch.stack(intermediate)
-
-        return output.unsqueeze(0)
-
-
-class TransformerEncoderLayer(nn.Module):
-
-    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
-                 activation="relu", normalize_before=False):
-        super().__init__()
-        self.d_model = d_model
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(self,
-                     src,
-                     src_mask: Optional[Tensor] = None,
-                     src_key_padding_mask: Optional[Tensor] = None,
-                     pos: Optional[Tensor] = None):
-        q = k = self.with_pos_embed(src, pos)
-        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,
-                              key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward_pre(self, src,
-                    src_mask: Optional[Tensor] = None,
-                    src_key_padding_mask: Optional[Tensor] = None,
-                    pos: Optional[Tensor] = None):
-        src2 = self.norm1(src)
-        q = k = self.with_pos_embed(src2, pos)
-        src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask,
-                              key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.dropout1(src2)
-        src2 = self.norm2(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
-        src = src + self.dropout2(src2)
-        return src
-
-    def forward(self, src,
-                src_mask: Optional[Tensor] = None,
-                src_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None):
-        if self.normalize_before:
-            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
-        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
-
-
-class TransformerDecoderLayer(nn.Module):
-
-    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
-                 activation="relu", normalize_before=False):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.d_model = d_model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.norm3 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-        self.dropout3 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(self, tgt, memory,
-                     tgt_mask: Optional[Tensor] = None,
-                     memory_mask: Optional[Tensor] = None,
-                     tgt_key_padding_mask: Optional[Tensor] = None,
-                     memory_key_padding_mask: Optional[Tensor] = None,
-                     pos: Optional[Tensor] = None,
-                     query_pos: Optional[Tensor] = None):
-                     
-        q = k = self.with_pos_embed(tgt, query_pos)
-        tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
-                              key_padding_mask=tgt_key_padding_mask)[0]
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
-                                   key=self.with_pos_embed(memory, pos),
-                                   value=memory, attn_mask=memory_mask,
-                                   key_padding_mask=memory_key_padding_mask)[0]
-        tgt = tgt + self.dropout2(tgt2)
-        tgt = self.norm2(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout3(tgt2)
-        tgt = self.norm3(tgt)
-        return tgt
-
-    def forward_pre(self, tgt, memory,
-                    tgt_mask: Optional[Tensor] = None,
-                    memory_mask: Optional[Tensor] = None,
-                    tgt_key_padding_mask: Optional[Tensor] = None,
-                    memory_key_padding_mask: Optional[Tensor] = None,
-                    pos: Optional[Tensor] = None,
-                    query_pos: Optional[Tensor] = None):
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
-                              key_padding_mask=tgt_key_padding_mask)[0]
-        tgt = tgt + self.dropout1(tgt2)
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
-                                   key=self.with_pos_embed(memory, pos),
-                                   value=memory, attn_mask=memory_mask,
-                                   key_padding_mask=memory_key_padding_mask)[0]
-        tgt = tgt + self.dropout2(tgt2)
-        tgt2 = self.norm3(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout3(tgt2)
-        return tgt
-
-    def forward(self, tgt, memory,
-                tgt_mask: Optional[Tensor] = None,
-                memory_mask: Optional[Tensor] = None,
-                tgt_key_padding_mask: Optional[Tensor] = None,
-                memory_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None,
-                query_pos: Optional[Tensor] = None):
-        if self.normalize_before:
-            return self.forward_pre(tgt, memory, tgt_mask, memory_mask,
-                                    tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)
-        return self.forward_post(tgt, memory, tgt_mask, memory_mask,
-                                 tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)
-
-
-def _get_clone(module):
-    return copy.deepcopy(module)
-
-def _get_clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def build_transformer(args):
-    return Transformer(
-        d_model=args.hidden_dim,
-        dropout=args.dropout,
-        nhead=args.nheads,
-        dim_feedforward=args.dim_feedforward,
-        num_encoder_layers=args.enc_layers,
-        num_decoder_layers=args.dec_layers,
-        normalize_before=args.pre_norm,
-        return_intermediate_dec=True,
-    )
-
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/openpose/hand.py b/spaces/PAIR/Text2Video-Zero/annotator/openpose/hand.py
deleted file mode 100644
index 3d0bf17165ad7eb225332b51f4a2aa16718664b2..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/openpose/hand.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import cv2
-import json
-import numpy as np
-import math
-import time
-from scipy.ndimage.filters import gaussian_filter
-import matplotlib.pyplot as plt
-import matplotlib
-import torch
-from skimage.measure import label
-
-from .model import handpose_model
-from . import util
-
-class Hand(object):
-    def __init__(self, model_path):
-        self.model = handpose_model()
-        if torch.cuda.is_available():
-            self.model = self.model.cuda()
-            print('cuda')
-        model_dict = util.transfer(self.model, torch.load(model_path))
-        self.model.load_state_dict(model_dict)
-        self.model.eval()
-
-    def __call__(self, oriImg):
-        scale_search = [0.5, 1.0, 1.5, 2.0]
-        # scale_search = [0.5]
-        boxsize = 368
-        stride = 8
-        padValue = 128
-        thre = 0.05
-        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
-        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 22))
-        # paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
-
-        for m in range(len(multiplier)):
-            scale = multiplier[m]
-            imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
-            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
-            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
-            im = np.ascontiguousarray(im)
-
-            data = torch.from_numpy(im).float()
-            if torch.cuda.is_available():
-                data = data.cuda()
-            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
-            with torch.no_grad():
-                output = self.model(data).cpu().numpy()
-                # output = self.model(data).numpy()q
-
-            # extract outputs, resize, and remove padding
-            heatmap = np.transpose(np.squeeze(output), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
-            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
-            heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
-
-            heatmap_avg += heatmap / len(multiplier)
-
-        all_peaks = []
-        for part in range(21):
-            map_ori = heatmap_avg[:, :, part]
-            one_heatmap = gaussian_filter(map_ori, sigma=3)
-            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
-            # 全部小于阈值
-            if np.sum(binary) == 0:
-                all_peaks.append([0, 0])
-                continue
-            label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
-            max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
-            label_img[label_img != max_index] = 0
-            map_ori[label_img == 0] = 0
-
-            y, x = util.npmax(map_ori)
-            all_peaks.append([x, y])
-        return np.array(all_peaks)
-
-if __name__ == "__main__":
-    hand_estimation = Hand('../model/hand_pose_model.pth')
-
-    # test_image = '../images/hand.jpg'
-    test_image = '../images/hand.jpg'
-    oriImg = cv2.imread(test_image)  # B,G,R order
-    peaks = hand_estimation(oriImg)
-    canvas = util.draw_handpose(oriImg, peaks, True)
-    cv2.imshow('', canvas)
-    cv2.waitKey(0)
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/openpose/util.py b/spaces/PAIR/Text2Video-Zero/annotator/openpose/util.py
deleted file mode 100644
index 6f91ae0e65abaf0cbd62d803f56498991141e61b..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/openpose/util.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import math
-import numpy as np
-import matplotlib
-import cv2
-
-
-def padRightDownCorner(img, stride, padValue):
-    h = img.shape[0]
-    w = img.shape[1]
-
-    pad = 4 * [None]
-    pad[0] = 0 # up
-    pad[1] = 0 # left
-    pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
-    pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
-
-    img_padded = img
-    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
-    img_padded = np.concatenate((pad_up, img_padded), axis=0)
-    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
-    img_padded = np.concatenate((pad_left, img_padded), axis=1)
-    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
-    img_padded = np.concatenate((img_padded, pad_down), axis=0)
-    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
-    img_padded = np.concatenate((img_padded, pad_right), axis=1)
-
-    return img_padded, pad
-
-# transfer caffe model to pytorch which will match the layer name
-def transfer(model, model_weights):
-    transfered_model_weights = {}
-    for weights_name in model.state_dict().keys():
-        transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
-    return transfered_model_weights
-
-# draw the body keypoint and lims
-def draw_bodypose(canvas, candidate, subset):
-    stickwidth = 4
-    limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
-               [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
-               [1, 16], [16, 18], [3, 17], [6, 18]]
-
-    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
-              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
-              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
-    for i in range(18):
-        for n in range(len(subset)):
-            index = int(subset[n][i])
-            if index == -1:
-                continue
-            x, y = candidate[index][0:2]
-            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
-    for i in range(17):
-        for n in range(len(subset)):
-            index = subset[n][np.array(limbSeq[i]) - 1]
-            if -1 in index:
-                continue
-            cur_canvas = canvas.copy()
-            Y = candidate[index.astype(int), 0]
-            X = candidate[index.astype(int), 1]
-            mX = np.mean(X)
-            mY = np.mean(Y)
-            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
-            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
-            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
-            cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
-            canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
-    # plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]])
-    # plt.imshow(canvas[:, :, [2, 1, 0]])
-    return canvas
-
-
-# image drawed by opencv is not good.
-def draw_handpose(canvas, all_hand_peaks, show_number=False):
-    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
-             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
-
-    for peaks in all_hand_peaks:
-        for ie, e in enumerate(edges):
-            if np.sum(np.all(peaks[e], axis=1)==0)==0:
-                x1, y1 = peaks[e[0]]
-                x2, y2 = peaks[e[1]]
-                cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2)
-
-        for i, keyponit in enumerate(peaks):
-            x, y = keyponit
-            cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
-            if show_number:
-                cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA)
-    return canvas
-
-# detect hand according to body pose keypoints
-# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
-def handDetect(candidate, subset, oriImg):
-    # right hand: wrist 4, elbow 3, shoulder 2
-    # left hand: wrist 7, elbow 6, shoulder 5
-    ratioWristElbow = 0.33
-    detect_result = []
-    image_height, image_width = oriImg.shape[0:2]
-    for person in subset.astype(int):
-        # if any of three not detected
-        has_left = np.sum(person[[5, 6, 7]] == -1) == 0
-        has_right = np.sum(person[[2, 3, 4]] == -1) == 0
-        if not (has_left or has_right):
-            continue
-        hands = []
-        #left hand
-        if has_left:
-            left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
-            x1, y1 = candidate[left_shoulder_index][:2]
-            x2, y2 = candidate[left_elbow_index][:2]
-            x3, y3 = candidate[left_wrist_index][:2]
-            hands.append([x1, y1, x2, y2, x3, y3, True])
-        # right hand
-        if has_right:
-            right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]]
-            x1, y1 = candidate[right_shoulder_index][:2]
-            x2, y2 = candidate[right_elbow_index][:2]
-            x3, y3 = candidate[right_wrist_index][:2]
-            hands.append([x1, y1, x2, y2, x3, y3, False])
-
-        for x1, y1, x2, y2, x3, y3, is_left in hands:
-            # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
-            # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
-            # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
-            # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
-            # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
-            # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
-            x = x3 + ratioWristElbow * (x3 - x2)
-            y = y3 + ratioWristElbow * (y3 - y2)
-            distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
-            distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-            width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
-            # x-y refers to the center --> offset to topLeft point
-            # handRectangle.x -= handRectangle.width / 2.f;
-            # handRectangle.y -= handRectangle.height / 2.f;
-            x -= width / 2
-            y -= width / 2  # width = height
-            # overflow the image
-            if x < 0: x = 0
-            if y < 0: y = 0
-            width1 = width
-            width2 = width
-            if x + width > image_width: width1 = image_width - x
-            if y + width > image_height: width2 = image_height - y
-            width = min(width1, width2)
-            # the max hand box value is 20 pixels
-            if width >= 20:
-                detect_result.append([int(x), int(y), int(width), is_left])
-
-    '''
-    return value: [[x, y, w, True if left hand else False]].
-    width=height since the network require squared input.
-    x, y is the coordinate of top left 
-    '''
-    return detect_result
-
-# get max index of 2d array
-def npmax(array):
-    arrayindex = array.argmax(1)
-    arrayvalue = array.max(1)
-    i = arrayvalue.argmax()
-    j = arrayindex[i]
-    return i, j
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/nonlocal_r50-d8.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/nonlocal_r50-d8.py
deleted file mode 100644
index 5674a39854cafd1f2e363bac99c58ccae62f24da..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/nonlocal_r50-d8.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='NLHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        dropout_ratio=0.1,
-        reduction=2,
-        use_scale=True,
-        mode='embedded_gaussian',
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/ParthRangarajan/Centauri_Pilot/README.md b/spaces/ParthRangarajan/Centauri_Pilot/README.md
deleted file mode 100644
index 114f770d5f591020c148e6a76e08a83eadc9f51a..0000000000000000000000000000000000000000
--- a/spaces/ParthRangarajan/Centauri_Pilot/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: Centauri_Pilot
-emoji: 🐢
-colorFrom: gray
-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`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/vm/trap-state.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/vm/trap-state.go
deleted file mode 100644
index 95da72ed2bc53a6bae2d98f15835c96cc3fbb1d2..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/vm/trap-state.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/OpenAssistant-falcon-7b-sft-mix-2000/app.py b/spaces/PeepDaSlan9/OpenAssistant-falcon-7b-sft-mix-2000/app.py
deleted file mode 100644
index 37573fd48e2618cf081342b84bd04e484d860504..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/OpenAssistant-falcon-7b-sft-mix-2000/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/OpenAssistant/falcon-7b-sft-mix-2000").launch()
\ No newline at end of file
diff --git a/spaces/PeepDaSlan9/SDXL-artists-browser/style.css b/spaces/PeepDaSlan9/SDXL-artists-browser/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/SDXL-artists-browser/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/decode_heads/sep_aspp_head.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/decode_heads/sep_aspp_head.py
deleted file mode 100644
index 3339a7ac56e77dfc638e9bffb557d4699148686b..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/decode_heads/sep_aspp_head.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .aspp_head import ASPPHead, ASPPModule
-
-
-class DepthwiseSeparableASPPModule(ASPPModule):
-    """Atrous Spatial Pyramid Pooling (ASPP) Module with depthwise separable
-    conv."""
-
-    def __init__(self, **kwargs):
-        super(DepthwiseSeparableASPPModule, self).__init__(**kwargs)
-        for i, dilation in enumerate(self.dilations):
-            if dilation > 1:
-                self[i] = DepthwiseSeparableConvModule(
-                    self.in_channels,
-                    self.channels,
-                    3,
-                    dilation=dilation,
-                    padding=dilation,
-                    norm_cfg=self.norm_cfg,
-                    act_cfg=self.act_cfg)
-
-
-@HEADS.register_module()
-class DepthwiseSeparableASPPHead(ASPPHead):
-    """Encoder-Decoder with Atrous Separable Convolution for Semantic Image
-    Segmentation.
-
-    This head is the implementation of `DeepLabV3+
-    <https://arxiv.org/abs/1802.02611>`_.
-
-    Args:
-        c1_in_channels (int): The input channels of c1 decoder. If is 0,
-            the no decoder will be used.
-        c1_channels (int): The intermediate channels of c1 decoder.
-    """
-
-    def __init__(self, c1_in_channels, c1_channels, **kwargs):
-        super(DepthwiseSeparableASPPHead, self).__init__(**kwargs)
-        assert c1_in_channels >= 0
-        self.aspp_modules = DepthwiseSeparableASPPModule(
-            dilations=self.dilations,
-            in_channels=self.in_channels,
-            channels=self.channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        if c1_in_channels > 0:
-            self.c1_bottleneck = ConvModule(
-                c1_in_channels,
-                c1_channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-        else:
-            self.c1_bottleneck = None
-        self.sep_bottleneck = nn.Sequential(
-            DepthwiseSeparableConvModule(
-                self.channels + c1_channels,
-                self.channels,
-                3,
-                padding=1,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg),
-            DepthwiseSeparableConvModule(
-                self.channels,
-                self.channels,
-                3,
-                padding=1,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg))
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        aspp_outs = [
-            resize(
-                self.image_pool(x),
-                size=x.size()[2:],
-                mode='bilinear',
-                align_corners=self.align_corners)
-        ]
-        aspp_outs.extend(self.aspp_modules(x))
-        aspp_outs = torch.cat(aspp_outs, dim=1)
-        output = self.bottleneck(aspp_outs)
-        if self.c1_bottleneck is not None:
-            c1_output = self.c1_bottleneck(inputs[0])
-            output = resize(
-                input=output,
-                size=c1_output.shape[2:],
-                mode='bilinear',
-                align_corners=self.align_corners)
-            output = torch.cat([output, c1_output], dim=1)
-        output = self.sep_bottleneck(output)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/Prathap/summarization/README.md b/spaces/Prathap/summarization/README.md
deleted file mode 100644
index ee9f7cd7066457b6fdd193b079f5f04216b3b2ab..0000000000000000000000000000000000000000
--- a/spaces/Prathap/summarization/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Summarization
-emoji: ⚡
-colorFrom: blue
-colorTo: green
-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/Qiwei97/Airbnb_tool/app.py b/spaces/Qiwei97/Airbnb_tool/app.py
deleted file mode 100644
index a9468e3a192fa13bed4dbbf393bdfebacf0e81ee..0000000000000000000000000000000000000000
--- a/spaces/Qiwei97/Airbnb_tool/app.py
+++ /dev/null
@@ -1,257 +0,0 @@
-
-import streamlit as st
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-import numpy_financial as npf
-import pandas as pd
-from streamlit_folium import folium_static
-import leafmap.foliumap as leafmap
-import folium
-from shapely.geometry import Point, Polygon
-import geopandas
-import geopy
-from geopy.geocoders import Nominatim
-from geopy.extra.rate_limiter import RateLimiter
-from scipy.spatial import cKDTree
-
-#-----------------------------------------
-# Set page settings
-st.set_page_config(layout="wide")
-
-#-----------------------------------------
-# Sidebar
-with st.sidebar:
-  st.header('Welcome to the Airbnb Investment Tool!')
-  nav = st.selectbox('Navigation', ['Heuristic Pricing', 
-                                    'Investment Analysis'])
-
-#-----------------------------------------
-# Additional Functions
-
-def p_title(title):
-    st.markdown(f'<h3 style="text-align: left; color:#F63366; font-size:28px;">{title}</h3>', unsafe_allow_html=True)
-
-
-# Function to return a GeoPandas DataFrame containing the listings
-# that are within a specified radius from a specified lat, long.
-def getNearbyListings(gdf_proj, input_long, input_lat, radius):
-  # Build Tree
-  airbnbCoords = np.array(list(gdf_proj.geometry.apply(lambda x: (x.x, x.y))))
-  airbnbTree = cKDTree(airbnbCoords)
-
-  # Convert lat-long to projected coords
-  gdf_input = geopandas.GeoSeries.from_xy(x=[input_long], y=[input_lat], crs=4326)
-  gdf_input_proj = gdf_input.to_crs(crs=32634)
-
-  coords = np.array(list((gdf_input_proj.x[0], gdf_input_proj.y[0])))
-
-  # Returns list of indices whose distance is <= radius
-  neighbours_indices = airbnbTree.query_ball_point(coords, radius)
-  gdf_neighbours_proj = gdf_proj.iloc[neighbours_indices, :]
-  gdf_neighbours = gdf_neighbours_proj.to_crs(crs=4326)
-
-  return gdf_neighbours
-
-# Function to return IRR.
-# Financial Modelling Tool.
-def investment_tool(house_price, loan_amount, loan_period, percentage_loan_interest_annual, 
-                    rental_charged_monthly, percentage_rental_tax, percentage_increase_in_rental_yearly, utilisation_rate, 
-                    yearly_refurbishment_costs, percentage_increase_in_refurbishment_yearly, ending_value_of_house):
-    
-  #expected format of percentage parameters is whole number and not decimals i.e., 5 instead of 0.05
-  #all non-% parameters are expected to be positive
-
-  house_price = int(house_price)
-  loan_amount = int(loan_amount)
-  loan_period = int(loan_period)
-  percentage_loan_interest_annual = int(percentage_loan_interest_annual)
-  rental_charged_monthly = int(rental_charged_monthly)
-  percentage_rental_tax = int(percentage_rental_tax)
-  percentage_increase_in_rental_yearly = int(percentage_increase_in_rental_yearly)
-  percentage_utilisation_rate = int(utilisation_rate)
-  yearly_refurbishment_costs = int(yearly_refurbishment_costs)
-  percentage_increase_in_refurbishment_yearly = int(percentage_increase_in_refurbishment_yearly)
-  ending_value_of_house = int(ending_value_of_house)
-
-  #ensuring the figures make sense
-  if loan_amount > house_price:
-    return print("Loan Amount cannot exceed House Price")
-
-  #creating the list of cash flows to be used to calculate internal rate of return
-  initial_cashflow = -(1 - loan_amount/house_price) * house_price
-  cashflow_list = [initial_cashflow]
-
-  #finding the annual mortgage assuming equal amortization
-  mortgage = npf.pmt(percentage_loan_interest_annual / 100, loan_period,  loan_amount) #the np.pmt function will automatically put mortgage as a negative cashflow
-
-  #finding the annual cashflows & loan balance changes during the loan period and appending them to the respective lists
-  for i in range(loan_period):
-    rental = 12 * rental_charged_monthly * ((1 + (percentage_increase_in_rental_yearly / 100)) ** i) * (1 - (percentage_rental_tax / 100)) * utilisation_rate / 100
-    refurbishment_cost = -1 * yearly_refurbishment_costs * ((1 + (percentage_increase_in_refurbishment_yearly / 100)) ** i)
-    
-    #the condition here is to include the salvage/ending value of the house to cashflows after loan repayments are finished
-    if i == (loan_period-1):
-      yearly_cashflow = ending_value_of_house + rental + mortgage + refurbishment_cost
-    else:
-      yearly_cashflow = rental + mortgage + refurbishment_cost
-    cashflow_list.append(yearly_cashflow)
-  
-  #finding the internal rate of return
-  irr = round(npf.irr(cashflow_list), 4)
-
-  #-----------------------------------
-  #Dataframe for plotting of graph
-  loan_dict = {'Year': [0], 'Starting Loan Balance': [0], 'Cumulative Interest Paid': [0], 'Cumulative Principal Paid': [0], 'Remaining Loan Balance': [0]}
-    
-  # Create DataFrame  
-  loan_dataframe = pd.DataFrame(loan_dict)
-    
-  #finding the annual mortgage assuming equal amortization
-  mortgage = npf.pmt(percentage_loan_interest_annual / 100, loan_period,  loan_amount) #the np.pmt function will automatically put mortgage as a negative cashflow
-
-  #updating the global dataframe
-  loan_dataframe.loc[0,'Starting Loan Balance'] = loan_amount
-
-  for i in range(loan_period):
-    loan_dataframe.loc[i,'Year'] = i+1
-
-    #the condition here is to calculate principal and interest paid
-    if i == 0:
-      loan_dataframe.loc[i,'Cumulative Interest Paid'] = loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)
-      loan_dataframe.loc[i,'Cumulative Principal Paid'] = (-1 * mortgage) - (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100))
-    else:
-      loan_dataframe.loc[i,'Cumulative Interest Paid'] = loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100) + loan_dataframe.loc[i-1,'Cumulative Interest Paid']
-      loan_dataframe.loc[i,'Cumulative Principal Paid'] = (-1 * mortgage) - (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)) + loan_dataframe.loc[i-1,'Cumulative Principal Paid']
-          
-    loan_dataframe.loc[i,'Remaining Loan Balance'] = loan_dataframe.loc[i,'Starting Loan Balance'] + (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)) + mortgage
-      
-      #condition to update starting loan balance
-    if i != loan_period-1:
-      loan_dataframe.loc[i+1,'Starting Loan Balance'] = loan_dataframe.loc[i,'Remaining Loan Balance']
-
-  loan_dataframe['Remaining Loan Balance'] = pd.to_numeric(loan_dataframe['Remaining Loan Balance'])
- 
-  return irr, loan_dataframe
-
-#-----------------------------------------
-# Load Airbnb listings data
-df_raw = pd.read_csv("data/listings_sf_withamenities.csv")
-df = df_raw.copy()
-gdf = geopandas.GeoDataFrame(
-    df, 
-    geometry=geopandas.points_from_xy(df.longitude, df.latitude),
-    crs=4326)
-gdf_proj = gdf.to_crs(crs=32634)
-
-#-----------------------------------------
-# Tab 1: Heuristic Pricing
-if nav == 'Heuristic Pricing':
-    
-    st.markdown("<h3 style='text-align: center; color:grey;'>Airbnb &#127968;</h3>", unsafe_allow_html=True)
-    st.text('')
-    p_title('Heuristic Pricing')
-    st.text('')
-
-    # Get address inputs
-    st.caption('Enter your address:')
-    with st.form("heuristics_form"):
-      col1, col2 = st.columns(2)
-      with col1:
-        postalcode = st.text_input("Postal Code", "94109")
-        street = st.text_input("Street", "1788 Clay Street")
-        city = st.selectbox("City", ["San Francisco"])
-      with col2:
-        state = st.selectbox("State", ["California"])
-        country = st.selectbox("Country", ["United States"])
-        radius = st.slider("Distance of nearest listings (metres)", min_value=500, max_value=2000, value=500, step=500)
-      submitted = st.form_submit_button("Submit")
-
-      if submitted:
-        # Get geolocation
-        geolocator = Nominatim(user_agent="GTA Lookup")
-        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
-        location = geolocator.geocode({"postalcode": postalcode, "street": street, "city": city, "state": state, "country": country})
-
-        # If the search address yields no result, set to default coords of San Fran
-        if location is None:
-          lat = 37.773972
-          lon = -122.431297
-          st.error("Address is not found. Please try again.")
-        else:
-          lat = location.latitude
-          lon = location.longitude
-
-          # Compute Stats
-          st.markdown('___')
-          st.caption('Recommended Pricing:')
-          gdf_nearby_listings = getNearbyListings(gdf_proj, lon, lat, radius=radius)
-          if len(gdf_nearby_listings) == 0:
-            st.error("There are no nearby listings.")
-          else:
-            col3, col4 = st.columns(2)
-            with col3:
-              df_nearby_stats = gdf_nearby_listings[["price"]].describe().round(2)
-              df_nearby_stats.columns = pd.Index(["Price"])
-              st.table(df_nearby_stats.style.format("{:.2f}"))
-            with col4:
-              # Plot Stats
-              fig = plt.figure(figsize=(10, 4))
-              sns.boxplot(x="price", data=gdf_nearby_listings, showfliers=False)
-              st.pyplot(fig)
-
-            # Plot using leafmap. Responsive width.
-            m = leafmap.Map(tiles="OpenStreetMap", location=[lat, lon], zoom_start=15)
-            m.add_marker(location=[lat, lon])
-            m.add_points_from_xy(gdf_nearby_listings, x="longitude", y="latitude", 
-                     popup=["id", "price", "review_scores_rating"],
-                     color_options=['red'])
-            m.add_heatmap(data=gdf_nearby_listings, 
-                          latitude="latitude", longitude="longitude", 
-                          value="price", min_opacity=0.1,
-                          name="Price Heatmap", blue=50)
-            m.to_streamlit()
-
-#-----------------------------------------
-# Tab 2: Investment Analysis
-if nav == 'Investment Analysis':
-    st.markdown("<h3 style='text-align: center; color:grey;'>Airbnb &#127968;</h3>", unsafe_allow_html=True)
-    st.text('')
-    p_title('Investment Analysis')
-    
-    # Financial Projections
-    st.caption("Enter data here")
-    with st.form("investment_form"):
-      col1_2, col2_2, col3_2 = st.columns(3)
-      with col1_2:
-        house_price = st.number_input("Purchase Price of House ($)", min_value=0, value=250000)
-        loan_amount = st.number_input("Loan Amount ($)", min_value=0, value=150000)
-        loan_period = st.number_input("Loan Period (Years)", min_value=0, value=15)
-        percentage_loan_interest_annual = st.number_input("Annual Loan I/R (%)", min_value=0.0, max_value=100.0, value=2.1)
-      with col2_2:
-        rental_charged_monthly = st.number_input("Monthly Rental ($)", min_value=0, value=2000)
-        percentage_rental_tax = st.number_input("Rental Tax (%)", min_value=0.0, value=0.0)
-        percentage_increase_in_rental_yearly = st.number_input("Annual Rental Increase (%)", min_value=0.0, value=1.0)
-        utilisation_rate = st.number_input("Utilisation Rate (%)", min_value=0.0, value=50.0)
-      with col3_2:
-        yearly_refurbishment_costs = st.number_input("Yearly Refurbishment Costs ($)", min_value=0, value=3000)
-        percentage_increase_in_refurbishment_yearly = st.number_input("Yearly Refurbishment Costs Increase (%)", min_value=0.0, value=2.0)
-        ending_value_of_house = st.number_input("Ending Value of House ($)", min_value=0, value=300000)
-      submitted2 = st.form_submit_button("Submit")
-
-      if submitted2:
-        irr, loan_dataframe = investment_tool(house_price, loan_amount, loan_period, percentage_loan_interest_annual, 
-                rental_charged_monthly, percentage_rental_tax, percentage_increase_in_rental_yearly, utilisation_rate, 
-                yearly_refurbishment_costs, percentage_increase_in_refurbishment_yearly, ending_value_of_house)
-        st.markdown('___')
-        st.caption("Expected Internal Rate of Return")
-        st.text("{:.2%}".format(irr))
-        # Print plots
-        fig = plt.figure(figsize=(10, 4))
-        plt.bar(loan_dataframe['Year'], loan_dataframe['Cumulative Principal Paid'], color='lightcoral')
-        plt.bar(loan_dataframe['Year'], loan_dataframe['Cumulative Interest Paid'], bottom=loan_dataframe['Cumulative Principal Paid'], color='lightsalmon')
-        plt.plot(loan_dataframe['Year'], loan_dataframe['Remaining Loan Balance'], color='crimson')
-        plt.ylabel('Amount')
-        plt.title('Loan Balance')
-        plt.legend(('Loan Balance Remaining','Cumulative Principal Paid', 'Cumulative Interest Paid'))
-        st.pyplot(fig)
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/cli/chardetect.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/cli/chardetect.py
deleted file mode 100644
index 7926fa37e380d87dbcd30f9fce6cb46359f7d3bb..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/cli/chardetect.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
-Script which takes one or more file paths and reports on their detected
-encodings
-
-Example::
-
-    % chardetect somefile someotherfile
-    somefile: windows-1252 with confidence 0.5
-    someotherfile: ascii with confidence 1.0
-
-If no paths are provided, it takes its input from stdin.
-
-"""
-
-
-import argparse
-import sys
-
-from .. import __version__
-from ..universaldetector import UniversalDetector
-
-
-def description_of(lines, name="stdin"):
-    """
-    Return a string describing the probable encoding of a file or
-    list of strings.
-
-    :param lines: The lines to get the encoding of.
-    :type lines: Iterable of bytes
-    :param name: Name of file or collection of lines
-    :type name: str
-    """
-    u = UniversalDetector()
-    for line in lines:
-        line = bytearray(line)
-        u.feed(line)
-        # shortcut out of the loop to save reading further - particularly useful if we read a BOM.
-        if u.done:
-            break
-    u.close()
-    result = u.result
-    if result["encoding"]:
-        return f'{name}: {result["encoding"]} with confidence {result["confidence"]}'
-    return f"{name}: no result"
-
-
-def main(argv=None):
-    """
-    Handles command line arguments and gets things started.
-
-    :param argv: List of arguments, as if specified on the command-line.
-                 If None, ``sys.argv[1:]`` is used instead.
-    :type argv: list of str
-    """
-    # Get command line arguments
-    parser = argparse.ArgumentParser(
-        description="Takes one or more file paths and reports their detected \
-                     encodings"
-    )
-    parser.add_argument(
-        "input",
-        help="File whose encoding we would like to determine. \
-                              (default: stdin)",
-        type=argparse.FileType("rb"),
-        nargs="*",
-        default=[sys.stdin.buffer],
-    )
-    parser.add_argument(
-        "--version", action="version", version=f"%(prog)s {__version__}"
-    )
-    args = parser.parse_args(argv)
-
-    for f in args.input:
-        if f.isatty():
-            print(
-                "You are running chardetect interactively. Press "
-                "CTRL-D twice at the start of a blank line to signal the "
-                "end of your input. If you want help, run chardetect "
-                "--help\n",
-                file=sys.stderr,
-            )
-        print(description_of(f, f.name))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/langturkishmodel.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/langturkishmodel.py
deleted file mode 100644
index 291857c25c83f91a151c1d7760e8e5e09c1ee238..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/langturkishmodel.py
+++ /dev/null
@@ -1,4380 +0,0 @@
-from pip._vendor.chardet.sbcharsetprober import SingleByteCharSetModel
-
-# 3: Positive
-# 2: Likely
-# 1: Unlikely
-# 0: Negative
-
-TURKISH_LANG_MODEL = {
-    23: {  # 'A'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 1,  # 'h'
-        3: 1,  # 'i'
-        24: 0,  # 'j'
-        10: 2,  # 'k'
-        5: 1,  # 'l'
-        13: 1,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 1,  # 'r'
-        8: 1,  # 's'
-        9: 1,  # 't'
-        14: 1,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 0,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    37: {  # 'B'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 2,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 1,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 1,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 1,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 0,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    47: {  # 'C'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 1,  # 'L'
-        20: 0,  # 'M'
-        46: 1,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 1,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 2,  # 'j'
-        10: 1,  # 'k'
-        5: 2,  # 'l'
-        13: 2,  # 'm'
-        4: 2,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 2,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    39: {  # 'D'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 1,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 1,  # 'l'
-        13: 3,  # 'm'
-        4: 0,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 1,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 1,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    29: {  # 'E'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 1,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 0,  # 'h'
-        3: 1,  # 'i'
-        24: 1,  # 'j'
-        10: 0,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 1,  # 's'
-        9: 1,  # 't'
-        14: 1,  # 'u'
-        32: 1,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    52: {  # 'F'
-        23: 0,  # 'A'
-        37: 1,  # 'B'
-        47: 1,  # 'C'
-        39: 1,  # 'D'
-        29: 1,  # 'E'
-        52: 2,  # 'F'
-        36: 0,  # 'G'
-        45: 2,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 1,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 1,  # 'b'
-        28: 1,  # 'c'
-        12: 1,  # 'd'
-        2: 0,  # 'e'
-        18: 1,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 2,  # 'i'
-        24: 1,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 1,  # 'm'
-        4: 2,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 2,  # 'r'
-        8: 1,  # 's'
-        9: 1,  # 't'
-        14: 1,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 1,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 2,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 2,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 2,  # 'ş'
-    },
-    36: {  # 'G'
-        23: 1,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 2,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 2,  # 'N'
-        42: 1,  # 'O'
-        48: 1,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 1,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 1,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 0,  # 'r'
-        8: 1,  # 's'
-        9: 1,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 1,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 2,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    45: {  # 'H'
-        23: 0,  # 'A'
-        37: 1,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 2,  # 'G'
-        45: 1,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 1,  # 'L'
-        20: 0,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 2,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 2,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 0,  # 'n'
-        15: 1,  # 'o'
-        26: 1,  # 'p'
-        7: 1,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 2,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 0,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    53: {  # 'I'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 2,  # 'l'
-        13: 2,  # 'm'
-        4: 0,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 0,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    60: {  # 'J'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 1,  # 'd'
-        2: 0,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 1,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 1,  # 's'
-        9: 0,  # 't'
-        14: 0,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 0,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    16: {  # 'K'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 3,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 1,  # 'e'
-        18: 3,  # 'f'
-        27: 3,  # 'g'
-        25: 3,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 0,  # 'u'
-        32: 3,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 2,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    49: {  # 'L'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 2,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 2,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 0,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 2,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 2,  # 'n'
-        15: 1,  # 'o'
-        26: 1,  # 'p'
-        7: 1,  # 'r'
-        8: 1,  # 's'
-        9: 1,  # 't'
-        14: 0,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 2,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 1,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    20: {  # 'M'
-        23: 1,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 1,  # 'h'
-        3: 2,  # 'i'
-        24: 2,  # 'j'
-        10: 2,  # 'k'
-        5: 2,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 3,  # 'r'
-        8: 0,  # 's'
-        9: 2,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 3,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    46: {  # 'N'
-        23: 0,  # 'A'
-        37: 1,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 1,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 2,  # 'j'
-        10: 1,  # 'k'
-        5: 1,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 1,  # 'o'
-        26: 1,  # 'p'
-        7: 1,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 1,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 2,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    42: {  # 'O'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 0,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 1,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 0,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 2,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 2,  # 'İ'
-        6: 1,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    48: {  # 'P'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 2,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 1,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 0,  # 'n'
-        15: 2,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 2,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 2,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 0,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    44: {  # 'R'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 1,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 2,  # 'l'
-        13: 2,  # 'm'
-        4: 0,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 1,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 1,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    35: {  # 'S'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 1,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 1,  # 'l'
-        13: 2,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 1,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 2,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 3,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    31: {  # 'T'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 0,  # 'c'
-        12: 1,  # 'd'
-        2: 3,  # 'e'
-        18: 2,  # 'f'
-        27: 2,  # 'g'
-        25: 0,  # 'h'
-        3: 1,  # 'i'
-        24: 1,  # 'j'
-        10: 2,  # 'k'
-        5: 2,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 2,  # 'p'
-        7: 2,  # 'r'
-        8: 0,  # 's'
-        9: 2,  # 't'
-        14: 2,  # 'u'
-        32: 1,  # 'v'
-        57: 1,  # 'w'
-        58: 1,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    51: {  # 'U'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 1,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 1,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 1,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 1,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 2,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 1,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    38: {  # 'V'
-        23: 1,  # 'A'
-        37: 1,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 1,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 2,  # 'l'
-        13: 2,  # 'm'
-        4: 0,  # 'n'
-        15: 2,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 1,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 1,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 1,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 3,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    62: {  # 'W'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 0,  # 'd'
-        2: 0,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 0,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 0,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 0,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    43: {  # 'Y'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 0,  # 'G'
-        45: 1,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 2,  # 'N'
-        42: 0,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 1,  # 'j'
-        10: 1,  # 'k'
-        5: 1,  # 'l'
-        13: 3,  # 'm'
-        4: 0,  # 'n'
-        15: 2,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 1,  # 'Ü'
-        59: 1,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 0,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    56: {  # 'Z'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 2,  # 'Z'
-        1: 2,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 2,  # 'i'
-        24: 1,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 1,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 1,  # 'r'
-        8: 1,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 1,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    1: {  # 'a'
-        23: 3,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 3,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 1,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 3,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 2,  # 'Z'
-        1: 2,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 2,  # 'e'
-        18: 3,  # 'f'
-        27: 3,  # 'g'
-        25: 3,  # 'h'
-        3: 3,  # 'i'
-        24: 3,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 3,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 3,  # 'v'
-        57: 2,  # 'w'
-        58: 0,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 1,  # 'î'
-        34: 1,  # 'ö'
-        17: 3,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    21: {  # 'b'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 3,  # 'g'
-        25: 1,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 3,  # 'p'
-        7: 1,  # 'r'
-        8: 2,  # 's'
-        9: 2,  # 't'
-        14: 2,  # 'u'
-        32: 1,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    28: {  # 'c'
-        23: 0,  # 'A'
-        37: 1,  # 'B'
-        47: 1,  # 'C'
-        39: 1,  # 'D'
-        29: 2,  # 'E'
-        52: 0,  # 'F'
-        36: 2,  # 'G'
-        45: 2,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 2,  # 'T'
-        51: 2,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 3,  # 'Y'
-        56: 0,  # 'Z'
-        1: 1,  # 'a'
-        21: 1,  # 'b'
-        28: 2,  # 'c'
-        12: 2,  # 'd'
-        2: 1,  # 'e'
-        18: 1,  # 'f'
-        27: 2,  # 'g'
-        25: 2,  # 'h'
-        3: 3,  # 'i'
-        24: 1,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 3,  # 'n'
-        15: 2,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 1,  # 'u'
-        32: 0,  # 'v'
-        57: 1,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 1,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 1,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 1,  # 'î'
-        34: 2,  # 'ö'
-        17: 2,  # 'ü'
-        30: 2,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 2,  # 'ş'
-    },
-    12: {  # 'd'
-        23: 1,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 2,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 1,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 1,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 1,  # 'f'
-        27: 3,  # 'g'
-        25: 3,  # 'h'
-        3: 2,  # 'i'
-        24: 3,  # 'j'
-        10: 2,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 2,  # 's'
-        9: 2,  # 't'
-        14: 3,  # 'u'
-        32: 1,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 3,  # 'y'
-        22: 1,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    2: {  # 'e'
-        23: 2,  # 'A'
-        37: 0,  # 'B'
-        47: 2,  # 'C'
-        39: 0,  # 'D'
-        29: 3,  # 'E'
-        52: 1,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 1,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 1,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 1,  # 'R'
-        35: 0,  # 'S'
-        31: 3,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 2,  # 'e'
-        18: 3,  # 'f'
-        27: 3,  # 'g'
-        25: 3,  # 'h'
-        3: 3,  # 'i'
-        24: 3,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 3,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 3,  # 'v'
-        57: 2,  # 'w'
-        58: 0,  # 'x'
-        11: 3,  # 'y'
-        22: 1,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 3,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    18: {  # 'f'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 2,  # 'f'
-        27: 1,  # 'g'
-        25: 1,  # 'h'
-        3: 1,  # 'i'
-        24: 1,  # 'j'
-        10: 1,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 2,  # 'p'
-        7: 1,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 1,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 1,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 1,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    27: {  # 'g'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 1,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 1,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 2,  # 'g'
-        25: 1,  # 'h'
-        3: 2,  # 'i'
-        24: 3,  # 'j'
-        10: 2,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 2,  # 'r'
-        8: 2,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 1,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 1,  # 'y'
-        22: 0,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    25: {  # 'h'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 2,  # 'h'
-        3: 2,  # 'i'
-        24: 3,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 1,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 2,  # 't'
-        14: 3,  # 'u'
-        32: 2,  # 'v'
-        57: 1,  # 'w'
-        58: 0,  # 'x'
-        11: 1,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    3: {  # 'i'
-        23: 2,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 0,  # 'N'
-        42: 1,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 1,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 2,  # 'f'
-        27: 3,  # 'g'
-        25: 1,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 3,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 2,  # 'v'
-        57: 1,  # 'w'
-        58: 1,  # 'x'
-        11: 3,  # 'y'
-        22: 1,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 1,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 3,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    24: {  # 'j'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 2,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 1,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 2,  # 'f'
-        27: 1,  # 'g'
-        25: 1,  # 'h'
-        3: 2,  # 'i'
-        24: 1,  # 'j'
-        10: 2,  # 'k'
-        5: 2,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 2,  # 'r'
-        8: 3,  # 's'
-        9: 2,  # 't'
-        14: 3,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 2,  # 'x'
-        11: 1,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    10: {  # 'k'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 3,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 3,  # 'e'
-        18: 1,  # 'f'
-        27: 2,  # 'g'
-        25: 2,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 2,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 3,  # 'p'
-        7: 2,  # 'r'
-        8: 2,  # 's'
-        9: 2,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 3,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 3,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    5: {  # 'l'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 3,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 1,  # 'e'
-        18: 3,  # 'f'
-        27: 3,  # 'g'
-        25: 2,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 1,  # 'l'
-        13: 1,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 2,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 2,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    13: {  # 'm'
-        23: 1,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 3,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 3,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 2,  # 'e'
-        18: 3,  # 'f'
-        27: 3,  # 'g'
-        25: 3,  # 'h'
-        3: 3,  # 'i'
-        24: 3,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 2,  # 'u'
-        32: 2,  # 'v'
-        57: 1,  # 'w'
-        58: 0,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 3,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    4: {  # 'n'
-        23: 1,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 2,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 1,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 1,  # 'f'
-        27: 2,  # 'g'
-        25: 3,  # 'h'
-        3: 2,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 3,  # 'p'
-        7: 2,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 2,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 2,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 1,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    15: {  # 'o'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 2,  # 'L'
-        20: 0,  # 'M'
-        46: 2,  # 'N'
-        42: 1,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 1,  # 'i'
-        24: 2,  # 'j'
-        10: 1,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 2,  # 'o'
-        26: 0,  # 'p'
-        7: 1,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 2,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 3,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 2,  # 'ğ'
-        41: 2,  # 'İ'
-        6: 3,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 2,  # 'ş'
-    },
-    26: {  # 'p'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 1,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 1,  # 'h'
-        3: 2,  # 'i'
-        24: 3,  # 'j'
-        10: 1,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 0,  # 'o'
-        26: 2,  # 'p'
-        7: 2,  # 'r'
-        8: 1,  # 's'
-        9: 1,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 1,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 3,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    7: {  # 'r'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 1,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 2,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 1,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 2,  # 'g'
-        25: 3,  # 'h'
-        3: 2,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 3,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    8: {  # 's'
-        23: 1,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 1,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 2,  # 'g'
-        25: 2,  # 'h'
-        3: 2,  # 'i'
-        24: 3,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 3,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 2,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 2,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    9: {  # 't'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 2,  # 'f'
-        27: 2,  # 'g'
-        25: 2,  # 'h'
-        3: 2,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 3,  # 'v'
-        57: 0,  # 'w'
-        58: 2,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 3,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 2,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    14: {  # 'u'
-        23: 3,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 3,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 2,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 3,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 2,  # 'Z'
-        1: 2,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 2,  # 'e'
-        18: 2,  # 'f'
-        27: 3,  # 'g'
-        25: 3,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 3,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 2,  # 'v'
-        57: 2,  # 'w'
-        58: 0,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 3,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    32: {  # 'v'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 1,  # 'j'
-        10: 1,  # 'k'
-        5: 3,  # 'l'
-        13: 2,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 1,  # 'r'
-        8: 2,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 1,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 1,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    57: {  # 'w'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 1,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 1,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 1,  # 's'
-        9: 0,  # 't'
-        14: 1,  # 'u'
-        32: 0,  # 'v'
-        57: 2,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 0,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    58: {  # 'x'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 1,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 1,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 2,  # 'i'
-        24: 2,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 2,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 1,  # 'r'
-        8: 2,  # 's'
-        9: 1,  # 't'
-        14: 0,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    11: {  # 'y'
-        23: 1,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 2,  # 'g'
-        25: 2,  # 'h'
-        3: 2,  # 'i'
-        24: 1,  # 'j'
-        10: 2,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 2,  # 'r'
-        8: 1,  # 's'
-        9: 2,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 1,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 3,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 2,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    22: {  # 'z'
-        23: 2,  # 'A'
-        37: 2,  # 'B'
-        47: 1,  # 'C'
-        39: 2,  # 'D'
-        29: 3,  # 'E'
-        52: 1,  # 'F'
-        36: 2,  # 'G'
-        45: 2,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 2,  # 'N'
-        42: 2,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 3,  # 'T'
-        51: 2,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 1,  # 'Z'
-        1: 1,  # 'a'
-        21: 2,  # 'b'
-        28: 1,  # 'c'
-        12: 2,  # 'd'
-        2: 2,  # 'e'
-        18: 3,  # 'f'
-        27: 2,  # 'g'
-        25: 2,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 3,  # 'n'
-        15: 2,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 0,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 3,  # 'y'
-        22: 2,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 2,  # 'Ü'
-        59: 1,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 2,  # 'ö'
-        17: 2,  # 'ü'
-        30: 2,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 3,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 2,  # 'ş'
-    },
-    63: {  # '·'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 0,  # 'd'
-        2: 1,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 0,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 0,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    54: {  # 'Ç'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 1,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 1,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 1,  # 'b'
-        28: 0,  # 'c'
-        12: 1,  # 'd'
-        2: 0,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 0,  # 'h'
-        3: 3,  # 'i'
-        24: 0,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 2,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 2,  # 'r'
-        8: 0,  # 's'
-        9: 1,  # 't'
-        14: 0,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 2,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    50: {  # 'Ö'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 1,  # 'D'
-        29: 2,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 2,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 1,  # 'N'
-        42: 2,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 2,  # 'b'
-        28: 1,  # 'c'
-        12: 2,  # 'd'
-        2: 0,  # 'e'
-        18: 1,  # 'f'
-        27: 1,  # 'g'
-        25: 1,  # 'h'
-        3: 2,  # 'i'
-        24: 0,  # 'j'
-        10: 2,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 3,  # 'n'
-        15: 2,  # 'o'
-        26: 2,  # 'p'
-        7: 3,  # 'r'
-        8: 1,  # 's'
-        9: 2,  # 't'
-        14: 0,  # 'u'
-        32: 1,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 2,  # 'ö'
-        17: 2,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    55: {  # 'Ü'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 1,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 1,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 1,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 1,  # 'l'
-        13: 1,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 1,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 1,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 0,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    59: {  # 'â'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 1,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 2,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 0,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 2,  # 'm'
-        4: 0,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 2,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 1,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    33: {  # 'ç'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 3,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 0,  # 'Z'
-        1: 0,  # 'a'
-        21: 3,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 0,  # 'e'
-        18: 2,  # 'f'
-        27: 1,  # 'g'
-        25: 3,  # 'h'
-        3: 3,  # 'i'
-        24: 0,  # 'j'
-        10: 3,  # 'k'
-        5: 0,  # 'l'
-        13: 0,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 3,  # 'r'
-        8: 2,  # 's'
-        9: 3,  # 't'
-        14: 0,  # 'u'
-        32: 2,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 1,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    61: {  # 'î'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 0,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 0,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 2,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 1,  # 'j'
-        10: 0,  # 'k'
-        5: 0,  # 'l'
-        13: 1,  # 'm'
-        4: 1,  # 'n'
-        15: 0,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 1,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 1,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 1,  # 'î'
-        34: 0,  # 'ö'
-        17: 0,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 1,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    34: {  # 'ö'
-        23: 0,  # 'A'
-        37: 1,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 1,  # 'G'
-        45: 1,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 1,  # 'L'
-        20: 0,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 2,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 1,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 2,  # 'c'
-        12: 1,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 2,  # 'g'
-        25: 2,  # 'h'
-        3: 1,  # 'i'
-        24: 2,  # 'j'
-        10: 1,  # 'k'
-        5: 2,  # 'l'
-        13: 3,  # 'm'
-        4: 2,  # 'n'
-        15: 2,  # 'o'
-        26: 0,  # 'p'
-        7: 0,  # 'r'
-        8: 3,  # 's'
-        9: 1,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 1,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 2,  # 'ö'
-        17: 0,  # 'ü'
-        30: 2,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 1,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    17: {  # 'ü'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 0,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 1,  # 'J'
-        16: 1,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 0,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 0,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 0,  # 'c'
-        12: 1,  # 'd'
-        2: 3,  # 'e'
-        18: 1,  # 'f'
-        27: 2,  # 'g'
-        25: 0,  # 'h'
-        3: 1,  # 'i'
-        24: 1,  # 'j'
-        10: 2,  # 'k'
-        5: 3,  # 'l'
-        13: 2,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 2,  # 'p'
-        7: 2,  # 'r'
-        8: 3,  # 's'
-        9: 2,  # 't'
-        14: 3,  # 'u'
-        32: 1,  # 'v'
-        57: 1,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 2,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    30: {  # 'ğ'
-        23: 0,  # 'A'
-        37: 2,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 1,  # 'G'
-        45: 0,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 1,  # 'M'
-        46: 2,  # 'N'
-        42: 2,  # 'O'
-        48: 1,  # 'P'
-        44: 1,  # 'R'
-        35: 0,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 2,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 0,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 2,  # 'e'
-        18: 0,  # 'f'
-        27: 0,  # 'g'
-        25: 0,  # 'h'
-        3: 0,  # 'i'
-        24: 3,  # 'j'
-        10: 1,  # 'k'
-        5: 2,  # 'l'
-        13: 3,  # 'm'
-        4: 0,  # 'n'
-        15: 1,  # 'o'
-        26: 0,  # 'p'
-        7: 1,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 2,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 0,  # 'î'
-        34: 2,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 2,  # 'İ'
-        6: 2,  # 'ı'
-        40: 2,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    41: {  # 'İ'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 1,  # 'D'
-        29: 1,  # 'E'
-        52: 0,  # 'F'
-        36: 2,  # 'G'
-        45: 2,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 2,  # 'P'
-        44: 0,  # 'R'
-        35: 1,  # 'S'
-        31: 1,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 0,  # 'Z'
-        1: 1,  # 'a'
-        21: 2,  # 'b'
-        28: 1,  # 'c'
-        12: 2,  # 'd'
-        2: 1,  # 'e'
-        18: 0,  # 'f'
-        27: 3,  # 'g'
-        25: 2,  # 'h'
-        3: 2,  # 'i'
-        24: 2,  # 'j'
-        10: 2,  # 'k'
-        5: 0,  # 'l'
-        13: 1,  # 'm'
-        4: 3,  # 'n'
-        15: 1,  # 'o'
-        26: 1,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 2,  # 't'
-        14: 0,  # 'u'
-        32: 0,  # 'v'
-        57: 1,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 1,  # 'Ü'
-        59: 1,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 1,  # 'ö'
-        17: 1,  # 'ü'
-        30: 2,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-    6: {  # 'ı'
-        23: 2,  # 'A'
-        37: 0,  # 'B'
-        47: 0,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 2,  # 'J'
-        16: 3,  # 'K'
-        49: 0,  # 'L'
-        20: 3,  # 'M'
-        46: 1,  # 'N'
-        42: 0,  # 'O'
-        48: 0,  # 'P'
-        44: 0,  # 'R'
-        35: 0,  # 'S'
-        31: 2,  # 'T'
-        51: 0,  # 'U'
-        38: 0,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 1,  # 'Z'
-        1: 3,  # 'a'
-        21: 2,  # 'b'
-        28: 1,  # 'c'
-        12: 3,  # 'd'
-        2: 3,  # 'e'
-        18: 3,  # 'f'
-        27: 3,  # 'g'
-        25: 2,  # 'h'
-        3: 3,  # 'i'
-        24: 3,  # 'j'
-        10: 3,  # 'k'
-        5: 3,  # 'l'
-        13: 3,  # 'm'
-        4: 3,  # 'n'
-        15: 0,  # 'o'
-        26: 3,  # 'p'
-        7: 3,  # 'r'
-        8: 3,  # 's'
-        9: 3,  # 't'
-        14: 3,  # 'u'
-        32: 3,  # 'v'
-        57: 1,  # 'w'
-        58: 1,  # 'x'
-        11: 3,  # 'y'
-        22: 0,  # 'z'
-        63: 1,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 2,  # 'ç'
-        61: 0,  # 'î'
-        34: 0,  # 'ö'
-        17: 3,  # 'ü'
-        30: 0,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 3,  # 'ı'
-        40: 0,  # 'Ş'
-        19: 0,  # 'ş'
-    },
-    40: {  # 'Ş'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 1,  # 'D'
-        29: 1,  # 'E'
-        52: 0,  # 'F'
-        36: 1,  # 'G'
-        45: 2,  # 'H'
-        53: 1,  # 'I'
-        60: 0,  # 'J'
-        16: 0,  # 'K'
-        49: 0,  # 'L'
-        20: 2,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 2,  # 'P'
-        44: 2,  # 'R'
-        35: 1,  # 'S'
-        31: 1,  # 'T'
-        51: 0,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 2,  # 'Y'
-        56: 1,  # 'Z'
-        1: 0,  # 'a'
-        21: 2,  # 'b'
-        28: 0,  # 'c'
-        12: 2,  # 'd'
-        2: 0,  # 'e'
-        18: 3,  # 'f'
-        27: 0,  # 'g'
-        25: 2,  # 'h'
-        3: 3,  # 'i'
-        24: 2,  # 'j'
-        10: 1,  # 'k'
-        5: 0,  # 'l'
-        13: 1,  # 'm'
-        4: 3,  # 'n'
-        15: 2,  # 'o'
-        26: 0,  # 'p'
-        7: 3,  # 'r'
-        8: 2,  # 's'
-        9: 2,  # 't'
-        14: 1,  # 'u'
-        32: 3,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 2,  # 'y'
-        22: 0,  # 'z'
-        63: 0,  # '·'
-        54: 0,  # 'Ç'
-        50: 0,  # 'Ö'
-        55: 1,  # 'Ü'
-        59: 0,  # 'â'
-        33: 0,  # 'ç'
-        61: 0,  # 'î'
-        34: 2,  # 'ö'
-        17: 1,  # 'ü'
-        30: 2,  # 'ğ'
-        41: 0,  # 'İ'
-        6: 2,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 2,  # 'ş'
-    },
-    19: {  # 'ş'
-        23: 0,  # 'A'
-        37: 0,  # 'B'
-        47: 1,  # 'C'
-        39: 0,  # 'D'
-        29: 0,  # 'E'
-        52: 2,  # 'F'
-        36: 1,  # 'G'
-        45: 0,  # 'H'
-        53: 0,  # 'I'
-        60: 0,  # 'J'
-        16: 3,  # 'K'
-        49: 2,  # 'L'
-        20: 0,  # 'M'
-        46: 1,  # 'N'
-        42: 1,  # 'O'
-        48: 1,  # 'P'
-        44: 1,  # 'R'
-        35: 1,  # 'S'
-        31: 0,  # 'T'
-        51: 1,  # 'U'
-        38: 1,  # 'V'
-        62: 0,  # 'W'
-        43: 1,  # 'Y'
-        56: 0,  # 'Z'
-        1: 3,  # 'a'
-        21: 1,  # 'b'
-        28: 2,  # 'c'
-        12: 0,  # 'd'
-        2: 3,  # 'e'
-        18: 0,  # 'f'
-        27: 2,  # 'g'
-        25: 1,  # 'h'
-        3: 1,  # 'i'
-        24: 0,  # 'j'
-        10: 2,  # 'k'
-        5: 2,  # 'l'
-        13: 3,  # 'm'
-        4: 0,  # 'n'
-        15: 0,  # 'o'
-        26: 1,  # 'p'
-        7: 3,  # 'r'
-        8: 0,  # 's'
-        9: 0,  # 't'
-        14: 3,  # 'u'
-        32: 0,  # 'v'
-        57: 0,  # 'w'
-        58: 0,  # 'x'
-        11: 0,  # 'y'
-        22: 2,  # 'z'
-        63: 0,  # '·'
-        54: 1,  # 'Ç'
-        50: 2,  # 'Ö'
-        55: 0,  # 'Ü'
-        59: 0,  # 'â'
-        33: 1,  # 'ç'
-        61: 1,  # 'î'
-        34: 2,  # 'ö'
-        17: 0,  # 'ü'
-        30: 1,  # 'ğ'
-        41: 1,  # 'İ'
-        6: 1,  # 'ı'
-        40: 1,  # 'Ş'
-        19: 1,  # 'ş'
-    },
-}
-
-# 255: Undefined characters that did not exist in training text
-# 254: Carriage/Return
-# 253: symbol (punctuation) that does not belong to word
-# 252: 0 - 9
-# 251: Control characters
-
-# Character Mapping Table(s):
-ISO_8859_9_TURKISH_CHAR_TO_ORDER = {
-    0: 255,  # '\x00'
-    1: 255,  # '\x01'
-    2: 255,  # '\x02'
-    3: 255,  # '\x03'
-    4: 255,  # '\x04'
-    5: 255,  # '\x05'
-    6: 255,  # '\x06'
-    7: 255,  # '\x07'
-    8: 255,  # '\x08'
-    9: 255,  # '\t'
-    10: 255,  # '\n'
-    11: 255,  # '\x0b'
-    12: 255,  # '\x0c'
-    13: 255,  # '\r'
-    14: 255,  # '\x0e'
-    15: 255,  # '\x0f'
-    16: 255,  # '\x10'
-    17: 255,  # '\x11'
-    18: 255,  # '\x12'
-    19: 255,  # '\x13'
-    20: 255,  # '\x14'
-    21: 255,  # '\x15'
-    22: 255,  # '\x16'
-    23: 255,  # '\x17'
-    24: 255,  # '\x18'
-    25: 255,  # '\x19'
-    26: 255,  # '\x1a'
-    27: 255,  # '\x1b'
-    28: 255,  # '\x1c'
-    29: 255,  # '\x1d'
-    30: 255,  # '\x1e'
-    31: 255,  # '\x1f'
-    32: 255,  # ' '
-    33: 255,  # '!'
-    34: 255,  # '"'
-    35: 255,  # '#'
-    36: 255,  # '$'
-    37: 255,  # '%'
-    38: 255,  # '&'
-    39: 255,  # "'"
-    40: 255,  # '('
-    41: 255,  # ')'
-    42: 255,  # '*'
-    43: 255,  # '+'
-    44: 255,  # ','
-    45: 255,  # '-'
-    46: 255,  # '.'
-    47: 255,  # '/'
-    48: 255,  # '0'
-    49: 255,  # '1'
-    50: 255,  # '2'
-    51: 255,  # '3'
-    52: 255,  # '4'
-    53: 255,  # '5'
-    54: 255,  # '6'
-    55: 255,  # '7'
-    56: 255,  # '8'
-    57: 255,  # '9'
-    58: 255,  # ':'
-    59: 255,  # ';'
-    60: 255,  # '<'
-    61: 255,  # '='
-    62: 255,  # '>'
-    63: 255,  # '?'
-    64: 255,  # '@'
-    65: 23,  # 'A'
-    66: 37,  # 'B'
-    67: 47,  # 'C'
-    68: 39,  # 'D'
-    69: 29,  # 'E'
-    70: 52,  # 'F'
-    71: 36,  # 'G'
-    72: 45,  # 'H'
-    73: 53,  # 'I'
-    74: 60,  # 'J'
-    75: 16,  # 'K'
-    76: 49,  # 'L'
-    77: 20,  # 'M'
-    78: 46,  # 'N'
-    79: 42,  # 'O'
-    80: 48,  # 'P'
-    81: 69,  # 'Q'
-    82: 44,  # 'R'
-    83: 35,  # 'S'
-    84: 31,  # 'T'
-    85: 51,  # 'U'
-    86: 38,  # 'V'
-    87: 62,  # 'W'
-    88: 65,  # 'X'
-    89: 43,  # 'Y'
-    90: 56,  # 'Z'
-    91: 255,  # '['
-    92: 255,  # '\\'
-    93: 255,  # ']'
-    94: 255,  # '^'
-    95: 255,  # '_'
-    96: 255,  # '`'
-    97: 1,  # 'a'
-    98: 21,  # 'b'
-    99: 28,  # 'c'
-    100: 12,  # 'd'
-    101: 2,  # 'e'
-    102: 18,  # 'f'
-    103: 27,  # 'g'
-    104: 25,  # 'h'
-    105: 3,  # 'i'
-    106: 24,  # 'j'
-    107: 10,  # 'k'
-    108: 5,  # 'l'
-    109: 13,  # 'm'
-    110: 4,  # 'n'
-    111: 15,  # 'o'
-    112: 26,  # 'p'
-    113: 64,  # 'q'
-    114: 7,  # 'r'
-    115: 8,  # 's'
-    116: 9,  # 't'
-    117: 14,  # 'u'
-    118: 32,  # 'v'
-    119: 57,  # 'w'
-    120: 58,  # 'x'
-    121: 11,  # 'y'
-    122: 22,  # 'z'
-    123: 255,  # '{'
-    124: 255,  # '|'
-    125: 255,  # '}'
-    126: 255,  # '~'
-    127: 255,  # '\x7f'
-    128: 180,  # '\x80'
-    129: 179,  # '\x81'
-    130: 178,  # '\x82'
-    131: 177,  # '\x83'
-    132: 176,  # '\x84'
-    133: 175,  # '\x85'
-    134: 174,  # '\x86'
-    135: 173,  # '\x87'
-    136: 172,  # '\x88'
-    137: 171,  # '\x89'
-    138: 170,  # '\x8a'
-    139: 169,  # '\x8b'
-    140: 168,  # '\x8c'
-    141: 167,  # '\x8d'
-    142: 166,  # '\x8e'
-    143: 165,  # '\x8f'
-    144: 164,  # '\x90'
-    145: 163,  # '\x91'
-    146: 162,  # '\x92'
-    147: 161,  # '\x93'
-    148: 160,  # '\x94'
-    149: 159,  # '\x95'
-    150: 101,  # '\x96'
-    151: 158,  # '\x97'
-    152: 157,  # '\x98'
-    153: 156,  # '\x99'
-    154: 155,  # '\x9a'
-    155: 154,  # '\x9b'
-    156: 153,  # '\x9c'
-    157: 152,  # '\x9d'
-    158: 151,  # '\x9e'
-    159: 106,  # '\x9f'
-    160: 150,  # '\xa0'
-    161: 149,  # '¡'
-    162: 148,  # '¢'
-    163: 147,  # '£'
-    164: 146,  # '¤'
-    165: 145,  # '¥'
-    166: 144,  # '¦'
-    167: 100,  # '§'
-    168: 143,  # '¨'
-    169: 142,  # '©'
-    170: 141,  # 'ª'
-    171: 140,  # '«'
-    172: 139,  # '¬'
-    173: 138,  # '\xad'
-    174: 137,  # '®'
-    175: 136,  # '¯'
-    176: 94,  # '°'
-    177: 80,  # '±'
-    178: 93,  # '²'
-    179: 135,  # '³'
-    180: 105,  # '´'
-    181: 134,  # 'µ'
-    182: 133,  # '¶'
-    183: 63,  # '·'
-    184: 132,  # '¸'
-    185: 131,  # '¹'
-    186: 130,  # 'º'
-    187: 129,  # '»'
-    188: 128,  # '¼'
-    189: 127,  # '½'
-    190: 126,  # '¾'
-    191: 125,  # '¿'
-    192: 124,  # 'À'
-    193: 104,  # 'Á'
-    194: 73,  # 'Â'
-    195: 99,  # 'Ã'
-    196: 79,  # 'Ä'
-    197: 85,  # 'Å'
-    198: 123,  # 'Æ'
-    199: 54,  # 'Ç'
-    200: 122,  # 'È'
-    201: 98,  # 'É'
-    202: 92,  # 'Ê'
-    203: 121,  # 'Ë'
-    204: 120,  # 'Ì'
-    205: 91,  # 'Í'
-    206: 103,  # 'Î'
-    207: 119,  # 'Ï'
-    208: 68,  # 'Ğ'
-    209: 118,  # 'Ñ'
-    210: 117,  # 'Ò'
-    211: 97,  # 'Ó'
-    212: 116,  # 'Ô'
-    213: 115,  # 'Õ'
-    214: 50,  # 'Ö'
-    215: 90,  # '×'
-    216: 114,  # 'Ø'
-    217: 113,  # 'Ù'
-    218: 112,  # 'Ú'
-    219: 111,  # 'Û'
-    220: 55,  # 'Ü'
-    221: 41,  # 'İ'
-    222: 40,  # 'Ş'
-    223: 86,  # 'ß'
-    224: 89,  # 'à'
-    225: 70,  # 'á'
-    226: 59,  # 'â'
-    227: 78,  # 'ã'
-    228: 71,  # 'ä'
-    229: 82,  # 'å'
-    230: 88,  # 'æ'
-    231: 33,  # 'ç'
-    232: 77,  # 'è'
-    233: 66,  # 'é'
-    234: 84,  # 'ê'
-    235: 83,  # 'ë'
-    236: 110,  # 'ì'
-    237: 75,  # 'í'
-    238: 61,  # 'î'
-    239: 96,  # 'ï'
-    240: 30,  # 'ğ'
-    241: 67,  # 'ñ'
-    242: 109,  # 'ò'
-    243: 74,  # 'ó'
-    244: 87,  # 'ô'
-    245: 102,  # 'õ'
-    246: 34,  # 'ö'
-    247: 95,  # '÷'
-    248: 81,  # 'ø'
-    249: 108,  # 'ù'
-    250: 76,  # 'ú'
-    251: 72,  # 'û'
-    252: 17,  # 'ü'
-    253: 6,  # 'ı'
-    254: 19,  # 'ş'
-    255: 107,  # 'ÿ'
-}
-
-ISO_8859_9_TURKISH_MODEL = SingleByteCharSetModel(
-    charset_name="ISO-8859-9",
-    language="Turkish",
-    char_to_order_map=ISO_8859_9_TURKISH_CHAR_TO_ORDER,
-    language_model=TURKISH_LANG_MODEL,
-    typical_positive_ratio=0.97029,
-    keep_ascii_letters=True,
-    alphabet="ABCDEFGHIJKLMNOPRSTUVYZabcdefghijklmnoprstuvyzÂÇÎÖÛÜâçîöûüĞğİıŞş",
-)
diff --git a/spaces/Rbrq/DeticChatGPT/detic/data/custom_build_augmentation.py b/spaces/Rbrq/DeticChatGPT/detic/data/custom_build_augmentation.py
deleted file mode 100644
index 9642c15e582fc953ecaa378a325b4fa02f4e7d28..0000000000000000000000000000000000000000
--- a/spaces/Rbrq/DeticChatGPT/detic/data/custom_build_augmentation.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-import pycocotools.mask as mask_util
-import torch
-from fvcore.common.file_io import PathManager
-from PIL import Image
-
-
-from detectron2.data import transforms as T
-from .transforms.custom_augmentation_impl import EfficientDetResizeCrop
-
-def build_custom_augmentation(cfg, is_train, scale=None, size=None, \
-    min_size=None, max_size=None):
-    """
-    Create a list of default :class:`Augmentation` from config.
-    Now it includes resizing and flipping.
-
-    Returns:
-        list[Augmentation]
-    """
-    if cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge':
-        if is_train:
-            min_size = cfg.INPUT.MIN_SIZE_TRAIN if min_size is None else min_size
-            max_size = cfg.INPUT.MAX_SIZE_TRAIN if max_size is None else max_size
-            sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
-        else:
-            min_size = cfg.INPUT.MIN_SIZE_TEST
-            max_size = cfg.INPUT.MAX_SIZE_TEST
-            sample_style = "choice"
-        augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)]
-    elif cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop':
-        if is_train:
-            scale = cfg.INPUT.SCALE_RANGE if scale is None else scale
-            size = cfg.INPUT.TRAIN_SIZE if size is None else size
-        else:
-            scale = (1, 1)
-            size = cfg.INPUT.TEST_SIZE
-        augmentation = [EfficientDetResizeCrop(size, scale)]
-    else:
-        assert 0, cfg.INPUT.CUSTOM_AUG
-
-    if is_train:
-        augmentation.append(T.RandomFlip())
-    return augmentation
-
-
-build_custom_transform_gen = build_custom_augmentation
-"""
-Alias for backward-compatibility.
-"""
\ No newline at end of file
diff --git a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/docs/TRAINING.md b/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/docs/TRAINING.md
deleted file mode 100644
index 99238b612d961a5a6aa29885bad23808c7aa6e07..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/docs/TRAINING.md
+++ /dev/null
@@ -1,72 +0,0 @@
-
-# Traininig ASpanFormer
-
-## Dataset setup
-Generally, two parts of data are needed for training ASpanFormer, the original dataset, i.e., ScanNet and MegaDepth, and the offline generated dataset indices. The dataset indices store scenes, image pairs, and other metadata within each dataset used for training/validation/testing. For the MegaDepth dataset, the relative poses between images used for training are directly cached in the indexing files. However, the relative poses of ScanNet image pairs are not stored due to the enormous resulting file size.
-
-### Download datasets
-#### MegaDepth
-We use depth maps provided in the [original MegaDepth dataset](https://www.cs.cornell.edu/projects/megadepth/) as well as undistorted images, corresponding camera intrinsics and extrinsics preprocessed by [D2-Net](https://github.com/mihaidusmanu/d2-net#downloading-and-preprocessing-the-megadepth-dataset). You can download them separately from the following links. 
-- [MegaDepth undistorted images and processed depths](https://www.cs.cornell.edu/projects/megadepth/dataset/Megadepth_v1/MegaDepth_v1.tar.gz)
-    - Note that we only use depth maps.
-    - Path of the download data will be referreed to as `/path/to/megadepth`
-- [D2-Net preprocessed images](https://drive.google.com/drive/folders/1hxpOsqOZefdrba_BqnW490XpNX_LgXPB)
-    - Images are undistorted manually in D2-Net since the undistorted images from MegaDepth do not come with corresponding intrinsics.
-    - Path of the download data will be referreed to as `/path/to/megadepth_d2net`
-
-#### ScanNet
-Please set up the ScanNet dataset following [the official guide](https://github.com/ScanNet/ScanNet#scannet-data)
-> NOTE: We use the [python exported data](https://github.com/ScanNet/ScanNet/tree/master/SensReader/python),
-instead of the [c++ exported one](https://github.com/ScanNet/ScanNet/tree/master/SensReader/c%2B%2B).
-
-### Download the dataset indices
-
-You can download the required dataset indices from the [following link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf).
-After downloading, unzip the required files.
-```shell
-unzip downloaded-file.zip
-
-# extract dataset indices
-tar xf train-data/megadepth_indices.tar
-tar xf train-data/scannet_indices.tar
-
-# extract testing data (optional)
-tar xf testdata/megadepth_test_1500.tar
-tar xf testdata/scannet_test_1500.tar
-```
-
-### Build the dataset symlinks
-
-We symlink the datasets to the `data` directory under the main ASpanFormer project directory.
-
-```shell
-# scannet
-# -- # train and test dataset
-ln -s /path/to/scannet_train/* /path/to/ASpanFormer/data/scannet/train
-ln -s /path/to/scannet_test/* /path/to/ASpanFormer/data/scannet/test
-# -- # dataset indices
-ln -s /path/to/scannet_indices/* /path/to/ASpanFormer/data/scannet/index
-
-# megadepth
-# -- # train and test dataset (train and test share the same dataset)
-ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/ASpanFormer/data/megadepth/train
-ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/ASpanFormer/data/megadepth/test
-# -- # dataset indices
-ln -s /path/to/megadepth_indices/* /path/to/ASpanFormer/data/megadepth/index
-```
-
-
-## Training
-We provide training scripts of ScanNet and MegaDepth. The results in the ASpanFormer paper can be reproduced with 8 v100 GPUs. For a different setup, we scale the learning rate and its warm-up linearly, but the final evaluation results might vary due to the different batch size & learning rate used. Thus the reproduction of results in our paper is not guaranteed.
-
-
-### Training on ScanNet
-``` shell
-scripts/reproduce_train/indoor.sh
-```
-
-
-### Training on MegaDepth
-``` shell
-scripts/reproduce_train/outdoor.sh
-```
\ No newline at end of file
diff --git a/spaces/RedBaron5/PatentSolver/functions.py b/spaces/RedBaron5/PatentSolver/functions.py
deleted file mode 100644
index 89aa53ba7ad6d6682d5b2dc087b5c4b4971c6706..0000000000000000000000000000000000000000
--- a/spaces/RedBaron5/PatentSolver/functions.py
+++ /dev/null
@@ -1,648 +0,0 @@
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~ #
-# ~~~ Import libraries ~~~ #
-# ~~~~~~~~~~~~~~~~~~~~~~~~ #
-
-# Google Scraper Class #
-from google_patent_scraper import scraper_class
-
-# Context Manager #
-from contextlib import contextmanager
-
-# Writing/Reading
-import csv
-import numpy as np
-import pandas as pd
-
-# clean patent #
-import re
-
-# Multiprocessing #
-import multiprocessing as mp
-
-# parse xml to text
-from bs4 import BeautifulSoup as bs
-
-# zip folder to download
-import shutil
-import base64
-import streamlit as st
-import os
-
-# extract problems
-from App.bin import constants
-from App.bin.InputHandler import InputHandler
-from App.bin.PatentHandler import PatentHandler
-from App.bin.CorpusProcessor import CorpusProcessor
-import json
-from pandas import json_normalize
-import glob
-
-
-
-# ~~~~~~~~~~~~~~~~~~~ #
-# ~~~~ Functions ~~~~ #
-# ~~~~~~~~~~~~~~~~~~~ #
-
-def single_process_scraper(patent,path_to_data_file,data_column_order):
-    """Scrapes a single google patent using the google scraper class
-       
-       Function does not return any values, instead it writes the output
-         of the data_patent_details into a csv file specified in the path_to_data_file
-         parameter
-
-       Inputs:
-         patent (str) : patent number including country prefix
-         lock (obj) : to prevent collisions, function uses a lock. You can pass whichever
-                      lock you want to this parameter
-         path_to_data_file : absolute path to csv file to write data_patent_details to
-         data_column_order : name of columns in order they will be saved in csv file
-
-    """
-    # ~ Initialize scraper class ~ #
-    scraper=scraper_class() 
-
-    # ~ Scrape single patent ~ #
-    err, soup, url = scraper.request_single_patent(patent)
-
-    # Checks if the scrape is successful.
-    # If successful -> parse text and deposit into csv file
-    # Else          -> print error statement
-
-    if err=='Success':
-        patent_parsed = scraper.get_scraped_data(soup,url,patent)
-
-        # Save the parsed data_patent_details to a csv file
-        #  using multiprocessing lock function
-        #  to prevent collisions
-        with lock:
-            with open(path_to_data_file,'a',newline='') as ofile:
-                writer = csv.DictWriter(ofile, fieldnames=data_column_order)
-                writer.writerow(patent_parsed)
-    else:
-        print('Patent {0} has error code {1}'.format(patent,err))
-
-# Allow pool to accept keyword arguments
-@contextmanager
-def poolcontext(*args, **kwargs):
-    pool = mp.Pool(*args, **kwargs)
-    yield pool
-    pool.terminate()
-
-def init(l):
-    """Creates lock object that is global, for use in sharing 
-       across processes
-    """
-    global lock
-    lock = l
-
-
-def patentinput(patent_string):
-    """
-    remove space among patent numbers from users' inputs
-    """
-    patent_string = patent_string.replace(" ", "") #remove space that user tpyed
-    list_results = list(patent_string.split(","))
-    return list_results
-
-def clean_patent(table):
-    """clean raw patent details from website
-    """
-
-    list_inventor_name = np.array([]) # create an empty list
-
-    inventor_name = table['inventor_name']
-    for line in inventor_name:
-        new_line = re.sub(r'"inventor_name":', '', line)
-        new_line = re.sub(r'\{|\}|\[|\]|\"', '', new_line)
-        # print(new_line)
-        list_inventor_name = np.append(list_inventor_name, new_line)
-
-    new_table_inventor_name = pd.DataFrame(list_inventor_name, columns=['inventor_name'])
-    # new_table.to_csv('saved_data/cleaned_patent_details')
-
-    ##clean assignee_name_orig feature
-    list_assignee_name = np.array([])
-    assignee_name = table['assignee_name_orig']
-    for line in assignee_name:
-        new_line = re.sub(r'"assignee_name":', '', line)  ##### errors
-        new_line = re.sub(r'\{|\}|\[|\]|\"', '', new_line)
-        list_assignee_name = np.append(list_assignee_name, new_line)
-
-    new_table_assignee_name = pd.DataFrame(list_assignee_name, columns=['assignee_name_orig'])
-    # print(new_table_assignee_name)
-    #
-    ##clean assignee_name_current feature
-    list_assignee_name_current = np.array([])
-    assignee_name_current = table['assignee_name_current']
-    for line in assignee_name_current:
-        new_line = re.sub(r'("assignee_name":)|(\\n\s\s)|(\{|\}|\[|\]|\")', '', line)
-        list_assignee_name_current = np.append(list_assignee_name_current, new_line)
-
-    new_table_assignee_name_current = pd.DataFrame(list_assignee_name_current, columns=['assignee_name_current'])
-    # print(new_table_assignee_name_current)
-    #
-    ##clean forward_cite_no_family feature
-    list_forward_cite_no_family = np.array([])
-    forward_cite_no_family = table['forward_cite_no_family']
-    for line in forward_cite_no_family:
-        new_line = re.sub(
-            r'("patent_number":)|(\\n)|(\{|\}|\[|\]|\")|(priority_date)|(:)|(pub_date)|(\d{4}-\d{2}-\d{2})', '', line)
-        new_line = re.sub(r'\s\,\s', '', new_line)
-        list_forward_cite_no_family = np.append(list_forward_cite_no_family, new_line)
-
-    new_table_forward_cite_no_family = pd.DataFrame(list_forward_cite_no_family, columns=['forward_cite_no_family'])
-    # print(new_table_forward_cite_no_family)
-    #
-    ##clean forward_cite_yes_family feature
-    list_forward_cite_yes_family = np.array([])
-    forward_cite_yes_family = table['forward_cite_yes_family']
-    for line in forward_cite_yes_family:
-        new_line = re.sub(
-            r'("patent_number":)|(\\n)|(\{|\}|\[|\]|\")|(priority_date)|(:)|(pub_date)|(\d{4}-\d{2}-\d{2})', '', line)
-        new_line = re.sub(r'\s\,\s', '', new_line)
-        list_forward_cite_yes_family = np.append(list_forward_cite_yes_family, new_line)
-
-    new_table_forward_cite_yes_family = pd.DataFrame(list_forward_cite_yes_family, columns=['forward_cite_yes_family'])
-    # print(new_table_forward_cite_yes_family)
-
-    ##clean backward_cite_no_family feature
-    list_backward_cite_no_family = np.array([])
-    backward_cite_no_family = table['backward_cite_no_family']
-    for line in backward_cite_no_family:
-        new_line = re.sub(
-            r'("patent_number":)|(\\n)|(\{|\}|\[|\]|\")|(priority_date)|(:)|(pub_date)|(\d{4}-\d{2}-\d{2})', '', line)
-        new_line = re.sub(r'\s\,\s', '', new_line)
-        list_backward_cite_no_family = np.append(list_backward_cite_no_family, new_line)
-
-    new_table_backward_cite_no_family = pd.DataFrame(list_backward_cite_no_family, columns=['backward_cite_no_family'])
-    # print(new_table_backward_cite_no_family)
-
-    ##clean backward_cite_yes_family feature
-    list_backward_cite_yes_family = np.array([])
-    backward_cite_yes_family = table['backward_cite_yes_family']
-    for line in backward_cite_yes_family:
-        new_line = re.sub(
-            r'("patent_number":)|(\\n)|(\{|\}|\[|\]|\")|(priority_date)|(:)|(pub_date)|(\d{4}-\d{2}-\d{2})', '', line)
-        new_line = re.sub(r'\s\,\s', '', new_line)
-        list_backward_cite_yes_family = np.append(list_backward_cite_yes_family, new_line)
-
-    new_table_backward_cite_yes_family = pd.DataFrame(list_backward_cite_yes_family,
-                                                      columns=['backward_cite_yes_family'])
-    # print(new_table_backward_cite_yes_family)
-
-    ##rename url feature
-    list_patent_number = np.array([])
-    patent_number = table['url']
-    for line in patent_number:
-        list_patent_number = np.append(list_patent_number, line)
-
-    new_table_patent_number = pd.DataFrame(list_patent_number, columns=['patent_number'])
-    # print(new_table_patent_number)
-
-    ##rename patent feature
-    list_patent_link = np.array([])
-    patent_link = table['patent']
-    for line in patent_link:
-        list_patent_link = np.append(list_patent_link, line)
-
-    new_table_patent_link = pd.DataFrame(list_patent_link, columns=['patent_link'])
-    # print(new_table_patent_link)
-
-    ##rename abstract_text
-    list_abstract_text = np.array([])
-    abstract_text = table['abstract_text']
-    for line in abstract_text:
-        list_abstract_text = np.append(list_abstract_text, line)
-
-    new_table_abstract_text = pd.DataFrame(abstract_text, columns=['abstract_text'])
-    # print(new_table_patent_link)
-
-    ###################################
-
-    ## concatenate all of sub dataframes to the final results
-    results = pd.concat([new_table_patent_number, table[['pub_date', 'priority_date', 'grant_date', 'filing_date']],
-                         new_table_inventor_name, new_table_assignee_name, new_table_assignee_name_current,
-                         new_table_forward_cite_no_family, new_table_forward_cite_yes_family,
-                         new_table_backward_cite_yes_family, new_table_backward_cite_no_family, new_table_patent_link,
-                         new_table_abstract_text], axis=1)
-
-    return results
-
-
-def count_patent(patent_table):
-    """count the patent features"""
-
-    ##count the number of assignee_name feature
-    assignee_name = pd.DataFrame(patent_table['assignee_name_orig'])
-    count_assignee_name = assignee_name.applymap(lambda x: str.count(x, ',') + 1)
-    count_assignee_name = count_assignee_name.rename(columns={'assignee_name_orig': 'count_assignee_name'})
-    # print(count_assignee_name)
-
-    ##count the number of inventor_name feature
-    inventor_name = pd.DataFrame(patent_table['inventor_name'])
-    count_inventor_name = inventor_name.applymap(lambda x: str.count(x, ',') + 1)
-    count_inventor_name = count_inventor_name.rename(columns={'inventor_name': 'count_inventor_name'})
-    # print(count_inventor_name)
-
-    ##count the number of assignee_name_current feature
-    assignee_name_current = pd.DataFrame(patent_table['assignee_name_current'])
-    # print(assignee_name_current)
-
-    ##replace NaN as int(0)
-    assignee_name_current_replace_NaN = lambda x: int(0) if pd.isnull(x) else str.count(x, ',') + 1
-    count_assignee_name_current = assignee_name_current.applymap(assignee_name_current_replace_NaN)
-    count_assignee_name_current = count_assignee_name_current.rename(
-        columns={'assignee_name_current': 'count_assignee_name_current'})
-    # print(count_assignee_name_current)
-
-    ##count forward_cite_no_family
-    forward_cite_no_family = pd.DataFrame(patent_table['forward_cite_no_family'])
-    forward_cite_no_family_replace_NaN = lambda x: int(0) if pd.isnull(x) else str.count(x, ',')
-    count_forward_cite_no_family = forward_cite_no_family.applymap(forward_cite_no_family_replace_NaN)
-    count_forward_cite_no_family = count_forward_cite_no_family.rename(
-        columns={'forward_cite_no_family': 'count_forward_cite_no_family'})
-    # print(count_forward_cite_no_family)
-
-    ##count forward_cite_yes_family
-    forward_cite_yes_family = pd.DataFrame(patent_table['forward_cite_yes_family'])
-    forward_cite_yes_family_replace_NaN = lambda x: int(0) if pd.isnull(x) else str.count(x, ',')
-    count_forward_cite_yes_family = forward_cite_yes_family.applymap(forward_cite_yes_family_replace_NaN)
-    count_forward_cite_yes_family = count_forward_cite_yes_family.rename(
-        columns={'forward_cite_yes_family': 'count_forward_cite_yes_family'})
-    # print(count_forward_cite_yes_family)
-
-    ##count backward_cite_no_family
-    backward_cite_no_family = pd.DataFrame(patent_table['backward_cite_no_family'])
-    backward_cite_no_family_replace_NaN = lambda x: int(0) if pd.isnull(x) else str.count(x, ',')
-    count_backward_cite_no_family = backward_cite_no_family.applymap(backward_cite_no_family_replace_NaN)
-    count_backward_cite_no_family = count_backward_cite_no_family.rename(
-        columns={'backward_cite_no_family': 'count_backward_cite_no_family'})
-    # print(count_backward_cite_no_family)
-
-    ##count backward_cite_yes_family
-    backward_cite_yes_family = pd.DataFrame(patent_table['backward_cite_yes_family'])
-    backward_cite_yes_family_replace_NaN = lambda x: int(0) if pd.isnull(x) else str.count(x, ',')
-    count_backward_cite_yes_family = backward_cite_yes_family.applymap(backward_cite_yes_family_replace_NaN)
-    count_backward_cite_yes_family = count_backward_cite_yes_family.rename(
-        columns={'backward_cite_yes_family': 'count_backward_cite_yes_family'})
-    # print(count_backward_cite_yes_family)
-
-    ##concate dataframes to the final cleaned dataset
-    results = pd.concat([patent_table[['patent_number', 'pub_date', 'priority_date',
-                                'grant_date', 'filing_date', 'inventor_name']], count_inventor_name,
-                         patent_table[['assignee_name_orig']], count_assignee_name,
-                         patent_table[['assignee_name_current']], count_assignee_name_current,
-                         patent_table[['forward_cite_no_family']], count_forward_cite_no_family,
-                         patent_table[['forward_cite_yes_family']], count_forward_cite_yes_family,
-                         patent_table[['backward_cite_no_family']], count_backward_cite_no_family,
-                         patent_table[['backward_cite_yes_family']], count_backward_cite_yes_family,
-                         patent_table[['patent_link', 'abstract_text']]], axis=1)
-
-    return results
-
-
-def XMLtoTEXT(patent_xml, saved_file_path):
-    # read file
-    tree = bs(patent_xml, "html.parser")
-
-    # get title
-
-    print('Title:')
-    title = tree.find_all("invention-title")
-    patent_title = title[0].text
-    print(patent_title)
-
-    # get number
-    print("Patent number:")
-    patent_number = tree.find_all('doc-number')
-    patent_number = 'US' + patent_number[0].text
-    patent_number_new = re.sub(r'US0', 'US', patent_number)
-    print(patent_number_new)
-
-    # get domain
-    print('Domain:')
-    domain = tree.find_all('classification-level')
-    patent_domain = domain[0].text
-    print(patent_domain)
-
-    # get date of publication
-    print("Publication date:")
-    date = tree.find_all("date")
-    patent_pubdate = date[0].text
-    print(patent_pubdate)
-
-    # get abstract
-    print('Abstract:')
-    ab = tree.find_all("abstract")
-    patent_abstract = ab[0].text
-    print(patent_abstract)
-
-    # get claim
-    print('Claims:')
-    claims = tree.find_all("claim-text")
-    for claim in claims:
-        print(claim.text)
-
-    # get description
-    print('Description:')
-    description = tree.find_all('description')
-    for des in description:
-        print(des.text)
-
-    # save file to the place
-    with open(saved_file_path + patent_number_new + '.txt', 'w') as text_file:
-        text_file.write("Patent title" + '\n' + patent_title +
-                        '\n' * 2 + "Patent number" + '\n' +
-                        patent_number_new + '\n' * 2 + "Domain" + '\n' + patent_domain + '\n' * 2 + "Publication date" + '\n' + patent_pubdate
-                        + '\n' * 2 + "Abstract" + '\n' + patent_abstract
-                        + '\n' * 2 + 'Claims' + '\n')  # save patent title, number, domain, publication data_patent_details, abstract
-        for claim in claims:
-            text_file.write(claim.text + '\n')
-        text_file.write('\n' + 'Description' + '\n')
-        for des in description:
-            text_file.write('\n' + des.text + '\n')
-
-    return text_file
-
-
-# to download patents (.txt) by zip file
-def create_download_zip(zip_directory, zip_path, filename):
-    """
-        zip_directory (str): path to directory you want to zip
-        zip_path (str): where you want to save zip file
-        filename (str): download filename for user who download this
-    """
-    shutil.make_archive(zip_path+filename, 'zip', zip_directory)
-
-    with open(zip_path+filename+'.zip', 'rb') as f:
-        st.download_button(
-            label = 'Download',
-            data = f,
-            file_name='patent.zip',
-            mime= 'zip'
-        )
-
-
-
-# save input files (txt) into the folder
-def save_uploadedfile(uploadedfile):
-    with open(os.path.join('Data/input/US_patents/',uploadedfile.name ), 'wb') as f:
-        f.write(uploadedfile.getbuffer())
-        # return st.success('Saved File:{}'.format(uploadedfile.name))
-
-# to extract problems from patents
-def extractor (folder):
-    input_folder = constants.DATA_INPUT + folder
-    files_extension = "*." + 'txt'
-
-    iInput = InputHandler(input_folder, files_extension)
-    input_data = iInput.get_input()
-
-    pretreat_data = PatentHandler(input_data)
-    clean_patent_data = pretreat_data.pretreat_data()
-
-    process_data = CorpusProcessor(clean_patent_data, input_folder, files_extension)
-    processed_data = process_data.process_corpus()
-
-    # convert json to dataframe
-    with open('Data/graphs/US_patents/graph.json') as json_data:
-        data = json.load(json_data)
-
-    concept_df = json_normalize(data['problem_graph'], sep="_")
-
-    concept_df = concept_df[['concept_sentence', 'concept_source', 'concept_type']]
-    problem_df = concept_df.rename(columns={"concept_sentence": "problem", 'concept_source': 'patent_number',
-                                            'concept_type': 'type'})
-    # choose problems
-    problem_new = problem_df.loc[problem_df['type'] == 'problem']
-
-    print(problem_new)
-
-    new_table_test = problem_new['patent_number'].apply(
-        lambda x: re.search(r'(?<=US_patents\/).*?(?=.txt)', x).group())
-
-    # assign patent number to the corresponding feature
-    problem_results = problem_new.assign(patent_number=new_table_test)
-
-    print(problem_results[['problem', 'patent_number']])
-    problem_results = problem_results[['patent_number', 'problem']]
-    problem_results.to_csv('data_problem/problem.csv',
-                           index=False)
-
-@st.cache
-def convert_df(df):
-    # IMPORTANT: Cache the conversion to prevent computation on every rerun
-    return df.to_csv().encode('utf-8')
-
-
-def extract_info_text():
-    new = pd.DataFrame(columns=['title', 'patent_number', 'domain', 'publication_date'])
-
-    # use glob to get all the txt files in the folder
-    path = 'Data/input/US_patents'
-    txt_files = glob.glob(os.path.join(path, "*.txt"))
-    for f in txt_files:
-        df = pd.read_csv(f, sep='\n', header=None, names=['content'])
-        print(df)
-        # extract patent information from text
-        new = new.append({'patent_number': df.iloc[3, 0], 'title': df.iloc[1, 0],
-                          'domain': df.iloc[5, 0], 'publication_date': df.iloc[7, 0]}, ignore_index=True)
-
-    print(new)
-
-    problem = pd.read_csv('data_problem/problem.csv')
-    final = pd.merge(problem, new, on='patent_number', how='left')
-    return final
-
-def input_domain(user_input_domain):
-    if user_input_domain == 'A (Human necessities)':
-        domain = 'A'
-    elif user_input_domain == 'B (Performing operations; transporting)':
-        domain = 'B'
-    elif user_input_domain == 'C (Chemistry; metallurgy)':
-        domain = 'C'
-    elif user_input_domain == 'D (Textiles; paper)':
-        domain = 'D'
-    elif user_input_domain == 'E (Fixed constructions)':
-        domain = 'E'
-    elif user_input_domain == 'F (Mechanical engineering; lighting; heating; weapons; blasting engines or pumps':
-        domain = 'F'
-    elif user_input_domain == 'G (Physics)':
-        domain = 'G'
-    elif user_input_domain == 'H (Electricity)':
-        domain = 'H'
-    return domain
-
-# the function for choosing month period that user choosed
-def choosing_month_period(problem_corpus,start_year, end_year, start_month, end_month):
-    problem_corpus = problem_corpus[problem_corpus['publication_year'].between(start_year, end_year)]
-    if start_year != end_year:  # 2014- 2015 #2014- 2016
-        if start_month == end_month:  # /01/  /01/
-            if end_year == start_year + 1:  # 2014/03/01 - 2015/03/01 #2014/01/01 - 2015/01/23 #2014/12/01 - 2015/12/23
-                problem_corpus.loc[(problem_corpus['publication_year'] == start_year) & (
-                    problem_corpus['publication_month'].between(start_month, 12)), 'label'] = 'true'
-                problem_corpus.loc[(problem_corpus['publication_year'] == end_year) & (
-                    problem_corpus['publication_month'].between(1, end_month)), 'label'] = 'true'
-
-            elif end_year > start_year + 1:  # 2014/01/01 - 2016/01/23 #2014/12/01 - 2016/12/23 # 2014/03/01 - 2016/03/01
-                if start_month == 1:  # 2014/01/01 - 2016/01/23
-                    problem_corpus.loc[(
-                                               problem_corpus['publication_year'] == end_year) & (
-                                       problem_corpus['publication_month'].between(
-                                           end_month + 1, 12)), 'label'] = 'false'
-                    problem_corpus.loc[(problem_corpus.label != 'false'), 'label'] = 'true'
-                elif start_month == 12:  # 2014/12/01 - 2016/12/23
-                    problem_corpus.loc[(
-                                               problem_corpus['publication_year'] == start_year) & (
-                                       problem_corpus['publication_month'].between(
-                                           1, start_month - 1)), 'label'] = 'false'
-                    problem_corpus.loc[(problem_corpus.label != 'false'), 'label'] = 'true'
-                else:  # 2014/03/01 - 2016/03/01
-                    problem_corpus.loc[(
-                                               problem_corpus['publication_year'] == start_year) & (
-                                       problem_corpus['publication_month'].between(
-                                           1, start_month - 1)), 'label'] = 'false'
-                    problem_corpus.loc[(
-                                               problem_corpus['publication_year'] == end_year) & (
-                                       problem_corpus['publication_month'].between(
-                                           end_month + 1, 12)), 'label'] = 'false'
-                    problem_corpus.loc[(problem_corpus.label != 'false'), 'label'] = 'true'
-        if start_month > end_month:  # /03/  /01/
-            if end_year == start_year + 1:  # 2014/12/01 - 2015/03/01 #2014/02/01 - 2015/01/23
-                problem_corpus.loc[(problem_corpus['publication_year'] == start_year) & (
-                    problem_corpus['publication_month'].between(start_month, 12)), 'label'] = 'true'
-                problem_corpus.loc[(problem_corpus['publication_year'] == end_year) & (
-                    problem_corpus['publication_month'].between(1, end_month)), 'label'] = 'true'
-
-            elif end_year > start_year + 1:  # 2014/12/01 - 2016/03/01 #2014/02/01 - 2016/01/23
-                problem_corpus.loc[(
-                                           problem_corpus['publication_year'] == start_year) & (
-                                   problem_corpus['publication_month'].between(
-                                       1, start_month - 1)), 'label'] = 'false'
-                problem_corpus.loc[(
-                                           problem_corpus['publication_year'] == end_year) & (
-                                   problem_corpus['publication_month'].between(
-                                       end_month + 1, 12)), 'label'] = 'false'
-                problem_corpus.loc[(problem_corpus.label != 'false'), 'label'] = 'true'
-
-        if start_month < end_month:  # /01/  /03/
-            if end_year == start_year + 1:  # 2014/01/01 - 2015/12/01 #2014/02/01 - 2015/11/23
-                problem_corpus.loc[(problem_corpus['publication_year'] == start_year) & (
-                    problem_corpus['publication_month'].between(start_month, 12)), 'label'] = 'true'
-                problem_corpus.loc[(problem_corpus['publication_year'] == end_year) & (
-                    problem_corpus['publication_month'].between(1, end_month)), 'label'] = 'true'
-
-            elif end_year > start_year + 1:  # 2014/01/01 - 2016/12/01 #2014/02/01 - 2016/11/23
-                if start_month == 1 & end_month == 12:  # 2014/01/01 - 2016/12/01
-                    problem_corpus['label'] = 'true'
-                elif start_month == 1:  # 2014/01/01 - 2016/03/01 #2014/01/01 - 2016/11/01
-                    problem_corpus.loc[(problem_corpus['publication_year'] == end_year) & (problem_corpus[
-                                                                                               'publication_month'].between(
-                        end_month + 1, 12)), 'label'] = 'false'
-                    problem_corpus.loc[(problem_corpus.label != 'false'), 'label'] = 'true'
-                elif end_month == 12:  # 2014/02/01 - 2016/12/01 #2015/02/01 - 2016/12/01
-                    problem_corpus.loc[(problem_corpus['publication_year'] == start_year) & (problem_corpus[
-                                                                                                 'publication_month'].between(
-                        1, start_month - 1)), 'label'] = 'false'
-                    problem_corpus.loc[(problem_corpus.label != 'false'), 'label'] = 'true'
-                else:  # 2014/02/01 - 2016/11/23
-                    problem_corpus.loc[(problem_corpus['publication_year'] == start_year) & (problem_corpus[
-                                                                                                 'publication_month'].between(
-                        1, start_month - 1)), 'label'] = 'false'
-                    problem_corpus.loc[(problem_corpus['publication_year'] == end_year) & (problem_corpus[
-                                                                                               'publication_month'].between(
-                        end_month + 1, 12)), 'label'] = 'false'
-                    problem_corpus.loc[(problem_corpus.label != 'false'), 'label'] = 'true'
-
-
-
-    else:  # start_year == end_year: 2012-2012
-        problem_corpus = problem_corpus[problem_corpus['publication_year'] == start_year]
-        if start_month != end_month:  # 2014/03/01 - 2014/05/01 2014/01/01 - 2014/05/01 2014/03/01 - 2014/12/01
-            problem_corpus.loc[problem_corpus['publication_month'].between(start_month, end_month), 'label'] = 'true'
-        else:  # 2014/03/01 - 2014/03/20 #2014/01/01 - 2014/01/20
-            problem_corpus.loc[problem_corpus['publication_month'] == start_month, 'label'] = 'true'
-
-    problem_corpus = problem_corpus.loc[problem_corpus['label'] == 'true']
-    problem_corpus= problem_corpus[['patent_number', 'Domain', 'First part Contradiction',
-                                     'Second part Contradiction', 'publication_date', 'publication_year',
-                                     'publication_month', 'label']]
-    return problem_corpus
-
-# for IDM-Similar model (word2vec)
-def avg_feature_vector(sentence, model, num_features, index2word_set):
-    words = sentence.split()
-    feature_vec = np.zeros((num_features, ), dtype='float32')
-    n_words = 0
-    for word in words:
-        if word in index2word_set:
-            n_words += 1
-            feature_vec = np.add(feature_vec, model[word])
-    if (n_words > 0):
-        feature_vec = np.divide(feature_vec, n_words)
-    return feature_vec
-
-def creat_query_id(dataset):
-    # create query
-    question = []
-    for each in dataset['problem']:
-        new = "What is the solution for the problem that " + each + "?"
-        question.append(new)
-    dataset['question'] = question
-
-    # create id
-    data = dataset.rename(columns={'Unnamed: 0': 'id'})
-    return data
-
-def csv_to_json (csv_file,json_file):
-    results = []
-    with open(csv_file) as csv_file:
-        csvReader = csv.DictReader(csv_file)
-        for row in csvReader:
-            context = row['Context']
-            qas = []
-            content = {}
-            content['id'] = row['id']
-            content['question'] = row['question']
-            qas.append(content)
-            result = {}
-            result['context'] = context
-            result['qas'] = qas
-            results.append(result)
-
-    # write data to a json file
-    with open(json_file, 'w') as jsonFile:
-        jsonFile.write(json.dumps(results, indent=4))
-
-
-
-def QA_prediction(prediction_file, prediction_output, model):
-    # if __name__ == '__main__':
-    with open(prediction_file, 'r') as pre_file:
-        temp = json.loads(pre_file.read())
-        predictions = model.predict(temp)
-
-    with open(prediction_output, 'w') as json_file:
-        json_file.write(json.dumps(predictions, indent=4))
-    print(predictions)
-
-def json_to_csv(input_file, output_file):
-    result = pd.read_json(input_file)
-    print(result.head())
-
-    result_answer = result.iloc[0][:]
-    print(result_answer.head())
-    print(len(result_answer))
-
-    df = pd.DataFrame(index=np.arange(len(result_answer)), columns=['id', 'answer'])
-    print(df)
-
-    for i in range(len(result_answer)):
-        line = result_answer[i]
-        print(line)
-        df.iloc[i, 0] = line['id']
-        df.iloc[i, 1] = line['answer']
-
-    print(df.head())
-    df.to_csv(output_file, index=False)
diff --git a/spaces/Reself/StableVideo/stablevideo/atlas_data.py b/spaces/Reself/StableVideo/stablevideo/atlas_data.py
deleted file mode 100644
index b9dfff4a5fe85a50a7c916cf1822524dcff61c40..0000000000000000000000000000000000000000
--- a/spaces/Reself/StableVideo/stablevideo/atlas_data.py
+++ /dev/null
@@ -1,291 +0,0 @@
-import random
-import json
-
-import torch
-import torch.nn.functional as F
-from torch.utils.data import Dataset
-from torchvision import transforms
-from torchvision.transforms.functional import crop
-
-from stablevideo.atlas_utils import (
-    load_neural_atlases_models,
-    get_frames_data,
-    get_high_res_atlas,
-    get_atlas_crops,
-    reconstruct_video_layer,
-    create_uv_mask,
-    get_masks_boundaries,
-    get_random_crop_params,
-    get_atlas_bounding_box,
-    load_video
-)
-
-class AtlasData():
-    def __init__(self, video_name):
-        with open(f"data/{video_name}/config.json", "r") as f:
-            json_dict = json.load(f)
-        try:
-            maximum_number_of_frames = json_dict["number_of_frames"]
-        except:
-            maximum_number_of_frames = json_dict["maximum_number_of_frames"]
-
-        config = {
-            "device": "cpu",
-            "checkpoint_path": f"data/{video_name}/checkpoint.ckpt",
-            "resx": json_dict["resx"],
-            "resy": json_dict["resy"],
-            "maximum_number_of_frames": maximum_number_of_frames,
-            "return_atlas_alpha": False,
-            "grid_atlas_resolution": 2000,
-            "num_scales": 7,
-            "masks_border_expansion": 30,
-            "mask_alpha_threshold": 0.99, # 0.95
-            "align_corners": False
-        }
-        self.config = config
-        self.device = config["device"]
-
-        self.min_size = min(self.config["resx"], self.config["resy"])
-        self.max_size = max(self.config["resx"], self.config["resy"])
-        data_folder = f"data/{video_name}/{video_name}"
-        self.original_video = load_video(
-            data_folder,
-            resize=(self.config["resy"], self.config["resx"]),
-            num_frames=self.config["maximum_number_of_frames"],
-        )
-        self.original_video = self.original_video.to(self.device) # tensor
-
-        (
-            foreground_mapping,
-            background_mapping,
-            foreground_atlas_model,
-            background_atlas_model,
-            alpha_model,
-        ) = load_neural_atlases_models(config)
-        (
-            original_background_all_uvs,
-            original_foreground_all_uvs,
-            self.all_alpha,
-            foreground_atlas_alpha,
-        ) = get_frames_data(
-            config,
-            foreground_mapping,
-            background_mapping,
-            alpha_model,
-        )
-
-        self.background_reconstruction = reconstruct_video_layer(original_background_all_uvs, background_atlas_model)
-        # using original video for the foreground layer
-        self.foreground_reconstruction = self.original_video * self.all_alpha
-
-        (
-            self.background_all_uvs,
-            self.scaled_background_uvs,
-            self.background_min_u,
-            self.background_min_v,
-            self.background_max_u,
-            self.background_max_v,
-        ) = self.preprocess_uv_values(
-            original_background_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="background"
-        )
-        (
-            self.foreground_all_uvs,
-            self.scaled_foreground_uvs,
-            self.foreground_min_u,
-            self.foreground_min_v,
-            self.foreground_max_u,
-            self.foreground_max_v,
-        ) = self.preprocess_uv_values(
-            original_foreground_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="foreground"
-        )
-
-        self.background_uv_mask = create_uv_mask(
-            config,
-            background_mapping,
-            self.background_min_u,
-            self.background_min_v,
-            self.background_max_u,
-            self.background_max_v,
-            uv_shift=-0.5,
-            resolution_shift=1,
-        )
-        self.foreground_uv_mask = create_uv_mask(
-            config,
-            foreground_mapping,
-            self.foreground_min_u,
-            self.foreground_min_v,
-            self.foreground_max_u,
-            self.foreground_max_v,
-            uv_shift=0.5,
-            resolution_shift=0,
-        )
-        self.background_grid_atlas = get_high_res_atlas(
-            background_atlas_model,
-            self.background_min_v,
-            self.background_min_u,
-            self.background_max_v,
-            self.background_max_u,
-            config["grid_atlas_resolution"],
-            device=config["device"],
-            layer="background",
-        )
-        self.foreground_grid_atlas = get_high_res_atlas(
-            foreground_atlas_model,
-            self.foreground_min_v,
-            self.foreground_min_u,
-            self.foreground_max_v,
-            self.foreground_max_u,
-            config["grid_atlas_resolution"],
-            device=config["device"],
-            layer="foreground",
-        )
-        if config["return_atlas_alpha"]:
-            self.foreground_atlas_alpha = foreground_atlas_alpha  # used for visualizations
-        self.cnn_min_crop_size = 2 ** self.config["num_scales"] + 1
-
-        self.mask_boundaries = get_masks_boundaries(
-            alpha_video=self.all_alpha.cpu(),
-            border=self.config["masks_border_expansion"],
-            threshold=self.config["mask_alpha_threshold"],
-            min_crop_size=self.cnn_min_crop_size,
-        )
-        self.cropped_foreground_atlas, self.foreground_atlas_bbox = get_atlas_bounding_box(
-            self.mask_boundaries, self.foreground_grid_atlas, self.foreground_all_uvs
-        )
-
-        self.step = -1
-        self.edited_atlas_dict, self.edit_dict, self.uv_mask = {}, {}, {}
-    
-    @staticmethod
-    def preprocess_uv_values(layer_uv_values, resolution, device="cuda", layer="background"):
-        if layer == "background":
-            shift = 1
-        else:
-            shift = 0
-        uv_values = (layer_uv_values + shift) * resolution
-        min_u, min_v = uv_values.reshape(-1, 2).min(dim=0).values.long()
-        uv_values -= torch.tensor([min_u, min_v], device=device)
-        max_u, max_v = uv_values.reshape(-1, 2).max(dim=0).values.ceil().long()
-
-        edge_size = torch.tensor([max_u, max_v], device=device)
-        scaled_uv_values = ((uv_values.reshape(-1, 2) / edge_size) * 2 - 1).unsqueeze(1).unsqueeze(0)
-
-        return uv_values, scaled_uv_values, min_u, min_v, max_u, max_v
-
-    def get_random_crop_data(self, crop_size):
-        t = random.randint(0, self.config["maximum_number_of_frames"] - 1)
-        y_start, x_start, h_crop, w_crop = get_random_crop_params((self.config["resx"], self.config["resy"]), crop_size)
-        return y_start, x_start, h_crop, w_crop, t
-
-    def get_global_crops_multi(self, keyframes, res):
-        foreground_atlas_crops = []
-        background_atlas_crops = []
-        foreground_uvs = []
-        background_uvs = []
-        background_alpha_crops = []
-        foreground_alpha_crops = []
-        original_background_crops = []
-        original_foreground_crops = []
-        output_dict = {}
-
-        self.config["crops_min_cover"] = 0.95
-        self.config["grid_atlas_resolution"] = res
-        
-        for cur_frame in keyframes:
-            y_start, x_start, frame_h, frame_w = self.mask_boundaries[cur_frame].tolist()
-            crop_size = (
-                max(
-                    random.randint(round(self.config["crops_min_cover"] * frame_h), frame_h),
-                    self.cnn_min_crop_size,
-                ),
-                max(
-                    random.randint(round(self.config["crops_min_cover"] * frame_w), frame_w),
-                    self.cnn_min_crop_size,
-                ),
-            )
-            y_crop, x_crop, h_crop, w_crop = get_random_crop_params((frame_w, frame_h), crop_size)
-
-            foreground_uv = self.foreground_all_uvs[
-                cur_frame,
-                y_start + y_crop : y_start + y_crop + h_crop,
-                x_start + x_crop : x_start + x_crop + w_crop,
-            ]
-            alpha = self.all_alpha[
-                [cur_frame],
-                :,
-                y_start + y_crop : y_start + y_crop + h_crop,
-                x_start + x_crop : x_start + x_crop + w_crop,
-            ]
-
-            original_foreground_crop = self.foreground_reconstruction[
-                [cur_frame],
-                :,
-                y_start + y_crop : y_start + y_crop + h_crop,
-                x_start + x_crop : x_start + x_crop + w_crop,
-            ]
-
-            foreground_alpha_crops.append(alpha)
-            foreground_uvs.append(foreground_uv)  # not scaled
-            original_foreground_crops.append(original_foreground_crop)
-
-        foreground_max_vals = torch.tensor(
-            [self.config["grid_atlas_resolution"]] * 2, device=self.device, dtype=torch.long
-        )
-        foreground_min_vals = torch.tensor([0] * 2, device=self.device, dtype=torch.long)
-        for uv_values in foreground_uvs:
-            min_uv = uv_values.amin(dim=[0, 1]).long()
-            max_uv = uv_values.amax(dim=[0, 1]).ceil().long()
-            foreground_min_vals = torch.minimum(foreground_min_vals, min_uv)
-            foreground_max_vals = torch.maximum(foreground_max_vals, max_uv)
-        
-        h_v = foreground_max_vals[1] - foreground_min_vals[1]
-        w_u = foreground_max_vals[0] - foreground_min_vals[0]
-        foreground_atlas_crop = crop(
-            self.foreground_grid_atlas,
-            foreground_min_vals[1],
-            foreground_min_vals[0],
-            h_v,
-            w_u,
-        )
-        foreground_atlas_crops.append(foreground_atlas_crop)
-        
-        for i, uv_values in enumerate(foreground_uvs):
-            foreground_uvs[i] = (
-                2 * (uv_values - foreground_min_vals) / (foreground_max_vals - foreground_min_vals) - 1
-            ).unsqueeze(0)
-
-
-        crop_size = (
-            random.randint(round(self.config["crops_min_cover"] * self.min_size), self.min_size),
-            random.randint(round(self.config["crops_min_cover"] * self.max_size), self.max_size),
-        )
-        crop_data = self.get_random_crop_data(crop_size)
-        y, x, h, w, _ = crop_data
-        background_uv = self.background_all_uvs[keyframes, y : y + h, x : x + w]
-        original_background_crop = self.background_reconstruction[
-            keyframes, :, y : y + h, x : x + w
-        ]
-        alpha = self.all_alpha[keyframes, :, y : y + h, x : x + w]
-
-
-        original_background_crops = [el.unsqueeze(0) for el in original_background_crop]
-        background_alpha_crops = [el.unsqueeze(0) for el in alpha]
-
-        background_atlas_crop, background_min_vals, background_max_vals = get_atlas_crops(
-            background_uv,
-            self.background_grid_atlas,
-        )
-        background_uv = 2 * (background_uv - background_min_vals) / (background_max_vals - background_min_vals) - 1
-        background_atlas_crops = [el.unsqueeze(0) for el in background_atlas_crop]
-        background_uvs = [el.unsqueeze(0) for el in background_uv]
-
-        output_dict["foreground_alpha"] = foreground_alpha_crops
-        output_dict["foreground_uvs"] = foreground_uvs
-        output_dict["original_foreground_crops"] = original_foreground_crops
-        output_dict["foreground_atlas_crops"] = foreground_atlas_crops
-
-        output_dict["background_alpha"] = background_alpha_crops
-        output_dict["background_uvs"] = background_uvs
-        output_dict["original_background_crops"] = original_background_crops
-        output_dict["background_atlas_crops"] = background_atlas_crops
-        return output_dict
\ No newline at end of file
diff --git a/spaces/Riksarkivet/htr_demo/helper/text/overview/contributions/contributions.md b/spaces/Riksarkivet/htr_demo/helper/text/overview/contributions/contributions.md
deleted file mode 100644
index bcaa71cec148b6096486831237efcc06321f334f..0000000000000000000000000000000000000000
--- a/spaces/Riksarkivet/htr_demo/helper/text/overview/contributions/contributions.md
+++ /dev/null
@@ -1,33 +0,0 @@
-## HTRFLOW – Contributions
-
-The AI models used in HTRFLOW is the result of a collaborative effort, involving the National Archives in both Sweden and Finland, in partnership with the Stockholm City Archives, Jämtlands läns fornskriftsällskap, citizen science volunteers and researchers from Stockholm and Uppsala Universities.
-
-Several datasets have been created by participants through Citizen Science using the Handwritten Text Recognition (HTR) software, Transkribus, provided by [READ-COOP SCE](https://readcoop.eu/) .
-
-### Archives used to train models for HTRFLOW
-
-[Svea hovrätt (Renskrivna protokoll), 1713–1735](https://sok.riksarkivet.se/arkiv/H2hpDbNn14scxjzdWqAaJ1)
-
-[Bergmästaren i Nora m fl bergslag (Hammartingsprotokoll), 1698–1765](https://sok.riksarkivet.se/arkiv/M5Fe2TT9rH6cxG02H087k3)
-
-[Trolldomskommissionen, mainly 1670s](https://sok.riksarkivet.se/trolldomskommissionen)
-
-[Bergskollegium, 1718–1758](https://sok.riksarkivet.se/arkiv/SMFky31ekQ80Qsk0UCZZE2)
-
-[Jämtlands domsaga, 1647–1688](https://sok.riksarkivet.se/arkiv/2l4NYFT8rH6cxG02H087k3)
-
-[Stockholms domkapitel, 1728–1759](https://sok.riksarkivet.se/arkiv/etg1tyeEaIPMBzKbUKTjw1)
-
-[Politikollegiet, 1729–1759](https://sok.riksarkivet.se/arkiv/1lQnXIDiKaYxRLBlK1dGF3)
-
-[Göteborgs poliskammare före 1900 (Detektiva polisens rapportböcker), 1868–1901](https://sok.riksarkivet.se/arkiv/oLTOi9yxweZJUG018W43t3)
-
-[Renovated Court Records, the National Archives of Finland, 1800s](https://tuomiokirjat.kansallisarkisto.fi/)
-
-### Ongoing research collaborations
-
-[Transcription node Sweden – machine interpretation and citizen research combined](https://riksarkivet.se/forskning), Swedish National Archives and University of Gothenburg, funded by the Swedish National Heritage Board.
-
-[Mapping the geographies of early modern mining knowledge. A digital history of the study tours of the Swedish Bureau of Mines, 1691–1826](https://www.idehist.uu.se/forskning/projekt/den-tidigmoderna-bergsvetenskapens-geografier), Uppsala University and Stockholm University, funded by the Swedish Research Council.
-
-The Swedish National Archives' research and development on HTR is part of the Swedish national infrastructure Huminfra. [Click here](https://riksarkivet.se/huminfra) for more information.
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv_custom/checkpoint.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv_custom/checkpoint.py
deleted file mode 100644
index 19b87fef0a52d31babcdb3edb8f3089b6420173f..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv_custom/checkpoint.py
+++ /dev/null
@@ -1,500 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-import io
-import os
-import os.path as osp
-import pkgutil
-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
-from torch.nn import functional as F
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.fileio import FileClient
-from annotator.uniformer.mmcv.fileio import load as load_file
-from annotator.uniformer.mmcv.parallel import is_module_wrapper
-from annotator.uniformer.mmcv.utils import mkdir_or_exist
-from annotator.uniformer.mmcv.runner 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 load_url_dist(url, model_dir=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    return checkpoint
-
-
-def load_pavimodel_dist(model_path, map_location=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    try:
-        from pavi import modelcloud
-    except ImportError:
-        raise ImportError(
-            'Please install pavi to load checkpoint from modelcloud.')
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        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)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            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
-
-
-def load_fileclient_dist(filename, backend, map_location):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    allowed_backends = ['ceph']
-    if backend not in allowed_backends:
-        raise ValueError(f'Load from Backend {backend} is not supported.')
-    if rank == 0:
-        fileclient = FileClient(backend=backend)
-        buffer = io.BytesIO(fileclient.get(filename))
-        checkpoint = torch.load(buffer, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            fileclient = FileClient(backend=backend)
-            buffer = io.BytesIO(fileclient.get(filename))
-            checkpoint = torch.load(buffer, map_location=map_location)
-    return checkpoint
-
-
-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
-
-
-def _load_checkpoint(filename, map_location=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 | None): Same as :func:`torch.load`. Default: None.
-
-    Returns:
-        dict | OrderedDict: The loaded checkpoint. It can be either an
-            OrderedDict storing model weights or a dict containing other
-            information, which depends on the checkpoint.
-    """
-    if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
-                      'use "torchvision://" instead')
-        model_urls = get_torchvision_models()
-        model_name = filename[11:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('torchvision://'):
-        model_urls = get_torchvision_models()
-        model_name = filename[14:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('open-mmlab://'):
-        model_urls = get_external_models()
-        model_name = filename[13:]
-        deprecated_urls = get_deprecated_model_names()
-        if model_name in deprecated_urls:
-            warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '
-                          f'of open-mmlab://{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_url_dist(model_url)
-        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)
-    elif filename.startswith('mmcls://'):
-        model_urls = get_mmcls_models()
-        model_name = filename[8:]
-        checkpoint = load_url_dist(model_urls[model_name])
-        checkpoint = _process_mmcls_checkpoint(checkpoint)
-    elif filename.startswith(('http://', 'https://')):
-        checkpoint = load_url_dist(filename)
-    elif filename.startswith('pavi://'):
-        model_path = filename[7:]
-        checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
-    elif filename.startswith('s3://'):
-        checkpoint = load_fileclient_dist(
-            filename, backend='ceph', map_location=map_location)
-    else:
-        if not osp.isfile(filename):
-            raise IOError(f'{filename} is not a checkpoint file')
-        checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-def load_checkpoint(model,
-                    filename,
-                    map_location='cpu',
-                    strict=False,
-                    logger=None):
-    """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.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = _load_checkpoint(filename, map_location)
-    # 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']
-    elif 'model' in checkpoint:
-        state_dict = checkpoint['model']
-    else:
-        state_dict = checkpoint
-    # strip prefix of state_dict
-    if list(state_dict.keys())[0].startswith('module.'):
-        state_dict = {k[7:]: v for k, v in state_dict.items()}
-
-    # for MoBY, load model of online branch
-    if sorted(list(state_dict.keys()))[0].startswith('encoder'):
-        state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
-
-    # reshape absolute position embedding
-    if state_dict.get('absolute_pos_embed') is not None:
-        absolute_pos_embed = state_dict['absolute_pos_embed']
-        N1, L, C1 = absolute_pos_embed.size()
-        N2, C2, H, W = model.absolute_pos_embed.size()
-        if N1 != N2 or C1 != C2 or L != H*W:
-            logger.warning("Error in loading absolute_pos_embed, pass")
-        else:
-            state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
-
-    # interpolate position bias table if needed
-    relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
-    for table_key in relative_position_bias_table_keys:
-        table_pretrained = state_dict[table_key]
-        table_current = model.state_dict()[table_key]
-        L1, nH1 = table_pretrained.size()
-        L2, nH2 = table_current.size()
-        if nH1 != nH2:
-            logger.warning(f"Error in loading {table_key}, pass")
-        else:
-            if L1 != L2:
-                S1 = int(L1 ** 0.5)
-                S2 = int(L2 ** 0.5)
-                table_pretrained_resized = F.interpolate(
-                     table_pretrained.permute(1, 0).view(1, nH1, S1, S1),
-                     size=(S2, S2), mode='bicubic')
-                state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
-
-    # 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()
-    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):
-    """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.
-    """
-    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://'):
-        try:
-            from pavi import modelcloud
-            from pavi.exception import NodeNotFoundError
-        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 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:
-        mmcv.mkdir_or_exist(osp.dirname(filename))
-        # immediately flush buffer
-        with open(filename, 'wb') as f:
-            torch.save(checkpoint, f)
-            f.flush()
\ No newline at end of file
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/bbox_heads/dii_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/bbox_heads/dii_head.py
deleted file mode 100644
index 8c970a78184672aaaa95edcdaecec03a26604390..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/bbox_heads/dii_head.py
+++ /dev/null
@@ -1,415 +0,0 @@
-import torch
-import torch.nn as nn
-from mmcv.cnn import (bias_init_with_prob, build_activation_layer,
-                      build_norm_layer)
-from mmcv.runner import auto_fp16, force_fp32
-
-from mmdet.core import multi_apply
-from mmdet.models.builder import HEADS, build_loss
-from mmdet.models.dense_heads.atss_head import reduce_mean
-from mmdet.models.losses import accuracy
-from mmdet.models.utils import FFN, MultiheadAttention, build_transformer
-from .bbox_head import BBoxHead
-
-
-@HEADS.register_module()
-class DIIHead(BBoxHead):
-    r"""Dynamic Instance Interactive Head for `Sparse R-CNN: End-to-End Object
-    Detection with Learnable Proposals <https://arxiv.org/abs/2011.12450>`_
-
-    Args:
-        num_classes (int): Number of class in dataset.
-            Defaults to 80.
-        num_ffn_fcs (int): The number of fully-connected
-            layers in FFNs. Defaults to 2.
-        num_heads (int): The hidden dimension of FFNs.
-            Defaults to 8.
-        num_cls_fcs (int): The number of fully-connected
-            layers in classification subnet. Defaults to 1.
-        num_reg_fcs (int): The number of fully-connected
-            layers in regression subnet. Defaults to 3.
-        feedforward_channels (int): The hidden dimension
-            of FFNs. Defaults to 2048
-        in_channels (int): Hidden_channels of MultiheadAttention.
-            Defaults to 256.
-        dropout (float): Probability of drop the channel.
-            Defaults to 0.0
-        ffn_act_cfg (dict): The activation config for FFNs.
-        dynamic_conv_cfg (dict): The convolution config
-            for DynamicConv.
-        loss_iou (dict): The config for iou or giou loss.
-
-    """
-
-    def __init__(self,
-                 num_classes=80,
-                 num_ffn_fcs=2,
-                 num_heads=8,
-                 num_cls_fcs=1,
-                 num_reg_fcs=3,
-                 feedforward_channels=2048,
-                 in_channels=256,
-                 dropout=0.0,
-                 ffn_act_cfg=dict(type='ReLU', inplace=True),
-                 dynamic_conv_cfg=dict(
-                     type='DynamicConv',
-                     in_channels=256,
-                     feat_channels=64,
-                     out_channels=256,
-                     input_feat_shape=7,
-                     act_cfg=dict(type='ReLU', inplace=True),
-                     norm_cfg=dict(type='LN')),
-                 loss_iou=dict(type='GIoULoss', loss_weight=2.0),
-                 **kwargs):
-        super(DIIHead, self).__init__(
-            num_classes=num_classes,
-            reg_decoded_bbox=True,
-            reg_class_agnostic=True,
-            **kwargs)
-        self.loss_iou = build_loss(loss_iou)
-        self.in_channels = in_channels
-        self.fp16_enabled = False
-        self.attention = MultiheadAttention(in_channels, num_heads, dropout)
-        self.attention_norm = build_norm_layer(dict(type='LN'), in_channels)[1]
-
-        self.instance_interactive_conv = build_transformer(dynamic_conv_cfg)
-        self.instance_interactive_conv_dropout = nn.Dropout(dropout)
-        self.instance_interactive_conv_norm = build_norm_layer(
-            dict(type='LN'), in_channels)[1]
-
-        self.ffn = FFN(
-            in_channels,
-            feedforward_channels,
-            num_ffn_fcs,
-            act_cfg=ffn_act_cfg,
-            dropout=dropout)
-        self.ffn_norm = build_norm_layer(dict(type='LN'), in_channels)[1]
-
-        self.cls_fcs = nn.ModuleList()
-        for _ in range(num_cls_fcs):
-            self.cls_fcs.append(
-                nn.Linear(in_channels, in_channels, bias=False))
-            self.cls_fcs.append(
-                build_norm_layer(dict(type='LN'), in_channels)[1])
-            self.cls_fcs.append(
-                build_activation_layer(dict(type='ReLU', inplace=True)))
-
-        # over load the self.fc_cls in BBoxHead
-        if self.loss_cls.use_sigmoid:
-            self.fc_cls = nn.Linear(in_channels, self.num_classes)
-        else:
-            self.fc_cls = nn.Linear(in_channels, self.num_classes + 1)
-
-        self.reg_fcs = nn.ModuleList()
-        for _ in range(num_reg_fcs):
-            self.reg_fcs.append(
-                nn.Linear(in_channels, in_channels, bias=False))
-            self.reg_fcs.append(
-                build_norm_layer(dict(type='LN'), in_channels)[1])
-            self.reg_fcs.append(
-                build_activation_layer(dict(type='ReLU', inplace=True)))
-        # over load the self.fc_cls in BBoxHead
-        self.fc_reg = nn.Linear(in_channels, 4)
-
-        assert self.reg_class_agnostic, 'DIIHead only ' \
-            'suppport `reg_class_agnostic=True` '
-        assert self.reg_decoded_bbox, 'DIIHead only ' \
-            'suppport `reg_decoded_bbox=True`'
-
-    def init_weights(self):
-        """Use xavier initialization for all weight parameter and set
-        classification head bias as a specific value when use focal loss."""
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-            else:
-                # adopt the default initialization for
-                # the weight and bias of the layer norm
-                pass
-        if self.loss_cls.use_sigmoid:
-            bias_init = bias_init_with_prob(0.01)
-            nn.init.constant_(self.fc_cls.bias, bias_init)
-
-    @auto_fp16()
-    def forward(self, roi_feat, proposal_feat):
-        """Forward function of Dynamic Instance Interactive Head.
-
-        Args:
-            roi_feat (Tensor): Roi-pooling features with shape
-                (batch_size*num_proposals, feature_dimensions,
-                pooling_h , pooling_w).
-            proposal_feat (Tensor): Intermediate feature get from
-                diihead in last stage, has shape
-                (batch_size, num_proposals, feature_dimensions)
-
-          Returns:
-                tuple[Tensor]: Usually a tuple of classification scores
-                and bbox prediction and a intermediate feature.
-
-                    - cls_scores (Tensor): Classification scores for
-                      all proposals, has shape
-                      (batch_size, num_proposals, num_classes).
-                    - bbox_preds (Tensor): Box energies / deltas for
-                      all proposals, has shape
-                      (batch_size, num_proposals, 4).
-                    - obj_feat (Tensor): Object feature before classification
-                      and regression subnet, has shape
-                      (batch_size, num_proposal, feature_dimensions).
-        """
-        N, num_proposals = proposal_feat.shape[:2]
-
-        # Self attention
-        proposal_feat = proposal_feat.permute(1, 0, 2)
-        proposal_feat = self.attention_norm(self.attention(proposal_feat))
-
-        # instance interactive
-        proposal_feat = proposal_feat.permute(1, 0,
-                                              2).reshape(-1, self.in_channels)
-        proposal_feat_iic = self.instance_interactive_conv(
-            proposal_feat, roi_feat)
-        proposal_feat = proposal_feat + self.instance_interactive_conv_dropout(
-            proposal_feat_iic)
-        obj_feat = self.instance_interactive_conv_norm(proposal_feat)
-
-        # FFN
-        obj_feat = self.ffn_norm(self.ffn(obj_feat))
-
-        cls_feat = obj_feat
-        reg_feat = obj_feat
-
-        for cls_layer in self.cls_fcs:
-            cls_feat = cls_layer(cls_feat)
-        for reg_layer in self.reg_fcs:
-            reg_feat = reg_layer(reg_feat)
-
-        cls_score = self.fc_cls(cls_feat).view(N, num_proposals, -1)
-        bbox_delta = self.fc_reg(reg_feat).view(N, num_proposals, -1)
-
-        return cls_score, bbox_delta, obj_feat.view(N, num_proposals, -1)
-
-    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
-    def loss(self,
-             cls_score,
-             bbox_pred,
-             labels,
-             label_weights,
-             bbox_targets,
-             bbox_weights,
-             imgs_whwh=None,
-             reduction_override=None,
-             **kwargs):
-        """"Loss function of DIIHead, get loss of all images.
-
-        Args:
-            cls_score (Tensor): Classification prediction
-                results of all class, has shape
-                (batch_size * num_proposals_single_image, num_classes)
-            bbox_pred (Tensor): Regression prediction results,
-                has shape
-                (batch_size * num_proposals_single_image, 4), the last
-                dimension 4 represents [tl_x, tl_y, br_x, br_y].
-            labels (Tensor): Label of each proposals, has shape
-                (batch_size * num_proposals_single_image
-            label_weights (Tensor): Classification loss
-                weight of each proposals, has shape
-                (batch_size * num_proposals_single_image
-            bbox_targets (Tensor): Regression targets of each
-                proposals, has shape
-                (batch_size * num_proposals_single_image, 4),
-                the last dimension 4 represents
-                [tl_x, tl_y, br_x, br_y].
-            bbox_weights (Tensor): Regression loss weight of each
-                proposals's coordinate, has shape
-                (batch_size * num_proposals_single_image, 4),
-            imgs_whwh (Tensor): imgs_whwh (Tensor): Tensor with\
-                shape (batch_size, num_proposals, 4), the last
-                dimension means
-                [img_width,img_height, img_width, img_height].
-            reduction_override (str, optional): The reduction
-                method used to override the original reduction
-                method of the loss. Options are "none",
-                "mean" and "sum". Defaults to None,
-
-            Returns:
-                dict[str, Tensor]: Dictionary of loss components
-        """
-        losses = dict()
-        bg_class_ind = self.num_classes
-        # note in spare rcnn num_gt == num_pos
-        pos_inds = (labels >= 0) & (labels < bg_class_ind)
-        num_pos = pos_inds.sum().float()
-        avg_factor = reduce_mean(num_pos)
-        if cls_score is not None:
-            if cls_score.numel() > 0:
-                losses['loss_cls'] = self.loss_cls(
-                    cls_score,
-                    labels,
-                    label_weights,
-                    avg_factor=avg_factor,
-                    reduction_override=reduction_override)
-                losses['pos_acc'] = accuracy(cls_score[pos_inds],
-                                             labels[pos_inds])
-        if bbox_pred is not None:
-            # 0~self.num_classes-1 are FG, self.num_classes is BG
-            # do not perform bounding box regression for BG anymore.
-            if pos_inds.any():
-                pos_bbox_pred = bbox_pred.reshape(bbox_pred.size(0),
-                                                  4)[pos_inds.type(torch.bool)]
-                imgs_whwh = imgs_whwh.reshape(bbox_pred.size(0),
-                                              4)[pos_inds.type(torch.bool)]
-                losses['loss_bbox'] = self.loss_bbox(
-                    pos_bbox_pred / imgs_whwh,
-                    bbox_targets[pos_inds.type(torch.bool)] / imgs_whwh,
-                    bbox_weights[pos_inds.type(torch.bool)],
-                    avg_factor=avg_factor)
-                losses['loss_iou'] = self.loss_iou(
-                    pos_bbox_pred,
-                    bbox_targets[pos_inds.type(torch.bool)],
-                    bbox_weights[pos_inds.type(torch.bool)],
-                    avg_factor=avg_factor)
-            else:
-                losses['loss_bbox'] = bbox_pred.sum() * 0
-                losses['loss_iou'] = bbox_pred.sum() * 0
-        return losses
-
-    def _get_target_single(self, pos_inds, neg_inds, pos_bboxes, neg_bboxes,
-                           pos_gt_bboxes, pos_gt_labels, cfg):
-        """Calculate the ground truth for proposals in the single image
-        according to the sampling results.
-
-        Almost the same as the implementation in `bbox_head`,
-        we add pos_inds and neg_inds to select positive and
-        negative samples instead of selecting the first num_pos
-        as positive samples.
-
-        Args:
-            pos_inds (Tensor): The length is equal to the
-                positive sample numbers contain all index
-                of the positive sample in the origin proposal set.
-            neg_inds (Tensor): The length is equal to the
-                negative sample numbers contain all index
-                of the negative sample in the origin proposal set.
-            pos_bboxes (Tensor): Contains all the positive boxes,
-                has shape (num_pos, 4), the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            neg_bboxes (Tensor): Contains all the negative boxes,
-                has shape (num_neg, 4), the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            pos_gt_bboxes (Tensor): Contains all the gt_boxes,
-                has shape (num_gt, 4), the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            pos_gt_labels (Tensor): Contains all the gt_labels,
-                has shape (num_gt).
-            cfg (obj:`ConfigDict`): `train_cfg` of R-CNN.
-
-        Returns:
-            Tuple[Tensor]: Ground truth for proposals in a single image.
-            Containing the following Tensors:
-
-                - labels(Tensor): Gt_labels for all proposals, has
-                  shape (num_proposals,).
-                - label_weights(Tensor): Labels_weights for all proposals, has
-                  shape (num_proposals,).
-                - bbox_targets(Tensor):Regression target for all proposals, has
-                  shape (num_proposals, 4), the last dimension 4
-                  represents [tl_x, tl_y, br_x, br_y].
-                - bbox_weights(Tensor):Regression weights for all proposals,
-                  has shape (num_proposals, 4).
-        """
-        num_pos = pos_bboxes.size(0)
-        num_neg = neg_bboxes.size(0)
-        num_samples = num_pos + num_neg
-
-        # original implementation uses new_zeros since BG are set to be 0
-        # now use empty & fill because BG cat_id = num_classes,
-        # FG cat_id = [0, num_classes-1]
-        labels = pos_bboxes.new_full((num_samples, ),
-                                     self.num_classes,
-                                     dtype=torch.long)
-        label_weights = pos_bboxes.new_zeros(num_samples)
-        bbox_targets = pos_bboxes.new_zeros(num_samples, 4)
-        bbox_weights = pos_bboxes.new_zeros(num_samples, 4)
-        if num_pos > 0:
-            labels[pos_inds] = pos_gt_labels
-            pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
-            label_weights[pos_inds] = pos_weight
-            if not self.reg_decoded_bbox:
-                pos_bbox_targets = self.bbox_coder.encode(
-                    pos_bboxes, pos_gt_bboxes)
-            else:
-                pos_bbox_targets = pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1
-        if num_neg > 0:
-            label_weights[neg_inds] = 1.0
-
-        return labels, label_weights, bbox_targets, bbox_weights
-
-    def get_targets(self,
-                    sampling_results,
-                    gt_bboxes,
-                    gt_labels,
-                    rcnn_train_cfg,
-                    concat=True):
-        """Calculate the ground truth for all samples in a batch according to
-        the sampling_results.
-
-        Almost the same as the implementation in bbox_head, we passed
-        additional parameters pos_inds_list and neg_inds_list to
-        `_get_target_single` function.
-
-        Args:
-            sampling_results (List[obj:SamplingResults]): Assign results of
-                all images in a batch after sampling.
-            gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch,
-                each tensor has shape (num_gt, 4),  the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            gt_labels (list[Tensor]): Gt_labels of all images in a batch,
-                each tensor has shape (num_gt,).
-            rcnn_train_cfg (obj:`ConfigDict`): `train_cfg` of RCNN.
-            concat (bool): Whether to concatenate the results of all
-                the images in a single batch.
-
-        Returns:
-            Tuple[Tensor]: Ground truth for proposals in a single image.
-            Containing the following list of Tensors:
-
-                - labels (list[Tensor],Tensor): Gt_labels for all
-                  proposals in a batch, each tensor in list has
-                  shape (num_proposals,) when `concat=False`, otherwise just
-                  a single tensor has shape (num_all_proposals,).
-                - label_weights (list[Tensor]): Labels_weights for
-                  all proposals in a batch, each tensor in list has shape
-                  (num_proposals,) when `concat=False`, otherwise just a
-                  single tensor has shape (num_all_proposals,).
-                - bbox_targets (list[Tensor],Tensor): Regression target
-                  for all proposals in a batch, each tensor in list has
-                  shape (num_proposals, 4) when `concat=False`, otherwise
-                  just a single tensor has shape (num_all_proposals, 4),
-                  the last dimension 4 represents [tl_x, tl_y, br_x, br_y].
-                - bbox_weights (list[tensor],Tensor): Regression weights for
-                  all proposals in a batch, each tensor in list has shape
-                  (num_proposals, 4) when `concat=False`, otherwise just a
-                  single tensor has shape (num_all_proposals, 4).
-        """
-        pos_inds_list = [res.pos_inds for res in sampling_results]
-        neg_inds_list = [res.neg_inds for res in sampling_results]
-        pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
-        neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
-        pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
-        pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
-        labels, label_weights, bbox_targets, bbox_weights = multi_apply(
-            self._get_target_single,
-            pos_inds_list,
-            neg_inds_list,
-            pos_bboxes_list,
-            neg_bboxes_list,
-            pos_gt_bboxes_list,
-            pos_gt_labels_list,
-            cfg=rcnn_train_cfg)
-        if concat:
-            labels = torch.cat(labels, 0)
-            label_weights = torch.cat(label_weights, 0)
-            bbox_targets = torch.cat(bbox_targets, 0)
-            bbox_weights = torch.cat(bbox_weights, 0)
-        return labels, label_weights, bbox_targets, bbox_weights
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/profiler.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/profiler.py
deleted file mode 100644
index b70236997eec59c2209ef351ae38863b4112d0ec..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/profiler.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-from typing import Callable, List, Optional, Union
-
-import torch
-
-from ..dist_utils import master_only
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class ProfilerHook(Hook):
-    """Profiler to analyze performance during training.
-
-    PyTorch Profiler is a tool that allows the collection of the performance
-    metrics during the training. More details on Profiler can be found at
-    https://pytorch.org/docs/1.8.1/profiler.html#torch.profiler.profile
-
-    Args:
-        by_epoch (bool): Profile performance by epoch or by iteration.
-            Default: True.
-        profile_iters (int): Number of iterations for profiling.
-            If ``by_epoch=True``, profile_iters indicates that they are the
-            first profile_iters epochs at the beginning of the
-            training, otherwise it indicates the first profile_iters
-            iterations. Default: 1.
-        activities (list[str]): List of activity groups (CPU, CUDA) to use in
-            profiling. Default: ['cpu', 'cuda'].
-        schedule (dict, optional): Config of generating the callable schedule.
-            if schedule is None, profiler will not add step markers into the
-            trace and table view. Default: None.
-        on_trace_ready (callable, dict): Either a handler or a dict of generate
-            handler. Default: None.
-        record_shapes (bool): Save information about operator's input shapes.
-            Default: False.
-        profile_memory (bool): Track tensor memory allocation/deallocation.
-            Default: False.
-        with_stack (bool): Record source information (file and line number)
-            for the ops. Default: False.
-        with_flops (bool): Use formula to estimate the FLOPS of specific
-            operators (matrix multiplication and 2D convolution).
-            Default: False.
-        json_trace_path (str, optional): Exports the collected trace in Chrome
-            JSON format. Default: None.
-
-    Example:
-        >>> runner = ... # instantiate a Runner
-        >>> # tensorboard trace
-        >>> trace_config = dict(type='tb_trace', dir_name='work_dir')
-        >>> profiler_config = dict(on_trace_ready=trace_config)
-        >>> runner.register_profiler_hook(profiler_config)
-        >>> runner.run(data_loaders=[trainloader], workflow=[('train', 1)])
-    """
-
-    def __init__(self,
-                 by_epoch: bool = True,
-                 profile_iters: int = 1,
-                 activities: List[str] = ['cpu', 'cuda'],
-                 schedule: Optional[dict] = None,
-                 on_trace_ready: Optional[Union[Callable, dict]] = None,
-                 record_shapes: bool = False,
-                 profile_memory: bool = False,
-                 with_stack: bool = False,
-                 with_flops: bool = False,
-                 json_trace_path: Optional[str] = None) -> None:
-        try:
-            from torch import profiler  # torch version >= 1.8.1
-        except ImportError:
-            raise ImportError('profiler is the new feature of torch1.8.1, '
-                              f'but your version is {torch.__version__}')
-
-        assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean.'
-        self.by_epoch = by_epoch
-
-        if profile_iters < 1:
-            raise ValueError('profile_iters should be greater than 0, but got '
-                             f'{profile_iters}')
-        self.profile_iters = profile_iters
-
-        if not isinstance(activities, list):
-            raise ValueError(
-                f'activities should be list, but got {type(activities)}')
-        self.activities = []
-        for activity in activities:
-            activity = activity.lower()
-            if activity == 'cpu':
-                self.activities.append(profiler.ProfilerActivity.CPU)
-            elif activity == 'cuda':
-                self.activities.append(profiler.ProfilerActivity.CUDA)
-            else:
-                raise ValueError(
-                    f'activity should be "cpu" or "cuda", but got {activity}')
-
-        if schedule is not None:
-            self.schedule = profiler.schedule(**schedule)
-        else:
-            self.schedule = None
-
-        self.on_trace_ready = on_trace_ready
-        self.record_shapes = record_shapes
-        self.profile_memory = profile_memory
-        self.with_stack = with_stack
-        self.with_flops = with_flops
-        self.json_trace_path = json_trace_path
-
-    @master_only
-    def before_run(self, runner):
-        if self.by_epoch and runner.max_epochs < self.profile_iters:
-            raise ValueError('self.profile_iters should not be greater than '
-                             f'{runner.max_epochs}')
-
-        if not self.by_epoch and runner.max_iters < self.profile_iters:
-            raise ValueError('self.profile_iters should not be greater than '
-                             f'{runner.max_iters}')
-
-        if callable(self.on_trace_ready):  # handler
-            _on_trace_ready = self.on_trace_ready
-        elif isinstance(self.on_trace_ready, dict):  # config of handler
-            trace_cfg = self.on_trace_ready.copy()
-            trace_type = trace_cfg.pop('type')  # log_trace handler
-            if trace_type == 'log_trace':
-
-                def _log_handler(prof):
-                    print(prof.key_averages().table(**trace_cfg))
-
-                _on_trace_ready = _log_handler
-            elif trace_type == 'tb_trace':  # tensorboard_trace handler
-                try:
-                    import torch_tb_profiler  # noqa: F401
-                except ImportError:
-                    raise ImportError('please run "pip install '
-                                      'torch-tb-profiler" to install '
-                                      'torch_tb_profiler')
-                _on_trace_ready = torch.profiler.tensorboard_trace_handler(
-                    **trace_cfg)
-            else:
-                raise ValueError('trace_type should be "log_trace" or '
-                                 f'"tb_trace", but got {trace_type}')
-        elif self.on_trace_ready is None:
-            _on_trace_ready = None  # type: ignore
-        else:
-            raise ValueError('on_trace_ready should be handler, dict or None, '
-                             f'but got {type(self.on_trace_ready)}')
-
-        if runner.max_epochs > 1:
-            warnings.warn(f'profiler will profile {runner.max_epochs} epochs '
-                          'instead of 1 epoch. Since profiler will slow down '
-                          'the training, it is recommended to train 1 epoch '
-                          'with ProfilerHook and adjust your setting according'
-                          ' to the profiler summary. During normal training '
-                          '(epoch > 1), you may disable the ProfilerHook.')
-
-        self.profiler = torch.profiler.profile(
-            activities=self.activities,
-            schedule=self.schedule,
-            on_trace_ready=_on_trace_ready,
-            record_shapes=self.record_shapes,
-            profile_memory=self.profile_memory,
-            with_stack=self.with_stack,
-            with_flops=self.with_flops)
-
-        self.profiler.__enter__()
-        runner.logger.info('profiler is profiling...')
-
-    @master_only
-    def after_train_epoch(self, runner):
-        if self.by_epoch and runner.epoch == self.profile_iters - 1:
-            runner.logger.info('profiler may take a few minutes...')
-            self.profiler.__exit__(None, None, None)
-            if self.json_trace_path is not None:
-                self.profiler.export_chrome_trace(self.json_trace_path)
-
-    @master_only
-    def after_train_iter(self, runner):
-        self.profiler.step()
-        if not self.by_epoch and runner.iter == self.profile_iters - 1:
-            runner.logger.info('profiler may take a few minutes...')
-            self.profiler.__exit__(None, None, None)
-            if self.json_trace_path is not None:
-                self.profiler.export_chrome_trace(self.json_trace_path)
diff --git a/spaces/Rongjiehuang/ProDiff/egs/datasets/audio/libritts/pre_align.py b/spaces/Rongjiehuang/ProDiff/egs/datasets/audio/libritts/pre_align.py
deleted file mode 100644
index 335b43d913edb02fb02e10c2479aa3dd9e07bb2f..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/egs/datasets/audio/libritts/pre_align.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import os
-
-from data_gen.tts.base_pre_align import BasePreAlign
-import glob
-
-
-class LibrittsPreAlign(BasePreAlign):
-    def meta_data(self):
-        wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*/*.wav'))
-        for wav_fn in wav_fns:
-            item_name = os.path.basename(wav_fn)[:-4]
-            txt_fn = f'{wav_fn[:-4]}.normalized.txt'
-            spk = item_name.split("_")[0]
-            yield item_name, wav_fn, (self.load_txt, txt_fn), spk
-
-
-if __name__ == "__main__":
-    LibrittsPreAlign().process()
diff --git a/spaces/Rongjiehuang/ProDiff/modules/parallel_wavegan/models/parallel_wavegan.py b/spaces/Rongjiehuang/ProDiff/modules/parallel_wavegan/models/parallel_wavegan.py
deleted file mode 100644
index c63b59f67aa48342179415c1d1beac68574a5498..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/modules/parallel_wavegan/models/parallel_wavegan.py
+++ /dev/null
@@ -1,434 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""Parallel WaveGAN Modules."""
-
-import logging
-import math
-
-import torch
-from torch import nn
-
-from modules.parallel_wavegan.layers import Conv1d
-from modules.parallel_wavegan.layers import Conv1d1x1
-from modules.parallel_wavegan.layers import ResidualBlock
-from modules.parallel_wavegan.layers import upsample
-from modules.parallel_wavegan import models
-
-
-class ParallelWaveGANGenerator(torch.nn.Module):
-    """Parallel WaveGAN Generator module."""
-
-    def __init__(self,
-                 in_channels=1,
-                 out_channels=1,
-                 kernel_size=3,
-                 layers=30,
-                 stacks=3,
-                 residual_channels=64,
-                 gate_channels=128,
-                 skip_channels=64,
-                 aux_channels=80,
-                 aux_context_window=2,
-                 dropout=0.0,
-                 bias=True,
-                 use_weight_norm=True,
-                 use_causal_conv=False,
-                 upsample_conditional_features=True,
-                 upsample_net="ConvInUpsampleNetwork",
-                 upsample_params={"upsample_scales": [4, 4, 4, 4]},
-                 use_pitch_embed=False,
-                 ):
-        """Initialize Parallel WaveGAN Generator module.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            kernel_size (int): Kernel size of dilated convolution.
-            layers (int): Number of residual block layers.
-            stacks (int): Number of stacks i.e., dilation cycles.
-            residual_channels (int): Number of channels in residual conv.
-            gate_channels (int):  Number of channels in gated conv.
-            skip_channels (int): Number of channels in skip conv.
-            aux_channels (int): Number of channels for auxiliary feature conv.
-            aux_context_window (int): Context window size for auxiliary feature.
-            dropout (float): Dropout rate. 0.0 means no dropout applied.
-            bias (bool): Whether to use bias parameter in conv layer.
-            use_weight_norm (bool): Whether to use weight norm.
-                If set to true, it will be applied to all of the conv layers.
-            use_causal_conv (bool): Whether to use causal structure.
-            upsample_conditional_features (bool): Whether to use upsampling network.
-            upsample_net (str): Upsampling network architecture.
-            upsample_params (dict): Upsampling network parameters.
-
-        """
-        super(ParallelWaveGANGenerator, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.aux_channels = aux_channels
-        self.layers = layers
-        self.stacks = stacks
-        self.kernel_size = kernel_size
-
-        # check the number of layers and stacks
-        assert layers % stacks == 0
-        layers_per_stack = layers // stacks
-
-        # define first convolution
-        self.first_conv = Conv1d1x1(in_channels, residual_channels, bias=True)
-
-        # define conv + upsampling network
-        if upsample_conditional_features:
-            upsample_params.update({
-                "use_causal_conv": use_causal_conv,
-            })
-            if upsample_net == "MelGANGenerator":
-                assert aux_context_window == 0
-                upsample_params.update({
-                    "use_weight_norm": False,  # not to apply twice
-                    "use_final_nonlinear_activation": False,
-                })
-                self.upsample_net = getattr(models, upsample_net)(**upsample_params)
-            else:
-                if upsample_net == "ConvInUpsampleNetwork":
-                    upsample_params.update({
-                        "aux_channels": aux_channels,
-                        "aux_context_window": aux_context_window,
-                    })
-                self.upsample_net = getattr(upsample, upsample_net)(**upsample_params)
-        else:
-            self.upsample_net = None
-
-        # define residual blocks
-        self.conv_layers = torch.nn.ModuleList()
-        for layer in range(layers):
-            dilation = 2 ** (layer % layers_per_stack)
-            conv = ResidualBlock(
-                kernel_size=kernel_size,
-                residual_channels=residual_channels,
-                gate_channels=gate_channels,
-                skip_channels=skip_channels,
-                aux_channels=aux_channels,
-                dilation=dilation,
-                dropout=dropout,
-                bias=bias,
-                use_causal_conv=use_causal_conv,
-            )
-            self.conv_layers += [conv]
-
-        # define output layers
-        self.last_conv_layers = torch.nn.ModuleList([
-            torch.nn.ReLU(inplace=True),
-            Conv1d1x1(skip_channels, skip_channels, bias=True),
-            torch.nn.ReLU(inplace=True),
-            Conv1d1x1(skip_channels, out_channels, bias=True),
-        ])
-
-        self.use_pitch_embed = use_pitch_embed
-        if use_pitch_embed:
-            self.pitch_embed = nn.Embedding(300, aux_channels, 0)
-            self.c_proj = nn.Linear(2 * aux_channels, aux_channels)
-
-        # apply weight norm
-        if use_weight_norm:
-            self.apply_weight_norm()
-
-    def forward(self, x, c=None, pitch=None, **kwargs):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input noise signal (B, C_in, T).
-            c (Tensor): Local conditioning auxiliary features (B, C ,T').
-            pitch (Tensor): Local conditioning pitch (B, T').
-
-        Returns:
-            Tensor: Output tensor (B, C_out, T)
-
-        """
-        # perform upsampling
-        if c is not None and self.upsample_net is not None:
-            if self.use_pitch_embed:
-                p = self.pitch_embed(pitch)
-                c = self.c_proj(torch.cat([c.transpose(1, 2), p], -1)).transpose(1, 2)
-            c = self.upsample_net(c)
-            assert c.size(-1) == x.size(-1), (c.size(-1), x.size(-1))
-
-        # encode to hidden representation
-        x = self.first_conv(x)
-        skips = 0
-        for f in self.conv_layers:
-            x, h = f(x, c)
-            skips += h
-        skips *= math.sqrt(1.0 / len(self.conv_layers))
-
-        # apply final layers
-        x = skips
-        for f in self.last_conv_layers:
-            x = f(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        """Remove weight normalization module from all of the layers."""
-        def _remove_weight_norm(m):
-            try:
-                logging.debug(f"Weight norm is removed from {m}.")
-                torch.nn.utils.remove_weight_norm(m)
-            except ValueError:  # this module didn't have weight norm
-                return
-
-        self.apply(_remove_weight_norm)
-
-    def apply_weight_norm(self):
-        """Apply weight normalization module from all of the layers."""
-        def _apply_weight_norm(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
-                torch.nn.utils.weight_norm(m)
-                logging.debug(f"Weight norm is applied to {m}.")
-
-        self.apply(_apply_weight_norm)
-
-    @staticmethod
-    def _get_receptive_field_size(layers, stacks, kernel_size,
-                                  dilation=lambda x: 2 ** x):
-        assert layers % stacks == 0
-        layers_per_cycle = layers // stacks
-        dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
-        return (kernel_size - 1) * sum(dilations) + 1
-
-    @property
-    def receptive_field_size(self):
-        """Return receptive field size."""
-        return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
-
-
-class ParallelWaveGANDiscriminator(torch.nn.Module):
-    """Parallel WaveGAN Discriminator module."""
-
-    def __init__(self,
-                 in_channels=1,
-                 out_channels=1,
-                 kernel_size=3,
-                 layers=10,
-                 conv_channels=64,
-                 dilation_factor=1,
-                 nonlinear_activation="LeakyReLU",
-                 nonlinear_activation_params={"negative_slope": 0.2},
-                 bias=True,
-                 use_weight_norm=True,
-                 ):
-        """Initialize Parallel WaveGAN Discriminator module.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            kernel_size (int): Number of output channels.
-            layers (int): Number of conv layers.
-            conv_channels (int): Number of chnn layers.
-            dilation_factor (int): Dilation factor. For example, if dilation_factor = 2,
-                the dilation will be 2, 4, 8, ..., and so on.
-            nonlinear_activation (str): Nonlinear function after each conv.
-            nonlinear_activation_params (dict): Nonlinear function parameters
-            bias (bool): Whether to use bias parameter in conv.
-            use_weight_norm (bool) Whether to use weight norm.
-                If set to true, it will be applied to all of the conv layers.
-
-        """
-        super(ParallelWaveGANDiscriminator, self).__init__()
-        assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
-        assert dilation_factor > 0, "Dilation factor must be > 0."
-        self.conv_layers = torch.nn.ModuleList()
-        conv_in_channels = in_channels
-        for i in range(layers - 1):
-            if i == 0:
-                dilation = 1
-            else:
-                dilation = i if dilation_factor == 1 else dilation_factor ** i
-                conv_in_channels = conv_channels
-            padding = (kernel_size - 1) // 2 * dilation
-            conv_layer = [
-                Conv1d(conv_in_channels, conv_channels,
-                       kernel_size=kernel_size, padding=padding,
-                       dilation=dilation, bias=bias),
-                getattr(torch.nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params)
-            ]
-            self.conv_layers += conv_layer
-        padding = (kernel_size - 1) // 2
-        last_conv_layer = Conv1d(
-            conv_in_channels, out_channels,
-            kernel_size=kernel_size, padding=padding, bias=bias)
-        self.conv_layers += [last_conv_layer]
-
-        # apply weight norm
-        if use_weight_norm:
-            self.apply_weight_norm()
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input noise signal (B, 1, T).
-
-        Returns:
-            Tensor: Output tensor (B, 1, T)
-
-        """
-        for f in self.conv_layers:
-            x = f(x)
-        return x
-
-    def apply_weight_norm(self):
-        """Apply weight normalization module from all of the layers."""
-        def _apply_weight_norm(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
-                torch.nn.utils.weight_norm(m)
-                logging.debug(f"Weight norm is applied to {m}.")
-
-        self.apply(_apply_weight_norm)
-
-    def remove_weight_norm(self):
-        """Remove weight normalization module from all of the layers."""
-        def _remove_weight_norm(m):
-            try:
-                logging.debug(f"Weight norm is removed from {m}.")
-                torch.nn.utils.remove_weight_norm(m)
-            except ValueError:  # this module didn't have weight norm
-                return
-
-        self.apply(_remove_weight_norm)
-
-
-class ResidualParallelWaveGANDiscriminator(torch.nn.Module):
-    """Parallel WaveGAN Discriminator module."""
-
-    def __init__(self,
-                 in_channels=1,
-                 out_channels=1,
-                 kernel_size=3,
-                 layers=30,
-                 stacks=3,
-                 residual_channels=64,
-                 gate_channels=128,
-                 skip_channels=64,
-                 dropout=0.0,
-                 bias=True,
-                 use_weight_norm=True,
-                 use_causal_conv=False,
-                 nonlinear_activation="LeakyReLU",
-                 nonlinear_activation_params={"negative_slope": 0.2},
-                 ):
-        """Initialize Parallel WaveGAN Discriminator module.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            kernel_size (int): Kernel size of dilated convolution.
-            layers (int): Number of residual block layers.
-            stacks (int): Number of stacks i.e., dilation cycles.
-            residual_channels (int): Number of channels in residual conv.
-            gate_channels (int):  Number of channels in gated conv.
-            skip_channels (int): Number of channels in skip conv.
-            dropout (float): Dropout rate. 0.0 means no dropout applied.
-            bias (bool): Whether to use bias parameter in conv.
-            use_weight_norm (bool): Whether to use weight norm.
-                If set to true, it will be applied to all of the conv layers.
-            use_causal_conv (bool): Whether to use causal structure.
-            nonlinear_activation_params (dict): Nonlinear function parameters
-
-        """
-        super(ResidualParallelWaveGANDiscriminator, self).__init__()
-        assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.layers = layers
-        self.stacks = stacks
-        self.kernel_size = kernel_size
-
-        # check the number of layers and stacks
-        assert layers % stacks == 0
-        layers_per_stack = layers // stacks
-
-        # define first convolution
-        self.first_conv = torch.nn.Sequential(
-            Conv1d1x1(in_channels, residual_channels, bias=True),
-            getattr(torch.nn, nonlinear_activation)(
-                inplace=True, **nonlinear_activation_params),
-        )
-
-        # define residual blocks
-        self.conv_layers = torch.nn.ModuleList()
-        for layer in range(layers):
-            dilation = 2 ** (layer % layers_per_stack)
-            conv = ResidualBlock(
-                kernel_size=kernel_size,
-                residual_channels=residual_channels,
-                gate_channels=gate_channels,
-                skip_channels=skip_channels,
-                aux_channels=-1,
-                dilation=dilation,
-                dropout=dropout,
-                bias=bias,
-                use_causal_conv=use_causal_conv,
-            )
-            self.conv_layers += [conv]
-
-        # define output layers
-        self.last_conv_layers = torch.nn.ModuleList([
-            getattr(torch.nn, nonlinear_activation)(
-                inplace=True, **nonlinear_activation_params),
-            Conv1d1x1(skip_channels, skip_channels, bias=True),
-            getattr(torch.nn, nonlinear_activation)(
-                inplace=True, **nonlinear_activation_params),
-            Conv1d1x1(skip_channels, out_channels, bias=True),
-        ])
-
-        # apply weight norm
-        if use_weight_norm:
-            self.apply_weight_norm()
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input noise signal (B, 1, T).
-
-        Returns:
-            Tensor: Output tensor (B, 1, T)
-
-        """
-        x = self.first_conv(x)
-
-        skips = 0
-        for f in self.conv_layers:
-            x, h = f(x, None)
-            skips += h
-        skips *= math.sqrt(1.0 / len(self.conv_layers))
-
-        # apply final layers
-        x = skips
-        for f in self.last_conv_layers:
-            x = f(x)
-        return x
-
-    def apply_weight_norm(self):
-        """Apply weight normalization module from all of the layers."""
-        def _apply_weight_norm(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
-                torch.nn.utils.weight_norm(m)
-                logging.debug(f"Weight norm is applied to {m}.")
-
-        self.apply(_apply_weight_norm)
-
-    def remove_weight_norm(self):
-        """Remove weight normalization module from all of the layers."""
-        def _remove_weight_norm(m):
-            try:
-                logging.debug(f"Weight norm is removed from {m}.")
-                torch.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/Rubens/semantic_similarity/README.md b/spaces/Rubens/semantic_similarity/README.md
deleted file mode 100644
index f3d02069692c2dc55011d407d9e1239bbb5be1f1..0000000000000000000000000000000000000000
--- a/spaces/Rubens/semantic_similarity/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Semantic Similarity
-emoji: 🐢
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SERER/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py b/spaces/SERER/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/SERER/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/SIVAPRASATH/tamil-translator/app.py b/spaces/SIVAPRASATH/tamil-translator/app.py
deleted file mode 100644
index 311405a7a875a7815be9db93b2a6ec6a173b7401..0000000000000000000000000000000000000000
--- a/spaces/SIVAPRASATH/tamil-translator/app.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import gradio as gr
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast,MBartTokenizerFast,MBart50Tokenizer
-
-
-from transformers import MBartTokenizer,MBartForConditionalGeneration, MBartConfig
-model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
-tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt",src_lang="en_XX")
-
-def get_input(text):
-  models_input = tokenizer(text,return_tensors="pt")
-  generated_tokens = model.generate(**models_input,forced_bos_token_id=tokenizer.lang_code_to_id["ta_IN"])
-  translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-  return translation
-  
-iface = gr.Interface(fn=get_input,inputs="text",outputs="text", title = "English to Tamil Translator",description="Get Tamil translation for your text in English")  
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/SRankChatGpt/Presentation-Assistant/README.md b/spaces/SRankChatGpt/Presentation-Assistant/README.md
deleted file mode 100644
index 74e79008f24ac2a0c022f072715b5ebf81f313e0..0000000000000000000000000000000000000000
--- a/spaces/SRankChatGpt/Presentation-Assistant/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PA(Presentation Assistant)
-emoji: 🧑‍🏫
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-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/SantoshKumar/SD-H5-AR-VR-IOT/style.css b/spaces/SantoshKumar/SD-H5-AR-VR-IOT/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/SantoshKumar/SD-H5-AR-VR-IOT/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js b/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js
deleted file mode 100644
index 85296fbcdf93ad959ffc0963af842b8640cbb27e..0000000000000000000000000000000000000000
--- a/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/SeyedAli/Musical-genres-Detection/README.md b/spaces/SeyedAli/Musical-genres-Detection/README.md
deleted file mode 100644
index 06e02f3cb0490d9dbd0ee98814aa702a1f4f6348..0000000000000000000000000000000000000000
--- a/spaces/SeyedAli/Musical-genres-Detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Musical Genres Detection
-emoji: 🎵
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.4
-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/Shad0ws/AI-Agent-with-Google-Search-APIs/src/baby_agi.py b/spaces/Shad0ws/AI-Agent-with-Google-Search-APIs/src/baby_agi.py
deleted file mode 100644
index 3011e659a283e2bad3139969c56f20df40073e55..0000000000000000000000000000000000000000
--- a/spaces/Shad0ws/AI-Agent-with-Google-Search-APIs/src/baby_agi.py
+++ /dev/null
@@ -1,221 +0,0 @@
-from langchain.vectorstores.base import VectorStore
-from pydantic import BaseModel, Field
-from langchain.chains.base import Chain
-from collections import deque
-from typing import Dict, List, Optional, Any
-from langchain.agents import ZeroShotAgent, AgentExecutor
-from src.task_creation_chain import TaskCreationChain
-from src.task_prio_chain import TaskPrioritizationChain
-import streamlit as st
-from langchain import LLMChain
-from langchain.llms import BaseLLM
-
-# -----------------helpers
-
-def get_next_task(
-    task_creation_chain: LLMChain,
-    result: Dict,
-    task_description: str,
-    task_list: List[str],
-    objective: str,
-) -> List[Dict]:
-    """Get the next task."""
-    incomplete_tasks = ", ".join(task_list)
-    response = task_creation_chain.run(
-        result=result,
-        task_description=task_description,
-        incomplete_tasks=incomplete_tasks,
-        objective=objective,
-    )
-    new_tasks = response.split("\n")
-    return [{"task_name": task_name} for task_name in new_tasks if task_name.strip()]
-
-def prioritize_tasks(
-    task_prioritization_chain: LLMChain,
-    this_task_id: int,
-    task_list: List[Dict],
-    objective: str,
-) -> List[Dict]:
-    """Prioritize tasks."""
-    task_names = [t["task_name"] for t in task_list]
-    next_task_id = int(this_task_id) + 1
-    response = task_prioritization_chain.run(
-        task_names=task_names, next_task_id=next_task_id, objective=objective
-    )
-    new_tasks = response.split("\n")
-    prioritized_task_list = []
-    for task_string in new_tasks:
-        if not task_string.strip():
-            continue
-        task_parts = task_string.strip().split(".", 1)
-        if len(task_parts) == 2:
-            task_id = task_parts[0].strip()
-            task_name = task_parts[1].strip()
-            prioritized_task_list.append({"task_id": task_id, "task_name": task_name})
-    return prioritized_task_list
-
-def _get_top_tasks(vectorstore, query: str, k: int) -> List[str]:
-    """Get the top k tasks based on the query."""
-    results = vectorstore.similarity_search_with_score(query, k=k)
-    if not results:
-        return []
-    sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))
-    return [str(item.metadata["task"]) for item in sorted_results]
-
-
-def execute_task(
-    vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5
-) -> str:
-    """Execute a task."""
-    context = _get_top_tasks(vectorstore, query=objective, k=k)
-    return execution_chain.run(objective=objective, context=context, task=task)
-
-
-# ---------------Class-------------
-
-
-class BabyAGI(Chain, BaseModel):
-    """Controller model for the BabyAGI agent."""
-
-    task_list: deque = Field(default_factory=deque)
-    task_creation_chain: TaskCreationChain = Field(...)
-    task_prioritization_chain: TaskPrioritizationChain = Field(...)
-    execution_chain: AgentExecutor = Field(...)
-    task_id_counter: int = Field(1)
-    vectorstore: VectorStore = Field(init=False)
-    max_iterations: Optional[int] = None
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        arbitrary_types_allowed = True
-
-    def add_task(self, task: Dict):
-        self.task_list.append(task)
-
-    def print_task_list(self):
-        print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m")
-        if len(self.task_list) > 1:
-            st.write('**Task List:** \n')
-        for t in self.task_list:
-            print(str(t["task_id"]) + ": " + t["task_name"])
-            if len(self.task_list) > 1:
-                st.write(str(t["task_id"]) + ": " + t["task_name"])
-
-    def print_next_task(self, task: Dict):
-        print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
-        print(str(task["task_name"]))
-        return (str(task["task_name"]))
-
-    def print_task_result(self, result: str):
-        print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m")
-        return(result)
-
-    @property
-    def input_keys(self) -> List[str]:
-        return ["objective"]
-
-    @property
-    def output_keys(self) -> List[str]:
-        return []
-    
-    def _call(_self, inputs: Dict[str, Any]) -> Dict[str, Any]:
-        result_list = []
-        """Run the agent."""
-        objective = inputs["objective"]
-        first_task = inputs.get("first_task", f"Make a todo list to accomplish the objective: {objective}")
-        _self.add_task({"task_id": 1, "task_name": first_task})
-        num_iters = 0
-        while True:
-            if _self.task_list:
-                _self.print_task_list()
-
-                # Step 1: Pull the first task
-                task = _self.task_list.popleft()
-                _self.print_next_task(task)
-                st.write('**Next Task:** \n')
-                st.write(_self.print_next_task(task))
-
-                # Step 2: Execute the task
-                result = execute_task(
-                    _self.vectorstore, _self.execution_chain, objective, task["task_name"]
-                )
-                this_task_id = int(task["task_id"])
-                _self.print_task_result(result)
-                st.write('**Result from Task:** \n')
-                st.write(_self.print_task_result(result))
-                result_list.append(result)
-
-                # Step 3: Store the result in Pinecone
-                result_id = f"result_{task['task_id']}"
-                _self.vectorstore.add_texts(
-                    texts=[result],
-                    metadatas=[{"task": task["task_name"]}],
-                    ids=[result_id],
-                )
-
-                # Step 4: Create new tasks and reprioritize task list
-                new_tasks = get_next_task(
-                    _self.task_creation_chain,
-                    result,
-                    task["task_name"],
-                    [t["task_name"] for t in _self.task_list],
-                    objective,
-                )
-                for new_task in new_tasks:
-                    _self.task_id_counter += 1
-                    new_task.update({"task_id": _self.task_id_counter})
-                    _self.add_task(new_task)
-                _self.task_list = deque(
-                    prioritize_tasks(
-                        _self.task_prioritization_chain,
-                        this_task_id,
-                        list(_self.task_list),
-                        objective,
-                    )
-                )
-            num_iters += 1
-            if _self.max_iterations is not None and num_iters == _self.max_iterations:
-                print(
-                    "\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m"
-                )
-                st.success('Task Completed!', icon="✅")
-                break
-
-        # Create a temporary file to hold the text
-        with open('output.txt', 'w') as f:
-            for item in result_list:
-                f.write(item)
-                f.write("\n\n")
-
-        return {}
-
-    @classmethod
-    def from_llm(
-        cls, 
-        prompt: str,
-        tools: list,
-        llm: BaseLLM, 
-        vectorstore: VectorStore, 
-        verbose: bool = False, 
-        **kwargs
-    ) -> "BabyAGI":
-        """Initialize the BabyAGI Controller."""
-        task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
-        task_prioritization_chain = TaskPrioritizationChain.from_llm(
-            llm, verbose=verbose
-        )
-        llm_chain = LLMChain(llm=llm, prompt=prompt)
-        tool_names = [tool.name for tool in tools]
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
-        agent_executor = AgentExecutor.from_agent_and_tools(
-            agent=agent, tools=tools, verbose=True
-        )
-        return cls(
-            task_creation_chain=task_creation_chain,
-            task_prioritization_chain=task_prioritization_chain,
-            execution_chain=agent_executor,
-            vectorstore=vectorstore,
-            **kwargs,
-        )
-    
\ No newline at end of file
diff --git a/spaces/Siddhant/ESPnet2-SLU/README.md b/spaces/Siddhant/ESPnet2-SLU/README.md
deleted file mode 100644
index 751e74621f74665d26ed2c634da021a0732f63a8..0000000000000000000000000000000000000000
--- a/spaces/Siddhant/ESPnet2-SLU/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: ESPnet2 SLU
-emoji: 📈
-colorFrom: green
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` 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/SujanMidatani/speechToText/README.md b/spaces/SujanMidatani/speechToText/README.md
deleted file mode 100644
index 413d0b0795f773aae321a793ccc71fc5e2bce35c..0000000000000000000000000000000000000000
--- a/spaces/SujanMidatani/speechToText/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SpeechToText
-emoji: 🐢
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/shortcuts/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/shortcuts/__init__.py
deleted file mode 100644
index 12890f4ab6e57dfa3393554e532783e0e05de91c..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/shortcuts/__init__.py
+++ /dev/null
@@ -1,630 +0,0 @@
-"""
-Module to define and register Terminal IPython shortcuts with
-:mod:`prompt_toolkit`
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-import os
-import signal
-import sys
-import warnings
-from dataclasses import dataclass
-from typing import Callable, Any, Optional, List
-
-from prompt_toolkit.application.current import get_app
-from prompt_toolkit.key_binding import KeyBindings
-from prompt_toolkit.key_binding.key_processor import KeyPressEvent
-from prompt_toolkit.key_binding.bindings import named_commands as nc
-from prompt_toolkit.key_binding.bindings.completion import (
-    display_completions_like_readline,
-)
-from prompt_toolkit.key_binding.vi_state import InputMode, ViState
-from prompt_toolkit.filters import Condition
-
-from IPython.core.getipython import get_ipython
-from IPython.terminal.shortcuts import auto_match as match
-from IPython.terminal.shortcuts import auto_suggest
-from IPython.terminal.shortcuts.filters import filter_from_string
-from IPython.utils.decorators import undoc
-
-from prompt_toolkit.enums import DEFAULT_BUFFER
-
-__all__ = ["create_ipython_shortcuts"]
-
-
-@dataclass
-class BaseBinding:
-    command: Callable[[KeyPressEvent], Any]
-    keys: List[str]
-
-
-@dataclass
-class RuntimeBinding(BaseBinding):
-    filter: Condition
-
-
-@dataclass
-class Binding(BaseBinding):
-    # while filter could be created by referencing variables directly (rather
-    # than created from strings), by using strings we ensure that users will
-    # be able to create filters in configuration (e.g. JSON) files too, which
-    # also benefits the documentation by enforcing human-readable filter names.
-    condition: Optional[str] = None
-
-    def __post_init__(self):
-        if self.condition:
-            self.filter = filter_from_string(self.condition)
-        else:
-            self.filter = None
-
-
-def create_identifier(handler: Callable):
-    parts = handler.__module__.split(".")
-    name = handler.__name__
-    package = parts[0]
-    if len(parts) > 1:
-        final_module = parts[-1]
-        return f"{package}:{final_module}.{name}"
-    else:
-        return f"{package}:{name}"
-
-
-AUTO_MATCH_BINDINGS = [
-    *[
-        Binding(
-            cmd, [key], "focused_insert & auto_match & followed_by_closing_paren_or_end"
-        )
-        for key, cmd in match.auto_match_parens.items()
-    ],
-    *[
-        # raw string
-        Binding(cmd, [key], "focused_insert & auto_match & preceded_by_raw_str_prefix")
-        for key, cmd in match.auto_match_parens_raw_string.items()
-    ],
-    Binding(
-        match.double_quote,
-        ['"'],
-        "focused_insert"
-        " & auto_match"
-        " & not_inside_unclosed_string"
-        " & preceded_by_paired_double_quotes"
-        " & followed_by_closing_paren_or_end",
-    ),
-    Binding(
-        match.single_quote,
-        ["'"],
-        "focused_insert"
-        " & auto_match"
-        " & not_inside_unclosed_string"
-        " & preceded_by_paired_single_quotes"
-        " & followed_by_closing_paren_or_end",
-    ),
-    Binding(
-        match.docstring_double_quotes,
-        ['"'],
-        "focused_insert"
-        " & auto_match"
-        " & not_inside_unclosed_string"
-        " & preceded_by_two_double_quotes",
-    ),
-    Binding(
-        match.docstring_single_quotes,
-        ["'"],
-        "focused_insert"
-        " & auto_match"
-        " & not_inside_unclosed_string"
-        " & preceded_by_two_single_quotes",
-    ),
-    Binding(
-        match.skip_over,
-        [")"],
-        "focused_insert & auto_match & followed_by_closing_round_paren",
-    ),
-    Binding(
-        match.skip_over,
-        ["]"],
-        "focused_insert & auto_match & followed_by_closing_bracket",
-    ),
-    Binding(
-        match.skip_over,
-        ["}"],
-        "focused_insert & auto_match & followed_by_closing_brace",
-    ),
-    Binding(
-        match.skip_over, ['"'], "focused_insert & auto_match & followed_by_double_quote"
-    ),
-    Binding(
-        match.skip_over, ["'"], "focused_insert & auto_match & followed_by_single_quote"
-    ),
-    Binding(
-        match.delete_pair,
-        ["backspace"],
-        "focused_insert"
-        " & preceded_by_opening_round_paren"
-        " & auto_match"
-        " & followed_by_closing_round_paren",
-    ),
-    Binding(
-        match.delete_pair,
-        ["backspace"],
-        "focused_insert"
-        " & preceded_by_opening_bracket"
-        " & auto_match"
-        " & followed_by_closing_bracket",
-    ),
-    Binding(
-        match.delete_pair,
-        ["backspace"],
-        "focused_insert"
-        " & preceded_by_opening_brace"
-        " & auto_match"
-        " & followed_by_closing_brace",
-    ),
-    Binding(
-        match.delete_pair,
-        ["backspace"],
-        "focused_insert"
-        " & preceded_by_double_quote"
-        " & auto_match"
-        " & followed_by_double_quote",
-    ),
-    Binding(
-        match.delete_pair,
-        ["backspace"],
-        "focused_insert"
-        " & preceded_by_single_quote"
-        " & auto_match"
-        " & followed_by_single_quote",
-    ),
-]
-
-AUTO_SUGGEST_BINDINGS = [
-    # there are two reasons for re-defining bindings defined upstream:
-    # 1) prompt-toolkit does not execute autosuggestion bindings in vi mode,
-    # 2) prompt-toolkit checks if we are at the end of text, not end of line
-    #    hence it does not work in multi-line mode of navigable provider
-    Binding(
-        auto_suggest.accept_or_jump_to_end,
-        ["end"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.accept_or_jump_to_end,
-        ["c-e"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.accept,
-        ["c-f"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.accept,
-        ["right"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.accept_word,
-        ["escape", "f"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.accept_token,
-        ["c-right"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.discard,
-        ["escape"],
-        # note this one is using `emacs_insert_mode`, not `emacs_like_insert_mode`
-        # as in `vi_insert_mode` we do not want `escape` to be shadowed (ever).
-        "has_suggestion & default_buffer_focused & emacs_insert_mode",
-    ),
-    Binding(
-        auto_suggest.discard,
-        ["delete"],
-        "has_suggestion & default_buffer_focused & emacs_insert_mode",
-    ),
-    Binding(
-        auto_suggest.swap_autosuggestion_up,
-        ["c-up"],
-        "navigable_suggestions"
-        " & ~has_line_above"
-        " & has_suggestion"
-        " & default_buffer_focused",
-    ),
-    Binding(
-        auto_suggest.swap_autosuggestion_down,
-        ["c-down"],
-        "navigable_suggestions"
-        " & ~has_line_below"
-        " & has_suggestion"
-        " & default_buffer_focused",
-    ),
-    Binding(
-        auto_suggest.up_and_update_hint,
-        ["c-up"],
-        "has_line_above & navigable_suggestions & default_buffer_focused",
-    ),
-    Binding(
-        auto_suggest.down_and_update_hint,
-        ["c-down"],
-        "has_line_below & navigable_suggestions & default_buffer_focused",
-    ),
-    Binding(
-        auto_suggest.accept_character,
-        ["escape", "right"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.accept_and_move_cursor_left,
-        ["c-left"],
-        "has_suggestion & default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.accept_and_keep_cursor,
-        ["escape", "down"],
-        "has_suggestion & default_buffer_focused & emacs_insert_mode",
-    ),
-    Binding(
-        auto_suggest.backspace_and_resume_hint,
-        ["backspace"],
-        # no `has_suggestion` here to allow resuming if no suggestion
-        "default_buffer_focused & emacs_like_insert_mode",
-    ),
-    Binding(
-        auto_suggest.resume_hinting,
-        ["right"],
-        "is_cursor_at_the_end_of_line"
-        " & default_buffer_focused"
-        " & emacs_like_insert_mode"
-        " & pass_through",
-    ),
-]
-
-
-SIMPLE_CONTROL_BINDINGS = [
-    Binding(cmd, [key], "vi_insert_mode & default_buffer_focused & ebivim")
-    for key, cmd in {
-        "c-a": nc.beginning_of_line,
-        "c-b": nc.backward_char,
-        "c-k": nc.kill_line,
-        "c-w": nc.backward_kill_word,
-        "c-y": nc.yank,
-        "c-_": nc.undo,
-    }.items()
-]
-
-
-ALT_AND_COMOBO_CONTROL_BINDINGS = [
-    Binding(cmd, list(keys), "vi_insert_mode & default_buffer_focused & ebivim")
-    for keys, cmd in {
-        # Control Combos
-        ("c-x", "c-e"): nc.edit_and_execute,
-        ("c-x", "e"): nc.edit_and_execute,
-        # Alt
-        ("escape", "b"): nc.backward_word,
-        ("escape", "c"): nc.capitalize_word,
-        ("escape", "d"): nc.kill_word,
-        ("escape", "h"): nc.backward_kill_word,
-        ("escape", "l"): nc.downcase_word,
-        ("escape", "u"): nc.uppercase_word,
-        ("escape", "y"): nc.yank_pop,
-        ("escape", "."): nc.yank_last_arg,
-    }.items()
-]
-
-
-def add_binding(bindings: KeyBindings, binding: Binding):
-    bindings.add(
-        *binding.keys,
-        **({"filter": binding.filter} if binding.filter is not None else {}),
-    )(binding.command)
-
-
-def create_ipython_shortcuts(shell, skip=None) -> KeyBindings:
-    """Set up the prompt_toolkit keyboard shortcuts for IPython.
-
-    Parameters
-    ----------
-    shell: InteractiveShell
-        The current IPython shell Instance
-    skip: List[Binding]
-        Bindings to skip.
-
-    Returns
-    -------
-    KeyBindings
-        the keybinding instance for prompt toolkit.
-
-    """
-    kb = KeyBindings()
-    skip = skip or []
-    for binding in KEY_BINDINGS:
-        skip_this_one = False
-        for to_skip in skip:
-            if (
-                to_skip.command == binding.command
-                and to_skip.filter == binding.filter
-                and to_skip.keys == binding.keys
-            ):
-                skip_this_one = True
-                break
-        if skip_this_one:
-            continue
-        add_binding(kb, binding)
-
-    def get_input_mode(self):
-        app = get_app()
-        app.ttimeoutlen = shell.ttimeoutlen
-        app.timeoutlen = shell.timeoutlen
-
-        return self._input_mode
-
-    def set_input_mode(self, mode):
-        shape = {InputMode.NAVIGATION: 2, InputMode.REPLACE: 4}.get(mode, 6)
-        cursor = "\x1b[{} q".format(shape)
-
-        sys.stdout.write(cursor)
-        sys.stdout.flush()
-
-        self._input_mode = mode
-
-    if shell.editing_mode == "vi" and shell.modal_cursor:
-        ViState._input_mode = InputMode.INSERT  # type: ignore
-        ViState.input_mode = property(get_input_mode, set_input_mode)  # type: ignore
-
-    return kb
-
-
-def reformat_and_execute(event):
-    """Reformat code and execute it"""
-    shell = get_ipython()
-    reformat_text_before_cursor(
-        event.current_buffer, event.current_buffer.document, shell
-    )
-    event.current_buffer.validate_and_handle()
-
-
-def reformat_text_before_cursor(buffer, document, shell):
-    text = buffer.delete_before_cursor(len(document.text[: document.cursor_position]))
-    try:
-        formatted_text = shell.reformat_handler(text)
-        buffer.insert_text(formatted_text)
-    except Exception as e:
-        buffer.insert_text(text)
-
-
-def handle_return_or_newline_or_execute(event):
-    shell = get_ipython()
-    if getattr(shell, "handle_return", None):
-        return shell.handle_return(shell)(event)
-    else:
-        return newline_or_execute_outer(shell)(event)
-
-
-def newline_or_execute_outer(shell):
-    def newline_or_execute(event):
-        """When the user presses return, insert a newline or execute the code."""
-        b = event.current_buffer
-        d = b.document
-
-        if b.complete_state:
-            cc = b.complete_state.current_completion
-            if cc:
-                b.apply_completion(cc)
-            else:
-                b.cancel_completion()
-            return
-
-        # If there's only one line, treat it as if the cursor is at the end.
-        # See https://github.com/ipython/ipython/issues/10425
-        if d.line_count == 1:
-            check_text = d.text
-        else:
-            check_text = d.text[: d.cursor_position]
-        status, indent = shell.check_complete(check_text)
-
-        # if all we have after the cursor is whitespace: reformat current text
-        # before cursor
-        after_cursor = d.text[d.cursor_position :]
-        reformatted = False
-        if not after_cursor.strip():
-            reformat_text_before_cursor(b, d, shell)
-            reformatted = True
-        if not (
-            d.on_last_line
-            or d.cursor_position_row >= d.line_count - d.empty_line_count_at_the_end()
-        ):
-            if shell.autoindent:
-                b.insert_text("\n" + indent)
-            else:
-                b.insert_text("\n")
-            return
-
-        if (status != "incomplete") and b.accept_handler:
-            if not reformatted:
-                reformat_text_before_cursor(b, d, shell)
-            b.validate_and_handle()
-        else:
-            if shell.autoindent:
-                b.insert_text("\n" + indent)
-            else:
-                b.insert_text("\n")
-
-    return newline_or_execute
-
-
-def previous_history_or_previous_completion(event):
-    """
-    Control-P in vi edit mode on readline is history next, unlike default prompt toolkit.
-
-    If completer is open this still select previous completion.
-    """
-    event.current_buffer.auto_up()
-
-
-def next_history_or_next_completion(event):
-    """
-    Control-N in vi edit mode on readline is history previous, unlike default prompt toolkit.
-
-    If completer is open this still select next completion.
-    """
-    event.current_buffer.auto_down()
-
-
-def dismiss_completion(event):
-    """Dismiss completion"""
-    b = event.current_buffer
-    if b.complete_state:
-        b.cancel_completion()
-
-
-def reset_buffer(event):
-    """Reset buffer"""
-    b = event.current_buffer
-    if b.complete_state:
-        b.cancel_completion()
-    else:
-        b.reset()
-
-
-def reset_search_buffer(event):
-    """Reset search buffer"""
-    if event.current_buffer.document.text:
-        event.current_buffer.reset()
-    else:
-        event.app.layout.focus(DEFAULT_BUFFER)
-
-
-def suspend_to_bg(event):
-    """Suspend to background"""
-    event.app.suspend_to_background()
-
-
-def quit(event):
-    """
-    Quit application with ``SIGQUIT`` if supported or ``sys.exit`` otherwise.
-
-    On platforms that support SIGQUIT, send SIGQUIT to the current process.
-    On other platforms, just exit the process with a message.
-    """
-    sigquit = getattr(signal, "SIGQUIT", None)
-    if sigquit is not None:
-        os.kill(0, signal.SIGQUIT)
-    else:
-        sys.exit("Quit")
-
-
-def indent_buffer(event):
-    """Indent buffer"""
-    event.current_buffer.insert_text(" " * 4)
-
-
-def newline_autoindent(event):
-    """Insert a newline after the cursor indented appropriately.
-
-    Fancier version of former ``newline_with_copy_margin`` which should
-    compute the correct indentation of the inserted line. That is to say, indent
-    by 4 extra space after a function definition, class definition, context
-    manager... And dedent by 4 space after ``pass``, ``return``, ``raise ...``.
-    """
-    shell = get_ipython()
-    inputsplitter = shell.input_transformer_manager
-    b = event.current_buffer
-    d = b.document
-
-    if b.complete_state:
-        b.cancel_completion()
-    text = d.text[: d.cursor_position] + "\n"
-    _, indent = inputsplitter.check_complete(text)
-    b.insert_text("\n" + (" " * (indent or 0)), move_cursor=False)
-
-
-def open_input_in_editor(event):
-    """Open code from input in external editor"""
-    event.app.current_buffer.open_in_editor()
-
-
-if sys.platform == "win32":
-    from IPython.core.error import TryNext
-    from IPython.lib.clipboard import (
-        ClipboardEmpty,
-        tkinter_clipboard_get,
-        win32_clipboard_get,
-    )
-
-    @undoc
-    def win_paste(event):
-        try:
-            text = win32_clipboard_get()
-        except TryNext:
-            try:
-                text = tkinter_clipboard_get()
-            except (TryNext, ClipboardEmpty):
-                return
-        except ClipboardEmpty:
-            return
-        event.current_buffer.insert_text(text.replace("\t", " " * 4))
-
-else:
-
-    @undoc
-    def win_paste(event):
-        """Stub used on other platforms"""
-        pass
-
-
-KEY_BINDINGS = [
-    Binding(
-        handle_return_or_newline_or_execute,
-        ["enter"],
-        "default_buffer_focused & ~has_selection & insert_mode",
-    ),
-    Binding(
-        reformat_and_execute,
-        ["escape", "enter"],
-        "default_buffer_focused & ~has_selection & insert_mode & ebivim",
-    ),
-    Binding(quit, ["c-\\"]),
-    Binding(
-        previous_history_or_previous_completion,
-        ["c-p"],
-        "vi_insert_mode & default_buffer_focused",
-    ),
-    Binding(
-        next_history_or_next_completion,
-        ["c-n"],
-        "vi_insert_mode & default_buffer_focused",
-    ),
-    Binding(dismiss_completion, ["c-g"], "default_buffer_focused & has_completions"),
-    Binding(reset_buffer, ["c-c"], "default_buffer_focused"),
-    Binding(reset_search_buffer, ["c-c"], "search_buffer_focused"),
-    Binding(suspend_to_bg, ["c-z"], "supports_suspend"),
-    Binding(
-        indent_buffer,
-        ["tab"],  # Ctrl+I == Tab
-        "default_buffer_focused"
-        " & ~has_selection"
-        " & insert_mode"
-        " & cursor_in_leading_ws",
-    ),
-    Binding(newline_autoindent, ["c-o"], "default_buffer_focused & emacs_insert_mode"),
-    Binding(open_input_in_editor, ["f2"], "default_buffer_focused"),
-    *AUTO_MATCH_BINDINGS,
-    *AUTO_SUGGEST_BINDINGS,
-    Binding(
-        display_completions_like_readline,
-        ["c-i"],
-        "readline_like_completions"
-        " & default_buffer_focused"
-        " & ~has_selection"
-        " & insert_mode"
-        " & ~cursor_in_leading_ws",
-    ),
-    Binding(win_paste, ["c-v"], "default_buffer_focused & ~vi_mode & is_windows_os"),
-    *SIMPLE_CONTROL_BINDINGS,
-    *ALT_AND_COMOBO_CONTROL_BINDINGS,
-]
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/version.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/version.py
deleted file mode 100644
index 1cce4e50bd692d4002e3cac3c545a3fb2efe95d0..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/version.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-__version__ = '1.3.17'
-
-
-def parse_version_info(version_str: str, length: int = 4) -> tuple:
-    """Parse a version string into a tuple.
-
-    Args:
-        version_str (str): The version string.
-        length (int): The maximum number of version levels. Default: 4.
-
-    Returns:
-        tuple[int | str]: The version info, e.g., "1.3.0" is parsed into
-            (1, 3, 0, 0, 0, 0), and "2.0.0rc1" is parsed into
-            (2, 0, 0, 0, 'rc', 1) (when length is set to 4).
-    """
-    from packaging.version import parse
-    version = parse(version_str)
-    assert version.release, f'failed to parse version {version_str}'
-    release = list(version.release)
-    release = release[:length]
-    if len(release) < length:
-        release = release + [0] * (length - len(release))
-    if version.is_prerelease:
-        release.extend(list(version.pre))
-    elif version.is_postrelease:
-        release.extend(list(version.post))
-    else:
-        release.extend([0, 0])
-    return tuple(release)
-
-
-version_info = tuple(int(x) for x in __version__.split('.')[:3])
-
-__all__ = ['__version__', 'version_info', 'parse_version_info']
diff --git a/spaces/TEnngal/bingo/src/components/chat-attachments.tsx b/spaces/TEnngal/bingo/src/components/chat-attachments.tsx
deleted file mode 100644
index ef43d4e262935d263b6099138c56f7daade5299d..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/components/chat-attachments.tsx
+++ /dev/null
@@ -1,37 +0,0 @@
-import Image from 'next/image'
-import ClearIcon from '@/assets/images/clear.svg'
-import RefreshIcon from '@/assets/images/refresh.svg'
-import { FileItem } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-import { useBing } from '@/lib/hooks/use-bing'
-
-type ChatAttachmentsProps = Pick<ReturnType<typeof useBing>, 'attachmentList' | 'setAttachmentList' | 'uploadImage'>
-
-export function ChatAttachments({ attachmentList = [], setAttachmentList, uploadImage }: ChatAttachmentsProps) {
-  return attachmentList.length ? (
-    <div className="attachment-list">
-      {attachmentList.map(file => (
-        <div className="file-item" key={file.url}>
-          {file.status === 'loading' && (
-            <div className="loading">
-              <div className="bar" />
-            </div>)
-          }
-          {file.status !== 'error' && (
-            <div className="thumbnail">
-              <img draggable="false" src={file.url} />
-            </div>)
-          }
-          {file.status === 'error' && (
-            <div className="error">
-              <Image alt="refresh" src={RefreshIcon} width={18} onClick={() => uploadImage(file.url)} />
-            </div>
-          )}
-          <button className={cn('dismiss', { 'no-file': file.status === 'error' })} type="button">
-            <Image alt="clear" src={ClearIcon} width={16} onClick={() => setAttachmentList([])} />
-          </button>
-        </div>
-      ))}
-    </div>
-  ) : null
-}
diff --git a/spaces/TEnngal/bingo/src/components/toaster.tsx b/spaces/TEnngal/bingo/src/components/toaster.tsx
deleted file mode 100644
index 4d2693460b61307a1d4c127fd01df9bee16e59ff..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/components/toaster.tsx
+++ /dev/null
@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distro/__main__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distro/__main__.py
deleted file mode 100644
index 0c01d5b08b6b44379b931d54d7fcf5221fdc9fde..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distro/__main__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .distro import main
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/proxy.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/proxy.py
deleted file mode 100644
index 2199cc7b7f004009493d032720c36d6568f9d89e..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/proxy.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from .ssl_ import create_urllib3_context, resolve_cert_reqs, resolve_ssl_version
-
-
-def connection_requires_http_tunnel(
-    proxy_url=None, proxy_config=None, destination_scheme=None
-):
-    """
-    Returns True if the connection requires an HTTP CONNECT through the proxy.
-
-    :param URL proxy_url:
-        URL of the proxy.
-    :param ProxyConfig proxy_config:
-        Proxy configuration from poolmanager.py
-    :param str destination_scheme:
-        The scheme of the destination. (i.e https, http, etc)
-    """
-    # If we're not using a proxy, no way to use a tunnel.
-    if proxy_url is None:
-        return False
-
-    # HTTP destinations never require tunneling, we always forward.
-    if destination_scheme == "http":
-        return False
-
-    # Support for forwarding with HTTPS proxies and HTTPS destinations.
-    if (
-        proxy_url.scheme == "https"
-        and proxy_config
-        and proxy_config.use_forwarding_for_https
-    ):
-        return False
-
-    # Otherwise always use a tunnel.
-    return True
-
-
-def create_proxy_ssl_context(
-    ssl_version, cert_reqs, ca_certs=None, ca_cert_dir=None, ca_cert_data=None
-):
-    """
-    Generates a default proxy ssl context if one hasn't been provided by the
-    user.
-    """
-    ssl_context = create_urllib3_context(
-        ssl_version=resolve_ssl_version(ssl_version),
-        cert_reqs=resolve_cert_reqs(cert_reqs),
-    )
-
-    if (
-        not ca_certs
-        and not ca_cert_dir
-        and not ca_cert_data
-        and hasattr(ssl_context, "load_default_certs")
-    ):
-        ssl_context.load_default_certs()
-
-    return ssl_context
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py
deleted file mode 100644
index c9efa287fc71315f633347023b390fe4ce57913a..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import cv2
-import torch
-from torch import nn
-from detectron2.utils.comm import get_world_size
-from detectron2.structures import pairwise_iou, Boxes
-# from .data import CenterNetCrop
-import torch.nn.functional as F
-import numpy as np
-from detectron2.structures import Boxes, ImageList, Instances
-
-__all__ = ['reduce_sum', '_transpose']
-
-INF = 1000000000
-
-def _transpose(training_targets, num_loc_list):
-    '''
-    This function is used to transpose image first training targets to 
-        level first ones
-    :return: level first training targets
-    '''
-    for im_i in range(len(training_targets)):
-        training_targets[im_i] = torch.split(
-            training_targets[im_i], num_loc_list, dim=0)
-
-    targets_level_first = []
-    for targets_per_level in zip(*training_targets):
-        targets_level_first.append(
-            torch.cat(targets_per_level, dim=0))
-    return targets_level_first
-
-
-def reduce_sum(tensor):
-    world_size = get_world_size()
-    if world_size < 2:
-        return tensor
-    tensor = tensor.clone()
-    torch.distributed.all_reduce(tensor, op=torch.distributed.ReduceOp.SUM)
-    return tensor
\ No newline at end of file
diff --git a/spaces/VikasKumar01/My_AI_chatbot/README.md b/spaces/VikasKumar01/My_AI_chatbot/README.md
deleted file mode 100644
index 0fabb51accd04c09ea27887174a314c3af9639a6..0000000000000000000000000000000000000000
--- a/spaces/VikasKumar01/My_AI_chatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: My AI Chatbot
-emoji: 👀
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Wayben/ChatGPT/run_Windows.bat b/spaces/Wayben/ChatGPT/run_Windows.bat
deleted file mode 100644
index 4c18f9ccaeea0af972301ffdf48778641221f76d..0000000000000000000000000000000000000000
--- a/spaces/Wayben/ChatGPT/run_Windows.bat
+++ /dev/null
@@ -1,5 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
diff --git a/spaces/Xalphinions/tab-cot/app.py b/spaces/Xalphinions/tab-cot/app.py
deleted file mode 100644
index 44e87153fb7932b9266c7ca9e79da64ee405478a..0000000000000000000000000000000000000000
--- a/spaces/Xalphinions/tab-cot/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import gradio as gr
-from statistics import mean
-from torch.utils.data import Dataset
-from collections import OrderedDict
-import xml.etree.ElementTree as ET
-import openai # For GPT-3 API ...
-import os
-import multiprocessing
-import json
-import numpy as np
-import random
-import torch
-import torchtext
-import re
-import random
-import time
-import datetime
-import pandas as pd
-import sys
-
-openai.api_key = os.getenv("api_key")
-
-def greet(question):
-    input = question + '\n\n' + "|step|subquestion|process|result|"
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-                {"role": "system", "content": "You are a helpful assistant that generate table to solve reasoning problem."},
-                {"role": "user", "content": input},
-
-            ]
-        )
-    response = response["choices"][0]["message"]["content"]
-    return "|step|subquestion|process|result|\n" + response
-
-
-iface = gr.Interface(
-    fn=greet, 
-    inputs="text", 
-    outputs="text",
-    title="Tab-CoT: Zero-Shot Tabular Chain-of-Thought",
-    examples=[
-        ["Tommy is fundraising for his charity by selling brownies for $3 a slice and cheesecakes for $4 a slice. If Tommy sells 43 brownies and 23 slices of cheesecake, how much money does Tommy raise?"],
-        ["Judy teaches 5 dance classes, every day, on the weekdays and 8 classes on Saturday.  If each class has 15 students and she charges $15.00 per student, how much money does she make in 1 week?"],
-        ["According to its nutritional info, a bag of chips has 250 calories per serving. If a 300g bag has 5 servings, how many grams can you eat if your daily calorie target is 2000 and you have already consumed 1800 calories?"],
-    ]
-    )
-iface.launch()
-
diff --git a/spaces/XingHe0127/Chatbot/Dockerfile b/spaces/XingHe0127/Chatbot/Dockerfile
deleted file mode 100644
index 335c2dba28ba8c365de9306858462a59dea25f28..0000000000000000000000000000000000000000
--- a/spaces/XingHe0127/Chatbot/Dockerfile
+++ /dev/null
@@ -1,15 +0,0 @@
-FROM python:3.9 as builder
-RUN apt-get update && apt-get install -y build-essential
-COPY requirements.txt .
-COPY requirements_advanced.txt .
-RUN pip install --user -r requirements.txt
-# RUN pip install --user -r requirements_advanced.txt
-
-FROM python:3.9
-MAINTAINER iskoldt
-COPY --from=builder /root/.local /root/.local
-ENV PATH=/root/.local/bin:$PATH
-COPY . /app
-WORKDIR /app
-ENV dockerrun yes
-CMD ["python3", "-u", "ChuanhuChatbot.py", "2>&1", "|", "tee", "/var/log/application.log"]
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/stable_diffusion_safe/safety_checker.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/stable_diffusion_safe/safety_checker.py
deleted file mode 100644
index f9dbf51e86440847646e168e5a50ebf835440f2a..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/stable_diffusion_safe/safety_checker.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright 2022 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.
-
-import torch
-import torch.nn as nn
-
-from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
-
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-def cosine_distance(image_embeds, text_embeds):
-    normalized_image_embeds = nn.functional.normalize(image_embeds)
-    normalized_text_embeds = nn.functional.normalize(text_embeds)
-    return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
-
-
-class SafeStableDiffusionSafetyChecker(PreTrainedModel):
-    config_class = CLIPConfig
-
-    _no_split_modules = ["CLIPEncoderLayer"]
-
-    def __init__(self, config: CLIPConfig):
-        super().__init__(config)
-
-        self.vision_model = CLIPVisionModel(config.vision_config)
-        self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
-
-        self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
-        self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
-
-        self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
-        self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
-
-    @torch.no_grad()
-    def forward(self, clip_input, images):
-        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
-        image_embeds = self.visual_projection(pooled_output)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
-
-        result = []
-        batch_size = image_embeds.shape[0]
-        for i in range(batch_size):
-            result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
-
-            # increase this value to create a stronger `nfsw` filter
-            # at the cost of increasing the possibility of filtering benign images
-            adjustment = 0.0
-
-            for concept_idx in range(len(special_cos_dist[0])):
-                concept_cos = special_cos_dist[i][concept_idx]
-                concept_threshold = self.special_care_embeds_weights[concept_idx].item()
-                result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["special_scores"][concept_idx] > 0:
-                    result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
-                    adjustment = 0.01
-
-            for concept_idx in range(len(cos_dist[0])):
-                concept_cos = cos_dist[i][concept_idx]
-                concept_threshold = self.concept_embeds_weights[concept_idx].item()
-                result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["concept_scores"][concept_idx] > 0:
-                    result_img["bad_concepts"].append(concept_idx)
-
-            result.append(result_img)
-
-        has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
-
-        return images, has_nsfw_concepts
-
-    @torch.no_grad()
-    def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
-        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
-        image_embeds = self.visual_projection(pooled_output)
-
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds)
-
-        # increase this value to create a stronger `nsfw` filter
-        # at the cost of increasing the possibility of filtering benign images
-        adjustment = 0.0
-
-        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
-        # special_scores = special_scores.round(decimals=3)
-        special_care = torch.any(special_scores > 0, dim=1)
-        special_adjustment = special_care * 0.01
-        special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
-
-        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
-        # concept_scores = concept_scores.round(decimals=3)
-        has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
-
-        return images, has_nsfw_concepts
diff --git a/spaces/Yiqin/ChatVID/model/fastchat/serve/monkey_patch_non_inplace.py b/spaces/Yiqin/ChatVID/model/fastchat/serve/monkey_patch_non_inplace.py
deleted file mode 100644
index 9661d70751261a11bbc33b57967efcf09d3cbe0c..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/fastchat/serve/monkey_patch_non_inplace.py
+++ /dev/null
@@ -1,118 +0,0 @@
-"""
-Monkey patch the llama implementation in the huggingface/transformers library.
-Avoid bugs in mps backend by not using in-place operations.
-"""
-import math
-from typing import List, Optional, Tuple
-
-import torch
-from torch import nn
-import transformers
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2].clone()
-    x2 = x[..., x.shape[-1] // 2 :].clone()
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
-    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
-    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
-    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-def forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    bsz, q_len, _ = hidden_states.size()
-
-    query_states = (
-        self.q_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-    key_states = (
-        self.k_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-    value_states = (
-        self.v_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = apply_rotary_pos_emb(
-        query_states, key_states, cos, sin, position_ids
-    )
-    # [bsz, nh, t, hd]
-
-    if past_key_value is not None:
-        # reuse k, v, self_attention
-        key_states = torch.cat([past_key_value[0], key_states], dim=2)
-        value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(
-        self.head_dim
-    )
-
-    if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-        raise ValueError(
-            f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
-            f" {attn_weights.size()}"
-        )
-
-    if attention_mask is not None:
-        if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-            )
-        attn_weights = attn_weights + attention_mask
-        attn_weights = torch.max(
-            attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
-        )
-
-    # upcast attention to fp32
-    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
-        query_states.dtype
-    )
-    attn_output = torch.matmul(attn_weights, value_states)
-
-    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-        raise ValueError(
-            f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-            f" {attn_output.size()}"
-        )
-
-    attn_output = attn_output.transpose(1, 2)
-    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    attn_output = self.o_proj(attn_output)
-
-    if not output_attentions:
-        attn_weights = None
-
-    return attn_output, attn_weights, past_key_value
-
-
-def replace_llama_attn_with_non_inplace_operations():
-    """Avoid bugs in mps backend by not using in-place operations."""
-    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/data/benchmark.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/data/benchmark.py
deleted file mode 100644
index ac2f372a4b111ad40b8e720adea208608271bab6..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/data/benchmark.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-from itertools import count
-from typing import List, Tuple
-import torch
-import tqdm
-from fvcore.common.timer import Timer
-
-from detectron2.utils import comm
-
-from .build import build_batch_data_loader
-from .common import DatasetFromList, MapDataset
-from .samplers import TrainingSampler
-
-logger = logging.getLogger(__name__)
-
-
-class _EmptyMapDataset(torch.utils.data.Dataset):
-    """
-    Map anything to emptiness.
-    """
-
-    def __init__(self, dataset):
-        self.ds = dataset
-
-    def __len__(self):
-        return len(self.ds)
-
-    def __getitem__(self, idx):
-        _ = self.ds[idx]
-        return [0]
-
-
-def iter_benchmark(
-    iterator, num_iter: int, warmup: int = 5, max_time_seconds: float = 60
-) -> Tuple[float, List[float]]:
-    """
-    Benchmark an iterator/iterable for `num_iter` iterations with an extra
-    `warmup` iterations of warmup.
-    End early if `max_time_seconds` time is spent on iterations.
-
-    Returns:
-        float: average time (seconds) per iteration
-        list[float]: time spent on each iteration. Sometimes useful for further analysis.
-    """
-    num_iter, warmup = int(num_iter), int(warmup)
-
-    iterator = iter(iterator)
-    for _ in range(warmup):
-        next(iterator)
-    timer = Timer()
-    all_times = []
-    for curr_iter in tqdm.trange(num_iter):
-        start = timer.seconds()
-        if start > max_time_seconds:
-            num_iter = curr_iter
-            break
-        next(iterator)
-        all_times.append(timer.seconds() - start)
-    avg = timer.seconds() / num_iter
-    return avg, all_times
-
-
-class DataLoaderBenchmark:
-    """
-    Some common benchmarks that help understand perf bottleneck of a standard dataloader
-    made of dataset, mapper and sampler.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        *,
-        mapper,
-        sampler=None,
-        total_batch_size,
-        num_workers=0,
-        max_time_seconds: int = 90,
-    ):
-        """
-        Args:
-            max_time_seconds (int): maximum time to spent for each benchmark
-            other args: same as in `build.py:build_detection_train_loader`
-        """
-        if isinstance(dataset, list):
-            dataset = DatasetFromList(dataset, copy=False, serialize=True)
-        if sampler is None:
-            sampler = TrainingSampler(len(dataset))
-
-        self.dataset = dataset
-        self.mapper = mapper
-        self.sampler = sampler
-        self.total_batch_size = total_batch_size
-        self.num_workers = num_workers
-        self.per_gpu_batch_size = self.total_batch_size // comm.get_world_size()
-
-        self.max_time_seconds = max_time_seconds
-
-    def _benchmark(self, iterator, num_iter, warmup, msg=None):
-        avg, all_times = iter_benchmark(iterator, num_iter, warmup, self.max_time_seconds)
-        if msg is not None:
-            self._log_time(msg, avg, all_times)
-        return avg, all_times
-
-    def _log_time(self, msg, avg, all_times, distributed=False):
-        percentiles = [np.percentile(all_times, k, interpolation="nearest") for k in [1, 5, 95, 99]]
-        if not distributed:
-            logger.info(
-                f"{msg}: avg={1.0/avg:.1f} it/s, "
-                f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, "
-                f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s."
-            )
-            return
-        avg_per_gpu = comm.all_gather(avg)
-        percentiles_per_gpu = comm.all_gather(percentiles)
-        if comm.get_rank() > 0:
-            return
-        for idx, avg, percentiles in zip(count(), avg_per_gpu, percentiles_per_gpu):
-            logger.info(
-                f"GPU{idx} {msg}: avg={1.0/avg:.1f} it/s, "
-                f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, "
-                f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s."
-            )
-
-    def benchmark_dataset(self, num_iter, warmup=5):
-        """
-        Benchmark the speed of taking raw samples from the dataset.
-        """
-
-        def loader():
-            while True:
-                for k in self.sampler:
-                    yield self.dataset[k]
-
-        self._benchmark(loader(), num_iter, warmup, "Dataset Alone")
-
-    def benchmark_mapper(self, num_iter, warmup=5):
-        """
-        Benchmark the speed of taking raw samples from the dataset and map
-        them in a single process.
-        """
-
-        def loader():
-            while True:
-                for k in self.sampler:
-                    yield self.mapper(self.dataset[k])
-
-        self._benchmark(loader(), num_iter, warmup, "Single Process Mapper (sec/sample)")
-
-    def benchmark_workers(self, num_iter, warmup=10):
-        """
-        Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers].
-        """
-        candidates = [0, 1]
-        if self.num_workers not in candidates:
-            candidates.append(self.num_workers)
-
-        dataset = MapDataset(self.dataset, self.mapper)
-        for n in candidates:
-            loader = build_batch_data_loader(
-                dataset,
-                self.sampler,
-                self.total_batch_size,
-                num_workers=n,
-            )
-            self._benchmark(
-                iter(loader),
-                num_iter * max(n, 1),
-                warmup * max(n, 1),
-                f"DataLoader ({n} workers, bs={self.per_gpu_batch_size})",
-            )
-            del loader
-
-    def benchmark_IPC(self, num_iter, warmup=10):
-        """
-        Benchmark the dataloader where each worker outputs nothing. This
-        eliminates the IPC overhead compared to the regular dataloader.
-
-        PyTorch multiprocessing's IPC only optimizes for torch tensors.
-        Large numpy arrays or other data structure may incur large IPC overhead.
-        """
-        n = self.num_workers
-        dataset = _EmptyMapDataset(MapDataset(self.dataset, self.mapper))
-        loader = build_batch_data_loader(
-            dataset, self.sampler, self.total_batch_size, num_workers=n
-        )
-        self._benchmark(
-            iter(loader),
-            num_iter * max(n, 1),
-            warmup * max(n, 1),
-            f"DataLoader ({n} workers, bs={self.per_gpu_batch_size}) w/o comm",
-        )
-
-    def benchmark_distributed(self, num_iter, warmup=10):
-        """
-        Benchmark the dataloader in each distributed worker, and log results of
-        all workers. This helps understand the final performance as well as
-        the variances among workers.
-
-        It also prints startup time (first iter) of the dataloader.
-        """
-        gpu = comm.get_world_size()
-        dataset = MapDataset(self.dataset, self.mapper)
-        n = self.num_workers
-        loader = build_batch_data_loader(
-            dataset, self.sampler, self.total_batch_size, num_workers=n
-        )
-
-        timer = Timer()
-        loader = iter(loader)
-        next(loader)
-        startup_time = timer.seconds()
-        logger.info("Dataloader startup time: {:.2f} seconds".format(startup_time))
-
-        comm.synchronize()
-
-        avg, all_times = self._benchmark(loader, num_iter * max(n, 1), warmup * max(n, 1))
-        del loader
-        self._log_time(
-            f"DataLoader ({gpu} GPUs x {n} workers, total bs={self.total_batch_size})",
-            avg,
-            all_times,
-            True,
-        )
diff --git a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/bleu/__init__.py b/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/bleu/__init__.py
deleted file mode 100644
index 3f7d85bba884ea8f83fc6ab2a1e6ade80d98d4d9..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/bleu/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__author__ = 'tylin'
diff --git a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/util/text_encoders.py b/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/util/text_encoders.py
deleted file mode 100644
index b49c5603afa2d41ad6e0145b719443f0f4ce9301..0000000000000000000000000000000000000000
--- a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/util/text_encoders.py
+++ /dev/null
@@ -1,160 +0,0 @@
-from . import text_cleaners
-from typing import Dict, List, Optional
-from .constants import ALL_POSSIBLE_HARAQAT
-import sentencepiece as spm
-
-
-class TextEncoder:
-    pad = "P"
-
-    def __init__(
-        self,
-        input_chars: List[str],
-        target_charts: List[str],
-        cleaner_fn: Optional[str] = None,
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        if cleaner_fn:
-            self.cleaner_fn = getattr(text_cleaners, cleaner_fn)
-        else:
-            self.cleaner_fn = None
-
-        self.input_symbols: List[str] = [TextEncoder.pad] + input_chars
-        self.target_symbols: List[str] = [TextEncoder.pad] + target_charts
-
-        if sp_model_path is None:
-            self.input_symbol_to_id: Dict[str, int] = {
-                s: i for i, s in enumerate(self.input_symbols)
-            }
-            self.input_id_to_symbol: Dict[int, str] = {
-                i: s for i, s in enumerate(self.input_symbols)
-            }
-        else:
-            sp_model = spm.SentencePieceProcessor()
-            sp_model.load(sp_model_path + "/sp.model")
-            self.input_symbol_to_id: Dict[str, int] = {
-                s: sp_model.PieceToId(s+'▁') for s in self.input_symbols
-            }
-            self.input_symbol_to_id[" "] = sp_model.PieceToId("|")  # encode space
-            self.input_symbol_to_id[TextEncoder.pad] = 0  # encode padding
-
-            self.input_space_id = sp_model.PieceToId("|")
-            self.input_id_to_symbol: Dict[int, str] = {
-                i: s for s, i in self.input_symbol_to_id.items()
-            }
-
-        self.target_symbol_to_id: Dict[str, int] = {
-            s: i for i, s in enumerate(self.target_symbols)
-        }
-        self.target_id_to_symbol: Dict[int, str] = {
-            i: s for i, s in enumerate(self.target_symbols)
-        }
-
-        self.reverse_input = reverse_input
-        self.reverse_target = reverse_target
-        self.input_pad_id = self.input_symbol_to_id[self.pad]
-        self.target_pad_id = self.target_symbol_to_id[self.pad]
-        self.start_symbol_id = None
-
-    def input_to_sequence(self, text: str) -> List[int]:
-        if self.reverse_input:
-            text = "".join(list(reversed(text)))
-        sequence = [self.input_symbol_to_id[s] for s in text if s not in [self.pad]]
-
-        return sequence
-
-    def target_to_sequence(self, text: str) -> List[int]:
-        if self.reverse_target:
-            text = "".join(list(reversed(text)))
-        sequence = [self.target_symbol_to_id[s] for s in text if s not in [self.pad]]
-
-        return sequence
-
-    def sequence_to_input(self, sequence: List[int]):
-        return [
-            self.input_id_to_symbol[symbol]
-            for symbol in sequence
-            if symbol in self.input_id_to_symbol and symbol not in [self.input_pad_id]
-        ]
-
-    def sequence_to_target(self, sequence: List[int]):
-        return [
-            self.target_id_to_symbol[symbol]
-            for symbol in sequence
-            if symbol in self.target_id_to_symbol and symbol not in [self.target_pad_id]
-        ]
-
-    def clean(self, text):
-        if self.cleaner_fn:
-            return self.cleaner_fn(text)
-        return text
-
-    def combine_text_and_haraqat(self, input_ids: List[int], output_ids: List[int]):
-        """
-        Combines the  input text with its corresponding  haraqat
-        Args:
-            inputs: a list of ids representing the input text
-            outputs: a list of ids representing the output text
-        Returns:
-        text: the text after merging the inputs text representation with the output
-        representation
-        """
-        output = ""
-        for i, input_id in enumerate(input_ids):
-            if input_id == self.input_pad_id:
-                break
-            output += self.input_id_to_symbol[input_id]
-            # if input_id == self.input_space_id:
-            #   continue
-            output += self.target_id_to_symbol[output_ids[i]]
-        return output
-
-    def __str__(self):
-        return type(self).__name__
-
-
-class BasicArabicEncoder(TextEncoder):
-    def __init__(
-        self,
-        cleaner_fn="basic_cleaners",
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        input_chars: List[str] = list("بض.غىهظخة؟:طس،؛فندؤلوئآك-يذاصشحزءمأجإ ترقعث")
-        target_charts: List[str] = list(ALL_POSSIBLE_HARAQAT.keys())
-
-        super().__init__(
-            input_chars,
-            target_charts,
-            cleaner_fn=cleaner_fn,
-            reverse_input=reverse_input,
-            reverse_target=reverse_target,
-            sp_model_path=sp_model_path,
-        )
-
-
-class ArabicEncoderWithStartSymbol(TextEncoder):
-    def __init__(
-        self,
-        cleaner_fn="basic_cleaners",
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        input_chars: List[str] = list("بض.غىهظخة؟:طس،؛فندؤلوئآك-يذاصشحزءمأجإ ترقعث")
-        # the only difference from the basic encoder is adding the start symbol
-        target_charts: List[str] = list(ALL_POSSIBLE_HARAQAT.keys()) + ["s"]
-
-        super().__init__(
-            input_chars,
-            target_charts,
-            cleaner_fn=cleaner_fn,
-            reverse_input=reverse_input,
-            reverse_target=reverse_target,
-            sp_model_path=sp_model_path,
-        )
-
-        self.start_symbol_id = self.target_symbol_to_id["s"]
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/__init__.py
deleted file mode 100644
index 378a0068432a371af364de9d73785901c0f83383..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/__init__.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# flake8: noqa
-# Copyright (c) OpenMMLab. All rights reserved.
-from .config import Config, ConfigDict, DictAction
-from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
-                   has_method, import_modules_from_strings, is_list_of,
-                   is_method_overridden, is_seq_of, is_str, is_tuple_of,
-                   iter_cast, list_cast, requires_executable, requires_package,
-                   slice_list, to_1tuple, to_2tuple, to_3tuple, to_4tuple,
-                   to_ntuple, tuple_cast)
-from .path import (check_file_exist, fopen, is_filepath, mkdir_or_exist,
-                   scandir, symlink)
-from .progressbar import (ProgressBar, track_iter_progress,
-                          track_parallel_progress, track_progress)
-from .testing import (assert_attrs_equal, assert_dict_contains_subset,
-                      assert_dict_has_keys, assert_is_norm_layer,
-                      assert_keys_equal, assert_params_all_zeros,
-                      check_python_script)
-from .timer import Timer, TimerError, check_time
-from .version_utils import digit_version, get_git_hash
-
-try:
-    import torch
-except ImportError:
-    __all__ = [
-        'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
-        'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
-        'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
-        'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
-        'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
-        'track_progress', 'track_iter_progress', 'track_parallel_progress',
-        'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
-        'digit_version', 'get_git_hash', 'import_modules_from_strings',
-        'assert_dict_contains_subset', 'assert_attrs_equal',
-        'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
-        'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
-        'is_method_overridden', 'has_method'
-    ]
-else:
-    from .env import collect_env
-    from .logging import get_logger, print_log
-    from .parrots_jit import jit, skip_no_elena
-    from .parrots_wrapper import (
-        TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
-        PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
-        _AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
-        _MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
-    from .registry import Registry, build_from_cfg
-    from .trace import is_jit_tracing
-    __all__ = [
-        'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
-        'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
-        'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
-        'check_prerequisites', 'requires_package', 'requires_executable',
-        'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
-        'symlink', 'scandir', 'ProgressBar', 'track_progress',
-        'track_iter_progress', 'track_parallel_progress', 'Registry',
-        'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
-        '_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
-        '_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
-        'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
-        'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
-        'deprecated_api_warning', 'digit_version', 'get_git_hash',
-        'import_modules_from_strings', 'jit', 'skip_no_elena',
-        'assert_dict_contains_subset', 'assert_attrs_equal',
-        'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
-        'assert_params_all_zeros', 'check_python_script',
-        'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
-        '_get_cuda_home', 'has_method'
-    ]
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/vgg.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/vgg.py
deleted file mode 100644
index 8778b649561a45a9652b1a15a26c2d171e58f3e1..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/vgg.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-
-import torch.nn as nn
-
-from .utils import constant_init, kaiming_init, normal_init
-
-
-def conv3x3(in_planes, out_planes, dilation=1):
-    """3x3 convolution with padding."""
-    return nn.Conv2d(
-        in_planes,
-        out_planes,
-        kernel_size=3,
-        padding=dilation,
-        dilation=dilation)
-
-
-def make_vgg_layer(inplanes,
-                   planes,
-                   num_blocks,
-                   dilation=1,
-                   with_bn=False,
-                   ceil_mode=False):
-    layers = []
-    for _ in range(num_blocks):
-        layers.append(conv3x3(inplanes, planes, dilation))
-        if with_bn:
-            layers.append(nn.BatchNorm2d(planes))
-        layers.append(nn.ReLU(inplace=True))
-        inplanes = planes
-    layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode))
-
-    return layers
-
-
-class VGG(nn.Module):
-    """VGG backbone.
-
-    Args:
-        depth (int): Depth of vgg, from {11, 13, 16, 19}.
-        with_bn (bool): Use BatchNorm or not.
-        num_classes (int): number of classes for classification.
-        num_stages (int): VGG stages, normally 5.
-        dilations (Sequence[int]): Dilation of each stage.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
-            not freezing any parameters.
-        bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
-            running stats (mean and var).
-        bn_frozen (bool): Whether to freeze weight and bias of BN layers.
-    """
-
-    arch_settings = {
-        11: (1, 1, 2, 2, 2),
-        13: (2, 2, 2, 2, 2),
-        16: (2, 2, 3, 3, 3),
-        19: (2, 2, 4, 4, 4)
-    }
-
-    def __init__(self,
-                 depth,
-                 with_bn=False,
-                 num_classes=-1,
-                 num_stages=5,
-                 dilations=(1, 1, 1, 1, 1),
-                 out_indices=(0, 1, 2, 3, 4),
-                 frozen_stages=-1,
-                 bn_eval=True,
-                 bn_frozen=False,
-                 ceil_mode=False,
-                 with_last_pool=True):
-        super(VGG, self).__init__()
-        if depth not in self.arch_settings:
-            raise KeyError(f'invalid depth {depth} for vgg')
-        assert num_stages >= 1 and num_stages <= 5
-        stage_blocks = self.arch_settings[depth]
-        self.stage_blocks = stage_blocks[:num_stages]
-        assert len(dilations) == num_stages
-        assert max(out_indices) <= num_stages
-
-        self.num_classes = num_classes
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.bn_eval = bn_eval
-        self.bn_frozen = bn_frozen
-
-        self.inplanes = 3
-        start_idx = 0
-        vgg_layers = []
-        self.range_sub_modules = []
-        for i, num_blocks in enumerate(self.stage_blocks):
-            num_modules = num_blocks * (2 + with_bn) + 1
-            end_idx = start_idx + num_modules
-            dilation = dilations[i]
-            planes = 64 * 2**i if i < 4 else 512
-            vgg_layer = make_vgg_layer(
-                self.inplanes,
-                planes,
-                num_blocks,
-                dilation=dilation,
-                with_bn=with_bn,
-                ceil_mode=ceil_mode)
-            vgg_layers.extend(vgg_layer)
-            self.inplanes = planes
-            self.range_sub_modules.append([start_idx, end_idx])
-            start_idx = end_idx
-        if not with_last_pool:
-            vgg_layers.pop(-1)
-            self.range_sub_modules[-1][1] -= 1
-        self.module_name = 'features'
-        self.add_module(self.module_name, nn.Sequential(*vgg_layers))
-
-        if self.num_classes > 0:
-            self.classifier = nn.Sequential(
-                nn.Linear(512 * 7 * 7, 4096),
-                nn.ReLU(True),
-                nn.Dropout(),
-                nn.Linear(4096, 4096),
-                nn.ReLU(True),
-                nn.Dropout(),
-                nn.Linear(4096, num_classes),
-            )
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            from ..runner import load_checkpoint
-            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)
-                elif isinstance(m, nn.Linear):
-                    normal_init(m, std=0.01)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        outs = []
-        vgg_layers = getattr(self, self.module_name)
-        for i in range(len(self.stage_blocks)):
-            for j in range(*self.range_sub_modules[i]):
-                vgg_layer = vgg_layers[j]
-                x = vgg_layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-        if self.num_classes > 0:
-            x = x.view(x.size(0), -1)
-            x = self.classifier(x)
-            outs.append(x)
-        if len(outs) == 1:
-            return outs[0]
-        else:
-            return tuple(outs)
-
-    def train(self, mode=True):
-        super(VGG, self).train(mode)
-        if self.bn_eval:
-            for m in self.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eval()
-                    if self.bn_frozen:
-                        for params in m.parameters():
-                            params.requires_grad = False
-        vgg_layers = getattr(self, self.module_name)
-        if mode and self.frozen_stages >= 0:
-            for i in range(self.frozen_stages):
-                for j in range(*self.range_sub_modules[i]):
-                    mod = vgg_layers[j]
-                    mod.eval()
-                    for param in mod.parameters():
-                        param.requires_grad = False
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/quartz.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/quartz.py
deleted file mode 100644
index 9b33fe22f3cb5a1a1bcb636fef3d68e26695c1f9..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/quartz.py
+++ /dev/null
@@ -1,109 +0,0 @@
-from ctypes import c_void_p, c_ubyte
-
-from pyglet.image import ImageData, Animation, AnimationFrame
-from pyglet.image.codecs import *
-
-from pyglet.libs.darwin.cocoapy import cf, quartz, NSMakeRect
-from pyglet.libs.darwin.cocoapy import cfnumber_to_number
-from pyglet.libs.darwin.cocoapy import kCGImageAlphaPremultipliedLast
-from pyglet.libs.darwin.cocoapy import kCGImagePropertyGIFDictionary
-from pyglet.libs.darwin.cocoapy import kCGImagePropertyGIFDelayTime
-
-
-class QuartzImageDecoder(ImageDecoder):
-    def get_file_extensions(self):
-        # Quartz can actually decode many more formats, but these are the most common.
-        return [ '.bmp', '.cur', '.gif', '.ico', '.jp2', '.jpg', '.jpeg',
-                 '.pcx', '.png', '.tga', '.tif', '.tiff', '.xbm', '.xpm' ]
-
-    def get_animation_file_extensions(self):
-        return ['.gif']
-
-    def _get_pyglet_ImageData_from_source_at_index(self, sourceRef, index):
-        imageRef = c_void_p(quartz.CGImageSourceCreateImageAtIndex(sourceRef, index, None))
-
-        # Regardless of the internal format of the image (L, LA, RGB, RGBA, etc)
-        # we just automatically convert everything to an RGBA format.
-        format = 'RGBA'
-        rgbColorSpace = c_void_p(quartz.CGColorSpaceCreateDeviceRGB())
-        bitsPerComponent = 8
-        width = quartz.CGImageGetWidth(imageRef)
-        height = quartz.CGImageGetHeight(imageRef)
-        bytesPerRow = 4 * width
-
-        # Create a buffer to store the RGBA formatted data.
-        bufferSize = height * bytesPerRow
-        buffer = (c_ubyte * bufferSize)()
-
-        # Create a bitmap context for the RGBA formatted data.
-        # Note that premultiplied alpha is required:
-        # http://developer.apple.com/library/mac/#qa/qa1037/_index.html
-        bitmap = c_void_p(quartz.CGBitmapContextCreate(buffer,
-                                                       width, height,
-                                                       bitsPerComponent,
-                                                       bytesPerRow,
-                                                       rgbColorSpace,
-                                                       kCGImageAlphaPremultipliedLast))
-
-        # Write the image data into the bitmap.
-        quartz.CGContextDrawImage(bitmap, NSMakeRect(0,0,width,height), imageRef)
-
-        quartz.CGImageRelease(imageRef)
-        quartz.CGContextRelease(bitmap)
-        quartz.CGColorSpaceRelease(rgbColorSpace)
-
-        pitch = bytesPerRow
-        return ImageData(width, height, format, buffer, -pitch)
-
-    def decode(self, filename, file):
-        if not file:
-            file = open(filename, 'rb')
-        file_bytes = file.read()
-        data = c_void_p(cf.CFDataCreate(None, file_bytes, len(file_bytes)))
-        # Second argument is an options dictionary.  It might be a good idea to provide
-        # a value for kCGImageSourceTypeIdentifierHint here using filename extension.
-        sourceRef = c_void_p(quartz.CGImageSourceCreateWithData(data, None))
-        image = self._get_pyglet_ImageData_from_source_at_index(sourceRef, 0)
-
-        cf.CFRelease(data)
-        cf.CFRelease(sourceRef)
-
-        return image
-
-    def decode_animation(self, filename, file):
-        if not file:
-            file = open(filename, 'rb')
-        # If file is not an animated GIF, it will be loaded as a single-frame animation.
-        file_bytes = file.read()
-        data = c_void_p(cf.CFDataCreate(None, file_bytes, len(file_bytes)))
-        sourceRef = c_void_p(quartz.CGImageSourceCreateWithData(data, None))
-
-        # Get number of frames in the animation.
-        count = quartz.CGImageSourceGetCount(sourceRef)
-
-        frames = []
-
-        for index in range(count):
-            # Try to determine frame duration from GIF properties dictionary.
-            duration = 0.1  # default duration if none found
-            props = c_void_p(quartz.CGImageSourceCopyPropertiesAtIndex(sourceRef, index, None))
-            if cf.CFDictionaryContainsKey(props, kCGImagePropertyGIFDictionary):
-                gif_props = c_void_p(cf.CFDictionaryGetValue(props, kCGImagePropertyGIFDictionary))
-                if cf.CFDictionaryContainsKey(gif_props, kCGImagePropertyGIFDelayTime):
-                    duration = cfnumber_to_number(c_void_p(cf.CFDictionaryGetValue(gif_props, kCGImagePropertyGIFDelayTime)))
-
-            cf.CFRelease(props)
-            image = self._get_pyglet_ImageData_from_source_at_index(sourceRef, index)
-            frames.append( AnimationFrame(image, duration) )
-
-        cf.CFRelease(data)
-        cf.CFRelease(sourceRef)
-
-        return Animation(frames)
-
-
-def get_decoders():
-    return [ QuartzImageDecoder() ]
-
-def get_encoders():
-    return []
diff --git a/spaces/active-learning/webhook/Dockerfile b/spaces/active-learning/webhook/Dockerfile
deleted file mode 100644
index e9e2e4a87e0f859680a49a0243b1022a6cb645b7..0000000000000000000000000000000000000000
--- a/spaces/active-learning/webhook/Dockerfile
+++ /dev/null
@@ -1,27 +0,0 @@
-# Use the official Python 3.9 image
-FROM python:3.9-bullseye
-
-# Set the working directory to /code
-WORKDIR /code
-
-# Copy the current directory contents into the container at /code
-COPY ./requirements.txt /code/requirements.txt
-
-# Install requirements.txt 
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-# Switch to the "user" user
-USER user
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-CMD ["uvicorn", "main:app","--proxy-headers", "--host", "0.0.0.0", "--port", "7860"]
diff --git a/spaces/akhaliq/deeplab2/data/preprocessing/__init__.py b/spaces/akhaliq/deeplab2/data/preprocessing/__init__.py
deleted file mode 100644
index 35e4ce02ff422f3aa84ab644b88d65b13e0cbc03..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/data/preprocessing/__init__.py
+++ /dev/null
@@ -1,15 +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.
-
diff --git a/spaces/akhaliq/deeplab2/model/layers/axial_blocks.py b/spaces/akhaliq/deeplab2/model/layers/axial_blocks.py
deleted file mode 100644
index bb21189461979d87aa5a8294959053a5960dfe76..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/layers/axial_blocks.py
+++ /dev/null
@@ -1,308 +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.
-
-"""Implements Axial-Blocks proposed in Axial-DeepLab [1].
-
-Axial-Blocks are based on residual bottleneck blocks, but with the 3x3
-convolution replaced with two axial-attention layers, one on the height-axis,
-followed by the other on the width-axis.
-
-[1] Axial-Deeplab: Stand-Alone Axial-Attention for Panoptic Segmentation,
-    ECCV 2020 Spotlight.
-      Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille,
-      Liang-Chieh Chen.
-"""
-import tensorflow as tf
-
-from deeplab2.model import utils
-from deeplab2.model.layers import activations
-from deeplab2.model.layers import axial_layers
-from deeplab2.model.layers import convolutions
-from deeplab2.model.layers import squeeze_and_excite
-
-
-class AxialBlock(tf.keras.layers.Layer):
-  """An AxialBlock as a building block for an Axial-ResNet model.
-
-  We implement the Axial-Block proposed in [1] in a general way that also
-  includes convolutional residual blocks, such as the basic block and the
-  bottleneck block (w/ and w/o Switchable Atrous Convolution).
-
-  A basic block consists of two 3x3 convolutions and a residual connection. It
-  is the main building block for wide-resnet variants.
-
-  A bottleneck block consists of consecutive 1x1, 3x3, 1x1 convolutions and a
-  residual connection. It is the main building block for standard resnet
-  variants.
-
-  An axial block consists of a 1x1 input convolution, a self-attention layer
-  (either axial-attention or global attention), a 1x1 output convolution, and a
-  residual connection. It is the main building block for axial-resnet variants.
-
-  Note: We apply the striding in the first spatial operation (i.e. 3x3
-  convolution or self-attention layer).
-  """
-
-  def __init__(self,
-               filters_list,
-               kernel_size=3,
-               strides=1,
-               atrous_rate=1,
-               use_squeeze_and_excite=False,
-               use_sac=False,
-               bn_layer=tf.keras.layers.BatchNormalization,
-               activation='relu',
-               name=None,
-               conv_kernel_weight_decay=0.0,
-               basic_block_second_conv_atrous_rate=None,
-               attention_type=None,
-               axial_layer_config=None):
-    """Initializes an AxialBlock.
-
-    Args:
-      filters_list: A list of filter numbers in the residual block. We currently
-        support filters_list with two or three elements. Two elements specify
-        the filters for two consecutive 3x3 convolutions, while three elements
-        specify the filters for three convolutions (1x1, 3x3, and 1x1).
-      kernel_size: The size of the convolution kernels (default: 3).
-      strides: The strides of the block (default: 1).
-      atrous_rate: The atrous rate of the 3x3 convolutions (default: 1). If this
-        residual block is a basic block, it is recommendeded to specify correct
-        basic_block_second_conv_atrous_rate for the second 3x3 convolution.
-        Otherwise, the second conv will also use atrous rate, which might cause
-        atrous inconsistency with different output strides, as tested in
-        axial_block_groups_test.test_atrous_consistency_basic_block.
-      use_squeeze_and_excite: A boolean flag indicating whether
-        squeeze-and-excite (SE) is used.
-      use_sac: A boolean, using the Switchable Atrous Convolution (SAC) or not.
-      bn_layer: A tf.keras.layers.Layer that computes the normalization
-        (default: tf.keras.layers.BatchNormalization).
-      activation: A string specifying the activation function to apply.
-      name: An string specifying the name of the layer (default: None).
-      conv_kernel_weight_decay: A float, the weight decay for convolution
-        kernels.
-      basic_block_second_conv_atrous_rate: An integer, the atrous rate for the
-        second convolution of basic block. This is necessary to ensure atrous
-        consistency with different output_strides. Defaults to atrous_rate.
-      attention_type: A string, type of attention to apply. Support 'axial' and
-        'global'.
-      axial_layer_config: A dict, an argument dictionary for the axial layer.
-
-    Raises:
-      ValueError: If filters_list does not have two or three elements.
-      ValueError: If attention_type is not supported.
-      ValueError: If double_global_attention is True in axial_layer_config.
-    """
-    super(AxialBlock, self).__init__(name=name)
-
-    self._filters_list = filters_list
-    self._strides = strides
-    self._use_squeeze_and_excite = use_squeeze_and_excite
-    self._bn_layer = bn_layer
-    self._activate_fn = activations.get_activation(activation)
-    self._attention_type = attention_type
-
-    if axial_layer_config is None:
-      axial_layer_config = {}
-
-    if basic_block_second_conv_atrous_rate is None:
-      basic_block_second_conv_atrous_rate = atrous_rate
-
-    if len(filters_list) == 3:
-      # Three consecutive convolutions: 1x1, 3x3, and 1x1.
-      self._conv1_bn_act = convolutions.Conv2DSame(
-          filters_list[0], 1, 'conv1_bn_act',
-          use_bias=False,
-          use_bn=True,
-          bn_layer=bn_layer,
-          activation=activation,
-          conv_kernel_weight_decay=conv_kernel_weight_decay)
-
-      if attention_type is None or attention_type.lower() == 'none':
-        self._conv2_bn_act = convolutions.Conv2DSame(
-            filters_list[1], kernel_size, 'conv2_bn_act',
-            strides=strides,
-            atrous_rate=atrous_rate,
-            use_bias=False,
-            use_bn=True,
-            bn_layer=bn_layer,
-            activation=activation,
-            use_switchable_atrous_conv=use_sac,
-            # We default to use global context in SAC if use_sac is True. This
-            # setting is experimentally found effective.
-            use_global_context_in_sac=use_sac,
-            conv_kernel_weight_decay=conv_kernel_weight_decay)
-      elif attention_type == 'axial':
-        if 'double_global_attention' in axial_layer_config:
-          if axial_layer_config['double_global_attention']:
-            raise ValueError('Double_global_attention takes no effect in '
-                             'AxialAttention2D.')
-          del axial_layer_config['double_global_attention']
-        self._attention = axial_layers.AxialAttention2D(
-            strides=strides,
-            filters=filters_list[1],
-            name='attention',
-            bn_layer=bn_layer,
-            conv_kernel_weight_decay=conv_kernel_weight_decay,
-            **axial_layer_config)
-      elif attention_type == 'global':
-        self._attention = axial_layers.GlobalAttention2D(
-            strides=strides,
-            filters=filters_list[1],
-            name='attention',
-            bn_layer=bn_layer,
-            conv_kernel_weight_decay=conv_kernel_weight_decay,
-            **axial_layer_config)
-      else:
-        raise ValueError(attention_type + ' is not supported.')
-
-      # Here we apply a batch norm with gamma initialized at zero. This ensures
-      # that at random initialization of the model, the skip connections
-      # dominate all residual blocks. In this way, all the skip connections
-      # construct an identity mapping that passes the gradients (without any
-      # distortion from the randomly initialized blocks) to all residual blocks.
-      # This trick helps training at early epochs.
-      # Reference: "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour".
-      # https://arxiv.org/abs/1706.02677
-      self._conv3_bn = convolutions.Conv2DSame(
-          filters_list[2], 1, 'conv3_bn',
-          use_bias=False,
-          use_bn=True,
-          bn_layer=bn_layer,
-          bn_gamma_initializer='zeros',
-          activation='none',
-          conv_kernel_weight_decay=conv_kernel_weight_decay)
-    elif len(filters_list) == 2:
-      # Two consecutive convolutions: 3x3 and 3x3.
-      self._conv1_bn_act = convolutions.Conv2DSame(
-          filters_list[0], kernel_size, 'conv1_bn_act',
-          strides=strides,
-          atrous_rate=atrous_rate,
-          use_bias=False,
-          use_bn=True,
-          bn_layer=bn_layer,
-          activation=activation,
-          use_switchable_atrous_conv=use_sac,
-          use_global_context_in_sac=use_sac,
-          conv_kernel_weight_decay=conv_kernel_weight_decay)
-      # Here we apply a batch norm with gamma initialized at zero. This ensures
-      # that at random initialization of the model, the skip connections
-      # dominate all residual blocks. In this way, all the skip connections
-      # construct an identity mapping that passes the gradients (without any
-      # distortion from the randomly initialized blocks) to all residual blocks.
-      # This trick helps training at early epochs.
-      # Reference: "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour".
-      # https://arxiv.org/abs/1706.02677
-      self._conv2_bn = convolutions.Conv2DSame(
-          filters_list[1], kernel_size, 'conv2_bn',
-          strides=1,
-          atrous_rate=basic_block_second_conv_atrous_rate,
-          use_bias=False,
-          use_bn=True,
-          bn_layer=bn_layer,
-          bn_gamma_initializer='zeros',
-          activation='none',
-          use_switchable_atrous_conv=use_sac,
-          use_global_context_in_sac=use_sac,
-          conv_kernel_weight_decay=conv_kernel_weight_decay)
-    else:
-      raise ValueError('Expect filters_list to have length 2 or 3; got %d' %
-                       len(filters_list))
-
-    if self._use_squeeze_and_excite:
-      self._squeeze_and_excite = squeeze_and_excite.SimplifiedSqueezeAndExcite(
-          filters_list[-1])
-    self._conv_kernel_weight_decay = conv_kernel_weight_decay
-
-  def build(self, input_shape_list):
-    input_tensor_shape = input_shape_list[0]
-    self._shortcut = None
-    if input_tensor_shape[3] != self._filters_list[-1]:
-      self._shortcut = convolutions.Conv2DSame(
-          self._filters_list[-1], 1, 'shortcut',
-          strides=self._strides,
-          use_bias=False,
-          use_bn=True,
-          bn_layer=self._bn_layer,
-          activation='none',
-          conv_kernel_weight_decay=self._conv_kernel_weight_decay)
-
-  def call(self, inputs):
-    """Performs a forward pass.
-
-    We have to define drop_path_random_mask outside the layer call and pass it
-    into the layer, because recompute_grad (gradient checkpointing) does not
-    allow any randomness within the function call. In addition, recompute_grad
-    only supports float tensors as inputs. For this reason, the training flag
-    should be also passed as a float tensor. For the same reason, we cannot
-    support passing drop_path_random_mask as None. Instead, we ask the users to
-    pass only the first two tensors when drop path is not used.
-
-    Args:
-      inputs: A tuple of 2 or 3 tensors, containing
-        input_tensor should be an input tensor of type tf.Tensor with shape
-          [batch, height, width, channels].
-        float_tensor_training should be a float tensor of 0.0 or 1.0, whether
-          the model is in training mode.
-        (optional) drop_path_random_mask is a drop path random mask of type
-          tf.Tensor with shape [batch, 1, 1, 1].
-
-    Returns:
-      outputs: two tensors. The first tensor does not use the last activation
-        function. The second tensor uses the activation. We return non-activated
-        output to support MaX-DeepLab which uses non-activated feature for the
-        stacked decoders.
-
-    Raises:
-      ValueError: If the length of inputs is not 2 or 3.
-    """
-    if len(inputs) not in (2, 3):
-      raise ValueError('The length of inputs should be either 2 or 3.')
-
-    # Unpack the inputs.
-    input_tensor, float_tensor_training, drop_path_random_mask = (
-        utils.pad_sequence_with_none(inputs, target_length=3))
-
-    # Recompute_grad takes only float tensors as inputs. It does not allow
-    # bools or boolean tensors. For this reason, we cast training to a float
-    # tensor outside this call, and now we cast it back to a boolean tensor.
-    training = tf.cast(float_tensor_training, tf.bool)
-
-    shortcut = input_tensor
-    if self._shortcut is not None:
-      shortcut = self._shortcut(shortcut, training=training)
-    elif self._strides != 1:
-      shortcut = shortcut[:, ::self._strides, ::self._strides, :]
-
-    if len(self._filters_list) == 3:
-      x = self._conv1_bn_act(input_tensor, training=training)
-      if (self._attention_type is None or
-          self._attention_type.lower() == 'none'):
-        x = self._conv2_bn_act(x, training=training)
-      else:
-        x = self._attention(x, training=training)
-        x = self._activate_fn(x)
-      x = self._conv3_bn(x, training=training)
-    if len(self._filters_list) == 2:
-      x = self._conv1_bn_act(input_tensor, training=training)
-      x = self._conv2_bn(x, training=training)
-
-    if self._use_squeeze_and_excite:
-      x = self._squeeze_and_excite(x)
-
-    if drop_path_random_mask is not None:
-      x = x * drop_path_random_mask
-    x = x + shortcut
-    return x, self._activate_fn(x)
diff --git a/spaces/akhaliq/deeplab2/model/post_processor/vip_deeplab_test.py b/spaces/akhaliq/deeplab2/model/post_processor/vip_deeplab_test.py
deleted file mode 100644
index e742fe470f5d2410b5c69005130977e9ee50e8a0..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/post_processor/vip_deeplab_test.py
+++ /dev/null
@@ -1,67 +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.
-
-"""Test for vip_deeplab.py."""
-import numpy as np
-import tensorflow as tf
-
-from deeplab2.model.post_processor import vip_deeplab
-
-
-class PostProcessingTest(tf.test.TestCase):
-
-  def test_stitch_video_panoptic_prediction(self):
-    concat_semantic = np.array(
-        [[[0, 0, 0, 0],
-          [0, 1, 1, 0],
-          [0, 2, 2, 0],
-          [2, 2, 3, 3]]], dtype=np.int32)
-    concat_instance = np.array(
-        [[[1, 1, 2, 2],
-          [1, 0, 0, 2],
-          [1, 1, 1, 2],
-          [2, 2, 1, 1]]], dtype=np.int32)
-    next_semantic = np.array(
-        [[[0, 1, 1, 0],
-          [0, 1, 1, 0],
-          [0, 2, 2, 0],
-          [2, 2, 3, 3]]], dtype=np.int32)
-    next_instance = np.array(
-        [[[2, 0, 0, 1],
-          [2, 0, 0, 1],
-          [2, 4, 4, 1],
-          [5, 5, 3, 3]]], dtype=np.int32)
-    label_divisor = 1000
-    concat_panoptic = concat_semantic * label_divisor + concat_instance
-    next_panoptic = next_semantic * label_divisor + next_instance
-    new_panoptic = vip_deeplab.stitch_video_panoptic_prediction(
-        concat_panoptic,
-        next_panoptic,
-        label_divisor)
-    # The expected instance is manually computed. It should receive the IDs
-    # propagated from concat_instance by IoU matching between concat_panoptic
-    # and next_panoptic.
-    expected_semantic = next_semantic
-    expected_instance = np.array(
-        [[[1, 0, 0, 2],
-          [1, 0, 0, 2],
-          [1, 1, 1, 2],
-          [2, 2, 1, 1]]], dtype=np.int32)
-    expected_panoptic = expected_semantic * label_divisor + expected_instance
-    np.testing.assert_array_equal(expected_panoptic, new_panoptic)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/spaces/akhaliq/lama/bin/blur_predicts.py b/spaces/akhaliq/lama/bin/blur_predicts.py
deleted file mode 100644
index a14fcc28d5a906ad3a21ab4ba482f38b4fc411cb..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/bin/blur_predicts.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import cv2
-import numpy as np
-import tqdm
-
-from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
-from saicinpainting.evaluation.utils import load_yaml
-
-
-def main(args):
-    config = load_yaml(args.config)
-
-    if not args.predictdir.endswith('/'):
-        args.predictdir += '/'
-
-    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
-
-    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)
-
-    for img_i in tqdm.trange(len(dataset)):
-        pred_fname = dataset.pred_filenames[img_i]
-        cur_out_fname = os.path.join(args.outpath, pred_fname[len(args.predictdir):])
-        os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
-        sample = dataset[img_i]
-        img = sample['image']
-        mask = sample['mask']
-        inpainted = sample['inpainted']
-
-        inpainted_blurred = cv2.GaussianBlur(np.transpose(inpainted, (1, 2, 0)),
-                                             ksize=(args.k, args.k),
-                                             sigmaX=args.s, sigmaY=args.s,
-                                             borderType=cv2.BORDER_REFLECT)
-
-        cur_res = (1 - mask) * np.transpose(img, (1, 2, 0)) + mask * inpainted_blurred
-        cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
-        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
-        cv2.imwrite(cur_out_fname, cur_res)
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('config', type=str, help='Path to evaluation config')
-    aparser.add_argument('datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('predictdir', type=str,
-                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
-    aparser.add_argument('outpath', type=str, help='Where to put results')
-    aparser.add_argument('-s', type=float, default=0.1, help='Gaussian blur sigma')
-    aparser.add_argument('-k', type=int, default=5, help='Kernel size in gaussian blur')
-
-    main(aparser.parse_args())
diff --git a/spaces/akhaliq/yolov7/hubconf.py b/spaces/akhaliq/yolov7/hubconf.py
deleted file mode 100644
index f8a8cbe940d89fa0ca293183407ec7bf6a453305..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/yolov7/hubconf.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""PyTorch Hub models
-
-Usage:
-    import torch
-    model = torch.hub.load('repo', 'model')
-"""
-
-from pathlib import Path
-
-import torch
-
-from models.yolo import Model
-from utils.general import check_requirements, set_logging
-from utils.google_utils import attempt_download
-from utils.torch_utils import select_device
-
-dependencies = ['torch', 'yaml']
-check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop'))
-set_logging()
-
-
-def create(name, pretrained, channels, classes, autoshape):
-    """Creates a specified model
-
-    Arguments:
-        name (str): name of model, i.e. 'yolov7'
-        pretrained (bool): load pretrained weights into the model
-        channels (int): number of input channels
-        classes (int): number of model classes
-
-    Returns:
-        pytorch model
-    """
-    try:
-        cfg = list((Path(__file__).parent / 'cfg').rglob(f'{name}.yaml'))[0]  # model.yaml path
-        model = Model(cfg, channels, classes)
-        if pretrained:
-            fname = f'{name}.pt'  # checkpoint filename
-            attempt_download(fname)  # download if not found locally
-            ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
-            msd = model.state_dict()  # model state_dict
-            csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-            csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
-            model.load_state_dict(csd, strict=False)  # load
-            if len(ckpt['model'].names) == classes:
-                model.names = ckpt['model'].names  # set class names attribute
-            if autoshape:
-                model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
-        device = select_device('0' if torch.cuda.is_available() else 'cpu')  # default to GPU if available
-        return model.to(device)
-
-    except Exception as e:
-        s = 'Cache maybe be out of date, try force_reload=True.'
-        raise Exception(s) from e
-
-
-def custom(path_or_model='path/to/model.pt', autoshape=True):
-    """custom mode
-
-    Arguments (3 options):
-        path_or_model (str): 'path/to/model.pt'
-        path_or_model (dict): torch.load('path/to/model.pt')
-        path_or_model (nn.Module): torch.load('path/to/model.pt')['model']
-
-    Returns:
-        pytorch model
-    """
-    model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model  # load checkpoint
-    if isinstance(model, dict):
-        model = model['ema' if model.get('ema') else 'model']  # load model
-
-    hub_model = Model(model.yaml).to(next(model.parameters()).device)  # create
-    hub_model.load_state_dict(model.float().state_dict())  # load state_dict
-    hub_model.names = model.names  # class names
-    if autoshape:
-        hub_model = hub_model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
-    device = select_device('0' if torch.cuda.is_available() else 'cpu')  # default to GPU if available
-    return hub_model.to(device)
-
-
-def yolov7(pretrained=True, channels=3, classes=80, autoshape=True):
-    return create('yolov7', pretrained, channels, classes, autoshape)
-
-
-if __name__ == '__main__':
-    model = custom(path_or_model='yolov7.pt')  # custom example
-    # model = create(name='yolov7', pretrained=True, channels=3, classes=80, autoshape=True)  # pretrained example
-
-    # Verify inference
-    import numpy as np
-    from PIL import Image
-
-    imgs = [np.zeros((640, 480, 3))]
-
-    results = model(imgs)  # batched inference
-    results.print()
-    results.save()
diff --git a/spaces/alamin655/Personas/conversant/prompt_chatbot.py b/spaces/alamin655/Personas/conversant/prompt_chatbot.py
deleted file mode 100644
index 79602d355ed420686faa494cb002a6f3efe799c8..0000000000000000000000000000000000000000
--- a/spaces/alamin655/Personas/conversant/prompt_chatbot.py
+++ /dev/null
@@ -1,608 +0,0 @@
-# Copyright (c) 2022 Cohere Inc. and its affiliates.
-#
-# Licensed under the MIT License (the "License");
-# you may not use this file except in compliance with the License.
-#
-# You may obtain a copy of the License in the LICENSE file at the top
-# level of this repository.
-
-import json
-import logging
-import os
-import warnings
-from concurrent.futures import Future, ThreadPoolExecutor
-from typing import Any, Dict, Tuple
-
-import cohere
-import jsonschema
-
-import conversant
-from conversant.chatbot import Chatbot, Interaction
-from conversant.prompts.chat_prompt import ChatPrompt
-from conversant.prompts.prompt import Prompt
-
-MAX_GENERATE_TOKENS = 2048
-TOKENS_PER_REQUEST = 10
-PERSONA_MODEL_DIRECTORY = f"{os.path.dirname(conversant.__file__)}/personas"
-PERSONA_JSON_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "chatbot_config": {
-            "type": "object",
-            "properties": {
-                "max_context_examples": {"type": "integer"},
-                "avatar": {"type": "string"},
-            },
-        },
-        "client_config": {
-            "type": "object",
-            "properties": {
-                "model": {"type": "string"},
-                "max_tokens": {"type": "integer"},
-                "temperature": {"type": "number"},
-                "frequency_penalty": {"type": "number"},
-                "presence_penalty": {"type": "number"},
-                "stop_sequences": {"type": "array"},
-            },
-        },
-        "prompt_config": {
-            "type": "object",
-        },
-    },
-}
-
-
-class PromptChatbot(Chatbot):
-    """Use prompt templates and LLM generation to define a chatbot.
-
-    This bot makes no use of external knowledge sources.
-    """
-
-    def __init__(
-        self,
-        client: cohere.Client,
-        prompt: Prompt,
-        persona_name: str = "",
-        chatbot_config: Dict[str, Any] = {},
-        client_config: Dict[str, Any] = {},
-    ):
-        """Enriches init by adding a prompt.
-
-        Args:
-            client (cohere.Client): Cohere client for API
-            prompt (Prompt): Prompt object to direct behavior.
-            persona_name (str, optional): Bot's persona name. Defaults to empty string.
-            chatbot_config: (Dict[str, Any], optional): Bot's chat config. Defaults to
-                empty dict.
-            client_config (Dict[str, Any], optional): Bot's client config. Defaults to
-                empty dict.
-        """
-
-        super().__init__(client)
-        self.prompt = prompt
-        self.persona_name = persona_name
-
-        self.configure_chatbot(chatbot_config)
-        self.configure_client(client_config)
-        self.chat_history = []
-        self.prompt_size_history = []
-        self.prompt_history = [self.prompt.to_string()]
-        self.curr_max_context_examples = self.chatbot_config["max_context_examples"]
-
-        # For the generation models, the maximum token length is 2048
-        # (prompt and generation). So the prompt sent to .generate should be
-        # MAX_GENERATE_TOKENS minus max tokens generated
-        self.max_prompt_size = MAX_GENERATE_TOKENS - self.client_config["max_tokens"]
-        self._check_prompt_size()
-
-    def __repr__(self) -> str:
-        return json.dumps(self.to_dict(), indent=4, default=str)
-
-    @property
-    def user_name(self):
-        """
-        Returns:
-            str: The name of the user, defined in the prompt. Defaults to "User".
-        """
-        if hasattr(self.prompt, "user_name"):
-            return self.prompt.user_name
-        else:
-            return "User"
-
-    @property
-    def bot_name(self):
-        """
-        Returns:
-            str: The name of the chatbot, defined in the prompt. Defaults to
-                "PromptChatbot".
-        """
-        if hasattr(self.prompt, "bot_name"):
-            return self.prompt.bot_name
-        else:
-            return "PromptChatbot"
-
-    @property
-    def latest_prompt(self) -> str:
-        """Retrieves the latest prompt.
-
-        Returns:
-            str: The prompt most recently added to the prompt history.
-        """
-        return self.prompt_history[-1]
-
-    def _update_max_context_examples(
-        self, prompt_size: int, max_context_examples: int
-    ) -> int:
-        """Adjust max_context_examples until a possible prompt size.
-
-        if this is not possible, send an error message.
-
-        Args:
-            prompt_size (int): Number of tokens of the prompt
-            max_context_examples (int): The length of the chat history for
-            the chatbot to use in reply.
-
-        Returns:
-           int: updated max_context_examples
-        """
-        # Store original values
-        original_size = prompt_size
-        # If the size of chat_history is smaller than max_context_examples
-        # the value of the variable is already updated with the size value
-        trimmed_max_examples = min(len(self.chat_history), max_context_examples)
-
-        # Check if the max_context_examples is bigger than 0 so it can be reduced
-        if max_context_examples > 0:
-            # Reduce max_context_examples until the number of token of the prompt
-            # is less than maximum or reaches 1
-            for size in self.prompt_size_history[-max_context_examples:]:
-                prompt_size -= size
-                trimmed_max_examples -= 1
-                if prompt_size <= self.max_prompt_size:
-                    if self.curr_max_context_examples == trimmed_max_examples:
-                        warnings.warn(
-                            "The parameter max_context_examples continues "
-                            f"{self.curr_max_context_examples}"
-                            ", so that the total amount of tokens does not"
-                            f" exceed {MAX_GENERATE_TOKENS}."
-                        )
-                    else:
-                        warnings.warn(
-                            "The parameter max_context_examples was changed for"
-                            f" this turn, from {self.curr_max_context_examples} to "
-                            f"{trimmed_max_examples}, so that "
-                            "the total amount of tokens does not"
-                            f" exceed {MAX_GENERATE_TOKENS}."
-                        )
-                    self.curr_max_context_examples = trimmed_max_examples
-                    return trimmed_max_examples
-
-        raise ValueError(
-            "The total number of tokens (prompt and prediction) cannot exceed "
-            f"{MAX_GENERATE_TOKENS}. Try using a shorter start prompt, sending "
-            "smaller text messages in the chat, or setting a smaller value "
-            "for the parameter max_tokens. More details:\n"
-            f" - Start Prompt: {self.start_prompt_size} tokens\n"
-            f" - Messages sent in chat: {original_size - self.start_prompt_size} "
-            f"tokens\n - Parameter max_tokens: {self.client_config['max_tokens']} "
-            "tokens"
-        )
-
-    def _dispatch_concurrent_generate_call(self, **kwargs) -> Future:
-        """Dispatches a concurrent call to co.generate.
-
-        This allows a network bound co.generate call to proceed while also
-        yielding the current response in a partial reply generator.
-
-        Args:
-            kwargs: Keyword arguments for the call to co.generate.
-
-        Returns:
-            Future: A future object that will be called to retrieve the result of
-                co.generate.
-        """
-        with ThreadPoolExecutor(max_workers=1) as exe:
-            future = exe.submit(self.co.generate, **kwargs)
-        return future
-
-    def get_stop_seq(self, response: str) -> str:
-        """Given a response, returns the stop sequence it has if any.
-
-        Args:
-            response (str): Response coming from prompt chatbot.
-
-        Returns:
-            str: The stop sequence in the response. If no stop sequence is found, then
-                an empty string is returned.
-
-        """
-        for stop_seq in self.client_config["stop_sequences"]:
-            if stop_seq in response:
-                return stop_seq
-        return ""
-
-    def generate_prompt_update_examples(self, query: str) -> str:
-        """Generate prompt from query and update max context examples if necessary
-
-        Args:
-            query (str): A query passed to the prompt chatbot.
-
-        Returns:
-            current_prompt (str): Returns the current prompt using
-            query and chat history
-
-        """
-        # The current prompt is assembled from the initial prompt,
-        # from the chat history with a maximum of max_context_examples,
-        # and from the current query
-
-        current_prompt = self.get_current_prompt(query)
-
-        current_prompt_size = self.co.tokenize(current_prompt).length
-
-        if current_prompt_size > self.max_prompt_size:
-            max_context_examples = self._update_max_context_examples(
-                current_prompt_size, self.chatbot_config["max_context_examples"]
-            )
-            current_prompt = self.get_current_prompt(query, max_context_examples)
-
-        elif (
-            self.curr_max_context_examples
-            != self.chatbot_config["max_context_examples"]
-        ):
-            warnings.warn(
-                "The max_context_examples value returned"
-                f" to {self.chatbot_config['max_context_examples']} - "
-                f"value set in the original config"
-            )
-        return current_prompt
-
-    def partial_reply(self, query: str) -> Tuple[str, str]:
-        """Generates (partial) reply to a query given a chat history.
-
-        Args:
-            query (str): A query passed to the prompt chatbot.
-
-        Yields:
-
-            Tuple[str, str]: A tuple of the response before the co.generate call,
-                and the response after.
-        """
-        current_prompt = self.generate_prompt_update_examples(query)
-        self.prompt_history.append(current_prompt)
-
-        response_before_current = ""
-        response_so_far = ""
-        num_requests_made = 0
-        max_requests = int(self.client_config["max_tokens"] / TOKENS_PER_REQUEST)
-        reply_complete = False
-
-        # As soon as the function is called (and the generator is created), dispatch
-        # a concurrent call to co.generate
-        future = self._dispatch_concurrent_generate_call(
-            model=self.client_config["model"],
-            prompt=current_prompt,
-            max_tokens=TOKENS_PER_REQUEST,
-            temperature=self.client_config["temperature"],
-            frequency_penalty=self.client_config["frequency_penalty"],
-            presence_penalty=self.client_config["presence_penalty"],
-            stop_sequences=self.client_config["stop_sequences"],
-        )
-
-        while num_requests_made < max_requests and not reply_complete:
-            generated_object = future.result()
-            partial_response = generated_object.generations[0].text
-
-            # If the partial response is an empty string, then this iteration is a no-op
-            # (we indicate that the reply is completely generated).
-            if not partial_response:
-                reply_complete = True
-
-            else:
-
-                # Concatenate the candidate response, then fetches the stop sequence if
-                # it exists in the candidate response
-                candidate_response = response_so_far + partial_response
-                stop_seq = self.get_stop_seq(response_so_far + partial_response)
-
-                # Truncate the candidate response if a stop sequence was found
-                if stop_seq:
-                    candidate_response = candidate_response[
-                        : candidate_response.index(stop_seq)
-                    ]
-
-                    # If the stop sequence is found across two partial replies,
-                    # then the response_so_far has to be truncated. Example:
-                    #
-                    #   stop_seq: "\nUser"
-                    #   response_so_far: "Thank you!\n"
-                    #   partial_response: "User: You are welcome"
-                    #
-                    # Then the candidate response is:
-                    #
-                    #   candidate_response: "Thank you!"
-                    #
-                    # In this case, what is yielded at the end of the loop needs to be:
-                    #
-                    #   response_before_current: "Thank you!"
-                    #   response_so_far: "Thank you!"
-                    #
-                    # So we'll truncate the response_so_far to be candidate_response
-                    if len(candidate_response) < len(response_so_far):
-                        response_so_far = candidate_response
-
-                    reply_complete = True
-
-                # Save candidate response
-                current_prompt += partial_response
-                response_before_current = response_so_far
-                response_so_far = candidate_response
-
-                # If this is the first partial_reply, append a new element to
-                # chat history after removing the leading whitespace
-                if num_requests_made == 0:
-                    response_so_far = response_so_far.lstrip()
-                    self.chat_history.append(
-                        self.prompt.create_interaction(query, response_so_far)
-                    )
-                    self.prompt_size_history.append(
-                        self.co.tokenize(
-                            self.prompt.create_interaction_string(
-                                query, response_so_far
-                            )
-                        ).length
-                    )
-                # Otherwise, overwrite the current chat history with the current
-                # response so far
-                else:
-                    self.chat_history[-1] = self.prompt.create_interaction(
-                        query, response_so_far
-                    )
-                    self.prompt_size_history[-1] = self.co.tokenize(
-                        self.prompt.create_interaction_string(query, response_so_far)
-                    ).length
-
-                num_requests_made += 1
-
-                # This dispatches a concurrent call to co.generate, which can be
-                # later accessed on the next iteration of the generator.
-                if num_requests_made < max_requests and not reply_complete:
-                    future = self._dispatch_concurrent_generate_call(
-                        model=self.client_config["model"],
-                        prompt=current_prompt,
-                        max_tokens=TOKENS_PER_REQUEST,
-                        temperature=self.client_config["temperature"],
-                        frequency_penalty=self.client_config["frequency_penalty"],
-                        presence_penalty=self.client_config["presence_penalty"],
-                        stop_sequences=self.client_config["stop_sequences"],
-                    )
-
-                yield response_before_current, response_so_far
-
-    def reply(self, query: str) -> Interaction:
-        """Replies to a query given a chat history.
-
-        The reply is then generated directly from a call to a LLM.
-
-        Args:
-            query (str): A query passed to the prompt chatbot.
-
-        Returns:
-            Interaction: Dictionary of query and generated LLM response
-        """
-
-        current_prompt = self.generate_prompt_update_examples(query)
-
-        # Make a call to Cohere's co.generate API
-        generated_object = self.co.generate(
-            model=self.client_config["model"],
-            prompt=current_prompt,
-            max_tokens=self.client_config["max_tokens"],
-            temperature=self.client_config["temperature"],
-            frequency_penalty=self.client_config["frequency_penalty"],
-            presence_penalty=self.client_config["presence_penalty"],
-            stop_sequences=self.client_config["stop_sequences"],
-        )
-        # If response was cut off by .generate() finding a stop sequence,
-        # remove that sequence from the response.
-        response = generated_object.generations[0].text
-        for stop_seq in self.client_config["stop_sequences"]:
-            if response.endswith(stop_seq):
-                response = response[: -len(stop_seq)]
-        response = response.lstrip()
-
-        # We need to remember the current response in the chat history for future
-        # responses.
-        self.chat_history.append(self.prompt.create_interaction(query, response))
-        self.prompt_size_history.append(
-            self.co.tokenize(
-                self.prompt.create_interaction_string(query, response)
-            ).length
-        )
-        self.prompt_history.append(current_prompt)
-
-        return response
-
-    def get_current_prompt(self, query: str, max_context_examples: int = None) -> str:
-        """Stitches the prompt with a trailing window of the chat.
-        Args:
-            query (str): The current user query.
-            max_context_examples (int): The length of the chat history for
-            the chatbot to use in reply.
-
-        Returns:
-            str: The current prompt given a query.
-        """
-        if max_context_examples is None:
-            max_context_examples = self.chatbot_config["max_context_examples"]
-
-        # get base prompt
-        base_prompt = self.prompt.to_string() + "\n"
-
-        # get context prompt
-        context_prompt_lines = []
-        trimmed_chat_history = (
-            self.chat_history[-max_context_examples:]
-            if max_context_examples > 0
-            else []
-        )
-        # TODO when prompt is updated, the history is mutated
-        # as it is recreated using the new prompt. A possible fix is to save the old
-        # prompt in history and use it when recreating.
-        for turn in trimmed_chat_history:
-            context_prompt_lines.append(self.prompt.create_interaction_string(**turn))
-        context_prompt = self.prompt.example_separator + "".join(context_prompt_lines)
-
-        current_prompt = base_prompt + context_prompt
-
-        # get query prompt
-        if query != "":
-            query_prompt = self.prompt.create_interaction_string(query)
-            current_prompt += query_prompt
-        return current_prompt.strip()
-
-    def configure_chatbot(self, chatbot_config: Dict = {}) -> None:
-        """Configures chatbot options.
-
-        Args:
-            chatbot_config (Dict, optional): Updates self.chatbot_config. Defaults
-                to {}.
-        """
-        # We initialize the chatbot to these default config values.
-        if not hasattr(self, "chatbot_config"):
-            self.chatbot_config = {"max_context_examples": 10, "avatar": ":robot:"}
-        # Override default config values with the config passed in
-        if isinstance(chatbot_config, Dict):
-            self.chatbot_config.update(chatbot_config)
-        else:
-            raise TypeError(
-                "chatbot_config must be of type Dict, but was passed in as "
-                f"{type(chatbot_config)}"
-            )
-
-    def configure_client(self, client_config: Dict = {}) -> None:
-        """Configures client options.
-
-        Args:
-            client_config (Dict, optional): Updates self.client_config. Defaults to {}.
-        """
-        # We initialize the client to these default config values.
-        if not hasattr(self, "client_config"):
-            self.client_config = {
-                "model": "xlarge",
-                "max_tokens": 200,
-                "temperature": 0.75,
-                "frequency_penalty": 0.0,
-                "presence_penalty": 0.0,
-                "stop_sequences": ["\\n", "\n"],
-            }
-        # Override default config values with the config passed in
-        if isinstance(client_config, Dict):
-            self.client_config.update(client_config)
-        else:
-            raise TypeError(
-                "client_config must be of type Dict, but was passed in as "
-                f"{type(client_config)}"
-            )
-
-        # Checks if the parameter is equal or bigger than MAX_GENERATE_TOKENS
-        if self.client_config["max_tokens"] >= MAX_GENERATE_TOKENS:
-            raise ValueError(
-                f"The parameter max_tokens needs to be smaller than "
-                f"{MAX_GENERATE_TOKENS}. Try using a smaller value."
-            )
-        elif self.client_config["max_tokens"] > (MAX_GENERATE_TOKENS * 0.75):
-            warnings.warn(
-                "The parameter max_tokens has a value "
-                f"({self.client_config['max_tokens']}) close to the total allowed"
-                f" for prompt and prediction - {MAX_GENERATE_TOKENS} tokens"
-            )
-
-    @classmethod
-    def from_persona(
-        cls,
-        persona_name: str,
-        client: cohere.Client,
-        persona_dir: str = PERSONA_MODEL_DIRECTORY,
-    ):
-        """Initializes a PromptChatbot using a persona.
-
-        Args:
-            persona (str): Name of persona, corresponding to a .json file.
-            client (cohere.Client): Cohere client for API
-            persona_dir (str): Path to where pre-defined personas are.
-        """
-        # Load the persona from a local directory
-        persona_path = os.path.join(persona_dir, persona_name, "config.json")
-        if os.path.isfile(persona_path):
-            logging.info(f"loading persona from {persona_path}")
-        else:
-            raise FileNotFoundError(f"{persona_path} cannot be found.")
-        with open(persona_path) as f:
-            persona = json.load(f)
-
-        # Validate that the persona follows our predefined schema
-        cls._validate_persona_dict(persona, persona_path)
-        return cls(
-            client=client,
-            prompt=ChatPrompt.from_dict(persona["chat_prompt_config"]),
-            persona_name=persona_name,
-            chatbot_config=persona["chatbot_config"],
-            client_config=persona["client_config"],
-        )
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Serializes this instance into a Python dictionary.
-
-        Returns:
-            Dict[str, Any]: Dictionary of attributes that defines this instance of a
-                PromptChatbot.
-        """
-        attr_dict = {k: v for k, v in vars(self).items()}
-        attr_dict["prompt"] = attr_dict["prompt"].to_dict()
-        return attr_dict
-
-    def _check_prompt_size(self) -> None:
-
-        self.start_prompt_size = self.co.tokenize(self.prompt.to_string()).length
-        if self.start_prompt_size > self.max_prompt_size:
-            raise ValueError(
-                f"The prompt given to PromptChatbot has {self.start_prompt_size}"
-                " tokens. And the value of the parameter max_tokens is"
-                f" {self.client_config['max_tokens']}. Adding the two values "
-                f"the total cannot exceed {MAX_GENERATE_TOKENS}. "
-                "Try using a shorter preamble or less examples."
-            )
-        elif self.start_prompt_size > (0.75 * self.max_prompt_size):
-            warnings.warn(
-                "The prompt given to PromptChatbot has "
-                f"{self.start_prompt_size} tokens. And the value of the parameter"
-                f" max_tokens is {self.client_config['max_tokens']}. "
-                "Adding the two together gives a value close to the total allowed"
-                f" for prompt and prediction - {MAX_GENERATE_TOKENS} tokens"
-            )
-
-    @staticmethod
-    def _validate_persona_dict(persona: Dict[str, Any], persona_path: str) -> None:
-        """Validates formatting of a persona defined as a dictionary.
-
-        Args:
-            persona (Dict[str, Any]): A dictionary containing the persona.
-            persona_path: The path from which the persona was loaded.
-        """
-        try:
-            jsonschema.validate(instance=persona, schema=PERSONA_JSON_SCHEMA)
-        except jsonschema.exceptions.ValidationError as e:
-            raise jsonschema.exceptions.ValidationError(
-                f"Type of values in given dictionary (persona from {persona_path}) do "
-                f"not match schema': {e}"
-            )
-        except KeyError as e:
-            raise KeyError(
-                f"Invalid key in given dictionary (persona from {persona_path})': {e}"
-            )
-        except Exception as e:
-            raise Exception(
-                "Failed to validate persona in given dictionary (persona from "
-                f"{persona_path}): {e}"
-            )
diff --git a/spaces/alamin655/websurfx/public/static/colorschemes/oceanic-next.css b/spaces/alamin655/websurfx/public/static/colorschemes/oceanic-next.css
deleted file mode 100644
index 896bae13eca023fba05eb8b91500ceb51c3b34fd..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/public/static/colorschemes/oceanic-next.css
+++ /dev/null
@@ -1,11 +0,0 @@
-:root {
-  --background-color: #1b2b34;
-  --foreground-color: #d8dee9;
-  --color-one: #343d46;
-  --color-two: #5FB3B3ff;
-  --color-three: #69Cf;
-  --color-four: #99c794;
-  --color-five: #69c;
-  --color-six: #c594c5;
-  --color-seven: #D8DEE9ff;
-}
diff --git a/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Parser.pod b/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Parser.pod
deleted file mode 100644
index b8cd46ec91963eec25511df32d5e9d1f8aa1b5cb..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Parser.pod
+++ /dev/null
@@ -1,67 +0,0 @@
-=head1 NAME
-
-XML::DOM::Parser - An XML::Parser that builds XML::DOM document structures
-
-=head1 SYNOPSIS
-
- use XML::DOM;
-
- my $parser = new XML::DOM::Parser;
- my $doc = $parser->parsefile ("file.xml");
- $doc->dispose; # Avoid memory leaks - cleanup circular references
-
-=head1 DESCRIPTION
-
-XML::DOM::Parser extends L<XML::Parser>
-
-The XML::Parser module was written by Clark Cooper and
-is built on top of XML::Parser::Expat, 
-which is a lower level interface to James Clark's expat library.
-
-XML::DOM::Parser parses XML strings or files
-and builds a data structure that conforms to the API of the Document Object 
-Model as described at L<http://www.w3.org/TR/REC-DOM-Level-1>.
-See the L<XML::Parser> manpage for other additional properties of the 
-XML::DOM::Parser class. 
-Note that the 'Style' property should not be used (it is set internally.)
-
-The XML::Parser B<NoExpand> option is more or less supported, in that it will
-generate EntityReference objects whenever an entity reference is encountered
-in character data. I'm not sure how useful this is. Any comments are welcome.
-
-As described in the synopsis, when you create an XML::DOM::Parser object, 
-the parse and parsefile methods create an L<XML::DOM::Document> object
-from the specified input. This Document object can then be examined, modified and
-written back out to a file or converted to a string.
-
-When using XML::DOM with XML::Parser version 2.19 and up, setting the 
-XML::DOM::Parser option B<KeepCDATA> to 1 will store CDATASections in
-CDATASection nodes, instead of converting them to Text nodes.
-Subsequent CDATASection nodes will be merged into one. Let me know if this
-is a problem.
-
-=head1 Using LWP to parse URLs
-
-The parsefile() method now also supports URLs, e.g. I<http://www.erols.com/enno/xsa.xml>.
-It uses LWP to download the file and then calls parse() on the resulting string.
-By default it will use a L<LWP::UserAgent> that is created as follows:
-
- use LWP::UserAgent;
- $LWP_USER_AGENT = LWP::UserAgent->new;
- $LWP_USER_AGENT->env_proxy;
-
-Note that env_proxy reads proxy settings from environment variables, which is what I need to
-do to get thru our firewall. If you want to use a different LWP::UserAgent, you can either set
-it globally with:
-
- XML::DOM::Parser::set_LWP_UserAgent ($my_agent);
-
-or, you can specify it for a specific XML::DOM::Parser by passing it to the constructor:
-
- my $parser = new XML::DOM::Parser (LWP_UserAgent => $my_agent);
-
-Currently, LWP is used when the filename (passed to parsefile) starts with one of
-the following URL schemes: http, https, ftp, wais, gopher, or file (followed by a colon.)
-If I missed one, please let me know. 
-
-The LWP modules are part of libwww-perl which is available at CPAN.
diff --git a/spaces/aliabid94/crossword/README.md b/spaces/aliabid94/crossword/README.md
deleted file mode 100644
index 185b8b452016e4322a709d8356e318ddccac934f..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/crossword/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Crossword
-emoji: 🧩
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.32.0
-app_file: run.py
-pinned: false
-license: mit
----
-
diff --git a/spaces/allknowingroger/Image-Models-Test10/README.md b/spaces/allknowingroger/Image-Models-Test10/README.md
deleted file mode 100644
index f4b76ae3d674676241555ac4d1f174a460c9c976..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test10/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-Test9
----
-
-<!--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-Test39/README.md b/spaces/allknowingroger/Image-Models-Test39/README.md
deleted file mode 100644
index 2cadaafbd0ac28469c87d116537d57a759150744..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test39/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 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-Test38
----
-
-<!--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-Test63/app.py b/spaces/allknowingroger/Image-Models-Test63/app.py
deleted file mode 100644
index 3ffcd571e3f2bbc44fa853595d2c112eeb93e30c..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test63/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "govindkrishnan123/my-pet-dog",
-    "anjali0610/my-dog",
-    "MakAttack/6537c748746a76fc12454f8b",
-    "Yntec/CultClassic",
-    "oljike/all-kzkhs-lora",
-    "Yntec/photoMovieX",
-    "Yntec/YiffyMix",
-    "Yntec/CinematicReality",
-    "aigrils2/beautifulv6",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test75/app.py b/spaces/allknowingroger/Image-Models-Test75/app.py
deleted file mode 100644
index 92885a147fed06b8e5a4a7ff30c554c7cb79ce92..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test75/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "Yntec/Toonify2",
-    "alkalinevk/testrep",
-    "hashu/my-pet-dog-hsq",
-    "hashu/my-pet-dog-xzh",
-    "skramd/db-sdxl-whitebg-product-photography",
-    "abin-regi/my-pet-dog-xzk",
-    "skshreyas714/lora-trained-xl-colab",
-    "Crosstyan/BPModel",
-    "NoCrypt/SomethingV2_2",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/amankishore/sjc/sd1/ldm/__init__.py b/spaces/amankishore/sjc/sd1/ldm/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Xiaor.py b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Xiaor.py
deleted file mode 100644
index 5757f9971157116cbbfabbe5420e3b7e88fed4e7..0000000000000000000000000000000000000000
--- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/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/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/prompt.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/prompt.py
deleted file mode 100644
index f81aa7c281376933c95c854ed2ecc8ae99ad92a7..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/prompt.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import re
-
-def check_is_number(value):
-    float_pattern = r'^(?=.)([+-]?([0-9]*)(\.([0-9]+))?)$'
-    return re.match(float_pattern, value)
-
-def parse_weight(match, frame = 0)->float:
-    import numexpr
-    w_raw = match.group("weight")
-    if w_raw == None:
-        return 1
-    if check_is_number(w_raw):
-        return float(w_raw)
-    else:
-        t = frame
-        if len(w_raw) < 3:
-            print('the value inside `-characters cannot represent a math function')
-            return 1
-        return float(numexpr.evaluate(w_raw[1:-1]))
-
-def split_weighted_subprompts(text, frame = 0):
-    """
-    splits the prompt based on deforum webui implementation, moved from generate.py 
-    """
-    math_parser = re.compile("""
-            (?P<weight>(
-            `[\S\s]*?`# a math function wrapped in `-characters
-            ))
-            """, re.VERBOSE)
-    
-    parsed_prompt = re.sub(math_parser, lambda m: str(parse_weight(m, frame)), text)
-
-    negative_prompts = []
-    positive_prompts = []
-
-    prompt_split = parsed_prompt.split("--neg")
-    if len(prompt_split) > 1:
-        positive_prompts, negative_prompts = parsed_prompt.split("--neg") #TODO: add --neg to vanilla Deforum for compat
-    else:
-        positive_prompts = prompt_split[0]
-        negative_prompts = ""
-
-    return positive_prompts, negative_prompts
-
-def interpolate_prompts(animation_prompts, max_frames):
-    import numpy as np
-    import pandas as pd
-    # Get prompts sorted by keyframe 
-    sorted_prompts = sorted(animation_prompts.items(), key=lambda item: int(item[0]))
-
-    # Setup container for interpolated prompts
-    prompt_series = pd.Series([np.nan for a in range(max_frames)])
-
-    # For every keyframe prompt except the last
-    for i in range(0,len(sorted_prompts)-1):
-        
-        # Get current and next keyframe
-        current_frame = int(sorted_prompts[i][0])
-        next_frame = int(sorted_prompts[i+1][0])
-        
-        # Ensure there's no weird ordering issues or duplication in the animation prompts
-        # (unlikely because we sort above, and the json parser will strip dupes)
-        if current_frame>=next_frame:
-            print(f"WARNING: Sequential prompt keyframes {i}:{current_frame} and {i+1}:{next_frame} are not monotonously increasing; skipping interpolation.")
-            continue
-            
-        # Get current and next keyframes' positive and negative prompts (if any)
-        current_prompt = sorted_prompts[i][1]
-        next_prompt = sorted_prompts[i+1][1]
-        current_positive, current_negative, *_ = current_prompt.split("--neg") + [None]
-        next_positive, next_negative, *_ = next_prompt.split("--neg") + [None]
-        
-        # Calculate how much to shift the weight from current to next prompt at each frame
-        weight_step = 1/(next_frame-current_frame)
-        
-        # Apply weighted prompt interpolation for each frame between current and next keyframe
-        # using the syntax:  prompt1 :weight1 AND prompt1 :weight2 --neg nprompt1 :weight1 AND nprompt1 :weight2
-        # (See: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#composable-diffusion )
-        for f in range(current_frame,next_frame):
-            next_weight = weight_step * (f-current_frame)
-            current_weight = 1 - next_weight
-            
-            # We will build the prompt incrementally depending on which prompts are present
-            prompt_series[f] = ''
-
-            # Cater for the case where neither, either or both current & next have positive prompts:
-            if current_positive:
-                prompt_series[f] += f"{current_positive} :{current_weight}"
-            if current_positive and next_positive:
-                prompt_series[f] += f" AND "
-            if next_positive:
-                prompt_series[f] += f"{next_positive} :{next_weight}"
-            
-            # Cater for the case where neither, either or both current & next have negative prompts:
-            if current_negative or next_negative:
-                prompt_series[f] += " --neg "
-                if current_negative:
-                    prompt_series[f] += f" {current_negative} :{current_weight}"
-                if current_negative and next_negative:
-                    prompt_series[f] += f" AND "
-                if next_negative:
-                    prompt_series[f] += f" {next_negative} :{next_weight}"
-    
-    # Set explicitly declared keyframe prompts (overwriting interpolated values at the keyframe idx). This ensures:
-    # - That final prompt is set, and
-    # - Gives us a chance to emit warnings if any keyframe prompts are already using composable diffusion
-    for i, prompt in animation_prompts.items():
-        prompt_series[int(i)] = prompt
-        if ' AND ' in prompt:
-            print(f"WARNING: keyframe {i}'s prompt is using composable diffusion (aka the 'AND' keyword). This will cause unexpected behaviour with interpolation.")
-    
-    # Return the filled series, in case max_frames is greater than the last keyframe or any ranges were skipped.
-    return prompt_series.ffill().bfill()
diff --git a/spaces/appl044/Chat-GPT-LangChain/polly_utils.py b/spaces/appl044/Chat-GPT-LangChain/polly_utils.py
deleted file mode 100644
index 7cb38abff2aaac3c5b24f20914d464151173780d..0000000000000000000000000000000000000000
--- a/spaces/appl044/Chat-GPT-LangChain/polly_utils.py
+++ /dev/null
@@ -1,635 +0,0 @@
-# This class stores Polly voice data. Specifically, the class stores several records containing
-# language, lang_code, gender, voice_id and engine. The class also has a method to return the
-# voice_id, lang_code and engine given a language and gender.
-
-NEURAL_ENGINE = "neural"
-STANDARD_ENGINE = "standard"
-
-
-class PollyVoiceData:
-    def get_voice(self, language, gender):
-        for voice in self.voice_data:
-            if voice['language'] == language and voice['gender'] == gender:
-                if voice['neural'] == 'Yes':
-                    return voice['voice_id'], voice['lang_code'], NEURAL_ENGINE
-        for voice in self.voice_data:
-            if voice['language'] == language and voice['gender'] == gender:
-                if voice['standard'] == 'Yes':
-                    return voice['voice_id'], voice['lang_code'], STANDARD_ENGINE
-        return None, None, None
-
-    def get_whisper_lang_code(self, language):
-        for voice in self.voice_data:
-            if voice['language'] == language:
-                return voice['whisper_lang_code']
-        return "en"
-
-    def __init__(self):
-        self.voice_data = [
-            {'language': 'Arabic',
-             'lang_code': 'arb',
-             'whisper_lang_code': 'ar',
-             'voice_id': 'Zeina',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Arabic (Gulf)',
-             'lang_code': 'ar-AE',
-             'whisper_lang_code': 'ar',
-             'voice_id': 'Hala',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Catalan',
-             'lang_code': 'ca-ES',
-             'whisper_lang_code': 'ca',
-             'voice_id': 'Arlet',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Chinese (Cantonese)',
-             'lang_code': 'yue-CN',
-             'whisper_lang_code': 'zh',
-             'voice_id': 'Hiujin',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Chinese (Mandarin)',
-             'lang_code': 'cmn-CN',
-             'whisper_lang_code': 'zh',
-             'voice_id': 'Zhiyu',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Danish',
-             'lang_code': 'da-DK',
-             'whisper_lang_code': 'da',
-             'voice_id': 'Naja',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Danish',
-             'lang_code': 'da-DK',
-             'whisper_lang_code': 'da',
-             'voice_id': 'Mads',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Dutch',
-             'lang_code': 'nl-NL',
-             'whisper_lang_code': 'nl',
-             'voice_id': 'Laura',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Dutch',
-             'lang_code': 'nl-NL',
-             'whisper_lang_code': 'nl',
-             'voice_id': 'Lotte',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Dutch',
-             'lang_code': 'nl-NL',
-             'whisper_lang_code': 'nl',
-             'voice_id': 'Ruben',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'English (Australian)',
-             'lang_code': 'en-AU',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Nicole',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'English (Australian)',
-             'lang_code': 'en-AU',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Olivia',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'English (Australian)',
-             'lang_code': 'en-AU',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Russell',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'English (British)',
-             'lang_code': 'en-GB',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Amy',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (British)',
-             'lang_code': 'en-GB',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Emma',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (British)',
-             'lang_code': 'en-GB',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Brian',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (British)',
-             'lang_code': 'en-GB',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Arthur',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'English (Indian)',
-             'lang_code': 'en-IN',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Aditi',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'English (Indian)',
-             'lang_code': 'en-IN',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Raveena',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'English (Indian)',
-             'lang_code': 'en-IN',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Kajal',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'English (New Zealand)',
-             'lang_code': 'en-NZ',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Aria',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'English (South African)',
-             'lang_code': 'en-ZA',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Ayanda',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Ivy',
-             'gender': 'Female (child)',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Joanna',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Kendra',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Kimberly',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Salli',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Joey',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Justin',
-             'gender': 'Male (child)',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Kevin',
-             'gender': 'Male (child)',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'English (US)',
-             'lang_code': 'en-US',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Matthew',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'English (Welsh)',
-             'lang_code': 'en-GB-WLS',
-             'whisper_lang_code': 'en',
-             'voice_id': 'Geraint',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Finnish',
-             'lang_code': 'fi-FI',
-             'whisper_lang_code': 'fi',
-             'voice_id': 'Suvi',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'French',
-             'lang_code': 'fr-FR',
-             'whisper_lang_code': 'fr',
-             'voice_id': 'Celine',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'French',
-             'lang_code': 'fr-FR',
-             'whisper_lang_code': 'fr',
-             'voice_id': 'Lea',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'French',
-             'lang_code': 'fr-FR',
-             'whisper_lang_code': 'fr',
-             'voice_id': 'Mathieu',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'French (Canadian)',
-             'lang_code': 'fr-CA',
-             'whisper_lang_code': 'fr',
-             'voice_id': 'Chantal',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'French (Canadian)',
-             'lang_code': 'fr-CA',
-             'whisper_lang_code': 'fr',
-             'voice_id': 'Gabrielle',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'French (Canadian)',
-             'lang_code': 'fr-CA',
-             'whisper_lang_code': 'fr',
-             'voice_id': 'Liam',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'German',
-             'lang_code': 'de-DE',
-             'whisper_lang_code': 'de',
-             'voice_id': 'Marlene',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'German',
-             'lang_code': 'de-DE',
-             'whisper_lang_code': 'de',
-             'voice_id': 'Vicki',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'German',
-             'lang_code': 'de-DE',
-             'whisper_lang_code': 'de',
-             'voice_id': 'Hans',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'German',
-             'lang_code': 'de-DE',
-             'whisper_lang_code': 'de',
-             'voice_id': 'Daniel',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'German (Austrian)',
-             'lang_code': 'de-AT',
-             'whisper_lang_code': 'de',
-             'voice_id': 'Hannah',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Hindi',
-             'lang_code': 'hi-IN',
-             'whisper_lang_code': 'hi',
-             'voice_id': 'Aditi',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Hindi',
-             'lang_code': 'hi-IN',
-             'whisper_lang_code': 'hi',
-             'voice_id': 'Kajal',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Icelandic',
-             'lang_code': 'is-IS',
-             'whisper_lang_code': 'is',
-             'voice_id': 'Dora',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Icelandic',
-             'lang_code': 'is-IS',
-             'whisper_lang_code': 'is',
-             'voice_id': 'Karl',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Italian',
-             'lang_code': 'it-IT',
-             'whisper_lang_code': 'it',
-             'voice_id': 'Carla',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Italian',
-             'lang_code': 'it-IT',
-             'whisper_lang_code': 'it',
-             'voice_id': 'Bianca',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Japanese',
-             'lang_code': 'ja-JP',
-             'whisper_lang_code': 'ja',
-             'voice_id': 'Mizuki',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Japanese',
-             'lang_code': 'ja-JP',
-             'whisper_lang_code': 'ja',
-             'voice_id': 'Takumi',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Korean',
-             'lang_code': 'ko-KR',
-             'whisper_lang_code': 'ko',
-             'voice_id': 'Seoyeon',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Norwegian',
-             'lang_code': 'nb-NO',
-             'whisper_lang_code': 'no',
-             'voice_id': 'Liv',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Norwegian',
-             'lang_code': 'nb-NO',
-             'whisper_lang_code': 'no',
-             'voice_id': 'Ida',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Polish',
-             'lang_code': 'pl-PL',
-             'whisper_lang_code': 'pl',
-             'voice_id': 'Ewa',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Polish',
-             'lang_code': 'pl-PL',
-             'whisper_lang_code': 'pl',
-             'voice_id': 'Maja',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Polish',
-             'lang_code': 'pl-PL',
-             'whisper_lang_code': 'pl',
-             'voice_id': 'Jacek',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Polish',
-             'lang_code': 'pl-PL',
-             'whisper_lang_code': 'pl',
-             'voice_id': 'Jan',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Polish',
-             'lang_code': 'pl-PL',
-             'whisper_lang_code': 'pl',
-             'voice_id': 'Ola',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Portuguese (Brazilian)',
-             'lang_code': 'pt-BR',
-             'whisper_lang_code': 'pt',
-             'voice_id': 'Camila',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Portuguese (Brazilian)',
-             'lang_code': 'pt-BR',
-             'whisper_lang_code': 'pt',
-             'voice_id': 'Vitoria',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Portuguese (Brazilian)',
-             'lang_code': 'pt-BR',
-             'whisper_lang_code': 'pt',
-             'voice_id': 'Ricardo',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Portuguese (European)',
-             'lang_code': 'pt-PT',
-             'whisper_lang_code': 'pt',
-             'voice_id': 'Ines',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Portuguese (European)',
-             'lang_code': 'pt-PT',
-             'whisper_lang_code': 'pt',
-             'voice_id': 'Cristiano',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Romanian',
-             'lang_code': 'ro-RO',
-             'whisper_lang_code': 'ro',
-             'voice_id': 'Carmen',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Russian',
-             'lang_code': 'ru-RU',
-             'whisper_lang_code': 'ru',
-             'voice_id': 'Tatyana',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Russian',
-             'lang_code': 'ru-RU',
-             'whisper_lang_code': 'ru',
-             'voice_id': 'Maxim',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Spanish (European)',
-             'lang_code': 'es-ES',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Conchita',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Spanish (European)',
-             'lang_code': 'es-ES',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Lucia',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Spanish (European)',
-             'lang_code': 'es-ES',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Enrique',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Spanish (Mexican)',
-             'lang_code': 'es-MX',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Mia',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Spanish (US)',
-             'lang_code': 'es-US',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Lupe',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'Yes'},
-            {'language': 'Spanish (US)',
-             'lang_code': 'es-US',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Penelope',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Spanish (US)',
-             'lang_code': 'es-US',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Miguel',
-             'gender': 'Male',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Spanish (US)',
-             'lang_code': 'es-US',
-             'whisper_lang_code': 'es',
-             'voice_id': 'Pedro',
-             'gender': 'Male',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Swedish',
-             'lang_code': 'sv-SE',
-             'whisper_lang_code': 'sv',
-             'voice_id': 'Astrid',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Swedish',
-             'lang_code': 'sv-SE',
-             'whisper_lang_code': 'sv',
-             'voice_id': 'Elin',
-             'gender': 'Female',
-             'neural': 'Yes',
-             'standard': 'No'},
-            {'language': 'Turkish',
-             'lang_code': 'tr-TR',
-             'whisper_lang_code': 'tr',
-             'voice_id': 'Filiz',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'},
-            {'language': 'Welsh',
-             'lang_code': 'cy-GB',
-             'whisper_lang_code': 'cy',
-             'voice_id': 'Gwyneth',
-             'gender': 'Female',
-             'neural': 'No',
-             'standard': 'Yes'}
-        ]
-
-
-# Run from the command-line
-if __name__ == '__main__':
-    polly_voice_data = PollyVoiceData()
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('English (US)', 'Male')
-    print('English (US)', 'Male', voice_id, language_code, engine)
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('English (US)', 'Female')
-    print('English (US)', 'Female', voice_id, language_code, engine)
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('French', 'Female')
-    print('French', 'Female', voice_id, language_code, engine)
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('French', 'Male')
-    print('French', 'Male', voice_id, language_code, engine)
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('Japanese', 'Female')
-    print('Japanese', 'Female', voice_id, language_code, engine)
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('Japanese', 'Male')
-    print('Japanese', 'Male', voice_id, language_code, engine)
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('Hindi', 'Female')
-    print('Hindi', 'Female', voice_id, language_code, engine)
-
-    voice_id, language_code, engine = polly_voice_data.get_voice('Hindi', 'Male')
-    print('Hindi', 'Male', voice_id, language_code, engine)
-
-    whisper_lang_code = polly_voice_data.get_whisper_lang_code('English (US)')
-    print('English (US) whisper_lang_code:', whisper_lang_code)
-
-    whisper_lang_code = polly_voice_data.get_whisper_lang_code('Chinese (Mandarin)')
-    print('Chinese (Mandarin) whisper_lang_code:', whisper_lang_code)
-
-    whisper_lang_code = polly_voice_data.get_whisper_lang_code('Norwegian')
-    print('Norwegian whisper_lang_code:', whisper_lang_code)
-
-    whisper_lang_code = polly_voice_data.get_whisper_lang_code('Dutch')
-    print('Dutch whisper_lang_code:', whisper_lang_code)
-
-    whisper_lang_code = polly_voice_data.get_whisper_lang_code('Foo')
-    print('Foo whisper_lang_code:', whisper_lang_code)
-
-
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/LL1Analyzer.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/LL1Analyzer.py
deleted file mode 100644
index 6b398fcd99db853b289eb57f5f0cbc1b880cf8ea..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/LL1Analyzer.py
+++ /dev/null
@@ -1,172 +0,0 @@
-#
-# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
-# Use of this file is governed by the BSD 3-clause license that
-# can be found in the LICENSE.txt file in the project root.
-#/
-from antlr4.IntervalSet import IntervalSet
-from antlr4.Token import Token
-from antlr4.PredictionContext import PredictionContext, SingletonPredictionContext, PredictionContextFromRuleContext
-from antlr4.RuleContext import RuleContext
-from antlr4.atn.ATN import ATN
-from antlr4.atn.ATNConfig import ATNConfig
-from antlr4.atn.ATNState import ATNState, RuleStopState
-from antlr4.atn.Transition import WildcardTransition, NotSetTransition, AbstractPredicateTransition, RuleTransition
-
-
-class LL1Analyzer (object):
-
-    #* Special value added to the lookahead sets to indicate that we hit
-    #  a predicate during analysis if {@code seeThruPreds==false}.
-    #/
-    HIT_PRED = Token.INVALID_TYPE
-
-    def __init__(self, atn:ATN):
-        self.atn = atn
-
-    #*
-    # Calculates the SLL(1) expected lookahead set for each outgoing transition
-    # of an {@link ATNState}. The returned array has one element for each
-    # outgoing transition in {@code s}. If the closure from transition
-    # <em>i</em> leads to a semantic predicate before matching a symbol, the
-    # element at index <em>i</em> of the result will be {@code null}.
-    #
-    # @param s the ATN state
-    # @return the expected symbols for each outgoing transition of {@code s}.
-    #/
-    def getDecisionLookahead(self, s:ATNState):
-        if s is None:
-            return None
-
-        count = len(s.transitions)
-        look = [] * count
-        for alt in range(0, count):
-            look[alt] = set()
-            lookBusy = set()
-            seeThruPreds = False # fail to get lookahead upon pred
-            self._LOOK(s.transition(alt).target, None, PredictionContext.EMPTY,
-                  look[alt], lookBusy, set(), seeThruPreds, False)
-            # Wipe out lookahead for this alternative if we found nothing
-            # or we had a predicate when we !seeThruPreds
-            if len(look[alt])==0 or self.HIT_PRED in look[alt]:
-                look[alt] = None
-        return look
-
-    #*
-    # Compute set of tokens that can follow {@code s} in the ATN in the
-    # specified {@code ctx}.
-    #
-    # <p>If {@code ctx} is {@code null} and the end of the rule containing
-    # {@code s} is reached, {@link Token#EPSILON} is added to the result set.
-    # If {@code ctx} is not {@code null} and the end of the outermost rule is
-    # reached, {@link Token#EOF} is added to the result set.</p>
-    #
-    # @param s the ATN state
-    # @param stopState the ATN state to stop at. This can be a
-    # {@link BlockEndState} to detect epsilon paths through a closure.
-    # @param ctx the complete parser context, or {@code null} if the context
-    # should be ignored
-    #
-    # @return The set of tokens that can follow {@code s} in the ATN in the
-    # specified {@code ctx}.
-    #/
-    def LOOK(self, s:ATNState, stopState:ATNState=None, ctx:RuleContext=None):
-        r = IntervalSet()
-        seeThruPreds = True # ignore preds; get all lookahead
-        lookContext = PredictionContextFromRuleContext(s.atn, ctx) if ctx is not None else None
-        self._LOOK(s, stopState, lookContext, r, set(), set(), seeThruPreds, True)
-        return r
-
-    #*
-    # Compute set of tokens that can follow {@code s} in the ATN in the
-    # specified {@code ctx}.
-    #
-    # <p>If {@code ctx} is {@code null} and {@code stopState} or the end of the
-    # rule containing {@code s} is reached, {@link Token#EPSILON} is added to
-    # the result set. If {@code ctx} is not {@code null} and {@code addEOF} is
-    # {@code true} and {@code stopState} or the end of the outermost rule is
-    # reached, {@link Token#EOF} is added to the result set.</p>
-    #
-    # @param s the ATN state.
-    # @param stopState the ATN state to stop at. This can be a
-    # {@link BlockEndState} to detect epsilon paths through a closure.
-    # @param ctx The outer context, or {@code null} if the outer context should
-    # not be used.
-    # @param look The result lookahead set.
-    # @param lookBusy A set used for preventing epsilon closures in the ATN
-    # from causing a stack overflow. Outside code should pass
-    # {@code new HashSet<ATNConfig>} for this argument.
-    # @param calledRuleStack A set used for preventing left recursion in the
-    # ATN from causing a stack overflow. Outside code should pass
-    # {@code new BitSet()} for this argument.
-    # @param seeThruPreds {@code true} to true semantic predicates as
-    # implicitly {@code true} and "see through them", otherwise {@code false}
-    # to treat semantic predicates as opaque and add {@link #HIT_PRED} to the
-    # result if one is encountered.
-    # @param addEOF Add {@link Token#EOF} to the result if the end of the
-    # outermost context is reached. This parameter has no effect if {@code ctx}
-    # is {@code null}.
-    #/
-    def _LOOK(self, s:ATNState, stopState:ATNState , ctx:PredictionContext, look:IntervalSet, lookBusy:set,
-                     calledRuleStack:set, seeThruPreds:bool, addEOF:bool):
-        c = ATNConfig(s, 0, ctx)
-
-        if c in lookBusy:
-            return
-        lookBusy.add(c)
-
-        if s == stopState:
-            if ctx is None:
-                look.addOne(Token.EPSILON)
-                return
-            elif ctx.isEmpty() and addEOF:
-                look.addOne(Token.EOF)
-                return
-
-        if isinstance(s, RuleStopState ):
-            if ctx is None:
-                look.addOne(Token.EPSILON)
-                return
-            elif ctx.isEmpty() and addEOF:
-                look.addOne(Token.EOF)
-                return
-
-            if ctx != PredictionContext.EMPTY:
-                # run thru all possible stack tops in ctx
-                for i in range(0, len(ctx)):
-                    returnState = self.atn.states[ctx.getReturnState(i)]
-                    removed = returnState.ruleIndex in calledRuleStack
-                    try:
-                        calledRuleStack.discard(returnState.ruleIndex)
-                        self._LOOK(returnState, stopState, ctx.getParent(i), look, lookBusy, calledRuleStack, seeThruPreds, addEOF)
-                    finally:
-                        if removed:
-                            calledRuleStack.add(returnState.ruleIndex)
-                return
-
-        for t in s.transitions:
-            if type(t) == RuleTransition:
-                if t.target.ruleIndex in calledRuleStack:
-                    continue
-
-                newContext = SingletonPredictionContext.create(ctx, t.followState.stateNumber)
-
-                try:
-                    calledRuleStack.add(t.target.ruleIndex)
-                    self._LOOK(t.target, stopState, newContext, look, lookBusy, calledRuleStack, seeThruPreds, addEOF)
-                finally:
-                    calledRuleStack.remove(t.target.ruleIndex)
-            elif isinstance(t, AbstractPredicateTransition ):
-                if seeThruPreds:
-                    self._LOOK(t.target, stopState, ctx, look, lookBusy, calledRuleStack, seeThruPreds, addEOF)
-                else:
-                    look.addOne(self.HIT_PRED)
-            elif t.isEpsilon:
-                self._LOOK(t.target, stopState, ctx, look, lookBusy, calledRuleStack, seeThruPreds, addEOF)
-            elif type(t) == WildcardTransition:
-                look.addRange( range(Token.MIN_USER_TOKEN_TYPE, self.atn.maxTokenType + 1) )
-            else:
-                set_ = t.label
-                if set_ is not None:
-                    if isinstance(t, NotSetTransition):
-                        set_ = set_.complement(Token.MIN_USER_TOKEN_TYPE, self.atn.maxTokenType)
-                    look.addSet(set_)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/_termui_impl.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/_termui_impl.py
deleted file mode 100644
index 4b979bcc1edcb33feb9b385156492221d92253dc..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/_termui_impl.py
+++ /dev/null
@@ -1,717 +0,0 @@
-"""
-This module contains implementations for the termui module. To keep the
-import time of Click down, some infrequently used functionality is
-placed in this module and only imported as needed.
-"""
-import contextlib
-import math
-import os
-import sys
-import time
-import typing as t
-from gettext import gettext as _
-
-from ._compat import _default_text_stdout
-from ._compat import CYGWIN
-from ._compat import get_best_encoding
-from ._compat import isatty
-from ._compat import open_stream
-from ._compat import strip_ansi
-from ._compat import term_len
-from ._compat import WIN
-from .exceptions import ClickException
-from .utils import echo
-
-V = t.TypeVar("V")
-
-if os.name == "nt":
-    BEFORE_BAR = "\r"
-    AFTER_BAR = "\n"
-else:
-    BEFORE_BAR = "\r\033[?25l"
-    AFTER_BAR = "\033[?25h\n"
-
-
-class ProgressBar(t.Generic[V]):
-    def __init__(
-        self,
-        iterable: t.Optional[t.Iterable[V]],
-        length: t.Optional[int] = None,
-        fill_char: str = "#",
-        empty_char: str = " ",
-        bar_template: str = "%(bar)s",
-        info_sep: str = "  ",
-        show_eta: bool = True,
-        show_percent: t.Optional[bool] = None,
-        show_pos: bool = False,
-        item_show_func: t.Optional[t.Callable[[t.Optional[V]], t.Optional[str]]] = None,
-        label: t.Optional[str] = None,
-        file: t.Optional[t.TextIO] = None,
-        color: t.Optional[bool] = None,
-        update_min_steps: int = 1,
-        width: int = 30,
-    ) -> None:
-        self.fill_char = fill_char
-        self.empty_char = empty_char
-        self.bar_template = bar_template
-        self.info_sep = info_sep
-        self.show_eta = show_eta
-        self.show_percent = show_percent
-        self.show_pos = show_pos
-        self.item_show_func = item_show_func
-        self.label = label or ""
-        if file is None:
-            file = _default_text_stdout()
-        self.file = file
-        self.color = color
-        self.update_min_steps = update_min_steps
-        self._completed_intervals = 0
-        self.width = width
-        self.autowidth = width == 0
-
-        if length is None:
-            from operator import length_hint
-
-            length = length_hint(iterable, -1)
-
-            if length == -1:
-                length = None
-        if iterable is None:
-            if length is None:
-                raise TypeError("iterable or length is required")
-            iterable = t.cast(t.Iterable[V], range(length))
-        self.iter = iter(iterable)
-        self.length = length
-        self.pos = 0
-        self.avg: t.List[float] = []
-        self.start = self.last_eta = time.time()
-        self.eta_known = False
-        self.finished = False
-        self.max_width: t.Optional[int] = None
-        self.entered = False
-        self.current_item: t.Optional[V] = None
-        self.is_hidden = not isatty(self.file)
-        self._last_line: t.Optional[str] = None
-
-    def __enter__(self) -> "ProgressBar":
-        self.entered = True
-        self.render_progress()
-        return self
-
-    def __exit__(self, exc_type, exc_value, tb):  # type: ignore
-        self.render_finish()
-
-    def __iter__(self) -> t.Iterator[V]:
-        if not self.entered:
-            raise RuntimeError("You need to use progress bars in a with block.")
-        self.render_progress()
-        return self.generator()
-
-    def __next__(self) -> V:
-        # Iteration is defined in terms of a generator function,
-        # returned by iter(self); use that to define next(). This works
-        # because `self.iter` is an iterable consumed by that generator,
-        # so it is re-entry safe. Calling `next(self.generator())`
-        # twice works and does "what you want".
-        return next(iter(self))
-
-    def render_finish(self) -> None:
-        if self.is_hidden:
-            return
-        self.file.write(AFTER_BAR)
-        self.file.flush()
-
-    @property
-    def pct(self) -> float:
-        if self.finished:
-            return 1.0
-        return min(self.pos / (float(self.length or 1) or 1), 1.0)
-
-    @property
-    def time_per_iteration(self) -> float:
-        if not self.avg:
-            return 0.0
-        return sum(self.avg) / float(len(self.avg))
-
-    @property
-    def eta(self) -> float:
-        if self.length is not None and not self.finished:
-            return self.time_per_iteration * (self.length - self.pos)
-        return 0.0
-
-    def format_eta(self) -> str:
-        if self.eta_known:
-            t = int(self.eta)
-            seconds = t % 60
-            t //= 60
-            minutes = t % 60
-            t //= 60
-            hours = t % 24
-            t //= 24
-            if t > 0:
-                return f"{t}d {hours:02}:{minutes:02}:{seconds:02}"
-            else:
-                return f"{hours:02}:{minutes:02}:{seconds:02}"
-        return ""
-
-    def format_pos(self) -> str:
-        pos = str(self.pos)
-        if self.length is not None:
-            pos += f"/{self.length}"
-        return pos
-
-    def format_pct(self) -> str:
-        return f"{int(self.pct * 100): 4}%"[1:]
-
-    def format_bar(self) -> str:
-        if self.length is not None:
-            bar_length = int(self.pct * self.width)
-            bar = self.fill_char * bar_length
-            bar += self.empty_char * (self.width - bar_length)
-        elif self.finished:
-            bar = self.fill_char * self.width
-        else:
-            chars = list(self.empty_char * (self.width or 1))
-            if self.time_per_iteration != 0:
-                chars[
-                    int(
-                        (math.cos(self.pos * self.time_per_iteration) / 2.0 + 0.5)
-                        * self.width
-                    )
-                ] = self.fill_char
-            bar = "".join(chars)
-        return bar
-
-    def format_progress_line(self) -> str:
-        show_percent = self.show_percent
-
-        info_bits = []
-        if self.length is not None and show_percent is None:
-            show_percent = not self.show_pos
-
-        if self.show_pos:
-            info_bits.append(self.format_pos())
-        if show_percent:
-            info_bits.append(self.format_pct())
-        if self.show_eta and self.eta_known and not self.finished:
-            info_bits.append(self.format_eta())
-        if self.item_show_func is not None:
-            item_info = self.item_show_func(self.current_item)
-            if item_info is not None:
-                info_bits.append(item_info)
-
-        return (
-            self.bar_template
-            % {
-                "label": self.label,
-                "bar": self.format_bar(),
-                "info": self.info_sep.join(info_bits),
-            }
-        ).rstrip()
-
-    def render_progress(self) -> None:
-        import shutil
-
-        if self.is_hidden:
-            # Only output the label as it changes if the output is not a
-            # TTY. Use file=stderr if you expect to be piping stdout.
-            if self._last_line != self.label:
-                self._last_line = self.label
-                echo(self.label, file=self.file, color=self.color)
-
-            return
-
-        buf = []
-        # Update width in case the terminal has been resized
-        if self.autowidth:
-            old_width = self.width
-            self.width = 0
-            clutter_length = term_len(self.format_progress_line())
-            new_width = max(0, shutil.get_terminal_size().columns - clutter_length)
-            if new_width < old_width:
-                buf.append(BEFORE_BAR)
-                buf.append(" " * self.max_width)  # type: ignore
-                self.max_width = new_width
-            self.width = new_width
-
-        clear_width = self.width
-        if self.max_width is not None:
-            clear_width = self.max_width
-
-        buf.append(BEFORE_BAR)
-        line = self.format_progress_line()
-        line_len = term_len(line)
-        if self.max_width is None or self.max_width < line_len:
-            self.max_width = line_len
-
-        buf.append(line)
-        buf.append(" " * (clear_width - line_len))
-        line = "".join(buf)
-        # Render the line only if it changed.
-
-        if line != self._last_line:
-            self._last_line = line
-            echo(line, file=self.file, color=self.color, nl=False)
-            self.file.flush()
-
-    def make_step(self, n_steps: int) -> None:
-        self.pos += n_steps
-        if self.length is not None and self.pos >= self.length:
-            self.finished = True
-
-        if (time.time() - self.last_eta) < 1.0:
-            return
-
-        self.last_eta = time.time()
-
-        # self.avg is a rolling list of length <= 7 of steps where steps are
-        # defined as time elapsed divided by the total progress through
-        # self.length.
-        if self.pos:
-            step = (time.time() - self.start) / self.pos
-        else:
-            step = time.time() - self.start
-
-        self.avg = self.avg[-6:] + [step]
-
-        self.eta_known = self.length is not None
-
-    def update(self, n_steps: int, current_item: t.Optional[V] = None) -> None:
-        """Update the progress bar by advancing a specified number of
-        steps, and optionally set the ``current_item`` for this new
-        position.
-
-        :param n_steps: Number of steps to advance.
-        :param current_item: Optional item to set as ``current_item``
-            for the updated position.
-
-        .. versionchanged:: 8.0
-            Added the ``current_item`` optional parameter.
-
-        .. versionchanged:: 8.0
-            Only render when the number of steps meets the
-            ``update_min_steps`` threshold.
-        """
-        if current_item is not None:
-            self.current_item = current_item
-
-        self._completed_intervals += n_steps
-
-        if self._completed_intervals >= self.update_min_steps:
-            self.make_step(self._completed_intervals)
-            self.render_progress()
-            self._completed_intervals = 0
-
-    def finish(self) -> None:
-        self.eta_known = False
-        self.current_item = None
-        self.finished = True
-
-    def generator(self) -> t.Iterator[V]:
-        """Return a generator which yields the items added to the bar
-        during construction, and updates the progress bar *after* the
-        yielded block returns.
-        """
-        # WARNING: the iterator interface for `ProgressBar` relies on
-        # this and only works because this is a simple generator which
-        # doesn't create or manage additional state. If this function
-        # changes, the impact should be evaluated both against
-        # `iter(bar)` and `next(bar)`. `next()` in particular may call
-        # `self.generator()` repeatedly, and this must remain safe in
-        # order for that interface to work.
-        if not self.entered:
-            raise RuntimeError("You need to use progress bars in a with block.")
-
-        if self.is_hidden:
-            yield from self.iter
-        else:
-            for rv in self.iter:
-                self.current_item = rv
-
-                # This allows show_item_func to be updated before the
-                # item is processed. Only trigger at the beginning of
-                # the update interval.
-                if self._completed_intervals == 0:
-                    self.render_progress()
-
-                yield rv
-                self.update(1)
-
-            self.finish()
-            self.render_progress()
-
-
-def pager(generator: t.Iterable[str], color: t.Optional[bool] = None) -> None:
-    """Decide what method to use for paging through text."""
-    stdout = _default_text_stdout()
-    if not isatty(sys.stdin) or not isatty(stdout):
-        return _nullpager(stdout, generator, color)
-    pager_cmd = (os.environ.get("PAGER", None) or "").strip()
-    if pager_cmd:
-        if WIN:
-            return _tempfilepager(generator, pager_cmd, color)
-        return _pipepager(generator, pager_cmd, color)
-    if os.environ.get("TERM") in ("dumb", "emacs"):
-        return _nullpager(stdout, generator, color)
-    if WIN or sys.platform.startswith("os2"):
-        return _tempfilepager(generator, "more <", color)
-    if hasattr(os, "system") and os.system("(less) 2>/dev/null") == 0:
-        return _pipepager(generator, "less", color)
-
-    import tempfile
-
-    fd, filename = tempfile.mkstemp()
-    os.close(fd)
-    try:
-        if hasattr(os, "system") and os.system(f'more "{filename}"') == 0:
-            return _pipepager(generator, "more", color)
-        return _nullpager(stdout, generator, color)
-    finally:
-        os.unlink(filename)
-
-
-def _pipepager(generator: t.Iterable[str], cmd: str, color: t.Optional[bool]) -> None:
-    """Page through text by feeding it to another program.  Invoking a
-    pager through this might support colors.
-    """
-    import subprocess
-
-    env = dict(os.environ)
-
-    # If we're piping to less we might support colors under the
-    # condition that
-    cmd_detail = cmd.rsplit("/", 1)[-1].split()
-    if color is None and cmd_detail[0] == "less":
-        less_flags = f"{os.environ.get('LESS', '')}{' '.join(cmd_detail[1:])}"
-        if not less_flags:
-            env["LESS"] = "-R"
-            color = True
-        elif "r" in less_flags or "R" in less_flags:
-            color = True
-
-    c = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, env=env)
-    stdin = t.cast(t.BinaryIO, c.stdin)
-    encoding = get_best_encoding(stdin)
-    try:
-        for text in generator:
-            if not color:
-                text = strip_ansi(text)
-
-            stdin.write(text.encode(encoding, "replace"))
-    except (OSError, KeyboardInterrupt):
-        pass
-    else:
-        stdin.close()
-
-    # Less doesn't respect ^C, but catches it for its own UI purposes (aborting
-    # search or other commands inside less).
-    #
-    # That means when the user hits ^C, the parent process (click) terminates,
-    # but less is still alive, paging the output and messing up the terminal.
-    #
-    # If the user wants to make the pager exit on ^C, they should set
-    # `LESS='-K'`. It's not our decision to make.
-    while True:
-        try:
-            c.wait()
-        except KeyboardInterrupt:
-            pass
-        else:
-            break
-
-
-def _tempfilepager(
-    generator: t.Iterable[str], cmd: str, color: t.Optional[bool]
-) -> None:
-    """Page through text by invoking a program on a temporary file."""
-    import tempfile
-
-    fd, filename = tempfile.mkstemp()
-    # TODO: This never terminates if the passed generator never terminates.
-    text = "".join(generator)
-    if not color:
-        text = strip_ansi(text)
-    encoding = get_best_encoding(sys.stdout)
-    with open_stream(filename, "wb")[0] as f:
-        f.write(text.encode(encoding))
-    try:
-        os.system(f'{cmd} "{filename}"')
-    finally:
-        os.close(fd)
-        os.unlink(filename)
-
-
-def _nullpager(
-    stream: t.TextIO, generator: t.Iterable[str], color: t.Optional[bool]
-) -> None:
-    """Simply print unformatted text.  This is the ultimate fallback."""
-    for text in generator:
-        if not color:
-            text = strip_ansi(text)
-        stream.write(text)
-
-
-class Editor:
-    def __init__(
-        self,
-        editor: t.Optional[str] = None,
-        env: t.Optional[t.Mapping[str, str]] = None,
-        require_save: bool = True,
-        extension: str = ".txt",
-    ) -> None:
-        self.editor = editor
-        self.env = env
-        self.require_save = require_save
-        self.extension = extension
-
-    def get_editor(self) -> str:
-        if self.editor is not None:
-            return self.editor
-        for key in "VISUAL", "EDITOR":
-            rv = os.environ.get(key)
-            if rv:
-                return rv
-        if WIN:
-            return "notepad"
-        for editor in "sensible-editor", "vim", "nano":
-            if os.system(f"which {editor} >/dev/null 2>&1") == 0:
-                return editor
-        return "vi"
-
-    def edit_file(self, filename: str) -> None:
-        import subprocess
-
-        editor = self.get_editor()
-        environ: t.Optional[t.Dict[str, str]] = None
-
-        if self.env:
-            environ = os.environ.copy()
-            environ.update(self.env)
-
-        try:
-            c = subprocess.Popen(f'{editor} "{filename}"', env=environ, shell=True)
-            exit_code = c.wait()
-            if exit_code != 0:
-                raise ClickException(
-                    _("{editor}: Editing failed").format(editor=editor)
-                )
-        except OSError as e:
-            raise ClickException(
-                _("{editor}: Editing failed: {e}").format(editor=editor, e=e)
-            ) from e
-
-    def edit(self, text: t.Optional[t.AnyStr]) -> t.Optional[t.AnyStr]:
-        import tempfile
-
-        if not text:
-            data = b""
-        elif isinstance(text, (bytes, bytearray)):
-            data = text
-        else:
-            if text and not text.endswith("\n"):
-                text += "\n"
-
-            if WIN:
-                data = text.replace("\n", "\r\n").encode("utf-8-sig")
-            else:
-                data = text.encode("utf-8")
-
-        fd, name = tempfile.mkstemp(prefix="editor-", suffix=self.extension)
-        f: t.BinaryIO
-
-        try:
-            with os.fdopen(fd, "wb") as f:
-                f.write(data)
-
-            # If the filesystem resolution is 1 second, like Mac OS
-            # 10.12 Extended, or 2 seconds, like FAT32, and the editor
-            # closes very fast, require_save can fail. Set the modified
-            # time to be 2 seconds in the past to work around this.
-            os.utime(name, (os.path.getatime(name), os.path.getmtime(name) - 2))
-            # Depending on the resolution, the exact value might not be
-            # recorded, so get the new recorded value.
-            timestamp = os.path.getmtime(name)
-
-            self.edit_file(name)
-
-            if self.require_save and os.path.getmtime(name) == timestamp:
-                return None
-
-            with open(name, "rb") as f:
-                rv = f.read()
-
-            if isinstance(text, (bytes, bytearray)):
-                return rv
-
-            return rv.decode("utf-8-sig").replace("\r\n", "\n")  # type: ignore
-        finally:
-            os.unlink(name)
-
-
-def open_url(url: str, wait: bool = False, locate: bool = False) -> int:
-    import subprocess
-
-    def _unquote_file(url: str) -> str:
-        from urllib.parse import unquote
-
-        if url.startswith("file://"):
-            url = unquote(url[7:])
-
-        return url
-
-    if sys.platform == "darwin":
-        args = ["open"]
-        if wait:
-            args.append("-W")
-        if locate:
-            args.append("-R")
-        args.append(_unquote_file(url))
-        null = open("/dev/null", "w")
-        try:
-            return subprocess.Popen(args, stderr=null).wait()
-        finally:
-            null.close()
-    elif WIN:
-        if locate:
-            url = _unquote_file(url.replace('"', ""))
-            args = f'explorer /select,"{url}"'
-        else:
-            url = url.replace('"', "")
-            wait_str = "/WAIT" if wait else ""
-            args = f'start {wait_str} "" "{url}"'
-        return os.system(args)
-    elif CYGWIN:
-        if locate:
-            url = os.path.dirname(_unquote_file(url).replace('"', ""))
-            args = f'cygstart "{url}"'
-        else:
-            url = url.replace('"', "")
-            wait_str = "-w" if wait else ""
-            args = f'cygstart {wait_str} "{url}"'
-        return os.system(args)
-
-    try:
-        if locate:
-            url = os.path.dirname(_unquote_file(url)) or "."
-        else:
-            url = _unquote_file(url)
-        c = subprocess.Popen(["xdg-open", url])
-        if wait:
-            return c.wait()
-        return 0
-    except OSError:
-        if url.startswith(("http://", "https://")) and not locate and not wait:
-            import webbrowser
-
-            webbrowser.open(url)
-            return 0
-        return 1
-
-
-def _translate_ch_to_exc(ch: str) -> t.Optional[BaseException]:
-    if ch == "\x03":
-        raise KeyboardInterrupt()
-
-    if ch == "\x04" and not WIN:  # Unix-like, Ctrl+D
-        raise EOFError()
-
-    if ch == "\x1a" and WIN:  # Windows, Ctrl+Z
-        raise EOFError()
-
-    return None
-
-
-if WIN:
-    import msvcrt
-
-    @contextlib.contextmanager
-    def raw_terminal() -> t.Iterator[int]:
-        yield -1
-
-    def getchar(echo: bool) -> str:
-        # The function `getch` will return a bytes object corresponding to
-        # the pressed character. Since Windows 10 build 1803, it will also
-        # return \x00 when called a second time after pressing a regular key.
-        #
-        # `getwch` does not share this probably-bugged behavior. Moreover, it
-        # returns a Unicode object by default, which is what we want.
-        #
-        # Either of these functions will return \x00 or \xe0 to indicate
-        # a special key, and you need to call the same function again to get
-        # the "rest" of the code. The fun part is that \u00e0 is
-        # "latin small letter a with grave", so if you type that on a French
-        # keyboard, you _also_ get a \xe0.
-        # E.g., consider the Up arrow. This returns \xe0 and then \x48. The
-        # resulting Unicode string reads as "a with grave" + "capital H".
-        # This is indistinguishable from when the user actually types
-        # "a with grave" and then "capital H".
-        #
-        # When \xe0 is returned, we assume it's part of a special-key sequence
-        # and call `getwch` again, but that means that when the user types
-        # the \u00e0 character, `getchar` doesn't return until a second
-        # character is typed.
-        # The alternative is returning immediately, but that would mess up
-        # cross-platform handling of arrow keys and others that start with
-        # \xe0. Another option is using `getch`, but then we can't reliably
-        # read non-ASCII characters, because return values of `getch` are
-        # limited to the current 8-bit codepage.
-        #
-        # Anyway, Click doesn't claim to do this Right(tm), and using `getwch`
-        # is doing the right thing in more situations than with `getch`.
-        func: t.Callable[[], str]
-
-        if echo:
-            func = msvcrt.getwche  # type: ignore
-        else:
-            func = msvcrt.getwch  # type: ignore
-
-        rv = func()
-
-        if rv in ("\x00", "\xe0"):
-            # \x00 and \xe0 are control characters that indicate special key,
-            # see above.
-            rv += func()
-
-        _translate_ch_to_exc(rv)
-        return rv
-
-else:
-    import tty
-    import termios
-
-    @contextlib.contextmanager
-    def raw_terminal() -> t.Iterator[int]:
-        f: t.Optional[t.TextIO]
-        fd: int
-
-        if not isatty(sys.stdin):
-            f = open("/dev/tty")
-            fd = f.fileno()
-        else:
-            fd = sys.stdin.fileno()
-            f = None
-
-        try:
-            old_settings = termios.tcgetattr(fd)
-
-            try:
-                tty.setraw(fd)
-                yield fd
-            finally:
-                termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
-                sys.stdout.flush()
-
-                if f is not None:
-                    f.close()
-        except termios.error:
-            pass
-
-    def getchar(echo: bool) -> str:
-        with raw_terminal() as fd:
-            ch = os.read(fd, 32).decode(get_best_encoding(sys.stdin), "replace")
-
-            if echo and isatty(sys.stdout):
-                sys.stdout.write(ch)
-
-            _translate_ch_to_exc(ch)
-            return ch
diff --git a/spaces/avivdm1/AutoGPT/autogpt/speech/base.py b/spaces/avivdm1/AutoGPT/autogpt/speech/base.py
deleted file mode 100644
index d74fa51be75b5078134c510b393a06deb0267b2a..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/speech/base.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Base class for all voice classes."""
-import abc
-from threading import Lock
-
-from autogpt.config import AbstractSingleton
-
-
-class VoiceBase(AbstractSingleton):
-    """
-    Base class for all voice classes.
-    """
-
-    def __init__(self):
-        """
-        Initialize the voice class.
-        """
-        self._url = None
-        self._headers = None
-        self._api_key = None
-        self._voices = []
-        self._mutex = Lock()
-        self._setup()
-
-    def say(self, text: str, voice_index: int = 0) -> bool:
-        """
-        Say the given text.
-
-        Args:
-            text (str): The text to say.
-            voice_index (int): The index of the voice to use.
-        """
-        with self._mutex:
-            return self._speech(text, voice_index)
-
-    @abc.abstractmethod
-    def _setup(self) -> None:
-        """
-        Setup the voices, API key, etc.
-        """
-        pass
-
-    @abc.abstractmethod
-    def _speech(self, text: str, voice_index: int = 0) -> bool:
-        """
-        Play the given text.
-
-        Args:
-            text (str): The text to play.
-        """
-        pass
diff --git a/spaces/awacke1/Bloom.Generative.Writer/generators/title_to_abstract.py b/spaces/awacke1/Bloom.Generative.Writer/generators/title_to_abstract.py
deleted file mode 100644
index a5ff1dda8edc9a75e7befa4d8d7a16efe0722e67..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bloom.Generative.Writer/generators/title_to_abstract.py
+++ /dev/null
@@ -1,5 +0,0 @@
- 
-from .model import model
-
-def title_to_abstract_generator(template):
-    return model('title', template)
diff --git a/spaces/awacke1/Github-Create-Read-Update-Delete/app.py b/spaces/awacke1/Github-Create-Read-Update-Delete/app.py
deleted file mode 100644
index e9f256549f71e2eb8bbab491cdb14477cad4bd3b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Github-Create-Read-Update-Delete/app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#Write a streamlit program in short number of lines that has four buttons on a sidebar also with a textbox labeled Github URL.  Create python code that performs the four functions with github : Pull & Read, Append and Write, Create a file on github, Delete a file on github.
-
-import streamlit as st
-
-st.markdown("""
-
-https://pypi.org/project/github3api/
-github3api
-
-https://pypi.org/project/github-archive/
-# Install tool
-pip3 install github-archive
-
-# Install locally
-make install
-
-""")
-    
-
-def pull_files_from_github_url(url):
-    import os
-
-    #client = GitHubAPI()
-    #client.get('/rate_limit')['resources']['core']
-    
-    # Using GitPython library
-    # from git import Repo
-
-    # Get the directory where the file will be downloaded
-    # dir_name = os.path.basename(url).split('.')[0]
-
-    # Clone the repo
-    # 
-    dir_name = 'c:\\users\\awacke1\\'
-    Repo.clone_from(url, dir_name)
-
-    # Get the list of files
-    # repo = Repo(dir_name)
-    # return [item.a_path for item in repo.tree.traverse()]
-
-url = st.sidebar.text_input("GitHub URL")
-url = 'https://github.com/AaronCWacker/Yggdrasil'
-
-
-st.sidebar.title('GitHub Functions')
-
-if st.sidebar.button("Pull & Read"):
-    pull_files_from_github_url(url)
-
-if st.sidebar.button("Append & Write"):
-    pull_files_from_github_url(url)
-
-if st.sidebar.button("Create File"):
-    pull_files_from_github_url(url)
-
-if st.sidebar.button("Delete File"):
-    pull_files_from_github_url(url)
diff --git a/spaces/awacke1/MistralGradioFast/README.md b/spaces/awacke1/MistralGradioFast/README.md
deleted file mode 100644
index 60d82cb450ca9ad6e6c5f83cdcfd7fa545490a00..0000000000000000000000000000000000000000
--- a/spaces/awacke1/MistralGradioFast/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MistralGradioFast
-emoji: 👀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.47.1
-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/Named-entity-resolution/app.py b/spaces/awacke1/Named-entity-resolution/app.py
deleted file mode 100644
index 7fb67b2c343f6cdd0ccd377aaa3956f5c18cb23a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Named-entity-resolution/app.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import streamlit as st
-import transformers
-
-# Load the pre-trained NER model
-model = transformers.pipeline('ner', model='dslim/bert-base-NER')
-
-# Define the Streamlit app
-def app():
-    # Set the app title
-    st.title('Named Entity Recognition with HuggingFace')
-
-    # Define the input text area
-    text = st.text_area('Enter some text:', value='', height=200)
-
-    # Define the button to submit the text
-    if st.button('Submit'):
-        # Run the NER model on the input text
-        entities = model(text)
-
-        # Display the named entities in the input text
-        for entity in entities:
-            st.write(f'{entity["entity"]}: {entity["word"]}')
-
-# Run the Streamlit app
-if __name__ == '__main__':
-    app()
diff --git a/spaces/awacke1/Streamlit.ChatWikiwriter.Multiplayer/README.md b/spaces/awacke1/Streamlit.ChatWikiwriter.Multiplayer/README.md
deleted file mode 100644
index a61338d67504d00a18374442b1768c7d02eb2a52..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit.ChatWikiwriter.Multiplayer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit.ChatWikiwriter.Multiplayer
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-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/StreamlitClipboardInteraction/app.py b/spaces/awacke1/StreamlitClipboardInteraction/app.py
deleted file mode 100644
index 11e8fd22408a85bd542a83b7c1faf3dfce1bedb9..0000000000000000000000000000000000000000
--- a/spaces/awacke1/StreamlitClipboardInteraction/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import streamlit as st
-from bokeh.models.widgets import Button
-from bokeh.models import CustomJS
-from streamlit_bokeh_events import streamlit_bokeh_events
-import pandas as pd
-
-df = pd.DataFrame({"x": [1, 2, 3, 4], "y": ["a", "b", "c", "d"]})
-
-st.dataframe(df)
-
-copy_button = Button(label="Copy Dataframe as Text To Clipboard")
-copy_button.js_on_event("button_click", CustomJS(args=dict(df=df.to_csv(sep='\t')), code="""
-    navigator.clipboard.writeText(df);
-    """))
-
-no_event = streamlit_bokeh_events(
-    copy_button,
-    events="GET_TEXT",
-    key="get_text",
-    refresh_on_update=True,
-    override_height=75,
-    debounce_time=0)
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/misc/TextureCubeUVNode.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/misc/TextureCubeUVNode.js
deleted file mode 100644
index e4f98189f819e82d8b8b36a72bc29afbdfbad6de..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/misc/TextureCubeUVNode.js
+++ /dev/null
@@ -1,209 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { TempNode } from '../core/TempNode.js';
-import { ConstNode } from '../core/ConstNode.js';
-import { StructNode } from '../core/StructNode.js';
-import { FunctionNode } from '../core/FunctionNode.js';
-import { ReflectNode } from '../accessors/ReflectNode.js';
-import { FloatNode } from '../inputs/FloatNode.js';
-import { BlinnExponentToRoughnessNode } from '../bsdfs/BlinnExponentToRoughnessNode.js';
-
-function TextureCubeUVNode( uv, textureSize, blinnExponentToRoughness ) {
-
-	TempNode.call( this, 'TextureCubeUVData' ); // TextureCubeUVData is type as StructNode
-
-	this.uv = uv || new ReflectNode( ReflectNode.VECTOR );
-	this.textureSize = textureSize || new FloatNode( 1024 );
-	this.blinnExponentToRoughness = blinnExponentToRoughness || new BlinnExponentToRoughnessNode();
-
-}
-
-TextureCubeUVNode.Nodes = ( function () {
-
-	var TextureCubeUVData = new StructNode( [
-		"struct TextureCubeUVData {",
-		"	vec2 uv_10;",
-		"	vec2 uv_20;",
-		"	float t;",
-		"}"
-	].join( "\n" ) );
-
-	var getFaceFromDirection = new FunctionNode( [
-		"int getFaceFromDirection(vec3 direction) {",
-		"	vec3 absDirection = abs(direction);",
-		"	int face = -1;",
-		"	if( absDirection.x > absDirection.z ) {",
-		"		if(absDirection.x > absDirection.y )",
-		"			face = direction.x > 0.0 ? 0 : 3;",
-		"		else",
-		"			face = direction.y > 0.0 ? 1 : 4;",
-		"	}",
-		"	else {",
-		"		if(absDirection.z > absDirection.y )",
-		"			face = direction.z > 0.0 ? 2 : 5;",
-		"		else",
-		"			face = direction.y > 0.0 ? 1 : 4;",
-		"	}",
-		"	return face;",
-		"}"
-	].join( "\n" ) );
-
-	var cubeUV_maxLods1 = new ConstNode( "#define cubeUV_maxLods1 ( log2( cubeUV_textureSize * 0.25 ) - 1.0 )" );
-	var cubeUV_rangeClamp = new ConstNode( "#define cubeUV_rangeClamp ( exp2( ( 6.0 - 1.0 ) * 2.0 ) )" );
-
-	var MipLevelInfo = new FunctionNode( [
-		"vec2 MipLevelInfo( vec3 vec, float roughnessLevel, float roughness, in float cubeUV_textureSize ) {",
-		"	float scale = exp2(cubeUV_maxLods1 - roughnessLevel);",
-		"	float dxRoughness = dFdx(roughness);",
-		"	float dyRoughness = dFdy(roughness);",
-		"	vec3 dx = dFdx( vec * scale * dxRoughness );",
-		"	vec3 dy = dFdy( vec * scale * dyRoughness );",
-		"	float d = max( dot( dx, dx ), dot( dy, dy ) );",
-		// Clamp the value to the max mip level counts. hard coded to 6 mips"
-		"	d = clamp(d, 1.0, cubeUV_rangeClamp);",
-		"	float mipLevel = 0.5 * log2(d);",
-		"	return vec2(floor(mipLevel), fract(mipLevel));",
-		"}"
-	].join( "\n" ), [ cubeUV_maxLods1, cubeUV_rangeClamp ], { derivatives: true } );
-
-	var cubeUV_maxLods2 = new ConstNode( "#define cubeUV_maxLods2 ( log2( cubeUV_textureSize * 0.25 ) - 2.0 )" );
-	var cubeUV_rcpTextureSize = new ConstNode( "#define cubeUV_rcpTextureSize ( 1.0 / cubeUV_textureSize )" );
-
-	var getCubeUV = new FunctionNode( [
-		"vec2 getCubeUV( vec3 direction, float roughnessLevel, float mipLevel, in float cubeUV_textureSize ) {",
-		"	mipLevel = roughnessLevel > cubeUV_maxLods2 - 3.0 ? 0.0 : mipLevel;",
-		"	float a = 16.0 * cubeUV_rcpTextureSize;",
-		"",
-		"	vec2 exp2_packed = exp2( vec2( roughnessLevel, mipLevel ) );",
-		"	vec2 rcp_exp2_packed = vec2( 1.0 ) / exp2_packed;",
-		// float powScale = exp2(roughnessLevel + mipLevel);"
-		"	float powScale = exp2_packed.x * exp2_packed.y;",
-		// float scale =  1.0 / exp2(roughnessLevel + 2.0 + mipLevel);"
-		"	float scale = rcp_exp2_packed.x * rcp_exp2_packed.y * 0.25;",
-		// float mipOffset = 0.75*(1.0 - 1.0/exp2(mipLevel))/exp2(roughnessLevel);"
-		"	float mipOffset = 0.75*(1.0 - rcp_exp2_packed.y) * rcp_exp2_packed.x;",
-		"",
-		"	bool bRes = mipLevel == 0.0;",
-		"	scale =  bRes && (scale < a) ? a : scale;",
-		"",
-		"	vec3 r;",
-		"	vec2 offset;",
-		"	int face = getFaceFromDirection(direction);",
-		"",
-		"	float rcpPowScale = 1.0 / powScale;",
-		"",
-		"	if( face == 0) {",
-		"		r = vec3(direction.x, -direction.z, direction.y);",
-		"		offset = vec2(0.0+mipOffset,0.75 * rcpPowScale);",
-		"		offset.y = bRes && (offset.y < 2.0*a) ? a : offset.y;",
-		"	}",
-		"	else if( face == 1) {",
-		"		r = vec3(direction.y, direction.x, direction.z);",
-		"		offset = vec2(scale+mipOffset, 0.75 * rcpPowScale);",
-		"		offset.y = bRes && (offset.y < 2.0*a) ? a : offset.y;",
-		"	}",
-		"	else if( face == 2) {",
-		"		r = vec3(direction.z, direction.x, direction.y);",
-		"		offset = vec2(2.0*scale+mipOffset, 0.75 * rcpPowScale);",
-		"		offset.y = bRes && (offset.y < 2.0*a) ? a : offset.y;",
-		"	}",
-		"	else if( face == 3) {",
-		"		r = vec3(direction.x, direction.z, direction.y);",
-		"		offset = vec2(0.0+mipOffset,0.5 * rcpPowScale);",
-		"		offset.y = bRes && (offset.y < 2.0*a) ? 0.0 : offset.y;",
-		"	}",
-		"	else if( face == 4) {",
-		"		r = vec3(direction.y, direction.x, -direction.z);",
-		"		offset = vec2(scale+mipOffset, 0.5 * rcpPowScale);",
-		"		offset.y = bRes && (offset.y < 2.0*a) ? 0.0 : offset.y;",
-		"	}",
-		"	else {",
-		"		r = vec3(direction.z, -direction.x, direction.y);",
-		"		offset = vec2(2.0*scale+mipOffset, 0.5 * rcpPowScale);",
-		"		offset.y = bRes && (offset.y < 2.0*a) ? 0.0 : offset.y;",
-		"	}",
-		"	r = normalize(r);",
-		"	float texelOffset = 0.5 * cubeUV_rcpTextureSize;",
-		"	vec2 s = ( r.yz / abs( r.x ) + vec2( 1.0 ) ) * 0.5;",
-		"	vec2 base = offset + vec2( texelOffset );",
-		"	return base + s * ( scale - 2.0 * texelOffset );",
-		"}"
-	].join( "\n" ), [ cubeUV_maxLods2, cubeUV_rcpTextureSize, getFaceFromDirection ] );
-
-	var cubeUV_maxLods3 = new ConstNode( "#define cubeUV_maxLods3 ( log2( cubeUV_textureSize * 0.25 ) - 3.0 )" );
-
-	var textureCubeUV = new FunctionNode( [
-		"TextureCubeUVData textureCubeUV( vec3 reflectedDirection, float roughness, in float cubeUV_textureSize ) {",
-		"	float roughnessVal = roughness * cubeUV_maxLods3;",
-		"	float r1 = floor(roughnessVal);",
-		"	float r2 = r1 + 1.0;",
-		"	float t = fract(roughnessVal);",
-		"	vec2 mipInfo = MipLevelInfo(reflectedDirection, r1, roughness, cubeUV_textureSize);",
-		"	float s = mipInfo.y;",
-		"	float level0 = mipInfo.x;",
-		"	float level1 = level0 + 1.0;",
-		"	level1 = level1 > 5.0 ? 5.0 : level1;",
-		"",
-		// round to nearest mipmap if we are not interpolating."
-		"	level0 += min( floor( s + 0.5 ), 5.0 );",
-		"",
-		// Tri linear interpolation."
-		"	vec2 uv_10 = getCubeUV(reflectedDirection, r1, level0, cubeUV_textureSize);",
-		"	vec2 uv_20 = getCubeUV(reflectedDirection, r2, level0, cubeUV_textureSize);",
-		"",
-		"	return TextureCubeUVData(uv_10, uv_20, t);",
-		"}"
-	].join( "\n" ), [ TextureCubeUVData, cubeUV_maxLods3, MipLevelInfo, getCubeUV ] );
-
-	return {
-		TextureCubeUVData: TextureCubeUVData,
-		textureCubeUV: textureCubeUV
-	};
-
-} )();
-
-TextureCubeUVNode.prototype = Object.create( TempNode.prototype );
-TextureCubeUVNode.prototype.constructor = TextureCubeUVNode;
-TextureCubeUVNode.prototype.nodeType = "TextureCubeUV";
-
-TextureCubeUVNode.prototype.generate = function ( builder, output ) {
-
-	if ( builder.isShader( 'fragment' ) ) {
-
-		var textureCubeUV = builder.include( TextureCubeUVNode.Nodes.textureCubeUV );
-
-		return builder.format( textureCubeUV + '( ' + this.uv.build( builder, 'v3' ) + ', ' +
-			this.blinnExponentToRoughness.build( builder, 'f' ) + ', ' +
-			this.textureSize.build( builder, 'f' ) + ' )', this.getType( builder ), output );
-
-	} else {
-
-		console.warn( "THREE.TextureCubeUVNode is not compatible with " + builder.shader + " shader." );
-
-		return builder.format( 'vec4( 0.0 )', this.getType( builder ), output );
-
-	}
-
-};
-
-TextureCubeUVNode.prototype.toJSON = function ( meta ) {
-
-	var data = this.getJSONNode( meta );
-
-	if ( ! data ) {
-
-		data = this.createJSONNode( meta );
-
-		data.uv = this.uv.toJSON( meta ).uuid;
-		data.textureSize = this.textureSize.toJSON( meta ).uuid;
-		data.blinnExponentToRoughness = this.blinnExponentToRoughness.toJSON( meta ).uuid;
-
-	}
-
-	return data;
-
-};
-
-export { TextureCubeUVNode };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/fog_pars_vertex.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/fog_pars_vertex.glsl.js
deleted file mode 100644
index 284d460f48543b3b1b192f9f28f46f0882ab7b11..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/fog_pars_vertex.glsl.js
+++ /dev/null
@@ -1,7 +0,0 @@
-export default /* glsl */`
-#ifdef USE_FOG
-
-	varying float fogDepth;
-
-#endif
-`;
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327092728.py b/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327092728.py
deleted file mode 100644
index dfc311232833ab0cc1dc8d7bc111303f1ff3bbea..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327092728.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import os
-#os.system("pip install gfpgan")
-
-#os.system("pip freeze")
-#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg')
-
-
-import cv2
-import glob
-import numpy as np
-from basicsr.utils import imwrite
-from gfpgan import GFPGANer
-
-bg_upsampler = None
-
-
- 
-# set up GFPGAN restorer
-restorer = GFPGANer(
-    model_path='experiments/pretrained_models/GFPGANv1.3.pth',
-    upscale=2,
-    arch='clean',
-    channel_multiplier=2,
-    bg_upsampler=bg_upsampler)
-
-
-def inference(img):
-    input_img = cv2.imread(img, cv2.IMREAD_COLOR)
-    cropped_faces, restored_faces, restored_img = restorer.enhance(
-        input_img, has_aligned=False, only_center_face=False, paste_back=True)
-    
-    #return Image.fromarray(restored_faces[0][:,:,::-1])
-    return Image.fromarray(restored_img[:, :, ::-1])
-
-title = "让美好回忆更清晰"
-description = "上传老照片，点击Submit，稍等片刻，右侧Output框将照片另存为即可。"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2101.04061' target='_blank'论文地址 By 腾讯PCG</a> | <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="filepath", label="Input")], 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-    ['lincoln.jpg'],
-    ['einstein.png'],
-    ['edison.jpg'],
-    ['Henry.jpg'],
-    ['Frida.jpg']
-    ]
-    ).launch(enable_queue=True,cache_examples=True,share=True)
-    
-    
diff --git a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620160828.py b/spaces/beihai/PDF-Table-Extractor/.history/app_20220620160828.py
deleted file mode 100644
index e5638ae2f7ce8d334984a3f6c44bb36bd3e2e77e..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620160828.py
+++ /dev/null
@@ -1,42 +0,0 @@
-#-*- coding : utf-8-*-
-import pandas as pd 
-import os,base64,subprocess
-os.system("apt-get install -y ghostscript")
-import streamlit as st
-
-from subprocess import STDOUT #os process manipuation
-
-@st.cache
-def gh():
-    """install ghostscript on the linux machine"""
-    proc = subprocess.Popen('apt-get install -y ghostscript', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-    proc.wait()
-
-gh()
-
-import camelot as cam 
-
-st.title("PDF Table Extractor")
-
-input_pdf = st.file_uploader(label = "", type = 'pdf')
-
-page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1)
-
-if input_pdf is not None:
-    # byte object into a PDF file 
-    with open("input.pdf", "wb") as f:
-        base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
-        f.write(base64.b64decode(base64_pdf))
-    f.close()
-
-    # read the pdf and parse it using stream
-    tables = cam.read_pdf("input.pdf", pages=page_number)
-    result = pd.ExcelWriter('result.xlsx', engine='xlsxwriter') 
-    tables[0].to_excel(result,index=False) 
-    # for i in range(0,len(tables)):
-    #     table = tables[i].df
-    #     sheetname = str(i)
-    #     table.to_excel(result, sheetname,index=False) 
-
-    with open('result.xlsx','rb') as f:
-       st.download_button('提取完成，点击下载！', f,file_name='result.xlsx',mime="application/vnd.ms-excel")
\ No newline at end of file
diff --git a/spaces/belectron/Seen-Zan5/README.md b/spaces/belectron/Seen-Zan5/README.md
deleted file mode 100644
index 1f74bcbc49522e49429a3925abbd1d5050460077..0000000000000000000000000000000000000000
--- a/spaces/belectron/Seen-Zan5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Seen Zan5
-emoji: ⚡
-colorFrom: red
-colorTo: green
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bhanuprasad3245/mygenAIchatbot/app.py b/spaces/bhanuprasad3245/mygenAIchatbot/app.py
deleted file mode 100644
index 5da3f3c9d079994a8bc59cd86ed5a8ba0c0db5ee..0000000000000000000000000000000000000000
--- a/spaces/bhanuprasad3245/mygenAIchatbot/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet sanju, your youthful and witty personal assistant! At 21 years old, he's full of energy and always eager to help. sanju's goal is to assist you with any questions or problems you might have. his enthusiasm shines through in every response, making interactions with his enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/bioriAsaeru/text-to-voice/Advanced SystemCare Pro Crack 13.2.0 Keygen Full Download 2020 The Best PC Optimization Tool for Windows.md b/spaces/bioriAsaeru/text-to-voice/Advanced SystemCare Pro Crack 13.2.0 Keygen Full Download 2020 The Best PC Optimization Tool for Windows.md
deleted file mode 100644
index 813ed6576eb8c2a64084b05f9c9f6240245b3e37..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Advanced SystemCare Pro Crack 13.2.0 Keygen Full Download 2020 The Best PC Optimization Tool for Windows.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Advanced SystemCare Pro Crack 13.2.0 Keygen Full Download 2020</h2><br /><p><b><b>Download File</b> &#10038;&#10038;&#10038; <a href="https://urloso.com/2uyPxa">https://urloso.com/2uyPxa</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Eyewitness Movie Free Download Hd.md b/spaces/bioriAsaeru/text-to-voice/Eyewitness Movie Free Download Hd.md
deleted file mode 100644
index 43288ec26c10f06e3041aabdafe707c18b9825e3..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Eyewitness Movie Free Download Hd.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>Recallingthis day in <i>Something Like an Autobiography, </i>Kurosawa explains the movieto them. The explanation is reprinted in the booklet that comes with the newCriterion DVD of "Rashomon." Two of the assistants are satisfied withhis explanation, but the third leaves looking puzzled. What he doesn'tunderstand is that while there is an <i>explanation </i>of the film's foureyewitness accounts of a murder, there is not a<i> solution.</i></p>
-<h2>Eyewitness movie free download hd</h2><br /><p><b><b>Download</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://urloso.com/2uyOP6">https://urloso.com/2uyOP6</a></b></p><br /><br />
-<p>How should MB handle Internet-only releases? "Alternate Dimension Series 1" by "Steve Coleman and Five Elements" =82845 is available as a free-for-download CD release on the artists' home page -base.com/. Here are the issues:</p>
-<p>download ?search=9 unlimited Movies and videos Download Here.?search=9 Hd,3gp. mp4 320p and More Videos You Can Download Easyly. tamilrockers and movierulz, tamilgun, filmywap, and pagalworld videos and Movies download.</p> aaccfb2cb3<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/bradarrML/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js b/spaces/bradarrML/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js
deleted file mode 100644
index 85296fbcdf93ad959ffc0963af842b8640cbb27e..0000000000000000000000000000000000000000
--- a/spaces/bradarrML/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/evaluation/__init__.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/evaluation/__init__.py
deleted file mode 100644
index e5ae1f20cdc822ebf3c870f1289a0ad210c57ae7..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/evaluation/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .evaluator import DensePoseCOCOEvaluator
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/doc/TOOL_QUERY_DB.md b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/doc/TOOL_QUERY_DB.md
deleted file mode 100644
index b0a764b8740597c6af634127b80b53d28913726f..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/doc/TOOL_QUERY_DB.md
+++ /dev/null
@@ -1,105 +0,0 @@
-
-# Query Dataset
-
-`query_db` is a tool to print or visualize DensePose data from a dataset.
-It has two modes: `print` and `show` to output dataset entries to standard
-output or to visualize them on images.
-
-## Print Mode
-
-The general command form is:
-```bash
-python query_db.py print [-h] [-v] [--max-entries N] <dataset> <selector>
-```
-
-There are two mandatory arguments:
- - `<dataset>`, DensePose dataset specification, from which to select
-   the entries (e.g. `densepose_coco_2014_train`).
- - `<selector>`, dataset entry selector which can be a single specification,
-   or a comma-separated list of specifications of the form
-   `field[:type]=value` for exact match with the value
-   or `field[:type]=min-max` for a range of values
-
-One can additionally limit the maximum number of entries to output
-by providing `--max-entries` argument.
-
-Examples:
-
-1. Output at most 10 first entries from the `densepose_coco_2014_train` dataset:
-```bash
-python query_db.py print densepose_coco_2014_train \* --max-entries 10 -v
-```
-
-2. Output all entries with `file_name` equal to `COCO_train2014_000000000036.jpg`: 
-```bash
-python query_db.py print densepose_coco_2014_train file_name=COCO_train2014_000000000036.jpg -v
-```
-
-3. Output all entries with `image_id` between 36 and 156:
-```bash
-python query_db.py print densepose_coco_2014_train image_id:int=36-156 -v
-```
-
-## Visualization Mode
-
-The general command form is:
-```bash
-python query_db.py show [-h] [-v] [--max-entries N] [--output <image_file>] <dataset> <selector> <visualizations>
-```
-
-There are three mandatory arguments:
- - `<dataset>`, DensePose dataset specification, from which to select
-   the entries (e.g. `densepose_coco_2014_train`).
- - `<selector>`, dataset entry selector which can be a single specification,
-   or a comma-separated list of specifications of the form
-   `field[:type]=value` for exact match with the value
-   or `field[:type]=min-max` for a range of values
- - `<visualizations>`, visualizations specifier; currently available visualizations are:
-   * `bbox` - bounding boxes of annotated persons;
-   * `dp_i` - annotated points colored according to the containing part;
-   * `dp_pts` - annotated points in green color;
-   * `dp_segm` - segmentation masks for annotated persons;
-   * `dp_u` - annotated points colored according to their U coordinate in part parameterization;
-   * `dp_v` - annotated points colored according to their V coordinate in part parameterization;
-
-One can additionally provide one of the two optional arguments:
- - `--max_entries` to limit the maximum number of entries to visualize
- - `--output` to provide visualization file name template, which defaults
-   to `output.png`. To distinguish file names for different dataset
-   entries, the tool appends 1-based entry index to the output file name,
-   e.g. output.0001.png, output.0002.png, etc.
-
-The following examples show how to output different visualizations for image with `id = 322`
-from `densepose_coco_2014_train` dataset:
-
-1. Show bounding box and segmentation:
-```bash
-python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_segm -v
-```
-![Bounding Box + Segmentation Visualization](images/vis_bbox_dp_segm.jpg)
-
-2. Show bounding box and points colored according to the containing part:
-```bash
-python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_i -v
-```
-![Bounding Box + Point Label Visualization](images/vis_bbox_dp_i.jpg)
-
-3. Show bounding box and annotated points in green color:
-```bash
-python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_segm -v
-```
-![Bounding Box + Point Visualization](images/vis_bbox_dp_pts.jpg)
-
-4. Show bounding box and annotated points colored according to their U coordinate in part parameterization:
-```bash
-python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_u -v
-```
-![Bounding Box + Point U Visualization](images/vis_bbox_dp_u.jpg)
-
-5. Show bounding box and annotated points colored according to their V coordinate in part parameterization:
-```bash
-python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_v -v
-```
-![Bounding Box + Point V Visualization](images/vis_bbox_dp_v.jpg)
-
-
diff --git a/spaces/cactusAtSea/influencerGPT/README.md b/spaces/cactusAtSea/influencerGPT/README.md
deleted file mode 100644
index 0a321b50b451d8e8a678c28f98ba0f9e28fe2a87..0000000000000000000000000000000000000000
--- a/spaces/cactusAtSea/influencerGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: InfluencerGPT
-emoji: 👁
-colorFrom: red
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/op_gpu/upfirdn2d.py b/spaces/caffeinum/VToonify/vtoonify/model/stylegan/op_gpu/upfirdn2d.py
deleted file mode 100644
index 3a12f15b3c2347194e3bf0fdfda736415693775f..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/op_gpu/upfirdn2d.py
+++ /dev/null
@@ -1,209 +0,0 @@
-from collections import abc
-import os
-
-import torch
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-
-module_path = os.path.dirname(__file__)
-upfirdn2d_op = load(
-    "upfirdn2d",
-    sources=[
-        os.path.join(module_path, "upfirdn2d.cpp"),
-        os.path.join(module_path, "upfirdn2d_kernel.cu"),
-    ],
-)
-
-
-class UpFirDn2dBackward(Function):
-    @staticmethod
-    def forward(
-        ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
-    ):
-
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_op.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_op.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(
-            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
-        )
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_op.upfirdn2d(
-            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-        )
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = None
-
-        if ctx.needs_input_grad[0]:
-            grad_input = UpFirDn2dBackward.apply(
-                grad_output,
-                kernel,
-                grad_kernel,
-                ctx.up,
-                ctx.down,
-                ctx.pad,
-                ctx.g_pad,
-                ctx.in_size,
-                ctx.out_size,
-            )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    if not isinstance(up, abc.Iterable):
-        up = (up, up)
-
-    if not isinstance(down, abc.Iterable):
-        down = (down, down)
-
-    if len(pad) == 2:
-        pad = (pad[0], pad[1], pad[0], pad[1])
-
-    if input.device.type == "cpu":
-        out = upfirdn2d_native(input, kernel, *up, *down, *pad)
-
-    else:
-        out = UpFirDn2d.apply(input, kernel, up, down, pad)
-
-    return out
-
-
-def upfirdn2d_native(
-    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-        :,
-        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
-        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
-        :,
-    ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/cahya/image-search/app.py b/spaces/cahya/image-search/app.py
deleted file mode 100644
index 600ebcd591453417fddcae680685330fda917c07..0000000000000000000000000000000000000000
--- a/spaces/cahya/image-search/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import streamlit as st
-import SessionState
-from prompts import PROMPT_LIST
-from wit_index import WitIndex
-import random
-import time
-
-# st.set_page_config(page_title="Image Search")
-
-# vector_length = 128
-wit_index_path = f"./models/wit_faiss.idx"
-model_name = f"./models/distilbert-base-wit"
-wit_dataset_path = "./models/wit_dataset.pkl"
-
-
-@st.cache(suppress_st_warning=True, allow_output_mutation=True)
-def get_wit_index():
-    st.write("Loading the WIT index, dataset and the DistillBERT model..")
-    wit_index = WitIndex(wit_index_path, model_name, wit_dataset_path, gpu=False)
-    return wit_index
-
-# st.cache is disabled temporarily because the inference could take forever using newer streamlit version.
-# @st.cache(suppress_st_warning=True)
-def process(text: str, top_k: int = 10):
-    # st.write("Cache miss: process")
-    distance, index, image_info = wit_index.search(text, top_k=top_k)
-    return distance, index, image_info
-
-
-st.title("Image Search")
-
-st.markdown(
-    """
-    This application is a demo for sentence-based image search using 
-    [WIT dataset](https://github.com/google-research-datasets/wit). We use DistillBert to encode the sentences 
-    and Facebook's Faiss to search the vector embeddings.
-    """
-)
-session_state = SessionState.get(prompt=None, prompt_box=None, text=None)
-ALL_PROMPTS = list(PROMPT_LIST.keys())+["Custom"]
-prompt = st.selectbox('Prompt', ALL_PROMPTS, index=len(ALL_PROMPTS)-1)
-# Update prompt
-if session_state.prompt is None:
-    session_state.prompt = prompt
-elif session_state.prompt is not None and (prompt != session_state.prompt):
-    session_state.prompt = prompt
-    session_state.prompt_box = None
-    session_state.text = None
-else:
-    session_state.prompt = prompt
-
-# Update prompt box
-if session_state.prompt == "Custom":
-    session_state.prompt_box = "Enter your text here"
-else:
-    if session_state.prompt is not None and session_state.prompt_box is None:
-        session_state.prompt_box = random.choice(PROMPT_LIST[session_state.prompt])
-
-session_state.text = st.text_area("Enter text", session_state.prompt_box)
-
-top_k = st.sidebar.number_input(
-    "Top k",
-    value=6,
-    min_value=1,
-    max_value=10
-)
-
-wit_index = get_wit_index()
-if st.button("Run"):
-    with st.spinner(text="Getting results..."):
-        st.subheader("Result")
-        time_start = time.time()
-        distances, index, image_info = process(text=session_state.text, top_k=int(top_k))
-        time_end = time.time()
-        time_diff = time_end-time_start
-        print(f"Search in {time_diff} seconds")
-        st.markdown(f"*Search in {time_diff:.5f} seconds*")
-        for i, distance in enumerate(distances):
-            try:
-                st.image(image_info[i][0].replace("http:", "https:"), width=400)
-            except FileNotFoundError:
-                st.write(f"{image_info[i][0]} can't be displayed")
-            st.write(f"{image_info[i][1]}. (D: {distance:.2f})")
-
-        # Reset state
-        session_state.prompt = None
-        session_state.prompt_box = None
-        session_state.text = None
diff --git a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/model_selection.py b/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/model_selection.py
deleted file mode 100644
index 94f0b3584cba8e1cf248226c8f3c304392c09cd1..0000000000000000000000000000000000000000
--- a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/model_selection.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import asyncio
-import json
-import logging
-
-import aiohttp
-from langchain import LLMChain
-from langchain.llms.base import BaseLLM
-from langchain.output_parsers import OutputFixingParser, PydanticOutputParser
-from langchain.prompts import load_prompt
-from pydantic import BaseModel, Field
-
-from hugginggpt.exceptions import ModelSelectionException, async_wrap_exceptions
-from hugginggpt.model_scraper import get_top_k_models
-from hugginggpt.resources import get_prompt_resource
-from hugginggpt.task_parsing import Task
-
-logger = logging.getLogger(__name__)
-
-
-class Model(BaseModel):
-    id: str = Field(description="ID of the model")
-    reason: str = Field(description="Reason for selecting this model")
-
-
-async def select_hf_models(
-    user_input: str,
-    tasks: list[Task],
-    model_selection_llm: BaseLLM,
-    output_fixing_llm: BaseLLM,
-) -> dict[int, Model]:
-    """Use LLM agent to select the best available HuggingFace model for each task, given model metadata.
-    Runs concurrently."""
-    async with aiohttp.ClientSession() as session:
-        async with asyncio.TaskGroup() as tg:
-            aio_tasks = []
-            for task in tasks:
-                aio_tasks.append(
-                    tg.create_task(
-                        select_model(
-                            user_input=user_input,
-                            task=task,
-                            model_selection_llm=model_selection_llm,
-                            output_fixing_llm=output_fixing_llm,
-                            session=session,
-                        )
-                    )
-                )
-        results = await asyncio.gather(*aio_tasks)
-        return {task_id: model for task_id, model in results}
-
-
-@async_wrap_exceptions(ModelSelectionException, "Failed to select model")
-async def select_model(
-    user_input: str,
-    task: Task,
-    model_selection_llm: BaseLLM,
-    output_fixing_llm: BaseLLM,
-    session: aiohttp.ClientSession,
-) -> (int, Model):
-    logger.info(f"Starting model selection for task: {task.task}")
-
-    top_k_models = await get_top_k_models(
-        task=task.task, top_k=5, max_description_length=100, session=session
-    )
-
-    if task.task in [
-        "summarization",
-        "translation",
-        "conversational",
-        "text-generation",
-        "text2text-generation",
-    ]:
-        model = Model(
-            id="openai",
-            reason="Text generation tasks are best handled by OpenAI models",
-        )
-    else:
-        prompt_template = load_prompt(
-            get_prompt_resource("model-selection-prompt.json")
-        )
-        llm_chain = LLMChain(prompt=prompt_template, llm=model_selection_llm)
-        # Need to replace double quotes with single quotes for correct response generation
-        task_str = task.json().replace('"', "'")
-        models_str = json.dumps(top_k_models).replace('"', "'")
-        output = await llm_chain.apredict(
-            user_input=user_input, task=task_str, models=models_str, stop=["<im_end>"]
-        )
-        logger.debug(f"Model selection raw output: {output}")
-
-        parser = PydanticOutputParser(pydantic_object=Model)
-        fixing_parser = OutputFixingParser.from_llm(
-            parser=parser, llm=output_fixing_llm
-        )
-        model = fixing_parser.parse(output)
-
-    logger.info(f"For task: {task.task}, selected model: {model}")
-    return task.id, model
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/utils/transform.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/utils/transform.py
deleted file mode 100644
index 8dc4ae7be878302ec39b7f235e3ae1b7a3ca29ee..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/utils/transform.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.data import MetadataCatalog
-from detectron2.utils.file_io import PathManager
-
-from densepose import DensePoseTransformData
-
-
-def load_for_dataset(dataset_name):
-    path = MetadataCatalog.get(dataset_name).densepose_transform_src
-    densepose_transform_data_fpath = PathManager.get_local_path(path)
-    return DensePoseTransformData.load(densepose_transform_data_fpath)
-
-
-def load_from_cfg(cfg):
-    return load_for_dataset(cfg.DATASETS.TEST[0])
diff --git a/spaces/cc1799/vits-uma-genshin-honkai/models.py b/spaces/cc1799/vits-uma-genshin-honkai/models.py
deleted file mode 100644
index 8353b867f441de7e4d05aef980e672899c3a8889..0000000000000000000000000000000000000000
--- a/spaces/cc1799/vits-uma-genshin-honkai/models.py
+++ /dev/null
@@ -1,533 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    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.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    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, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, m, logs, 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
-
-
-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
-
-
-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 SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    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,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    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.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    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, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/codeparrot/examples/README.md b/spaces/chendl/compositional_test/transformers/examples/research_projects/codeparrot/examples/README.md
deleted file mode 100644
index c1980262d8275b9e0a9abe3d5a00d3c955ddb73d..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/codeparrot/examples/README.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Examples
-In this folder we showcase some examples to use code models for downstream tasks.
-
-## Complexity prediction
-In this task we want to predict the complexity of Java programs in [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) dataset. Using Hugging Face `trainer`, we finetuned [multilingual CodeParrot](https://huggingface.co/codeparrot/codeparrot-small-multi) and [UniXcoder](https://huggingface.co/microsoft/unixcoder-base-nine) on it, and we used the latter to build this Java complexity prediction [space](https://huggingface.co/spaces/codeparrot/code-complexity-predictor) on Hugging Face hub.
-
-To fine-tune a model on this dataset you can use the following commands:
-
-```python
-python train_complexity_predictor.py \
-    --model_ckpt microsoft/unixcoder-base-nine \
-    --num_epochs 60 \
-    --num_warmup_steps 10 \
-    --batch_size 8 \
-    --learning_rate 5e-4 
-```
-
-## Code generation: text to python
-In this task we want to train a model to generate code from english text. We finetuned Codeparrot-small on [github-jupyter-text-to-code](https://huggingface.co/datasets/codeparrot/github-jupyter-text-to-code), a dataset where the samples are a succession of docstrings and their Python code, originally extracted from Jupyter notebooks parsed in this [dataset](https://huggingface.co/datasets/codeparrot/github-jupyter-parsed).
-
-To fine-tune a model on this dataset we use the same [script](https://github.com/huggingface/transformers/blob/main/examples/research_projects/codeparrot/scripts/codeparrot_training.py) as the pretraining of codeparrot:
-
-```python
-accelerate launch scripts/codeparrot_training.py \
-    --model_ckpt codeparrot/codeparrot-small \
-    --dataset_name_train codeparrot/github-jupyter-text-to-code \
-    --dataset_name_valid codeparrot/github-jupyter-text-to-code \
-    --train_batch_size 12 \
-    --valid_batch_size 12 \
-    --learning_rate 5e-4 \
-    --num_warmup_steps 100 \
-    --gradient_accumulation 1 \
-    --gradient_checkpointing False \
-    --max_train_steps 3000 \
-    --save_checkpoint_steps 200 \
-    --save_dir jupyter-text-to-python
-```
-
-## Code explanation: python to text
-In this task we want to train a model to explain python code. We finetuned Codeparrot-small on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text), a dataset where the samples are a succession of Python code and its explanation as a docstring, we just inverted the order of text and code pairs in github-jupyter-code-to-text dataset and added the delimiters "Explanation:" and "End of explanation" inside the doctrings.
-
-To fine-tune a model on this dataset we use the same [script](https://github.com/huggingface/transformers/blob/main/examples/research_projects/codeparrot/scripts/codeparrot_training.py) as the pretraining of codeparrot:
-
-```python
-accelerate launch scripts/codeparrot_training.py \
-    --model_ckpt codeparrot/codeparrot-small \
-    --dataset_name_train codeparrot/github-jupyter-code-to-text \
-    --dataset_name_valid codeparrot/github-jupyter-code-to-text \
-    --train_batch_size 12 \
-    --valid_batch_size 12 \
-    --learning_rate 5e-4 \
-    --num_warmup_steps 100 \
-    --gradient_accumulation 1 \
-    --gradient_checkpointing False \
-    --max_train_steps 3000 \
-    --save_checkpoint_steps 200 \
-    --save_dir jupyter-python-to-text
-```
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/longform-qa/README.md b/spaces/chendl/compositional_test/transformers/examples/research_projects/longform-qa/README.md
deleted file mode 100644
index eaa29d4542260c553c2403f311c2ee45df6c3fd9..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/longform-qa/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Long Form Question Answering
-
-Author: @yjernite
-
-This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and [🤗datasets](https://github.com/huggingface/datasets) libraries.
-
-You can use these methods to train your own system by following along the associate [notebook](https://github.com/huggingface/notebooks/blob/master/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb) or [blog post](https://yjernite.github.io/lfqa.html).
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/feaLib/parser.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/feaLib/parser.py
deleted file mode 100644
index 49667f4503e15be8c00388a72cb0d428dc7dafe9..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/feaLib/parser.py
+++ /dev/null
@@ -1,2365 +0,0 @@
-from fontTools.feaLib.error import FeatureLibError
-from fontTools.feaLib.lexer import Lexer, IncludingLexer, NonIncludingLexer
-from fontTools.feaLib.variableScalar import VariableScalar
-from fontTools.misc.encodingTools import getEncoding
-from fontTools.misc.textTools import bytechr, tobytes, tostr
-import fontTools.feaLib.ast as ast
-import logging
-import os
-import re
-
-
-log = logging.getLogger(__name__)
-
-
-class Parser(object):
-    """Initializes a Parser object.
-
-    Example:
-
-        .. code:: python
-
-            from fontTools.feaLib.parser import Parser
-            parser = Parser(file, font.getReverseGlyphMap())
-            parsetree = parser.parse()
-
-    Note: the ``glyphNames`` iterable serves a double role to help distinguish
-    glyph names from ranges in the presence of hyphens and to ensure that glyph
-    names referenced in a feature file are actually part of a font's glyph set.
-    If the iterable is left empty, no glyph name in glyph set checking takes
-    place, and all glyph tokens containing hyphens are treated as literal glyph
-    names, not as ranges. (Adding a space around the hyphen can, in any case,
-    help to disambiguate ranges from glyph names containing hyphens.)
-
-    By default, the parser will follow ``include()`` statements in the feature
-    file. To turn this off, pass ``followIncludes=False``. Pass a directory string as
-    ``includeDir`` to explicitly declare a directory to search included feature files
-    in.
-    """
-
-    extensions = {}
-    ast = ast
-    SS_FEATURE_TAGS = {"ss%02d" % i for i in range(1, 20 + 1)}
-    CV_FEATURE_TAGS = {"cv%02d" % i for i in range(1, 99 + 1)}
-
-    def __init__(
-        self, featurefile, glyphNames=(), followIncludes=True, includeDir=None, **kwargs
-    ):
-
-        if "glyphMap" in kwargs:
-            from fontTools.misc.loggingTools import deprecateArgument
-
-            deprecateArgument("glyphMap", "use 'glyphNames' (iterable) instead")
-            if glyphNames:
-                raise TypeError(
-                    "'glyphNames' and (deprecated) 'glyphMap' are " "mutually exclusive"
-                )
-            glyphNames = kwargs.pop("glyphMap")
-        if kwargs:
-            raise TypeError(
-                "unsupported keyword argument%s: %s"
-                % ("" if len(kwargs) == 1 else "s", ", ".join(repr(k) for k in kwargs))
-            )
-
-        self.glyphNames_ = set(glyphNames)
-        self.doc_ = self.ast.FeatureFile()
-        self.anchors_ = SymbolTable()
-        self.glyphclasses_ = SymbolTable()
-        self.lookups_ = SymbolTable()
-        self.valuerecords_ = SymbolTable()
-        self.symbol_tables_ = {self.anchors_, self.valuerecords_}
-        self.next_token_type_, self.next_token_ = (None, None)
-        self.cur_comments_ = []
-        self.next_token_location_ = None
-        lexerClass = IncludingLexer if followIncludes else NonIncludingLexer
-        self.lexer_ = lexerClass(featurefile, includeDir=includeDir)
-        self.missing = {}
-        self.advance_lexer_(comments=True)
-
-    def parse(self):
-        """Parse the file, and return a :class:`fontTools.feaLib.ast.FeatureFile`
-        object representing the root of the abstract syntax tree containing the
-        parsed contents of the file."""
-        statements = self.doc_.statements
-        while self.next_token_type_ is not None or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("include"):
-                statements.append(self.parse_include_())
-            elif self.cur_token_type_ is Lexer.GLYPHCLASS:
-                statements.append(self.parse_glyphclass_definition_())
-            elif self.is_cur_keyword_(("anon", "anonymous")):
-                statements.append(self.parse_anonymous_())
-            elif self.is_cur_keyword_("anchorDef"):
-                statements.append(self.parse_anchordef_())
-            elif self.is_cur_keyword_("languagesystem"):
-                statements.append(self.parse_languagesystem_())
-            elif self.is_cur_keyword_("lookup"):
-                statements.append(self.parse_lookup_(vertical=False))
-            elif self.is_cur_keyword_("markClass"):
-                statements.append(self.parse_markClass_())
-            elif self.is_cur_keyword_("feature"):
-                statements.append(self.parse_feature_block_())
-            elif self.is_cur_keyword_("conditionset"):
-                statements.append(self.parse_conditionset_())
-            elif self.is_cur_keyword_("variation"):
-                statements.append(self.parse_feature_block_(variation=True))
-            elif self.is_cur_keyword_("table"):
-                statements.append(self.parse_table_())
-            elif self.is_cur_keyword_("valueRecordDef"):
-                statements.append(self.parse_valuerecord_definition_(vertical=False))
-            elif (
-                self.cur_token_type_ is Lexer.NAME
-                and self.cur_token_ in self.extensions
-            ):
-                statements.append(self.extensions[self.cur_token_](self))
-            elif self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected feature, languagesystem, lookup, markClass, "
-                    'table, or glyph class definition, got {} "{}"'.format(
-                        self.cur_token_type_, self.cur_token_
-                    ),
-                    self.cur_token_location_,
-                )
-        # Report any missing glyphs at the end of parsing
-        if self.missing:
-            error = [
-                " %s (first found at %s)" % (name, loc)
-                for name, loc in self.missing.items()
-            ]
-            raise FeatureLibError(
-                "The following glyph names are referenced but are missing from the "
-                "glyph set:\n" + ("\n".join(error)),
-                None,
-            )
-        return self.doc_
-
-    def parse_anchor_(self):
-        # Parses an anchor in any of the four formats given in the feature
-        # file specification (2.e.vii).
-        self.expect_symbol_("<")
-        self.expect_keyword_("anchor")
-        location = self.cur_token_location_
-
-        if self.next_token_ == "NULL":  # Format D
-            self.expect_keyword_("NULL")
-            self.expect_symbol_(">")
-            return None
-
-        if self.next_token_type_ == Lexer.NAME:  # Format E
-            name = self.expect_name_()
-            anchordef = self.anchors_.resolve(name)
-            if anchordef is None:
-                raise FeatureLibError(
-                    'Unknown anchor "%s"' % name, self.cur_token_location_
-                )
-            self.expect_symbol_(">")
-            return self.ast.Anchor(
-                anchordef.x,
-                anchordef.y,
-                name=name,
-                contourpoint=anchordef.contourpoint,
-                xDeviceTable=None,
-                yDeviceTable=None,
-                location=location,
-            )
-
-        x, y = self.expect_number_(variable=True), self.expect_number_(variable=True)
-
-        contourpoint = None
-        if self.next_token_ == "contourpoint":  # Format B
-            self.expect_keyword_("contourpoint")
-            contourpoint = self.expect_number_()
-
-        if self.next_token_ == "<":  # Format C
-            xDeviceTable = self.parse_device_()
-            yDeviceTable = self.parse_device_()
-        else:
-            xDeviceTable, yDeviceTable = None, None
-
-        self.expect_symbol_(">")
-        return self.ast.Anchor(
-            x,
-            y,
-            name=None,
-            contourpoint=contourpoint,
-            xDeviceTable=xDeviceTable,
-            yDeviceTable=yDeviceTable,
-            location=location,
-        )
-
-    def parse_anchor_marks_(self):
-        # Parses a sequence of ``[<anchor> mark @MARKCLASS]*.``
-        anchorMarks = []  # [(self.ast.Anchor, markClassName)*]
-        while self.next_token_ == "<":
-            anchor = self.parse_anchor_()
-            if anchor is None and self.next_token_ != "mark":
-                continue  # <anchor NULL> without mark, eg. in GPOS type 5
-            self.expect_keyword_("mark")
-            markClass = self.expect_markClass_reference_()
-            anchorMarks.append((anchor, markClass))
-        return anchorMarks
-
-    def parse_anchordef_(self):
-        # Parses a named anchor definition (`section 2.e.viii <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#2.e.vii>`_).
-        assert self.is_cur_keyword_("anchorDef")
-        location = self.cur_token_location_
-        x, y = self.expect_number_(), self.expect_number_()
-        contourpoint = None
-        if self.next_token_ == "contourpoint":
-            self.expect_keyword_("contourpoint")
-            contourpoint = self.expect_number_()
-        name = self.expect_name_()
-        self.expect_symbol_(";")
-        anchordef = self.ast.AnchorDefinition(
-            name, x, y, contourpoint=contourpoint, location=location
-        )
-        self.anchors_.define(name, anchordef)
-        return anchordef
-
-    def parse_anonymous_(self):
-        # Parses an anonymous data block (`section 10 <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#10>`_).
-        assert self.is_cur_keyword_(("anon", "anonymous"))
-        tag = self.expect_tag_()
-        _, content, location = self.lexer_.scan_anonymous_block(tag)
-        self.advance_lexer_()
-        self.expect_symbol_("}")
-        end_tag = self.expect_tag_()
-        assert tag == end_tag, "bad splitting in Lexer.scan_anonymous_block()"
-        self.expect_symbol_(";")
-        return self.ast.AnonymousBlock(tag, content, location=location)
-
-    def parse_attach_(self):
-        # Parses a GDEF Attach statement (`section 9.b <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.b>`_)
-        assert self.is_cur_keyword_("Attach")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        contourPoints = {self.expect_number_()}
-        while self.next_token_ != ";":
-            contourPoints.add(self.expect_number_())
-        self.expect_symbol_(";")
-        return self.ast.AttachStatement(glyphs, contourPoints, location=location)
-
-    def parse_enumerate_(self, vertical):
-        # Parse an enumerated pair positioning rule (`section 6.b.ii <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#6.b.ii>`_).
-        assert self.cur_token_ in {"enumerate", "enum"}
-        self.advance_lexer_()
-        return self.parse_position_(enumerated=True, vertical=vertical)
-
-    def parse_GlyphClassDef_(self):
-        # Parses 'GlyphClassDef @BASE, @LIGATURES, @MARKS, @COMPONENTS;'
-        assert self.is_cur_keyword_("GlyphClassDef")
-        location = self.cur_token_location_
-        if self.next_token_ != ",":
-            baseGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            baseGlyphs = None
-        self.expect_symbol_(",")
-        if self.next_token_ != ",":
-            ligatureGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            ligatureGlyphs = None
-        self.expect_symbol_(",")
-        if self.next_token_ != ",":
-            markGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            markGlyphs = None
-        self.expect_symbol_(",")
-        if self.next_token_ != ";":
-            componentGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            componentGlyphs = None
-        self.expect_symbol_(";")
-        return self.ast.GlyphClassDefStatement(
-            baseGlyphs, markGlyphs, ligatureGlyphs, componentGlyphs, location=location
-        )
-
-    def parse_glyphclass_definition_(self):
-        # Parses glyph class definitions such as '@UPPERCASE = [A-Z];'
-        location, name = self.cur_token_location_, self.cur_token_
-        self.expect_symbol_("=")
-        glyphs = self.parse_glyphclass_(accept_glyphname=False)
-        self.expect_symbol_(";")
-        glyphclass = self.ast.GlyphClassDefinition(name, glyphs, location=location)
-        self.glyphclasses_.define(name, glyphclass)
-        return glyphclass
-
-    def split_glyph_range_(self, name, location):
-        # Since v1.20, the OpenType Feature File specification allows
-        # for dashes in glyph names. A sequence like "a-b-c-d" could
-        # therefore mean a single glyph whose name happens to be
-        # "a-b-c-d", or it could mean a range from glyph "a" to glyph
-        # "b-c-d", or a range from glyph "a-b" to glyph "c-d", or a
-        # range from glyph "a-b-c" to glyph "d".Technically, this
-        # example could be resolved because the (pretty complex)
-        # definition of glyph ranges renders most of these splits
-        # invalid. But the specification does not say that a compiler
-        # should try to apply such fancy heuristics. To encourage
-        # unambiguous feature files, we therefore try all possible
-        # splits and reject the feature file if there are multiple
-        # splits possible. It is intentional that we don't just emit a
-        # warning; warnings tend to get ignored. To fix the problem,
-        # font designers can trivially add spaces around the intended
-        # split point, and we emit a compiler error that suggests
-        # how exactly the source should be rewritten to make things
-        # unambiguous.
-        parts = name.split("-")
-        solutions = []
-        for i in range(len(parts)):
-            start, limit = "-".join(parts[0:i]), "-".join(parts[i:])
-            if start in self.glyphNames_ and limit in self.glyphNames_:
-                solutions.append((start, limit))
-        if len(solutions) == 1:
-            start, limit = solutions[0]
-            return start, limit
-        elif len(solutions) == 0:
-            raise FeatureLibError(
-                '"%s" is not a glyph in the font, and it can not be split '
-                "into a range of known glyphs" % name,
-                location,
-            )
-        else:
-            ranges = " or ".join(['"%s - %s"' % (s, l) for s, l in solutions])
-            raise FeatureLibError(
-                'Ambiguous glyph range "%s"; '
-                "please use %s to clarify what you mean" % (name, ranges),
-                location,
-            )
-
-    def parse_glyphclass_(self, accept_glyphname, accept_null=False):
-        # Parses a glyph class, either named or anonymous, or (if
-        # ``bool(accept_glyphname)``) a glyph name. If ``bool(accept_null)`` then
-        # also accept the special NULL glyph.
-        if accept_glyphname and self.next_token_type_ in (Lexer.NAME, Lexer.CID):
-            if accept_null and self.next_token_ == "NULL":
-                # If you want a glyph called NULL, you should escape it.
-                self.advance_lexer_()
-                return self.ast.NullGlyph(location=self.cur_token_location_)
-            glyph = self.expect_glyph_()
-            self.check_glyph_name_in_glyph_set(glyph)
-            return self.ast.GlyphName(glyph, location=self.cur_token_location_)
-        if self.next_token_type_ is Lexer.GLYPHCLASS:
-            self.advance_lexer_()
-            gc = self.glyphclasses_.resolve(self.cur_token_)
-            if gc is None:
-                raise FeatureLibError(
-                    "Unknown glyph class @%s" % self.cur_token_,
-                    self.cur_token_location_,
-                )
-            if isinstance(gc, self.ast.MarkClass):
-                return self.ast.MarkClassName(gc, location=self.cur_token_location_)
-            else:
-                return self.ast.GlyphClassName(gc, location=self.cur_token_location_)
-
-        self.expect_symbol_("[")
-        location = self.cur_token_location_
-        glyphs = self.ast.GlyphClass(location=location)
-        while self.next_token_ != "]":
-            if self.next_token_type_ is Lexer.NAME:
-                glyph = self.expect_glyph_()
-                location = self.cur_token_location_
-                if "-" in glyph and self.glyphNames_ and glyph not in self.glyphNames_:
-                    start, limit = self.split_glyph_range_(glyph, location)
-                    self.check_glyph_name_in_glyph_set(start, limit)
-                    glyphs.add_range(
-                        start, limit, self.make_glyph_range_(location, start, limit)
-                    )
-                elif self.next_token_ == "-":
-                    start = glyph
-                    self.expect_symbol_("-")
-                    limit = self.expect_glyph_()
-                    self.check_glyph_name_in_glyph_set(start, limit)
-                    glyphs.add_range(
-                        start, limit, self.make_glyph_range_(location, start, limit)
-                    )
-                else:
-                    if "-" in glyph and not self.glyphNames_:
-                        log.warning(
-                            str(
-                                FeatureLibError(
-                                    f"Ambiguous glyph name that looks like a range: {glyph!r}",
-                                    location,
-                                )
-                            )
-                        )
-                    self.check_glyph_name_in_glyph_set(glyph)
-                    glyphs.append(glyph)
-            elif self.next_token_type_ is Lexer.CID:
-                glyph = self.expect_glyph_()
-                if self.next_token_ == "-":
-                    range_location = self.cur_token_location_
-                    range_start = self.cur_token_
-                    self.expect_symbol_("-")
-                    range_end = self.expect_cid_()
-                    self.check_glyph_name_in_glyph_set(
-                        f"cid{range_start:05d}",
-                        f"cid{range_end:05d}",
-                    )
-                    glyphs.add_cid_range(
-                        range_start,
-                        range_end,
-                        self.make_cid_range_(range_location, range_start, range_end),
-                    )
-                else:
-                    glyph_name = f"cid{self.cur_token_:05d}"
-                    self.check_glyph_name_in_glyph_set(glyph_name)
-                    glyphs.append(glyph_name)
-            elif self.next_token_type_ is Lexer.GLYPHCLASS:
-                self.advance_lexer_()
-                gc = self.glyphclasses_.resolve(self.cur_token_)
-                if gc is None:
-                    raise FeatureLibError(
-                        "Unknown glyph class @%s" % self.cur_token_,
-                        self.cur_token_location_,
-                    )
-                if isinstance(gc, self.ast.MarkClass):
-                    gc = self.ast.MarkClassName(gc, location=self.cur_token_location_)
-                else:
-                    gc = self.ast.GlyphClassName(gc, location=self.cur_token_location_)
-                glyphs.add_class(gc)
-            else:
-                raise FeatureLibError(
-                    "Expected glyph name, glyph range, "
-                    f"or glyph class reference, found {self.next_token_!r}",
-                    self.next_token_location_,
-                )
-        self.expect_symbol_("]")
-        return glyphs
-
-    def parse_glyph_pattern_(self, vertical):
-        # Parses a glyph pattern, including lookups and context, e.g.::
-        #
-        #    a b
-        #    a b c' d e
-        #    a b c' lookup ChangeC d e
-        prefix, glyphs, lookups, values, suffix = ([], [], [], [], [])
-        hasMarks = False
-        while self.next_token_ not in {"by", "from", ";", ","}:
-            gc = self.parse_glyphclass_(accept_glyphname=True)
-            marked = False
-            if self.next_token_ == "'":
-                self.expect_symbol_("'")
-                hasMarks = marked = True
-            if marked:
-                if suffix:
-                    # makeotf also reports this as an error, while FontForge
-                    # silently inserts ' in all the intervening glyphs.
-                    # https://github.com/fonttools/fonttools/pull/1096
-                    raise FeatureLibError(
-                        "Unsupported contextual target sequence: at most "
-                        "one run of marked (') glyph/class names allowed",
-                        self.cur_token_location_,
-                    )
-                glyphs.append(gc)
-            elif glyphs:
-                suffix.append(gc)
-            else:
-                prefix.append(gc)
-
-            if self.is_next_value_():
-                values.append(self.parse_valuerecord_(vertical))
-            else:
-                values.append(None)
-
-            lookuplist = None
-            while self.next_token_ == "lookup":
-                if lookuplist is None:
-                    lookuplist = []
-                self.expect_keyword_("lookup")
-                if not marked:
-                    raise FeatureLibError(
-                        "Lookups can only follow marked glyphs",
-                        self.cur_token_location_,
-                    )
-                lookup_name = self.expect_name_()
-                lookup = self.lookups_.resolve(lookup_name)
-                if lookup is None:
-                    raise FeatureLibError(
-                        'Unknown lookup "%s"' % lookup_name, self.cur_token_location_
-                    )
-                lookuplist.append(lookup)
-            if marked:
-                lookups.append(lookuplist)
-
-        if not glyphs and not suffix:  # eg., "sub f f i by"
-            assert lookups == []
-            return ([], prefix, [None] * len(prefix), values, [], hasMarks)
-        else:
-            if any(values[: len(prefix)]):
-                raise FeatureLibError(
-                    "Positioning cannot be applied in the bactrack glyph sequence, "
-                    "before the marked glyph sequence.",
-                    self.cur_token_location_,
-                )
-            marked_values = values[len(prefix) : len(prefix) + len(glyphs)]
-            if any(marked_values):
-                if any(values[len(prefix) + len(glyphs) :]):
-                    raise FeatureLibError(
-                        "Positioning values are allowed only in the marked glyph "
-                        "sequence, or after the final glyph node when only one glyph "
-                        "node is marked.",
-                        self.cur_token_location_,
-                    )
-                values = marked_values
-            elif values and values[-1]:
-                if len(glyphs) > 1 or any(values[:-1]):
-                    raise FeatureLibError(
-                        "Positioning values are allowed only in the marked glyph "
-                        "sequence, or after the final glyph node when only one glyph "
-                        "node is marked.",
-                        self.cur_token_location_,
-                    )
-                values = values[-1:]
-            elif any(values):
-                raise FeatureLibError(
-                    "Positioning values are allowed only in the marked glyph "
-                    "sequence, or after the final glyph node when only one glyph "
-                    "node is marked.",
-                    self.cur_token_location_,
-                )
-            return (prefix, glyphs, lookups, values, suffix, hasMarks)
-
-    def parse_ignore_glyph_pattern_(self, sub):
-        location = self.cur_token_location_
-        prefix, glyphs, lookups, values, suffix, hasMarks = self.parse_glyph_pattern_(
-            vertical=False
-        )
-        if any(lookups):
-            raise FeatureLibError(
-                f'No lookups can be specified for "ignore {sub}"', location
-            )
-        if not hasMarks:
-            error = FeatureLibError(
-                f'Ambiguous "ignore {sub}", there should be least one marked glyph',
-                location,
-            )
-            log.warning(str(error))
-            suffix, glyphs = glyphs[1:], glyphs[0:1]
-        chainContext = (prefix, glyphs, suffix)
-        return chainContext
-
-    def parse_ignore_context_(self, sub):
-        location = self.cur_token_location_
-        chainContext = [self.parse_ignore_glyph_pattern_(sub)]
-        while self.next_token_ == ",":
-            self.expect_symbol_(",")
-            chainContext.append(self.parse_ignore_glyph_pattern_(sub))
-        self.expect_symbol_(";")
-        return chainContext
-
-    def parse_ignore_(self):
-        # Parses an ignore sub/pos rule.
-        assert self.is_cur_keyword_("ignore")
-        location = self.cur_token_location_
-        self.advance_lexer_()
-        if self.cur_token_ in ["substitute", "sub"]:
-            chainContext = self.parse_ignore_context_("sub")
-            return self.ast.IgnoreSubstStatement(chainContext, location=location)
-        if self.cur_token_ in ["position", "pos"]:
-            chainContext = self.parse_ignore_context_("pos")
-            return self.ast.IgnorePosStatement(chainContext, location=location)
-        raise FeatureLibError(
-            'Expected "substitute" or "position"', self.cur_token_location_
-        )
-
-    def parse_include_(self):
-        assert self.cur_token_ == "include"
-        location = self.cur_token_location_
-        filename = self.expect_filename_()
-        # self.expect_symbol_(";")
-        return ast.IncludeStatement(filename, location=location)
-
-    def parse_language_(self):
-        assert self.is_cur_keyword_("language")
-        location = self.cur_token_location_
-        language = self.expect_language_tag_()
-        include_default, required = (True, False)
-        if self.next_token_ in {"exclude_dflt", "include_dflt"}:
-            include_default = self.expect_name_() == "include_dflt"
-        if self.next_token_ == "required":
-            self.expect_keyword_("required")
-            required = True
-        self.expect_symbol_(";")
-        return self.ast.LanguageStatement(
-            language, include_default, required, location=location
-        )
-
-    def parse_ligatureCaretByIndex_(self):
-        assert self.is_cur_keyword_("LigatureCaretByIndex")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        carets = [self.expect_number_()]
-        while self.next_token_ != ";":
-            carets.append(self.expect_number_())
-        self.expect_symbol_(";")
-        return self.ast.LigatureCaretByIndexStatement(glyphs, carets, location=location)
-
-    def parse_ligatureCaretByPos_(self):
-        assert self.is_cur_keyword_("LigatureCaretByPos")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        carets = [self.expect_number_(variable=True)]
-        while self.next_token_ != ";":
-            carets.append(self.expect_number_(variable=True))
-        self.expect_symbol_(";")
-        return self.ast.LigatureCaretByPosStatement(glyphs, carets, location=location)
-
-    def parse_lookup_(self, vertical):
-        # Parses a ``lookup`` - either a lookup block, or a lookup reference
-        # inside a feature.
-        assert self.is_cur_keyword_("lookup")
-        location, name = self.cur_token_location_, self.expect_name_()
-
-        if self.next_token_ == ";":
-            lookup = self.lookups_.resolve(name)
-            if lookup is None:
-                raise FeatureLibError(
-                    'Unknown lookup "%s"' % name, self.cur_token_location_
-                )
-            self.expect_symbol_(";")
-            return self.ast.LookupReferenceStatement(lookup, location=location)
-
-        use_extension = False
-        if self.next_token_ == "useExtension":
-            self.expect_keyword_("useExtension")
-            use_extension = True
-
-        block = self.ast.LookupBlock(name, use_extension, location=location)
-        self.parse_block_(block, vertical)
-        self.lookups_.define(name, block)
-        return block
-
-    def parse_lookupflag_(self):
-        # Parses a ``lookupflag`` statement, either specified by number or
-        # in words.
-        assert self.is_cur_keyword_("lookupflag")
-        location = self.cur_token_location_
-
-        # format B: "lookupflag 6;"
-        if self.next_token_type_ == Lexer.NUMBER:
-            value = self.expect_number_()
-            self.expect_symbol_(";")
-            return self.ast.LookupFlagStatement(value, location=location)
-
-        # format A: "lookupflag RightToLeft MarkAttachmentType @M;"
-        value_seen = False
-        value, markAttachment, markFilteringSet = 0, None, None
-        flags = {
-            "RightToLeft": 1,
-            "IgnoreBaseGlyphs": 2,
-            "IgnoreLigatures": 4,
-            "IgnoreMarks": 8,
-        }
-        seen = set()
-        while self.next_token_ != ";":
-            if self.next_token_ in seen:
-                raise FeatureLibError(
-                    "%s can be specified only once" % self.next_token_,
-                    self.next_token_location_,
-                )
-            seen.add(self.next_token_)
-            if self.next_token_ == "MarkAttachmentType":
-                self.expect_keyword_("MarkAttachmentType")
-                markAttachment = self.parse_glyphclass_(accept_glyphname=False)
-            elif self.next_token_ == "UseMarkFilteringSet":
-                self.expect_keyword_("UseMarkFilteringSet")
-                markFilteringSet = self.parse_glyphclass_(accept_glyphname=False)
-            elif self.next_token_ in flags:
-                value_seen = True
-                value = value | flags[self.expect_name_()]
-            else:
-                raise FeatureLibError(
-                    '"%s" is not a recognized lookupflag' % self.next_token_,
-                    self.next_token_location_,
-                )
-        self.expect_symbol_(";")
-
-        if not any([value_seen, markAttachment, markFilteringSet]):
-            raise FeatureLibError(
-                "lookupflag must have a value", self.next_token_location_
-            )
-
-        return self.ast.LookupFlagStatement(
-            value,
-            markAttachment=markAttachment,
-            markFilteringSet=markFilteringSet,
-            location=location,
-        )
-
-    def parse_markClass_(self):
-        assert self.is_cur_keyword_("markClass")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        if not glyphs.glyphSet():
-            raise FeatureLibError(
-                "Empty glyph class in mark class definition", location
-            )
-        anchor = self.parse_anchor_()
-        name = self.expect_class_name_()
-        self.expect_symbol_(";")
-        markClass = self.doc_.markClasses.get(name)
-        if markClass is None:
-            markClass = self.ast.MarkClass(name)
-            self.doc_.markClasses[name] = markClass
-            self.glyphclasses_.define(name, markClass)
-        mcdef = self.ast.MarkClassDefinition(
-            markClass, anchor, glyphs, location=location
-        )
-        markClass.addDefinition(mcdef)
-        return mcdef
-
-    def parse_position_(self, enumerated, vertical):
-        assert self.cur_token_ in {"position", "pos"}
-        if self.next_token_ == "cursive":  # GPOS type 3
-            return self.parse_position_cursive_(enumerated, vertical)
-        elif self.next_token_ == "base":  # GPOS type 4
-            return self.parse_position_base_(enumerated, vertical)
-        elif self.next_token_ == "ligature":  # GPOS type 5
-            return self.parse_position_ligature_(enumerated, vertical)
-        elif self.next_token_ == "mark":  # GPOS type 6
-            return self.parse_position_mark_(enumerated, vertical)
-
-        location = self.cur_token_location_
-        prefix, glyphs, lookups, values, suffix, hasMarks = self.parse_glyph_pattern_(
-            vertical
-        )
-        self.expect_symbol_(";")
-
-        if any(lookups):
-            # GPOS type 8: Chaining contextual positioning; explicit lookups
-            if any(values):
-                raise FeatureLibError(
-                    'If "lookup" is present, no values must be specified', location
-                )
-            return self.ast.ChainContextPosStatement(
-                prefix, glyphs, suffix, lookups, location=location
-            )
-
-        # Pair positioning, format A: "pos V 10 A -10;"
-        # Pair positioning, format B: "pos V A -20;"
-        if not prefix and not suffix and len(glyphs) == 2 and not hasMarks:
-            if values[0] is None:  # Format B: "pos V A -20;"
-                values.reverse()
-            return self.ast.PairPosStatement(
-                glyphs[0],
-                values[0],
-                glyphs[1],
-                values[1],
-                enumerated=enumerated,
-                location=location,
-            )
-
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is only allowed with pair positionings', location
-            )
-        return self.ast.SinglePosStatement(
-            list(zip(glyphs, values)),
-            prefix,
-            suffix,
-            forceChain=hasMarks,
-            location=location,
-        )
-
-    def parse_position_cursive_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("cursive")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with ' "cursive attachment positioning",
-                location,
-            )
-        glyphclass = self.parse_glyphclass_(accept_glyphname=True)
-        entryAnchor = self.parse_anchor_()
-        exitAnchor = self.parse_anchor_()
-        self.expect_symbol_(";")
-        return self.ast.CursivePosStatement(
-            glyphclass, entryAnchor, exitAnchor, location=location
-        )
-
-    def parse_position_base_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("base")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with '
-                "mark-to-base attachment positioning",
-                location,
-            )
-        base = self.parse_glyphclass_(accept_glyphname=True)
-        marks = self.parse_anchor_marks_()
-        self.expect_symbol_(";")
-        return self.ast.MarkBasePosStatement(base, marks, location=location)
-
-    def parse_position_ligature_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("ligature")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with '
-                "mark-to-ligature attachment positioning",
-                location,
-            )
-        ligatures = self.parse_glyphclass_(accept_glyphname=True)
-        marks = [self.parse_anchor_marks_()]
-        while self.next_token_ == "ligComponent":
-            self.expect_keyword_("ligComponent")
-            marks.append(self.parse_anchor_marks_())
-        self.expect_symbol_(";")
-        return self.ast.MarkLigPosStatement(ligatures, marks, location=location)
-
-    def parse_position_mark_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("mark")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with '
-                "mark-to-mark attachment positioning",
-                location,
-            )
-        baseMarks = self.parse_glyphclass_(accept_glyphname=True)
-        marks = self.parse_anchor_marks_()
-        self.expect_symbol_(";")
-        return self.ast.MarkMarkPosStatement(baseMarks, marks, location=location)
-
-    def parse_script_(self):
-        assert self.is_cur_keyword_("script")
-        location, script = self.cur_token_location_, self.expect_script_tag_()
-        self.expect_symbol_(";")
-        return self.ast.ScriptStatement(script, location=location)
-
-    def parse_substitute_(self):
-        assert self.cur_token_ in {"substitute", "sub", "reversesub", "rsub"}
-        location = self.cur_token_location_
-        reverse = self.cur_token_ in {"reversesub", "rsub"}
-        (
-            old_prefix,
-            old,
-            lookups,
-            values,
-            old_suffix,
-            hasMarks,
-        ) = self.parse_glyph_pattern_(vertical=False)
-        if any(values):
-            raise FeatureLibError(
-                "Substitution statements cannot contain values", location
-            )
-        new = []
-        if self.next_token_ == "by":
-            keyword = self.expect_keyword_("by")
-            while self.next_token_ != ";":
-                gc = self.parse_glyphclass_(accept_glyphname=True, accept_null=True)
-                new.append(gc)
-        elif self.next_token_ == "from":
-            keyword = self.expect_keyword_("from")
-            new = [self.parse_glyphclass_(accept_glyphname=False)]
-        else:
-            keyword = None
-        self.expect_symbol_(";")
-        if len(new) == 0 and not any(lookups):
-            raise FeatureLibError(
-                'Expected "by", "from" or explicit lookup references',
-                self.cur_token_location_,
-            )
-
-        # GSUB lookup type 3: Alternate substitution.
-        # Format: "substitute a from [a.1 a.2 a.3];"
-        if keyword == "from":
-            if reverse:
-                raise FeatureLibError(
-                    'Reverse chaining substitutions do not support "from"', location
-                )
-            if len(old) != 1 or len(old[0].glyphSet()) != 1:
-                raise FeatureLibError('Expected a single glyph before "from"', location)
-            if len(new) != 1:
-                raise FeatureLibError(
-                    'Expected a single glyphclass after "from"', location
-                )
-            return self.ast.AlternateSubstStatement(
-                old_prefix, old[0], old_suffix, new[0], location=location
-            )
-
-        num_lookups = len([l for l in lookups if l is not None])
-
-        is_deletion = False
-        if len(new) == 1 and isinstance(new[0], ast.NullGlyph):
-            new = []  # Deletion
-            is_deletion = True
-
-        # GSUB lookup type 1: Single substitution.
-        # Format A: "substitute a by a.sc;"
-        # Format B: "substitute [one.fitted one.oldstyle] by one;"
-        # Format C: "substitute [a-d] by [A.sc-D.sc];"
-        if not reverse and len(old) == 1 and len(new) == 1 and num_lookups == 0:
-            glyphs = list(old[0].glyphSet())
-            replacements = list(new[0].glyphSet())
-            if len(replacements) == 1:
-                replacements = replacements * len(glyphs)
-            if len(glyphs) != len(replacements):
-                raise FeatureLibError(
-                    'Expected a glyph class with %d elements after "by", '
-                    "but found a glyph class with %d elements"
-                    % (len(glyphs), len(replacements)),
-                    location,
-                )
-            return self.ast.SingleSubstStatement(
-                old, new, old_prefix, old_suffix, forceChain=hasMarks, location=location
-            )
-
-        # Glyph deletion, built as GSUB lookup type 2: Multiple substitution
-        # with empty replacement.
-        if is_deletion and len(old) == 1 and num_lookups == 0:
-            return self.ast.MultipleSubstStatement(
-                old_prefix,
-                old[0],
-                old_suffix,
-                (),
-                forceChain=hasMarks,
-                location=location,
-            )
-
-        # GSUB lookup type 2: Multiple substitution.
-        # Format: "substitute f_f_i by f f i;"
-        #
-        # GlyphsApp introduces two additional formats:
-        # Format 1: "substitute [f_i f_l] by [f f] [i l];"
-        # Format 2: "substitute [f_i f_l] by f [i l];"
-        # http://handbook.glyphsapp.com/en/layout/multiple-substitution-with-classes/
-        if not reverse and len(old) == 1 and len(new) > 1 and num_lookups == 0:
-            count = len(old[0].glyphSet())
-            for n in new:
-                if not list(n.glyphSet()):
-                    raise FeatureLibError("Empty class in replacement", location)
-                if len(n.glyphSet()) != 1 and len(n.glyphSet()) != count:
-                    raise FeatureLibError(
-                        f'Expected a glyph class with 1 or {count} elements after "by", '
-                        f"but found a glyph class with {len(n.glyphSet())} elements",
-                        location,
-                    )
-            return self.ast.MultipleSubstStatement(
-                old_prefix,
-                old[0],
-                old_suffix,
-                new,
-                forceChain=hasMarks,
-                location=location,
-            )
-
-        # GSUB lookup type 4: Ligature substitution.
-        # Format: "substitute f f i by f_f_i;"
-        if (
-            not reverse
-            and len(old) > 1
-            and len(new) == 1
-            and len(new[0].glyphSet()) == 1
-            and num_lookups == 0
-        ):
-            return self.ast.LigatureSubstStatement(
-                old_prefix,
-                old,
-                old_suffix,
-                list(new[0].glyphSet())[0],
-                forceChain=hasMarks,
-                location=location,
-            )
-
-        # GSUB lookup type 8: Reverse chaining substitution.
-        if reverse:
-            if len(old) != 1:
-                raise FeatureLibError(
-                    "In reverse chaining single substitutions, "
-                    "only a single glyph or glyph class can be replaced",
-                    location,
-                )
-            if len(new) != 1:
-                raise FeatureLibError(
-                    "In reverse chaining single substitutions, "
-                    'the replacement (after "by") must be a single glyph '
-                    "or glyph class",
-                    location,
-                )
-            if num_lookups != 0:
-                raise FeatureLibError(
-                    "Reverse chaining substitutions cannot call named lookups", location
-                )
-            glyphs = sorted(list(old[0].glyphSet()))
-            replacements = sorted(list(new[0].glyphSet()))
-            if len(replacements) == 1:
-                replacements = replacements * len(glyphs)
-            if len(glyphs) != len(replacements):
-                raise FeatureLibError(
-                    'Expected a glyph class with %d elements after "by", '
-                    "but found a glyph class with %d elements"
-                    % (len(glyphs), len(replacements)),
-                    location,
-                )
-            return self.ast.ReverseChainSingleSubstStatement(
-                old_prefix, old_suffix, old, new, location=location
-            )
-
-        if len(old) > 1 and len(new) > 1:
-            raise FeatureLibError(
-                "Direct substitution of multiple glyphs by multiple glyphs "
-                "is not supported",
-                location,
-            )
-
-        # If there are remaining glyphs to parse, this is an invalid GSUB statement
-        if len(new) != 0 or is_deletion:
-            raise FeatureLibError("Invalid substitution statement", location)
-
-        # GSUB lookup type 6: Chaining contextual substitution.
-        rule = self.ast.ChainContextSubstStatement(
-            old_prefix, old, old_suffix, lookups, location=location
-        )
-        return rule
-
-    def parse_subtable_(self):
-        assert self.is_cur_keyword_("subtable")
-        location = self.cur_token_location_
-        self.expect_symbol_(";")
-        return self.ast.SubtableStatement(location=location)
-
-    def parse_size_parameters_(self):
-        # Parses a ``parameters`` statement used in ``size`` features. See
-        # `section 8.b <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#8.b>`_.
-        assert self.is_cur_keyword_("parameters")
-        location = self.cur_token_location_
-        DesignSize = self.expect_decipoint_()
-        SubfamilyID = self.expect_number_()
-        RangeStart = 0.0
-        RangeEnd = 0.0
-        if self.next_token_type_ in (Lexer.NUMBER, Lexer.FLOAT) or SubfamilyID != 0:
-            RangeStart = self.expect_decipoint_()
-            RangeEnd = self.expect_decipoint_()
-
-        self.expect_symbol_(";")
-        return self.ast.SizeParameters(
-            DesignSize, SubfamilyID, RangeStart, RangeEnd, location=location
-        )
-
-    def parse_size_menuname_(self):
-        assert self.is_cur_keyword_("sizemenuname")
-        location = self.cur_token_location_
-        platformID, platEncID, langID, string = self.parse_name_()
-        return self.ast.FeatureNameStatement(
-            "size", platformID, platEncID, langID, string, location=location
-        )
-
-    def parse_table_(self):
-        assert self.is_cur_keyword_("table")
-        location, name = self.cur_token_location_, self.expect_tag_()
-        table = self.ast.TableBlock(name, location=location)
-        self.expect_symbol_("{")
-        handler = {
-            "GDEF": self.parse_table_GDEF_,
-            "head": self.parse_table_head_,
-            "hhea": self.parse_table_hhea_,
-            "vhea": self.parse_table_vhea_,
-            "name": self.parse_table_name_,
-            "BASE": self.parse_table_BASE_,
-            "OS/2": self.parse_table_OS_2_,
-            "STAT": self.parse_table_STAT_,
-        }.get(name)
-        if handler:
-            handler(table)
-        else:
-            raise FeatureLibError(
-                '"table %s" is not supported' % name.strip(), location
-            )
-        self.expect_symbol_("}")
-        end_tag = self.expect_tag_()
-        if end_tag != name:
-            raise FeatureLibError(
-                'Expected "%s"' % name.strip(), self.cur_token_location_
-            )
-        self.expect_symbol_(";")
-        return table
-
-    def parse_table_GDEF_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("Attach"):
-                statements.append(self.parse_attach_())
-            elif self.is_cur_keyword_("GlyphClassDef"):
-                statements.append(self.parse_GlyphClassDef_())
-            elif self.is_cur_keyword_("LigatureCaretByIndex"):
-                statements.append(self.parse_ligatureCaretByIndex_())
-            elif self.is_cur_keyword_("LigatureCaretByPos"):
-                statements.append(self.parse_ligatureCaretByPos_())
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected Attach, LigatureCaretByIndex, " "or LigatureCaretByPos",
-                    self.cur_token_location_,
-                )
-
-    def parse_table_head_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("FontRevision"):
-                statements.append(self.parse_FontRevision_())
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError("Expected FontRevision", self.cur_token_location_)
-
-    def parse_table_hhea_(self, table):
-        statements = table.statements
-        fields = ("CaretOffset", "Ascender", "Descender", "LineGap")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME and self.cur_token_ in fields:
-                key = self.cur_token_.lower()
-                value = self.expect_number_()
-                statements.append(
-                    self.ast.HheaField(key, value, location=self.cur_token_location_)
-                )
-                if self.next_token_ != ";":
-                    raise FeatureLibError(
-                        "Incomplete statement", self.next_token_location_
-                    )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected CaretOffset, Ascender, " "Descender or LineGap",
-                    self.cur_token_location_,
-                )
-
-    def parse_table_vhea_(self, table):
-        statements = table.statements
-        fields = ("VertTypoAscender", "VertTypoDescender", "VertTypoLineGap")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME and self.cur_token_ in fields:
-                key = self.cur_token_.lower()
-                value = self.expect_number_()
-                statements.append(
-                    self.ast.VheaField(key, value, location=self.cur_token_location_)
-                )
-                if self.next_token_ != ";":
-                    raise FeatureLibError(
-                        "Incomplete statement", self.next_token_location_
-                    )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected VertTypoAscender, "
-                    "VertTypoDescender or VertTypoLineGap",
-                    self.cur_token_location_,
-                )
-
-    def parse_table_name_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("nameid"):
-                statement = self.parse_nameid_()
-                if statement:
-                    statements.append(statement)
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError("Expected nameid", self.cur_token_location_)
-
-    def parse_name_(self):
-        """Parses a name record. See `section 9.e <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.e>`_."""
-        platEncID = None
-        langID = None
-        if self.next_token_type_ in Lexer.NUMBERS:
-            platformID = self.expect_any_number_()
-            location = self.cur_token_location_
-            if platformID not in (1, 3):
-                raise FeatureLibError("Expected platform id 1 or 3", location)
-            if self.next_token_type_ in Lexer.NUMBERS:
-                platEncID = self.expect_any_number_()
-                langID = self.expect_any_number_()
-        else:
-            platformID = 3
-            location = self.cur_token_location_
-
-        if platformID == 1:  # Macintosh
-            platEncID = platEncID or 0  # Roman
-            langID = langID or 0  # English
-        else:  # 3, Windows
-            platEncID = platEncID or 1  # Unicode
-            langID = langID or 0x0409  # English
-
-        string = self.expect_string_()
-        self.expect_symbol_(";")
-
-        encoding = getEncoding(platformID, platEncID, langID)
-        if encoding is None:
-            raise FeatureLibError("Unsupported encoding", location)
-        unescaped = self.unescape_string_(string, encoding)
-        return platformID, platEncID, langID, unescaped
-
-    def parse_stat_name_(self):
-        platEncID = None
-        langID = None
-        if self.next_token_type_ in Lexer.NUMBERS:
-            platformID = self.expect_any_number_()
-            location = self.cur_token_location_
-            if platformID not in (1, 3):
-                raise FeatureLibError("Expected platform id 1 or 3", location)
-            if self.next_token_type_ in Lexer.NUMBERS:
-                platEncID = self.expect_any_number_()
-                langID = self.expect_any_number_()
-        else:
-            platformID = 3
-            location = self.cur_token_location_
-
-        if platformID == 1:  # Macintosh
-            platEncID = platEncID or 0  # Roman
-            langID = langID or 0  # English
-        else:  # 3, Windows
-            platEncID = platEncID or 1  # Unicode
-            langID = langID or 0x0409  # English
-
-        string = self.expect_string_()
-        encoding = getEncoding(platformID, platEncID, langID)
-        if encoding is None:
-            raise FeatureLibError("Unsupported encoding", location)
-        unescaped = self.unescape_string_(string, encoding)
-        return platformID, platEncID, langID, unescaped
-
-    def parse_nameid_(self):
-        assert self.cur_token_ == "nameid", self.cur_token_
-        location, nameID = self.cur_token_location_, self.expect_any_number_()
-        if nameID > 32767:
-            raise FeatureLibError(
-                "Name id value cannot be greater than 32767", self.cur_token_location_
-            )
-        platformID, platEncID, langID, string = self.parse_name_()
-        return self.ast.NameRecord(
-            nameID, platformID, platEncID, langID, string, location=location
-        )
-
-    def unescape_string_(self, string, encoding):
-        if encoding == "utf_16_be":
-            s = re.sub(r"\\[0-9a-fA-F]{4}", self.unescape_unichr_, string)
-        else:
-            unescape = lambda m: self.unescape_byte_(m, encoding)
-            s = re.sub(r"\\[0-9a-fA-F]{2}", unescape, string)
-        # We now have a Unicode string, but it might contain surrogate pairs.
-        # We convert surrogates to actual Unicode by round-tripping through
-        # Python's UTF-16 codec in a special mode.
-        utf16 = tobytes(s, "utf_16_be", "surrogatepass")
-        return tostr(utf16, "utf_16_be")
-
-    @staticmethod
-    def unescape_unichr_(match):
-        n = match.group(0)[1:]
-        return chr(int(n, 16))
-
-    @staticmethod
-    def unescape_byte_(match, encoding):
-        n = match.group(0)[1:]
-        return bytechr(int(n, 16)).decode(encoding)
-
-    def parse_table_BASE_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("HorizAxis.BaseTagList"):
-                horiz_bases = self.parse_base_tag_list_()
-            elif self.is_cur_keyword_("HorizAxis.BaseScriptList"):
-                horiz_scripts = self.parse_base_script_list_(len(horiz_bases))
-                statements.append(
-                    self.ast.BaseAxis(
-                        horiz_bases,
-                        horiz_scripts,
-                        False,
-                        location=self.cur_token_location_,
-                    )
-                )
-            elif self.is_cur_keyword_("VertAxis.BaseTagList"):
-                vert_bases = self.parse_base_tag_list_()
-            elif self.is_cur_keyword_("VertAxis.BaseScriptList"):
-                vert_scripts = self.parse_base_script_list_(len(vert_bases))
-                statements.append(
-                    self.ast.BaseAxis(
-                        vert_bases,
-                        vert_scripts,
-                        True,
-                        location=self.cur_token_location_,
-                    )
-                )
-            elif self.cur_token_ == ";":
-                continue
-
-    def parse_table_OS_2_(self, table):
-        statements = table.statements
-        numbers = (
-            "FSType",
-            "TypoAscender",
-            "TypoDescender",
-            "TypoLineGap",
-            "winAscent",
-            "winDescent",
-            "XHeight",
-            "CapHeight",
-            "WeightClass",
-            "WidthClass",
-            "LowerOpSize",
-            "UpperOpSize",
-        )
-        ranges = ("UnicodeRange", "CodePageRange")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME:
-                key = self.cur_token_.lower()
-                value = None
-                if self.cur_token_ in numbers:
-                    value = self.expect_number_()
-                elif self.is_cur_keyword_("Panose"):
-                    value = []
-                    for i in range(10):
-                        value.append(self.expect_number_())
-                elif self.cur_token_ in ranges:
-                    value = []
-                    while self.next_token_ != ";":
-                        value.append(self.expect_number_())
-                elif self.is_cur_keyword_("Vendor"):
-                    value = self.expect_string_()
-                statements.append(
-                    self.ast.OS2Field(key, value, location=self.cur_token_location_)
-                )
-            elif self.cur_token_ == ";":
-                continue
-
-    def parse_STAT_ElidedFallbackName(self):
-        assert self.is_cur_keyword_("ElidedFallbackName")
-        self.expect_symbol_("{")
-        names = []
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_()
-            if self.is_cur_keyword_("name"):
-                platformID, platEncID, langID, string = self.parse_stat_name_()
-                nameRecord = self.ast.STATNameStatement(
-                    "stat",
-                    platformID,
-                    platEncID,
-                    langID,
-                    string,
-                    location=self.cur_token_location_,
-                )
-                names.append(nameRecord)
-            else:
-                if self.cur_token_ != ";":
-                    raise FeatureLibError(
-                        f"Unexpected token {self.cur_token_} " f"in ElidedFallbackName",
-                        self.cur_token_location_,
-                    )
-        self.expect_symbol_("}")
-        if not names:
-            raise FeatureLibError('Expected "name"', self.cur_token_location_)
-        return names
-
-    def parse_STAT_design_axis(self):
-        assert self.is_cur_keyword_("DesignAxis")
-        names = []
-        axisTag = self.expect_tag_()
-        if (
-            axisTag not in ("ital", "opsz", "slnt", "wdth", "wght")
-            and not axisTag.isupper()
-        ):
-            log.warning(f"Unregistered axis tag {axisTag} should be uppercase.")
-        axisOrder = self.expect_number_()
-        self.expect_symbol_("{")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_()
-            if self.cur_token_type_ is Lexer.COMMENT:
-                continue
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_stat_name_()
-                name = self.ast.STATNameStatement(
-                    "stat", platformID, platEncID, langID, string, location=location
-                )
-                names.append(name)
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    f'Expected "name", got {self.cur_token_}', self.cur_token_location_
-                )
-
-        self.expect_symbol_("}")
-        return self.ast.STATDesignAxisStatement(
-            axisTag, axisOrder, names, self.cur_token_location_
-        )
-
-    def parse_STAT_axis_value_(self):
-        assert self.is_cur_keyword_("AxisValue")
-        self.expect_symbol_("{")
-        locations = []
-        names = []
-        flags = 0
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                continue
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_stat_name_()
-                name = self.ast.STATNameStatement(
-                    "stat", platformID, platEncID, langID, string, location=location
-                )
-                names.append(name)
-            elif self.is_cur_keyword_("location"):
-                location = self.parse_STAT_location()
-                locations.append(location)
-            elif self.is_cur_keyword_("flag"):
-                flags = self.expect_stat_flags()
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    f"Unexpected token {self.cur_token_} " f"in AxisValue",
-                    self.cur_token_location_,
-                )
-        self.expect_symbol_("}")
-        if not names:
-            raise FeatureLibError('Expected "Axis Name"', self.cur_token_location_)
-        if not locations:
-            raise FeatureLibError('Expected "Axis location"', self.cur_token_location_)
-        if len(locations) > 1:
-            for location in locations:
-                if len(location.values) > 1:
-                    raise FeatureLibError(
-                        "Only one value is allowed in a "
-                        "Format 4 Axis Value Record, but "
-                        f"{len(location.values)} were found.",
-                        self.cur_token_location_,
-                    )
-            format4_tags = []
-            for location in locations:
-                tag = location.tag
-                if tag in format4_tags:
-                    raise FeatureLibError(
-                        f"Axis tag {tag} already " "defined.", self.cur_token_location_
-                    )
-                format4_tags.append(tag)
-
-        return self.ast.STATAxisValueStatement(
-            names, locations, flags, self.cur_token_location_
-        )
-
-    def parse_STAT_location(self):
-        values = []
-        tag = self.expect_tag_()
-        if len(tag.strip()) != 4:
-            raise FeatureLibError(
-                f"Axis tag {self.cur_token_} must be 4 " "characters",
-                self.cur_token_location_,
-            )
-
-        while self.next_token_ != ";":
-            if self.next_token_type_ is Lexer.FLOAT:
-                value = self.expect_float_()
-                values.append(value)
-            elif self.next_token_type_ is Lexer.NUMBER:
-                value = self.expect_number_()
-                values.append(value)
-            else:
-                raise FeatureLibError(
-                    f'Unexpected value "{self.next_token_}". '
-                    "Expected integer or float.",
-                    self.next_token_location_,
-                )
-        if len(values) == 3:
-            nominal, min_val, max_val = values
-            if nominal < min_val or nominal > max_val:
-                raise FeatureLibError(
-                    f"Default value {nominal} is outside "
-                    f"of specified range "
-                    f"{min_val}-{max_val}.",
-                    self.next_token_location_,
-                )
-        return self.ast.AxisValueLocationStatement(tag, values)
-
-    def parse_table_STAT_(self, table):
-        statements = table.statements
-        design_axes = []
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME:
-                if self.is_cur_keyword_("ElidedFallbackName"):
-                    names = self.parse_STAT_ElidedFallbackName()
-                    statements.append(self.ast.ElidedFallbackName(names))
-                elif self.is_cur_keyword_("ElidedFallbackNameID"):
-                    value = self.expect_number_()
-                    statements.append(self.ast.ElidedFallbackNameID(value))
-                    self.expect_symbol_(";")
-                elif self.is_cur_keyword_("DesignAxis"):
-                    designAxis = self.parse_STAT_design_axis()
-                    design_axes.append(designAxis.tag)
-                    statements.append(designAxis)
-                    self.expect_symbol_(";")
-                elif self.is_cur_keyword_("AxisValue"):
-                    axisValueRecord = self.parse_STAT_axis_value_()
-                    for location in axisValueRecord.locations:
-                        if location.tag not in design_axes:
-                            # Tag must be defined in a DesignAxis before it
-                            # can be referenced
-                            raise FeatureLibError(
-                                "DesignAxis not defined for " f"{location.tag}.",
-                                self.cur_token_location_,
-                            )
-                    statements.append(axisValueRecord)
-                    self.expect_symbol_(";")
-                else:
-                    raise FeatureLibError(
-                        f"Unexpected token {self.cur_token_}", self.cur_token_location_
-                    )
-            elif self.cur_token_ == ";":
-                continue
-
-    def parse_base_tag_list_(self):
-        # Parses BASE table entries. (See `section 9.a <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.a>`_)
-        assert self.cur_token_ in (
-            "HorizAxis.BaseTagList",
-            "VertAxis.BaseTagList",
-        ), self.cur_token_
-        bases = []
-        while self.next_token_ != ";":
-            bases.append(self.expect_script_tag_())
-        self.expect_symbol_(";")
-        return bases
-
-    def parse_base_script_list_(self, count):
-        assert self.cur_token_ in (
-            "HorizAxis.BaseScriptList",
-            "VertAxis.BaseScriptList",
-        ), self.cur_token_
-        scripts = [(self.parse_base_script_record_(count))]
-        while self.next_token_ == ",":
-            self.expect_symbol_(",")
-            scripts.append(self.parse_base_script_record_(count))
-        self.expect_symbol_(";")
-        return scripts
-
-    def parse_base_script_record_(self, count):
-        script_tag = self.expect_script_tag_()
-        base_tag = self.expect_script_tag_()
-        coords = [self.expect_number_() for i in range(count)]
-        return script_tag, base_tag, coords
-
-    def parse_device_(self):
-        result = None
-        self.expect_symbol_("<")
-        self.expect_keyword_("device")
-        if self.next_token_ == "NULL":
-            self.expect_keyword_("NULL")
-        else:
-            result = [(self.expect_number_(), self.expect_number_())]
-            while self.next_token_ == ",":
-                self.expect_symbol_(",")
-                result.append((self.expect_number_(), self.expect_number_()))
-            result = tuple(result)  # make it hashable
-        self.expect_symbol_(">")
-        return result
-
-    def is_next_value_(self):
-        return (
-            self.next_token_type_ is Lexer.NUMBER
-            or self.next_token_ == "<"
-            or self.next_token_ == "("
-        )
-
-    def parse_valuerecord_(self, vertical):
-        if (
-            self.next_token_type_ is Lexer.SYMBOL and self.next_token_ == "("
-        ) or self.next_token_type_ is Lexer.NUMBER:
-            number, location = (
-                self.expect_number_(variable=True),
-                self.cur_token_location_,
-            )
-            if vertical:
-                val = self.ast.ValueRecord(
-                    yAdvance=number, vertical=vertical, location=location
-                )
-            else:
-                val = self.ast.ValueRecord(
-                    xAdvance=number, vertical=vertical, location=location
-                )
-            return val
-        self.expect_symbol_("<")
-        location = self.cur_token_location_
-        if self.next_token_type_ is Lexer.NAME:
-            name = self.expect_name_()
-            if name == "NULL":
-                self.expect_symbol_(">")
-                return self.ast.ValueRecord()
-            vrd = self.valuerecords_.resolve(name)
-            if vrd is None:
-                raise FeatureLibError(
-                    'Unknown valueRecordDef "%s"' % name, self.cur_token_location_
-                )
-            value = vrd.value
-            xPlacement, yPlacement = (value.xPlacement, value.yPlacement)
-            xAdvance, yAdvance = (value.xAdvance, value.yAdvance)
-        else:
-            xPlacement, yPlacement, xAdvance, yAdvance = (
-                self.expect_number_(variable=True),
-                self.expect_number_(variable=True),
-                self.expect_number_(variable=True),
-                self.expect_number_(variable=True),
-            )
-
-        if self.next_token_ == "<":
-            xPlaDevice, yPlaDevice, xAdvDevice, yAdvDevice = (
-                self.parse_device_(),
-                self.parse_device_(),
-                self.parse_device_(),
-                self.parse_device_(),
-            )
-            allDeltas = sorted(
-                [
-                    delta
-                    for size, delta in (xPlaDevice if xPlaDevice else ())
-                    + (yPlaDevice if yPlaDevice else ())
-                    + (xAdvDevice if xAdvDevice else ())
-                    + (yAdvDevice if yAdvDevice else ())
-                ]
-            )
-            if allDeltas[0] < -128 or allDeltas[-1] > 127:
-                raise FeatureLibError(
-                    "Device value out of valid range (-128..127)",
-                    self.cur_token_location_,
-                )
-        else:
-            xPlaDevice, yPlaDevice, xAdvDevice, yAdvDevice = (None, None, None, None)
-
-        self.expect_symbol_(">")
-        return self.ast.ValueRecord(
-            xPlacement,
-            yPlacement,
-            xAdvance,
-            yAdvance,
-            xPlaDevice,
-            yPlaDevice,
-            xAdvDevice,
-            yAdvDevice,
-            vertical=vertical,
-            location=location,
-        )
-
-    def parse_valuerecord_definition_(self, vertical):
-        # Parses a named value record definition. (See section `2.e.v <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#2.e.v>`_)
-        assert self.is_cur_keyword_("valueRecordDef")
-        location = self.cur_token_location_
-        value = self.parse_valuerecord_(vertical)
-        name = self.expect_name_()
-        self.expect_symbol_(";")
-        vrd = self.ast.ValueRecordDefinition(name, value, location=location)
-        self.valuerecords_.define(name, vrd)
-        return vrd
-
-    def parse_languagesystem_(self):
-        assert self.cur_token_ == "languagesystem"
-        location = self.cur_token_location_
-        script = self.expect_script_tag_()
-        language = self.expect_language_tag_()
-        self.expect_symbol_(";")
-        return self.ast.LanguageSystemStatement(script, language, location=location)
-
-    def parse_feature_block_(self, variation=False):
-        if variation:
-            assert self.cur_token_ == "variation"
-        else:
-            assert self.cur_token_ == "feature"
-        location = self.cur_token_location_
-        tag = self.expect_tag_()
-        vertical = tag in {"vkrn", "vpal", "vhal", "valt"}
-
-        stylisticset = None
-        cv_feature = None
-        size_feature = False
-        if tag in self.SS_FEATURE_TAGS:
-            stylisticset = tag
-        elif tag in self.CV_FEATURE_TAGS:
-            cv_feature = tag
-        elif tag == "size":
-            size_feature = True
-
-        if variation:
-            conditionset = self.expect_name_()
-
-        use_extension = False
-        if self.next_token_ == "useExtension":
-            self.expect_keyword_("useExtension")
-            use_extension = True
-
-        if variation:
-            block = self.ast.VariationBlock(
-                tag, conditionset, use_extension=use_extension, location=location
-            )
-        else:
-            block = self.ast.FeatureBlock(
-                tag, use_extension=use_extension, location=location
-            )
-        self.parse_block_(block, vertical, stylisticset, size_feature, cv_feature)
-        return block
-
-    def parse_feature_reference_(self):
-        assert self.cur_token_ == "feature", self.cur_token_
-        location = self.cur_token_location_
-        featureName = self.expect_tag_()
-        self.expect_symbol_(";")
-        return self.ast.FeatureReferenceStatement(featureName, location=location)
-
-    def parse_featureNames_(self, tag):
-        """Parses a ``featureNames`` statement found in stylistic set features.
-        See section `8.c <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#8.c>`_."""
-        assert self.cur_token_ == "featureNames", self.cur_token_
-        block = self.ast.NestedBlock(
-            tag, self.cur_token_, location=self.cur_token_location_
-        )
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                block.statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_name_()
-                block.statements.append(
-                    self.ast.FeatureNameStatement(
-                        tag, platformID, platEncID, langID, string, location=location
-                    )
-                )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError('Expected "name"', self.cur_token_location_)
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-        self.expect_symbol_(";")
-        return block
-
-    def parse_cvParameters_(self, tag):
-        # Parses a ``cvParameters`` block found in Character Variant features.
-        # See section `8.d <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#8.d>`_.
-        assert self.cur_token_ == "cvParameters", self.cur_token_
-        block = self.ast.NestedBlock(
-            tag, self.cur_token_, location=self.cur_token_location_
-        )
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-
-        statements = block.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_(
-                {
-                    "FeatUILabelNameID",
-                    "FeatUITooltipTextNameID",
-                    "SampleTextNameID",
-                    "ParamUILabelNameID",
-                }
-            ):
-                statements.append(self.parse_cvNameIDs_(tag, self.cur_token_))
-            elif self.is_cur_keyword_("Character"):
-                statements.append(self.parse_cvCharacter_(tag))
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected statement: got {} {}".format(
-                        self.cur_token_type_, self.cur_token_
-                    ),
-                    self.cur_token_location_,
-                )
-
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-        self.expect_symbol_(";")
-        return block
-
-    def parse_cvNameIDs_(self, tag, block_name):
-        assert self.cur_token_ == block_name, self.cur_token_
-        block = self.ast.NestedBlock(tag, block_name, location=self.cur_token_location_)
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                block.statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_name_()
-                block.statements.append(
-                    self.ast.CVParametersNameStatement(
-                        tag,
-                        platformID,
-                        platEncID,
-                        langID,
-                        string,
-                        block_name,
-                        location=location,
-                    )
-                )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError('Expected "name"', self.cur_token_location_)
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-        self.expect_symbol_(";")
-        return block
-
-    def parse_cvCharacter_(self, tag):
-        assert self.cur_token_ == "Character", self.cur_token_
-        location, character = self.cur_token_location_, self.expect_any_number_()
-        self.expect_symbol_(";")
-        if not (0xFFFFFF >= character >= 0):
-            raise FeatureLibError(
-                "Character value must be between "
-                "{:#x} and {:#x}".format(0, 0xFFFFFF),
-                location,
-            )
-        return self.ast.CharacterStatement(character, tag, location=location)
-
-    def parse_FontRevision_(self):
-        # Parses a ``FontRevision`` statement found in the head table. See
-        # `section 9.c <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.c>`_.
-        assert self.cur_token_ == "FontRevision", self.cur_token_
-        location, version = self.cur_token_location_, self.expect_float_()
-        self.expect_symbol_(";")
-        if version <= 0:
-            raise FeatureLibError("Font revision numbers must be positive", location)
-        return self.ast.FontRevisionStatement(version, location=location)
-
-    def parse_conditionset_(self):
-        name = self.expect_name_()
-
-        conditions = {}
-        self.expect_symbol_("{")
-
-        while self.next_token_ != "}":
-            self.advance_lexer_()
-            if self.cur_token_type_ is not Lexer.NAME:
-                raise FeatureLibError("Expected an axis name", self.cur_token_location_)
-
-            axis = self.cur_token_
-            if axis in conditions:
-                raise FeatureLibError(
-                    f"Repeated condition for axis {axis}", self.cur_token_location_
-                )
-
-            if self.next_token_type_ is Lexer.FLOAT:
-                min_value = self.expect_float_()
-            elif self.next_token_type_ is Lexer.NUMBER:
-                min_value = self.expect_number_(variable=False)
-
-            if self.next_token_type_ is Lexer.FLOAT:
-                max_value = self.expect_float_()
-            elif self.next_token_type_ is Lexer.NUMBER:
-                max_value = self.expect_number_(variable=False)
-            self.expect_symbol_(";")
-
-            conditions[axis] = (min_value, max_value)
-
-        self.expect_symbol_("}")
-
-        finalname = self.expect_name_()
-        if finalname != name:
-            raise FeatureLibError('Expected "%s"' % name, self.cur_token_location_)
-        return self.ast.ConditionsetStatement(name, conditions)
-
-    def parse_block_(
-        self, block, vertical, stylisticset=None, size_feature=False, cv_feature=None
-    ):
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-
-        statements = block.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.GLYPHCLASS:
-                statements.append(self.parse_glyphclass_definition_())
-            elif self.is_cur_keyword_("anchorDef"):
-                statements.append(self.parse_anchordef_())
-            elif self.is_cur_keyword_({"enum", "enumerate"}):
-                statements.append(self.parse_enumerate_(vertical=vertical))
-            elif self.is_cur_keyword_("feature"):
-                statements.append(self.parse_feature_reference_())
-            elif self.is_cur_keyword_("ignore"):
-                statements.append(self.parse_ignore_())
-            elif self.is_cur_keyword_("language"):
-                statements.append(self.parse_language_())
-            elif self.is_cur_keyword_("lookup"):
-                statements.append(self.parse_lookup_(vertical))
-            elif self.is_cur_keyword_("lookupflag"):
-                statements.append(self.parse_lookupflag_())
-            elif self.is_cur_keyword_("markClass"):
-                statements.append(self.parse_markClass_())
-            elif self.is_cur_keyword_({"pos", "position"}):
-                statements.append(
-                    self.parse_position_(enumerated=False, vertical=vertical)
-                )
-            elif self.is_cur_keyword_("script"):
-                statements.append(self.parse_script_())
-            elif self.is_cur_keyword_({"sub", "substitute", "rsub", "reversesub"}):
-                statements.append(self.parse_substitute_())
-            elif self.is_cur_keyword_("subtable"):
-                statements.append(self.parse_subtable_())
-            elif self.is_cur_keyword_("valueRecordDef"):
-                statements.append(self.parse_valuerecord_definition_(vertical))
-            elif stylisticset and self.is_cur_keyword_("featureNames"):
-                statements.append(self.parse_featureNames_(stylisticset))
-            elif cv_feature and self.is_cur_keyword_("cvParameters"):
-                statements.append(self.parse_cvParameters_(cv_feature))
-            elif size_feature and self.is_cur_keyword_("parameters"):
-                statements.append(self.parse_size_parameters_())
-            elif size_feature and self.is_cur_keyword_("sizemenuname"):
-                statements.append(self.parse_size_menuname_())
-            elif (
-                self.cur_token_type_ is Lexer.NAME
-                and self.cur_token_ in self.extensions
-            ):
-                statements.append(self.extensions[self.cur_token_](self))
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected glyph class definition or statement: got {} {}".format(
-                        self.cur_token_type_, self.cur_token_
-                    ),
-                    self.cur_token_location_,
-                )
-
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-
-        name = self.expect_name_()
-        if name != block.name.strip():
-            raise FeatureLibError(
-                'Expected "%s"' % block.name.strip(), self.cur_token_location_
-            )
-        self.expect_symbol_(";")
-
-        # A multiple substitution may have a single destination, in which case
-        # it will look just like a single substitution. So if there are both
-        # multiple and single substitutions, upgrade all the single ones to
-        # multiple substitutions.
-
-        # Check if we have a mix of non-contextual singles and multiples.
-        has_single = False
-        has_multiple = False
-        for s in statements:
-            if isinstance(s, self.ast.SingleSubstStatement):
-                has_single = not any([s.prefix, s.suffix, s.forceChain])
-            elif isinstance(s, self.ast.MultipleSubstStatement):
-                has_multiple = not any([s.prefix, s.suffix, s.forceChain])
-
-        # Upgrade all single substitutions to multiple substitutions.
-        if has_single and has_multiple:
-            statements = []
-            for s in block.statements:
-                if isinstance(s, self.ast.SingleSubstStatement):
-                    glyphs = s.glyphs[0].glyphSet()
-                    replacements = s.replacements[0].glyphSet()
-                    if len(replacements) == 1:
-                        replacements *= len(glyphs)
-                    for i, glyph in enumerate(glyphs):
-                        statements.append(
-                            self.ast.MultipleSubstStatement(
-                                s.prefix,
-                                glyph,
-                                s.suffix,
-                                [replacements[i]],
-                                s.forceChain,
-                                location=s.location,
-                            )
-                        )
-                else:
-                    statements.append(s)
-            block.statements = statements
-
-    def is_cur_keyword_(self, k):
-        if self.cur_token_type_ is Lexer.NAME:
-            if isinstance(k, type("")):  # basestring is gone in Python3
-                return self.cur_token_ == k
-            else:
-                return self.cur_token_ in k
-        return False
-
-    def expect_class_name_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.GLYPHCLASS:
-            raise FeatureLibError("Expected @NAME", self.cur_token_location_)
-        return self.cur_token_
-
-    def expect_cid_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.CID:
-            return self.cur_token_
-        raise FeatureLibError("Expected a CID", self.cur_token_location_)
-
-    def expect_filename_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.FILENAME:
-            raise FeatureLibError("Expected file name", self.cur_token_location_)
-        return self.cur_token_
-
-    def expect_glyph_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME:
-            self.cur_token_ = self.cur_token_.lstrip("\\")
-            if len(self.cur_token_) > 63:
-                raise FeatureLibError(
-                    "Glyph names must not be longer than 63 characters",
-                    self.cur_token_location_,
-                )
-            return self.cur_token_
-        elif self.cur_token_type_ is Lexer.CID:
-            return "cid%05d" % self.cur_token_
-        raise FeatureLibError("Expected a glyph name or CID", self.cur_token_location_)
-
-    def check_glyph_name_in_glyph_set(self, *names):
-        """Adds a glyph name (just `start`) or glyph names of a
-        range (`start` and `end`) which are not in the glyph set
-        to the "missing list" for future error reporting.
-
-        If no glyph set is present, does nothing.
-        """
-        if self.glyphNames_:
-            for name in names:
-                if name in self.glyphNames_:
-                    continue
-                if name not in self.missing:
-                    self.missing[name] = self.cur_token_location_
-
-    def expect_markClass_reference_(self):
-        name = self.expect_class_name_()
-        mc = self.glyphclasses_.resolve(name)
-        if mc is None:
-            raise FeatureLibError(
-                "Unknown markClass @%s" % name, self.cur_token_location_
-            )
-        if not isinstance(mc, self.ast.MarkClass):
-            raise FeatureLibError(
-                "@%s is not a markClass" % name, self.cur_token_location_
-            )
-        return mc
-
-    def expect_tag_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.NAME:
-            raise FeatureLibError("Expected a tag", self.cur_token_location_)
-        if len(self.cur_token_) > 4:
-            raise FeatureLibError(
-                "Tags cannot be longer than 4 characters", self.cur_token_location_
-            )
-        return (self.cur_token_ + "    ")[:4]
-
-    def expect_script_tag_(self):
-        tag = self.expect_tag_()
-        if tag == "dflt":
-            raise FeatureLibError(
-                '"dflt" is not a valid script tag; use "DFLT" instead',
-                self.cur_token_location_,
-            )
-        return tag
-
-    def expect_language_tag_(self):
-        tag = self.expect_tag_()
-        if tag == "DFLT":
-            raise FeatureLibError(
-                '"DFLT" is not a valid language tag; use "dflt" instead',
-                self.cur_token_location_,
-            )
-        return tag
-
-    def expect_symbol_(self, symbol):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == symbol:
-            return symbol
-        raise FeatureLibError("Expected '%s'" % symbol, self.cur_token_location_)
-
-    def expect_keyword_(self, keyword):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME and self.cur_token_ == keyword:
-            return self.cur_token_
-        raise FeatureLibError('Expected "%s"' % keyword, self.cur_token_location_)
-
-    def expect_name_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME:
-            return self.cur_token_
-        raise FeatureLibError("Expected a name", self.cur_token_location_)
-
-    def expect_number_(self, variable=False):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NUMBER:
-            return self.cur_token_
-        if variable and self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == "(":
-            return self.expect_variable_scalar_()
-        raise FeatureLibError("Expected a number", self.cur_token_location_)
-
-    def expect_variable_scalar_(self):
-        self.advance_lexer_()  # "("
-        scalar = VariableScalar()
-        while True:
-            if self.cur_token_type_ == Lexer.SYMBOL and self.cur_token_ == ")":
-                break
-            location, value = self.expect_master_()
-            scalar.add_value(location, value)
-        return scalar
-
-    def expect_master_(self):
-        location = {}
-        while True:
-            if self.cur_token_type_ is not Lexer.NAME:
-                raise FeatureLibError("Expected an axis name", self.cur_token_location_)
-            axis = self.cur_token_
-            self.advance_lexer_()
-            if not (self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == "="):
-                raise FeatureLibError(
-                    "Expected an equals sign", self.cur_token_location_
-                )
-            value = self.expect_number_()
-            location[axis] = value
-            if self.next_token_type_ is Lexer.NAME and self.next_token_[0] == ":":
-                # Lexer has just read the value as a glyph name. We'll correct it later
-                break
-            self.advance_lexer_()
-            if not (self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == ","):
-                raise FeatureLibError(
-                    "Expected an comma or an equals sign", self.cur_token_location_
-                )
-            self.advance_lexer_()
-        self.advance_lexer_()
-        value = int(self.cur_token_[1:])
-        self.advance_lexer_()
-        return location, value
-
-    def expect_any_number_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ in Lexer.NUMBERS:
-            return self.cur_token_
-        raise FeatureLibError(
-            "Expected a decimal, hexadecimal or octal number", self.cur_token_location_
-        )
-
-    def expect_float_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.FLOAT:
-            return self.cur_token_
-        raise FeatureLibError(
-            "Expected a floating-point number", self.cur_token_location_
-        )
-
-    def expect_decipoint_(self):
-        if self.next_token_type_ == Lexer.FLOAT:
-            return self.expect_float_()
-        elif self.next_token_type_ is Lexer.NUMBER:
-            return self.expect_number_() / 10
-        else:
-            raise FeatureLibError(
-                "Expected an integer or floating-point number", self.cur_token_location_
-            )
-
-    def expect_stat_flags(self):
-        value = 0
-        flags = {
-            "OlderSiblingFontAttribute": 1,
-            "ElidableAxisValueName": 2,
-        }
-        while self.next_token_ != ";":
-            if self.next_token_ in flags:
-                name = self.expect_name_()
-                value = value | flags[name]
-            else:
-                raise FeatureLibError(
-                    f"Unexpected STAT flag {self.cur_token_}", self.cur_token_location_
-                )
-        return value
-
-    def expect_stat_values_(self):
-        if self.next_token_type_ == Lexer.FLOAT:
-            return self.expect_float_()
-        elif self.next_token_type_ is Lexer.NUMBER:
-            return self.expect_number_()
-        else:
-            raise FeatureLibError(
-                "Expected an integer or floating-point number", self.cur_token_location_
-            )
-
-    def expect_string_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.STRING:
-            return self.cur_token_
-        raise FeatureLibError("Expected a string", self.cur_token_location_)
-
-    def advance_lexer_(self, comments=False):
-        if comments and self.cur_comments_:
-            self.cur_token_type_ = Lexer.COMMENT
-            self.cur_token_, self.cur_token_location_ = self.cur_comments_.pop(0)
-            return
-        else:
-            self.cur_token_type_, self.cur_token_, self.cur_token_location_ = (
-                self.next_token_type_,
-                self.next_token_,
-                self.next_token_location_,
-            )
-        while True:
-            try:
-                (
-                    self.next_token_type_,
-                    self.next_token_,
-                    self.next_token_location_,
-                ) = next(self.lexer_)
-            except StopIteration:
-                self.next_token_type_, self.next_token_ = (None, None)
-            if self.next_token_type_ != Lexer.COMMENT:
-                break
-            self.cur_comments_.append((self.next_token_, self.next_token_location_))
-
-    @staticmethod
-    def reverse_string_(s):
-        """'abc' --> 'cba'"""
-        return "".join(reversed(list(s)))
-
-    def make_cid_range_(self, location, start, limit):
-        """(location, 999, 1001) --> ["cid00999", "cid01000", "cid01001"]"""
-        result = list()
-        if start > limit:
-            raise FeatureLibError(
-                "Bad range: start should be less than limit", location
-            )
-        for cid in range(start, limit + 1):
-            result.append("cid%05d" % cid)
-        return result
-
-    def make_glyph_range_(self, location, start, limit):
-        """(location, "a.sc", "d.sc") --> ["a.sc", "b.sc", "c.sc", "d.sc"]"""
-        result = list()
-        if len(start) != len(limit):
-            raise FeatureLibError(
-                'Bad range: "%s" and "%s" should have the same length' % (start, limit),
-                location,
-            )
-
-        rev = self.reverse_string_
-        prefix = os.path.commonprefix([start, limit])
-        suffix = rev(os.path.commonprefix([rev(start), rev(limit)]))
-        if len(suffix) > 0:
-            start_range = start[len(prefix) : -len(suffix)]
-            limit_range = limit[len(prefix) : -len(suffix)]
-        else:
-            start_range = start[len(prefix) :]
-            limit_range = limit[len(prefix) :]
-
-        if start_range >= limit_range:
-            raise FeatureLibError(
-                "Start of range must be smaller than its end", location
-            )
-
-        uppercase = re.compile(r"^[A-Z]$")
-        if uppercase.match(start_range) and uppercase.match(limit_range):
-            for c in range(ord(start_range), ord(limit_range) + 1):
-                result.append("%s%c%s" % (prefix, c, suffix))
-            return result
-
-        lowercase = re.compile(r"^[a-z]$")
-        if lowercase.match(start_range) and lowercase.match(limit_range):
-            for c in range(ord(start_range), ord(limit_range) + 1):
-                result.append("%s%c%s" % (prefix, c, suffix))
-            return result
-
-        digits = re.compile(r"^[0-9]{1,3}$")
-        if digits.match(start_range) and digits.match(limit_range):
-            for i in range(int(start_range, 10), int(limit_range, 10) + 1):
-                number = ("000" + str(i))[-len(start_range) :]
-                result.append("%s%s%s" % (prefix, number, suffix))
-            return result
-
-        raise FeatureLibError('Bad range: "%s-%s"' % (start, limit), location)
-
-
-class SymbolTable(object):
-    def __init__(self):
-        self.scopes_ = [{}]
-
-    def enter_scope(self):
-        self.scopes_.append({})
-
-    def exit_scope(self):
-        self.scopes_.pop()
-
-    def define(self, name, item):
-        self.scopes_[-1][name] = item
-
-    def resolve(self, name):
-        for scope in reversed(self.scopes_):
-            item = scope.get(name)
-            if item:
-                return item
-        return None
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/cached.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/cached.py
deleted file mode 100644
index 379cf04cffeedc85618952c0dcea152c9ebc6eaa..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/cached.py
+++ /dev/null
@@ -1,867 +0,0 @@
-from __future__ import annotations
-
-import contextlib
-import hashlib
-import inspect
-import logging
-import os
-import pickle
-import tempfile
-import time
-from shutil import rmtree
-from typing import ClassVar
-
-from fsspec import AbstractFileSystem, filesystem
-from fsspec.callbacks import _DEFAULT_CALLBACK
-from fsspec.compression import compr
-from fsspec.core import BaseCache, MMapCache
-from fsspec.exceptions import BlocksizeMismatchError
-from fsspec.spec import AbstractBufferedFile
-from fsspec.utils import infer_compression
-
-logger = logging.getLogger("fsspec.cached")
-
-
-class CachingFileSystem(AbstractFileSystem):
-    """Locally caching filesystem, layer over any other FS
-
-    This class implements chunk-wise local storage of remote files, for quick
-    access after the initial download. The files are stored in a given
-    directory with hashes of URLs for the filenames. If no directory is given,
-    a temporary one is used, which should be cleaned up by the OS after the
-    process ends. The files themselves are sparse (as implemented in
-    :class:`~fsspec.caching.MMapCache`), so only the data which is accessed
-    takes up space.
-
-    Restrictions:
-
-    - the block-size must be the same for each access of a given file, unless
-      all blocks of the file have already been read
-    - caching can only be applied to file-systems which produce files
-      derived from fsspec.spec.AbstractBufferedFile ; LocalFileSystem is also
-      allowed, for testing
-    """
-
-    protocol: ClassVar[str | tuple[str, ...]] = ("blockcache", "cached")
-
-    def __init__(
-        self,
-        target_protocol=None,
-        cache_storage="TMP",
-        cache_check=10,
-        check_files=False,
-        expiry_time=604800,
-        target_options=None,
-        fs=None,
-        same_names=False,
-        compression=None,
-        **kwargs,
-    ):
-        """
-
-        Parameters
-        ----------
-        target_protocol: str (optional)
-            Target filesystem protocol. Provide either this or ``fs``.
-        cache_storage: str or list(str)
-            Location to store files. If "TMP", this is a temporary directory,
-            and will be cleaned up by the OS when this process ends (or later).
-            If a list, each location will be tried in the order given, but
-            only the last will be considered writable.
-        cache_check: int
-            Number of seconds between reload of cache metadata
-        check_files: bool
-            Whether to explicitly see if the UID of the remote file matches
-            the stored one before using. Warning: some file systems such as
-            HTTP cannot reliably give a unique hash of the contents of some
-            path, so be sure to set this option to False.
-        expiry_time: int
-            The time in seconds after which a local copy is considered useless.
-            Set to falsy to prevent expiry. The default is equivalent to one
-            week.
-        target_options: dict or None
-            Passed to the instantiation of the FS, if fs is None.
-        fs: filesystem instance
-            The target filesystem to run against. Provide this or ``protocol``.
-        same_names: bool (optional)
-            By default, target URLs are hashed, so that files from different backends
-            with the same basename do not conflict. If this is true, the original
-            basename is used.
-        compression: str (optional)
-            To decompress on download. Can be 'infer' (guess from the URL name),
-            one of the entries in ``fsspec.compression.compr``, or None for no
-            decompression.
-        """
-        super().__init__(**kwargs)
-        if fs is None and target_protocol is None:
-            raise ValueError(
-                "Please provide filesystem instance(fs) or target_protocol"
-            )
-        if not (fs is None) ^ (target_protocol is None):
-            raise ValueError(
-                "Both filesystems (fs) and target_protocol may not be both given."
-            )
-        if cache_storage == "TMP":
-            storage = [tempfile.mkdtemp()]
-        else:
-            if isinstance(cache_storage, str):
-                storage = [cache_storage]
-            else:
-                storage = cache_storage
-        os.makedirs(storage[-1], exist_ok=True)
-        self.storage = storage
-        self.kwargs = target_options or {}
-        self.cache_check = cache_check
-        self.check_files = check_files
-        self.expiry = expiry_time
-        self.compression = compression
-        # TODO: same_names should allow for variable prefix, not only
-        #  to keep the basename
-        self.same_names = same_names
-        self.target_protocol = (
-            target_protocol
-            if isinstance(target_protocol, str)
-            else (fs.protocol if isinstance(fs.protocol, str) else fs.protocol[0])
-        )
-        self.load_cache()
-        self.fs = fs if fs is not None else filesystem(target_protocol, **self.kwargs)
-
-        def _strip_protocol(path):
-            # acts as a method, since each instance has a difference target
-            return self.fs._strip_protocol(type(self)._strip_protocol(path))
-
-        self._strip_protocol = _strip_protocol
-
-    def _mkcache(self):
-        os.makedirs(self.storage[-1], exist_ok=True)
-
-    def load_cache(self):
-        """Read set of stored blocks from file"""
-        cached_files = []
-        for storage in self.storage:
-            fn = os.path.join(storage, "cache")
-            if os.path.exists(fn):
-                with open(fn, "rb") as f:
-                    # TODO: consolidate blocks here
-                    loaded_cached_files = pickle.load(f)
-                    for c in loaded_cached_files.values():
-                        if isinstance(c["blocks"], list):
-                            c["blocks"] = set(c["blocks"])
-                    cached_files.append(loaded_cached_files)
-            else:
-                cached_files.append({})
-        self._mkcache()
-        self.cached_files = cached_files or [{}]
-        self.last_cache = time.time()
-
-    def save_cache(self):
-        """Save set of stored blocks from file"""
-        fn = os.path.join(self.storage[-1], "cache")
-        # TODO: a file lock could be used to ensure file does not change
-        #  between re-read and write; but occasional duplicated reads ok.
-        cache = self.cached_files[-1]
-        if os.path.exists(fn):
-            with open(fn, "rb") as f:
-                cached_files = pickle.load(f)
-            for k, c in cached_files.items():
-                if k in cache:
-                    if c["blocks"] is True or cache[k]["blocks"] is True:
-                        c["blocks"] = True
-                    else:
-                        # self.cached_files[*][*]["blocks"] must continue to
-                        # point to the same set object so that updates
-                        # performed by MMapCache are propagated back to
-                        # self.cached_files.
-                        blocks = cache[k]["blocks"]
-                        blocks.update(c["blocks"])
-                        c["blocks"] = blocks
-                    c["time"] = max(c["time"], cache[k]["time"])
-                    c["uid"] = cache[k]["uid"]
-
-            # Files can be added to cache after it was written once
-            for k, c in cache.items():
-                if k not in cached_files:
-                    cached_files[k] = c
-        else:
-            cached_files = cache
-        cache = {k: v.copy() for k, v in cached_files.items()}
-        for c in cache.values():
-            if isinstance(c["blocks"], set):
-                c["blocks"] = list(c["blocks"])
-        self._mkcache()
-        with atomic_write(fn) as f:
-            pickle.dump(cache, f)
-        self.cached_files[-1] = cached_files
-        self.last_cache = time.time()
-
-    def _check_cache(self):
-        """Reload caches if time elapsed or any disappeared"""
-        self._mkcache()
-        if not self.cache_check:
-            # explicitly told not to bother checking
-            return
-        timecond = time.time() - self.last_cache > self.cache_check
-        existcond = all(os.path.exists(storage) for storage in self.storage)
-        if timecond or not existcond:
-            self.load_cache()
-
-    def _check_file(self, path):
-        """Is path in cache and still valid"""
-        path = self._strip_protocol(path)
-
-        self._check_cache()
-        for storage, cache in zip(self.storage, self.cached_files):
-            if path not in cache:
-                continue
-            detail = cache[path].copy()
-            if self.check_files:
-                if detail["uid"] != self.fs.ukey(path):
-                    continue
-            if self.expiry:
-                if time.time() - detail["time"] > self.expiry:
-                    continue
-            fn = os.path.join(storage, detail["fn"])
-            if os.path.exists(fn):
-                return detail, fn
-        return False
-
-    def clear_cache(self):
-        """Remove all files and metadat from the cache
-
-        In the case of multiple cache locations, this clears only the last one,
-        which is assumed to be the read/write one.
-        """
-        rmtree(self.storage[-1])
-        self.load_cache()
-
-    def clear_expired_cache(self, expiry_time=None):
-        """Remove all expired files and metadata from the cache
-
-        In the case of multiple cache locations, this clears only the last one,
-        which is assumed to be the read/write one.
-
-        Parameters
-        ----------
-        expiry_time: int
-            The time in seconds after which a local copy is considered useless.
-            If not defined the default is equivalent to the attribute from the
-            file caching instantiation.
-        """
-
-        if not expiry_time:
-            expiry_time = self.expiry
-
-        self._check_cache()
-
-        for path, detail in self.cached_files[-1].copy().items():
-            if time.time() - detail["time"] > expiry_time:
-                if self.same_names:
-                    basename = os.path.basename(detail["original"])
-                    fn = os.path.join(self.storage[-1], basename)
-                else:
-                    fn = os.path.join(self.storage[-1], detail["fn"])
-                if os.path.exists(fn):
-                    os.remove(fn)
-                    self.cached_files[-1].pop(path)
-
-        if self.cached_files[-1]:
-            cache_path = os.path.join(self.storage[-1], "cache")
-            with atomic_write(cache_path) as fc:
-                pickle.dump(self.cached_files[-1], fc)
-        else:
-            rmtree(self.storage[-1])
-            self.load_cache()
-
-    def pop_from_cache(self, path):
-        """Remove cached version of given file
-
-        Deletes local copy of the given (remote) path. If it is found in a cache
-        location which is not the last, it is assumed to be read-only, and
-        raises PermissionError
-        """
-        path = self._strip_protocol(path)
-        details = self._check_file(path)
-        if not details:
-            return
-        _, fn = details
-        if fn.startswith(self.storage[-1]):
-            # is in in writable cache
-            os.remove(fn)
-            self.cached_files[-1].pop(path)
-            self.save_cache()
-        else:
-            raise PermissionError(
-                "Can only delete cached file in last, writable cache location"
-            )
-
-    def _open(
-        self,
-        path,
-        mode="rb",
-        block_size=None,
-        autocommit=True,
-        cache_options=None,
-        **kwargs,
-    ):
-        """Wrap the target _open
-
-        If the whole file exists in the cache, just open it locally and
-        return that.
-
-        Otherwise, open the file on the target FS, and make it have a mmap
-        cache pointing to the location which we determine, in our cache.
-        The ``blocks`` instance is shared, so as the mmap cache instance
-        updates, so does the entry in our ``cached_files`` attribute.
-        We monkey-patch this file, so that when it closes, we call
-        ``close_and_update`` to save the state of the blocks.
-        """
-        path = self._strip_protocol(path)
-
-        path = self.fs._strip_protocol(path)
-        if "r" not in mode:
-            return self.fs._open(
-                path,
-                mode=mode,
-                block_size=block_size,
-                autocommit=autocommit,
-                cache_options=cache_options,
-                **kwargs,
-            )
-        detail = self._check_file(path)
-        if detail:
-            # file is in cache
-            detail, fn = detail
-            hash, blocks = detail["fn"], detail["blocks"]
-            if blocks is True:
-                # stored file is complete
-                logger.debug("Opening local copy of %s" % path)
-                return open(fn, mode)
-            # TODO: action where partial file exists in read-only cache
-            logger.debug("Opening partially cached copy of %s" % path)
-        else:
-            hash = self.hash_name(path, self.same_names)
-            fn = os.path.join(self.storage[-1], hash)
-            blocks = set()
-            detail = {
-                "original": path,
-                "fn": hash,
-                "blocks": blocks,
-                "time": time.time(),
-                "uid": self.fs.ukey(path),
-            }
-            self.cached_files[-1][path] = detail
-            logger.debug("Creating local sparse file for %s" % path)
-
-        # call target filesystems open
-        self._mkcache()
-        f = self.fs._open(
-            path,
-            mode=mode,
-            block_size=block_size,
-            autocommit=autocommit,
-            cache_options=cache_options,
-            cache_type="none",
-            **kwargs,
-        )
-        if self.compression:
-            comp = (
-                infer_compression(path)
-                if self.compression == "infer"
-                else self.compression
-            )
-            f = compr[comp](f, mode="rb")
-        if "blocksize" in detail:
-            if detail["blocksize"] != f.blocksize:
-                raise BlocksizeMismatchError(
-                    "Cached file must be reopened with same block"
-                    "size as original (old: %i, new %i)"
-                    "" % (detail["blocksize"], f.blocksize)
-                )
-        else:
-            detail["blocksize"] = f.blocksize
-        f.cache = MMapCache(f.blocksize, f._fetch_range, f.size, fn, blocks)
-        close = f.close
-        f.close = lambda: self.close_and_update(f, close)
-        self.save_cache()
-        return f
-
-    def hash_name(self, path, same_name):
-        return hash_name(path, same_name=same_name)
-
-    def close_and_update(self, f, close):
-        """Called when a file is closing, so store the set of blocks"""
-        if f.closed:
-            return
-        path = self._strip_protocol(f.path)
-
-        c = self.cached_files[-1][path]
-        if c["blocks"] is not True and len(c["blocks"]) * f.blocksize >= f.size:
-            c["blocks"] = True
-        try:
-            logger.debug("going to save")
-            self.save_cache()
-            logger.debug("saved")
-        except OSError:
-            logger.debug("Cache saving failed while closing file")
-        except NameError:
-            logger.debug("Cache save failed due to interpreter shutdown")
-        close()
-        f.closed = True
-
-    def __getattribute__(self, item):
-        if item in [
-            "load_cache",
-            "_open",
-            "save_cache",
-            "close_and_update",
-            "__init__",
-            "__getattribute__",
-            "__reduce__",
-            "_make_local_details",
-            "open",
-            "cat",
-            "cat_file",
-            "get",
-            "read_block",
-            "tail",
-            "head",
-            "_check_file",
-            "_check_cache",
-            "_mkcache",
-            "clear_cache",
-            "clear_expired_cache",
-            "pop_from_cache",
-            "_mkcache",
-            "local_file",
-            "_paths_from_path",
-            "get_mapper",
-            "open_many",
-            "commit_many",
-            "hash_name",
-            "__hash__",
-            "__eq__",
-            "to_json",
-        ]:
-            # all the methods defined in this class. Note `open` here, since
-            # it calls `_open`, but is actually in superclass
-            return lambda *args, **kw: getattr(type(self), item).__get__(self)(
-                *args, **kw
-            )
-        if item in ["__reduce_ex__"]:
-            raise AttributeError
-        if item in ["_cache"]:
-            # class attributes
-            return getattr(type(self), item)
-        if item == "__class__":
-            return type(self)
-        d = object.__getattribute__(self, "__dict__")
-        fs = d.get("fs", None)  # fs is not immediately defined
-        if item in d:
-            return d[item]
-        elif fs is not None:
-            if item in fs.__dict__:
-                # attribute of instance
-                return fs.__dict__[item]
-            # attributed belonging to the target filesystem
-            cls = type(fs)
-            m = getattr(cls, item)
-            if (inspect.isfunction(m) or inspect.isdatadescriptor(m)) and (
-                not hasattr(m, "__self__") or m.__self__ is None
-            ):
-                # instance method
-                return m.__get__(fs, cls)
-            return m  # class method or attribute
-        else:
-            # attributes of the superclass, while target is being set up
-            return super().__getattribute__(item)
-
-    def __eq__(self, other):
-        """Test for equality."""
-        if self is other:
-            return True
-        if not isinstance(other, type(self)):
-            return False
-        return (
-            self.storage == other.storage
-            and self.kwargs == other.kwargs
-            and self.cache_check == other.cache_check
-            and self.check_files == other.check_files
-            and self.expiry == other.expiry
-            and self.compression == other.compression
-            and self.same_names == other.same_names
-            and self.target_protocol == other.target_protocol
-        )
-
-    def __hash__(self):
-        """Calculate hash."""
-        return (
-            hash(tuple(self.storage))
-            ^ hash(str(self.kwargs))
-            ^ hash(self.cache_check)
-            ^ hash(self.check_files)
-            ^ hash(self.expiry)
-            ^ hash(self.compression)
-            ^ hash(self.same_names)
-            ^ hash(self.target_protocol)
-        )
-
-    def to_json(self):
-        """Calculate JSON representation.
-
-        Not implemented yet for CachingFileSystem.
-        """
-        raise NotImplementedError(
-            "CachingFileSystem JSON representation not implemented"
-        )
-
-
-class WholeFileCacheFileSystem(CachingFileSystem):
-    """Caches whole remote files on first access
-
-    This class is intended as a layer over any other file system, and
-    will make a local copy of each file accessed, so that all subsequent
-    reads are local. This is similar to ``CachingFileSystem``, but without
-    the block-wise functionality and so can work even when sparse files
-    are not allowed. See its docstring for definition of the init
-    arguments.
-
-    The class still needs access to the remote store for listing files,
-    and may refresh cached files.
-    """
-
-    protocol = "filecache"
-    local_file = True
-
-    def open_many(self, open_files):
-        paths = [of.path for of in open_files]
-        if "r" in open_files.mode:
-            self._mkcache()
-        else:
-            return [
-                LocalTempFile(self.fs, path, mode=open_files.mode) for path in paths
-            ]
-
-        if self.compression:
-            raise NotImplementedError
-        details = [self._check_file(sp) for sp in paths]
-        downpath = [p for p, d in zip(paths, details) if not d]
-        downfn0 = [
-            os.path.join(self.storage[-1], self.hash_name(p, self.same_names))
-            for p, d in zip(paths, details)
-        ]  # keep these path names for opening later
-        downfn = [fn for fn, d in zip(downfn0, details) if not d]
-        if downpath:
-            # skip if all files are already cached and up to date
-            self.fs.get(downpath, downfn)
-
-            # update metadata - only happens when downloads are successful
-            newdetail = [
-                {
-                    "original": path,
-                    "fn": self.hash_name(path, self.same_names),
-                    "blocks": True,
-                    "time": time.time(),
-                    "uid": self.fs.ukey(path),
-                }
-                for path in downpath
-            ]
-            self.cached_files[-1].update(
-                {path: detail for path, detail in zip(downpath, newdetail)}
-            )
-            self.save_cache()
-
-        def firstpart(fn):
-            # helper to adapt both whole-file and simple-cache
-            return fn[1] if isinstance(fn, tuple) else fn
-
-        return [
-            open(firstpart(fn0) if fn0 else fn1, mode=open_files.mode)
-            for fn0, fn1 in zip(details, downfn0)
-        ]
-
-    def commit_many(self, open_files):
-        self.fs.put([f.fn for f in open_files], [f.path for f in open_files])
-        [f.close() for f in open_files]
-        for f in open_files:
-            # in case autocommit is off, and so close did not already delete
-            try:
-                os.remove(f.name)
-            except FileNotFoundError:
-                pass
-
-    def _make_local_details(self, path):
-        hash = self.hash_name(path, self.same_names)
-        fn = os.path.join(self.storage[-1], hash)
-        detail = {
-            "original": path,
-            "fn": hash,
-            "blocks": True,
-            "time": time.time(),
-            "uid": self.fs.ukey(path),
-        }
-        self.cached_files[-1][path] = detail
-        logger.debug("Copying %s to local cache" % path)
-        return fn
-
-    def cat(
-        self,
-        path,
-        recursive=False,
-        on_error="raise",
-        callback=_DEFAULT_CALLBACK,
-        **kwargs,
-    ):
-        paths = self.expand_path(
-            path, recursive=recursive, maxdepth=kwargs.get("maxdepth", None)
-        )
-        getpaths = []
-        storepaths = []
-        fns = []
-        out = {}
-        for p in paths.copy():
-            try:
-                detail = self._check_file(p)
-                if not detail:
-                    fn = self._make_local_details(p)
-                    getpaths.append(p)
-                    storepaths.append(fn)
-                else:
-                    detail, fn = detail if isinstance(detail, tuple) else (None, detail)
-                fns.append(fn)
-            except Exception as e:
-                if on_error == "raise":
-                    raise
-                if on_error == "return":
-                    out[p] = e
-                paths.remove(p)
-
-        if getpaths:
-            self.fs.get(getpaths, storepaths)
-            self.save_cache()
-
-        callback.set_size(len(paths))
-        for p, fn in zip(paths, fns):
-            with open(fn, "rb") as f:
-                out[p] = f.read()
-            callback.relative_update(1)
-        if isinstance(path, str) and len(paths) == 1 and recursive is False:
-            out = out[paths[0]]
-        return out
-
-    def _open(self, path, mode="rb", **kwargs):
-        path = self._strip_protocol(path)
-        if "r" not in mode:
-            return LocalTempFile(self, path, mode=mode)
-        detail = self._check_file(path)
-        if detail:
-            detail, fn = detail
-            _, blocks = detail["fn"], detail["blocks"]
-            if blocks is True:
-                logger.debug("Opening local copy of %s" % path)
-
-                # In order to support downstream filesystems to be able to
-                # infer the compression from the original filename, like
-                # the `TarFileSystem`, let's extend the `io.BufferedReader`
-                # fileobject protocol by adding a dedicated attribute
-                # `original`.
-                f = open(fn, mode)
-                f.original = detail.get("original")
-                return f
-            else:
-                raise ValueError(
-                    "Attempt to open partially cached file %s"
-                    "as a wholly cached file" % path
-                )
-        else:
-            fn = self._make_local_details(path)
-        kwargs["mode"] = mode
-
-        # call target filesystems open
-        self._mkcache()
-        if self.compression:
-            with self.fs._open(path, **kwargs) as f, open(fn, "wb") as f2:
-                if isinstance(f, AbstractBufferedFile):
-                    # want no type of caching if just downloading whole thing
-                    f.cache = BaseCache(0, f.cache.fetcher, f.size)
-                comp = (
-                    infer_compression(path)
-                    if self.compression == "infer"
-                    else self.compression
-                )
-                f = compr[comp](f, mode="rb")
-                data = True
-                while data:
-                    block = getattr(f, "blocksize", 5 * 2**20)
-                    data = f.read(block)
-                    f2.write(data)
-        else:
-            self.fs.get(path, fn)
-        self.save_cache()
-        return self._open(path, mode)
-
-
-class SimpleCacheFileSystem(WholeFileCacheFileSystem):
-    """Caches whole remote files on first access
-
-    This class is intended as a layer over any other file system, and
-    will make a local copy of each file accessed, so that all subsequent
-    reads are local. This implementation only copies whole files, and
-    does not keep any metadata about the download time or file details.
-    It is therefore safer to use in multi-threaded/concurrent situations.
-
-    This is the only of the caching filesystems that supports write: you will
-    be given a real local open file, and upon close and commit, it will be
-    uploaded to the target filesystem; the writability or the target URL is
-    not checked until that time.
-
-    """
-
-    protocol = "simplecache"
-    local_file = True
-
-    def __init__(self, **kwargs):
-        kw = kwargs.copy()
-        for key in ["cache_check", "expiry_time", "check_files"]:
-            kw[key] = False
-        super().__init__(**kw)
-        for storage in self.storage:
-            if not os.path.exists(storage):
-                os.makedirs(storage, exist_ok=True)
-        self.cached_files = [{}]
-
-    def _check_file(self, path):
-        self._check_cache()
-        sha = self.hash_name(path, self.same_names)
-        for storage in self.storage:
-            fn = os.path.join(storage, sha)
-            if os.path.exists(fn):
-                return fn
-
-    def save_cache(self):
-        pass
-
-    def load_cache(self):
-        pass
-
-    def _open(self, path, mode="rb", **kwargs):
-        path = self._strip_protocol(path)
-
-        if "r" not in mode:
-            return LocalTempFile(self, path, mode=mode)
-        fn = self._check_file(path)
-        if fn:
-            return open(fn, mode)
-
-        sha = self.hash_name(path, self.same_names)
-        fn = os.path.join(self.storage[-1], sha)
-        logger.debug("Copying %s to local cache" % path)
-        kwargs["mode"] = mode
-
-        self._mkcache()
-        if self.compression:
-            with self.fs._open(path, **kwargs) as f, open(fn, "wb") as f2:
-                if isinstance(f, AbstractBufferedFile):
-                    # want no type of caching if just downloading whole thing
-                    f.cache = BaseCache(0, f.cache.fetcher, f.size)
-                comp = (
-                    infer_compression(path)
-                    if self.compression == "infer"
-                    else self.compression
-                )
-                f = compr[comp](f, mode="rb")
-                data = True
-                while data:
-                    block = getattr(f, "blocksize", 5 * 2**20)
-                    data = f.read(block)
-                    f2.write(data)
-        else:
-            self.fs.get(path, fn)
-        return self._open(path, mode)
-
-
-class LocalTempFile:
-    """A temporary local file, which will be uploaded on commit"""
-
-    def __init__(self, fs, path, fn=None, mode="wb", autocommit=True, seek=0):
-        if fn:
-            self.fn = fn
-            self.fh = open(fn, mode)
-        else:
-            fd, self.fn = tempfile.mkstemp()
-            self.fh = open(fd, mode)
-        self.mode = mode
-        if seek:
-            self.fh.seek(seek)
-        self.path = path
-        self.fs = fs
-        self.closed = False
-        self.autocommit = autocommit
-
-    def __reduce__(self):
-        # always open in rb+ to allow continuing writing at a location
-        return (
-            LocalTempFile,
-            (self.fs, self.path, self.fn, "rb+", self.autocommit, self.tell()),
-        )
-
-    def __enter__(self):
-        return self.fh
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.close()
-
-    def close(self):
-        if self.closed:
-            return
-        self.fh.close()
-        self.closed = True
-        if self.autocommit:
-            self.commit()
-
-    def discard(self):
-        self.fh.close()
-        os.remove(self.fn)
-
-    def commit(self):
-        self.fs.put(self.fn, self.path)
-        try:
-            os.remove(self.fn)
-        except (PermissionError, FileNotFoundError):
-            # file path may be held by new version of the file on windows
-            pass
-
-    @property
-    def name(self):
-        return self.fn
-
-    def __getattr__(self, item):
-        return getattr(self.fh, item)
-
-
-def hash_name(path, same_name):
-    if same_name:
-        hash = os.path.basename(path)
-    else:
-        hash = hashlib.sha256(path.encode()).hexdigest()
-    return hash
-
-
-@contextlib.contextmanager
-def atomic_write(path, mode="wb"):
-    """
-    A context manager that opens a temporary file next to `path` and, on exit,
-    replaces `path` with the temporary file, thereby updating `path`
-    atomically.
-    """
-    fd, fn = tempfile.mkstemp(
-        dir=os.path.dirname(path), prefix=os.path.basename(path) + "-"
-    )
-    try:
-        with open(fd, mode) as fp:
-            yield fp
-    except BaseException:
-        with contextlib.suppress(FileNotFoundError):
-            os.unlink(fn)
-        raise
-    else:
-        os.replace(fn, path)
diff --git a/spaces/cihyFjudo/fairness-paper-search/Hete avonturen means hot adventures in Dutch and it is also the title of a song by Geovanny Reyes on SoundCloud[3]..md b/spaces/cihyFjudo/fairness-paper-search/Hete avonturen means hot adventures in Dutch and it is also the title of a song by Geovanny Reyes on SoundCloud[3]..md
deleted file mode 100644
index e2c252e5a766aee11a870d111014198348afc6ee..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Hete avonturen means hot adventures in Dutch and it is also the title of a song by Geovanny Reyes on SoundCloud[3]..md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>jommeke hete avonturen</h2><br /><p><b><b>Download</b> ---> <a href="https://tinurli.com/2uwj44">https://tinurli.com/2uwj44</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/Why You Need DRM Converter 4 3 8 Keygen Mac OSX to Enjoy Your Media Files Without Limits.md b/spaces/cihyFjudo/fairness-paper-search/Why You Need DRM Converter 4 3 8 Keygen Mac OSX to Enjoy Your Media Files Without Limits.md
deleted file mode 100644
index cb91a8d86eaa1c9834c0c0d714276f2d2543a264..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Why You Need DRM Converter 4 3 8 Keygen Mac OSX to Enjoy Your Media Files Without Limits.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Re:Chips for security<br />Hard to tell if chips are already compromised. One thing for sure is that RNGs in them are weak (and probably backdoored). Good way to use these chips is to load a key instead of on-board keygen. Split the operations into tiny modules across multiple chips and vendors. This will disallow a single collusion.</p>
-<h2>drm converter 4 3 8 keygen mac osx</h2><br /><p><b><b>Download</b> &#9889; <a href="https://tinurli.com/2uwjyo">https://tinurli.com/2uwjyo</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/contourpy/_version.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/contourpy/_version.py
deleted file mode 100644
index 6849410aae0a8010e76d5f0a44ced13d750b0989..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/contourpy/_version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "1.1.0"
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/symfont.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/symfont.py
deleted file mode 100644
index 0bd69a386ec9f01c8951f0dfc8bc8c261718cf1f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/symfont.py
+++ /dev/null
@@ -1,251 +0,0 @@
-from fontTools.pens.basePen import BasePen
-from functools import partial
-from itertools import count
-import sympy as sp
-import sys
-
-n = 3  # Max Bezier degree; 3 for cubic, 2 for quadratic
-
-t, x, y = sp.symbols("t x y", real=True)
-c = sp.symbols("c", real=False)  # Complex representation instead of x/y
-
-X = tuple(sp.symbols("x:%d" % (n + 1), real=True))
-Y = tuple(sp.symbols("y:%d" % (n + 1), real=True))
-P = tuple(zip(*(sp.symbols("p:%d[%s]" % (n + 1, w), real=True) for w in "01")))
-C = tuple(sp.symbols("c:%d" % (n + 1), real=False))
-
-# Cubic Bernstein basis functions
-BinomialCoefficient = [(1, 0)]
-for i in range(1, n + 1):
-    last = BinomialCoefficient[-1]
-    this = tuple(last[j - 1] + last[j] for j in range(len(last))) + (0,)
-    BinomialCoefficient.append(this)
-BinomialCoefficient = tuple(tuple(item[:-1]) for item in BinomialCoefficient)
-del last, this
-
-BernsteinPolynomial = tuple(
-    tuple(c * t**i * (1 - t) ** (n - i) for i, c in enumerate(coeffs))
-    for n, coeffs in enumerate(BinomialCoefficient)
-)
-
-BezierCurve = tuple(
-    tuple(
-        sum(P[i][j] * bernstein for i, bernstein in enumerate(bernsteins))
-        for j in range(2)
-    )
-    for n, bernsteins in enumerate(BernsteinPolynomial)
-)
-BezierCurveC = tuple(
-    sum(C[i] * bernstein for i, bernstein in enumerate(bernsteins))
-    for n, bernsteins in enumerate(BernsteinPolynomial)
-)
-
-
-def green(f, curveXY):
-    f = -sp.integrate(sp.sympify(f), y)
-    f = f.subs({x: curveXY[0], y: curveXY[1]})
-    f = sp.integrate(f * sp.diff(curveXY[0], t), (t, 0, 1))
-    return f
-
-
-class _BezierFuncsLazy(dict):
-    def __init__(self, symfunc):
-        self._symfunc = symfunc
-        self._bezfuncs = {}
-
-    def __missing__(self, i):
-        args = ["p%d" % d for d in range(i + 1)]
-        f = green(self._symfunc, BezierCurve[i])
-        f = sp.gcd_terms(f.collect(sum(P, ())))  # Optimize
-        return sp.lambdify(args, f)
-
-
-class GreenPen(BasePen):
-
-    _BezierFuncs = {}
-
-    @classmethod
-    def _getGreenBezierFuncs(celf, func):
-        funcstr = str(func)
-        if not funcstr in celf._BezierFuncs:
-            celf._BezierFuncs[funcstr] = _BezierFuncsLazy(func)
-        return celf._BezierFuncs[funcstr]
-
-    def __init__(self, func, glyphset=None):
-        BasePen.__init__(self, glyphset)
-        self._funcs = self._getGreenBezierFuncs(func)
-        self.value = 0
-
-    def _moveTo(self, p0):
-        self.__startPoint = p0
-
-    def _closePath(self):
-        p0 = self._getCurrentPoint()
-        if p0 != self.__startPoint:
-            self._lineTo(self.__startPoint)
-
-    def _endPath(self):
-        p0 = self._getCurrentPoint()
-        if p0 != self.__startPoint:
-            # Green theorem is not defined on open contours.
-            raise NotImplementedError
-
-    def _lineTo(self, p1):
-        p0 = self._getCurrentPoint()
-        self.value += self._funcs[1](p0, p1)
-
-    def _qCurveToOne(self, p1, p2):
-        p0 = self._getCurrentPoint()
-        self.value += self._funcs[2](p0, p1, p2)
-
-    def _curveToOne(self, p1, p2, p3):
-        p0 = self._getCurrentPoint()
-        self.value += self._funcs[3](p0, p1, p2, p3)
-
-
-# Sample pens.
-# Do not use this in real code.
-# Use fontTools.pens.momentsPen.MomentsPen instead.
-AreaPen = partial(GreenPen, func=1)
-MomentXPen = partial(GreenPen, func=x)
-MomentYPen = partial(GreenPen, func=y)
-MomentXXPen = partial(GreenPen, func=x * x)
-MomentYYPen = partial(GreenPen, func=y * y)
-MomentXYPen = partial(GreenPen, func=x * y)
-
-
-def printGreenPen(penName, funcs, file=sys.stdout, docstring=None):
-
-    if docstring is not None:
-        print('"""%s"""' % docstring)
-
-    print(
-        """from fontTools.pens.basePen import BasePen, OpenContourError
-try:
-	import cython
-
-	COMPILED = cython.compiled
-except (AttributeError, ImportError):
-	# if cython not installed, use mock module with no-op decorators and types
-	from fontTools.misc import cython
-
-	COMPILED = False
-
-
-__all__ = ["%s"]
-
-class %s(BasePen):
-
-	def __init__(self, glyphset=None):
-		BasePen.__init__(self, glyphset)
-"""
-        % (penName, penName),
-        file=file,
-    )
-    for name, f in funcs:
-        print("		self.%s = 0" % name, file=file)
-    print(
-        """
-	def _moveTo(self, p0):
-		self.__startPoint = p0
-
-	def _closePath(self):
-		p0 = self._getCurrentPoint()
-		if p0 != self.__startPoint:
-			self._lineTo(self.__startPoint)
-
-	def _endPath(self):
-		p0 = self._getCurrentPoint()
-		if p0 != self.__startPoint:
-			# Green theorem is not defined on open contours.
-			raise OpenContourError(
-							"Green theorem is not defined on open contours."
-			)
-""",
-        end="",
-        file=file,
-    )
-
-    for n in (1, 2, 3):
-
-        subs = {P[i][j]: [X, Y][j][i] for i in range(n + 1) for j in range(2)}
-        greens = [green(f, BezierCurve[n]) for name, f in funcs]
-        greens = [sp.gcd_terms(f.collect(sum(P, ()))) for f in greens]  # Optimize
-        greens = [f.subs(subs) for f in greens]  # Convert to p to x/y
-        defs, exprs = sp.cse(
-            greens,
-            optimizations="basic",
-            symbols=(sp.Symbol("r%d" % i) for i in count()),
-        )
-
-        print()
-        for name, value in defs:
-            print("	@cython.locals(%s=cython.double)" % name, file=file)
-        if n == 1:
-            print(
-                """\
-	@cython.locals(x0=cython.double, y0=cython.double)
-	@cython.locals(x1=cython.double, y1=cython.double)
-	def _lineTo(self, p1):
-		x0,y0 = self._getCurrentPoint()
-		x1,y1 = p1
-""",
-                file=file,
-            )
-        elif n == 2:
-            print(
-                """\
-	@cython.locals(x0=cython.double, y0=cython.double)
-	@cython.locals(x1=cython.double, y1=cython.double)
-	@cython.locals(x2=cython.double, y2=cython.double)
-	def _qCurveToOne(self, p1, p2):
-		x0,y0 = self._getCurrentPoint()
-		x1,y1 = p1
-		x2,y2 = p2
-""",
-                file=file,
-            )
-        elif n == 3:
-            print(
-                """\
-	@cython.locals(x0=cython.double, y0=cython.double)
-	@cython.locals(x1=cython.double, y1=cython.double)
-	@cython.locals(x2=cython.double, y2=cython.double)
-	@cython.locals(x3=cython.double, y3=cython.double)
-	def _curveToOne(self, p1, p2, p3):
-		x0,y0 = self._getCurrentPoint()
-		x1,y1 = p1
-		x2,y2 = p2
-		x3,y3 = p3
-""",
-                file=file,
-            )
-        for name, value in defs:
-            print("		%s = %s" % (name, value), file=file)
-
-        print(file=file)
-        for name, value in zip([f[0] for f in funcs], exprs):
-            print("		self.%s += %s" % (name, value), file=file)
-
-    print(
-        """
-if __name__ == '__main__':
-	from fontTools.misc.symfont import x, y, printGreenPen
-	printGreenPen('%s', ["""
-        % penName,
-        file=file,
-    )
-    for name, f in funcs:
-        print("		      ('%s', %s)," % (name, str(f)), file=file)
-    print("		     ])", file=file)
-
-
-if __name__ == "__main__":
-    pen = AreaPen()
-    pen.moveTo((100, 100))
-    pen.lineTo((100, 200))
-    pen.lineTo((200, 200))
-    pen.curveTo((200, 250), (300, 300), (250, 350))
-    pen.lineTo((200, 100))
-    pen.closePath()
-    print(pen.value)
diff --git a/spaces/cncn102/bingo1/src/components/voice.tsx b/spaces/cncn102/bingo1/src/components/voice.tsx
deleted file mode 100644
index ab886394487445e4b0675770b76096bba0e61b0e..0000000000000000000000000000000000000000
--- a/spaces/cncn102/bingo1/src/components/voice.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import React, { useEffect } from 'react'
-import { useSetAtom } from 'jotai'
-import { useBing } from '@/lib/hooks/use-bing'
-import Image from 'next/image'
-import VoiceIcon from '@/assets/images/voice.svg'
-import VoiceButton from './ui/voice'
-import { SR } from '@/lib/bots/bing/sr'
-import { voiceListenAtom } from '@/state'
-
-const sr = new SR(['发送', '清空', '退出'])
-
-const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick<ReturnType<typeof useBing>, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => {
-  const setListen = useSetAtom(voiceListenAtom)
-  useEffect(() => {
-    if (sr.listening) return
-    sr.transcript = !isSpeaking
-  }, [isSpeaking])
-
-  useEffect(() => {
-    sr.onchange = (msg: string, command?: string) => {
-      switch (command) {
-        case '退出':
-          sr.stop()
-          break;
-        case '发送':
-          sendMessage(input)
-        case '清空':
-          setInput('')
-          break;
-        default:
-          setInput(input + msg)
-      }
-    }
-  }, [input, setInput, sendMessage])
-
-  const switchSR = (enable: boolean = false) => {
-    setListen(enable)
-    if (enable) {
-      sr.start()
-    } else {
-      sr.stop()
-    }
-  }
-
-  return sr.listening ? (
-    <VoiceButton onClick={() => switchSR(false)} />
-  ) : (
-    <Image alt="start voice" src={VoiceIcon} width={24} className="-mt-0.5" onClick={() => switchSR(true)} />
-  )
-};
-
-export default Voice;
diff --git a/spaces/codes4aryan/LLMs-QandA-AI/app.py b/spaces/codes4aryan/LLMs-QandA-AI/app.py
deleted file mode 100644
index af0c595dabfbbec7be7502256bdebec9f59e6d08..0000000000000000000000000000000000000000
--- a/spaces/codes4aryan/LLMs-QandA-AI/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-
-import streamlit as st
-
-
-from langchain.llms import OpenAI
-
-#Function to return the response
-def load_answer(question):
-    llm = OpenAI(model_name="text-davinci-003",temperature=0)
-    answer=llm(question)
-    return answer
-
-
-#App UI starts here
-st.set_page_config(page_title="Aryan's Q&A Chatbot", page_icon=":robot:")
-st.header("Aryan's Q&A Chatbot")
-
-#Gets the user input
-def get_text():
-    input_text = st.text_input("You: ", key="input")
-    return input_text
-
-
-user_input=get_text()
-response = load_answer(user_input)
-
-submit = st.button('Generate')  
-
-#If generate button is clicked
-if submit:
-
-    st.subheader("Answer:")
-
-    st.write(response)
-
diff --git a/spaces/colakin/video-generater/classes/OpenAI.php b/spaces/colakin/video-generater/classes/OpenAI.php
deleted file mode 100644
index 0bd21d18ee68d77ffc8639a2d579c3b14f45fb8d..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/classes/OpenAI.php
+++ /dev/null
@@ -1,196 +0,0 @@
-<?php
-
-declare(strict_types=1);
-
-use GuzzleHttp\Client;
-use GuzzleHttp\Exception\RequestException;
-use Psr\Log\LoggerInterface;
-use Psr\Log\NullLogger;
-
-/**
- * Class OpenAI
- */
-class OpenAI {
-	/**
-	 * @var string
-	 */
-	private $api_key;
-
-	/**
-	 * @var Client
-	 */
-	private $client;
-
-	/**
-	 * @var LoggerInterface
-	 */
-	private $log;
-
-	/**
-	 * OpenAI constructor.
-	 *
-	 * @param string $api_key
-	 * @param Client|null $client
-	 * @param LoggerInterface|null $log
-	 */
-	public function __construct(string $api_key, Client $client = null, LoggerInterface $log = null) {
-		$this->api_key = $api_key;
-		$this->client = $client ?: new Client([
-			'base_uri' => 'https://api.openai.com/v1/',
-			'headers' => [
-				'Content-Type' => 'application/json',
-				'Authorization' => 'Bearer ' . $this->api_key
-			]
-		]);
-		$this->log = $log ?: new NullLogger();
-		if (empty($api_key)) {
-			throw new RuntimeException('No valid API key was provided.');
-		}
-	}
-
-	/**
-	 * Generate an image using DALL-E API and save it locally.
-	 *
-	 * @param string $prompt
-	 * @param string $localDirectory
-	 * @param string $size
-	 * @param int $n
-	 * @return array|null
-	 */
-	public function generateImage(string $prompt, string $localDirectory, string $size = '1024x1024', int $n = 4): ?array {
-		$data = [
-			'prompt' => $prompt,
-			'n' => $n,
-			'size' => $size
-		];
-
-		try {
-			$response = $this->client->post('images/generations', ['json' => $data]);
-			$json = json_decode((string) $response->getBody(), true);
-
-			if (json_last_error() === JSON_ERROR_NONE) {
-				$savedImages = [];
-				foreach ($json['data'] as $imageData) {
-					$imageUrl = $imageData['url'];
-					$localFilePath = $this->saveImage($imageUrl, $localDirectory);
-					$savedImages[] = $localFilePath;
-				}
-
-				return $savedImages;
-			} else {
-				$this->log->error('Failed to decode JSON response', ['json_error' => json_last_error_msg()]);
-
-				return null;
-			}
-		} catch (RequestException $e) {
-			$this->log->error('RequestException encountered', ['message' => $e->getMessage()]);
-
-			return null;
-		}
-	}
-
-	/**
-	 * Save an image from a URL to a local file path.
-	 *
-	 * @param string $imageUrl
-	 * @param string $localDirectory
-	 * @return string
-	 */
-	private function saveImage(string $imageUrl, string $localDirectory): string {
-		$imageFileName = basename(parse_url($imageUrl, PHP_URL_PATH));
-		$localFilePath = $localDirectory . '/' . $imageFileName;
-
-		$client = new Client();
-		$response = $client->get($imageUrl, ['sink' => $localFilePath]);
-
-		if ($response->getStatusCode() == 200) {
-			$this->log->info('Image saved successfully', ['path' => $localFilePath]);
-
-			return $localFilePath;
-		} else {
-			$this->log->error('Failed to save the image', ['status_code' => $response->getStatusCode()]);
-
-			throw new RuntimeException('Failed to save the image: ' . $response->getStatusCode());
-		}
-	}
-
-	/**
-	 * Generate a script using the OpenAI GPT-3.5 Turbo model.
-	 *
-	 * @param string $role
-	 * @param string $prompt
-	 * @param int $maxTokens
-	 * @param float $temperature
-	 * @return string|null
-	 */
-	public function generateScript(string $role, string $prompt, int $maxTokens = 3600, float $temperature = 1.0): ?string {
-		$data = [
-			'model' => 'gpt-3.5-turbo',
-			'messages' => [
-				['role' => 'system', 'content' => $role],
-				['role' => 'user', 'content' => $prompt],
-			],
-			'max_tokens' => $maxTokens,
-			'temperature' => $temperature
-		];
-
-		try {
-			$response = $this->client->post('chat/completions', ['json' => $data]);
-			$json = json_decode((string) $response->getBody(), true);
-
-			if (json_last_error() === JSON_ERROR_NONE) {
-				$assistantResponse = $json['choices'][0]['message']['content'];
-
-				return $assistantResponse;
-			} else {
-				$this->log->error('Failed to decode JSON response', ['json_error' => json_last_error_msg()]);
-
-				return null;
-			}
-		} catch (RequestException $e) {
-			$this->log->error('RequestException encountered', ['message' => $e->getMessage()]);
-
-			return null;
-		}
-	}
-    /**
-     * Generate image variations using DALL-E API and save them locally.
-     *
-     * @param string $imagePath
-     * @param string $localDirectory
-     * @param int $n
-     * @param string $size
-     * @return array|null
-     */
-    public function generateImageVariations(string $imagePath, string $localDirectory, int $n = 4, string $size = '1024x1024'): ?array {
-        $data = [
-            'n' => $n,
-            'size' => $size,
-            'image' => curl_file_create($imagePath)
-        ];
-
-        try {
-            $response = $this->client->post('images/variations', ['multipart' => $data]);
-            $json = json_decode((string) $response->getBody(), true);
-
-            if (json_last_error() === JSON_ERROR_NONE) {
-                $savedImages = [];
-                foreach ($json['data'] as $imageData) {
-                    $imageUrl = $imageData['url'];
-                    $localFilePath = $this->saveImage($imageUrl, $localDirectory);
-                    $savedImages[] = $localFilePath;
-                }
-
-                return $savedImages;
-            } else {
-                $this->log->error('Failed to decode JSON response', ['json_error' => json_last_error_msg()]);
-
-                return null;
-            }
-        } catch (RequestException $e) {
-            $this->log->error('RequestException encountered', ['message' => $e->getMessage()]);
-
-            return null;
-        }
-    }
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vpx_arith.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vpx_arith.h
deleted file mode 100644
index 1ead2284c33c6609baf748fc332ef9ba412ca4fc..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vpx_arith.h
+++ /dev/null
@@ -1,121 +0,0 @@
-/*
- * Copyright (C) 2010 Mans Rullgard <mans@mansr.com>
- *
- * 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
- */
-
-#ifndef AVCODEC_ARM_VPX_ARITH_H
-#define AVCODEC_ARM_VPX_ARITH_H
-
-#if CONFIG_THUMB
-#   define A(x)
-#   define T(x) x
-#else
-#   define A(x) x
-#   define T(x)
-#endif
-
-#if CONFIG_THUMB || defined __clang__
-#   define L(x)
-#   define U(x) x
-#else
-#   define L(x) x
-#   define U(x)
-#endif
-
-#if HAVE_ARMV6_INLINE
-
-#define vpx_rac_get_prob vp56_rac_get_prob_armv6
-static inline int vp56_rac_get_prob_armv6(VPXRangeCoder *c, int pr)
-{
-    unsigned shift     = ff_vpx_norm_shift[c->high];
-    unsigned code_word = c->code_word << shift;
-    unsigned high      = c->high << shift;
-    unsigned bit;
-
-    __asm__ ("adds    %3,  %3,  %0           \n"
-             "itt     cs                     \n"
-             "cmpcs   %7,  %4                \n"
-           L("ldrcsh  %2,  [%4], #2          \n")
-           U("ldrhcs  %2,  [%4], #2          \n")
-             "rsb     %0,  %6,  #256         \n"
-             "smlabb  %0,  %5,  %6,  %0      \n"
-           T("itttt   cs                     \n")
-             "rev16cs %2,  %2                \n"
-           T("lslcs   %2,  %2,  %3           \n")
-           T("orrcs   %1,  %1,  %2           \n")
-           A("orrcs   %1,  %1,  %2,  lsl %3  \n")
-             "subcs   %3,  %3,  #16          \n"
-             "lsr     %0,  %0,  #8           \n"
-             "cmp     %1,  %0,  lsl #16      \n"
-             "ittte   ge                     \n"
-             "subge   %1,  %1,  %0,  lsl #16 \n"
-             "subge   %0,  %5,  %0           \n"
-             "movge   %2,  #1                \n"
-             "movlt   %2,  #0                \n"
-             : "=&r"(c->high), "=&r"(c->code_word), "=&r"(bit),
-               "+&r"(c->bits), "+&r"(c->buffer)
-             : "r"(high), "r"(pr), "r"(c->end - 1),
-               "0"(shift), "1"(code_word)
-             : "cc");
-
-    return bit;
-}
-
-#define vpx_rac_get_prob_branchy vp56_rac_get_prob_branchy_armv6
-static inline int vp56_rac_get_prob_branchy_armv6(VPXRangeCoder *c, int pr)
-{
-    unsigned shift     = ff_vpx_norm_shift[c->high];
-    unsigned code_word = c->code_word << shift;
-    unsigned high      = c->high << shift;
-    unsigned low;
-    unsigned tmp;
-
-    __asm__ ("adds    %3,  %3,  %0           \n"
-             "itt     cs                     \n"
-             "cmpcs   %7,  %4                \n"
-           L("ldrcsh  %2,  [%4], #2          \n")
-           U("ldrhcs  %2,  [%4], #2          \n")
-             "rsb     %0,  %6,  #256         \n"
-             "smlabb  %0,  %5,  %6,  %0      \n"
-           T("itttt   cs                     \n")
-             "rev16cs %2,  %2                \n"
-           T("lslcs   %2,  %2,  %3           \n")
-           T("orrcs   %1,  %1,  %2           \n")
-           A("orrcs   %1,  %1,  %2,  lsl %3  \n")
-             "subcs   %3,  %3,  #16          \n"
-             "lsr     %0,  %0,  #8           \n"
-             "lsl     %2,  %0,  #16          \n"
-             : "=&r"(low), "+&r"(code_word), "=&r"(tmp),
-               "+&r"(c->bits), "+&r"(c->buffer)
-             : "r"(high), "r"(pr), "r"(c->end - 1), "0"(shift)
-             : "cc");
-
-    if (code_word >= tmp) {
-        c->high      = high - low;
-        c->code_word = code_word - tmp;
-        return 1;
-    }
-
-    c->high      = low;
-    c->code_word = code_word;
-    return 0;
-}
-
-#endif
-
-#endif /* AVCODEC_ARM_VPX_ARITH_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dcahuff.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dcahuff.h
deleted file mode 100644
index fdfe7e922a6fcf5736bc71f0ccb3b7ab2ede3155..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dcahuff.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * DCA compatible decoder - huffman tables
- * Copyright (C) 2004 Gildas Bazin
- * Copyright (C) 2007 Konstantin Shishkov
- *
- * 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
- */
-
-#ifndef AVCODEC_DCAHUFF_H
-#define AVCODEC_DCAHUFF_H
-
-#include <stdint.h>
-
-#include "libavutil/attributes.h"
-
-#include "vlc.h"
-
-#define DCA_CODE_BOOKS      10
-#define DCA_BITALLOC_12_COUNT    5
-#define DCA_NUM_BITALLOC_CODES (1 * 3 + \
-                                3 * (5 + 7 + 9 + 13) \
-                                + 7 * (17 + 25 + 33 + 65 + 129))
-
-extern VLC  ff_dca_vlc_bit_allocation[5];
-#define DCA_TMODE_VLC_BITS 3
-extern VLC  ff_dca_vlc_transition_mode[4];
-#define DCA_SCALES_VLC_BITS 9
-extern VLC  ff_dca_vlc_scale_factor[5];
-extern VLC  ff_dca_vlc_quant_index[DCA_CODE_BOOKS][7];
-
-#define DCA_TNL_GRP_VLC_BITS 9
-extern VLC  ff_dca_vlc_tnl_grp[5];
-#define DCA_TNL_SCF_VLC_BITS 9
-extern VLC  ff_dca_vlc_tnl_scf;
-#define DCA_DAMP_VLC_BITS 6
-extern VLC  ff_dca_vlc_damp;
-#define DCA_DPH_VLC_BITS 6
-extern VLC  ff_dca_vlc_dph;
-#define DCA_FST_RSD_VLC_BITS 9
-extern VLC  ff_dca_vlc_fst_rsd_amp;
-#define DCA_RSD_APPRX_VLC_BITS 5
-extern VLC  ff_dca_vlc_rsd_apprx;
-#define DCA_RSD_AMP_VLC_BITS 9
-extern VLC  ff_dca_vlc_rsd_amp;
-#define DCA_AVG_G3_VLC_BITS 9
-extern VLC  ff_dca_vlc_avg_g3;
-#define DCA_ST_GRID_VLC_BITS 9
-extern VLC  ff_dca_vlc_st_grid;
-#define DCA_GRID_VLC_BITS 9
-extern VLC  ff_dca_vlc_grid_2;
-extern VLC  ff_dca_vlc_grid_3;
-#define DCA_RSD_VLC_BITS 6
-extern VLC  ff_dca_vlc_rsd;
-
-extern const int8_t  ff_dca_bitalloc_offsets[DCA_CODE_BOOKS];
-extern const uint8_t ff_dca_bitalloc_sizes[DCA_CODE_BOOKS];
-extern const uint8_t ff_dca_vlc_src_tables[][2];
-
-av_cold void ff_dca_init_vlcs(void);
-
-#endif /* AVCODEC_DCAHUFF_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/vp9_idct_msa.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/vp9_idct_msa.c
deleted file mode 100644
index 53bfbb476569287c8a87d1a179904a839c7a4671..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/vp9_idct_msa.c
+++ /dev/null
@@ -1,2171 +0,0 @@
-/*
- * Copyright (c) 2015 - 2017 Shivraj Patil (Shivraj.Patil@imgtec.com)
- *
- * 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 <string.h>
-#include "libavcodec/vp9dsp.h"
-#include "libavutil/mips/generic_macros_msa.h"
-#include "vp9dsp_mips.h"
-
-#define VP9_DCT_CONST_BITS   14
-#define ROUND_POWER_OF_TWO(value, n)  (((value) + (1 << ((n) - 1))) >> (n))
-
-static const int32_t cospi_1_64 = 16364;
-static const int32_t cospi_2_64 = 16305;
-static const int32_t cospi_3_64 = 16207;
-static const int32_t cospi_4_64 = 16069;
-static const int32_t cospi_5_64 = 15893;
-static const int32_t cospi_6_64 = 15679;
-static const int32_t cospi_7_64 = 15426;
-static const int32_t cospi_8_64 = 15137;
-static const int32_t cospi_9_64 = 14811;
-static const int32_t cospi_10_64 = 14449;
-static const int32_t cospi_11_64 = 14053;
-static const int32_t cospi_12_64 = 13623;
-static const int32_t cospi_13_64 = 13160;
-static const int32_t cospi_14_64 = 12665;
-static const int32_t cospi_15_64 = 12140;
-static const int32_t cospi_16_64 = 11585;
-static const int32_t cospi_17_64 = 11003;
-static const int32_t cospi_18_64 = 10394;
-static const int32_t cospi_19_64 = 9760;
-static const int32_t cospi_20_64 = 9102;
-static const int32_t cospi_21_64 = 8423;
-static const int32_t cospi_22_64 = 7723;
-static const int32_t cospi_23_64 = 7005;
-static const int32_t cospi_24_64 = 6270;
-static const int32_t cospi_25_64 = 5520;
-static const int32_t cospi_26_64 = 4756;
-static const int32_t cospi_27_64 = 3981;
-static const int32_t cospi_28_64 = 3196;
-static const int32_t cospi_29_64 = 2404;
-static const int32_t cospi_30_64 = 1606;
-static const int32_t cospi_31_64 = 804;
-
-//  16384 * sqrt(2) * sin(kPi/9) * 2 / 3
-static const int32_t sinpi_1_9 = 5283;
-static const int32_t sinpi_2_9 = 9929;
-static const int32_t sinpi_3_9 = 13377;
-static const int32_t sinpi_4_9 = 15212;
-
-#define VP9_DOTP_CONST_PAIR(reg0, reg1, cnst0, cnst1, out0, out1)  \
-{                                                                  \
-    v8i16 k0_m = __msa_fill_h(cnst0);                              \
-    v4i32 s0_m, s1_m, s2_m, s3_m;                                  \
-                                                                   \
-    s0_m = (v4i32) __msa_fill_h(cnst1);                            \
-    k0_m = __msa_ilvev_h((v8i16) s0_m, k0_m);                      \
-                                                                   \
-    ILVRL_H2_SW((-reg1), reg0, s1_m, s0_m);                        \
-    ILVRL_H2_SW(reg0, reg1, s3_m, s2_m);                           \
-    DOTP_SH2_SW(s1_m, s0_m, k0_m, k0_m, s1_m, s0_m);               \
-    SRARI_W2_SW(s1_m, s0_m, VP9_DCT_CONST_BITS);                   \
-    out0 = __msa_pckev_h((v8i16) s0_m, (v8i16) s1_m);              \
-                                                                   \
-    DOTP_SH2_SW(s3_m, s2_m, k0_m, k0_m, s1_m, s0_m);               \
-    SRARI_W2_SW(s1_m, s0_m, VP9_DCT_CONST_BITS);                   \
-    out1 = __msa_pckev_h((v8i16) s0_m, (v8i16) s1_m);              \
-}
-
-#define VP9_DOT_ADD_SUB_SRARI_PCK(in0, in1, in2, in3, in4, in5, in6, in7,  \
-                                      dst0, dst1, dst2, dst3)              \
-{                                                                          \
-    v4i32 tp0_m, tp1_m, tp2_m, tp3_m, tp4_m;                               \
-    v4i32 tp5_m, tp6_m, tp7_m, tp8_m, tp9_m;                               \
-                                                                           \
-    DOTP_SH4_SW(in0, in1, in0, in1, in4, in4, in5, in5,                    \
-                tp0_m, tp2_m, tp3_m, tp4_m);                               \
-    DOTP_SH4_SW(in2, in3, in2, in3, in6, in6, in7, in7,                    \
-                tp5_m, tp6_m, tp7_m, tp8_m);                               \
-    BUTTERFLY_4(tp0_m, tp3_m, tp7_m, tp5_m, tp1_m, tp9_m, tp7_m, tp5_m);   \
-    BUTTERFLY_4(tp2_m, tp4_m, tp8_m, tp6_m, tp3_m, tp0_m, tp4_m, tp2_m);   \
-    SRARI_W4_SW(tp1_m, tp9_m, tp7_m, tp5_m, VP9_DCT_CONST_BITS);           \
-    SRARI_W4_SW(tp3_m, tp0_m, tp4_m, tp2_m, VP9_DCT_CONST_BITS);           \
-    PCKEV_H4_SH(tp1_m, tp3_m, tp9_m, tp0_m, tp7_m, tp4_m, tp5_m, tp2_m,    \
-                dst0, dst1, dst2, dst3);                                   \
-}
-
-#define VP9_DOT_SHIFT_RIGHT_PCK_H(in0, in1, in2)          \
-( {                                                       \
-    v8i16 dst_m;                                          \
-    v4i32 tp0_m, tp1_m;                                   \
-                                                          \
-    DOTP_SH2_SW(in0, in1, in2, in2, tp1_m, tp0_m);        \
-    SRARI_W2_SW(tp1_m, tp0_m, VP9_DCT_CONST_BITS);        \
-    dst_m = __msa_pckev_h((v8i16) tp1_m, (v8i16) tp0_m);  \
-                                                          \
-    dst_m;                                                \
-} )
-
-#define VP9_ADST8(in0, in1, in2, in3, in4, in5, in6, in7,                 \
-                  out0, out1, out2, out3, out4, out5, out6, out7)         \
-{                                                                         \
-    v8i16 cnst0_m, cnst1_m, cnst2_m, cnst3_m, cnst4_m;                    \
-    v8i16 vec0_m, vec1_m, vec2_m, vec3_m, s0_m, s1_m;                     \
-    v8i16 coeff0_m = { cospi_2_64, cospi_6_64, cospi_10_64, cospi_14_64,  \
-        cospi_18_64, cospi_22_64, cospi_26_64, cospi_30_64 };             \
-    v8i16 coeff1_m = { cospi_8_64, -cospi_8_64, cospi_16_64,              \
-        -cospi_16_64, cospi_24_64, -cospi_24_64, 0, 0 };                  \
-                                                                          \
-    SPLATI_H2_SH(coeff0_m, 0, 7, cnst0_m, cnst1_m);                       \
-    cnst2_m = -cnst0_m;                                                   \
-    ILVEV_H2_SH(cnst0_m, cnst1_m, cnst1_m, cnst2_m, cnst0_m, cnst1_m);    \
-    SPLATI_H2_SH(coeff0_m, 4, 3, cnst2_m, cnst3_m);                       \
-    cnst4_m = -cnst2_m;                                                   \
-    ILVEV_H2_SH(cnst2_m, cnst3_m, cnst3_m, cnst4_m, cnst2_m, cnst3_m);    \
-                                                                          \
-    ILVRL_H2_SH(in0, in7, vec1_m, vec0_m);                                \
-    ILVRL_H2_SH(in4, in3, vec3_m, vec2_m);                                \
-    VP9_DOT_ADD_SUB_SRARI_PCK(vec0_m, vec1_m, vec2_m, vec3_m, cnst0_m,    \
-                              cnst1_m, cnst2_m, cnst3_m, in7, in0,        \
-                              in4, in3);                                  \
-                                                                          \
-    SPLATI_H2_SH(coeff0_m, 2, 5, cnst0_m, cnst1_m);                       \
-    cnst2_m = -cnst0_m;                                                   \
-    ILVEV_H2_SH(cnst0_m, cnst1_m, cnst1_m, cnst2_m, cnst0_m, cnst1_m);    \
-    SPLATI_H2_SH(coeff0_m, 6, 1, cnst2_m, cnst3_m);                       \
-    cnst4_m = -cnst2_m;                                                   \
-    ILVEV_H2_SH(cnst2_m, cnst3_m, cnst3_m, cnst4_m, cnst2_m, cnst3_m);    \
-                                                                          \
-    ILVRL_H2_SH(in2, in5, vec1_m, vec0_m);                                \
-    ILVRL_H2_SH(in6, in1, vec3_m, vec2_m);                                \
-                                                                          \
-    VP9_DOT_ADD_SUB_SRARI_PCK(vec0_m, vec1_m, vec2_m, vec3_m, cnst0_m,    \
-                              cnst1_m, cnst2_m, cnst3_m, in5, in2,        \
-                              in6, in1);                                  \
-    BUTTERFLY_4(in7, in0, in2, in5, s1_m, s0_m, in2, in5);                \
-    out7 = -s0_m;                                                         \
-    out0 = s1_m;                                                          \
-                                                                          \
-    SPLATI_H4_SH(coeff1_m, 0, 4, 1, 5,                                    \
-                 cnst0_m, cnst1_m, cnst2_m, cnst3_m);                     \
-                                                                          \
-    ILVEV_H2_SH(cnst3_m, cnst0_m, cnst1_m, cnst2_m, cnst3_m, cnst2_m);    \
-    cnst0_m = __msa_ilvev_h(cnst1_m, cnst0_m);                            \
-    cnst1_m = cnst0_m;                                                    \
-                                                                          \
-    ILVRL_H2_SH(in4, in3, vec1_m, vec0_m);                                \
-    ILVRL_H2_SH(in6, in1, vec3_m, vec2_m);                                \
-    VP9_DOT_ADD_SUB_SRARI_PCK(vec0_m, vec1_m, vec2_m, vec3_m, cnst0_m,    \
-                              cnst2_m, cnst3_m, cnst1_m, out1, out6,      \
-                              s0_m, s1_m);                                \
-                                                                          \
-    SPLATI_H2_SH(coeff1_m, 2, 3, cnst0_m, cnst1_m);                       \
-    cnst1_m = __msa_ilvev_h(cnst1_m, cnst0_m);                            \
-                                                                          \
-    ILVRL_H2_SH(in2, in5, vec1_m, vec0_m);                                \
-    ILVRL_H2_SH(s0_m, s1_m, vec3_m, vec2_m);                              \
-    out3 = VP9_DOT_SHIFT_RIGHT_PCK_H(vec0_m, vec1_m, cnst0_m);            \
-    out4 = VP9_DOT_SHIFT_RIGHT_PCK_H(vec0_m, vec1_m, cnst1_m);            \
-    out2 = VP9_DOT_SHIFT_RIGHT_PCK_H(vec2_m, vec3_m, cnst0_m);            \
-    out5 = VP9_DOT_SHIFT_RIGHT_PCK_H(vec2_m, vec3_m, cnst1_m);            \
-                                                                          \
-    out1 = -out1;                                                         \
-    out3 = -out3;                                                         \
-    out5 = -out5;                                                         \
-}
-
-#define VP9_MADD_SHORT(m0, m1, c0, c1, res0, res1)                        \
-{                                                                         \
-    v4i32 madd0_m, madd1_m, madd2_m, madd3_m;                             \
-    v8i16 madd_s0_m, madd_s1_m;                                           \
-                                                                          \
-    ILVRL_H2_SH(m1, m0, madd_s0_m, madd_s1_m);                            \
-    DOTP_SH4_SW(madd_s0_m, madd_s1_m, madd_s0_m, madd_s1_m,               \
-                c0, c0, c1, c1, madd0_m, madd1_m, madd2_m, madd3_m);      \
-    SRARI_W4_SW(madd0_m, madd1_m, madd2_m, madd3_m, VP9_DCT_CONST_BITS);  \
-    PCKEV_H2_SH(madd1_m, madd0_m, madd3_m, madd2_m, res0, res1);          \
-}
-
-#define VP9_MADD_BF(inp0, inp1, inp2, inp3, cst0, cst1, cst2, cst3,       \
-                    out0, out1, out2, out3)                               \
-{                                                                         \
-    v8i16 madd_s0_m, madd_s1_m, madd_s2_m, madd_s3_m;                     \
-    v4i32 tmp0_m, tmp1_m, tmp2_m, tmp3_m, m4_m, m5_m;                     \
-                                                                          \
-    ILVRL_H2_SH(inp1, inp0, madd_s0_m, madd_s1_m);                        \
-    ILVRL_H2_SH(inp3, inp2, madd_s2_m, madd_s3_m);                        \
-    DOTP_SH4_SW(madd_s0_m, madd_s1_m, madd_s2_m, madd_s3_m,               \
-                cst0, cst0, cst2, cst2, tmp0_m, tmp1_m, tmp2_m, tmp3_m);  \
-    BUTTERFLY_4(tmp0_m, tmp1_m, tmp3_m, tmp2_m,                           \
-                m4_m, m5_m, tmp3_m, tmp2_m);                              \
-    SRARI_W4_SW(m4_m, m5_m, tmp2_m, tmp3_m, VP9_DCT_CONST_BITS);          \
-    PCKEV_H2_SH(m5_m, m4_m, tmp3_m, tmp2_m, out0, out1);                  \
-    DOTP_SH4_SW(madd_s0_m, madd_s1_m, madd_s2_m, madd_s3_m,               \
-                cst1, cst1, cst3, cst3, tmp0_m, tmp1_m, tmp2_m, tmp3_m);  \
-    BUTTERFLY_4(tmp0_m, tmp1_m, tmp3_m, tmp2_m,                           \
-                m4_m, m5_m, tmp3_m, tmp2_m);                              \
-    SRARI_W4_SW(m4_m, m5_m, tmp2_m, tmp3_m, VP9_DCT_CONST_BITS);          \
-    PCKEV_H2_SH(m5_m, m4_m, tmp3_m, tmp2_m, out2, out3);                  \
-}
-
-#define VP9_SET_COSPI_PAIR(c0_h, c1_h)   \
-( {                                      \
-    v8i16 out0_m, r0_m, r1_m;            \
-                                         \
-    r0_m = __msa_fill_h(c0_h);           \
-    r1_m = __msa_fill_h(c1_h);           \
-    out0_m = __msa_ilvev_h(r1_m, r0_m);  \
-                                         \
-    out0_m;                              \
-} )
-
-#define VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in0, in1, in2, in3)  \
-{                                                                 \
-    uint8_t *dst_m = (uint8_t *) (dst);                           \
-    v16u8 dst0_m, dst1_m, dst2_m, dst3_m;                         \
-    v16i8 tmp0_m, tmp1_m;                                         \
-    v16i8 zero_m = { 0 };                                         \
-    v8i16 res0_m, res1_m, res2_m, res3_m;                         \
-                                                                  \
-    LD_UB4(dst_m, dst_stride, dst0_m, dst1_m, dst2_m, dst3_m);    \
-    ILVR_B4_SH(zero_m, dst0_m, zero_m, dst1_m, zero_m, dst2_m,    \
-               zero_m, dst3_m, res0_m, res1_m, res2_m, res3_m);   \
-    ADD4(res0_m, in0, res1_m, in1, res2_m, in2, res3_m, in3,      \
-         res0_m, res1_m, res2_m, res3_m);                         \
-    CLIP_SH4_0_255(res0_m, res1_m, res2_m, res3_m);               \
-    PCKEV_B2_SB(res1_m, res0_m, res3_m, res2_m, tmp0_m, tmp1_m);  \
-    ST_D4(tmp0_m, tmp1_m, 0, 1, 0, 1, dst_m, dst_stride);         \
-}
-
-#define VP9_IDCT4x4(in0, in1, in2, in3, out0, out1, out2, out3)       \
-{                                                                     \
-    v8i16 c0_m, c1_m, c2_m, c3_m;                                     \
-    v8i16 step0_m, step1_m;                                           \
-    v4i32 tmp0_m, tmp1_m, tmp2_m, tmp3_m;                             \
-    v16i8 zeros = { 0 };                                              \
-                                                                      \
-    c0_m = VP9_SET_COSPI_PAIR(cospi_16_64, cospi_16_64);              \
-    c1_m = VP9_SET_COSPI_PAIR(cospi_16_64, -cospi_16_64);             \
-    step0_m = __msa_ilvr_h(in2, in0);                                 \
-    DOTP_SH2_SW(step0_m, step0_m, c0_m, c1_m, tmp0_m, tmp1_m);        \
-                                                                      \
-    c2_m = VP9_SET_COSPI_PAIR(cospi_24_64, -cospi_8_64);              \
-    c3_m = VP9_SET_COSPI_PAIR(cospi_8_64, cospi_24_64);               \
-    step1_m = __msa_ilvr_h(in3, in1);                                 \
-    DOTP_SH2_SW(step1_m, step1_m, c2_m, c3_m, tmp2_m, tmp3_m);        \
-    SRARI_W4_SW(tmp0_m, tmp1_m, tmp2_m, tmp3_m, VP9_DCT_CONST_BITS);  \
-                                                                      \
-    PCKEV_H2_SW(tmp1_m, tmp0_m, tmp3_m, tmp2_m, tmp0_m, tmp2_m);      \
-    SLDI_B2_SW(zeros, tmp0_m, zeros, tmp2_m, 8, tmp1_m, tmp3_m);      \
-    BUTTERFLY_4((v8i16) tmp0_m, (v8i16) tmp1_m,                       \
-                (v8i16) tmp2_m, (v8i16) tmp3_m,                       \
-                out0, out1, out2, out3);                              \
-}
-
-#define VP9_IADST4x4(in0, in1, in2, in3, out0, out1, out2, out3)      \
-{                                                                     \
-    v8i16 res0_m, res1_m, c0_m, c1_m;                                 \
-    v8i16 k1_m, k2_m, k3_m, k4_m;                                     \
-    v8i16 zero_m = { 0 };                                             \
-    v4i32 tmp0_m, tmp1_m, tmp2_m, tmp3_m;                             \
-    v4i32 int0_m, int1_m, int2_m, int3_m;                             \
-    v8i16 mask_m = { sinpi_1_9, sinpi_2_9, sinpi_3_9,                 \
-        sinpi_4_9, -sinpi_1_9, -sinpi_2_9, -sinpi_3_9,                \
-        -sinpi_4_9 };                                                 \
-                                                                      \
-    SPLATI_H4_SH(mask_m, 3, 0, 1, 2, c0_m, c1_m, k1_m, k2_m);         \
-    ILVEV_H2_SH(c0_m, c1_m, k1_m, k2_m, c0_m, c1_m);                  \
-    ILVR_H2_SH(in0, in2, in1, in3, res0_m, res1_m);                   \
-    DOTP_SH2_SW(res0_m, res1_m, c0_m, c1_m, tmp2_m, tmp1_m);          \
-    int0_m = tmp2_m + tmp1_m;                                         \
-                                                                      \
-    SPLATI_H2_SH(mask_m, 4, 7, k4_m, k3_m);                           \
-    ILVEV_H2_SH(k4_m, k1_m, k3_m, k2_m, c0_m, c1_m);                  \
-    DOTP_SH2_SW(res0_m, res1_m, c0_m, c1_m, tmp0_m, tmp1_m);          \
-    int1_m = tmp0_m + tmp1_m;                                         \
-                                                                      \
-    c0_m = __msa_splati_h(mask_m, 6);                                 \
-    ILVL_H2_SH(k2_m, c0_m, zero_m, k2_m, c0_m, c1_m);                 \
-    ILVR_H2_SH(in0, in2, in1, in3, res0_m, res1_m);                   \
-    DOTP_SH2_SW(res0_m, res1_m, c0_m, c1_m, tmp0_m, tmp1_m);          \
-    int2_m = tmp0_m + tmp1_m;                                         \
-                                                                      \
-    c0_m = __msa_splati_h(mask_m, 6);                                 \
-    c0_m = __msa_ilvev_h(c0_m, k1_m);                                 \
-                                                                      \
-    res0_m = __msa_ilvr_h((in1), (in3));                              \
-    tmp0_m = __msa_dotp_s_w(res0_m, c0_m);                            \
-    int3_m = tmp2_m + tmp0_m;                                         \
-                                                                      \
-    res0_m = __msa_ilvr_h((in2), (in3));                              \
-    c1_m = __msa_ilvev_h(k4_m, k3_m);                                 \
-                                                                      \
-    tmp2_m = __msa_dotp_s_w(res0_m, c1_m);                            \
-    res1_m = __msa_ilvr_h((in0), (in2));                              \
-    c1_m = __msa_ilvev_h(k1_m, zero_m);                               \
-                                                                      \
-    tmp3_m = __msa_dotp_s_w(res1_m, c1_m);                            \
-    int3_m += tmp2_m;                                                 \
-    int3_m += tmp3_m;                                                 \
-                                                                      \
-    SRARI_W4_SW(int0_m, int1_m, int2_m, int3_m, VP9_DCT_CONST_BITS);  \
-    PCKEV_H2_SH(int0_m, int0_m, int1_m, int1_m, out0, out1);          \
-    PCKEV_H2_SH(int2_m, int2_m, int3_m, int3_m, out2, out3);          \
-}
-
-#define TRANSPOSE4X8_SH_SH(in0, in1, in2, in3, in4, in5, in6, in7,          \
-                           out0, out1, out2, out3, out4, out5, out6, out7)  \
-{                                                                           \
-    v8i16 tmp0_m, tmp1_m, tmp2_m, tmp3_m;                                   \
-    v8i16 tmp0_n, tmp1_n, tmp2_n, tmp3_n;                                   \
-    v8i16 zero_m = { 0 };                                                   \
-                                                                            \
-    ILVR_H4_SH(in1, in0, in3, in2, in5, in4, in7, in6,                      \
-               tmp0_n, tmp1_n, tmp2_n, tmp3_n);                             \
-    ILVRL_W2_SH(tmp1_n, tmp0_n, tmp0_m, tmp2_m);                            \
-    ILVRL_W2_SH(tmp3_n, tmp2_n, tmp1_m, tmp3_m);                            \
-                                                                            \
-    out0 = (v8i16) __msa_ilvr_d((v2i64) tmp1_m, (v2i64) tmp0_m);            \
-    out1 = (v8i16) __msa_ilvl_d((v2i64) tmp1_m, (v2i64) tmp0_m);            \
-    out2 = (v8i16) __msa_ilvr_d((v2i64) tmp3_m, (v2i64) tmp2_m);            \
-    out3 = (v8i16) __msa_ilvl_d((v2i64) tmp3_m, (v2i64) tmp2_m);            \
-                                                                            \
-    out4 = zero_m;                                                          \
-    out5 = zero_m;                                                          \
-    out6 = zero_m;                                                          \
-    out7 = zero_m;                                                          \
-}
-
-static void vp9_idct4x4_1_add_msa(int16_t *input, uint8_t *dst,
-                                  int32_t dst_stride)
-{
-    int16_t out;
-    v8i16 vec;
-
-    out = ROUND_POWER_OF_TWO((input[0] * cospi_16_64), VP9_DCT_CONST_BITS);
-    out = ROUND_POWER_OF_TWO((out * cospi_16_64), VP9_DCT_CONST_BITS);
-    out = ROUND_POWER_OF_TWO(out, 4);
-    vec = __msa_fill_h(out);
-    input[0] = 0;
-
-    ADDBLK_ST4x4_UB(vec, vec, vec, vec, dst, dst_stride);
-}
-
-static void vp9_idct4x4_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                          int32_t dst_stride)
-{
-    v8i16 in0, in1, in2, in3;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 4x4 block */
-    in0 = LD_SH(input);
-    in2 = LD_SH(input + 8);
-    in1 = (v8i16) __msa_ilvl_d((v2i64) in0, (v2i64) in0);
-    in3 = (v8i16) __msa_ilvl_d((v2i64) in2, (v2i64) in2);
-    ST_SH2(zero, zero, input, 8);
-    /* rows */
-    VP9_IDCT4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* columns */
-    TRANSPOSE4x4_SH_SH(in0, in1, in2, in3, in0, in1, in2, in3);
-    VP9_IDCT4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* rounding (add 2^3, divide by 2^4) */
-    SRARI_H4_SH(in0, in1, in2, in3, 4);
-    ADDBLK_ST4x4_UB(in0, in1, in2, in3, dst, dst_stride);
-}
-
-static void vp9_iadst4x4_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v8i16 in0, in1, in2, in3;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 4x4 block */
-    in0 = LD_SH(input);
-    in2 = LD_SH(input + 8);
-    in1 = (v8i16) __msa_ilvl_d((v2i64) in0, (v2i64) in0);
-    in3 = (v8i16) __msa_ilvl_d((v2i64) in2, (v2i64) in2);
-    ST_SH2(zero, zero, input, 8);
-    /* rows */
-    VP9_IADST4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* columns */
-    TRANSPOSE4x4_SH_SH(in0, in1, in2, in3, in0, in1, in2, in3);
-    VP9_IADST4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* rounding (add 2^3, divide by 2^4) */
-    SRARI_H4_SH(in0, in1, in2, in3, 4);
-    ADDBLK_ST4x4_UB(in0, in1, in2, in3, dst, dst_stride);
-}
-
-static void vp9_iadst_idct_4x4_add_msa(int16_t *input, uint8_t *dst,
-                                       int32_t dst_stride, int32_t eob)
-{
-    v8i16 in0, in1, in2, in3;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 4x4 block */
-    in0 = LD_SH(input);
-    in2 = LD_SH(input + 8);
-    in1 = (v8i16) __msa_ilvl_d((v2i64) in0, (v2i64) in0);
-    in3 = (v8i16) __msa_ilvl_d((v2i64) in2, (v2i64) in2);
-    ST_SH2(zero, zero, input, 8);
-    /* cols */
-    VP9_IADST4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* columns */
-    TRANSPOSE4x4_SH_SH(in0, in1, in2, in3, in0, in1, in2, in3);
-    VP9_IDCT4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* rounding (add 2^3, divide by 2^4) */
-    SRARI_H4_SH(in0, in1, in2, in3, 4);
-    ADDBLK_ST4x4_UB(in0, in1, in2, in3, dst, dst_stride);
-}
-
-static void vp9_idct_iadst_4x4_add_msa(int16_t *input, uint8_t *dst,
-                                       int32_t dst_stride, int32_t eob)
-{
-    v8i16 in0, in1, in2, in3;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 4x4 block */
-    in0 = LD_SH(input);
-    in2 = LD_SH(input + 8);
-    in1 = (v8i16) __msa_ilvl_d((v2i64) in0, (v2i64) in0);
-    in3 = (v8i16) __msa_ilvl_d((v2i64) in2, (v2i64) in2);
-    ST_SH2(zero, zero, input, 8);
-    /* cols */
-    VP9_IDCT4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* columns */
-    TRANSPOSE4x4_SH_SH(in0, in1, in2, in3, in0, in1, in2, in3);
-    VP9_IADST4x4(in0, in1, in2, in3, in0, in1, in2, in3);
-    /* rounding (add 2^3, divide by 2^4) */
-    SRARI_H4_SH(in0, in1, in2, in3, 4);
-    ADDBLK_ST4x4_UB(in0, in1, in2, in3, dst, dst_stride);
-}
-
-#define VP9_SET_CONST_PAIR(mask_h, idx1_h, idx2_h)     \
-( {                                                    \
-    v8i16 c0_m, c1_m;                                  \
-                                                       \
-    SPLATI_H2_SH(mask_h, idx1_h, idx2_h, c0_m, c1_m);  \
-    c0_m = __msa_ilvev_h(c1_m, c0_m);                  \
-                                                       \
-    c0_m;                                              \
-} )
-
-/* multiply and add macro */
-#define VP9_MADD(inp0, inp1, inp2, inp3, cst0, cst1, cst2, cst3,          \
-                 out0, out1, out2, out3)                                  \
-{                                                                         \
-    v8i16 madd_s0_m, madd_s1_m, madd_s2_m, madd_s3_m;                     \
-    v4i32 tmp0_m, tmp1_m, tmp2_m, tmp3_m;                                 \
-                                                                          \
-    ILVRL_H2_SH(inp1, inp0, madd_s1_m, madd_s0_m);                        \
-    ILVRL_H2_SH(inp3, inp2, madd_s3_m, madd_s2_m);                        \
-    DOTP_SH4_SW(madd_s1_m, madd_s0_m, madd_s1_m, madd_s0_m,               \
-                cst0, cst0, cst1, cst1, tmp0_m, tmp1_m, tmp2_m, tmp3_m);  \
-    SRARI_W4_SW(tmp0_m, tmp1_m, tmp2_m, tmp3_m, VP9_DCT_CONST_BITS);      \
-    PCKEV_H2_SH(tmp1_m, tmp0_m, tmp3_m, tmp2_m, out0, out1);              \
-    DOTP_SH4_SW(madd_s3_m, madd_s2_m, madd_s3_m, madd_s2_m,               \
-                cst2, cst2, cst3, cst3, tmp0_m, tmp1_m, tmp2_m, tmp3_m);  \
-    SRARI_W4_SW(tmp0_m, tmp1_m, tmp2_m, tmp3_m, VP9_DCT_CONST_BITS);      \
-    PCKEV_H2_SH(tmp1_m, tmp0_m, tmp3_m, tmp2_m, out2, out3);              \
-}
-
-/* idct 8x8 macro */
-#define VP9_IDCT8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,                 \
-                       out0, out1, out2, out3, out4, out5, out6, out7)         \
-{                                                                              \
-    v8i16 tp0_m, tp1_m, tp2_m, tp3_m, tp4_m, tp5_m, tp6_m, tp7_m;              \
-    v8i16 k0_m, k1_m, k2_m, k3_m, res0_m, res1_m, res2_m, res3_m;              \
-    v4i32 tmp0_m, tmp1_m, tmp2_m, tmp3_m;                                      \
-    v8i16 mask_m = { cospi_28_64, cospi_4_64, cospi_20_64, cospi_12_64,        \
-       cospi_16_64, -cospi_4_64, -cospi_20_64, -cospi_16_64 };                 \
-                                                                               \
-    k0_m = VP9_SET_CONST_PAIR(mask_m, 0, 5);                                   \
-    k1_m = VP9_SET_CONST_PAIR(mask_m, 1, 0);                                   \
-    k2_m = VP9_SET_CONST_PAIR(mask_m, 6, 3);                                   \
-    k3_m = VP9_SET_CONST_PAIR(mask_m, 3, 2);                                   \
-    VP9_MADD(in1, in7, in3, in5, k0_m, k1_m, k2_m, k3_m, in1, in7, in3, in5);  \
-    SUB2(in1, in3, in7, in5, res0_m, res1_m);                                  \
-    k0_m = VP9_SET_CONST_PAIR(mask_m, 4, 7);                                   \
-    k1_m = __msa_splati_h(mask_m, 4);                                          \
-                                                                               \
-    ILVRL_H2_SH(res0_m, res1_m, res2_m, res3_m);                               \
-    DOTP_SH4_SW(res2_m, res3_m, res2_m, res3_m, k0_m, k0_m, k1_m, k1_m,        \
-                tmp0_m, tmp1_m, tmp2_m, tmp3_m);                               \
-    SRARI_W4_SW(tmp0_m, tmp1_m, tmp2_m, tmp3_m, VP9_DCT_CONST_BITS);           \
-    tp4_m = in1 + in3;                                                         \
-    PCKEV_H2_SH(tmp1_m, tmp0_m, tmp3_m, tmp2_m, tp5_m, tp6_m);                 \
-    tp7_m = in7 + in5;                                                         \
-    k2_m = VP9_SET_COSPI_PAIR(cospi_24_64, -cospi_8_64);                       \
-    k3_m = VP9_SET_COSPI_PAIR(cospi_8_64, cospi_24_64);                        \
-    VP9_MADD(in0, in4, in2, in6, k1_m, k0_m, k2_m, k3_m,                       \
-             in0, in4, in2, in6);                                              \
-    BUTTERFLY_4(in0, in4, in2, in6, tp0_m, tp1_m, tp2_m, tp3_m);               \
-    BUTTERFLY_8(tp0_m, tp1_m, tp2_m, tp3_m, tp4_m, tp5_m, tp6_m, tp7_m,        \
-                out0, out1, out2, out3, out4, out5, out6, out7);               \
-}
-
-#define VP9_IADST8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,              \
-                        out0, out1, out2, out3, out4, out5, out6, out7)      \
-{                                                                            \
-    v4i32 r0_m, r1_m, r2_m, r3_m, r4_m, r5_m, r6_m, r7_m;                    \
-    v4i32 m0_m, m1_m, m2_m, m3_m, t0_m, t1_m;                                \
-    v8i16 res0_m, res1_m, res2_m, res3_m, k0_m, k1_m, in_s0, in_s1;          \
-    v8i16 mask1_m = { cospi_2_64, cospi_30_64, -cospi_2_64,                  \
-        cospi_10_64, cospi_22_64, -cospi_10_64, cospi_18_64, cospi_14_64 };  \
-    v8i16 mask2_m = { cospi_14_64, -cospi_18_64, cospi_26_64,                \
-        cospi_6_64, -cospi_26_64, cospi_8_64, cospi_24_64, -cospi_8_64 };    \
-    v8i16 mask3_m = { -cospi_24_64, cospi_8_64, cospi_16_64,                 \
-        -cospi_16_64, 0, 0, 0, 0 };                                          \
-                                                                             \
-    k0_m = VP9_SET_CONST_PAIR(mask1_m, 0, 1);                                \
-    k1_m = VP9_SET_CONST_PAIR(mask1_m, 1, 2);                                \
-    ILVRL_H2_SH(in1, in0, in_s1, in_s0);                                     \
-    DOTP_SH4_SW(in_s1, in_s0, in_s1, in_s0, k0_m, k0_m, k1_m, k1_m,          \
-                r0_m, r1_m, r2_m, r3_m);                                     \
-    k0_m = VP9_SET_CONST_PAIR(mask1_m, 6, 7);                                \
-    k1_m = VP9_SET_CONST_PAIR(mask2_m, 0, 1);                                \
-    ILVRL_H2_SH(in5, in4, in_s1, in_s0);                                     \
-    DOTP_SH4_SW(in_s1, in_s0, in_s1, in_s0, k0_m, k0_m, k1_m, k1_m,          \
-                r4_m, r5_m, r6_m, r7_m);                                     \
-    ADD4(r0_m, r4_m, r1_m, r5_m, r2_m, r6_m, r3_m, r7_m,                     \
-         m0_m, m1_m, m2_m, m3_m);                                            \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SH(m1_m, m0_m, m3_m, m2_m, res0_m, res1_m);                     \
-    SUB4(r0_m, r4_m, r1_m, r5_m, r2_m, r6_m, r3_m, r7_m,                     \
-         m0_m, m1_m, m2_m, m3_m);                                            \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SW(m1_m, m0_m, m3_m, m2_m, t0_m, t1_m);                         \
-    k0_m = VP9_SET_CONST_PAIR(mask1_m, 3, 4);                                \
-    k1_m = VP9_SET_CONST_PAIR(mask1_m, 4, 5);                                \
-    ILVRL_H2_SH(in3, in2, in_s1, in_s0);                                     \
-    DOTP_SH4_SW(in_s1, in_s0, in_s1, in_s0, k0_m, k0_m, k1_m, k1_m,          \
-                r0_m, r1_m, r2_m, r3_m);                                     \
-    k0_m = VP9_SET_CONST_PAIR(mask2_m, 2, 3);                                \
-    k1_m = VP9_SET_CONST_PAIR(mask2_m, 3, 4);                                \
-    ILVRL_H2_SH(in7, in6, in_s1, in_s0);                                     \
-    DOTP_SH4_SW(in_s1, in_s0, in_s1, in_s0, k0_m, k0_m, k1_m, k1_m,          \
-                r4_m, r5_m, r6_m, r7_m);                                     \
-    ADD4(r0_m, r4_m, r1_m, r5_m, r2_m, r6_m, r3_m, r7_m,                     \
-         m0_m, m1_m, m2_m, m3_m);                                            \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SH(m1_m, m0_m, m3_m, m2_m, res2_m, res3_m);                     \
-    SUB4(r0_m, r4_m, r1_m, r5_m, r2_m, r6_m, r3_m, r7_m,                     \
-         m0_m, m1_m, m2_m, m3_m);                                            \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SW(m1_m, m0_m, m3_m, m2_m, r2_m, r3_m);                         \
-    ILVRL_H2_SW(r3_m, r2_m, m2_m, m3_m);                                     \
-    BUTTERFLY_4(res0_m, res1_m, res3_m, res2_m, out0, in7, in4, in3);        \
-    k0_m = VP9_SET_CONST_PAIR(mask2_m, 5, 6);                                \
-    k1_m = VP9_SET_CONST_PAIR(mask2_m, 6, 7);                                \
-    ILVRL_H2_SH(t1_m, t0_m, in_s1, in_s0);                                   \
-    DOTP_SH4_SW(in_s1, in_s0, in_s1, in_s0, k0_m, k0_m, k1_m, k1_m,          \
-                r0_m, r1_m, r2_m, r3_m);                                     \
-    k1_m = VP9_SET_CONST_PAIR(mask3_m, 0, 1);                                \
-    DOTP_SH4_SW(m2_m, m3_m, m2_m, m3_m, k0_m, k0_m, k1_m, k1_m,              \
-                r4_m, r5_m, r6_m, r7_m);                                     \
-    ADD4(r0_m, r6_m, r1_m, r7_m, r2_m, r4_m, r3_m, r5_m,                     \
-         m0_m, m1_m, m2_m, m3_m);                                            \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SH(m1_m, m0_m, m3_m, m2_m, in1, out6);                          \
-    SUB4(r0_m, r6_m, r1_m, r7_m, r2_m, r4_m, r3_m, r5_m,                     \
-         m0_m, m1_m, m2_m, m3_m);                                            \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SH(m1_m, m0_m, m3_m, m2_m, in2, in5);                           \
-    k0_m = VP9_SET_CONST_PAIR(mask3_m, 2, 2);                                \
-    k1_m = VP9_SET_CONST_PAIR(mask3_m, 2, 3);                                \
-    ILVRL_H2_SH(in4, in3, in_s1, in_s0);                                     \
-    DOTP_SH4_SW(in_s1, in_s0, in_s1, in_s0, k0_m, k0_m, k1_m, k1_m,          \
-                m0_m, m1_m, m2_m, m3_m);                                     \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SH(m1_m, m0_m, m3_m, m2_m, in3, out4);                          \
-    ILVRL_H2_SW(in5, in2, m2_m, m3_m);                                       \
-    DOTP_SH4_SW(m2_m, m3_m, m2_m, m3_m, k0_m, k0_m, k1_m, k1_m,              \
-                m0_m, m1_m, m2_m, m3_m);                                     \
-    SRARI_W4_SW(m0_m, m1_m, m2_m, m3_m, VP9_DCT_CONST_BITS);                 \
-    PCKEV_H2_SH(m1_m, m0_m, m3_m, m2_m, out2, in5);                          \
-                                                                             \
-    out1 = -in1;                                                             \
-    out3 = -in3;                                                             \
-    out5 = -in5;                                                             \
-    out7 = -in7;                                                             \
-}
-
-static void vp9_idct8x8_1_add_msa(int16_t *input, uint8_t *dst,
-                                  int32_t dst_stride)
-{
-    int16_t out;
-    int32_t val;
-    v8i16 vec;
-
-    out = ROUND_POWER_OF_TWO((input[0] * cospi_16_64), VP9_DCT_CONST_BITS);
-    out = ROUND_POWER_OF_TWO((out * cospi_16_64), VP9_DCT_CONST_BITS);
-    val = ROUND_POWER_OF_TWO(out, 5);
-    vec = __msa_fill_h(val);
-    input[0] = 0;
-
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, vec, vec, vec, vec);
-    dst += (4 * dst_stride);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, vec, vec, vec, vec);
-}
-
-static void vp9_idct8x8_12_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-    v8i16 s0, s1, s2, s3, s4, s5, s6, s7, k0, k1, k2, k3, m0, m1, m2, m3;
-    v4i32 tmp0, tmp1, tmp2, tmp3;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 8x8 block */
-    LD_SH8(input, 8, in0, in1, in2, in3, in4, in5, in6, in7);
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 8);
-    ILVR_D2_SH(in1, in0, in3, in2, in0, in1);
-    ILVR_D2_SH(in5, in4, in7, in6, in2, in3);
-
-    /* stage1 */
-    ILVL_H2_SH(in3, in0, in2, in1, s0, s1);
-    k0 = VP9_SET_COSPI_PAIR(cospi_28_64, -cospi_4_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_4_64, cospi_28_64);
-    k2 = VP9_SET_COSPI_PAIR(-cospi_20_64, cospi_12_64);
-    k3 = VP9_SET_COSPI_PAIR(cospi_12_64, cospi_20_64);
-    DOTP_SH4_SW(s0, s0, s1, s1, k0, k1, k2, k3, tmp0, tmp1, tmp2, tmp3);
-    SRARI_W4_SW(tmp0, tmp1, tmp2, tmp3, VP9_DCT_CONST_BITS);
-    PCKEV_H2_SH(zero, tmp0, zero, tmp1, s0, s1);
-    PCKEV_H2_SH(zero, tmp2, zero, tmp3, s2, s3);
-    BUTTERFLY_4(s0, s1, s3, s2, s4, s7, s6, s5);
-
-    /* stage2 */
-    ILVR_H2_SH(in3, in1, in2, in0, s1, s0);
-    k0 = VP9_SET_COSPI_PAIR(cospi_16_64, cospi_16_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_16_64, -cospi_16_64);
-    k2 = VP9_SET_COSPI_PAIR(cospi_24_64, -cospi_8_64);
-    k3 = VP9_SET_COSPI_PAIR(cospi_8_64, cospi_24_64);
-    DOTP_SH4_SW(s0, s0, s1, s1, k0, k1, k2, k3, tmp0, tmp1, tmp2, tmp3);
-    SRARI_W4_SW(tmp0, tmp1, tmp2, tmp3, VP9_DCT_CONST_BITS);
-    PCKEV_H2_SH(zero, tmp0, zero, tmp1, s0, s1);
-    PCKEV_H2_SH(zero, tmp2, zero, tmp3, s2, s3);
-    BUTTERFLY_4(s0, s1, s2, s3, m0, m1, m2, m3);
-
-    /* stage3 */
-    s0 = __msa_ilvr_h(s6, s5);
-
-    k1 = VP9_SET_COSPI_PAIR(-cospi_16_64, cospi_16_64);
-    DOTP_SH2_SW(s0, s0, k1, k0, tmp0, tmp1);
-    SRARI_W2_SW(tmp0, tmp1, VP9_DCT_CONST_BITS);
-    PCKEV_H2_SH(zero, tmp0, zero, tmp1, s2, s3);
-
-    /* stage4 */
-    BUTTERFLY_8(m0, m1, m2, m3, s4, s2, s3, s7,
-                in0, in1, in2, in3, in4, in5, in6, in7);
-    TRANSPOSE4X8_SH_SH(in0, in1, in2, in3, in4, in5, in6, in7,
-                       in0, in1, in2, in3, in4, in5, in6, in7);
-    VP9_IDCT8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,
-                   in0, in1, in2, in3, in4, in5, in6, in7);
-
-    /* final rounding (add 2^4, divide by 2^5) and shift */
-    SRARI_H4_SH(in0, in1, in2, in3, 5);
-    SRARI_H4_SH(in4, in5, in6, in7, 5);
-
-    /* add block and store 8x8 */
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in0, in1, in2, in3);
-    dst += (4 * dst_stride);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in4, in5, in6, in7);
-}
-
-static void vp9_idct8x8_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                          int32_t dst_stride)
-{
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 8x8 block */
-    LD_SH8(input, 8, in0, in1, in2, in3, in4, in5, in6, in7);
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 8);
-    /* 1D idct8x8 */
-    VP9_IDCT8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,
-                   in0, in1, in2, in3, in4, in5, in6, in7);
-    /* columns transform */
-    TRANSPOSE8x8_SH_SH(in0, in1, in2, in3, in4, in5, in6, in7,
-                       in0, in1, in2, in3, in4, in5, in6, in7);
-    /* 1D idct8x8 */
-    VP9_IDCT8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,
-                   in0, in1, in2, in3, in4, in5, in6, in7);
-    /* final rounding (add 2^4, divide by 2^5) and shift */
-    SRARI_H4_SH(in0, in1, in2, in3, 5);
-    SRARI_H4_SH(in4, in5, in6, in7, 5);
-    /* add block and store 8x8 */
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in0, in1, in2, in3);
-    dst += (4 * dst_stride);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in4, in5, in6, in7);
-}
-
-static void vp9_iadst8x8_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-    v8i16 res0, res1, res2, res3, res4, res5, res6, res7;
-    v16u8 dst0, dst1, dst2, dst3, dst4, dst5, dst6, dst7;
-    v8i16 out0, out1, out2, out3, out4, out5, out6, out7;
-    v8i16 cnst0, cnst1, cnst2, cnst3, cnst4;
-    v8i16 temp0, temp1, temp2, temp3, s0, s1;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 8x8 block */
-    LD_SH8(input, 8, in0, in1, in2, in3, in4, in5, in6, in7);
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 8);
-
-    /* 1D adst8x8 */
-    VP9_ADST8(in0, in1, in2, in3, in4, in5, in6, in7,
-              in0, in1, in2, in3, in4, in5, in6, in7);
-
-    /* columns transform */
-    TRANSPOSE8x8_SH_SH(in0, in1, in2, in3, in4, in5, in6, in7,
-                       in0, in1, in2, in3, in4, in5, in6, in7);
-
-    cnst0 = __msa_fill_h(cospi_2_64);
-    cnst1 = __msa_fill_h(cospi_30_64);
-    cnst2 = -cnst0;
-    ILVEV_H2_SH(cnst0, cnst1, cnst1, cnst2, cnst0, cnst1);
-    cnst2 = __msa_fill_h(cospi_18_64);
-    cnst3 = __msa_fill_h(cospi_14_64);
-    cnst4 = -cnst2;
-    ILVEV_H2_SH(cnst2, cnst3, cnst3, cnst4, cnst2, cnst3);
-
-    ILVRL_H2_SH(in0, in7, temp1, temp0);
-    ILVRL_H2_SH(in4, in3, temp3, temp2);
-    VP9_DOT_ADD_SUB_SRARI_PCK(temp0, temp1, temp2, temp3, cnst0, cnst1, cnst2,
-                              cnst3, in7, in0, in4, in3);
-
-    cnst0 = __msa_fill_h(cospi_10_64);
-    cnst1 = __msa_fill_h(cospi_22_64);
-    cnst2 = -cnst0;
-    ILVEV_H2_SH(cnst0, cnst1, cnst1, cnst2, cnst0, cnst1);
-    cnst2 = __msa_fill_h(cospi_26_64);
-    cnst3 = __msa_fill_h(cospi_6_64);
-    cnst4 = -cnst2;
-    ILVEV_H2_SH(cnst2, cnst3, cnst3, cnst4, cnst2, cnst3);
-
-    ILVRL_H2_SH(in2, in5, temp1, temp0);
-    ILVRL_H2_SH(in6, in1, temp3, temp2);
-    VP9_DOT_ADD_SUB_SRARI_PCK(temp0, temp1, temp2, temp3, cnst0, cnst1, cnst2,
-                              cnst3, in5, in2, in6, in1);
-    BUTTERFLY_4(in7, in0, in2, in5, s1, s0, in2, in5);
-    out7 = -s0;
-    out0 = s1;
-    SRARI_H2_SH(out0, out7, 5);
-    dst0 = LD_UB(dst + 0 * dst_stride);
-    dst7 = LD_UB(dst + 7 * dst_stride);
-
-    res0 = (v8i16) __msa_ilvr_b((v16i8) zero, (v16i8) dst0);
-    res0 += out0;
-    CLIP_SH_0_255(res0);
-    res0 = (v8i16) __msa_pckev_b((v16i8) res0, (v16i8) res0);
-    ST_D1(res0, 0, dst);
-
-    res7 = (v8i16) __msa_ilvr_b((v16i8) zero, (v16i8) dst7);
-    res7 += out7;
-    CLIP_SH_0_255(res7);
-    res7 = (v8i16) __msa_pckev_b((v16i8) res7, (v16i8) res7);
-    ST_D1(res7, 0, dst + 7 * dst_stride);
-
-    cnst1 = __msa_fill_h(cospi_24_64);
-    cnst0 = __msa_fill_h(cospi_8_64);
-    cnst3 = -cnst1;
-    cnst2 = -cnst0;
-
-    ILVEV_H2_SH(cnst3, cnst0, cnst1, cnst2, cnst3, cnst2);
-    cnst0 = __msa_ilvev_h(cnst1, cnst0);
-    cnst1 = cnst0;
-
-    ILVRL_H2_SH(in4, in3, temp1, temp0);
-    ILVRL_H2_SH(in6, in1, temp3, temp2);
-    VP9_DOT_ADD_SUB_SRARI_PCK(temp0, temp1, temp2, temp3, cnst0, cnst2, cnst3,
-                              cnst1, out1, out6, s0, s1);
-    out1 = -out1;
-    SRARI_H2_SH(out1, out6, 5);
-    dst1 = LD_UB(dst + 1 * dst_stride);
-    dst6 = LD_UB(dst + 6 * dst_stride);
-    ILVR_B2_SH(zero, dst1, zero, dst6, res1, res6);
-    ADD2(res1, out1, res6, out6, res1, res6);
-    CLIP_SH2_0_255(res1, res6);
-    PCKEV_B2_SH(res1, res1, res6, res6, res1, res6);
-    ST_D1(res1, 0, dst + dst_stride);
-    ST_D1(res6, 0, dst + 6 * dst_stride);
-
-    cnst0 = __msa_fill_h(cospi_16_64);
-    cnst1 = -cnst0;
-    cnst1 = __msa_ilvev_h(cnst1, cnst0);
-
-    ILVRL_H2_SH(in2, in5, temp1, temp0);
-    ILVRL_H2_SH(s0, s1, temp3, temp2);
-    out3 = VP9_DOT_SHIFT_RIGHT_PCK_H(temp0, temp1, cnst0);
-    out4 = VP9_DOT_SHIFT_RIGHT_PCK_H(temp0, temp1, cnst1);
-    out3 = -out3;
-    SRARI_H2_SH(out3, out4, 5);
-    dst3 = LD_UB(dst + 3 * dst_stride);
-    dst4 = LD_UB(dst + 4 * dst_stride);
-    ILVR_B2_SH(zero, dst3, zero, dst4, res3, res4);
-    ADD2(res3, out3, res4, out4, res3, res4);
-    CLIP_SH2_0_255(res3, res4);
-    PCKEV_B2_SH(res3, res3, res4, res4, res3, res4);
-    ST_D1(res3, 0, dst + 3 * dst_stride);
-    ST_D1(res4, 0, dst + 4 * dst_stride);
-
-    out2 = VP9_DOT_SHIFT_RIGHT_PCK_H(temp2, temp3, cnst0);
-    out5 = VP9_DOT_SHIFT_RIGHT_PCK_H(temp2, temp3, cnst1);
-    out5 = -out5;
-    SRARI_H2_SH(out2, out5, 5);
-    dst2 = LD_UB(dst + 2 * dst_stride);
-    dst5 = LD_UB(dst + 5 * dst_stride);
-    ILVR_B2_SH(zero, dst2, zero, dst5, res2, res5);
-    ADD2(res2, out2, res5, out5, res2, res5);
-    CLIP_SH2_0_255(res2, res5);
-    PCKEV_B2_SH(res2, res2, res5, res5, res2, res5);
-    ST_D1(res2, 0, dst + 2 * dst_stride);
-    ST_D1(res5, 0, dst + 5 * dst_stride);
-}
-
-static void vp9_iadst_idct_8x8_add_msa(int16_t *input, uint8_t *dst,
-                                       int32_t dst_stride, int32_t eob)
-{
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 8x8 block */
-    LD_SH8(input, 8, in1, in6, in3, in4, in5, in2, in7, in0);
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 8);
-    /* 1D idct8x8 */
-    VP9_IADST8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,
-                    in0, in1, in2, in3, in4, in5, in6, in7);
-    /* columns transform */
-    TRANSPOSE8x8_SH_SH(in0, in1, in2, in3, in4, in5, in6, in7,
-                       in0, in1, in2, in3, in4, in5, in6, in7);
-    /* 1D idct8x8 */
-    VP9_IDCT8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,
-                   in0, in1, in2, in3, in4, in5, in6, in7);
-    /* final rounding (add 2^4, divide by 2^5) and shift */
-    SRARI_H4_SH(in0, in1, in2, in3, 5);
-    SRARI_H4_SH(in4, in5, in6, in7, 5);
-    /* add block and store 8x8 */
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in0, in1, in2, in3);
-    dst += (4 * dst_stride);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in4, in5, in6, in7);
-}
-
-static void vp9_idct_iadst_8x8_add_msa(int16_t *input, uint8_t *dst,
-                                       int32_t dst_stride, int32_t eob)
-{
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-    v8i16 zero = { 0 };
-
-    /* load vector elements of 8x8 block */
-    LD_SH8(input, 8, in0, in1, in2, in3, in4, in5, in6, in7);
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 8);
-
-    /* 1D idct8x8 */
-    VP9_IDCT8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,
-                   in0, in1, in2, in3, in4, in5, in6, in7);
-    /* columns transform */
-    TRANSPOSE8x8_SH_SH(in0, in1, in2, in3, in4, in5, in6, in7,
-                       in1, in6, in3, in4, in5, in2, in7, in0);
-    /* 1D idct8x8 */
-    VP9_IADST8x8_1D(in0, in1, in2, in3, in4, in5, in6, in7,
-                    in0, in1, in2, in3, in4, in5, in6, in7);
-    /* final rounding (add 2^4, divide by 2^5) and shift */
-    SRARI_H4_SH(in0, in1, in2, in3, 5);
-    SRARI_H4_SH(in4, in5, in6, in7, 5);
-    /* add block and store 8x8 */
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in0, in1, in2, in3);
-    dst += (4 * dst_stride);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, in4, in5, in6, in7);
-}
-
-#define VP9_IADST8x16_1D(r0, r1, r2, r3, r4, r5, r6, r7, r8,          \
-                         r9, r10, r11, r12, r13, r14, r15,            \
-                         out0, out1, out2, out3, out4, out5,          \
-                         out6, out7, out8, out9, out10, out11,        \
-                         out12, out13, out14, out15)                  \
-{                                                                     \
-    v8i16 g0_m, g1_m, g2_m, g3_m, g4_m, g5_m, g6_m, g7_m;             \
-    v8i16 g8_m, g9_m, g10_m, g11_m, g12_m, g13_m, g14_m, g15_m;       \
-    v8i16 h0_m, h1_m, h2_m, h3_m, h4_m, h5_m, h6_m, h7_m;             \
-    v8i16 h8_m, h9_m, h10_m, h11_m;                                   \
-    v8i16 k0_m, k1_m, k2_m, k3_m;                                     \
-                                                                      \
-    /* stage 1 */                                                     \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_1_64, cospi_31_64);               \
-    k1_m = VP9_SET_COSPI_PAIR(cospi_31_64, -cospi_1_64);              \
-    k2_m = VP9_SET_COSPI_PAIR(cospi_17_64, cospi_15_64);              \
-    k3_m = VP9_SET_COSPI_PAIR(cospi_15_64, -cospi_17_64);             \
-    VP9_MADD_BF(r15, r0, r7, r8, k0_m, k1_m, k2_m, k3_m,              \
-                g0_m, g1_m, g2_m, g3_m);                              \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_5_64, cospi_27_64);               \
-    k1_m = VP9_SET_COSPI_PAIR(cospi_27_64, -cospi_5_64);              \
-    k2_m = VP9_SET_COSPI_PAIR(cospi_21_64, cospi_11_64);              \
-    k3_m = VP9_SET_COSPI_PAIR(cospi_11_64, -cospi_21_64);             \
-    VP9_MADD_BF(r13, r2, r5, r10, k0_m, k1_m, k2_m, k3_m,             \
-                g4_m, g5_m, g6_m, g7_m);                              \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_9_64, cospi_23_64);               \
-    k1_m = VP9_SET_COSPI_PAIR(cospi_23_64, -cospi_9_64);              \
-    k2_m = VP9_SET_COSPI_PAIR(cospi_25_64, cospi_7_64);               \
-    k3_m = VP9_SET_COSPI_PAIR(cospi_7_64, -cospi_25_64);              \
-    VP9_MADD_BF(r11, r4, r3, r12, k0_m, k1_m, k2_m, k3_m,             \
-                g8_m, g9_m, g10_m, g11_m);                            \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_13_64, cospi_19_64);              \
-    k1_m = VP9_SET_COSPI_PAIR(cospi_19_64, -cospi_13_64);             \
-    k2_m = VP9_SET_COSPI_PAIR(cospi_29_64, cospi_3_64);               \
-    k3_m = VP9_SET_COSPI_PAIR(cospi_3_64, -cospi_29_64);              \
-    VP9_MADD_BF(r9, r6, r1, r14, k0_m, k1_m, k2_m, k3_m,              \
-                g12_m, g13_m, g14_m, g15_m);                          \
-                                                                      \
-    /* stage 2 */                                                     \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_4_64, cospi_28_64);               \
-    k1_m = VP9_SET_COSPI_PAIR(cospi_28_64, -cospi_4_64);              \
-    k2_m = VP9_SET_COSPI_PAIR(-cospi_28_64, cospi_4_64);              \
-    VP9_MADD_BF(g1_m, g3_m, g9_m, g11_m, k0_m, k1_m, k2_m, k0_m,      \
-                h0_m, h1_m, h2_m, h3_m);                              \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_12_64, cospi_20_64);              \
-    k1_m = VP9_SET_COSPI_PAIR(-cospi_20_64, cospi_12_64);             \
-    k2_m = VP9_SET_COSPI_PAIR(cospi_20_64, -cospi_12_64);             \
-    VP9_MADD_BF(g7_m, g5_m, g15_m, g13_m, k0_m, k1_m, k2_m, k0_m,     \
-                h4_m, h5_m, h6_m, h7_m);                              \
-    BUTTERFLY_4(h0_m, h2_m, h6_m, h4_m, out8, out9, out11, out10);    \
-    BUTTERFLY_8(g0_m, g2_m, g4_m, g6_m, g14_m, g12_m, g10_m, g8_m,    \
-                h8_m, h9_m, h10_m, h11_m, h6_m, h4_m, h2_m, h0_m);    \
-                                                                      \
-    /* stage 3 */                                                     \
-    BUTTERFLY_4(h8_m, h9_m, h11_m, h10_m, out0, out1, h11_m, h10_m);  \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_8_64, cospi_24_64);               \
-    k1_m = VP9_SET_COSPI_PAIR(cospi_24_64, -cospi_8_64);              \
-    k2_m = VP9_SET_COSPI_PAIR(-cospi_24_64, cospi_8_64);              \
-    VP9_MADD_BF(h0_m, h2_m, h4_m, h6_m, k0_m, k1_m, k2_m, k0_m,       \
-                out4, out6, out5, out7);                              \
-    VP9_MADD_BF(h1_m, h3_m, h5_m, h7_m, k0_m, k1_m, k2_m, k0_m,       \
-                out12, out14, out13, out15);                          \
-                                                                      \
-    /* stage 4 */                                                     \
-    k0_m = VP9_SET_COSPI_PAIR(cospi_16_64, cospi_16_64);              \
-    k1_m = VP9_SET_COSPI_PAIR(-cospi_16_64, -cospi_16_64);            \
-    k2_m = VP9_SET_COSPI_PAIR(cospi_16_64, -cospi_16_64);             \
-    k3_m = VP9_SET_COSPI_PAIR(-cospi_16_64, cospi_16_64);             \
-    VP9_MADD_SHORT(h10_m, h11_m, k1_m, k2_m, out2, out3);             \
-    VP9_MADD_SHORT(out6, out7, k0_m, k3_m, out6, out7);               \
-    VP9_MADD_SHORT(out10, out11, k0_m, k3_m, out10, out11);           \
-    VP9_MADD_SHORT(out14, out15, k1_m, k2_m, out14, out15);           \
-}
-
-static void vp9_idct16_1d_columns_addblk_msa(int16_t *input, uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    v8i16 loc0, loc1, loc2, loc3;
-    v8i16 reg0, reg2, reg4, reg6, reg8, reg10, reg12, reg14;
-    v8i16 reg3, reg13, reg11, reg5, reg7, reg9, reg1, reg15;
-    v8i16 tmp5, tmp6, tmp7;
-    v8i16 zero = { 0 };
-
-    /* load up 8x16 */
-    LD_SH16(input, 16,
-            reg0, reg1, reg2, reg3, reg4, reg5, reg6, reg7,
-            reg8, reg9, reg10, reg11, reg12, reg13, reg14, reg15);
-
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 16);
-    input += 8 * 16;
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 16);
-
-    VP9_DOTP_CONST_PAIR(reg2, reg14, cospi_28_64, cospi_4_64, reg2, reg14);
-    VP9_DOTP_CONST_PAIR(reg10, reg6, cospi_12_64, cospi_20_64, reg10, reg6);
-    BUTTERFLY_4(reg2, reg14, reg6, reg10, loc0, loc1, reg14, reg2);
-    VP9_DOTP_CONST_PAIR(reg14, reg2, cospi_16_64, cospi_16_64, loc2, loc3);
-    VP9_DOTP_CONST_PAIR(reg0, reg8, cospi_16_64, cospi_16_64, reg0, reg8);
-    VP9_DOTP_CONST_PAIR(reg4, reg12, cospi_24_64, cospi_8_64, reg4, reg12);
-    BUTTERFLY_4(reg8, reg0, reg4, reg12, reg2, reg6, reg10, reg14);
-
-    reg0 = reg2 - loc1;
-    reg2 = reg2 + loc1;
-    reg12 = reg14 - loc0;
-    reg14 = reg14 + loc0;
-    reg4 = reg6 - loc3;
-    reg6 = reg6 + loc3;
-    reg8 = reg10 - loc2;
-    reg10 = reg10 + loc2;
-
-    /* stage 2 */
-    VP9_DOTP_CONST_PAIR(reg1, reg15, cospi_30_64, cospi_2_64, reg1, reg15);
-    VP9_DOTP_CONST_PAIR(reg9, reg7, cospi_14_64, cospi_18_64, loc2, loc3);
-
-    reg9 = reg1 - loc2;
-    reg1 = reg1 + loc2;
-    reg7 = reg15 - loc3;
-    reg15 = reg15 + loc3;
-
-    VP9_DOTP_CONST_PAIR(reg5, reg11, cospi_22_64, cospi_10_64, reg5, reg11);
-    VP9_DOTP_CONST_PAIR(reg13, reg3, cospi_6_64, cospi_26_64, loc0, loc1);
-    BUTTERFLY_4(loc0, loc1, reg11, reg5, reg13, reg3, reg11, reg5);
-
-    loc1 = reg15 + reg3;
-    reg3 = reg15 - reg3;
-    loc2 = reg2 + loc1;
-    reg15 = reg2 - loc1;
-
-    loc1 = reg1 + reg13;
-    reg13 = reg1 - reg13;
-    loc0 = reg0 + loc1;
-    loc1 = reg0 - loc1;
-    tmp6 = loc0;
-    tmp7 = loc1;
-    reg0 = loc2;
-
-    VP9_DOTP_CONST_PAIR(reg7, reg9, cospi_24_64, cospi_8_64, reg7, reg9);
-    VP9_DOTP_CONST_PAIR((-reg5), (-reg11), cospi_8_64, cospi_24_64, reg5,
-                        reg11);
-
-    loc0 = reg9 + reg5;
-    reg5 = reg9 - reg5;
-    reg2 = reg6 + loc0;
-    reg1 = reg6 - loc0;
-
-    loc0 = reg7 + reg11;
-    reg11 = reg7 - reg11;
-    loc1 = reg4 + loc0;
-    loc2 = reg4 - loc0;
-    tmp5 = loc1;
-
-    VP9_DOTP_CONST_PAIR(reg5, reg11, cospi_16_64, cospi_16_64, reg5, reg11);
-    BUTTERFLY_4(reg8, reg10, reg11, reg5, loc0, reg4, reg9, loc1);
-
-    reg10 = loc0;
-    reg11 = loc1;
-
-    VP9_DOTP_CONST_PAIR(reg3, reg13, cospi_16_64, cospi_16_64, reg3, reg13);
-    BUTTERFLY_4(reg12, reg14, reg13, reg3, reg8, reg6, reg7, reg5);
-    reg13 = loc2;
-
-    /* Transpose and store the output */
-    reg12 = tmp5;
-    reg14 = tmp6;
-    reg3 = tmp7;
-
-    SRARI_H4_SH(reg0, reg2, reg4, reg6, 6);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, reg0, reg2, reg4, reg6);
-    dst += (4 * dst_stride);
-    SRARI_H4_SH(reg8, reg10, reg12, reg14, 6);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, reg8, reg10, reg12, reg14);
-    dst += (4 * dst_stride);
-    SRARI_H4_SH(reg3, reg13, reg11, reg5, 6);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, reg3, reg13, reg11, reg5);
-    dst += (4 * dst_stride);
-    SRARI_H4_SH(reg7, reg9, reg1, reg15, 6);
-    VP9_ADDBLK_ST8x4_UB(dst, dst_stride, reg7, reg9, reg1, reg15);
-}
-
-static void vp9_idct16_1d_columns_msa(int16_t *input, int16_t *output)
-{
-    v8i16 loc0, loc1, loc2, loc3;
-    v8i16 reg0, reg2, reg4, reg6, reg8, reg10, reg12, reg14;
-    v8i16 reg3, reg13, reg11, reg5, reg7, reg9, reg1, reg15;
-    v8i16 tmp5, tmp6, tmp7;
-    v8i16 zero = { 0 };
-
-    /* load up 8x16 */
-    LD_SH16(input, 16,
-            reg0, reg1, reg2, reg3, reg4, reg5, reg6, reg7,
-            reg8, reg9, reg10, reg11, reg12, reg13, reg14, reg15);
-
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 16);
-    input += 16 * 8;
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 16);
-
-    VP9_DOTP_CONST_PAIR(reg2, reg14, cospi_28_64, cospi_4_64, reg2, reg14);
-    VP9_DOTP_CONST_PAIR(reg10, reg6, cospi_12_64, cospi_20_64, reg10, reg6);
-    BUTTERFLY_4(reg2, reg14, reg6, reg10, loc0, loc1, reg14, reg2);
-    VP9_DOTP_CONST_PAIR(reg14, reg2, cospi_16_64, cospi_16_64, loc2, loc3);
-    VP9_DOTP_CONST_PAIR(reg0, reg8, cospi_16_64, cospi_16_64, reg0, reg8);
-    VP9_DOTP_CONST_PAIR(reg4, reg12, cospi_24_64, cospi_8_64, reg4, reg12);
-    BUTTERFLY_4(reg8, reg0, reg4, reg12, reg2, reg6, reg10, reg14);
-
-    reg0 = reg2 - loc1;
-    reg2 = reg2 + loc1;
-    reg12 = reg14 - loc0;
-    reg14 = reg14 + loc0;
-    reg4 = reg6 - loc3;
-    reg6 = reg6 + loc3;
-    reg8 = reg10 - loc2;
-    reg10 = reg10 + loc2;
-
-    /* stage 2 */
-    VP9_DOTP_CONST_PAIR(reg1, reg15, cospi_30_64, cospi_2_64, reg1, reg15);
-    VP9_DOTP_CONST_PAIR(reg9, reg7, cospi_14_64, cospi_18_64, loc2, loc3);
-
-    reg9 = reg1 - loc2;
-    reg1 = reg1 + loc2;
-    reg7 = reg15 - loc3;
-    reg15 = reg15 + loc3;
-
-    VP9_DOTP_CONST_PAIR(reg5, reg11, cospi_22_64, cospi_10_64, reg5, reg11);
-    VP9_DOTP_CONST_PAIR(reg13, reg3, cospi_6_64, cospi_26_64, loc0, loc1);
-    BUTTERFLY_4(loc0, loc1, reg11, reg5, reg13, reg3, reg11, reg5);
-
-    loc1 = reg15 + reg3;
-    reg3 = reg15 - reg3;
-    loc2 = reg2 + loc1;
-    reg15 = reg2 - loc1;
-
-    loc1 = reg1 + reg13;
-    reg13 = reg1 - reg13;
-    loc0 = reg0 + loc1;
-    loc1 = reg0 - loc1;
-    tmp6 = loc0;
-    tmp7 = loc1;
-    reg0 = loc2;
-
-    VP9_DOTP_CONST_PAIR(reg7, reg9, cospi_24_64, cospi_8_64, reg7, reg9);
-    VP9_DOTP_CONST_PAIR((-reg5), (-reg11), cospi_8_64, cospi_24_64, reg5,
-                        reg11);
-
-    loc0 = reg9 + reg5;
-    reg5 = reg9 - reg5;
-    reg2 = reg6 + loc0;
-    reg1 = reg6 - loc0;
-
-    loc0 = reg7 + reg11;
-    reg11 = reg7 - reg11;
-    loc1 = reg4 + loc0;
-    loc2 = reg4 - loc0;
-
-    tmp5 = loc1;
-
-    VP9_DOTP_CONST_PAIR(reg5, reg11, cospi_16_64, cospi_16_64, reg5, reg11);
-    BUTTERFLY_4(reg8, reg10, reg11, reg5, loc0, reg4, reg9, loc1);
-
-    reg10 = loc0;
-    reg11 = loc1;
-
-    VP9_DOTP_CONST_PAIR(reg3, reg13, cospi_16_64, cospi_16_64, reg3, reg13);
-    BUTTERFLY_4(reg12, reg14, reg13, reg3, reg8, reg6, reg7, reg5);
-    reg13 = loc2;
-
-    /* Transpose and store the output */
-    reg12 = tmp5;
-    reg14 = tmp6;
-    reg3 = tmp7;
-
-    /* transpose block */
-    TRANSPOSE8x8_SH_SH(reg0, reg2, reg4, reg6, reg8, reg10, reg12, reg14,
-                       reg0, reg2, reg4, reg6, reg8, reg10, reg12, reg14);
-    ST_SH4(reg0, reg2, reg4, reg6, output, 16);
-    ST_SH4(reg8, reg10, reg12, reg14, (output + 4 * 16), 16);
-
-    /* transpose block */
-    TRANSPOSE8x8_SH_SH(reg3, reg13, reg11, reg5, reg7, reg9, reg1, reg15,
-                       reg3, reg13, reg11, reg5, reg7, reg9, reg1, reg15);
-    ST_SH4(reg3, reg13, reg11, reg5, (output + 8), 16);
-    ST_SH4(reg7, reg9, reg1, reg15, (output + 8 + 4 * 16), 16);
-}
-
-static void vp9_idct16x16_1_add_msa(int16_t *input, uint8_t *dst,
-                                    int32_t dst_stride)
-{
-    uint8_t i;
-    int16_t out;
-    v8i16 vec, res0, res1, res2, res3, res4, res5, res6, res7;
-    v16u8 dst0, dst1, dst2, dst3, tmp0, tmp1, tmp2, tmp3;
-
-    out = ROUND_POWER_OF_TWO((input[0] * cospi_16_64), VP9_DCT_CONST_BITS);
-    out = ROUND_POWER_OF_TWO((out * cospi_16_64), VP9_DCT_CONST_BITS);
-    out = ROUND_POWER_OF_TWO(out, 6);
-    input[0] = 0;
-
-    vec = __msa_fill_h(out);
-
-    for (i = 4; i--;) {
-        LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-        UNPCK_UB_SH(dst0, res0, res4);
-        UNPCK_UB_SH(dst1, res1, res5);
-        UNPCK_UB_SH(dst2, res2, res6);
-        UNPCK_UB_SH(dst3, res3, res7);
-        ADD4(res0, vec, res1, vec, res2, vec, res3, vec, res0, res1, res2,
-             res3);
-        ADD4(res4, vec, res5, vec, res6, vec, res7, vec, res4, res5, res6,
-             res7);
-        CLIP_SH8_0_255(res0, res1, res2, res3, res4, res5, res6, res7);
-        PCKEV_B4_UB(res4, res0, res5, res1, res6, res2, res7, res3,
-                    tmp0, tmp1, tmp2, tmp3);
-        ST_UB4(tmp0, tmp1, tmp2, tmp3, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void vp9_idct16x16_10_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                               int32_t dst_stride)
-{
-    int32_t i;
-    int16_t out_arr[16 * 16] ALLOC_ALIGNED(ALIGNMENT);
-    int16_t *out = out_arr;
-
-    /* transform rows */
-    vp9_idct16_1d_columns_msa(input, out);
-
-    /* short case just considers top 4 rows as valid output */
-    out += 4 * 16;
-    for (i = 12; i--;) {
-        __asm__ volatile (
-            "sw     $zero,   0(%[out])     \n\t"
-            "sw     $zero,   4(%[out])     \n\t"
-            "sw     $zero,   8(%[out])     \n\t"
-            "sw     $zero,  12(%[out])     \n\t"
-            "sw     $zero,  16(%[out])     \n\t"
-            "sw     $zero,  20(%[out])     \n\t"
-            "sw     $zero,  24(%[out])     \n\t"
-            "sw     $zero,  28(%[out])     \n\t"
-
-            :
-            : [out] "r" (out)
-        );
-
-        out += 16;
-    }
-
-    out = out_arr;
-
-    /* transform columns */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_idct16_1d_columns_addblk_msa((out + (i << 3)), (dst + (i << 3)),
-                                         dst_stride);
-    }
-}
-
-static void vp9_idct16x16_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                            int32_t dst_stride)
-{
-    int32_t i;
-    int16_t out_arr[16 * 16] ALLOC_ALIGNED(ALIGNMENT);
-    int16_t *out = out_arr;
-
-    /* transform rows */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_idct16_1d_columns_msa((input + (i << 3)), (out + (i << 7)));
-    }
-
-    /* transform columns */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_idct16_1d_columns_addblk_msa((out + (i << 3)), (dst + (i << 3)),
-                                         dst_stride);
-    }
-}
-
-static void vp9_iadst16_1d_columns_msa(int16_t *input, int16_t *output)
-{
-    v8i16 r0, r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15;
-    v8i16 l0, l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14, l15;
-    v8i16 zero = { 0 };
-
-    /* load input data */
-    LD_SH16(input, 16,
-            l0, l1, l2, l3, l4, l5, l6, l7,
-            l8, l9, l10, l11, l12, l13, l14, l15);
-
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 16);
-    input += 16 * 8;
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, input, 16);
-
-    /* ADST in horizontal */
-    VP9_IADST8x16_1D(l0, l1, l2, l3, l4, l5, l6, l7,
-                     l8, l9, l10, l11, l12, l13, l14, l15,
-                     r0, r1, r2, r3, r4, r5, r6, r7,
-                     r8, r9, r10, r11, r12, r13, r14, r15);
-
-    l1 = -r8;
-    l3 = -r4;
-    l13 = -r13;
-    l15 = -r1;
-
-    TRANSPOSE8x8_SH_SH(r0, l1, r12, l3, r6, r14, r10, r2,
-                       l0, l1, l2, l3, l4, l5, l6, l7);
-    ST_SH8(l0, l1, l2, l3, l4, l5, l6, l7, output, 16);
-    TRANSPOSE8x8_SH_SH(r3, r11, r15, r7, r5, l13, r9, l15,
-                       l8, l9, l10, l11, l12, l13, l14, l15);
-    ST_SH8(l8, l9, l10, l11, l12, l13, l14, l15, (output + 8), 16);
-}
-
-static void vp9_iadst16_1d_columns_addblk_msa(int16_t *input, uint8_t *dst,
-                                              int32_t dst_stride)
-{
-    v8i16 v0, v2, v4, v6, k0, k1, k2, k3;
-    v8i16 r0, r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15;
-    v8i16 out0, out1, out2, out3, out4, out5, out6, out7;
-    v8i16 out8, out9, out10, out11, out12, out13, out14, out15;
-    v8i16 g0, g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, g11, g12, g13, g14, g15;
-    v8i16 h0, h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11;
-    v8i16 res0, res1, res2, res3, res4, res5, res6, res7;
-    v8i16 res8, res9, res10, res11, res12, res13, res14, res15;
-    v16u8 dst0, dst1, dst2, dst3, dst4, dst5, dst6, dst7;
-    v16u8 dst8, dst9, dst10, dst11, dst12, dst13, dst14, dst15;
-    v16i8 zero = { 0 };
-
-    r0 = LD_SH(input + 0 * 16);
-    r3 = LD_SH(input + 3 * 16);
-    r4 = LD_SH(input + 4 * 16);
-    r7 = LD_SH(input + 7 * 16);
-    r8 = LD_SH(input + 8 * 16);
-    r11 = LD_SH(input + 11 * 16);
-    r12 = LD_SH(input + 12 * 16);
-    r15 = LD_SH(input + 15 * 16);
-
-    /* stage 1 */
-    k0 = VP9_SET_COSPI_PAIR(cospi_1_64, cospi_31_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_31_64, -cospi_1_64);
-    k2 = VP9_SET_COSPI_PAIR(cospi_17_64, cospi_15_64);
-    k3 = VP9_SET_COSPI_PAIR(cospi_15_64, -cospi_17_64);
-    VP9_MADD_BF(r15, r0, r7, r8, k0, k1, k2, k3, g0, g1, g2, g3);
-    k0 = VP9_SET_COSPI_PAIR(cospi_9_64, cospi_23_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_23_64, -cospi_9_64);
-    k2 = VP9_SET_COSPI_PAIR(cospi_25_64, cospi_7_64);
-    k3 = VP9_SET_COSPI_PAIR(cospi_7_64, -cospi_25_64);
-    VP9_MADD_BF(r11, r4, r3, r12, k0, k1, k2, k3, g8, g9, g10, g11);
-    BUTTERFLY_4(g0, g2, g10, g8, h8, h9, v2, v0);
-    k0 = VP9_SET_COSPI_PAIR(cospi_4_64, cospi_28_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_28_64, -cospi_4_64);
-    k2 = VP9_SET_COSPI_PAIR(-cospi_28_64, cospi_4_64);
-    VP9_MADD_BF(g1, g3, g9, g11, k0, k1, k2, k0, h0, h1, h2, h3);
-
-    r1 = LD_SH(input + 1 * 16);
-    r2 = LD_SH(input + 2 * 16);
-    r5 = LD_SH(input + 5 * 16);
-    r6 = LD_SH(input + 6 * 16);
-    r9 = LD_SH(input + 9 * 16);
-    r10 = LD_SH(input + 10 * 16);
-    r13 = LD_SH(input + 13 * 16);
-    r14 = LD_SH(input + 14 * 16);
-
-    k0 = VP9_SET_COSPI_PAIR(cospi_5_64, cospi_27_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_27_64, -cospi_5_64);
-    k2 = VP9_SET_COSPI_PAIR(cospi_21_64, cospi_11_64);
-    k3 = VP9_SET_COSPI_PAIR(cospi_11_64, -cospi_21_64);
-    VP9_MADD_BF(r13, r2, r5, r10, k0, k1, k2, k3, g4, g5, g6, g7);
-    k0 = VP9_SET_COSPI_PAIR(cospi_13_64, cospi_19_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_19_64, -cospi_13_64);
-    k2 = VP9_SET_COSPI_PAIR(cospi_29_64, cospi_3_64);
-    k3 = VP9_SET_COSPI_PAIR(cospi_3_64, -cospi_29_64);
-    VP9_MADD_BF(r9, r6, r1, r14, k0, k1, k2, k3, g12, g13, g14, g15);
-    BUTTERFLY_4(g4, g6, g14, g12, h10, h11, v6, v4);
-    BUTTERFLY_4(h8, h9, h11, h10, out0, out1, h11, h10);
-    out1 = -out1;
-    SRARI_H2_SH(out0, out1, 6);
-    dst0 = LD_UB(dst + 0 * dst_stride);
-    dst1 = LD_UB(dst + 15 * dst_stride);
-    ILVR_B2_SH(zero, dst0, zero, dst1, res0, res1);
-    ADD2(res0, out0, res1, out1, res0, res1);
-    CLIP_SH2_0_255(res0, res1);
-    PCKEV_B2_SH(res0, res0, res1, res1, res0, res1);
-    ST_D1(res0, 0, dst);
-    ST_D1(res1, 0, dst + 15 * dst_stride);
-
-    k0 = VP9_SET_COSPI_PAIR(cospi_12_64, cospi_20_64);
-    k1 = VP9_SET_COSPI_PAIR(-cospi_20_64, cospi_12_64);
-    k2 = VP9_SET_COSPI_PAIR(cospi_20_64, -cospi_12_64);
-    VP9_MADD_BF(g7, g5, g15, g13, k0, k1, k2, k0, h4, h5, h6, h7);
-    BUTTERFLY_4(h0, h2, h6, h4, out8, out9, out11, out10);
-    out8 = -out8;
-
-    SRARI_H2_SH(out8, out9, 6);
-    dst8 = LD_UB(dst + 1 * dst_stride);
-    dst9 = LD_UB(dst + 14 * dst_stride);
-    ILVR_B2_SH(zero, dst8, zero, dst9, res8, res9);
-    ADD2(res8, out8, res9, out9, res8, res9);
-    CLIP_SH2_0_255(res8, res9);
-    PCKEV_B2_SH(res8, res8, res9, res9, res8, res9);
-    ST_D1(res8, 0, dst + dst_stride);
-    ST_D1(res9, 0, dst + 14 * dst_stride);
-
-    k0 = VP9_SET_COSPI_PAIR(cospi_8_64, cospi_24_64);
-    k1 = VP9_SET_COSPI_PAIR(cospi_24_64, -cospi_8_64);
-    k2 = VP9_SET_COSPI_PAIR(-cospi_24_64, cospi_8_64);
-    VP9_MADD_BF(v0, v2, v4, v6, k0, k1, k2, k0, out4, out6, out5, out7);
-    out4 = -out4;
-    SRARI_H2_SH(out4, out5, 6);
-    dst4 = LD_UB(dst + 3 * dst_stride);
-    dst5 = LD_UB(dst + 12 * dst_stride);
-    ILVR_B2_SH(zero, dst4, zero, dst5, res4, res5);
-    ADD2(res4, out4, res5, out5, res4, res5);
-    CLIP_SH2_0_255(res4, res5);
-    PCKEV_B2_SH(res4, res4, res5, res5, res4, res5);
-    ST_D1(res4, 0, dst + 3 * dst_stride);
-    ST_D1(res5, 0, dst + 12 * dst_stride);
-
-    VP9_MADD_BF(h1, h3, h5, h7, k0, k1, k2, k0, out12, out14, out13, out15);
-    out13 = -out13;
-    SRARI_H2_SH(out12, out13, 6);
-    dst12 = LD_UB(dst + 2 * dst_stride);
-    dst13 = LD_UB(dst + 13 * dst_stride);
-    ILVR_B2_SH(zero, dst12, zero, dst13, res12, res13);
-    ADD2(res12, out12, res13, out13, res12, res13);
-    CLIP_SH2_0_255(res12, res13);
-    PCKEV_B2_SH(res12, res12, res13, res13, res12, res13);
-    ST_D1(res12, 0, dst + 2 * dst_stride);
-    ST_D1(res13, 0, dst + 13 * dst_stride);
-
-    k0 = VP9_SET_COSPI_PAIR(cospi_16_64, cospi_16_64);
-    k3 = VP9_SET_COSPI_PAIR(-cospi_16_64, cospi_16_64);
-    VP9_MADD_SHORT(out6, out7, k0, k3, out6, out7);
-    SRARI_H2_SH(out6, out7, 6);
-    dst6 = LD_UB(dst + 4 * dst_stride);
-    dst7 = LD_UB(dst + 11 * dst_stride);
-    ILVR_B2_SH(zero, dst6, zero, dst7, res6, res7);
-    ADD2(res6, out6, res7, out7, res6, res7);
-    CLIP_SH2_0_255(res6, res7);
-    PCKEV_B2_SH(res6, res6, res7, res7, res6, res7);
-    ST_D1(res6, 0, dst + 4 * dst_stride);
-    ST_D1(res7, 0, dst + 11 * dst_stride);
-
-    VP9_MADD_SHORT(out10, out11, k0, k3, out10, out11);
-    SRARI_H2_SH(out10, out11, 6);
-    dst10 = LD_UB(dst + 6 * dst_stride);
-    dst11 = LD_UB(dst + 9 * dst_stride);
-    ILVR_B2_SH(zero, dst10, zero, dst11, res10, res11);
-    ADD2(res10, out10, res11, out11, res10, res11);
-    CLIP_SH2_0_255(res10, res11);
-    PCKEV_B2_SH(res10, res10, res11, res11, res10, res11);
-    ST_D1(res10, 0, dst + 6 * dst_stride);
-    ST_D1(res11, 0, dst + 9 * dst_stride);
-
-    k1 = VP9_SET_COSPI_PAIR(-cospi_16_64, -cospi_16_64);
-    k2 = VP9_SET_COSPI_PAIR(cospi_16_64, -cospi_16_64);
-    VP9_MADD_SHORT(h10, h11, k1, k2, out2, out3);
-    SRARI_H2_SH(out2, out3, 6);
-    dst2 = LD_UB(dst + 7 * dst_stride);
-    dst3 = LD_UB(dst + 8 * dst_stride);
-    ILVR_B2_SH(zero, dst2, zero, dst3, res2, res3);
-    ADD2(res2, out2, res3, out3, res2, res3);
-    CLIP_SH2_0_255(res2, res3);
-    PCKEV_B2_SH(res2, res2, res3, res3, res2, res3);
-    ST_D1(res2, 0, dst + 7 * dst_stride);
-    ST_D1(res3, 0, dst + 8 * dst_stride);
-
-    VP9_MADD_SHORT(out14, out15, k1, k2, out14, out15);
-    SRARI_H2_SH(out14, out15, 6);
-    dst14 = LD_UB(dst + 5 * dst_stride);
-    dst15 = LD_UB(dst + 10 * dst_stride);
-    ILVR_B2_SH(zero, dst14, zero, dst15, res14, res15);
-    ADD2(res14, out14, res15, out15, res14, res15);
-    CLIP_SH2_0_255(res14, res15);
-    PCKEV_B2_SH(res14, res14, res15, res15, res14, res15);
-    ST_D1(res14, 0, dst + 5 * dst_stride);
-    ST_D1(res15, 0, dst + 10 * dst_stride);
-}
-
-static void vp9_iadst16x16_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    int16_t out_arr[16 * 16] ALLOC_ALIGNED(ALIGNMENT);
-    int16_t *out = out_arr;
-    int32_t i;
-
-    /* transform rows */
-    for (i = 0; i < 2; i++) {
-        /* process 16 * 8 block */
-        vp9_iadst16_1d_columns_msa((input + (i << 3)), (out + (i << 7)));
-    }
-
-    /* transform columns */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_iadst16_1d_columns_addblk_msa((out + (i << 3)), (dst + (i << 3)),
-                                          dst_stride);
-    }
-}
-
-static void vp9_iadst_idct_16x16_add_msa(int16_t *input, uint8_t *dst,
-                                         int32_t dst_stride, int32_t eob)
-{
-    int32_t i;
-    int16_t out[16 * 16];
-    int16_t *out_ptr = &out[0];
-
-    /* transform rows */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_iadst16_1d_columns_msa((input + (i << 3)), (out_ptr + (i << 7)));
-    }
-
-    /* transform columns */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_idct16_1d_columns_addblk_msa((out_ptr + (i << 3)),
-                                         (dst + (i << 3)), dst_stride);
-    }
-}
-
-static void vp9_idct_iadst_16x16_add_msa(int16_t *input, uint8_t *dst,
-                                         int32_t dst_stride, int32_t eob)
-{
-    int32_t i;
-    int16_t out[16 * 16];
-    int16_t *out_ptr = &out[0];
-
-    /* transform rows */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_idct16_1d_columns_msa((input + (i << 3)), (out_ptr + (i << 7)));
-    }
-
-    /* transform columns */
-    for (i = 0; i < 2; i++) {
-        /* process 8 * 16 block */
-        vp9_iadst16_1d_columns_addblk_msa((out_ptr + (i << 3)),
-                                          (dst + (i << 3)), dst_stride);
-    }
-}
-
-static void vp9_idct_butterfly_transpose_store(int16_t *tmp_buf,
-                                               int16_t *tmp_eve_buf,
-                                               int16_t *tmp_odd_buf,
-                                               int16_t *dst)
-{
-    v8i16 vec0, vec1, vec2, vec3, loc0, loc1, loc2, loc3;
-    v8i16 m0, m1, m2, m3, m4, m5, m6, m7, n0, n1, n2, n3, n4, n5, n6, n7;
-
-    /* FINAL BUTTERFLY : Dependency on Even & Odd */
-    vec0 = LD_SH(tmp_odd_buf);
-    vec1 = LD_SH(tmp_odd_buf + 9 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 14 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 6 * 8);
-    loc0 = LD_SH(tmp_eve_buf);
-    loc1 = LD_SH(tmp_eve_buf + 8 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 4 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 12 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, m0, m4, m2, m6);
-
-    ST_SH((loc0 - vec3), (tmp_buf + 31 * 8));
-    ST_SH((loc1 - vec2), (tmp_buf + 23 * 8));
-    ST_SH((loc2 - vec1), (tmp_buf + 27 * 8));
-    ST_SH((loc3 - vec0), (tmp_buf + 19 * 8));
-
-    /* Load 8 & Store 8 */
-    vec0 = LD_SH(tmp_odd_buf + 4 * 8);
-    vec1 = LD_SH(tmp_odd_buf + 13 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 10 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 3 * 8);
-    loc0 = LD_SH(tmp_eve_buf + 2 * 8);
-    loc1 = LD_SH(tmp_eve_buf + 10 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 6 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 14 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, m1, m5, m3, m7);
-
-    ST_SH((loc0 - vec3), (tmp_buf + 29 * 8));
-    ST_SH((loc1 - vec2), (tmp_buf + 21 * 8));
-    ST_SH((loc2 - vec1), (tmp_buf + 25 * 8));
-    ST_SH((loc3 - vec0), (tmp_buf + 17 * 8));
-
-    /* Load 8 & Store 8 */
-    vec0 = LD_SH(tmp_odd_buf + 2 * 8);
-    vec1 = LD_SH(tmp_odd_buf + 11 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 12 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 7 * 8);
-    loc0 = LD_SH(tmp_eve_buf + 1 * 8);
-    loc1 = LD_SH(tmp_eve_buf + 9 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 5 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 13 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, n0, n4, n2, n6);
-
-    ST_SH((loc0 - vec3), (tmp_buf + 30 * 8));
-    ST_SH((loc1 - vec2), (tmp_buf + 22 * 8));
-    ST_SH((loc2 - vec1), (tmp_buf + 26 * 8));
-    ST_SH((loc3 - vec0), (tmp_buf + 18 * 8));
-
-    /* Load 8 & Store 8 */
-    vec0 = LD_SH(tmp_odd_buf + 5 * 8);
-    vec1 = LD_SH(tmp_odd_buf + 15 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 8 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 1 * 8);
-    loc0 = LD_SH(tmp_eve_buf + 3 * 8);
-    loc1 = LD_SH(tmp_eve_buf + 11 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 7 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 15 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, n1, n5, n3, n7);
-
-    ST_SH((loc0 - vec3), (tmp_buf + 28 * 8));
-    ST_SH((loc1 - vec2), (tmp_buf + 20 * 8));
-    ST_SH((loc2 - vec1), (tmp_buf + 24 * 8));
-    ST_SH((loc3 - vec0), (tmp_buf + 16 * 8));
-
-    /* Transpose : 16 vectors */
-    /* 1st & 2nd 8x8 */
-    TRANSPOSE8x8_SH_SH(m0, n0, m1, n1, m2, n2, m3, n3,
-                       m0, n0, m1, n1, m2, n2, m3, n3);
-    ST_SH4(m0, n0, m1, n1, (dst + 0), 32);
-    ST_SH4(m2, n2, m3, n3, (dst + 4 * 32), 32);
-
-    TRANSPOSE8x8_SH_SH(m4, n4, m5, n5, m6, n6, m7, n7,
-                       m4, n4, m5, n5, m6, n6, m7, n7);
-    ST_SH4(m4, n4, m5, n5, (dst + 8), 32);
-    ST_SH4(m6, n6, m7, n7, (dst + 8 + 4 * 32), 32);
-
-    /* 3rd & 4th 8x8 */
-    LD_SH8((tmp_buf + 8 * 16), 8, m0, n0, m1, n1, m2, n2, m3, n3);
-    LD_SH8((tmp_buf + 12 * 16), 8, m4, n4, m5, n5, m6, n6, m7, n7);
-    TRANSPOSE8x8_SH_SH(m0, n0, m1, n1, m2, n2, m3, n3,
-                       m0, n0, m1, n1, m2, n2, m3, n3);
-    ST_SH4(m0, n0, m1, n1, (dst + 16), 32);
-    ST_SH4(m2, n2, m3, n3, (dst + 16 + 4 * 32), 32);
-
-    TRANSPOSE8x8_SH_SH(m4, n4, m5, n5, m6, n6, m7, n7,
-                       m4, n4, m5, n5, m6, n6, m7, n7);
-    ST_SH4(m4, n4, m5, n5, (dst + 24), 32);
-    ST_SH4(m6, n6, m7, n7, (dst + 24 + 4 * 32), 32);
-}
-
-static void vp9_idct8x32_column_even_process_store(int16_t *tmp_buf,
-                                                   int16_t *tmp_eve_buf)
-{
-    v8i16 vec0, vec1, vec2, vec3, loc0, loc1, loc2, loc3;
-    v8i16 reg0, reg1, reg2, reg3, reg4, reg5, reg6, reg7;
-    v8i16 stp0, stp1, stp2, stp3, stp4, stp5, stp6, stp7;
-    v8i16 zero = { 0 };
-
-    /* Even stage 1 */
-    LD_SH8(tmp_buf, (4 * 32), reg0, reg1, reg2, reg3, reg4, reg5, reg6, reg7);
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, tmp_buf, (4 * 32));
-    tmp_buf += (2 * 32);
-
-    VP9_DOTP_CONST_PAIR(reg1, reg7, cospi_28_64, cospi_4_64, reg1, reg7);
-    VP9_DOTP_CONST_PAIR(reg5, reg3, cospi_12_64, cospi_20_64, reg5, reg3);
-    BUTTERFLY_4(reg1, reg7, reg3, reg5, vec1, vec3, vec2, vec0);
-    VP9_DOTP_CONST_PAIR(vec2, vec0, cospi_16_64, cospi_16_64, loc2, loc3);
-
-    loc1 = vec3;
-    loc0 = vec1;
-
-    VP9_DOTP_CONST_PAIR(reg0, reg4, cospi_16_64, cospi_16_64, reg0, reg4);
-    VP9_DOTP_CONST_PAIR(reg2, reg6, cospi_24_64, cospi_8_64, reg2, reg6);
-    BUTTERFLY_4(reg4, reg0, reg2, reg6, vec1, vec3, vec2, vec0);
-    BUTTERFLY_4(vec0, vec1, loc1, loc0, stp3, stp0, stp7, stp4);
-    BUTTERFLY_4(vec2, vec3, loc3, loc2, stp2, stp1, stp6, stp5);
-
-    /* Even stage 2 */
-    /* Load 8 */
-    LD_SH8(tmp_buf, (4 * 32), reg0, reg1, reg2, reg3, reg4, reg5, reg6, reg7);
-    ST_SH8(zero, zero, zero, zero, zero, zero, zero, zero, tmp_buf, (4 * 32));
-
-    VP9_DOTP_CONST_PAIR(reg0, reg7, cospi_30_64, cospi_2_64, reg0, reg7);
-    VP9_DOTP_CONST_PAIR(reg4, reg3, cospi_14_64, cospi_18_64, reg4, reg3);
-    VP9_DOTP_CONST_PAIR(reg2, reg5, cospi_22_64, cospi_10_64, reg2, reg5);
-    VP9_DOTP_CONST_PAIR(reg6, reg1, cospi_6_64, cospi_26_64, reg6, reg1);
-
-    vec0 = reg0 + reg4;
-    reg0 = reg0 - reg4;
-    reg4 = reg6 + reg2;
-    reg6 = reg6 - reg2;
-    reg2 = reg1 + reg5;
-    reg1 = reg1 - reg5;
-    reg5 = reg7 + reg3;
-    reg7 = reg7 - reg3;
-    reg3 = vec0;
-
-    vec1 = reg2;
-    reg2 = reg3 + reg4;
-    reg3 = reg3 - reg4;
-    reg4 = reg5 - vec1;
-    reg5 = reg5 + vec1;
-
-    VP9_DOTP_CONST_PAIR(reg7, reg0, cospi_24_64, cospi_8_64, reg0, reg7);
-    VP9_DOTP_CONST_PAIR((-reg6), reg1, cospi_24_64, cospi_8_64, reg6, reg1);
-
-    vec0 = reg0 - reg6;
-    reg0 = reg0 + reg6;
-    vec1 = reg7 - reg1;
-    reg7 = reg7 + reg1;
-
-    VP9_DOTP_CONST_PAIR(vec1, vec0, cospi_16_64, cospi_16_64, reg6, reg1);
-    VP9_DOTP_CONST_PAIR(reg4, reg3, cospi_16_64, cospi_16_64, reg3, reg4);
-
-    /* Even stage 3 : Dependency on Even stage 1 & Even stage 2 */
-    /* Store 8 */
-    BUTTERFLY_4(stp0, stp1, reg7, reg5, loc1, loc3, loc2, loc0);
-    ST_SH2(loc1, loc3, tmp_eve_buf, 8);
-    ST_SH2(loc2, loc0, (tmp_eve_buf + 14 * 8), 8);
-
-    BUTTERFLY_4(stp2, stp3, reg4, reg1, loc1, loc3, loc2, loc0);
-    ST_SH2(loc1, loc3, (tmp_eve_buf + 2 * 8), 8);
-    ST_SH2(loc2, loc0, (tmp_eve_buf + 12 * 8), 8);
-
-    /* Store 8 */
-    BUTTERFLY_4(stp4, stp5, reg6, reg3, loc1, loc3, loc2, loc0);
-    ST_SH2(loc1, loc3, (tmp_eve_buf + 4 * 8), 8);
-    ST_SH2(loc2, loc0, (tmp_eve_buf + 10 * 8), 8);
-
-    BUTTERFLY_4(stp6, stp7, reg2, reg0, loc1, loc3, loc2, loc0);
-    ST_SH2(loc1, loc3, (tmp_eve_buf + 6 * 8), 8);
-    ST_SH2(loc2, loc0, (tmp_eve_buf + 8 * 8), 8);
-}
-
-static void vp9_idct8x32_column_odd_process_store(int16_t *tmp_buf,
-                                                  int16_t *tmp_odd_buf)
-{
-    v8i16 vec0, vec1, vec2, vec3, loc0, loc1, loc2, loc3;
-    v8i16 reg0, reg1, reg2, reg3, reg4, reg5, reg6, reg7;
-    v8i16 zero = { 0 };
-
-    /* Odd stage 1 */
-    reg0 = LD_SH(tmp_buf + 32);
-    reg1 = LD_SH(tmp_buf + 7 * 32);
-    reg2 = LD_SH(tmp_buf + 9 * 32);
-    reg3 = LD_SH(tmp_buf + 15 * 32);
-    reg4 = LD_SH(tmp_buf + 17 * 32);
-    reg5 = LD_SH(tmp_buf + 23 * 32);
-    reg6 = LD_SH(tmp_buf + 25 * 32);
-    reg7 = LD_SH(tmp_buf + 31 * 32);
-
-    ST_SH(zero, tmp_buf + 32);
-    ST_SH(zero, tmp_buf + 7 * 32);
-    ST_SH(zero, tmp_buf + 9 * 32);
-    ST_SH(zero, tmp_buf + 15 * 32);
-    ST_SH(zero, tmp_buf + 17 * 32);
-    ST_SH(zero, tmp_buf + 23 * 32);
-    ST_SH(zero, tmp_buf + 25 * 32);
-    ST_SH(zero, tmp_buf + 31 * 32);
-
-    VP9_DOTP_CONST_PAIR(reg0, reg7, cospi_31_64, cospi_1_64, reg0, reg7);
-    VP9_DOTP_CONST_PAIR(reg4, reg3, cospi_15_64, cospi_17_64, reg3, reg4);
-    VP9_DOTP_CONST_PAIR(reg2, reg5, cospi_23_64, cospi_9_64, reg2, reg5);
-    VP9_DOTP_CONST_PAIR(reg6, reg1, cospi_7_64, cospi_25_64, reg1, reg6);
-
-    vec0 = reg0 + reg3;
-    reg0 = reg0 - reg3;
-    reg3 = reg7 + reg4;
-    reg7 = reg7 - reg4;
-    reg4 = reg1 + reg2;
-    reg1 = reg1 - reg2;
-    reg2 = reg6 + reg5;
-    reg6 = reg6 - reg5;
-    reg5 = vec0;
-
-    /* 4 Stores */
-    ADD2(reg5, reg4, reg3, reg2, vec0, vec1);
-    ST_SH2(vec0, vec1, (tmp_odd_buf + 4 * 8), 8);
-    SUB2(reg5, reg4, reg3, reg2, vec0, vec1);
-    VP9_DOTP_CONST_PAIR(vec1, vec0, cospi_24_64, cospi_8_64, vec0, vec1);
-    ST_SH2(vec0, vec1, tmp_odd_buf, 8);
-
-    /* 4 Stores */
-    VP9_DOTP_CONST_PAIR(reg7, reg0, cospi_28_64, cospi_4_64, reg0, reg7);
-    VP9_DOTP_CONST_PAIR(reg6, reg1, -cospi_4_64, cospi_28_64, reg1, reg6);
-    BUTTERFLY_4(reg0, reg7, reg6, reg1, vec0, vec1, vec2, vec3);
-    ST_SH2(vec0, vec1, (tmp_odd_buf + 6 * 8), 8);
-    VP9_DOTP_CONST_PAIR(vec2, vec3, cospi_24_64, cospi_8_64, vec2, vec3);
-    ST_SH2(vec2, vec3, (tmp_odd_buf + 2 * 8), 8);
-
-    /* Odd stage 2 */
-    /* 8 loads */
-    reg0 = LD_SH(tmp_buf + 3 * 32);
-    reg1 = LD_SH(tmp_buf + 5 * 32);
-    reg2 = LD_SH(tmp_buf + 11 * 32);
-    reg3 = LD_SH(tmp_buf + 13 * 32);
-    reg4 = LD_SH(tmp_buf + 19 * 32);
-    reg5 = LD_SH(tmp_buf + 21 * 32);
-    reg6 = LD_SH(tmp_buf + 27 * 32);
-    reg7 = LD_SH(tmp_buf + 29 * 32);
-
-    ST_SH(zero, tmp_buf + 3 * 32);
-    ST_SH(zero, tmp_buf + 5 * 32);
-    ST_SH(zero, tmp_buf + 11 * 32);
-    ST_SH(zero, tmp_buf + 13 * 32);
-    ST_SH(zero, tmp_buf + 19 * 32);
-    ST_SH(zero, tmp_buf + 21 * 32);
-    ST_SH(zero, tmp_buf + 27 * 32);
-    ST_SH(zero, tmp_buf + 29 * 32);
-
-    VP9_DOTP_CONST_PAIR(reg1, reg6, cospi_27_64, cospi_5_64, reg1, reg6);
-    VP9_DOTP_CONST_PAIR(reg5, reg2, cospi_11_64, cospi_21_64, reg2, reg5);
-    VP9_DOTP_CONST_PAIR(reg3, reg4, cospi_19_64, cospi_13_64, reg3, reg4);
-    VP9_DOTP_CONST_PAIR(reg7, reg0, cospi_3_64, cospi_29_64, reg0, reg7);
-
-    /* 4 Stores */
-    SUB4(reg1, reg2, reg6, reg5, reg0, reg3, reg7, reg4,
-         vec0, vec1, vec2, vec3);
-    VP9_DOTP_CONST_PAIR(vec1, vec0, cospi_12_64, cospi_20_64, loc0, loc1);
-    VP9_DOTP_CONST_PAIR(vec3, vec2, -cospi_20_64, cospi_12_64, loc2, loc3);
-    BUTTERFLY_4(loc2, loc3, loc1, loc0, vec0, vec1, vec3, vec2);
-    ST_SH2(vec0, vec1, (tmp_odd_buf + 12 * 8), 3 * 8);
-    VP9_DOTP_CONST_PAIR(vec3, vec2, -cospi_8_64, cospi_24_64, vec0, vec1);
-    ST_SH2(vec0, vec1, (tmp_odd_buf + 10 * 8), 8);
-
-    /* 4 Stores */
-    ADD4(reg0, reg3, reg1, reg2, reg5, reg6, reg4, reg7,
-         vec0, vec1, vec2, vec3);
-    BUTTERFLY_4(vec0, vec3, vec2, vec1, reg0, reg1, reg3, reg2);
-    ST_SH2(reg0, reg1, (tmp_odd_buf + 13 * 8), 8);
-    VP9_DOTP_CONST_PAIR(reg3, reg2, -cospi_8_64, cospi_24_64, reg0, reg1);
-    ST_SH2(reg0, reg1, (tmp_odd_buf + 8 * 8), 8);
-
-    /* Odd stage 3 : Dependency on Odd stage 1 & Odd stage 2 */
-    /* Load 8 & Store 8 */
-    LD_SH4(tmp_odd_buf, 8, reg0, reg1, reg2, reg3);
-    LD_SH4((tmp_odd_buf + 8 * 8), 8, reg4, reg5, reg6, reg7);
-
-    ADD4(reg0, reg4, reg1, reg5, reg2, reg6, reg3, reg7,
-         loc0, loc1, loc2, loc3);
-    ST_SH4(loc0, loc1, loc2, loc3, tmp_odd_buf, 8);
-
-    SUB2(reg0, reg4, reg1, reg5, vec0, vec1);
-    VP9_DOTP_CONST_PAIR(vec1, vec0, cospi_16_64, cospi_16_64, loc0, loc1);
-
-    SUB2(reg2, reg6, reg3, reg7, vec0, vec1);
-    VP9_DOTP_CONST_PAIR(vec1, vec0, cospi_16_64, cospi_16_64, loc2, loc3);
-    ST_SH4(loc0, loc1, loc2, loc3, (tmp_odd_buf + 8 * 8), 8);
-
-    /* Load 8 & Store 8 */
-    LD_SH4((tmp_odd_buf + 4 * 8), 8, reg1, reg2, reg0, reg3);
-    LD_SH4((tmp_odd_buf + 12 * 8), 8, reg4, reg5, reg6, reg7);
-
-    ADD4(reg0, reg4, reg1, reg5, reg2, reg6, reg3, reg7,
-         loc0, loc1, loc2, loc3);
-    ST_SH4(loc0, loc1, loc2, loc3, (tmp_odd_buf + 4 * 8), 8);
-
-    SUB2(reg0, reg4, reg3, reg7, vec0, vec1);
-    VP9_DOTP_CONST_PAIR(vec1, vec0, cospi_16_64, cospi_16_64, loc0, loc1);
-
-    SUB2(reg1, reg5, reg2, reg6, vec0, vec1);
-    VP9_DOTP_CONST_PAIR(vec1, vec0, cospi_16_64, cospi_16_64, loc2, loc3);
-    ST_SH4(loc0, loc1, loc2, loc3, (tmp_odd_buf + 12 * 8), 8);
-}
-
-static void vp9_idct8x32_column_butterfly_addblk(int16_t *tmp_eve_buf,
-                                                 int16_t *tmp_odd_buf,
-                                                 uint8_t *dst,
-                                                 int32_t dst_stride)
-{
-    v8i16 vec0, vec1, vec2, vec3, loc0, loc1, loc2, loc3;
-    v8i16 m0, m1, m2, m3, m4, m5, m6, m7, n0, n1, n2, n3, n4, n5, n6, n7;
-
-    /* FINAL BUTTERFLY : Dependency on Even & Odd */
-    vec0 = LD_SH(tmp_odd_buf);
-    vec1 = LD_SH(tmp_odd_buf + 9 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 14 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 6 * 8);
-    loc0 = LD_SH(tmp_eve_buf);
-    loc1 = LD_SH(tmp_eve_buf + 8 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 4 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 12 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, m0, m4, m2, m6);
-    SRARI_H4_SH(m0, m2, m4, m6, 6);
-    VP9_ADDBLK_ST8x4_UB(dst, (4 * dst_stride), m0, m2, m4, m6);
-
-    SUB4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, m6, m2, m4, m0);
-    SRARI_H4_SH(m0, m2, m4, m6, 6);
-    VP9_ADDBLK_ST8x4_UB((dst + 19 * dst_stride), (4 * dst_stride),
-                        m0, m2, m4, m6);
-
-    /* Load 8 & Store 8 */
-    vec0 = LD_SH(tmp_odd_buf + 4 * 8);
-    vec1 = LD_SH(tmp_odd_buf + 13 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 10 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 3 * 8);
-    loc0 = LD_SH(tmp_eve_buf + 2 * 8);
-    loc1 = LD_SH(tmp_eve_buf + 10 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 6 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 14 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, m1, m5, m3, m7);
-    SRARI_H4_SH(m1, m3, m5, m7, 6);
-    VP9_ADDBLK_ST8x4_UB((dst + 2 * dst_stride), (4 * dst_stride),
-                        m1, m3, m5, m7);
-
-    SUB4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, m7, m3, m5, m1);
-    SRARI_H4_SH(m1, m3, m5, m7, 6);
-    VP9_ADDBLK_ST8x4_UB((dst + 17 * dst_stride), (4 * dst_stride),
-                        m1, m3, m5, m7);
-
-    /* Load 8 & Store 8 */
-    vec0 = LD_SH(tmp_odd_buf + 2 * 8);
-    vec1 = LD_SH(tmp_odd_buf + 11 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 12 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 7 * 8);
-    loc0 = LD_SH(tmp_eve_buf + 1 * 8);
-    loc1 = LD_SH(tmp_eve_buf + 9 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 5 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 13 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, n0, n4, n2, n6);
-    SRARI_H4_SH(n0, n2, n4, n6, 6);
-    VP9_ADDBLK_ST8x4_UB((dst + 1 * dst_stride), (4 * dst_stride),
-                        n0, n2, n4, n6);
-
-    SUB4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, n6, n2, n4, n0);
-    SRARI_H4_SH(n0, n2, n4, n6, 6);
-    VP9_ADDBLK_ST8x4_UB((dst + 18 * dst_stride), (4 * dst_stride),
-                        n0, n2, n4, n6);
-
-    /* Load 8 & Store 8 */
-    vec0 = LD_SH(tmp_odd_buf + 5 * 8);
-    vec1 = LD_SH(tmp_odd_buf + 15 * 8);
-    vec2 = LD_SH(tmp_odd_buf + 8 * 8);
-    vec3 = LD_SH(tmp_odd_buf + 1 * 8);
-    loc0 = LD_SH(tmp_eve_buf + 3 * 8);
-    loc1 = LD_SH(tmp_eve_buf + 11 * 8);
-    loc2 = LD_SH(tmp_eve_buf + 7 * 8);
-    loc3 = LD_SH(tmp_eve_buf + 15 * 8);
-
-    ADD4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, n1, n5, n3, n7);
-    SRARI_H4_SH(n1, n3, n5, n7, 6);
-    VP9_ADDBLK_ST8x4_UB((dst + 3 * dst_stride), (4 * dst_stride),
-                        n1, n3, n5, n7);
-
-    SUB4(loc0, vec3, loc1, vec2, loc2, vec1, loc3, vec0, n7, n3, n5, n1);
-    SRARI_H4_SH(n1, n3, n5, n7, 6);
-    VP9_ADDBLK_ST8x4_UB((dst + 16 * dst_stride), (4 * dst_stride),
-                        n1, n3, n5, n7);
-}
-
-static void vp9_idct8x32_1d_columns_addblk_msa(int16_t *input, uint8_t *dst,
-                                               int32_t dst_stride)
-{
-    int16_t tmp_odd_buf[16 * 8] ALLOC_ALIGNED(ALIGNMENT);
-    int16_t tmp_eve_buf[16 * 8] ALLOC_ALIGNED(ALIGNMENT);
-
-    vp9_idct8x32_column_even_process_store(input, &tmp_eve_buf[0]);
-    vp9_idct8x32_column_odd_process_store(input, &tmp_odd_buf[0]);
-    vp9_idct8x32_column_butterfly_addblk(&tmp_eve_buf[0], &tmp_odd_buf[0],
-                                         dst, dst_stride);
-}
-
-static void vp9_idct8x32_1d_columns_msa(int16_t *input, int16_t *output,
-                                        int16_t *tmp_buf)
-{
-    int16_t tmp_odd_buf[16 * 8] ALLOC_ALIGNED(ALIGNMENT);
-    int16_t tmp_eve_buf[16 * 8] ALLOC_ALIGNED(ALIGNMENT);
-
-    vp9_idct8x32_column_even_process_store(input, &tmp_eve_buf[0]);
-    vp9_idct8x32_column_odd_process_store(input, &tmp_odd_buf[0]);
-    vp9_idct_butterfly_transpose_store(tmp_buf, &tmp_eve_buf[0],
-                                       &tmp_odd_buf[0], output);
-}
-
-static void vp9_idct32x32_1_add_msa(int16_t *input, uint8_t *dst,
-                                    int32_t dst_stride)
-{
-    int32_t i;
-    int16_t out;
-    v16u8 dst0, dst1, dst2, dst3, tmp0, tmp1, tmp2, tmp3;
-    v8i16 res0, res1, res2, res3, res4, res5, res6, res7, vec;
-
-    out = ROUND_POWER_OF_TWO((input[0] * cospi_16_64), VP9_DCT_CONST_BITS);
-    out = ROUND_POWER_OF_TWO((out * cospi_16_64), VP9_DCT_CONST_BITS);
-    out = ROUND_POWER_OF_TWO(out, 6);
-    input[0] = 0;
-
-    vec = __msa_fill_h(out);
-
-    for (i = 16; i--;) {
-        LD_UB2(dst, 16, dst0, dst1);
-        LD_UB2(dst + dst_stride, 16, dst2, dst3);
-
-        UNPCK_UB_SH(dst0, res0, res4);
-        UNPCK_UB_SH(dst1, res1, res5);
-        UNPCK_UB_SH(dst2, res2, res6);
-        UNPCK_UB_SH(dst3, res3, res7);
-        ADD4(res0, vec, res1, vec, res2, vec, res3, vec, res0, res1, res2,
-             res3);
-        ADD4(res4, vec, res5, vec, res6, vec, res7, vec, res4, res5, res6,
-             res7);
-        CLIP_SH8_0_255(res0, res1, res2, res3, res4, res5, res6, res7);
-        PCKEV_B4_UB(res4, res0, res5, res1, res6, res2, res7, res3,
-                    tmp0, tmp1, tmp2, tmp3);
-
-        ST_UB2(tmp0, tmp1, dst, 16);
-        dst += dst_stride;
-        ST_UB2(tmp2, tmp3, dst, 16);
-        dst += dst_stride;
-    }
-}
-
-static void vp9_idct32x32_34_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                               int32_t dst_stride)
-{
-    int32_t i;
-    int16_t out_arr[32 * 32] ALLOC_ALIGNED(ALIGNMENT);
-    int16_t *out_ptr = out_arr;
-    int16_t tmp_buf[8 * 32] ALLOC_ALIGNED(ALIGNMENT);
-
-    for (i = 32; i--;) {
-        __asm__ volatile (
-            "sw     $zero,       (%[out_ptr])     \n\t"
-            "sw     $zero,      4(%[out_ptr])     \n\t"
-            "sw     $zero,      8(%[out_ptr])     \n\t"
-            "sw     $zero,     12(%[out_ptr])     \n\t"
-            "sw     $zero,     16(%[out_ptr])     \n\t"
-            "sw     $zero,     20(%[out_ptr])     \n\t"
-            "sw     $zero,     24(%[out_ptr])     \n\t"
-            "sw     $zero,     28(%[out_ptr])     \n\t"
-            "sw     $zero,     32(%[out_ptr])     \n\t"
-            "sw     $zero,     36(%[out_ptr])     \n\t"
-            "sw     $zero,     40(%[out_ptr])     \n\t"
-            "sw     $zero,     44(%[out_ptr])     \n\t"
-            "sw     $zero,     48(%[out_ptr])     \n\t"
-            "sw     $zero,     52(%[out_ptr])     \n\t"
-            "sw     $zero,     56(%[out_ptr])     \n\t"
-            "sw     $zero,     60(%[out_ptr])     \n\t"
-
-            :
-            : [out_ptr] "r" (out_ptr)
-        );
-
-        out_ptr += 32;
-    }
-
-    out_ptr = out_arr;
-
-    /* process 8*32 block */
-    vp9_idct8x32_1d_columns_msa(input, out_ptr, &tmp_buf[0]);
-
-    /* transform columns */
-    for (i = 0; i < 4; i++) {
-        /* process 8*32 block */
-        vp9_idct8x32_1d_columns_addblk_msa((out_ptr + (i << 3)),
-                                           (dst + (i << 3)), dst_stride);
-    }
-}
-
-static void vp9_idct32x32_colcol_addblk_msa(int16_t *input, uint8_t *dst,
-                                            int32_t dst_stride)
-{
-    int32_t i;
-    int16_t out_arr[32 * 32] ALLOC_ALIGNED(ALIGNMENT);
-    int16_t *out_ptr = out_arr;
-    int16_t tmp_buf[8 * 32] ALLOC_ALIGNED(ALIGNMENT);
-
-    /* transform rows */
-    for (i = 0; i < 4; i++) {
-        /* process 8*32 block */
-        vp9_idct8x32_1d_columns_msa((input + (i << 3)), (out_ptr + (i << 8)),
-                                    &tmp_buf[0]);
-    }
-
-    /* transform columns */
-    for (i = 0; i < 4; i++) {
-        /* process 8*32 block */
-        vp9_idct8x32_1d_columns_addblk_msa((out_ptr + (i << 3)),
-                                           (dst + (i << 3)), dst_stride);
-    }
-}
-
-void ff_idct_idct_4x4_add_msa(uint8_t *dst, ptrdiff_t stride,
-                              int16_t *block, int eob)
-{
-    if (eob > 1) {
-        vp9_idct4x4_colcol_addblk_msa(block, dst, stride);
-    }
-    else {
-        vp9_idct4x4_1_add_msa(block, dst, stride);
-    }
-}
-
-void ff_idct_idct_8x8_add_msa(uint8_t *dst, ptrdiff_t stride,
-                              int16_t *block, int eob)
-{
-    if (eob == 1) {
-        vp9_idct8x8_1_add_msa(block, dst, stride);
-    }
-    else if (eob <= 12) {
-        vp9_idct8x8_12_colcol_addblk_msa(block, dst, stride);
-    }
-    else {
-        vp9_idct8x8_colcol_addblk_msa(block, dst, stride);
-    }
-}
-
-void ff_idct_idct_16x16_add_msa(uint8_t *dst, ptrdiff_t stride,
-                                int16_t *block, int eob)
-{
-    if (eob == 1) {
-        /* DC only DCT coefficient. */
-        vp9_idct16x16_1_add_msa(block, dst, stride);
-    }
-    else if (eob <= 10) {
-        vp9_idct16x16_10_colcol_addblk_msa(block, dst, stride);
-    }
-    else {
-        vp9_idct16x16_colcol_addblk_msa(block, dst, stride);
-    }
-}
-
-void ff_idct_idct_32x32_add_msa(uint8_t *dst, ptrdiff_t stride,
-                                int16_t *block, int eob)
-{
-    if (eob == 1) {
-        vp9_idct32x32_1_add_msa(block, dst, stride);
-    }
-    else if (eob <= 34) {
-        vp9_idct32x32_34_colcol_addblk_msa(block, dst, stride);
-    }
-    else {
-        vp9_idct32x32_colcol_addblk_msa(block, dst, stride);
-    }
-}
-
-void ff_iadst_iadst_4x4_add_msa(uint8_t *dst, ptrdiff_t stride,
-                                int16_t *block, int eob)
-{
-    vp9_iadst4x4_colcol_addblk_msa(block, dst, stride);
-}
-
-void ff_iadst_iadst_8x8_add_msa(uint8_t *dst, ptrdiff_t stride,
-                                int16_t *block, int eob)
-{
-    vp9_iadst8x8_colcol_addblk_msa(block, dst, stride);
-}
-
-void ff_iadst_iadst_16x16_add_msa(uint8_t *dst, ptrdiff_t stride,
-                                  int16_t *block, int eob)
-{
-    vp9_iadst16x16_colcol_addblk_msa(block, dst, stride);
-}
-
-void ff_idct_iadst_4x4_add_msa(uint8_t *dst, ptrdiff_t stride,
-                               int16_t *block, int eob)
-{
-    vp9_idct_iadst_4x4_add_msa(block, dst, stride, eob);
-}
-
-void ff_idct_iadst_8x8_add_msa(uint8_t *dst, ptrdiff_t stride,
-                               int16_t *block, int eob)
-{
-    vp9_idct_iadst_8x8_add_msa(block, dst, stride, eob);
-}
-
-void ff_idct_iadst_16x16_add_msa(uint8_t *dst, ptrdiff_t stride,
-                                 int16_t *block, int eob)
-{
-    vp9_idct_iadst_16x16_add_msa(block, dst, stride, eob);
-}
-
-void ff_iadst_idct_4x4_add_msa(uint8_t *dst, ptrdiff_t stride,
-                               int16_t *block, int eob)
-{
-    vp9_iadst_idct_4x4_add_msa(block, dst, stride, eob);
-}
-
-void ff_iadst_idct_8x8_add_msa(uint8_t *dst, ptrdiff_t stride,
-                               int16_t *block, int eob)
-{
-    vp9_iadst_idct_8x8_add_msa(block, dst, stride, eob);
-}
-
-void ff_iadst_idct_16x16_add_msa(uint8_t *dst, ptrdiff_t stride,
-                                 int16_t *block, int eob)
-{
-    vp9_iadst_idct_16x16_add_msa(block, dst, stride, eob);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Barbie Dreamhouse Adventures APK (MOD Unlocked) - The Ultimate Game for Barbie Fans.md b/spaces/congsaPfin/Manga-OCR/logs/Barbie Dreamhouse Adventures APK (MOD Unlocked) - The Ultimate Game for Barbie Fans.md
deleted file mode 100644
index a6438612e252857119a88455483e5427681b43f4..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Barbie Dreamhouse Adventures APK (MOD Unlocked) - The Ultimate Game for Barbie Fans.md	
+++ /dev/null
@@ -1,80 +0,0 @@
-<br />
-<h1>Download Barbie Dreamhouse Adventures APK (MOD Unlocked)</h1>
-<p>Do you love playing with Barbie dolls and designing your own dream house? If yes, then you will love Barbie Dreamhouse Adventures, a simulation game where you can create your own room, join fun activities with Barbie and her friends, and explore the city. But what if you want to enjoy all the features of the game without spending any money? Well, you can do that by downloading Barbie Dreamhouse Adventures APK (MOD Unlocked), a modified version of the game that gives you unlimited access to everything. In this article, we will tell you what is Barbie Dreamhouse Adventures, why you should download the APK file, and how to install it on your device.</p>
- <h2>What is Barbie Dreamhouse Adventures?</h2>
-<p>Barbie Dreamhouse Adventures is an exciting simulation game developed by Budge Studios, a company that specializes in creating games for kids. The game is based on the popular animated series of the same name, where Barbie and her friends live in a dream house and have fun adventures together. The game allows you to design your own room with various furniture, decorations, and accessories. You can also join Barbie and her friends in different activities, such as baking, dancing, swimming, and more. You can customize your character and outfits, and explore the city and discover new places. The game is suitable for children of all ages, as it has colorful graphics, easy controls, and positive messages.</p>
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- <h3>Features of Barbie Dreamhouse Adventures</h3>
-<h4>Create your own dream room</h4>
-<p>One of the main features of the game is that you can create your own dream room with Barbie. You can choose from different themes, such as glam, sporty, or cozy. You can also add various furniture, such as beds, sofas, tables, chairs, lamps, and more. You can decorate your room with wallpapers, rugs, curtains, paintings, and other items. You can even add pets, such as dogs, cats, or horses. You can change the layout of your room anytime you want, and make it as unique as you are.</p>
- <h4>Join fun activities with Barbie and her friends</h4>
-<p>Another feature of the game is that you can join fun activities with Barbie and her friends. You can bake delicious cakes and cookies in the kitchen, dance to your favorite music in the living room, swim in the pool or relax in the hot tub, play with your pets in the backyard, or have a sleepover party in your room. You can also go on exciting adventures with Barbie and her friends, such as camping in the mountains, going to the beach, or visiting a carnival. You can interact with different characters and objects in the game, and have a lot of fun.</p>
- <h4>Customize your character and outfits</h4>
-<p>The game also lets you customize your character and outfits. You can choose from different hairstyles, eye colors, skin tones, and facial features. You can also dress up your character with various clothes, shoes, accessories, and jewelry. You can mix and match different items to create your own style. You can also change your outfits according to the occasion or mood. You can be a fashionista like Barbie or a sporty girl like Teresa.</p>
- <h4>Explore the city and discover new places</h4>
-<p>The game also allows you to explore the city and discover new places. You can visit different locations in the city, such as the mall, the park, the cinema, or the salon <p>. You can also travel to different destinations, such as Paris, New York, or Tokyo. You can meet new people and make new friends. You can also learn about different cultures and traditions. You can have a lot of fun exploring the world with Barbie.</p>
- <h3>Why download Barbie Dreamhouse Adventures APK (MOD Unlocked)?</h3>
-<p>Barbie Dreamhouse Adventures is a free game that you can download from the Google Play Store or the App Store. However, the game has some limitations that may affect your gaming experience. For example, some of the rooms and activities are locked and require VIP membership to access. VIP membership costs $4.99 per month or $39.99 per year. Also, some of the items and outfits are expensive and require coins and gems to buy. Coins and gems are the in-game currencies that you can earn by playing the game or watching ads. However, earning enough coins and gems can be time-consuming and frustrating.</p>
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- <p>That's why you may want to download Barbie Dreamhouse Adventures APK (MOD Unlocked), a modified version of the game that gives you unlimited access to everything. By downloading this APK file, you can unlock VIP features for free, enjoy unlimited access to all rooms and activities, and get unlimited coins and gems to buy anything you want. You can also remove ads and enjoy a smoother gaming experience. You can have more fun and freedom playing the game with this APK file.</p>
- <h3>How to download and install Barbie Dreamhouse Adventures APK (MOD Unlocked)?</h3>
-<p>Downloading and installing Barbie Dreamhouse Adventures APK (MOD Unlocked) is easy and safe. Just follow these simple steps:</p>
- <h4>Step 1: Download the APK file from a trusted source</h4>
-<p>The first step is to download the APK file from a trusted source. You can find many websites that offer this APK file, but be careful as some of them may contain viruses or malware that can harm your device. We recommend you to use this link to download the APK file, as it is verified and tested by us.</p>
- <h4>Step 2: Enable unknown sources on your device</h4>
-<p>The second step is to enable unknown sources on your device. This is necessary because your device may not allow you to install apps from sources other than the official app stores. To enable unknown sources, go to your device settings, then security, then unknown sources, and turn it on.</p>
- <h4>Step 3: Install the APK file and launch the game</h4>
-<p>The third step is to install the APK file and launch the game. To install the APK file, locate it in your device storage, tap on it, and follow the instructions on the screen. To launch the game, find its icon on your home screen or app drawer, tap on it, and enjoy.</p>
- <h2>Conclusion</h2>
-<p>Barbie Dreamhouse Adventures is a fun and creative simulation game that lets you design your own room, join fun activities with Barbie and her friends, and explore the city and the world. However, if you want to enjoy all the features of the game without spending any money, you can download Barbie Dreamhouse Adventures APK (MOD Unlocked), a modified version of the game that gives you unlimited access to everything. You can download this APK file from this link and install it on your device following these simple steps. You can have more fun and freedom playing the game with this APK file.</p>
- <p>We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Barbie Dreamhouse Adventures APK (MOD Unlocked):</p>
- <ul>
-<li><b>Is Barbie Dreamhouse Adventures APK (MOD Unlocked) safe?</b></li>
-<p>Yes, Barbie Dreamhouse Adventures APK (MOD Unlocked) is safe to download and install on your device. It does not contain any viruses or malware that can harm your device. However, make sure you download it from a trusted source like this link, as some websites may offer fake or corrupted files.</p>
-<li><b>Is Barbie Dreamhouse Adventures APK (MOD Unlocked) legal?</b></li>
-<p>No, Barbie Dreamhouse Adventures APK (MOD Unlocked) is not legal, as it violates the terms and conditions of the original game developer Budge Studios. By downloading this APK file, you are using an unauthorized version of the game that gives you an unfair advantage over other players. This may result in legal action or account suspension by Budge Studios.</p>
-<li><b>Will Barbie Dreamhouse Adventures APK (MOD Unlocked) work on my device?</b></li>
-<p>Barbie Dreamhouse Adventures APK (MOD Unlocked) will work on most Android devices that have Android 4.4 or higher. However, some devices may not be compatible with the game or the APK file, and may experience crashes or errors. To check if your device is compatible, you can visit the official Google Play Store page of the game and see if it is available for your device.</p>
-<li><b>Can I update Barbie Dreamhouse Adventures APK (MOD Unlocked)?</b></li>
-<p>No, you cannot update Barbie Dreamhouse Adventures APK (MOD Unlocked) from the Google Play Store or the App Store, as it is a modified version of the game that is not recognized by the official app stores. If you try to update it, you may lose all your progress and data, and revert back to the original version of the game. To get the latest version of the APK file, you need to visit this link again and download it manually.</p>
-<li><b>Can I play Barbie Dreamhouse Adventures APK (MOD Unlocked) online with other players?</b></li>
-<p>Yes, you can play Barbie Dreamhouse Adventures APK (MOD Unlocked) online with other players who have the same version of the game. However, you may not be able to play with players who have the original version of the game, as they may have different features and settings. Also, you may face some issues or bugs while playing online, as the game is not designed to support the APK file.</p>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Stickman Zombies How to Unlock All the Weapons and Levels.md b/spaces/congsaPfin/Manga-OCR/logs/Stickman Zombies How to Unlock All the Weapons and Levels.md
deleted file mode 100644
index d166225b8add485d803a03ba90b2eaa9f45d7663..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Stickman Zombies How to Unlock All the Weapons and Levels.md	
+++ /dev/null
@@ -1,91 +0,0 @@
-<br />
-<h1>Stickman Zombies: What Are They and How to Survive Them</h1>
-<p>If you are a fan of stickman games and zombie games, you might have come across the term "stickman zombies". But what are they exactly and how did they come to be? And more importantly, how can you survive them in the games and animations that feature them? In this article, we will answer these questions and more, as we explore the world of stickman zombies.</p>
- <h2>Introduction</h2>
-<p>Stickman zombies are a type of undead creatures that resemble stick figures. They have thin limbs, round heads, and usually no facial features. They are often depicted as wearing clothes or carrying weapons, such as guns, knives, or axes. They are usually hostile and aggressive, and will attack any living being on sight.</p>
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- <h3>What are stickman zombies?</h3>
-<p>Stickman zombies are a combination of two popular genres: stickman games and zombie games. Stickman games are games that feature simple graphics and animations of stick figures performing various actions, such as fighting, running, jumping, or shooting. Zombie games are games that involve surviving a zombie apocalypse, where the player has to fend off hordes of undead creatures that want to eat their brains.</p>
-<p>Stickman zombies are a result of merging these two genres together, creating a unique and challenging gameplay experience. The player has to control a stick figure that has to survive in a world overrun by stick figure zombies. The player has to use various weapons and skills to kill the zombies and avoid getting bitten or infected.</p>
- <h3>How did they originate?</h3>
-<p>The origin of stickman zombies is not clear, but there are some possible explanations. One theory is that they are the result of a virus or a mutation that turned normal stick figures into mindless cannibals. Another theory is that they are the product of a scientific experiment gone wrong, where stick figures were exposed to radiation or chemicals that altered their DNA. A third theory is that they are the manifestation of a curse or a spell that brought the dead back to life as stick figures.</p>
-<p>Whatever the cause, stickman zombies have become a menace to society and a threat to humanity. They have spread across the globe, infecting and killing anyone they encounter. They have no remorse, no mercy, and no reason. They only have one goal: to feed on the living.</p>
- <h3>Why are they dangerous?</h3>
-<p>Stickman zombies are dangerous for several reasons. First of all, they are fast and agile. They can run, jump, climb, and dodge with ease. They can also use weapons and objects to attack their prey. Second of all, they are relentless and persistent. They will not stop chasing their target until they catch it or kill it. They will also work together in groups or hordes to overwhelm their enemies. Third of all, they are infectious and contagious. If they bite or scratch someone, they will transmit their virus or curse to them, turning them into one of them.</p>
-<p>Therefore, stickman zombies pose a serious challenge for anyone who wants to survive in their world. The player has to be careful, alert, and resourceful to avoid getting caught or killed by them.</p>
- <h2>Stickman Zombie Games</h2>
-<p>One way to experience the thrill and horror of stickman zombies is to play stickman zombie games. These are games that let you control a stick figure that has to fight against stick figure zombies in various scenarios and environments. Some of these games are: <h3>What are some popular stickman zombie games?</h3>
-<h4>Stickman Zombie 3D</h4>
-<p>This is a 3D shooting game that puts you in the role of a stickman soldier who has to survive a zombie outbreak in a military base. You have to use different guns and explosives to kill the zombies and find a way out of the base. The game has realistic graphics, sound effects, and physics, as well as multiple levels and modes to play.</p>
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- <h4>Stickman vs Zombies</h4>
-<p>This is a 2D side-scrolling game that lets you control a stickman who has to run and jump through a city infested by zombies. You have to collect coins and power-ups, as well as avoid obstacles and traps, while shooting the zombies with your gun. The game has colorful graphics, catchy music, and simple controls, as well as various characters and weapons to unlock.</p>
- <h4>Stickman Zombie Annihilation</h4>
-<p>This is a 2D top-down game that allows you to drive a car and run over zombies in a post-apocalyptic world. You have to upgrade your car and weapons, as well as complete missions and challenges, while avoiding the zombies and other hazards. The game has pixelated graphics, retro music, and addictive gameplay, as well as a leaderboard and achievements to compete with other players.</p>
- <h3>What are some tips and tricks to play stickman zombie games?</h3>
-<p>Here are some general tips and tricks that can help you play stickman zombie games better: - Aim for the head. This will deal more damage and kill the zombies faster. - Keep moving. This will make you harder to hit and allow you to dodge the zombies' attacks. - Use cover. This will protect you from the zombies' projectiles and give you time to reload or heal. - Conserve ammo. This will prevent you from running out of bullets when you need them most. - Upgrade your weapons. This will increase your firepower and effectiveness against the zombies. - Explore the environment. This will help you find hidden items, secrets, and shortcuts that can aid you in your survival.</p>
- <h2>Stickman Zombie Animation</h2>
-<p>Another way to enjoy the fun and horror of stickman zombies is to watch stickman zombie animation. These are short videos that feature stick figures in various situations involving zombies. They are usually humorous, creative, and action-packed, and showcase the skills and talents of the animators.</p>
- <h3>What are some examples of stickman zombie animation?</h3>
-<h4>"EVACUATION" THE WHITE</h4>
-<p>This is a stickman zombie animation by Alan Becker, the creator of the famous Animator vs Animation series. It tells the story of a white stick figure who has to escape from a building full of zombies with the help of his friends. The animation has smooth movements, detailed backgrounds, and clever references to other zombie movies and games.</p>
- <h4>Marbles VS Zombies Battle</h4>
-<p>This is a stickman zombie animation by Marbles Animations, a YouTube channel that specializes in marble racing videos. It shows a battle between marbles and zombies in a stadium, where the marbles have to use their speed and agility to avoid being eaten by the zombies. The animation has colorful visuals, dynamic camera angles, and suspenseful music.</p>
- <h4>Stickman vs Zombie - Epic Battle for Survival!</h4>
-<p>This is a stickman zombie animation by JZBoy Animations, another YouTube channel that produces stick figure videos. It depicts a fight between a stickman and a horde of zombies in an abandoned city, where the stickman has to use his martial arts skills and weapons to survive. The animation has fast-paced action, cool effects, and humorous moments.</p>
- <h3>What are some benefits of watching stickman zombie animation?</h3>
-<p>Watching stickman zombie animation can have several benefits for you, such as: - Entertaining you. Stickman zombie animation can make you laugh, gasp, or cheer with their stories, characters, and humor. - Inspiring you. Stickman zombie animation can motivate you to create your own animations or games with your imagination and creativity. - Educating you. Stickman zombie animation can teach you about animation techniques, software, or tools that you can use to improve your skills or learn new ones.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, stickman zombies are a fascinating phenomenon that combines two popular genres: stickman games and zombie games. They are undead creatures that resemble stick figures and attack any living being on sight. They can be found in various games and animations that challenge your skills and entertain your senses with their graphics, sound, and animation. If you are looking for a new and exciting way to have fun and test your abilities, you should try playing or watching stickman zombie games and animations. You will not regret it!</p>
- <h3>Summary of the main points</h3>
-<p>Here is a summary of the main points we covered in this article: - Stickman zombies are a type of undead creatures that resemble stick figures and attack any living being on sight. - They originated from a virus, a mutation, or a curse that turned normal stick figures into mindless cannibals. - They are dangerous because they are fast, agile, relentless, persistent, infectious, and contagious. - Stickman zombie games are games that let you control a stick figure that has to fight against stick figure zombies in various scenarios and environments. - Stickman zombie animation are short videos that feature stick figures in various situations involving zombies. - Playing or watching stickman zombie games and animations can entertain you, inspire you, and educate you.</p>
- <h3>Call to action</h3>
-<p>If you enjoyed this article, please share it with your friends and family who might also be interested in stickman zombies. You can also leave a comment below and tell us what you think about stickman zombies, or suggest other topics you would like us to write about. And don't forget to subscribe to our newsletter to get more articles like this one delivered to your inbox. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about stickman zombies:</p>
- <h3>What is the difference between stickman zombies and regular zombies?</h3>
-<p>The main difference between stickman zombies and regular zombies is their appearance. Stickman zombies look like stick figures, while regular zombies look like humans. However, they both share the same characteristics of being undead, mindless, and hungry for flesh.</p>
- <h3>Are stickman zombies real?</h3>
-<p>No, stickman zombies are not real. They are fictional creatures that only exist in games and animations. However, some people believe that zombies could become real in the future, due to a pandemic, a war, or a disaster.</p>
- <h3>How can I make my own stickman zombie game or animation?</h3>
-<p>If you want to make your own stickman zombie game or animation, you will need some software and tools to help you. For example, you can use Adobe Flash or Adobe Animate to create 2D graphics and animations of stick figures and zombies. You can also use Unity or Unreal Engine to create 3D games and simulations of stickman zombie scenarios. You can also use online platforms or apps that allow you to make your own stickman zombie games or animations without coding, such as Stick Nodes or Pivot Animator.</p>
- <h3>What are some other genres that feature stickman zombies?</h3>
-<p>Besides games and animations, there are some other genres that feature stickman zombies, such as comics, books, movies, or TV shows. For example, there is a comic series called The Walking Dead: Stick Edition, which is a parody of the popular zombie comic and TV show The Walking Dead. There is also a book called Stick Z: The Zombie Apocalypse Begins Here!, which is a novel about a group of kids who have to survive a stickman zombie outbreak in their school. There is also a movie called Stick Zombie Massacre!, which is a horror comedy film about a college student who has to fight off an army of stick figure zombies.</p>
- <h3>Where can I find more information about stickman zombies?</h3>
-<p>If you want to find more information about stickman zombies, you can visit some websites or blogs that are dedicated to them, such as StickZombie.com or StickZombieBlog.com. You can also join some online communities or forums that discuss them, such as Reddit.com/r/StickZombies or StickZombies.net/forum. You can also follow some social media accounts or channels that post about them, such as @StickZombie on Twitter or Stick Zombie on YouTube.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/consciousAI/question_answering/README.md b/spaces/consciousAI/question_answering/README.md
deleted file mode 100644
index e099bba8e2315aab4a57341a5fd46be2729ac536..0000000000000000000000000000000000000000
--- a/spaces/consciousAI/question_answering/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Question Answering Encoders vs Generative
-emoji: 🔥
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.8.2
-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/contluForse/HuggingGPT/assets/Ala A Little Agency Melissa Sets 001 026.md b/spaces/contluForse/HuggingGPT/assets/Ala A Little Agency Melissa Sets 001 026.md
deleted file mode 100644
index a4b81360bd964d8e8baa0413bd42c7fdef562fa9..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Ala A Little Agency Melissa Sets 001 026.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Ala A Little Agency Melissa Sets 001 026</h2><br /><p><b><b>DOWNLOAD</b> &#127383; <a href="https://ssurll.com/2uzwJd">https://ssurll.com/2uzwJd</a></b></p><br /><br />
-
-Part 1 is available . ... Let's Play Shogun II - Oda Campaign - Korean Mod - Part 1 - The Conquest ... Ala A Little Agency Melissa Sets 001 026 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/layers/median_pool.py b/spaces/cooelf/Multimodal-CoT/timm/models/layers/median_pool.py
deleted file mode 100644
index 40bd71a7a3840aaebefd2af0a99605b845054cd7..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/layers/median_pool.py
+++ /dev/null
@@ -1,49 +0,0 @@
-""" Median Pool
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import torch.nn as nn
-import torch.nn.functional as F
-from .helpers import to_2tuple, to_4tuple
-
-
-class MedianPool2d(nn.Module):
-    """ Median pool (usable as median filter when stride=1) module.
-
-    Args:
-         kernel_size: size of pooling kernel, int or 2-tuple
-         stride: pool stride, int or 2-tuple
-         padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad
-         same: override padding and enforce same padding, boolean
-    """
-    def __init__(self, kernel_size=3, stride=1, padding=0, same=False):
-        super(MedianPool2d, self).__init__()
-        self.k = to_2tuple(kernel_size)
-        self.stride = to_2tuple(stride)
-        self.padding = to_4tuple(padding)  # convert to l, r, t, b
-        self.same = same
-
-    def _padding(self, x):
-        if self.same:
-            ih, iw = x.size()[2:]
-            if ih % self.stride[0] == 0:
-                ph = max(self.k[0] - self.stride[0], 0)
-            else:
-                ph = max(self.k[0] - (ih % self.stride[0]), 0)
-            if iw % self.stride[1] == 0:
-                pw = max(self.k[1] - self.stride[1], 0)
-            else:
-                pw = max(self.k[1] - (iw % self.stride[1]), 0)
-            pl = pw // 2
-            pr = pw - pl
-            pt = ph // 2
-            pb = ph - pt
-            padding = (pl, pr, pt, pb)
-        else:
-            padding = self.padding
-        return padding
-
-    def forward(self, x):
-        x = F.pad(x, self._padding(x), mode='reflect')
-        x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1])
-        x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0]
-        return x
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/openpose/model.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/openpose/model.py
deleted file mode 100644
index 5dfc80de827a17beccb9b0f3f7588545be78c9de..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/openpose/model.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import torch
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-
-def make_layers(block, no_relu_layers):
-    layers = []
-    for layer_name, v in block.items():
-        if 'pool' in layer_name:
-            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
-                                    padding=v[2])
-            layers.append((layer_name, layer))
-        else:
-            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
-                               kernel_size=v[2], stride=v[3],
-                               padding=v[4])
-            layers.append((layer_name, conv2d))
-            if layer_name not in no_relu_layers:
-                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
-
-    return nn.Sequential(OrderedDict(layers))
-
-class bodypose_model(nn.Module):
-    def __init__(self):
-        super(bodypose_model, self).__init__()
-
-        # these layers have no relu layer
-        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
-                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
-                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
-                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
-        blocks = {}
-        block0 = OrderedDict([
-                      ('conv1_1', [3, 64, 3, 1, 1]),
-                      ('conv1_2', [64, 64, 3, 1, 1]),
-                      ('pool1_stage1', [2, 2, 0]),
-                      ('conv2_1', [64, 128, 3, 1, 1]),
-                      ('conv2_2', [128, 128, 3, 1, 1]),
-                      ('pool2_stage1', [2, 2, 0]),
-                      ('conv3_1', [128, 256, 3, 1, 1]),
-                      ('conv3_2', [256, 256, 3, 1, 1]),
-                      ('conv3_3', [256, 256, 3, 1, 1]),
-                      ('conv3_4', [256, 256, 3, 1, 1]),
-                      ('pool3_stage1', [2, 2, 0]),
-                      ('conv4_1', [256, 512, 3, 1, 1]),
-                      ('conv4_2', [512, 512, 3, 1, 1]),
-                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
-                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
-                  ])
-
-
-        # Stage 1
-        block1_1 = OrderedDict([
-                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
-                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
-                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
-                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
-                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
-                    ])
-
-        block1_2 = OrderedDict([
-                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
-                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
-                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
-                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
-                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
-                    ])
-        blocks['block1_1'] = block1_1
-        blocks['block1_2'] = block1_2
-
-        self.model0 = make_layers(block0, no_relu_layers)
-
-        # Stages 2 - 6
-        for i in range(2, 7):
-            blocks['block%d_1' % i] = OrderedDict([
-                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
-                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
-                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
-                ])
-
-            blocks['block%d_2' % i] = OrderedDict([
-                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
-                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
-                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
-                ])
-
-        for k in blocks.keys():
-            blocks[k] = make_layers(blocks[k], no_relu_layers)
-
-        self.model1_1 = blocks['block1_1']
-        self.model2_1 = blocks['block2_1']
-        self.model3_1 = blocks['block3_1']
-        self.model4_1 = blocks['block4_1']
-        self.model5_1 = blocks['block5_1']
-        self.model6_1 = blocks['block6_1']
-
-        self.model1_2 = blocks['block1_2']
-        self.model2_2 = blocks['block2_2']
-        self.model3_2 = blocks['block3_2']
-        self.model4_2 = blocks['block4_2']
-        self.model5_2 = blocks['block5_2']
-        self.model6_2 = blocks['block6_2']
-
-
-    def forward(self, x):
-
-        out1 = self.model0(x)
-
-        out1_1 = self.model1_1(out1)
-        out1_2 = self.model1_2(out1)
-        out2 = torch.cat([out1_1, out1_2, out1], 1)
-
-        out2_1 = self.model2_1(out2)
-        out2_2 = self.model2_2(out2)
-        out3 = torch.cat([out2_1, out2_2, out1], 1)
-
-        out3_1 = self.model3_1(out3)
-        out3_2 = self.model3_2(out3)
-        out4 = torch.cat([out3_1, out3_2, out1], 1)
-
-        out4_1 = self.model4_1(out4)
-        out4_2 = self.model4_2(out4)
-        out5 = torch.cat([out4_1, out4_2, out1], 1)
-
-        out5_1 = self.model5_1(out5)
-        out5_2 = self.model5_2(out5)
-        out6 = torch.cat([out5_1, out5_2, out1], 1)
-
-        out6_1 = self.model6_1(out6)
-        out6_2 = self.model6_2(out6)
-
-        return out6_1, out6_2
-
-class handpose_model(nn.Module):
-    def __init__(self):
-        super(handpose_model, self).__init__()
-
-        # these layers have no relu layer
-        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
-                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
-        # stage 1
-        block1_0 = OrderedDict([
-                ('conv1_1', [3, 64, 3, 1, 1]),
-                ('conv1_2', [64, 64, 3, 1, 1]),
-                ('pool1_stage1', [2, 2, 0]),
-                ('conv2_1', [64, 128, 3, 1, 1]),
-                ('conv2_2', [128, 128, 3, 1, 1]),
-                ('pool2_stage1', [2, 2, 0]),
-                ('conv3_1', [128, 256, 3, 1, 1]),
-                ('conv3_2', [256, 256, 3, 1, 1]),
-                ('conv3_3', [256, 256, 3, 1, 1]),
-                ('conv3_4', [256, 256, 3, 1, 1]),
-                ('pool3_stage1', [2, 2, 0]),
-                ('conv4_1', [256, 512, 3, 1, 1]),
-                ('conv4_2', [512, 512, 3, 1, 1]),
-                ('conv4_3', [512, 512, 3, 1, 1]),
-                ('conv4_4', [512, 512, 3, 1, 1]),
-                ('conv5_1', [512, 512, 3, 1, 1]),
-                ('conv5_2', [512, 512, 3, 1, 1]),
-                ('conv5_3_CPM', [512, 128, 3, 1, 1])
-            ])
-
-        block1_1 = OrderedDict([
-            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
-            ('conv6_2_CPM', [512, 22, 1, 1, 0])
-        ])
-
-        blocks = {}
-        blocks['block1_0'] = block1_0
-        blocks['block1_1'] = block1_1
-
-        # stage 2-6
-        for i in range(2, 7):
-            blocks['block%d' % i] = OrderedDict([
-                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
-                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
-                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
-                ])
-
-        for k in blocks.keys():
-            blocks[k] = make_layers(blocks[k], no_relu_layers)
-
-        self.model1_0 = blocks['block1_0']
-        self.model1_1 = blocks['block1_1']
-        self.model2 = blocks['block2']
-        self.model3 = blocks['block3']
-        self.model4 = blocks['block4']
-        self.model5 = blocks['block5']
-        self.model6 = blocks['block6']
-
-    def forward(self, x):
-        out1_0 = self.model1_0(x)
-        out1_1 = self.model1_1(out1_0)
-        concat_stage2 = torch.cat([out1_1, out1_0], 1)
-        out_stage2 = self.model2(concat_stage2)
-        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
-        out_stage3 = self.model3(concat_stage3)
-        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
-        out_stage4 = self.model4(concat_stage4)
-        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
-        out_stage5 = self.model5(concat_stage5)
-        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
-        out_stage6 = self.model6(concat_stage6)
-        return out_stage6
-
-
diff --git a/spaces/cscan/CodeFormer/README.md b/spaces/cscan/CodeFormer/README.md
deleted file mode 100644
index 6fafbe6f03ca8588a58a159d4ab39fe2256c9d88..0000000000000000000000000000000000000000
--- a/spaces/cscan/CodeFormer/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: CodeFormer
-emoji: 🐼
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: sczhou/CodeFormer
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/__init__.py
deleted file mode 100644
index 5d28506120e9e25c27858fb1d6b94bcdbcea095e..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import logging
-from fontTools.misc.loggingTools import configLogger
-
-log = logging.getLogger(__name__)
-
-version = __version__ = "4.42.0"
-
-__all__ = ["version", "log", "configLogger"]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/layout.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/layout.py
deleted file mode 100644
index 6b85cd503387291f326e937b36a5739b1de23ef1..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/layout.py
+++ /dev/null
@@ -1,530 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools import ttLib
-from fontTools.ttLib.tables.DefaultTable import DefaultTable
-from fontTools.ttLib.tables import otTables
-from fontTools.merge.base import add_method, mergeObjects
-from fontTools.merge.util import *
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-
-
-def mergeLookupLists(lst):
-    # TODO Do smarter merge.
-    return sumLists(lst)
-
-
-def mergeFeatures(lst):
-    assert lst
-    self = otTables.Feature()
-    self.FeatureParams = None
-    self.LookupListIndex = mergeLookupLists(
-        [l.LookupListIndex for l in lst if l.LookupListIndex]
-    )
-    self.LookupCount = len(self.LookupListIndex)
-    return self
-
-
-def mergeFeatureLists(lst):
-    d = {}
-    for l in lst:
-        for f in l:
-            tag = f.FeatureTag
-            if tag not in d:
-                d[tag] = []
-            d[tag].append(f.Feature)
-    ret = []
-    for tag in sorted(d.keys()):
-        rec = otTables.FeatureRecord()
-        rec.FeatureTag = tag
-        rec.Feature = mergeFeatures(d[tag])
-        ret.append(rec)
-    return ret
-
-
-def mergeLangSyses(lst):
-    assert lst
-
-    # TODO Support merging ReqFeatureIndex
-    assert all(l.ReqFeatureIndex == 0xFFFF for l in lst)
-
-    self = otTables.LangSys()
-    self.LookupOrder = None
-    self.ReqFeatureIndex = 0xFFFF
-    self.FeatureIndex = mergeFeatureLists(
-        [l.FeatureIndex for l in lst if l.FeatureIndex]
-    )
-    self.FeatureCount = len(self.FeatureIndex)
-    return self
-
-
-def mergeScripts(lst):
-    assert lst
-
-    if len(lst) == 1:
-        return lst[0]
-    langSyses = {}
-    for sr in lst:
-        for lsr in sr.LangSysRecord:
-            if lsr.LangSysTag not in langSyses:
-                langSyses[lsr.LangSysTag] = []
-            langSyses[lsr.LangSysTag].append(lsr.LangSys)
-    lsrecords = []
-    for tag, langSys_list in sorted(langSyses.items()):
-        lsr = otTables.LangSysRecord()
-        lsr.LangSys = mergeLangSyses(langSys_list)
-        lsr.LangSysTag = tag
-        lsrecords.append(lsr)
-
-    self = otTables.Script()
-    self.LangSysRecord = lsrecords
-    self.LangSysCount = len(lsrecords)
-    dfltLangSyses = [s.DefaultLangSys for s in lst if s.DefaultLangSys]
-    if dfltLangSyses:
-        self.DefaultLangSys = mergeLangSyses(dfltLangSyses)
-    else:
-        self.DefaultLangSys = None
-    return self
-
-
-def mergeScriptRecords(lst):
-    d = {}
-    for l in lst:
-        for s in l:
-            tag = s.ScriptTag
-            if tag not in d:
-                d[tag] = []
-            d[tag].append(s.Script)
-    ret = []
-    for tag in sorted(d.keys()):
-        rec = otTables.ScriptRecord()
-        rec.ScriptTag = tag
-        rec.Script = mergeScripts(d[tag])
-        ret.append(rec)
-    return ret
-
-
-otTables.ScriptList.mergeMap = {
-    "ScriptCount": lambda lst: None,  # TODO
-    "ScriptRecord": mergeScriptRecords,
-}
-otTables.BaseScriptList.mergeMap = {
-    "BaseScriptCount": lambda lst: None,  # TODO
-    # TODO: Merge duplicate entries
-    "BaseScriptRecord": lambda lst: sorted(
-        sumLists(lst), key=lambda s: s.BaseScriptTag
-    ),
-}
-
-otTables.FeatureList.mergeMap = {
-    "FeatureCount": sum,
-    "FeatureRecord": lambda lst: sorted(sumLists(lst), key=lambda s: s.FeatureTag),
-}
-
-otTables.LookupList.mergeMap = {
-    "LookupCount": sum,
-    "Lookup": sumLists,
-}
-
-otTables.Coverage.mergeMap = {
-    "Format": min,
-    "glyphs": sumLists,
-}
-
-otTables.ClassDef.mergeMap = {
-    "Format": min,
-    "classDefs": sumDicts,
-}
-
-otTables.LigCaretList.mergeMap = {
-    "Coverage": mergeObjects,
-    "LigGlyphCount": sum,
-    "LigGlyph": sumLists,
-}
-
-otTables.AttachList.mergeMap = {
-    "Coverage": mergeObjects,
-    "GlyphCount": sum,
-    "AttachPoint": sumLists,
-}
-
-# XXX Renumber MarkFilterSets of lookups
-otTables.MarkGlyphSetsDef.mergeMap = {
-    "MarkSetTableFormat": equal,
-    "MarkSetCount": sum,
-    "Coverage": sumLists,
-}
-
-otTables.Axis.mergeMap = {
-    "*": mergeObjects,
-}
-
-# XXX Fix BASE table merging
-otTables.BaseTagList.mergeMap = {
-    "BaseTagCount": sum,
-    "BaselineTag": sumLists,
-}
-
-otTables.GDEF.mergeMap = (
-    otTables.GSUB.mergeMap
-) = (
-    otTables.GPOS.mergeMap
-) = otTables.BASE.mergeMap = otTables.JSTF.mergeMap = otTables.MATH.mergeMap = {
-    "*": mergeObjects,
-    "Version": max,
-}
-
-ttLib.getTableClass("GDEF").mergeMap = ttLib.getTableClass(
-    "GSUB"
-).mergeMap = ttLib.getTableClass("GPOS").mergeMap = ttLib.getTableClass(
-    "BASE"
-).mergeMap = ttLib.getTableClass(
-    "JSTF"
-).mergeMap = ttLib.getTableClass(
-    "MATH"
-).mergeMap = {
-    "tableTag": onlyExisting(equal),  # XXX clean me up
-    "table": mergeObjects,
-}
-
-
-@add_method(ttLib.getTableClass("GSUB"))
-def merge(self, m, tables):
-    assert len(tables) == len(m.duplicateGlyphsPerFont)
-    for i, (table, dups) in enumerate(zip(tables, m.duplicateGlyphsPerFont)):
-        if not dups:
-            continue
-        if table is None or table is NotImplemented:
-            log.warning(
-                "Have non-identical duplicates to resolve for '%s' but no GSUB. Are duplicates intended?: %s",
-                m.fonts[i]._merger__name,
-                dups,
-            )
-            continue
-
-        synthFeature = None
-        synthLookup = None
-        for script in table.table.ScriptList.ScriptRecord:
-            if script.ScriptTag == "DFLT":
-                continue  # XXX
-            for langsys in [script.Script.DefaultLangSys] + [
-                l.LangSys for l in script.Script.LangSysRecord
-            ]:
-                if langsys is None:
-                    continue  # XXX Create!
-                feature = [v for v in langsys.FeatureIndex if v.FeatureTag == "locl"]
-                assert len(feature) <= 1
-                if feature:
-                    feature = feature[0]
-                else:
-                    if not synthFeature:
-                        synthFeature = otTables.FeatureRecord()
-                        synthFeature.FeatureTag = "locl"
-                        f = synthFeature.Feature = otTables.Feature()
-                        f.FeatureParams = None
-                        f.LookupCount = 0
-                        f.LookupListIndex = []
-                        table.table.FeatureList.FeatureRecord.append(synthFeature)
-                        table.table.FeatureList.FeatureCount += 1
-                    feature = synthFeature
-                    langsys.FeatureIndex.append(feature)
-                    langsys.FeatureIndex.sort(key=lambda v: v.FeatureTag)
-
-                if not synthLookup:
-                    subtable = otTables.SingleSubst()
-                    subtable.mapping = dups
-                    synthLookup = otTables.Lookup()
-                    synthLookup.LookupFlag = 0
-                    synthLookup.LookupType = 1
-                    synthLookup.SubTableCount = 1
-                    synthLookup.SubTable = [subtable]
-                    if table.table.LookupList is None:
-                        # mtiLib uses None as default value for LookupList,
-                        # while feaLib points to an empty array with count 0
-                        # TODO: make them do the same
-                        table.table.LookupList = otTables.LookupList()
-                        table.table.LookupList.Lookup = []
-                        table.table.LookupList.LookupCount = 0
-                    table.table.LookupList.Lookup.append(synthLookup)
-                    table.table.LookupList.LookupCount += 1
-
-                if feature.Feature.LookupListIndex[:1] != [synthLookup]:
-                    feature.Feature.LookupListIndex[:0] = [synthLookup]
-                    feature.Feature.LookupCount += 1
-
-    DefaultTable.merge(self, m, tables)
-    return self
-
-
-@add_method(
-    otTables.SingleSubst,
-    otTables.MultipleSubst,
-    otTables.AlternateSubst,
-    otTables.LigatureSubst,
-    otTables.ReverseChainSingleSubst,
-    otTables.SinglePos,
-    otTables.PairPos,
-    otTables.CursivePos,
-    otTables.MarkBasePos,
-    otTables.MarkLigPos,
-    otTables.MarkMarkPos,
-)
-def mapLookups(self, lookupMap):
-    pass
-
-
-# Copied and trimmed down from subset.py
-@add_method(
-    otTables.ContextSubst,
-    otTables.ChainContextSubst,
-    otTables.ContextPos,
-    otTables.ChainContextPos,
-)
-def __merge_classify_context(self):
-    class ContextHelper(object):
-        def __init__(self, klass, Format):
-            if klass.__name__.endswith("Subst"):
-                Typ = "Sub"
-                Type = "Subst"
-            else:
-                Typ = "Pos"
-                Type = "Pos"
-            if klass.__name__.startswith("Chain"):
-                Chain = "Chain"
-            else:
-                Chain = ""
-            ChainTyp = Chain + Typ
-
-            self.Typ = Typ
-            self.Type = Type
-            self.Chain = Chain
-            self.ChainTyp = ChainTyp
-
-            self.LookupRecord = Type + "LookupRecord"
-
-            if Format == 1:
-                self.Rule = ChainTyp + "Rule"
-                self.RuleSet = ChainTyp + "RuleSet"
-            elif Format == 2:
-                self.Rule = ChainTyp + "ClassRule"
-                self.RuleSet = ChainTyp + "ClassSet"
-
-    if self.Format not in [1, 2, 3]:
-        return None  # Don't shoot the messenger; let it go
-    if not hasattr(self.__class__, "_merge__ContextHelpers"):
-        self.__class__._merge__ContextHelpers = {}
-    if self.Format not in self.__class__._merge__ContextHelpers:
-        helper = ContextHelper(self.__class__, self.Format)
-        self.__class__._merge__ContextHelpers[self.Format] = helper
-    return self.__class__._merge__ContextHelpers[self.Format]
-
-
-@add_method(
-    otTables.ContextSubst,
-    otTables.ChainContextSubst,
-    otTables.ContextPos,
-    otTables.ChainContextPos,
-)
-def mapLookups(self, lookupMap):
-    c = self.__merge_classify_context()
-
-    if self.Format in [1, 2]:
-        for rs in getattr(self, c.RuleSet):
-            if not rs:
-                continue
-            for r in getattr(rs, c.Rule):
-                if not r:
-                    continue
-                for ll in getattr(r, c.LookupRecord):
-                    if not ll:
-                        continue
-                    ll.LookupListIndex = lookupMap[ll.LookupListIndex]
-    elif self.Format == 3:
-        for ll in getattr(self, c.LookupRecord):
-            if not ll:
-                continue
-            ll.LookupListIndex = lookupMap[ll.LookupListIndex]
-    else:
-        assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.ExtensionSubst, otTables.ExtensionPos)
-def mapLookups(self, lookupMap):
-    if self.Format == 1:
-        self.ExtSubTable.mapLookups(lookupMap)
-    else:
-        assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.Lookup)
-def mapLookups(self, lookupMap):
-    for st in self.SubTable:
-        if not st:
-            continue
-        st.mapLookups(lookupMap)
-
-
-@add_method(otTables.LookupList)
-def mapLookups(self, lookupMap):
-    for l in self.Lookup:
-        if not l:
-            continue
-        l.mapLookups(lookupMap)
-
-
-@add_method(otTables.Lookup)
-def mapMarkFilteringSets(self, markFilteringSetMap):
-    if self.LookupFlag & 0x0010:
-        self.MarkFilteringSet = markFilteringSetMap[self.MarkFilteringSet]
-
-
-@add_method(otTables.LookupList)
-def mapMarkFilteringSets(self, markFilteringSetMap):
-    for l in self.Lookup:
-        if not l:
-            continue
-        l.mapMarkFilteringSets(markFilteringSetMap)
-
-
-@add_method(otTables.Feature)
-def mapLookups(self, lookupMap):
-    self.LookupListIndex = [lookupMap[i] for i in self.LookupListIndex]
-
-
-@add_method(otTables.FeatureList)
-def mapLookups(self, lookupMap):
-    for f in self.FeatureRecord:
-        if not f or not f.Feature:
-            continue
-        f.Feature.mapLookups(lookupMap)
-
-
-@add_method(otTables.DefaultLangSys, otTables.LangSys)
-def mapFeatures(self, featureMap):
-    self.FeatureIndex = [featureMap[i] for i in self.FeatureIndex]
-    if self.ReqFeatureIndex != 65535:
-        self.ReqFeatureIndex = featureMap[self.ReqFeatureIndex]
-
-
-@add_method(otTables.Script)
-def mapFeatures(self, featureMap):
-    if self.DefaultLangSys:
-        self.DefaultLangSys.mapFeatures(featureMap)
-    for l in self.LangSysRecord:
-        if not l or not l.LangSys:
-            continue
-        l.LangSys.mapFeatures(featureMap)
-
-
-@add_method(otTables.ScriptList)
-def mapFeatures(self, featureMap):
-    for s in self.ScriptRecord:
-        if not s or not s.Script:
-            continue
-        s.Script.mapFeatures(featureMap)
-
-
-def layoutPreMerge(font):
-    # Map indices to references
-
-    GDEF = font.get("GDEF")
-    GSUB = font.get("GSUB")
-    GPOS = font.get("GPOS")
-
-    for t in [GSUB, GPOS]:
-        if not t:
-            continue
-
-        if t.table.LookupList:
-            lookupMap = {i: v for i, v in enumerate(t.table.LookupList.Lookup)}
-            t.table.LookupList.mapLookups(lookupMap)
-            t.table.FeatureList.mapLookups(lookupMap)
-
-            if (
-                GDEF
-                and GDEF.table.Version >= 0x00010002
-                and GDEF.table.MarkGlyphSetsDef
-            ):
-                markFilteringSetMap = {
-                    i: v for i, v in enumerate(GDEF.table.MarkGlyphSetsDef.Coverage)
-                }
-                t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
-        if t.table.FeatureList and t.table.ScriptList:
-            featureMap = {i: v for i, v in enumerate(t.table.FeatureList.FeatureRecord)}
-            t.table.ScriptList.mapFeatures(featureMap)
-
-    # TODO FeatureParams nameIDs
-
-
-def layoutPostMerge(font):
-    # Map references back to indices
-
-    GDEF = font.get("GDEF")
-    GSUB = font.get("GSUB")
-    GPOS = font.get("GPOS")
-
-    for t in [GSUB, GPOS]:
-        if not t:
-            continue
-
-        if t.table.FeatureList and t.table.ScriptList:
-            # Collect unregistered (new) features.
-            featureMap = GregariousIdentityDict(t.table.FeatureList.FeatureRecord)
-            t.table.ScriptList.mapFeatures(featureMap)
-
-            # Record used features.
-            featureMap = AttendanceRecordingIdentityDict(
-                t.table.FeatureList.FeatureRecord
-            )
-            t.table.ScriptList.mapFeatures(featureMap)
-            usedIndices = featureMap.s
-
-            # Remove unused features
-            t.table.FeatureList.FeatureRecord = [
-                f
-                for i, f in enumerate(t.table.FeatureList.FeatureRecord)
-                if i in usedIndices
-            ]
-
-            # Map back to indices.
-            featureMap = NonhashableDict(t.table.FeatureList.FeatureRecord)
-            t.table.ScriptList.mapFeatures(featureMap)
-
-            t.table.FeatureList.FeatureCount = len(t.table.FeatureList.FeatureRecord)
-
-        if t.table.LookupList:
-            # Collect unregistered (new) lookups.
-            lookupMap = GregariousIdentityDict(t.table.LookupList.Lookup)
-            t.table.FeatureList.mapLookups(lookupMap)
-            t.table.LookupList.mapLookups(lookupMap)
-
-            # Record used lookups.
-            lookupMap = AttendanceRecordingIdentityDict(t.table.LookupList.Lookup)
-            t.table.FeatureList.mapLookups(lookupMap)
-            t.table.LookupList.mapLookups(lookupMap)
-            usedIndices = lookupMap.s
-
-            # Remove unused lookups
-            t.table.LookupList.Lookup = [
-                l for i, l in enumerate(t.table.LookupList.Lookup) if i in usedIndices
-            ]
-
-            # Map back to indices.
-            lookupMap = NonhashableDict(t.table.LookupList.Lookup)
-            t.table.FeatureList.mapLookups(lookupMap)
-            t.table.LookupList.mapLookups(lookupMap)
-
-            t.table.LookupList.LookupCount = len(t.table.LookupList.Lookup)
-
-            if GDEF and GDEF.table.Version >= 0x00010002:
-                markFilteringSetMap = NonhashableDict(
-                    GDEF.table.MarkGlyphSetsDef.Coverage
-                )
-                t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
-    # TODO FeatureParams nameIDs
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/boundsPen.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/boundsPen.py
deleted file mode 100644
index d833cc89b90b38937aa0e21c26bc7e7e84f5ee7d..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/boundsPen.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from fontTools.misc.arrayTools import updateBounds, pointInRect, unionRect
-from fontTools.misc.bezierTools import calcCubicBounds, calcQuadraticBounds
-from fontTools.pens.basePen import BasePen
-
-
-__all__ = ["BoundsPen", "ControlBoundsPen"]
-
-
-class ControlBoundsPen(BasePen):
-
-    """Pen to calculate the "control bounds" of a shape. This is the
-    bounding box of all control points, so may be larger than the
-    actual bounding box if there are curves that don't have points
-    on their extremes.
-
-    When the shape has been drawn, the bounds are available as the
-    ``bounds`` attribute of the pen object. It's a 4-tuple::
-
-            (xMin, yMin, xMax, yMax).
-
-    If ``ignoreSinglePoints`` is True, single points are ignored.
-    """
-
-    def __init__(self, glyphSet, ignoreSinglePoints=False):
-        BasePen.__init__(self, glyphSet)
-        self.ignoreSinglePoints = ignoreSinglePoints
-        self.init()
-
-    def init(self):
-        self.bounds = None
-        self._start = None
-
-    def _moveTo(self, pt):
-        self._start = pt
-        if not self.ignoreSinglePoints:
-            self._addMoveTo()
-
-    def _addMoveTo(self):
-        if self._start is None:
-            return
-        bounds = self.bounds
-        if bounds:
-            self.bounds = updateBounds(bounds, self._start)
-        else:
-            x, y = self._start
-            self.bounds = (x, y, x, y)
-        self._start = None
-
-    def _lineTo(self, pt):
-        self._addMoveTo()
-        self.bounds = updateBounds(self.bounds, pt)
-
-    def _curveToOne(self, bcp1, bcp2, pt):
-        self._addMoveTo()
-        bounds = self.bounds
-        bounds = updateBounds(bounds, bcp1)
-        bounds = updateBounds(bounds, bcp2)
-        bounds = updateBounds(bounds, pt)
-        self.bounds = bounds
-
-    def _qCurveToOne(self, bcp, pt):
-        self._addMoveTo()
-        bounds = self.bounds
-        bounds = updateBounds(bounds, bcp)
-        bounds = updateBounds(bounds, pt)
-        self.bounds = bounds
-
-
-class BoundsPen(ControlBoundsPen):
-
-    """Pen to calculate the bounds of a shape. It calculates the
-    correct bounds even when the shape contains curves that don't
-    have points on their extremes. This is somewhat slower to compute
-    than the "control bounds".
-
-    When the shape has been drawn, the bounds are available as the
-    ``bounds`` attribute of the pen object. It's a 4-tuple::
-
-            (xMin, yMin, xMax, yMax)
-    """
-
-    def _curveToOne(self, bcp1, bcp2, pt):
-        self._addMoveTo()
-        bounds = self.bounds
-        bounds = updateBounds(bounds, pt)
-        if not pointInRect(bcp1, bounds) or not pointInRect(bcp2, bounds):
-            bounds = unionRect(
-                bounds, calcCubicBounds(self._getCurrentPoint(), bcp1, bcp2, pt)
-            )
-        self.bounds = bounds
-
-    def _qCurveToOne(self, bcp, pt):
-        self._addMoveTo()
-        bounds = self.bounds
-        bounds = updateBounds(bounds, pt)
-        if not pointInRect(bcp, bounds):
-            bounds = unionRect(
-                bounds, calcQuadraticBounds(self._getCurrentPoint(), bcp, pt)
-            )
-        self.bounds = bounds
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py
deleted file mode 100644
index 1b9fc5270a62bbb18d1393263101d4b9f73b7511..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .pipeline_latent_diffusion_uncond import LDMPipeline
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_flax_objects.py b/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_flax_objects.py
deleted file mode 100644
index 2bb80d136f338d193c67773266355956afd1d98a..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_flax_objects.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class FlaxControlNetModel(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxModelMixin(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxUNet2DConditionModel(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxAutoencoderKL(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxDDIMScheduler(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxDDPMScheduler(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxDPMSolverMultistepScheduler(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxKarrasVeScheduler(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxLMSDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxPNDMScheduler(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxSchedulerMixin(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-
-class FlaxScoreSdeVeScheduler(metaclass=DummyObject):
-    _backends = ["flax"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax"])
diff --git a/spaces/diacanFperku/AutoGPT/Marianas TrenchMasterpiece Theatre Full Album Zip REPACK.md b/spaces/diacanFperku/AutoGPT/Marianas TrenchMasterpiece Theatre Full Album Zip REPACK.md
deleted file mode 100644
index d5b4181f757acce9b281a84415e5fbfcfc4edb2a..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Marianas TrenchMasterpiece Theatre Full Album Zip REPACK.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Marianas TrenchMasterpiece Theatre Full Album Zip</h2><br /><p><b><b>Download File</b> &#127379; <a href="https://gohhs.com/2uFSYK">https://gohhs.com/2uFSYK</a></b></p><br /><br />
-
-Zip-Lock (4 stars out of 5) ... Mariana Trench: MASTERPIECE THEATER. Painting. We really enjoyed this CD with their unique arrangements and harmonies. ... Sir Paul McCartney and his Beatles. Beatlemania. potpourri. ... The Beatles. Remix. This is a very good classical composition written in the style of the Beatles. And it's a very good choice for a CD. ... The Beatles. yellow submarine. This is another great performance on CD. ... Paul McCartney and his band Wings. Live at Wembley. ... Paul McCartney is back with his band. ... Michael Jackson. black and white. ... Michael Jackson and his band. ... Michael Jackson. Michael Jackson: Live in New York. ... 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/README.md b/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/README.md
deleted file mode 100644
index 7904d4445f0da894113405b7b0b1c6ea9d0ba4c9..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 步非烟 Ver.b
-emoji: 🌟
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-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/digitalxingtong/Eileen-Bert-Vits2/text/symbols.py b/spaces/digitalxingtong/Eileen-Bert-Vits2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Eileen-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/dineshreddy/WALT/mmdet/models/dense_heads/__init__.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/__init__.py
deleted file mode 100644
index f004dd95d97df16167f932587b3ce73b05b04a37..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from .anchor_free_head import AnchorFreeHead
-from .anchor_head import AnchorHead
-from .atss_head import ATSSHead
-from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
-from .centripetal_head import CentripetalHead
-from .corner_head import CornerHead
-from .embedding_rpn_head import EmbeddingRPNHead
-from .fcos_head import FCOSHead
-from .fovea_head import FoveaHead
-from .free_anchor_retina_head import FreeAnchorRetinaHead
-from .fsaf_head import FSAFHead
-from .ga_retina_head import GARetinaHead
-from .ga_rpn_head import GARPNHead
-from .gfl_head import GFLHead
-from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
-from .ld_head import LDHead
-from .nasfcos_head import NASFCOSHead
-from .paa_head import PAAHead
-from .pisa_retinanet_head import PISARetinaHead
-from .pisa_ssd_head import PISASSDHead
-from .reppoints_head import RepPointsHead
-from .retina_head import RetinaHead
-from .retina_sepbn_head import RetinaSepBNHead
-from .rpn_head import RPNHead
-from .sabl_retina_head import SABLRetinaHead
-from .ssd_head import SSDHead
-from .transformer_head import TransformerHead
-from .vfnet_head import VFNetHead
-from .yolact_head import YOLACTHead, YOLACTProtonet, YOLACTSegmHead
-from .yolo_head import YOLOV3Head
-
-__all__ = [
-    'AnchorFreeHead', 'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption',
-    'RPNHead', 'GARPNHead', 'RetinaHead', 'RetinaSepBNHead', 'GARetinaHead',
-    'SSDHead', 'FCOSHead', 'RepPointsHead', 'FoveaHead',
-    'FreeAnchorRetinaHead', 'ATSSHead', 'FSAFHead', 'NASFCOSHead',
-    'PISARetinaHead', 'PISASSDHead', 'GFLHead', 'CornerHead', 'YOLACTHead',
-    'YOLACTSegmHead', 'YOLACTProtonet', 'YOLOV3Head', 'PAAHead',
-    'SABLRetinaHead', 'CentripetalHead', 'VFNetHead', 'TransformerHead',
-    'StageCascadeRPNHead', 'CascadeRPNHead', 'EmbeddingRPNHead', 'LDHead'
-]
diff --git a/spaces/dorkai/pygmalion/README.md b/spaces/dorkai/pygmalion/README.md
deleted file mode 100644
index 7aa040d767d6c8a53bb3db0cf8124b0dd0d1ee3b..0000000000000000000000000000000000000000
--- a/spaces/dorkai/pygmalion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pygmalion
-emoji: 🐠
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.28.3
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/demo/gradio_app.py b/spaces/dragonSwing/annotate-anything/GroundingDINO/demo/gradio_app.py
deleted file mode 100644
index 15e08323f485291df8b53eefd4691c087d7863f7..0000000000000000000000000000000000000000
--- a/spaces/dragonSwing/annotate-anything/GroundingDINO/demo/gradio_app.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import argparse
-from functools import partial
-import cv2
-import requests
-import os
-from io import BytesIO
-from PIL import Image
-import numpy as np
-from pathlib import Path
-
-
-import warnings
-
-import torch
-
-# prepare the environment
-os.system("python setup.py build develop --user")
-os.system("pip install packaging==21.3")
-os.system("pip install gradio")
-
-
-warnings.filterwarnings("ignore")
-
-import gradio as gr
-
-from groundingdino.models import build_model
-from groundingdino.util.slconfig import SLConfig
-from groundingdino.util.utils import clean_state_dict
-from groundingdino.util.inference import annotate, load_image, predict
-import groundingdino.datasets.transforms as T
-
-from huggingface_hub import hf_hub_download
-
-
-
-# Use this command for evaluate the GLIP-T model
-config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
-ckpt_repo_id = "ShilongLiu/GroundingDINO"
-ckpt_filenmae = "groundingdino_swint_ogc.pth"
-
-
-def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
-    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='cpu')
-    log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)
-    print("Model loaded from {} \n => {}".format(cache_file, log))
-    _ = model.eval()
-    return model    
-
-def image_transform_grounding(init_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])
-    ])
-    image, _ = transform(init_image, None) # 3, h, w
-    return init_image, image
-
-def image_transform_grounding_for_vis(init_image):
-    transform = T.Compose([
-        T.RandomResize([800], max_size=1333),
-    ])
-    image, _ = transform(init_image, None) # 3, h, w
-    return image
-
-model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
-
-def run_grounding(input_image, grounding_caption, box_threshold, text_threshold):
-    init_image = input_image.convert("RGB")
-    original_size = init_image.size
-
-    _, image_tensor = image_transform_grounding(init_image)
-    image_pil: Image = image_transform_grounding_for_vis(init_image)
-
-    # run grounidng
-    boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cpu')
-    annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases)
-    image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))
-
-
-    return image_with_box
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser("Grounding DINO 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()
-
-    block = gr.Blocks().queue()
-    with block:
-        gr.Markdown("# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)")
-        gr.Markdown("### Open-World Detection with Grounding DINO")
-
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type="pil")
-                grounding_caption = gr.Textbox(label="Detection Prompt")
-                run_button = gr.Button(label="Run")
-                with gr.Accordion("Advanced options", open=False):
-                    box_threshold = gr.Slider(
-                        label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
-                    )
-                    text_threshold = gr.Slider(
-                        label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
-                    )
-
-            with gr.Column():
-                gallery = gr.outputs.Image(
-                    type="pil",
-                    # label="grounding results"
-                ).style(full_width=True, full_height=True)
-                # gallery = gr.Gallery(label="Generated images", show_label=False).style(
-                #         grid=[1], height="auto", container=True, full_width=True, full_height=True)
-
-        run_button.click(fn=run_grounding, inputs=[
-                        input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
-
-
-    block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share)
-
diff --git a/spaces/duycse1603/math2tex/ScanSSD/gtdb/split_annotations_per_page.py b/spaces/duycse1603/math2tex/ScanSSD/gtdb/split_annotations_per_page.py
deleted file mode 100644
index 772cc9600ba477d8f31957369ab897ba8cddb615..0000000000000000000000000000000000000000
--- a/spaces/duycse1603/math2tex/ScanSSD/gtdb/split_annotations_per_page.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# Author: Parag Mali
-# This script generates page level annotations from the PDF level annotations
-# provided in the dataset
-
-import sys
-import os
-from multiprocessing import Pool
-import csv
-import cv2
-
-def split(args):
-
-    gt_dir, pdf_name, out_dir, ext = args
-
-    file_path = os.path.join(gt_dir, pdf_name + "." + ext)
-    img_dir = '/home/psm2208/data/GTDB/images/'
-
-    # create a map of page to list of math boxes
-    map = {}
-
-    if ext == "math":
-
-        file_ip = open(file_path, "r")
-        for line in file_ip:
-            entries = line.strip().split(",")
-
-            # if entry is not in map
-            if entries[0] not in map:
-                map[entries[0]] = []
-
-            map[entries[0]].append(entries[1:])
-
-        for key in map:
-
-            boxes = map[key]
-            key = float(key)
-            img_file = os.path.join(img_dir, pdf_name, str(int(key) + 1) + ".png")
-            img = cv2.imread(img_file)
-
-            height, width, channels = img.shape
-
-            #width_ratio = 512 / width
-            #height_ratio = 512 / height
-
-            width_ratio = 1
-            height_ratio = 1
-
-            # create processed math file
-            file_op = open(os.path.join(out_dir, pdf_name, str(int(key) + 1)) + ".p" + ext, "w")
-
-            for box in boxes:
-                # xmin, ymin, xmax, ymax
-
-                box[0] = float(box[0]) * width_ratio
-                box[1] = float(box[1]) * height_ratio
-                box[2] = float(box[2]) * width_ratio
-                box[3] = float(box[3]) * height_ratio
-
-                file_op.write(','.join(str(e) for e in box) + "\n")
-
-            file_op.close()
-            file_ip.close()
-
-    elif ext == "char":
-        with open(file_path, 'r') as csvfile:
-            reader = csv.reader(csvfile, delimiter=',')
-            for row in reader:
-                # if entry is not in map
-                if row[0] not in map:
-                    map[row[0]] = []
-
-                map[row[0]].append(row)
-
-        for key in map:
-
-            boxes = map[key]
-
-            with open(os.path.join(out_dir, pdf_name, str(int(key) + 1)) + ".p" + ext, "w") as csvfile:
-                writer = csv.writer(csvfile, delimiter=',')
-
-                for box in boxes:
-                    writer.writerow(box)
-
-def test():
-
-    filename = sys.argv[1] # file names to be processed
-    out_dir = sys.argv[2] # output dir
-    gt_dir = sys.argv[3] # gt dir
-    ext = sys.argv[4] # file extension
-
-    pdf_names_list = []
-    pdf_names = open(filename, 'r')
-
-    for pdf_name in pdf_names:
-        pdf_name = pdf_name.strip()
-
-        if not os.path.exists(os.path.join(out_dir, pdf_name)):
-            os.mkdir(os.path.join(out_dir, pdf_name))
-
-        if pdf_name != '':
-            pdf_names_list.append((gt_dir, pdf_name, out_dir, ext))
-
-    pdf_names.close()
-
-    pool = Pool(processes=32)
-    pool.map(split, pdf_names_list)
-    pool.close()
-    pool.join()
-
-
-if __name__ == "__main__":
-    test()
diff --git a/spaces/editing-images/ai-halloween-photobooth/cog_sdxl_dataset_and_utils.py b/spaces/editing-images/ai-halloween-photobooth/cog_sdxl_dataset_and_utils.py
deleted file mode 100644
index d0f5bd01c9e535390b68a298db944ff4ecf986b9..0000000000000000000000000000000000000000
--- a/spaces/editing-images/ai-halloween-photobooth/cog_sdxl_dataset_and_utils.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# dataset_and_utils.py file taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py
-import os
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import pandas as pd
-import PIL
-import torch
-import torch.utils.checkpoint
-from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
-from PIL import Image
-from safetensors import safe_open
-from safetensors.torch import save_file
-from torch.utils.data import Dataset
-from transformers import AutoTokenizer, PretrainedConfig
-
-
-def prepare_image(
-    pil_image: PIL.Image.Image, w: int = 512, h: int = 512
-) -> torch.Tensor:
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("RGB"))
-    arr = arr.astype(np.float32) / 127.5 - 1
-    arr = np.transpose(arr, [2, 0, 1])
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-def prepare_mask(
-    pil_image: PIL.Image.Image, w: int = 512, h: int = 512
-) -> torch.Tensor:
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("L"))
-    arr = arr.astype(np.float32) / 255.0
-    arr = np.expand_dims(arr, 0)
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-class PreprocessedDataset(Dataset):
-    def __init__(
-        self,
-        csv_path: str,
-        tokenizer_1,
-        tokenizer_2,
-        vae_encoder,
-        text_encoder_1=None,
-        text_encoder_2=None,
-        do_cache: bool = False,
-        size: int = 512,
-        text_dropout: float = 0.0,
-        scale_vae_latents: bool = True,
-        substitute_caption_map: Dict[str, str] = {},
-    ):
-        super().__init__()
-
-        self.data = pd.read_csv(csv_path)
-        self.csv_path = csv_path
-
-        self.caption = self.data["caption"]
-        # make it lowercase
-        self.caption = self.caption.str.lower()
-        for key, value in substitute_caption_map.items():
-            self.caption = self.caption.str.replace(key.lower(), value)
-
-        self.image_path = self.data["image_path"]
-
-        if "mask_path" not in self.data.columns:
-            self.mask_path = None
-        else:
-            self.mask_path = self.data["mask_path"]
-
-        if text_encoder_1 is None:
-            self.return_text_embeddings = False
-        else:
-            self.text_encoder_1 = text_encoder_1
-            self.text_encoder_2 = text_encoder_2
-            self.return_text_embeddings = True
-            assert (
-                NotImplementedError
-            ), "Preprocessing Text Encoder is not implemented yet"
-
-        self.tokenizer_1 = tokenizer_1
-        self.tokenizer_2 = tokenizer_2
-
-        self.vae_encoder = vae_encoder
-        self.scale_vae_latents = scale_vae_latents
-        self.text_dropout = text_dropout
-
-        self.size = size
-
-        if do_cache:
-            self.vae_latents = []
-            self.tokens_tuple = []
-            self.masks = []
-
-            self.do_cache = True
-
-            print("Captions to train on: ")
-            for idx in range(len(self.data)):
-                token, vae_latent, mask = self._process(idx)
-                self.vae_latents.append(vae_latent)
-                self.tokens_tuple.append(token)
-                self.masks.append(mask)
-
-            del self.vae_encoder
-
-        else:
-            self.do_cache = False
-
-    @torch.no_grad()
-    def _process(
-        self, idx: int
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
-        image_path = self.image_path[idx]
-        image_path = os.path.join(os.path.dirname(self.csv_path), image_path)
-
-        image = PIL.Image.open(image_path).convert("RGB")
-        image = prepare_image(image, self.size, self.size).to(
-            dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
-        )
-
-        caption = self.caption[idx]
-
-        print(caption)
-
-        # tokenizer_1
-        ti1 = self.tokenizer_1(
-            caption,
-            padding="max_length",
-            max_length=77,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        ).input_ids
-
-        ti2 = self.tokenizer_2(
-            caption,
-            padding="max_length",
-            max_length=77,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        ).input_ids
-
-        vae_latent = self.vae_encoder.encode(image).latent_dist.sample()
-
-        if self.scale_vae_latents:
-            vae_latent = vae_latent * self.vae_encoder.config.scaling_factor
-
-        if self.mask_path is None:
-            mask = torch.ones_like(
-                vae_latent, dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
-            )
-
-        else:
-            mask_path = self.mask_path[idx]
-            mask_path = os.path.join(os.path.dirname(self.csv_path), mask_path)
-
-            mask = PIL.Image.open(mask_path)
-            mask = prepare_mask(mask, self.size, self.size).to(
-                dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
-            )
-
-            mask = torch.nn.functional.interpolate(
-                mask, size=(vae_latent.shape[-2], vae_latent.shape[-1]), mode="nearest"
-            )
-            mask = mask.repeat(1, vae_latent.shape[1], 1, 1)
-
-        assert len(mask.shape) == 4 and len(vae_latent.shape) == 4
-
-        return (ti1.squeeze(), ti2.squeeze()), vae_latent.squeeze(), mask.squeeze()
-
-    def __len__(self) -> int:
-        return len(self.data)
-
-    def atidx(
-        self, idx: int
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
-        if self.do_cache:
-            return self.tokens_tuple[idx], self.vae_latents[idx], self.masks[idx]
-        else:
-            return self._process(idx)
-
-    def __getitem__(
-        self, idx: int
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
-        token, vae_latent, mask = self.atidx(idx)
-        return token, vae_latent, mask
-
-
-def import_model_class_from_model_name_or_path(
-    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
-):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "CLIPTextModelWithProjection":
-        from transformers import CLIPTextModelWithProjection
-
-        return CLIPTextModelWithProjection
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def load_models(pretrained_model_name_or_path, revision, device, weight_dtype):
-    tokenizer_one = AutoTokenizer.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=revision,
-        use_fast=False,
-    )
-    tokenizer_two = AutoTokenizer.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=revision,
-        use_fast=False,
-    )
-
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(
-        pretrained_model_name_or_path, subfolder="scheduler"
-    )
-    # import correct text encoder classes
-    text_encoder_cls_one = import_model_class_from_model_name_or_path(
-        pretrained_model_name_or_path, revision
-    )
-    text_encoder_cls_two = import_model_class_from_model_name_or_path(
-        pretrained_model_name_or_path, revision, subfolder="text_encoder_2"
-    )
-    text_encoder_one = text_encoder_cls_one.from_pretrained(
-        pretrained_model_name_or_path, subfolder="text_encoder", revision=revision
-    )
-    text_encoder_two = text_encoder_cls_two.from_pretrained(
-        pretrained_model_name_or_path, subfolder="text_encoder_2", revision=revision
-    )
-
-    vae = AutoencoderKL.from_pretrained(
-        pretrained_model_name_or_path, subfolder="vae", revision=revision
-    )
-    unet = UNet2DConditionModel.from_pretrained(
-        pretrained_model_name_or_path, subfolder="unet", revision=revision
-    )
-
-    vae.requires_grad_(False)
-    text_encoder_one.requires_grad_(False)
-    text_encoder_two.requires_grad_(False)
-
-    unet.to(device, dtype=weight_dtype)
-    vae.to(device, dtype=torch.float32)
-    text_encoder_one.to(device, dtype=weight_dtype)
-    text_encoder_two.to(device, dtype=weight_dtype)
-
-    return (
-        tokenizer_one,
-        tokenizer_two,
-        noise_scheduler,
-        text_encoder_one,
-        text_encoder_two,
-        vae,
-        unet,
-    )
-
-
-def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
-    """
-    Returns:
-        a state dict containing just the attention processor parameters.
-    """
-    attn_processors = unet.attn_processors
-
-    attn_processors_state_dict = {}
-
-    for attn_processor_key, attn_processor in attn_processors.items():
-        for parameter_key, parameter in attn_processor.state_dict().items():
-            attn_processors_state_dict[
-                f"{attn_processor_key}.{parameter_key}"
-            ] = parameter
-
-    return attn_processors_state_dict
-
-
-class TokenEmbeddingsHandler:
-    def __init__(self, text_encoders, tokenizers):
-        self.text_encoders = text_encoders
-        self.tokenizers = tokenizers
-
-        self.train_ids: Optional[torch.Tensor] = None
-        self.inserting_toks: Optional[List[str]] = None
-        self.embeddings_settings = {}
-
-    def initialize_new_tokens(self, inserting_toks: List[str]):
-        idx = 0
-        for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
-            assert isinstance(
-                inserting_toks, list
-            ), "inserting_toks should be a list of strings."
-            assert all(
-                isinstance(tok, str) for tok in inserting_toks
-            ), "All elements in inserting_toks should be strings."
-
-            self.inserting_toks = inserting_toks
-            special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
-            tokenizer.add_special_tokens(special_tokens_dict)
-            text_encoder.resize_token_embeddings(len(tokenizer))
-
-            self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
-
-            # random initialization of new tokens
-
-            std_token_embedding = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data.std()
-            )
-
-            print(f"{idx} text encodedr's std_token_embedding: {std_token_embedding}")
-
-            text_encoder.text_model.embeddings.token_embedding.weight.data[
-                self.train_ids
-            ] = (
-                torch.randn(
-                    len(self.train_ids), text_encoder.text_model.config.hidden_size
-                )
-                .to(device=self.device)
-                .to(dtype=self.dtype)
-                * std_token_embedding
-            )
-            self.embeddings_settings[
-                f"original_embeddings_{idx}"
-            ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
-            self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
-
-            inu = torch.ones((len(tokenizer),), dtype=torch.bool)
-            inu[self.train_ids] = False
-
-            self.embeddings_settings[f"index_no_updates_{idx}"] = inu
-
-            print(self.embeddings_settings[f"index_no_updates_{idx}"].shape)
-
-            idx += 1
-
-    def save_embeddings(self, file_path: str):
-        assert (
-            self.train_ids is not None
-        ), "Initialize new tokens before saving embeddings."
-        tensors = {}
-        for idx, text_encoder in enumerate(self.text_encoders):
-            assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[
-                0
-            ] == len(self.tokenizers[0]), "Tokenizers should be the same."
-            new_token_embeddings = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data[
-                    self.train_ids
-                ]
-            )
-            tensors[f"text_encoders_{idx}"] = new_token_embeddings
-
-        save_file(tensors, file_path)
-
-    @property
-    def dtype(self):
-        return self.text_encoders[0].dtype
-
-    @property
-    def device(self):
-        return self.text_encoders[0].device
-
-    def _load_embeddings(self, loaded_embeddings, tokenizer, text_encoder):
-        # Assuming new tokens are of the format <s_i>
-        self.inserting_toks = [f"<s{i}>" for i in range(loaded_embeddings.shape[0])]
-        special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
-        tokenizer.add_special_tokens(special_tokens_dict)
-        text_encoder.resize_token_embeddings(len(tokenizer))
-
-        self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
-        assert self.train_ids is not None, "New tokens could not be converted to IDs."
-        text_encoder.text_model.embeddings.token_embedding.weight.data[
-            self.train_ids
-        ] = loaded_embeddings.to(device=self.device).to(dtype=self.dtype)
-
-    @torch.no_grad()
-    def retract_embeddings(self):
-        for idx, text_encoder in enumerate(self.text_encoders):
-            index_no_updates = self.embeddings_settings[f"index_no_updates_{idx}"]
-            text_encoder.text_model.embeddings.token_embedding.weight.data[
-                index_no_updates
-            ] = (
-                self.embeddings_settings[f"original_embeddings_{idx}"][index_no_updates]
-                .to(device=text_encoder.device)
-                .to(dtype=text_encoder.dtype)
-            )
-
-            # for the parts that were updated, we need to normalize them
-            # to have the same std as before
-            std_token_embedding = self.embeddings_settings[f"std_token_embedding_{idx}"]
-
-            index_updates = ~index_no_updates
-            new_embeddings = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data[
-                    index_updates
-                ]
-            )
-            off_ratio = std_token_embedding / new_embeddings.std()
-
-            new_embeddings = new_embeddings * (off_ratio**0.1)
-            text_encoder.text_model.embeddings.token_embedding.weight.data[
-                index_updates
-            ] = new_embeddings
-
-    def load_embeddings(self, file_path: str):
-        with safe_open(file_path, framework="pt", device=self.device.type) as f:
-            for idx in range(len(self.text_encoders)):
-                text_encoder = self.text_encoders[idx]
-                tokenizer = self.tokenizers[idx]
-
-                loaded_embeddings = f.get_tensor(f"text_encoders_{idx}")
-                self._load_embeddings(loaded_embeddings, tokenizer, text_encoder)
\ No newline at end of file
diff --git a/spaces/eliolio/yelp-reviews/app.py b/spaces/eliolio/yelp-reviews/app.py
deleted file mode 100644
index edc4da72d090558ea739c631b9c28db5acb11d75..0000000000000000000000000000000000000000
--- a/spaces/eliolio/yelp-reviews/app.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import transformers
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
-import gradio as gr
-import os
-
-model_name = 'eliolio/bart-finetuned-yelpreviews'
-bert_model_name = 'eliolio/bert-correlation-yelpreviews'
-
-access_token = os.environ.get('private_token')
-
-model = AutoModelForSeq2SeqLM.from_pretrained(
-    model_name, use_auth_token=access_token
-)
-tokenizer = AutoTokenizer.from_pretrained(
-    model_name, use_auth_token=access_token
-)
-
-bert_tokenizer = AutoTokenizer.from_pretrained(
-    bert_model_name, use_auth_token=access_token
-)
-bert_model = AutoModelForSequenceClassification.from_pretrained(
-    bert_model_name, use_auth_token=access_token
-)
-
-
-def correlation_score(table, review):
-    # Compute the correlation score
-    args = ((table, review))
-    inputs = bert_tokenizer(*args, padding=True, max_length=128, truncation=True, return_tensors="pt")
-    logits = bert_model(**inputs).logits
-    probs = logits.softmax(dim=-1)
-    return {
-        "correlated": probs[:, 1].item(),
-        "uncorrelated": probs[:, 0].item()
-    }
-
-def create_prompt(stars, useful, funny, cool):
-    return f"Generate review: stars: {stars}, useful: {useful}, funny: {funny}, cool: {cool}"
-
-
-def postprocess(review):
-    dot = review.rfind('.')
-    return review[:dot+1]
-
-
-def generate_reviews(stars, useful, funny, cool):
-    text = create_prompt(stars, useful, funny, cool)
-    inputs = tokenizer(text, return_tensors='pt')
-    out = model.generate(
-        input_ids=inputs.input_ids,
-        attention_mask=inputs.attention_mask,
-        do_sample=True,
-        num_return_sequences=3,
-        temperature=1.2,
-        top_p=0.9
-    )
-    reviews = []
-    scores = []
-    for review in out:
-        reviews.append(postprocess(tokenizer.decode(review, skip_special_tokens=True)))
-    for review in reviews:
-        scores.append(
-            correlation_score(text[17:], review)
-        )
-
-    return reviews[0], reviews[1], reviews[2], scores[0], scores[1], scores[2]
-
-
-css = """
-    #ctr {text-align: center;}
-    #btn {color: white; background: linear-gradient( 90deg,  rgba(255,166,0,1) 14.7%, rgba(255,99,97,1) 73% );}
-"""
-
-
-md_text = """<h1 style='text-align: center; margin-bottom: 1rem'>Generating Yelp reviews with BART-base ⭐⭐⭐</h1>
-
-This space demonstrates how synthetic data generation can be performed on natural language columns, as found in the Yelp reviews dataset.   
-
-| review id | stars | useful | funny | cool | text |
-|:---:|:---:|:---:|:---:|:---:|:---:|
-| 0 | 5 | 1 | 0 | 1 | "Wow! Yummy, different, delicious. Our favorite is the lamb curry and korma. With 10 different kinds of naan!!!  Don't let the outside deter you (because we almost changed our minds)...go in and try something new! You'll be glad you did!"
-
-
-
-
-The model is a fine-tuned version of [facebook/bart-base](https://huggingface.com/facebook/bart-base) on Yelp reviews with the following input-output pairs:
-
-- **Input**: "Generate review: stars: 5, useful: 1, funny: 0, cool: 1"
-- **Output**: "Wow!  Yummy, different,  delicious.   Our favorite is the lamb curry and korma.  With 10 different kinds of naan!!!  Don't let the outside deter you (because we almost changed our minds)...go in and try something new!   You'll be glad you did!"
-"""
-
-resources = """## Resources
-- Code for training: [github repo](https://github.com/EliottZemour/yelp-reviews/)
-- The Yelp reviews dataset can be found in json format [here](https://www.yelp.com/dataset)."""
-
-demo = gr.Blocks(css=css)
-with demo:
-    with gr.Row():
-        gr.Markdown(md_text)
-
-    with gr.Row():
-        stars = gr.inputs.Slider(minimum=0, maximum=5,
-                                 step=1, default=0, label="stars")
-        useful = gr.inputs.Slider(
-            minimum=0, maximum=5, step=1, default=0, label="useful")
-        funny = gr.inputs.Slider(minimum=0, maximum=5,
-                                 step=1, default=0, label="funny")
-        cool = gr.inputs.Slider(minimum=0, maximum=5,
-                                step=1, default=0, label="cool")
-    with gr.Row():
-        button = gr.Button("Generate reviews !", elem_id='btn')
-
-    with gr.Row():
-        output1 = gr.Textbox(label="Review #1")
-        output2 = gr.Textbox(label="Review #2")
-        output3 = gr.Textbox(label="Review #3")
-
-    with gr.Row():
-        score1 = gr.Label(label="Correlation score #1")
-        score2 = gr.Label(label="Correlation score #2")
-        score3 = gr.Label(label="Correlation score #3")
-
-    with gr.Row():
-        gr.Markdown(resources)
-
-    button.click(
-        fn=generate_reviews,
-        inputs=[stars, useful, funny, cool],
-        outputs=[output1, output2, output3, score1, score2, score3]
-    )
-
-demo.launch()
diff --git a/spaces/elitecode/Detect_Emotions/app.py b/spaces/elitecode/Detect_Emotions/app.py
deleted file mode 100644
index f68dfdbe8003c892e27a2a4064a5d0a179107abe..0000000000000000000000000000000000000000
--- a/spaces/elitecode/Detect_Emotions/app.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/j-hartmann/emotion-english-distilroberta-base").launch()
-# gr.Interface.load("models/elitecode/Detect_Emotions").launch()
\ No newline at end of file
diff --git a/spaces/exnav29/Real_Estate_Bot/app.py b/spaces/exnav29/Real_Estate_Bot/app.py
deleted file mode 100644
index a4fae9fb19716c1f297abc0b922d6985780e9995..0000000000000000000000000000000000000000
--- a/spaces/exnav29/Real_Estate_Bot/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-import openai
-import gradio as gr
-
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-messages = [
-    {"role": "system", "content": "You are an AI specialized in Real Estate Investing Do not answer anything other than Real Estate-related queries"},
-]
-counter = 0
-def chatbot(input):
-    global counter
-    if input:
-        messages.append({"role": "user", "content": input})
-        chat = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo", messages=messages, temperature=0.2
-        )
-        reply = chat.choices[0].message.content
-        messages.append({"role": "assistant", "content": reply})
-        counter += 1
-        if counter >= 3:
-            gr.Interface.show_feedback_message("Thank you for using our chatbot! If you find our service helpful, please consider making a donation to support us: <a href='https://www.paypal.me/exnav29' target='_blank'>Donate Now</a>")
-            counter = 0
-        return reply
-
-inputs = gr.inputs.Textbox(lines=7, label="Ask your question")
-outputs = gr.outputs.Textbox(label="Reply")
-
-app = gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, 
-                   title="JChat Real Estate Investing Chatbot", 
-                   description="Ask anything you want about investing in real estate")
-app.launch()
\ No newline at end of file
diff --git a/spaces/failfast/2D-GameCreator/src/components/Examples.tsx b/spaces/failfast/2D-GameCreator/src/components/Examples.tsx
deleted file mode 100644
index 6e5e870473a1b4f909e640d1f20dbe2ee2004b81..0000000000000000000000000000000000000000
--- a/spaces/failfast/2D-GameCreator/src/components/Examples.tsx
+++ /dev/null
@@ -1,62 +0,0 @@
-import { Link, Typography } from "@mui/material";
-import { DividerBox, SectionBox } from "./base/boxes";
-import ExamplesGrid, { Example } from "@/components/ExamplesGrid";
-
-const examples: Example[] = [
-	{
-		title: "Space Invaders Extreme",
-		creatorLink: "https://nerddis.co",
-		creatorName: "NERDDISCO",
-		image: "img/space_invaders_extreme.jpg",
-		playLink: "https://codesandbox.io/s/space-invaders-extreme-7xhp4v",
-		model: "GPT 3.5 Turbo",
-		iterations: 20,
-		controls: "Keyboard: Movement (Arrow Left, Arrow right), Shooting (Space), Restart (R)",
-		hints: "The bigger the window, the easier it gets to play",
-	},
-	{
-		title: "Flappy Bird SPEED",
-		creatorLink: "https://nerddis.co",
-		creatorName: "NERDDISCO",
-		image: "img/flappy_bird_speed.jpg",
-		playLink: "https://codesandbox.io/s/flappy-bird-speed-rg9z6f",
-		model: "GPT 4.0",
-		iterations: 24,
-		controls: "Keyboard: Fly (space)",
-		hints: "Don't collide with the pipes ;)",
-	},
-];
-
-export default function Examples() {
-	return (
-		<>
-			<DividerBox />
-
-			<SectionBox>
-				<Typography component="h3" variant="h2">
-					Example Games
-				</Typography>
-				<Typography variant="body1">
-					We re-created some of our favorite games! You want to share your game with the
-					community? Then head over to our{" "}
-					<Link
-						href="https://discord.com/invite/m3TBB9XEkb"
-						target="_blank"
-						rel="noopener"
-						sx={{ verticalAlign: "bottom" }}
-					>
-						<img
-							style={{ verticalAlign: "bottom" }}
-							src="https://img.shields.io/discord/1091306623819059300?color=7289da&label=Discord&logo=discord&logoColor=fff&style=for-the-badge"
-						/>
-					</Link>
-					, post it in #showcase and we might add it to this section as well!
-				</Typography>
-			</SectionBox>
-
-			<ExamplesGrid examples={examples} />
-
-			<DividerBox />
-		</>
-	);
-}
diff --git a/spaces/falterWliame/Face_Mask_Detection/148 Yoga Muthirai In Tamil.md b/spaces/falterWliame/Face_Mask_Detection/148 Yoga Muthirai In Tamil.md
deleted file mode 100644
index 354665dcb59493ec1b1a37005abeb76c33f861c9..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/148 Yoga Muthirai In Tamil.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<br />
-<p>this mudra for the nose is performed by the person who is in the pose. place the tip of your right index finger and ring finger together. interlock the other fingers and place your hands on your nose. this yoga mudra will give you relief from nasal congestion.</p>
-<h2>148 Yoga Muthirai In Tamil</h2><br /><p><b><b>DOWNLOAD</b> ->->->-> <a href="https://urlca.com/2uDcYS">https://urlca.com/2uDcYS</a></b></p><br /><br />
-<p>the two yoga mudras for the stomach are performed by the person who is in the pose. cross your right index finger over your right thumb and place your right hand on your belly button. this mudra will aid digestion.</p>
-<p>this mudra for the jaw is performed by the person who is in the pose. place your index and middle fingers together. interlock the other fingers and place your hands on your jaw line. this yoga mudra will help you calm your nerves and boost your confidence.</p>
-<p>the two yoga mudras for the body are performed by the person who is in the pose. cross your right index finger over your right thumb and place your right hand on your chest. this mudra will ease your chest congestion. cross your right middle finger over your right index finger and place your right hand on your abdomen. this mudra will aid digestion.</p>
-<p>this mudra for the feet is performed by the person who is in the pose. cross your left index finger over your left thumb and place your left hand on your left foot. this yoga mudra will calm your nerves and boost your confidence.</p>
-<p>for many people, yoga is regarded as a type of religion. in fact, yoga has been practiced by monks, nuns and other religious devotees for a long time. there are certain hand gestures that people perform, similar to prayer, as part of yoga or meditation. they are called mudras. the following mudras are used in yoga. </p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Beauty And The Beast (English) 3 Telugu Dubbed Movie Onlinel [UPD].md b/spaces/falterWliame/Face_Mask_Detection/Beauty And The Beast (English) 3 Telugu Dubbed Movie Onlinel [UPD].md
deleted file mode 100644
index 927dac351685cfe7c9255693dbe892807757d1b6..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Beauty And The Beast (English) 3 Telugu Dubbed Movie Onlinel [UPD].md	
+++ /dev/null
@@ -1,43 +0,0 @@
-<br />
-<h1>Beauty And The Beast (English) 3 Telugu Dubbed Movie Online</h1>
-<p>Beauty and the Beast is one of the most beloved fairy tales of all time. It tells the story of Belle, a young woman who is taken prisoner by a beast in his enchanted castle. Despite her fears, she learns to look beyond his appearance and discover the kind heart and soul of the true prince within.</p>
-<p>There have been many adaptations of this classic story, but one of the most recent and popular ones is the 2017 live-action film starring Emma Watson and Dan Stevens. This film was a huge success, earning over $1 billion at the box office and receiving positive reviews from critics and audiences alike. It also won two Academy Awards for Best Production Design and Best Costume Design.</p>
-<h2>Beauty And The Beast (English) 3 Telugu Dubbed Movie Onlinel</h2><br /><p><b><b>Download Zip</b> &#10038;&#10038;&#10038; <a href="https://urlca.com/2uDcf1">https://urlca.com/2uDcf1</a></b></p><br /><br />
-<p>If you are a fan of this movie and want to watch it in Telugu, you are in luck. There are several online platforms where you can stream or download Beauty And The Beast (English) 3 Telugu Dubbed Movie Online. Here are some of them:</p>
-<ul>
-<li>Disney+ Hotstar: This is the official streaming service of Disney, where you can watch all their movies and shows in various languages, including Telugu. You can watch Beauty And The Beast Full Movie on Disney+ Hotstar with a subscription or a VIP plan.</li>
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diff --git a/spaces/fatiXbelha/sd/Blue Apron Home Cooking Made Easy and Fun - Download the App and Get 80 Off.md b/spaces/fatiXbelha/sd/Blue Apron Home Cooking Made Easy and Fun - Download the App and Get 80 Off.md
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diff --git a/spaces/fatiXbelha/sd/City of Crime Liberty Mod APK How to Use Unlimited Money and Gems to Dominate the City.md b/spaces/fatiXbelha/sd/City of Crime Liberty Mod APK How to Use Unlimited Money and Gems to Dominate the City.md
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diff --git a/spaces/fatiXbelha/sd/Download 1 Dollar Lawyer and Enjoy the Thrilling Story of a Low-Cost Lawyer with a High Success Rate.md b/spaces/fatiXbelha/sd/Download 1 Dollar Lawyer and Enjoy the Thrilling Story of a Low-Cost Lawyer with a High Success Rate.md
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diff --git a/spaces/fatiXbelha/sd/Download Pinkalicious Episodes for Free - Watch the Full Series Online.md b/spaces/fatiXbelha/sd/Download Pinkalicious Episodes for Free - Watch the Full Series Online.md
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--- a/spaces/fatiXbelha/sd/Download Pinkalicious Episodes for Free - Watch the Full Series Online.md	
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-<li>Open the PBS Kids app or website and find the episode you want to download.</li>
-<li>Tap or click on the download icon next to the episode title.</li>
-<li>Wait for the download to finish. You can check the progress on the downloads tab.</li>
-<li>Once the download is complete, you can watch the episode offline from the downloads tab.</li>
-</ul>
-<p>Note that you can only download up to 25 episodes at a time, and they will expire after 30 days or when you delete the app or website data.</p>
-<h3>You can also download Pinkalicious episodes from Amazon Prime Video or YouTube TV</h3>
-<p>Another way to download Pinkalicious episodes for offline viewing is to use Amazon Prime Video or YouTube TV. These platforms allow you to download episodes to your device and watch them without an internet connection. To download an episode from Amazon Prime Video or YouTube TV, follow these steps:</p>
-<ul>
-<li>Open the Amazon Prime Video or YouTube TV app or website and sign in with your account.</li>
-<li>Find the episode you want to download and tap or click on it.</li>
-<li>Tap or click on the download icon at the bottom of the screen.</li>
-<li>Select the quality and storage options you prefer and confirm the download.</li>
-<li>Wait for the download to finish. You can check the progress on the downloads tab.</li>
-<li>Once the download is complete, you can watch the episode offline from the downloads tab.</li>
-</ul>
-<p>Note that you need to have an active subscription and enough storage space on your device to download episodes. You also need to connect to the internet at least once every 30 days to keep your downloads active.</p>
- <h2>Conclusion</h2>
-<h3>Summary of the main points and benefits of downloading Pinkalicious episodes</h3>
-<p>Pinkalicious is a wonderful show for kids that teaches them about creativity, diversity, and learning. You can watch Pinkalicious episodes online for free on the PBS Kids website or app, or stream them on Amazon Prime Video, YouTube TV, or PBS Passport. You can also download Pinkalicious episodes for offline viewing from the PBS Kids app or website, or from Amazon Prime Video or YouTube TV. Downloading Pinkalicious episodes allows you to enjoy them anytime and anywhere, without worrying about internet access or data usage.</p>
-<h3>Call to action and invitation to share feedback or questions</h3>
-<p>We hope this article has helped you learn how to download Pinkalicious episodes for free. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you. And if you liked this article, please share it with your friends and family who might also be interested in watching Pinkalicious. Thank you for reading!</p>
- <h2>Frequently Asked Questions</h2>
-<h3>How many seasons and episodes of Pinkalicious are there?</h3>
-<p>As of June 2023, there are three seasons and 78 episodes of Pinkalicious. The show premiered in February 2018 and is still ongoing.</p>
-<h3>What are some of the other characters in Pinkalicious?</h3>
-<p>Some of the other characters in Pinkalicious are:</p>
-<ul>
-<li>Peter: Pinkalicious's younger brother who likes purple.</li>
-<li>Mommy and Daddy: Pinkalicious's parents who support her creativity and adventures.</li>
-<li>Rafael: Pinkalicious's best friend who likes yellow.</li>
-<li>Jasmine: Pinkalicious's other best friend who likes blue.</li>
-<li>Lila: Pinkalicious's neighbor who likes green.</li>
-<li>Kendra: Pinkalicious's rival who likes orange.</li>
-</ul>
- <h3>What are some of the activities and games related to Pinkalicious?</h3>
-<p>Some of the activities and games related to Pinkalicious are:</p>
-<ul>
-<li>Pinkcredible Story Maker: A game where you can create your own stories with Pinkalicious and her friends.</li>
-<li>Pinkatastic Stickers: A game where you can decorate scenes with stickers of Pinkalicious and her friends.</li>
-<li>Pinkamagine Fashion: A game where you can design outfits for Pinkalicious and her friends.</li>
-<li>Pinka-Perfect Band: A game where you can make music with Pinkalicious and her friends.</li>
-<li>Pinka-Paint: A game where you can paint pictures with Pinkalicious and her friends.</li>
-</ul>
- <h3>Where can I find more information about Pinkalicious?</h3>
-<p>You can find more information about Pinkalicious on the following websites:</p>
-<ul>
-<li><a href="">The official PBS Kids website for Pinkalicious</a></li>
-<li><a href="">The official Facebook page for Pinkalicious</a></li>
-<li><a href="">The official Instagram account for Pinkalicious </a></li>
-<li><a href="">The official YouTube channel for Pinkalicious</a></li>
-</ul>
- <h3>How can I support Pinkalicious and PBS Kids?</h3>
-<p>If you love Pinkalicious and PBS Kids, you can support them by:</p>
-<ul>
-<li>Donating to your local PBS station or becoming a PBS Passport member.</li>
-<li>Buying Pinkalicious books, DVDs, toys, and merchandise.</li>
-<li>Following and sharing Pinkalicious on social media and online platforms.</li>
-<li>Writing reviews and ratings for Pinkalicious on Amazon, IMDb, or other websites.</li>
-<li>Telling your friends and family about Pinkalicious and encouraging them to watch it.</li>
-</ul></p> 401be4b1e0<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/wsc_preprocessing.py b/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/wsc_preprocessing.py
deleted file mode 100644
index c9b5ec0a7625ac870e71e77cf8af6256e0a1609c..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/wsc_preprocessing.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import json
-from tqdm import tqdm
-import os
-import argparse
-
-label2desc={'true':'是','false':'不是'}
-
-
-def load_data(file_path,is_training=False):
-    with open(file_path, 'r', encoding='utf8') as f:
-        lines = f.readlines()
-        result=[]
-        for line in tqdm(lines): 
-            data = json.loads(line)
-            target = data['target']
-            text=list(data['text'])
-            if target['span2_index']<target['span1_index']:
-                text.insert(target['span2_index'],'_')
-                text.insert(target['span2_index']+len(target['span2_text'])+1,'_')
-                text.insert(target['span1_index']+2,'[')
-                text.insert(target['span1_index']+2+len(target['span1_text'])+1,']')
-            else:
-                text.insert(target['span1_index'],'[')
-                text.insert(target['span1_index']+len(target['span1_text'])+1,']')
-                text.insert(target['span2_index']+2,'_')
-                text.insert(target['span2_index']+2+len(target['span2_text'])+1,'_')
-
-            texta = ''.join(text)
-            textb = ''
-            span2_text=target['span2_text']
-            span1_text=target['span1_text']
-
-            question = ''
-
-            choice = []
-            for k,v in label2desc.items():
-                choice .append(f'{span2_text}{v}{span1_text}')
-            # print(choice)
-            answer = label2desc[data['label']] if 'label' in data.keys() else ''
-            answer = f'{span2_text}{answer}{span1_text}'
-
-            label = choice.index(answer) if 'label' in data.keys() else 0
-            text_id = data['id'] if 'id' in data.keys() else 0
-            result.append({'texta':texta,
-                            'textb':textb,
-                            'question':question,
-                            'choice':choice,
-                            'answer':answer,
-                            'label':label,
-                            'id':text_id}) 
-        for i in range(5):
-            print(result[i])
-        return result
-
-
-def save_data(data,file_path):
-    with open(file_path, 'w', encoding='utf8') as f:
-        for line in data:
-            json_data=json.dumps(line,ensure_ascii=False)
-            f.write(json_data+'\n')
-
-
-if __name__=="__main__":
-    parser = argparse.ArgumentParser(description="train")
-    parser.add_argument("--data_path", type=str,default="")
-    parser.add_argument("--save_path", type=str,default="")
-
-    args = parser.parse_args()
-    
-    
-    data_path = args.data_path
-    save_path = args.save_path
-    
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-    
-    file_list = ['train','dev','test1.0','test1.1']
-    for file in file_list:
-        file_path = os.path.join(data_path,file+'.json')
-        output_path = os.path.join(save_path,file+'.json')
-        save_data(load_data(file_path),output_path)
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Need For Speed Underground 2 APK  OBB and Enjoy the Ultimate Racing Experience on Your Android Phone.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Need For Speed Underground 2 APK  OBB and Enjoy the Ultimate Racing Experience on Your Android Phone.md
deleted file mode 100644
index 7f5e609bf7fce6f61b70befcdb0ab8451ba40802..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Need For Speed Underground 2 APK  OBB and Enjoy the Ultimate Racing Experience on Your Android Phone.md	
+++ /dev/null
@@ -1,155 +0,0 @@
-<br />
-<h1>Need for Speed Underground 2 APK + OBB Download: A Guide for Racing Fans</h1>
- <p>If you are a fan of racing games, you might have heard of Need for Speed Underground 2, one of the most popular titles in the Need for Speed series. This game was released in 2004 for various platforms, including PC, PlayStation 2, Xbox, GameCube, Nintendo DS, and PlayStation Portable. But did you know that you can also play this game on your Android device?</p>
- <p>In this article, we will show you how to download and install Need for Speed Underground 2 APK + OBB on your Android device, as well as some of the features, tips, and tricks that you can use to enjoy this game to the fullest. So buckle up and get ready to race!</p>
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- <h2>Introduction</h2>
- <h3>What is Need for Speed Underground 2?</h3>
- <p>Need for Speed Underground 2 is a racing game that focuses on the street racing culture and the import tuner scene. The game follows the story of a racer who arrives in a new city called Bayview after being ambushed by a rival crew in Olympic City. With the help of Rachel Teller, a friend of Samantha who was the protagonist's mentor in the previous game, the racer must rebuild his reputation and challenge the top racers in Bayview.</p>
- <h3>Why should you play Need for Speed Underground 2 on your Android device?</h3>
- <p>Need for Speed Underground 2 is a game that offers a lot of fun and excitement for racing fans. Here are some of the reasons why you should play this game on your Android device:</p>
- <ul>
-<li>You can experience the thrill of street racing anytime and anywhere.</li>
-<li>You can customize your car with hundreds of parts and accessories.</li>
-<li>You can explore a large open-world city with different neighborhoods and terrains.</li>
-<li>You can compete in various modes and events, such as circuit, drag, drift, sprint, outrun, street X, URL, and more.</li>
-<li>You can challenge your friends in multiplayer mode via Wi-Fi or Bluetooth.</li>
-</ul>
- <h2>How to download and install Need for Speed Underground 2 APK + OBB on your Android device</h2>
- <p>To play Need for Speed Underground 2 on your Android device, you will need to download two files: an APK file and an OBB file. The APK file is the application file that contains the game's code and data, while the OBB file is the additional file that contains the game's graphics and sound assets. Here are the steps to download and install these files on your device:</p>
- <h3>Step 1: Download the APK and OBB files from a trusted source</h3>
- <p>The first step is to download the APK and OBB files from a trusted source. You can search online for websites that offer these files, but make sure that they are safe and reliable. <h3>Step 2: Enable unknown sources on your device settings</h3>
- <p>The next step is to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store or other trusted sources. To do this, follow these steps:</p>
- <ol>
-<li>Go to your device settings and look for the security or privacy option.</li>
-<li>Depending on your device, you may need to tap on the lock screen and security tab or the install unknown apps switch.</li>
-<li>Then, you need to turn on the unknown sources switch or check the box next to it.</li>
-<li>You may see a warning message against enabling this option. Tap OK to confirm.</li>
-</ol>
- <p>Now you are ready to install the APK file and extract the OBB file.</p>
- <h3>Step 3: Install the APK file and extract the OBB file</h3>
- <p>The third step is to install the APK file and extract the OBB file. To do this, follow these steps:</p>
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- <ol>
-<li>Locate the APK file that you downloaded in your device storage. You can use a file manager app to help you with this.</li>
-<li>Tap on the APK file and follow the installation instructions. You may need to grant some permissions to the app.</li>
-<li>Once the installation is complete, do not open the app yet.</li>
-<li>Locate the OBB file that you downloaded in your device storage. It should have a .zip or .rar extension.</li>
-<li>Tap on the OBB file and extract it using a file extractor app. You should get a folder that is usually named com.developer-name.androidpackage.</li>
-</ol>
- <p>Now you have installed the APK file and extracted the OBB file.</p>
- <h3>Step 4: Move the OBB file to the appropriate folder</h3>
- <p>The fourth step is to move the OBB file to the appropriate folder. To do this, follow these steps:</p>
- <ol>
-<li>Cut or copy the folder that you extracted from the OBB file.</li>
-<li>Navigate to your device storage and look for a folder named Android.</li>
-<li>Open the Android folder and look for a folder named OBB. If you don't see it, create one.</li>
-<li>Paste the folder that you cut or copied into the OBB folder.</li>
-</ol>
- <p>Now you have moved the OBB file to the appropriate folder.</p>
- <h3>Step 5: Launch the game and enjoy</h3>
- <p>The final step is to launch the game and enjoy. To do this, follow these steps:</p>
- <ol>
-<li>Go back to your device home screen and look for the game icon.</li>
-<li>Tap on the game icon and wait for it to load.</li>
-<li>You may need to accept some terms and conditions or adjust some settings before playing.</li>
-<li>Once the game is ready, you can start racing and customizing your car.</li>
-</ol>
- <p>Congratulations! You have successfully downloaded and installed Need for Speed Underground 2 APK + OBB on your Android device.</p>
- <h2>Features of Need for Speed Underground 2 APK + OBB</h2>
- <h3>Stunning graphics and sound effects</h3>
- <p>One of the features of Need for Speed Underground 2 APK + OBB is its stunning graphics and sound effects. The game has been optimized for mobile devices, so you can enjoy high-quality visuals and realistic sounds. The game also features dynamic lighting and shadows, as well as weather effects and day-night cycles. You can also listen to various music tracks from different genres, such as hip-hop, rock, electronic, and more.</p>
- <h3>Wide range of cars and customization options</h3>
- <p>Another feature of Need for Speed Underground 2 APK + OBB is its wide range of cars and customization options. The game offers over 30 licensed cars from various manufacturers, such as Toyota, Nissan, Mitsubishi, Honda, Ford, Chevrolet, and more. You can also customize your car with hundreds of parts and accessories, such as spoilers, bumpers, hoods, rims, tires, decals, paint jobs, vinyls, neon lights, and more. You can also tune your car performance by adjusting engine, transmission, suspension, brakes, nitrous, turbo, weight reduction, and more.</p>
- <h3>Various modes and events to choose from</h3>
- <p>A third feature of Need for Speed Underground 2 APK + OBB is its various modes and events to choose from. The game has a story mode that follows the plot of the original game, where you have to compete in various races and events to earn money and reputation. You can also play in free roam mode, where you can explore the city and find hidden shops and races. You can also participate in different types of races, such as circuit, drag, drift, sprint, outrun, street X, URL, and more. Each race type has its own rules and challenges, so you have to adapt your strategy accordingly.</p>
- <h3>Open-world city to explore and race in</h3>
- <p>A fourth feature of Need for Speed Underground 2 APK + OBB is its open-world city to explore and race in. The game features a large city called Bayview, which is divided into five districts: City Core, Beacon Hill, Jackson Heights, Coal Harbor, and Airport. Each district has its own style and atmosphere, as well as different roads and terrains. You can drive around the city and discover new places and shortcuts, as well as hidden shops and races. You can also interact with other racers and challenge them to outrun or street X events.</p>
- <h3>Multiplayer mode to challenge your friends</h3>
- <p>A fifth feature of Need for Speed Underground 2 APK + OBB is its multiplayer mode to challenge your friends. The game supports multiplayer mode via Wi-Fi or Bluetooth, where you can race against up to four players in various modes and events. You can also customize your car and show it off to your friends. You can also chat with other players and exchange tips and tricks.</p>
- <h2>Tips and tricks for playing Need for Speed Underground 2 APK + OBB</h2>
- <h3>Choose the right car for each race type</h3>
- <p>One of the tips for playing Need for Speed Underground 2 APK + OBB is to choose the right car for each race type. Different cars have different strengths and weaknesses, such as speed, acceleration, handling, durability, and nitrous. You should choose a car that suits your style and preference, as well as the race type that you are competing in. For example, if you are racing in a circuit or sprint event, you may want a car that has high speed and acceleration. If you are racing in a drag or drift event, you may want a car that has good handling and nitrous.</p>
- <h3>Upgrade your car performance and appearance wisely</h3>
- <p>Another tip for playing Need for Speed Underground 2 APK + OBB is to upgrade your car performance and appearance wisely. You can upgrade your car with various parts and accessories that can improve its performance and appearance. However, you should be careful not to spend too much money on unnecessary upgrades that may not make a significant difference. You should also balance your performance upgrades with your appearance upgrades, as both affect your reputation and star rating. Your reputation determines how much respect you get from other racers and how many events you can unlock. Your star rating determines how much attention you get from sponsors and magazines.</p>
- <h3>Use nitrous and drift strategically</h3>
- <p>A third tip for playing Need for Speed Underground 2 APK + OBB is to use nitrous and drift strategically. Nitrous is a boost that can give you a temporary speed advantage over your opponents. Drift is a technique that allows you to slide around corners without losing speed or control. Both nitrous and drift are useful tools that can help you win races, but you should use them wisely. You should use nitrous when you need an extra burst of speed or when you want to overtake another racer. You should use drift when you want to take sharp turns or when you want to earn more points in drift events.</p>
- <h3>Avoid obstacles and traffic on the road</h3>
- <p>A fourth tip for playing Need for Speed Underground 2 APK + OBB is to avoid obstacles and traffic on the road. The game features a realistic environment where you have to deal with various obstacles and traffic on the road. These include pedestrians, cars, trucks, buses, trains, police cars, barriers, poles, signs, trees, walls, bridges, tunnels, ramps, jumps, water puddles, oil spills, fire hydrants, trash cans , and more. These can slow you down, damage your car, or cause you to lose control. You should try to avoid them as much as possible, or use them to your advantage. For example, you can use ramps and jumps to perform stunts and shortcuts, or use water puddles and oil spills to make your opponents slip and crash.</p>
- <h3>Find hidden shops and races in the city</h3>
- <p>A fifth tip for playing Need for Speed Underground 2 APK + OBB is to find hidden shops and races in the city. The game features a large open-world city that has many secrets and surprises. You can find hidden shops that sell rare and exclusive parts and accessories for your car, or hidden races that offer high rewards and challenges. You can also find magazines and DVDs that feature your car and your achievements. To find these hidden shops and races, you need to explore the city and look for clues and icons on the map. You can also get tips from Rachel or other racers via SMS or phone calls.</p>
- <h2>Conclusion</h2>
- <p>Need for Speed Underground 2 APK + OBB is a game that will satisfy your need for speed and adrenaline. It is a game that offers stunning graphics, sound effects, cars, customization options, modes, events, open-world city, multiplayer mode, and more. It is a game that you can download and install on your Android device easily and safely by following the steps in this article. It is a game that you can play anytime and anywhere, and challenge your friends and rivals. It is a game that you should not miss if you are a fan of racing games.</p>
- <p>So what are you waiting for? Download Need for Speed Underground 2 APK + OBB now and start racing!</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about Need for Speed Underground 2 APK + OBB:</p>
- <ol>
-<li><b>Is Need for Speed Underground 2 APK + OBB safe to download and install?</b></li>
-<p>Yes, Need for Speed Underground 2 APK + OBB is safe to download and install, as long as you get the files from a trusted source. You should also scan the files with an antivirus app before installing them.</p>
-<li><b>Is Need for Speed Underground 2 APK + OBB compatible with my device?</b></li>
-<p>Need for Speed Underground 2 APK + OBB is compatible with most Android devices that have at least 1 GB of RAM and 1 GB of free storage space. However, some devices may experience performance issues or crashes due to hardware limitations or software conflicts. You should check the compatibility of your device before downloading and installing the game.</p>
-<li><b>How can I update Need for Speed Underground 2 APK + OBB?</b></li>
-<p>To update Need for Speed Underground 2 APK + OBB, you need to download the latest version of the APK and OBB files from a trusted source. Then, you need to uninstall the previous version of the game from your device. After that, you need to follow the same steps as installing the game for the first time.</p>
-<li><b>How can I fix Need for Speed Underground 2 APK + OBB errors or bugs?</b></li>
-<p>If you encounter any errors or bugs while playing Need for Speed Underground 2 APK + OBB, you can try some of these solutions:</p>
-<ul>
-<li>Restart your device and launch the game again.</li>
-<li>Clear the cache and data of the game from your device settings.</li>
-<li>Reinstall the game by following the steps in this article.</li>
-<li>Contact the developer or support team of the game via email or social media.</li>
-</ul>
-<li><b>Where can I get more information or help about Need for Speed Underground 2 APK + OBB?</b></li>
-<p>If you want more information or help about Need for Speed Underground 2 APK + OBB, you can visit some of these sources:</p>
-<ul>
-<li>The official website of Need for Speed: https://www.ea.com/games/need-for-speed</li>
-<li>The official Facebook page of Need for Speed: https://www.facebook.com/needforspeed</li>
-<li>The official Twitter account of Need for Speed: https://twitter.com/needforspeed</li>
-<li>The official YouTube channel of Need for Speed: https://www.youtube.com/user/NeedForSpeed</li>
-<li>The official Reddit community of Need for Speed: https://www.reddit.com/r/needforspeed/</li>
-</ul>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/Music_Source_Separation/bytesep/optimizers/__init__.py b/spaces/fffiloni/Music_Source_Separation/bytesep/optimizers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/firsk/ai_otto/resample.py b/spaces/firsk/ai_otto/resample.py
deleted file mode 100644
index 87abdfe19bda902ae9e99ab2a9f1ea8998425557..0000000000000000000000000000000000000000
--- a/spaces/firsk/ai_otto/resample.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-import argparse
-import librosa
-from multiprocessing import Pool, cpu_count
-
-import soundfile
-from tqdm import tqdm
-
-
-def process(item):
-    spkdir, wav_name, args = item
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and ".wav" in wav_path:
-        os.makedirs(os.path.join(args.out_dir, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, sr=args.sr)
-        soundfile.write(os.path.join(args.out_dir, speaker, wav_name), wav, sr)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr", type=int, default=44100, help="sampling rate")
-    parser.add_argument(
-        "--in_dir", type=str, default="./raw", help="path to source dir"
-    )
-    parser.add_argument(
-        "--out_dir", type=str, default="./dataset", help="path to target dir"
-    )
-    args = parser.parse_args()
-    # processes = 8
-    processes = cpu_count() - 2 if cpu_count() > 4 else 1
-    pool = Pool(processes=processes)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(
-                pool.imap_unordered(
-                    process,
-                    [
-                        (spk_dir, i, args)
-                        for i in os.listdir(spk_dir)
-                        if i.endswith("wav")
-                    ],
-                )
-            ):
-                pass
diff --git a/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// Last updated Apr. 16, 2011
-// Alex Evans: Linear memory allocator (taken from jpge.h).
-//
-// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.
-//
-// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.
-// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain"
-// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html
-
-#include "jpgd.h"
-#include <string.h>
-
-#include <assert.h>
-// BEGIN EPIC MOD
-#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0
-// END EPIC MOD
-
-#ifdef _MSC_VER
-#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable
-#endif
-
-// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).
-// This is slower, but results in higher quality on images with highly saturated colors.
-#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1
-
-#define JPGD_TRUE (1)
-#define JPGD_FALSE (0)
-
-#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))
-#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))
-
-namespace jpgd {
-
-	static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-	static inline void jpgd_free(void *p) { FMemory::Free(p); }
-
-// BEGIN EPIC MOD
-//@UE3 - use UE3 BGRA encoding instead of assuming RGBA
-	// stolen from IImageWrapper.h
-	enum ERGBFormatJPG
-	{
-		Invalid = -1,
-		RGBA =  0,
-		BGRA =  1,
-		Gray =  2,
-	};
-	static ERGBFormatJPG jpg_format;
-// END EPIC MOD
-
-	// DCT coefficients are stored in this sequence.
-	static int g_ZAG[64] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-
-	enum JPEG_MARKER
-	{
-		M_SOF0  = 0xC0, M_SOF1  = 0xC1, M_SOF2  = 0xC2, M_SOF3  = 0xC3, M_SOF5  = 0xC5, M_SOF6  = 0xC6, M_SOF7  = 0xC7, M_JPG   = 0xC8,
-		M_SOF9  = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT   = 0xC4, M_DAC   = 0xCC,
-		M_RST0  = 0xD0, M_RST1  = 0xD1, M_RST2  = 0xD2, M_RST3  = 0xD3, M_RST4  = 0xD4, M_RST5  = 0xD5, M_RST6  = 0xD6, M_RST7  = 0xD7,
-		M_SOI   = 0xD8, M_EOI   = 0xD9, M_SOS   = 0xDA, M_DQT   = 0xDB, M_DNL   = 0xDC, M_DRI   = 0xDD, M_DHP   = 0xDE, M_EXP   = 0xDF,
-		M_APP0  = 0xE0, M_APP15 = 0xEF, M_JPG0  = 0xF0, M_JPG13 = 0xFD, M_COM   = 0xFE, M_TEM   = 0x01, M_ERROR = 0x100, RST0   = 0xD0
-	};
-
-	enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };
-
-#define CONST_BITS  13
-#define PASS1_BITS  2
-#define SCALEDONE ((int32)1)
-
-#define FIX_0_298631336  ((int32)2446)        /* FIX(0.298631336) */
-#define FIX_0_390180644  ((int32)3196)        /* FIX(0.390180644) */
-#define FIX_0_541196100  ((int32)4433)        /* FIX(0.541196100) */
-#define FIX_0_765366865  ((int32)6270)        /* FIX(0.765366865) */
-#define FIX_0_899976223  ((int32)7373)        /* FIX(0.899976223) */
-#define FIX_1_175875602  ((int32)9633)        /* FIX(1.175875602) */
-#define FIX_1_501321110  ((int32)12299)       /* FIX(1.501321110) */
-#define FIX_1_847759065  ((int32)15137)       /* FIX(1.847759065) */
-#define FIX_1_961570560  ((int32)16069)       /* FIX(1.961570560) */
-#define FIX_2_053119869  ((int32)16819)       /* FIX(2.053119869) */
-#define FIX_2_562915447  ((int32)20995)       /* FIX(2.562915447) */
-#define FIX_3_072711026  ((int32)25172)       /* FIX(3.072711026) */
-
-#define DESCALE(x,n)  (((x) + (SCALEDONE << ((n)-1))) >> (n))
-#define DESCALE_ZEROSHIFT(x,n)  (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))
-
-#define MULTIPLY(var, cnst)  ((var) * (cnst))
-
-#define CLAMP(i) ((static_cast<uint>(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))
-
-	// Compiler creates a fast path 1D IDCT for X non-zero columns
-	template <int NONZERO_COLS>
-	struct Row
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			// ACCESS_COL() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)
-
-			const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);
-			pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);
-		}
-	};
-
-	template <>
-	struct Row<0>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-#ifdef _MSC_VER
-			pTemp; pSrc;
-#endif
-		}
-	};
-
-	template <>
-	struct Row<1>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			const int dcval = (pSrc[0] << PASS1_BITS);
-
-			pTemp[0] = dcval;
-			pTemp[1] = dcval;
-			pTemp[2] = dcval;
-			pTemp[3] = dcval;
-			pTemp[4] = dcval;
-			pTemp[5] = dcval;
-			pTemp[6] = dcval;
-			pTemp[7] = dcval;
-		}
-	};
-
-	// Compiler creates a fast path 1D IDCT for X non-zero rows
-	template <int NONZERO_ROWS>
-	struct Col
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			// ACCESS_ROW() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)
-
-			const int z2 = ACCESS_ROW(2);
-			const int z3 = ACCESS_ROW(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*0] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*7] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*1] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*6] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*2] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*5] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*3] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*4] = (uint8)CLAMP(i);
-		}
-	};
-
-	template <>
-	struct Col<1>
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);
-			const uint8 dcval_clamped = (uint8)CLAMP(dcval);
-			pDst_ptr[0*8] = dcval_clamped;
-			pDst_ptr[1*8] = dcval_clamped;
-			pDst_ptr[2*8] = dcval_clamped;
-			pDst_ptr[3*8] = dcval_clamped;
-			pDst_ptr[4*8] = dcval_clamped;
-			pDst_ptr[5*8] = dcval_clamped;
-			pDst_ptr[6*8] = dcval_clamped;
-			pDst_ptr[7*8] = dcval_clamped;
-		}
-	};
-
-	static const uint8 s_idct_row_table[] =
-	{
-		1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,
-		4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,
-		6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,
-		6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,
-		8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,
-		8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,
-		8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,
-		8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,
-	};
-
-	static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };
-
-	void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)
-	{
-		JPGD_ASSERT(block_max_zag >= 1);
-		JPGD_ASSERT(block_max_zag <= 64);
-
-		if (block_max_zag == 1)
-		{
-			int k = ((pSrc_ptr[0] + 4) >> 3) + 128;
-			k = CLAMP(k);
-			k = k | (k<<8);
-			k = k | (k<<16);
-
-			for (int i = 8; i > 0; i--)
-			{
-				*(int*)&pDst_ptr[0] = k;
-				*(int*)&pDst_ptr[4] = k;
-				pDst_ptr += 8;
-			}
-			return;
-		}
-
-		int temp[64];
-
-		const jpgd_block_t* pSrc = pSrc_ptr;
-		int* pTemp = temp;
-
-		const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];
-		int i;
-		for (i = 8; i > 0; i--, pRow_tab++)
-		{
-			switch (*pRow_tab)
-			{
-			case 0: Row<0>::idct(pTemp, pSrc); break;
-			case 1: Row<1>::idct(pTemp, pSrc); break;
-			case 2: Row<2>::idct(pTemp, pSrc); break;
-			case 3: Row<3>::idct(pTemp, pSrc); break;
-			case 4: Row<4>::idct(pTemp, pSrc); break;
-			case 5: Row<5>::idct(pTemp, pSrc); break;
-			case 6: Row<6>::idct(pTemp, pSrc); break;
-			case 7: Row<7>::idct(pTemp, pSrc); break;
-			case 8: Row<8>::idct(pTemp, pSrc); break;
-			}
-
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-
-		const int nonzero_rows = s_idct_col_table[block_max_zag - 1];
-		for (i = 8; i > 0; i--)
-		{
-			switch (nonzero_rows)
-			{
-			case 1: Col<1>::idct(pDst_ptr, pTemp); break;
-			case 2: Col<2>::idct(pDst_ptr, pTemp); break;
-			case 3: Col<3>::idct(pDst_ptr, pTemp); break;
-			case 4: Col<4>::idct(pDst_ptr, pTemp); break;
-			case 5: Col<5>::idct(pDst_ptr, pTemp); break;
-			case 6: Col<6>::idct(pDst_ptr, pTemp); break;
-			case 7: Col<7>::idct(pDst_ptr, pTemp); break;
-			case 8: Col<8>::idct(pDst_ptr, pTemp); break;
-			}
-
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)
-	{
-		int temp[64];
-		int* pTemp = temp;
-		const jpgd_block_t* pSrc = pSrc_ptr;
-
-		for (int i = 4; i > 0; i--)
-		{
-			Row<4>::idct(pTemp, pSrc);
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-		for (int i = 8; i > 0; i--)
-		{
-			Col<4>::idct(pDst_ptr, pTemp);
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	// Retrieve one character from the input stream.
-	inline uint jpeg_decoder::get_char()
-	{
-		// Any bytes remaining in buffer?
-		if (!m_in_buf_left)
-		{
-			// Try to get more bytes.
-			prep_in_buffer();
-			// Still nothing to get?
-			if (!m_in_buf_left)
-			{
-				// Pad the end of the stream with 0xFF 0xD9 (EOI marker)
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Same as previous method, except can indicate if the character is a pad character or not.
-	inline uint jpeg_decoder::get_char(bool *pPadding_flag)
-	{
-		if (!m_in_buf_left)
-		{
-			prep_in_buffer();
-			if (!m_in_buf_left)
-			{
-				*pPadding_flag = true;
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		*pPadding_flag = false;
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Inserts a previously retrieved character back into the input buffer.
-	inline void jpeg_decoder::stuff_char(uint8 q)
-	{
-		*(--m_pIn_buf_ofs) = q;
-		m_in_buf_left++;
-	}
-
-	// Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.
-	inline uint8 jpeg_decoder::get_octet()
-	{
-		bool padding_flag;
-		int c = get_char(&padding_flag);
-
-		if (c == 0xFF)
-		{
-			if (padding_flag)
-				return 0xFF;
-
-			c = get_char(&padding_flag);
-			if (padding_flag)
-			{
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-
-			if (c == 0x00)
-				return 0xFF;
-			else
-			{
-				stuff_char(static_cast<uint8>(c));
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-		}
-
-		return static_cast<uint8>(c);
-	}
-
-	// Retrieves a variable number of bits from the input stream. Does not recognize markers.
-	inline uint jpeg_decoder::get_bits(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			uint c1 = get_char();
-			uint c2 = get_char();
-			m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.
-	inline uint jpeg_decoder::get_bits_no_markers(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))
-			{
-				uint c1 = get_octet();
-				uint c2 = get_octet();
-				m_bit_buf |= (c1 << 8) | c2;
-			}
-			else
-			{
-				m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];
-				m_in_buf_left -= 2;
-				m_pIn_buf_ofs += 2;
-			}
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)
-		{
-			// Decode more bits, use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-		}
-		else
-			get_bits_no_markers(pH->code_size[symbol]);
-
-		return symbol;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)
-		{
-			// Use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-
-			extra_bits = get_bits_no_markers(symbol & 0xF);
-		}
-		else
-		{
-			JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));
-
-			if (symbol & 0x8000)
-			{
-				get_bits_no_markers((symbol >> 8) & 31);
-				extra_bits = symbol >> 16;
-			}
-			else
-			{
-				int code_size = (symbol >> 8) & 31;
-				int num_extra_bits = symbol & 0xF;
-				int bits = code_size + num_extra_bits;
-				if (bits <= (m_bits_left + 16))
-					extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);
-				else
-				{
-					get_bits_no_markers(code_size);
-					extra_bits = get_bits_no_markers(num_extra_bits);
-				}
-			}
-
-			symbol &= 0xFF;
-		}
-
-		return symbol;
-	}
-
-	// Tables and macro used to fully decode the DPCM differences.
-	static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };
-	static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };
-	static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };
-#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))
-
-	// Clamps a value between 0-255.
-	inline uint8 jpeg_decoder::clamp(int i)
-	{
-		if (static_cast<uint>(i) > 255)
-			i = (((~i) >> 31) & 0xFF);
-
-		return static_cast<uint8>(i);
-	}
-
-	namespace DCT_Upsample
-	{
-		struct Matrix44
-		{
-			typedef int Element_Type;
-			enum { NUM_ROWS = 4, NUM_COLS = 4 };
-
-			Element_Type v[NUM_ROWS][NUM_COLS];
-
-			inline int rows() const { return NUM_ROWS; }
-			inline int cols() const { return NUM_COLS; }
-
-			inline const Element_Type & at(int r, int c) const { return v[r][c]; }
-			inline       Element_Type & at(int r, int c)       { return v[r][c]; }
-
-			inline Matrix44() { }
-
-			inline Matrix44& operator += (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) += a.at(r, 0);
-					at(r, 1) += a.at(r, 1);
-					at(r, 2) += a.at(r, 2);
-					at(r, 3) += a.at(r, 3);
-				}
-				return *this;
-			}
-
-			inline Matrix44& operator -= (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) -= a.at(r, 0);
-					at(r, 1) -= a.at(r, 1);
-					at(r, 2) -= a.at(r, 2);
-					at(r, 3) -= a.at(r, 3);
-				}
-				return *this;
-			}
-
-			friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) + b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) + b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) + b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) + b.at(r, 3);
-				}
-				return ret;
-			}
-
-			friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) - b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) - b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) - b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) - b.at(r, 3);
-				}
-				return ret;
-			}
-
-			static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) + b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) + b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) + b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) + b.at(r, 3));
-				}
-			}
-
-			static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) - b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) - b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) - b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) - b.at(r, 3));
-				}
-			}
-		};
-
-		const int FRACT_BITS = 10;
-		const int SCALE = 1 << FRACT_BITS;
-
-		typedef int Temp_Type;
-#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)
-#define F(i) ((int)((i) * SCALE + .5f))
-
-		// Any decent C++ compiler will optimize this at compile time to a 0, or an array access.
-#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])
-
-		// NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix
-		template<int NUM_ROWS, int NUM_COLS>
-		struct P_Q
-		{
-			static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X000 = AT(0, 0);
-				const Temp_Type X001 = AT(0, 1);
-				const Temp_Type X002 = AT(0, 2);
-				const Temp_Type X003 = AT(0, 3);
-				const Temp_Type X004 = AT(0, 4);
-				const Temp_Type X005 = AT(0, 5);
-				const Temp_Type X006 = AT(0, 6);
-				const Temp_Type X007 = AT(0, 7);
-				const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));
-				const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));
-				const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));
-				const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));
-				const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));
-				const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));
-				const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));
-				const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));
-				const Temp_Type X020 = AT(4, 0);
-				const Temp_Type X021 = AT(4, 1);
-				const Temp_Type X022 = AT(4, 2);
-				const Temp_Type X023 = AT(4, 3);
-				const Temp_Type X024 = AT(4, 4);
-				const Temp_Type X025 = AT(4, 5);
-				const Temp_Type X026 = AT(4, 6);
-				const Temp_Type X027 = AT(4, 7);
-				const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));
-				const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));
-				const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));
-				const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));
-				const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));
-				const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));
-				const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));
-				const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				P.at(0, 0) = X000;
-				P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));
-				P.at(0, 2) = X004;
-				P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));
-				P.at(1, 0) = X010;
-				P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));
-				P.at(1, 2) = X014;
-				P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));
-				P.at(2, 0) = X020;
-				P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));
-				P.at(2, 2) = X024;
-				P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));
-				P.at(3, 0) = X030;
-				P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));
-				P.at(3, 2) = X034;
-				P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));
-				// 40 muls 24 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));
-				Q.at(0, 1) = X002;
-				Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));
-				Q.at(0, 3) = X006;
-				Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));
-				Q.at(1, 1) = X012;
-				Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));
-				Q.at(1, 3) = X016;
-				Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));
-				Q.at(2, 1) = X022;
-				Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));
-				Q.at(2, 3) = X026;
-				Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));
-				Q.at(3, 1) = X032;
-				Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));
-				Q.at(3, 3) = X036;
-				// 40 muls 24 adds
-			}
-		};
-
-		template<int NUM_ROWS, int NUM_COLS>
-		struct R_S
-		{
-			static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));
-				const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));
-				const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));
-				const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));
-				const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));
-				const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));
-				const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));
-				const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));
-				const Temp_Type X110 = AT(2, 0);
-				const Temp_Type X111 = AT(2, 1);
-				const Temp_Type X112 = AT(2, 2);
-				const Temp_Type X113 = AT(2, 3);
-				const Temp_Type X114 = AT(2, 4);
-				const Temp_Type X115 = AT(2, 5);
-				const Temp_Type X116 = AT(2, 6);
-				const Temp_Type X117 = AT(2, 7);
-				const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));
-				const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));
-				const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));
-				const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));
-				const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));
-				const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));
-				const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));
-				const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));
-				const Temp_Type X130 = AT(6, 0);
-				const Temp_Type X131 = AT(6, 1);
-				const Temp_Type X132 = AT(6, 2);
-				const Temp_Type X133 = AT(6, 3);
-				const Temp_Type X134 = AT(6, 4);
-				const Temp_Type X135 = AT(6, 5);
-				const Temp_Type X136 = AT(6, 6);
-				const Temp_Type X137 = AT(6, 7);
-				// 80 muls 48 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				R.at(0, 0) = X100;
-				R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));
-				R.at(0, 2) = X104;
-				R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));
-				R.at(1, 0) = X110;
-				R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));
-				R.at(1, 2) = X114;
-				R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));
-				R.at(2, 0) = X120;
-				R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));
-				R.at(2, 2) = X124;
-				R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));
-				R.at(3, 0) = X130;
-				R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));
-				R.at(3, 2) = X134;
-				R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));
-				// 40 muls 24 adds
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));
-				S.at(0, 1) = X102;
-				S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));
-				S.at(0, 3) = X106;
-				S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));
-				S.at(1, 1) = X112;
-				S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));
-				S.at(1, 3) = X116;
-				S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));
-				S.at(2, 1) = X122;
-				S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));
-				S.at(2, 3) = X126;
-				S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));
-				S.at(3, 1) = X132;
-				S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));
-				S.at(3, 3) = X136;
-				// 40 muls 24 adds
-			}
-		};
-	} // end namespace DCT_Upsample
-
-	// Unconditionally frees all allocated m_blocks.
-	void jpeg_decoder::free_all_blocks()
-	{
-		m_pStream = NULL;
-		for (mem_block *b = m_pMem_blocks; b; )
-		{
-			mem_block *n = b->m_pNext;
-			jpgd_free(b);
-			b = n;
-		}
-		m_pMem_blocks = NULL;
-	}
-
-	// This method handles all errors.
-	// It could easily be changed to use C++ exceptions.
-	void jpeg_decoder::stop_decoding(jpgd_status status)
-	{
-		m_error_code = status;
-		free_all_blocks();
-		longjmp(m_jmp_state, status);
-
-		// we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit
-		// that this function doesn't return, otherwise we get this error:
-		// 
-		// error : function declared 'noreturn' should not return
-		exit(1);
-	}
-
-	void *jpeg_decoder::alloc(size_t nSize, bool zero)
-	{
-		nSize = (JPGD_MAX(nSize, 1) + 3) & ~3;
-		char *rv = NULL;
-		for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)
-		{
-			if ((b->m_used_count + nSize) <= b->m_size)
-			{
-				rv = b->m_data + b->m_used_count;
-				b->m_used_count += nSize;
-				break;
-			}
-		}
-		if (!rv)
-		{
-			int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);
-			mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);
-			if (!b) stop_decoding(JPGD_NOTENOUGHMEM);
-			b->m_pNext = m_pMem_blocks; m_pMem_blocks = b;
-			b->m_used_count = nSize;
-			b->m_size = capacity;
-			rv = b->m_data;
-		}
-		if (zero) memset(rv, 0, nSize);
-		return rv;
-	}
-
-	void jpeg_decoder::word_clear(void *p, uint16 c, uint n)
-	{
-		uint8 *pD = (uint8*)p;
-		const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;
-		while (n)
-		{
-			pD[0] = l; pD[1] = h; pD += 2;
-			n--;
-		}
-	}
-
-	// Refill the input buffer.
-	// This method will sit in a loop until (A) the buffer is full or (B)
-	// the stream's read() method reports and end of file condition.
-	void jpeg_decoder::prep_in_buffer()
-	{
-		m_in_buf_left = 0;
-		m_pIn_buf_ofs = m_in_buf;
-
-		if (m_eof_flag)
-			return;
-
-		do
-		{
-			int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);
-			if (bytes_read == -1)
-				stop_decoding(JPGD_STREAM_READ);
-
-			m_in_buf_left += bytes_read;
-		} while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));
-
-		m_total_bytes_read += m_in_buf_left;
-
-		// Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).
-		// (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)
-		word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);
-	}
-
-	// Read a Huffman code table.
-	void jpeg_decoder::read_dht_marker()
-	{
-		int i, index, count;
-		uint8 huff_num[17];
-		uint8 huff_val[256];
-
-		uint num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DHT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			index = get_bits(8);
-
-			huff_num[0] = 0;
-
-			count = 0;
-
-			for (i = 1; i <= 16; i++)
-			{
-				huff_num[i] = static_cast<uint8>(get_bits(8));
-				count += huff_num[i];
-			}
-
-			if (count > 255)
-				stop_decoding(JPGD_BAD_DHT_COUNTS);
-
-			for (i = 0; i < count; i++)
-				huff_val[i] = static_cast<uint8>(get_bits(8));
-
-			i = 1 + 16 + count;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DHT_MARKER);
-
-			num_left -= i;
-
-			if ((index & 0x10) > 0x10)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);
-
-			if (index >= JPGD_MAX_HUFF_TABLES)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			if (!m_huff_num[index])
-				m_huff_num[index] = (uint8 *)alloc(17);
-
-			if (!m_huff_val[index])
-				m_huff_val[index] = (uint8 *)alloc(256);
-
-			m_huff_ac[index] = (index & 0x10) != 0;
-			memcpy(m_huff_num[index], huff_num, 17);
-			memcpy(m_huff_val[index], huff_val, 256);
-		}
-	}
-
-	// Read a quantization table.
-	void jpeg_decoder::read_dqt_marker()
-	{
-		int n, i, prec;
-		uint num_left;
-		uint temp;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DQT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			n = get_bits(8);
-			prec = n >> 4;
-			n &= 0x0F;
-
-			if (n >= JPGD_MAX_QUANT_TABLES)
-				stop_decoding(JPGD_BAD_DQT_TABLE);
-
-			if (!m_quant[n])
-				m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));
-
-			// read quantization entries, in zag order
-			for (i = 0; i < 64; i++)
-			{
-				temp = get_bits(8);
-
-				if (prec)
-					temp = (temp << 8) + get_bits(8);
-
-				m_quant[n][i] = static_cast<jpgd_quant_t>(temp);
-			}
-
-			i = 64 + 1;
-
-			if (prec)
-				i += 64;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DQT_LENGTH);
-
-			num_left -= i;
-		}
-	}
-
-	// Read the start of frame (SOF) marker.
-	void jpeg_decoder::read_sof_marker()
-	{
-		int i;
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (get_bits(8) != 8)   /* precision: sorry, only 8-bit precision is supported right now */
-			stop_decoding(JPGD_BAD_PRECISION);
-
-		m_image_y_size = get_bits(16);
-
-		if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))
-			stop_decoding(JPGD_BAD_HEIGHT);
-
-		m_image_x_size = get_bits(16);
-
-		if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))
-			stop_decoding(JPGD_BAD_WIDTH);
-
-		m_comps_in_frame = get_bits(8);
-
-		if (m_comps_in_frame > JPGD_MAX_COMPONENTS)
-			stop_decoding(JPGD_TOO_MANY_COMPONENTS);
-
-		if (num_left != (uint)(m_comps_in_frame * 3 + 8))
-			stop_decoding(JPGD_BAD_SOF_LENGTH);
-
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_comp_ident[i]  = get_bits(8);
-			m_comp_h_samp[i] = get_bits(4);
-			m_comp_v_samp[i] = get_bits(4);
-			m_comp_quant[i]  = get_bits(8);
-		}
-	}
-
-	// Used to skip unrecognized markers.
-	void jpeg_decoder::skip_variable_marker()
-	{
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_VARIABLE_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Read a define restart interval (DRI) marker.
-	void jpeg_decoder::read_dri_marker()
-	{
-		if (get_bits(16) != 4)
-			stop_decoding(JPGD_BAD_DRI_LENGTH);
-
-		m_restart_interval = get_bits(16);
-	}
-
-	// Read a start of scan (SOS) marker.
-	void jpeg_decoder::read_sos_marker()
-	{
-		uint num_left;
-		int i, ci, n, c, cc;
-
-		num_left = get_bits(16);
-
-		n = get_bits(8);
-
-		m_comps_in_scan = n;
-
-		num_left -= 3;
-
-		if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )
-			stop_decoding(JPGD_BAD_SOS_LENGTH);
-
-		for (i = 0; i < n; i++)
-		{
-			cc = get_bits(8);
-			c = get_bits(8);
-			num_left -= 2;
-
-			for (ci = 0; ci < m_comps_in_frame; ci++)
-				if (cc == m_comp_ident[ci])
-					break;
-
-			if (ci >= m_comps_in_frame)
-				stop_decoding(JPGD_BAD_SOS_COMP_ID);
-
-			m_comp_list[i]    = ci;
-			m_comp_dc_tab[ci] = (c >> 4) & 15;
-			m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);
-		}
-
-		m_spectral_start  = get_bits(8);
-		m_spectral_end    = get_bits(8);
-		m_successive_high = get_bits(4);
-		m_successive_low  = get_bits(4);
-
-		if (!m_progressive_flag)
-		{
-			m_spectral_start = 0;
-			m_spectral_end = 63;
-		}
-
-		num_left -= 3;
-
-		while (num_left)                  /* read past whatever is num_left */
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Finds the next marker.
-	int jpeg_decoder::next_marker()
-	{
-		uint c, bytes;
-
-		bytes = 0;
-
-		do
-		{
-			do
-			{
-				bytes++;
-				c = get_bits(8);
-			} while (c != 0xFF);
-
-			do
-			{
-				c = get_bits(8);
-			} while (c == 0xFF);
-
-		} while (c == 0);
-
-		// If bytes > 0 here, there where extra bytes before the marker (not good).
-
-		return c;
-	}
-
-	// Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is
-	// encountered.
-	int jpeg_decoder::process_markers()
-	{
-		int c;
-
-		for ( ; ; )
-		{
-			c = next_marker();
-
-			switch (c)
-			{
-			case M_SOF0:
-			case M_SOF1:
-			case M_SOF2:
-			case M_SOF3:
-			case M_SOF5:
-			case M_SOF6:
-			case M_SOF7:
-				//      case M_JPG:
-			case M_SOF9:
-			case M_SOF10:
-			case M_SOF11:
-			case M_SOF13:
-			case M_SOF14:
-			case M_SOF15:
-			case M_SOI:
-			case M_EOI:
-			case M_SOS:
-				{
-					return c;
-				}
-			case M_DHT:
-				{
-					read_dht_marker();
-					break;
-				}
-				// No arithmitic support - dumb patents!
-			case M_DAC:
-				{
-					stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-					break;
-				}
-			case M_DQT:
-				{
-					read_dqt_marker();
-					break;
-				}
-			case M_DRI:
-				{
-					read_dri_marker();
-					break;
-				}
-				//case M_APP0:  /* no need to read the JFIF marker */
-
-			case M_JPG:
-			case M_RST0:    /* no parameters */
-			case M_RST1:
-			case M_RST2:
-			case M_RST3:
-			case M_RST4:
-			case M_RST5:
-			case M_RST6:
-			case M_RST7:
-			case M_TEM:
-				{
-					stop_decoding(JPGD_UNEXPECTED_MARKER);
-					break;
-				}
-			default:    /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */
-				{
-					skip_variable_marker();
-					break;
-				}
-			}
-		}
-	}
-
-	// Finds the start of image (SOI) marker.
-	// This code is rather defensive: it only checks the first 512 bytes to avoid
-	// false positives.
-	void jpeg_decoder::locate_soi_marker()
-	{
-		uint lastchar, thischar;
-		uint bytesleft;
-
-		lastchar = get_bits(8);
-
-		thischar = get_bits(8);
-
-		/* ok if it's a normal JPEG file without a special header */
-
-		if ((lastchar == 0xFF) && (thischar == M_SOI))
-			return;
-
-		bytesleft = 4096; //512;
-
-		for ( ; ; )
-		{
-			if (--bytesleft == 0)
-				stop_decoding(JPGD_NOT_JPEG);
-
-			lastchar = thischar;
-
-			thischar = get_bits(8);
-
-			if (lastchar == 0xFF)
-			{
-				if (thischar == M_SOI)
-					break;
-				else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end
-					stop_decoding(JPGD_NOT_JPEG);
-			}
-		}
-
-		// Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.
-		thischar = (m_bit_buf >> 24) & 0xFF;
-
-		if (thischar != 0xFF)
-			stop_decoding(JPGD_NOT_JPEG);
-	}
-
-	// Find a start of frame (SOF) marker.
-	void jpeg_decoder::locate_sof_marker()
-	{
-		locate_soi_marker();
-
-		int c = process_markers();
-
-		switch (c)
-		{
-		case M_SOF2:
-			m_progressive_flag = JPGD_TRUE;
-		case M_SOF0:  /* baseline DCT */
-		case M_SOF1:  /* extended sequential DCT */
-			{
-				read_sof_marker();
-				break;
-			}
-		case M_SOF9:  /* Arithmitic coding */
-			{
-				stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-				break;
-			}
-		default:
-			{
-				stop_decoding(JPGD_UNSUPPORTED_MARKER);
-				break;
-			}
-		}
-	}
-
-	// Find a start of scan (SOS) marker.
-	int jpeg_decoder::locate_sos_marker()
-	{
-		int c;
-
-		c = process_markers();
-
-		if (c == M_EOI)
-			return JPGD_FALSE;
-		else if (c != M_SOS)
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-
-		read_sos_marker();
-
-		return JPGD_TRUE;
-	}
-
-	// Reset everything to default/uninitialized state.
-	void jpeg_decoder::init(jpeg_decoder_stream *pStream)
-	{
-		m_pMem_blocks = NULL;
-		m_error_code = JPGD_SUCCESS;
-		m_ready_flag = false;
-		m_image_x_size = m_image_y_size = 0;
-		m_pStream = pStream;
-		m_progressive_flag = JPGD_FALSE;
-
-		memset(m_huff_ac, 0, sizeof(m_huff_ac));
-		memset(m_huff_num, 0, sizeof(m_huff_num));
-		memset(m_huff_val, 0, sizeof(m_huff_val));
-		memset(m_quant, 0, sizeof(m_quant));
-
-		m_scan_type = 0;
-		m_comps_in_frame = 0;
-
-		memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));
-		memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));
-		memset(m_comp_quant, 0, sizeof(m_comp_quant));
-		memset(m_comp_ident, 0, sizeof(m_comp_ident));
-		memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));
-		memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));
-
-		m_comps_in_scan = 0;
-		memset(m_comp_list, 0, sizeof(m_comp_list));
-		memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));
-		memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));
-
-		m_spectral_start = 0;
-		m_spectral_end = 0;
-		m_successive_low = 0;
-		m_successive_high = 0;
-		m_max_mcu_x_size = 0;
-		m_max_mcu_y_size = 0;
-		m_blocks_per_mcu = 0;
-		m_max_blocks_per_row = 0;
-		m_mcus_per_row = 0;
-		m_mcus_per_col = 0;
-		m_expanded_blocks_per_component = 0;
-		m_expanded_blocks_per_mcu = 0;
-		m_expanded_blocks_per_row = 0;
-		m_freq_domain_chroma_upsample = false;
-
-		memset(m_mcu_org, 0, sizeof(m_mcu_org));
-
-		m_total_lines_left = 0;
-		m_mcu_lines_left = 0;
-		m_real_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_pixel = 0;
-
-		memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));
-
-		memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));
-		memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_eob_run = 0;
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_pIn_buf_ofs = m_in_buf;
-		m_in_buf_left = 0;
-		m_eof_flag = false;
-		m_tem_flag = 0;
-
-		memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));
-		memset(m_in_buf, 0, sizeof(m_in_buf));
-		memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));
-
-		m_restart_interval = 0;
-		m_restarts_left    = 0;
-		m_next_restart_num = 0;
-
-		m_max_mcus_per_row = 0;
-		m_max_blocks_per_mcu = 0;
-		m_max_mcus_per_col = 0;
-
-		memset(m_last_dc_val, 0, sizeof(m_last_dc_val));
-		m_pMCU_coefficients = NULL;
-		m_pSample_buf = NULL;
-
-		m_total_bytes_read = 0;
-
-		m_pScan_line_0 = NULL;
-		m_pScan_line_1 = NULL;
-
-		// Ready the input buffer.
-		prep_in_buffer();
-
-		// Prime the bit buffer.
-		m_bits_left = 16;
-		m_bit_buf = 0;
-
-		get_bits(16);
-		get_bits(16);
-
-		for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)
-			m_mcu_block_max_zag[i] = 64;
-	}
-
-#define SCALEBITS 16
-#define ONE_HALF  ((int) 1 << (SCALEBITS-1))
-#define FIX(x)    ((int) ((x) * (1L<<SCALEBITS) + 0.5f))
-
-	// Create a few tables that allow us to quickly convert YCbCr to RGB.
-	void jpeg_decoder::create_look_ups()
-	{
-		for (int i = 0; i <= 255; i++)
-		{
-			int k = i - 128;
-			m_crr[i] = ( FIX(1.40200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_cbb[i] = ( FIX(1.77200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_crg[i] = (-FIX(0.71414f)) * k;
-			m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;
-		}
-	}
-
-	// This method throws back into the stream any bytes that where read
-	// into the bit buffer during initial marker scanning.
-	void jpeg_decoder::fix_in_buffer()
-	{
-		// In case any 0xFF's where pulled into the buffer during marker scanning.
-		JPGD_ASSERT((m_bits_left & 7) == 0);
-
-		if (m_bits_left == 16)
-			stuff_char( (uint8)(m_bit_buf & 0xFF));
-
-		if (m_bits_left >= 8)
-			stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));
-
-		stuff_char((uint8)((m_bit_buf >> 16) & 0xFF));
-		stuff_char((uint8)((m_bit_buf >> 24) & 0xFF));
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	void jpeg_decoder::transform_mcu(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;
-
-		for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-	}
-
-	static const uint8 s_max_rc[64] =
-	{
-		17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,
-		102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136
-	};
-
-	void jpeg_decoder::transform_mcu_expand(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;
-
-		// Y IDCT
-		int mcu_block;
-		for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-
-		// Chroma IDCT, with upsampling
-		jpgd_block_t temp_block[64];
-
-		for (int i = 0; i < 2; i++)
-		{
-			DCT_Upsample::Matrix44 P, Q, R, S;
-
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);
-
-			switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])
-			{
-			case 1*16+1:
-				DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);
-				break;
-			case 1*16+2:
-				DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 2*16+2:
-				DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+2:
-				DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+3:
-				DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+4:
-				DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 4*16+4:
-				DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+4:
-				DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+5:
-				DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+6:
-				DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 6*16+6:
-				DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+6:
-				DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+7:
-				DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+8:
-				DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);
-				break;
-			case 8*16+8:
-				DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);
-				break;
-			default:
-				JPGD_ASSERT(false);
-			}
-
-			DCT_Upsample::Matrix44 a(P + Q); P -= Q;
-			DCT_Upsample::Matrix44& b = P;
-			DCT_Upsample::Matrix44 c(R + S); R -= S;
-			DCT_Upsample::Matrix44& d = R;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			pSrc_ptr += 64;
-		}
-	}
-
-	// Loads and dequantizes the next row of (already decoded) coefficients.
-	// Progressive images only.
-	void jpeg_decoder::load_next_row()
-	{
-		int i;
-		jpgd_block_t *p;
-		jpgd_quant_t *q;
-		int mcu_row, mcu_block, row_block = 0;
-		int component_num, component_id;
-		int block_x_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));
-
-		for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-			for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-			{
-				component_id = m_mcu_org[mcu_block];
-				q = m_quant[m_comp_quant[component_id]];
-
-				p = m_pMCU_coefficients + 64 * mcu_block;
-
-				jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				p[0] = pDC[0];
-				memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));
-
-				for (i = 63; i > 0; i--)
-					if (p[g_ZAG[i]])
-						break;
-
-				m_mcu_block_max_zag[mcu_block] = i + 1;
-
-				for ( ; i >= 0; i--)
-					if (p[g_ZAG[i]])
-						p[g_ZAG[i]] = static_cast<jpgd_block_t>(p[g_ZAG[i]] * q[i]);
-
-				row_block++;
-
-				if (m_comps_in_scan == 1)
-					block_x_mcu[component_id]++;
-				else
-				{
-					if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-					{
-						block_x_mcu_ofs = 0;
-
-						if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-						{
-							block_y_mcu_ofs = 0;
-
-							block_x_mcu[component_id] += m_comp_h_samp[component_id];
-						}
-					}
-				}
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-		}
-
-		if (m_comps_in_scan == 1)
-			m_block_y_mcu[m_comp_list[0]]++;
-		else
-		{
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				component_id = m_comp_list[component_num];
-
-				m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-			}
-		}
-	}
-
-	// Restart interval processing.
-	void jpeg_decoder::process_restart()
-	{
-		int i;
-		int c = 0;
-
-		// Align to a byte boundry
-		// FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!
-		//get_bits_no_markers(m_bits_left & 7);
-
-		// Let's scan a little bit to find the marker, but not _too_ far.
-		// 1536 is a "fudge factor" that determines how much to scan.
-		for (i = 1536; i > 0; i--)
-			if (get_char() == 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		for ( ; i > 0; i--)
-			if ((c = get_char()) != 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Is it the expected marker? If not, something bad happened.
-		if (c != (m_next_restart_num + M_RST0))
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Reset each component's DC prediction values.
-		memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		m_restarts_left = m_restart_interval;
-
-		m_next_restart_num = (m_next_restart_num + 1) & 7;
-
-		// Get the bit buffer going again...
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	static inline int dequantize_ac(int c, int q) {	c *= q;	return c; }
-
-	// Decodes and dequantizes the next row of coefficients.
-	void jpeg_decoder::decode_next_row()
-	{
-		int row_block = 0;
-
-		for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			if ((m_restart_interval) && (m_restarts_left == 0))
-				process_restart();
-
-			jpgd_block_t* p = m_pMCU_coefficients;
-			for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)
-			{
-				int component_id = m_mcu_org[mcu_block];
-				jpgd_quant_t* q = m_quant[m_comp_quant[component_id]];
-
-				int r, s;
-				s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);
-				s = HUFF_EXTEND(r, s);
-
-				m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);
-
-				p[0] = static_cast<jpgd_block_t>(s * q[0]);
-
-				int prev_num_set = m_mcu_block_max_zag[mcu_block];
-
-				huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];
-
-				int k;
-				for (k = 1; k < 64; k++)
-				{
-					int extra_bits;
-					s = huff_decode(pH, extra_bits);
-
-					r = s >> 4;
-					s &= 15;
-
-					if (s)
-					{
-						if (r)
-						{
-							if ((k + r) > 63)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(r, prev_num_set - k);
-								int kt = k;
-								while (n--)
-									p[g_ZAG[kt++]] = 0;
-							}
-
-							k += r;
-						}
-
-						s = HUFF_EXTEND(extra_bits, s);
-
-						JPGD_ASSERT(k < 64);
-
-						p[g_ZAG[k]] = static_cast<jpgd_block_t>(dequantize_ac(s, q[k])); //s * q[k];
-					}
-					else
-					{
-						if (r == 15)
-						{
-							if ((k + 16) > 64)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(16, prev_num_set - k);
-								int kt = k;
-								while (n--)
-								{
-									JPGD_ASSERT(kt <= 63);
-									p[g_ZAG[kt++]] = 0;
-								}
-							}
-
-							k += 16 - 1; // - 1 because the loop counter is k
-							// BEGIN EPIC MOD
-							JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);
-							// END EPIC MOD
-						}
-						else
-							break;
-					}
-				}
-
-				if (k < prev_num_set)
-				{
-					int kt = k;
-					while (kt < prev_num_set)
-						p[g_ZAG[kt++]] = 0;
-				}
-
-				m_mcu_block_max_zag[mcu_block] = k;
-
-				row_block++;
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-
-			m_restarts_left--;
-		}
-	}
-
-	// YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int y = s[j];
-				int cb = s[64+j];
-				int cr = s[128+j];
-
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d[0] = clamp(y + m_cbb[cb]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_crr[cr]);
-					d[3] = 255;
-				}
-				else
-				{
-					d[0] = clamp(y + m_crr[cr]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_cbb[cb]);
-					d[3] = 255;
-				}
-				d += 4;
-			}
-
-			s += 64*3;
-		}
-	}
-
-	// YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *y = m_pSample_buf + row * 8;
-		uint8 *c = m_pSample_buf + 2*64 + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 4; j++)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j<<1];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-					}
-
-					d0 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*4 - 64*2;
-			c += 64*4 - 8;
-		}
-	}
-
-	// YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*1 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*2 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int cb = c[0+j];
-				int cr = c[64+j];
-
-				int rc = m_crr[cr];
-				int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-				int bc = m_cbb[cb];
-
-				int yy = y[j];
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d0[0] = clamp(yy+bc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+rc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+bc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+rc);
-					d1[3] = 255;
-				}
-				else
-				{
-					d0[0] = clamp(yy+rc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+bc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+rc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+bc);
-					d1[3] = 255;
-				}
-
-				d0 += 4;
-				d1 += 4;
-			}
-
-			y += 64*4;
-			c += 64*4;
-		}
-	}
-
-	// YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*2 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*4 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 8; j += 2)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+bc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+rc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+bc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+rc);
-						d1[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+rc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+bc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+rc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+bc);
-						d1[7] = 255;
-					}
-
-					d0 += 8;
-					d1 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*6 - 64*2;
-			c += 64*6 - 8;
-		}
-	}
-
-	// Y (1 block per MCU) to 8-bit grayscale
-	void jpeg_decoder::gray_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			*(uint *)d = *(uint *)s;
-			*(uint *)(&d[4]) = *(uint *)(&s[4]);
-
-			s += 64;
-			d += 8;
-		}
-	}
-
-	void jpeg_decoder::expanded_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-
-		uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;
-
-		uint8* d = m_pScan_line_0;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int k = 0; k < m_max_mcu_x_size; k += 8)
-			{
-				const int Y_ofs = k * 8;
-				const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;
-				const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;
-				for (int j = 0; j < 8; j++)
-				{
-					int y = Py[Y_ofs + j];
-					int cb = Py[Cb_ofs + j];
-					int cr = Py[Cr_ofs + j];
-
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d[0] = clamp(y + m_cbb[cb]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_crr[cr]);
-						d[3] = 255;
-					}
-					else
-					{
-						d[0] = clamp(y + m_crr[cr]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_cbb[cb]);
-						d[3] = 255;
-					}
-
-					d += 4;
-				}
-			}
-
-			Py += 64 * m_expanded_blocks_per_mcu;
-		}
-	}
-
-	// Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.
-	void jpeg_decoder::find_eoi()
-	{
-		if (!m_progressive_flag)
-		{
-			// Attempt to read the EOI marker.
-			//get_bits_no_markers(m_bits_left & 7);
-
-			// Prime the bit buffer
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-
-			// The next marker _should_ be EOI
-			process_markers();
-		}
-
-		m_total_bytes_read -= m_in_buf_left;
-	}
-
-	int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)
-	{
-		if ((m_error_code) || (!m_ready_flag))
-			return JPGD_FAILED;
-
-		if (m_total_lines_left == 0)
-			return JPGD_DONE;
-
-		if (m_mcu_lines_left == 0)
-		{
-			if (setjmp(m_jmp_state))
-				return JPGD_FAILED;
-
-			if (m_progressive_flag)
-				load_next_row();
-			else
-				decode_next_row();
-
-			// Find the EOI marker if that was the last row.
-			if (m_total_lines_left <= m_max_mcu_y_size)
-				find_eoi();
-
-			m_mcu_lines_left = m_max_mcu_y_size;
-		}
-
-		if (m_freq_domain_chroma_upsample)
-		{
-			expanded_convert();
-			*pScan_line = m_pScan_line_0;
-		}
-		else
-		{
-			switch (m_scan_type)
-			{
-			case JPGD_YH2V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H2V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH2V1:
-				{
-					H2V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_YH1V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H1V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH1V1:
-				{
-					H1V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_GRAYSCALE:
-				{
-					gray_convert();
-					*pScan_line = m_pScan_line_0;
-
-					break;
-				}
-			}
-		}
-
-		*pScan_line_len = m_real_dest_bytes_per_scan_line;
-
-		m_mcu_lines_left--;
-		m_total_lines_left--;
-
-		return JPGD_SUCCESS;
-	}
-
-	// Creates the tables needed for efficient Huffman decoding.
-	void jpeg_decoder::make_huff_table(int index, huff_tables *pH)
-	{
-		int p, i, l, si;
-		uint8 huffsize[257];
-		uint huffcode[257];
-		uint code;
-		uint subtree;
-		int code_size;
-		int lastp;
-		int nextfreeentry;
-		int currententry;
-
-		pH->ac_table = m_huff_ac[index] != 0;
-
-		p = 0;
-
-		for (l = 1; l <= 16; l++)
-		{
-			for (i = 1; i <= m_huff_num[index][l]; i++)
-				huffsize[p++] = static_cast<uint8>(l);
-		}
-
-		huffsize[p] = 0;
-
-		lastp = p;
-
-		code = 0;
-		si = huffsize[0];
-		p = 0;
-
-		while (huffsize[p])
-		{
-			while (huffsize[p] == si)
-			{
-				huffcode[p++] = code;
-				code++;
-			}
-
-			code <<= 1;
-			si++;
-		}
-
-		memset(pH->look_up, 0, sizeof(pH->look_up));
-		memset(pH->look_up2, 0, sizeof(pH->look_up2));
-		memset(pH->tree, 0, sizeof(pH->tree));
-		memset(pH->code_size, 0, sizeof(pH->code_size));
-
-		nextfreeentry = -1;
-
-		p = 0;
-
-		while (p < lastp)
-		{
-			i = m_huff_val[index][p];
-			code = huffcode[p];
-			code_size = huffsize[p];
-
-			pH->code_size[i] = static_cast<uint8>(code_size);
-
-			if (code_size <= 8)
-			{
-				code <<= (8 - code_size);
-
-				for (l = 1 << (8 - code_size); l > 0; l--)
-				{
-					JPGD_ASSERT(i < 256);
-
-					pH->look_up[code] = i;
-
-					bool has_extrabits = false;
-					int extra_bits = 0;
-					int num_extra_bits = i & 15;
-
-					int bits_to_fetch = code_size;
-					if (num_extra_bits)
-					{
-						int total_codesize = code_size + num_extra_bits;
-						if (total_codesize <= 8)
-						{
-							has_extrabits = true;
-							extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));
-							JPGD_ASSERT(extra_bits <= 0x7FFF);
-							bits_to_fetch += num_extra_bits;
-						}
-					}
-
-					if (!has_extrabits)
-						pH->look_up2[code] = i | (bits_to_fetch << 8);
-					else
-						pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);
-
-					code++;
-				}
-			}
-			else
-			{
-				subtree = (code >> (code_size - 8)) & 0xFF;
-
-				currententry = pH->look_up[subtree];
-
-				if (currententry == 0)
-				{
-					pH->look_up[subtree] = currententry = nextfreeentry;
-					pH->look_up2[subtree] = currententry = nextfreeentry;
-
-					nextfreeentry -= 2;
-				}
-
-				code <<= (16 - (code_size - 8));
-
-				for (l = code_size; l > 9; l--)
-				{
-					if ((code & 0x8000) == 0)
-						currententry--;
-
-					if (pH->tree[-currententry - 1] == 0)
-					{
-						pH->tree[-currententry - 1] = nextfreeentry;
-
-						currententry = nextfreeentry;
-
-						nextfreeentry -= 2;
-					}
-					else
-						currententry = pH->tree[-currententry - 1];
-
-					code <<= 1;
-				}
-
-				if ((code & 0x8000) == 0)
-					currententry--;
-
-				pH->tree[-currententry - 1] = i;
-			}
-
-			p++;
-		}
-	}
-
-	// Verifies the quantization tables needed for this scan are available.
-	void jpeg_decoder::check_quant_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-			if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)
-				stop_decoding(JPGD_UNDEFINED_QUANT_TABLE);
-	}
-
-	// Verifies that all the Huffman tables needed for this scan are available.
-	void jpeg_decoder::check_huff_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-		{
-			if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-
-			if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-		}
-
-		for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)
-			if (m_huff_num[i])
-			{
-				if (!m_pHuff_tabs[i])
-					m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));
-
-				make_huff_table(i, m_pHuff_tabs[i]);
-			}
-	}
-
-	// Determines the component order inside each MCU.
-	// Also calcs how many MCU's are on each row, etc.
-	void jpeg_decoder::calc_mcu_block_order()
-	{
-		int component_num, component_id;
-		int max_h_samp = 0, max_v_samp = 0;
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			if (m_comp_h_samp[component_id] > max_h_samp)
-				max_h_samp = m_comp_h_samp[component_id];
-
-			if (m_comp_v_samp[component_id] > max_v_samp)
-				max_v_samp = m_comp_v_samp[component_id];
-		}
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;
-			m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];
-			m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];
-		}
-		else
-		{
-			m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;
-			m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcu_org[0] = m_comp_list[0];
-
-			m_blocks_per_mcu = 1;
-		}
-		else
-		{
-			m_blocks_per_mcu = 0;
-
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				int num_blocks;
-
-				component_id = m_comp_list[component_num];
-
-				num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];
-
-				while (num_blocks--)
-					m_mcu_org[m_blocks_per_mcu++] = component_id;
-			}
-		}
-	}
-
-	// Starts a new scan.
-	int jpeg_decoder::init_scan()
-	{
-		if (!locate_sos_marker())
-			return JPGD_FALSE;
-
-		calc_mcu_block_order();
-
-		check_huff_tables();
-
-		check_quant_tables();
-
-		memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		if (m_restart_interval)
-		{
-			m_restarts_left = m_restart_interval;
-			m_next_restart_num = 0;
-		}
-
-		fix_in_buffer();
-
-		return JPGD_TRUE;
-	}
-
-	// Starts a frame. Determines if the number of components or sampling factors
-	// are supported.
-	void jpeg_decoder::init_frame()
-	{
-		int i;
-
-		if (m_comps_in_frame == 1)
-		{
-			if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			m_scan_type = JPGD_GRAYSCALE;
-			m_max_blocks_per_mcu = 1;
-			m_max_mcu_x_size = 8;
-			m_max_mcu_y_size = 8;
-		}
-		else if (m_comps_in_frame == 3)
-		{
-			if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||
-				((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH1V1;
-
-				m_max_blocks_per_mcu = 3;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH2V1;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH1V2;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 16;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH2V2;
-				m_max_blocks_per_mcu = 6;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 16;
-			}
-			else
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-		}
-		else
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;
-		m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;
-
-		// These values are for the *destination* pixels: after conversion.
-		if (m_scan_type == JPGD_GRAYSCALE)
-			m_dest_bytes_per_pixel = 1;
-		else
-			m_dest_bytes_per_pixel = 4;
-
-		m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;
-
-		m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);
-
-		// Initialize two scan line buffers.
-		m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-		if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))
-			m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-
-		m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;
-
-		// Should never happen
-		if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)
-			stop_decoding(JPGD_ASSERTION_ERROR);
-
-		// Allocate the coefficient buffer, enough for one MCU
-		m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));
-
-		for (i = 0; i < m_max_blocks_per_mcu; i++)
-			m_mcu_block_max_zag[i] = 64;
-
-		m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];
-		m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;
-		m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;
-		// Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.
-// BEGIN EPIC MOD
-#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING
-		m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);
-#else
-		m_freq_domain_chroma_upsample = 0;
-#endif
-// END EPIC MOD
-
-		if (m_freq_domain_chroma_upsample)
-			m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);
-		else
-			m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);
-
-		m_total_lines_left = m_image_y_size;
-
-		m_mcu_lines_left = 0;
-
-		create_look_ups();
-	}
-
-	// The coeff_buf series of methods originally stored the coefficients
-	// into a "virtual" file which was located in EMS, XMS, or a disk file. A cache
-	// was used to make this process more efficient. Now, we can store the entire
-	// thing in RAM.
-	jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)
-	{
-		coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));
-
-		cb->block_num_x = block_num_x;
-		cb->block_num_y = block_num_y;
-		cb->block_len_x = block_len_x;
-		cb->block_len_y = block_len_y;
-		cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);
-		cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);
-		return cb;
-	}
-
-	inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)
-	{
-		JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));
-		return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));
-	}
-
-	// The following methods decode the various types of m_blocks encountered
-	// in progressively encoded images.
-	void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, r;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-		if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)
-		{
-			r = pD->get_bits_no_markers(s);
-			s = HUFF_EXTEND(r, s);
-		}
-
-		pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);
-
-		p[0] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-	}
-
-	void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		if (pD->get_bits_no_markers(1))
-		{
-			jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-			p[0] |= (1 << pD->m_successive_low);
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int k, s, r;
-
-		if (pD->m_eob_run)
-		{
-			pD->m_eob_run--;
-			return;
-		}
-
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)
-		{
-			s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-			r = s >> 4;
-			s &= 15;
-
-			if (s)
-			{
-				if ((k += r) > 63)
-					pD->stop_decoding(JPGD_DECODE_ERROR);
-
-				r = pD->get_bits_no_markers(s);
-				s = HUFF_EXTEND(r, s);
-
-				p[g_ZAG[k]] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-			}
-			else
-			{
-				if (r == 15)
-				{
-					if ((k += 15) > 63)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-				}
-				else
-				{
-					pD->m_eob_run = 1 << r;
-
-					if (r)
-						pD->m_eob_run += pD->get_bits_no_markers(r);
-
-					pD->m_eob_run--;
-
-					break;
-				}
-			}
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, k, r;
-		int p1 = 1 << pD->m_successive_low;
-		int m1 = (-1) << pD->m_successive_low;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		k = pD->m_spectral_start;
-
-		if (pD->m_eob_run == 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-				r = s >> 4;
-				s &= 15;
-
-				if (s)
-				{
-					if (s != 1)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-
-					if (pD->get_bits_no_markers(1))
-						s = p1;
-					else
-						s = m1;
-				}
-				else
-				{
-					if (r != 15)
-					{
-						pD->m_eob_run = 1 << r;
-
-						if (r)
-							pD->m_eob_run += pD->get_bits_no_markers(r);
-
-						break;
-					}
-				}
-
-				do
-				{
-					// BEGIN EPIC MOD
-					JPGD_ASSERT(k < 64);
-					// END EPIC MOD
-
-					jpgd_block_t *this_coef = p + g_ZAG[k];
-
-					if (*this_coef != 0)
-					{
-						if (pD->get_bits_no_markers(1))
-						{
-							if ((*this_coef & p1) == 0)
-							{
-								if (*this_coef >= 0)
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-								else
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-							}
-						}
-					}
-					else
-					{
-						if (--r < 0)
-							break;
-					}
-
-					k++;
-
-				} while (k <= pD->m_spectral_end);
-
-				if ((s) && (k < 64))
-				{
-					p[g_ZAG[k]] = static_cast<jpgd_block_t>(s);
-				}
-			}
-		}
-
-		if (pD->m_eob_run > 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				// BEGIN EPIC MOD
-				JPGD_ASSERT(k < 64);
-				// END EPIC MOD
-
-				jpgd_block_t *this_coef = p + g_ZAG[k];
-
-				if (*this_coef != 0)
-				{
-					if (pD->get_bits_no_markers(1))
-					{
-						if ((*this_coef & p1) == 0)
-						{
-							if (*this_coef >= 0)
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-							else
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-						}
-					}
-				}
-			}
-
-			pD->m_eob_run--;
-		}
-	}
-
-	// Decode a scan in a progressively encoded image.
-	void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)
-	{
-		int mcu_row, mcu_col, mcu_block;
-		int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)
-		{
-			int component_num, component_id;
-
-			memset(block_x_mcu, 0, sizeof(block_x_mcu));
-
-			for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-			{
-				int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-				if ((m_restart_interval) && (m_restarts_left == 0))
-					process_restart();
-
-				for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-				{
-					component_id = m_mcu_org[mcu_block];
-
-					decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-
-					if (m_comps_in_scan == 1)
-						block_x_mcu[component_id]++;
-					else
-					{
-						if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-						{
-							block_x_mcu_ofs = 0;
-
-							if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-							{
-								block_y_mcu_ofs = 0;
-								block_x_mcu[component_id] += m_comp_h_samp[component_id];
-							}
-						}
-					}
-				}
-
-				m_restarts_left--;
-			}
-
-			if (m_comps_in_scan == 1)
-				m_block_y_mcu[m_comp_list[0]]++;
-			else
-			{
-				for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-				{
-					component_id = m_comp_list[component_num];
-					m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-				}
-			}
-		}
-	}
-
-	// Decode a progressively encoded image.
-	void jpeg_decoder::init_progressive()
-	{
-		int i;
-
-		if (m_comps_in_frame == 4)
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		// Allocate the coefficient buffers.
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);
-			m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);
-		}
-
-		for ( ; ; )
-		{
-			int dc_only_scan, refinement_scan;
-			pDecode_block_func decode_block_func;
-
-			if (!init_scan())
-				break;
-
-			dc_only_scan = (m_spectral_start == 0);
-			refinement_scan = (m_successive_high != 0);
-
-			if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if (dc_only_scan)
-			{
-				if (m_spectral_end)
-					stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-			}
-			else if (m_comps_in_scan != 1)  /* AC scans can only contain one component */
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if ((refinement_scan) && (m_successive_low != m_successive_high - 1))
-				stop_decoding(JPGD_BAD_SOS_SUCCESSIVE);
-
-			if (dc_only_scan)
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_dc_refine;
-				else
-					decode_block_func = decode_block_dc_first;
-			}
-			else
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_ac_refine;
-				else
-					decode_block_func = decode_block_ac_first;
-			}
-
-			decode_scan(decode_block_func);
-
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-		}
-
-		m_comps_in_scan = m_comps_in_frame;
-
-		for (i = 0; i < m_comps_in_frame; i++)
-			m_comp_list[i] = i;
-
-		calc_mcu_block_order();
-	}
-
-	void jpeg_decoder::init_sequential()
-	{
-		if (!init_scan())
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-	}
-
-	void jpeg_decoder::decode_start()
-	{
-		init_frame();
-
-		if (m_progressive_flag)
-			init_progressive();
-		else
-			init_sequential();
-	}
-
-	void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)
-	{
-		init(pStream);
-		locate_sof_marker();
-	}
-
-	jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)
-	{
-		if (setjmp(m_jmp_state))
-			return;
-		decode_init(pStream);
-	}
-
-	int jpeg_decoder::begin_decoding()
-	{
-		if (m_ready_flag)
-			return JPGD_SUCCESS;
-
-		if (m_error_code)
-			return JPGD_FAILED;
-
-		if (setjmp(m_jmp_state))
-			return JPGD_FAILED;
-
-		decode_start();
-
-		m_ready_flag = true;
-
-		return JPGD_SUCCESS;
-	}
-
-	jpeg_decoder::~jpeg_decoder()
-	{
-		free_all_blocks();
-	}
-
-	jpeg_decoder_file_stream::jpeg_decoder_file_stream()
-	{
-		m_pFile = NULL;
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	void jpeg_decoder_file_stream::close()
-	{
-		if (m_pFile)
-		{
-			fclose(m_pFile);
-			m_pFile = NULL;
-		}
-
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	jpeg_decoder_file_stream::~jpeg_decoder_file_stream()
-	{
-		close();
-	}
-
-	bool jpeg_decoder_file_stream::open(const char *Pfilename)
-	{
-		close();
-
-		m_eof_flag = false;
-		m_error_flag = false;
-
-#if defined(_MSC_VER)
-		m_pFile = NULL;
-		fopen_s(&m_pFile, Pfilename, "rb");
-#else
-		m_pFile = fopen(Pfilename, "rb");
-#endif
-		return m_pFile != NULL;
-	}
-
-	int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		if (!m_pFile)
-			return -1;
-
-		if (m_eof_flag)
-		{
-			*pEOF_flag = true;
-			return 0;
-		}
-
-		if (m_error_flag)
-			return -1;
-
-		int bytes_read = static_cast<int>(fread(pBuf, 1, max_bytes_to_read, m_pFile));
-		if (bytes_read < max_bytes_to_read)
-		{
-			if (ferror(m_pFile))
-			{
-				m_error_flag = true;
-				return -1;
-			}
-
-			m_eof_flag = true;
-			*pEOF_flag = true;
-		}
-
-		return bytes_read;
-	}
-
-	bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)
-	{
-		close();
-		m_pSrc_data = pSrc_data;
-		m_ofs = 0;
-		m_size = size;
-		return true;
-	}
-
-	int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		*pEOF_flag = false;
-
-		if (!m_pSrc_data)
-			return -1;
-
-		uint bytes_remaining = m_size - m_ofs;
-		if ((uint)max_bytes_to_read > bytes_remaining)
-		{
-			max_bytes_to_read = bytes_remaining;
-			*pEOF_flag = true;
-		}
-
-		memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);
-		m_ofs += max_bytes_to_read;
-
-		return max_bytes_to_read;
-	}
-
-	unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		if (!actual_comps)
-			return NULL;
-		*actual_comps = 0;
-
-		if ((!pStream) || (!width) || (!height) || (!req_comps))
-			return NULL;
-
-		if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))
-			return NULL;
-
-		jpeg_decoder decoder(pStream);
-		if (decoder.get_error_code() != JPGD_SUCCESS)
-			return NULL;
-
-		const int image_width = decoder.get_width(), image_height = decoder.get_height();
-		*width = image_width;
-		*height = image_height;
-		*actual_comps = decoder.get_num_components();
-
-		if (decoder.begin_decoding() != JPGD_SUCCESS)
-			return NULL;
-
-		const int dst_bpl = image_width * req_comps;
-
-		uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);
-		if (!pImage_data)
-			return NULL;
-
-		for (int y = 0; y < image_height; y++)
-		{
-			const uint8* pScan_line = 0;
-			uint scan_line_len;
-			if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)
-			{
-				jpgd_free(pImage_data);
-				return NULL;
-			}
-
-			uint8 *pDst = pImage_data + y * dst_bpl;
-
-			if (((req_comps == 4) && (decoder.get_num_components() == 3)) ||
-				((req_comps == 1) && (decoder.get_num_components() == 1)))
-			{
-				memcpy(pDst, pScan_line, dst_bpl);
-			}
-			else if (decoder.get_num_components() == 1)
-			{
-				if (req_comps == 3)
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst += 3;
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst[3] = 255;
-						pDst += 4;
-					}
-				}
-			}
-			else if (decoder.get_num_components() == 3)
-			{
-				if (req_comps == 1)
-				{
-					const int YR = 19595, YG = 38470, YB = 7471;
-					for (int x = 0; x < image_width; x++)
-					{
-						int r = pScan_line[x*4+0];
-						int g = pScan_line[x*4+1];
-						int b = pScan_line[x*4+2];
-						*pDst++ = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						pDst[0] = pScan_line[x*4+0];
-						pDst[1] = pScan_line[x*4+1];
-						pDst[2] = pScan_line[x*4+2];
-						pDst += 3;
-					}
-				}
-			}
-		}
-
-		return pImage_data;
-	}
-
-// BEGIN EPIC MOD
-	unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)
-	{
-		jpg_format = (ERGBFormatJPG)format;
-// EMD EPIC MOD
-		jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);
-		return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);
-	}
-
-	unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		jpgd::jpeg_decoder_file_stream file_stream;
-		if (!file_stream.open(pSrc_filename))
-			return NULL;
-		return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);
-	}
-
-} // namespace jpgd
diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/obstructedmaze.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/obstructedmaze.py
deleted file mode 100644
index 2b7987c12930d850e0ceceea88c4593771694f4b..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/obstructedmaze.py
+++ /dev/null
@@ -1,224 +0,0 @@
-from gym_minigrid.minigrid import *
-from gym_minigrid.roomgrid import RoomGrid
-from gym_minigrid.register import register
-
-class ObstructedMazeEnv(RoomGrid):
-    """
-    A blue ball is hidden in the maze. Doors may be locked,
-    doors may be obstructed by a ball and keys may be hidden in boxes.
-    """
-
-    def __init__(self,
-        num_rows,
-        num_cols,
-        num_rooms_visited,
-        seed=None
-    ):
-        room_size = 6
-        max_steps = 4*num_rooms_visited*room_size**2
-
-        super().__init__(
-            room_size=room_size,
-            num_rows=num_rows,
-            num_cols=num_cols,
-            max_steps=max_steps,
-            seed=seed
-        )
-
-    def _gen_grid(self, width, height):
-        super()._gen_grid(width, height)
-
-        # Define all possible colors for doors
-        self.door_colors = self._rand_subset(COLOR_NAMES, len(COLOR_NAMES))
-        # Define the color of the ball to pick up
-        self.ball_to_find_color = COLOR_NAMES[0]
-        # Define the color of the balls that obstruct doors
-        self.blocking_ball_color = COLOR_NAMES[1]
-        # Define the color of boxes in which keys are hidden
-        self.box_color = COLOR_NAMES[2]
-
-        self.mission = "pick up the %s ball" % self.ball_to_find_color
-
-    def step(self, action):
-        obs, reward, done, info = super().step(action)
-
-        if action == self.actions.pickup:
-            if self.carrying and self.carrying == self.obj:
-                reward = self._reward()
-                done = True
-
-        return obs, reward, done, info
-
-    def add_door(self, i, j, door_idx=0, color=None, locked=False, key_in_box=False, blocked=False):
-        """
-        Add a door. If the door must be locked, it also adds the key.
-        If the key must be hidden, it is put in a box. If the door must
-        be obstructed, it adds a ball in front of the door.
-        """
-
-        door, door_pos = super().add_door(i, j, door_idx, color, locked=locked)
-
-        if blocked:
-            vec = DIR_TO_VEC[door_idx]
-            blocking_ball = Ball(self.blocking_ball_color) if blocked else None
-            self.grid.set(door_pos[0]-vec[0], door_pos[1]-vec[1], blocking_ball)
-            
-        if locked:
-            obj = Key(door.color)
-            if key_in_box:
-                box = Box(self.box_color) if key_in_box else None
-                box.contains = obj
-                obj = box
-            self.place_in_room(i, j, obj)
-
-        return door, door_pos
-
-class ObstructedMaze_1Dlhb(ObstructedMazeEnv):
-    """
-    A blue ball is hidden in a 2x1 maze. A locked door separates
-    rooms. Doors are obstructed by a ball and keys are hidden in boxes.
-    """
-
-    def __init__(self, key_in_box=True, blocked=True, seed=None):
-        self.key_in_box = key_in_box
-        self.blocked = blocked
-
-        super().__init__(
-            num_rows=1,
-            num_cols=2,
-            num_rooms_visited=2,
-            seed=seed
-        )
-
-    def _gen_grid(self, width, height):
-        super()._gen_grid(width, height)
-
-        self.add_door(0, 0, door_idx=0, color=self.door_colors[0],
-                      locked=True,
-                      key_in_box=self.key_in_box,
-                      blocked=self.blocked)
-
-        self.obj, _ = self.add_object(1, 0, "ball", color=self.ball_to_find_color)
-        self.place_agent(0, 0)
-
-class ObstructedMaze_1Dl(ObstructedMaze_1Dlhb):
-    def __init__(self, seed=None):
-        super().__init__(False, False, seed)
-
-class ObstructedMaze_1Dlh(ObstructedMaze_1Dlhb):
-    def __init__(self, seed=None):
-        super().__init__(True, False, seed)
-
-class ObstructedMaze_Full(ObstructedMazeEnv):
-    """
-    A blue ball is hidden in one of the 4 corners of a 3x3 maze. Doors
-    are locked, doors are obstructed by a ball and keys are hidden in
-    boxes.
-    """
-
-    def __init__(self, agent_room=(1, 1), key_in_box=True, blocked=True,
-                 num_quarters=4, num_rooms_visited=25, seed=None):
-        self.agent_room = agent_room
-        self.key_in_box = key_in_box
-        self.blocked = blocked
-        self.num_quarters = num_quarters
-
-        super().__init__(
-            num_rows=3,
-            num_cols=3,
-            num_rooms_visited=num_rooms_visited,
-            seed=seed
-        )
-
-    def _gen_grid(self, width, height):
-        super()._gen_grid(width, height)
-
-        middle_room = (1, 1)
-        # Define positions of "side rooms" i.e. rooms that are neither
-        # corners nor the center.
-        side_rooms = [(2, 1), (1, 2), (0, 1), (1, 0)][:self.num_quarters]
-        for i in range(len(side_rooms)):
-            side_room = side_rooms[i]
-
-            # Add a door between the center room and the side room
-            self.add_door(*middle_room, door_idx=i, color=self.door_colors[i], locked=False)
-
-            for k in [-1, 1]:
-                # Add a door to each side of the side room
-                self.add_door(*side_room, locked=True,
-                              door_idx=(i+k)%4,
-                              color=self.door_colors[(i+k)%len(self.door_colors)],
-                              key_in_box=self.key_in_box,
-                              blocked=self.blocked)
-
-        corners = [(2, 0), (2, 2), (0, 2), (0, 0)][:self.num_quarters]
-        ball_room = self._rand_elem(corners)
-
-        self.obj, _ = self.add_object(*ball_room, "ball", color=self.ball_to_find_color)
-        self.place_agent(*self.agent_room)
-
-class ObstructedMaze_2Dl(ObstructedMaze_Full):
-    def __init__(self, seed=None):
-        super().__init__((2, 1), False, False, 1, 4, seed)
-
-class ObstructedMaze_2Dlh(ObstructedMaze_Full):
-    def __init__(self, seed=None):
-        super().__init__((2, 1), True, False, 1, 4, seed)
-
-
-class ObstructedMaze_2Dlhb(ObstructedMaze_Full):
-    def __init__(self, seed=None):
-        super().__init__((2, 1), True, True, 1, 4, seed)
-
-class ObstructedMaze_1Q(ObstructedMaze_Full):
-    def __init__(self, seed=None):
-        super().__init__((1, 1), True, True, 1, 5, seed)
-
-class ObstructedMaze_2Q(ObstructedMaze_Full):
-    def __init__(self, seed=None):
-        super().__init__((1, 1), True, True, 2, 11, seed)
-
-register(
-    id="MiniGrid-ObstructedMaze-1Dl-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_1Dl"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-1Dlh-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_1Dlh"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-1Dlhb-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_1Dlhb"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-2Dl-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_2Dl"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-2Dlh-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_2Dlh"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-2Dlhb-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_2Dlhb"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-1Q-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_1Q"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-2Q-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_2Q"
-)
-
-register(
-    id="MiniGrid-ObstructedMaze-Full-v0",
-    entry_point="gym_minigrid.envs:ObstructedMaze_Full"
-)
\ No newline at end of file
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_simple_squares/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_simple_squares/run.py
deleted file mode 100644
index 33519c743be15483757ced87b98ac9be20a8780d..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_simple_squares/run.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-
-demo = gr.Blocks(css="#btn {color: red}")
-
-with demo:
-    default_json = {"a": "a"}
-
-    num = gr.Variable(value=0)
-    squared = gr.Number(value=0)
-    btn = gr.Button("Next Square", elem_id="btn").style(rounded=False)
-
-    stats = gr.Variable(value=default_json)
-    table = gr.JSON()
-
-    def increase(var, stats_history):
-        var += 1
-        stats_history[str(var)] = var**2
-        return var, var**2, stats_history, stats_history
-
-    btn.click(increase, [num, stats], [num, squared, stats, table])
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/freddyaboulton/whisper-to-stable-diffusion/style.css b/spaces/freddyaboulton/whisper-to-stable-diffusion/style.css
deleted file mode 100644
index 38c5511c0b8702aae919be4524ab1883d11fb99c..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/whisper-to-stable-diffusion/style.css
+++ /dev/null
@@ -1,152 +0,0 @@
-.container {
-    max-width: 780px;
-    margin: auto;
-    padding-top: 1.5rem;
-}
-a {
-    text-decoration: underline;
-}
-h1 {
-    font-weight: 900; 
-    margin-bottom: 7px;
-    text-align: center;
-    font-size: 2em;
-    margin-bottom: 1em;
-}
-#w2sd_container{
-    margin-top: 20px;
-}
-.footer {
-    margin-bottom: 45px;
-    margin-top: 35px;
-    text-align: center;
-    border-bottom: 1px solid #e5e5e5;
-}
-.footer>p {
-    font-size: .8rem;
-    display: inline-block;
-    padding: 0 10px;
-    transform: translateY(10px);
-    background: white;
-}
-.dark .footer {
-    border-color: #303030;
-}
-.dark .footer>p {
-    background: #0b0f19;
-}
-.tabitem {
-    border-bottom-left-radius: 10px;
-    border-bottom-right-radius: 10px;
-}
-#record_tab, #upload_tab {
-    font-size: 1.2em;
-}
-#record_btn{
-    
-}
-#record_btn > div > button > span {
-    width: 2.375rem;
-    height: 2.375rem;
-}
-#record_btn > div > button > span > span {
-    width: 2.375rem;
-    height: 2.375rem;
-}
-audio {
-    margin-bottom: 10px;
-}
-div#record_btn > .mt-6{
-    margin-top: 0!important;
-}
-div#record_btn > .mt-6 button {
-    font-size: 2em;
-    width: 100%;
-    padding: 20px;
-    height: 160px;
-}
-div#upload_area {
-    height: 11.1rem;
-}
-div#upload_area > div.w-full > div {
-    min-height: 9rem;
-}
-#check_btn_1, #check_btn_2{
-    color: #fff;
-    --tw-gradient-from: #4caf50;
-    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
-    --tw-gradient-to: #4caf50;
-    border-color: #8bc34a;
-}
-#magic_btn_1, #magic_btn_2{
-    color: #fff;
-    --tw-gradient-from: #f44336;
-    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
-    --tw-gradient-to: #ff9800;
-    border-color: #ff9800;
-}
-input::-webkit-inner-spin-button, input::-webkit-outer-spin-button {
-    -webkit-appearance: none;
-}
-input[type=number] {
-    -moz-appearance: textfield;
-}
-input[type=range] {
-    -webkit-appearance: none;
-    cursor: pointer;
-    height: 1px;
-    background: currentColor;
-}
-input[type=range]::-webkit-slider-thumb {
-    -webkit-appearance: none;
-    width: 0.5em;
-    height: 1.2em;
-    border-radius: 10px;
-    background: currentColor;
-}
-input[type=range]::-moz-range-thumb{
-    width: 0.5em;
-    height: 1.2em;
-    border-radius: 10px;
-    background: currentColor;
-}
-div#spoken_lang textarea {
-    font-size: 4em;
-    line-height: 1em;
-    text-align: center;
-}
-div#transcripted {
-    flex: 4;
-}
-div#translated textarea {
-    font-size: 1.5em;
-    line-height: 1.25em;
-}
-#sd_settings {
-    margin-bottom: 20px;
-}
-#diffuse_btn {
-    color: #fff;
-    font-size: 1em;
-    margin-bottom: 20px;
-    --tw-gradient-from: #4caf50;
-    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
-    --tw-gradient-to: #4caf50;
-    border-color: #8bc34a;
-}
-#notice {
-    padding: 20px 14px 10px;
-    display: flex;
-    align-content: space-evenly;
-    gap: 20px;
-    line-height: 1em;
-    font-size: .8em;
-    border: 1px solid #374151;
-    border-radius: 10px;
-}
-#about {
-    padding: 20px;
-}
-#notice > div {
-    flex: 1;
-}
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/CODE_OF_CONDUCT.md b/spaces/gradio/HuBERT/CODE_OF_CONDUCT.md
deleted file mode 100644
index a0cbeaab7650bf08267fbdbc9bb54e845c88f392..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-  advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-  address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <conduct@pytorch.org>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
-
diff --git a/spaces/gradio/HuBERT/fairseq/criterions/fairseq_criterion.py b/spaces/gradio/HuBERT/fairseq/criterions/fairseq_criterion.py
deleted file mode 100644
index ff4beb02503ea48a6c09596630aad4c710be94b6..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/criterions/fairseq_criterion.py
+++ /dev/null
@@ -1,120 +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 inspect
-from typing import Any, Dict, List
-
-from fairseq import metrics, utils
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.utils import gen_parser_from_dataclass
-from torch.nn.modules.loss import _Loss
-
-
-class FairseqCriterion(_Loss):
-    def __init__(self, task):
-        super().__init__()
-        self.task = task
-        if hasattr(task, "target_dictionary"):
-            tgt_dict = task.target_dictionary
-            self.padding_idx = tgt_dict.pad() if tgt_dict is not None else -100
-
-    @classmethod
-    def add_args(cls, parser):
-        """Add criterion-specific arguments to the parser."""
-        dc = getattr(cls, "__dataclass", None)
-        if dc is not None:
-            gen_parser_from_dataclass(parser, dc())
-
-    @classmethod
-    def build_criterion(cls, cfg: FairseqDataclass, task):
-        """Construct a criterion from command-line args."""
-        # arguments in the __init__.
-        init_args = {}
-        for p in inspect.signature(cls).parameters.values():
-            if (
-                p.kind == p.POSITIONAL_ONLY
-                or p.kind == p.VAR_POSITIONAL
-                or p.kind == p.VAR_KEYWORD
-            ):
-                # we haven't implemented inference for these argument types,
-                # but PRs welcome :)
-                raise NotImplementedError("{} not supported".format(p.kind))
-
-            assert p.kind in {p.POSITIONAL_OR_KEYWORD, p.KEYWORD_ONLY}
-
-            if p.name == "task":
-                init_args["task"] = task
-            elif p.name == "cfg":
-                init_args["cfg"] = cfg
-            elif hasattr(cfg, p.name):
-                init_args[p.name] = getattr(cfg, p.name)
-            elif p.default != p.empty:
-                pass  # we'll use the default value
-            else:
-                raise NotImplementedError(
-                    "Unable to infer Criterion arguments, please implement "
-                    "{}.build_criterion".format(cls.__name__)
-                )
-        return cls(**init_args)
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        raise NotImplementedError
-
-    @staticmethod
-    def aggregate_logging_outputs(
-        logging_outputs: List[Dict[str, Any]]
-    ) -> Dict[str, Any]:
-        """Aggregate logging outputs from data parallel training."""
-        utils.deprecation_warning(
-            "The aggregate_logging_outputs API is deprecated. "
-            "Please use the reduce_metrics API instead."
-        )
-        raise NotImplementedError
-
-    @classmethod
-    def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        utils.deprecation_warning(
-            "Criterions should implement the reduce_metrics API. "
-            "Falling back to deprecated aggregate_logging_outputs API."
-        )
-        agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs)
-        for k, v in agg_logging_outputs.items():
-            if k in {"nsentences", "ntokens", "sample_size"}:
-                continue
-            metrics.log_scalar(k, v)
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return False
-
-
-class LegacyFairseqCriterion(FairseqCriterion):
-    def __init__(self, args, task):
-        super().__init__(task=task)
-        self.args = args
-
-        utils.deprecation_warning(
-            "Criterions should take explicit arguments instead of an "
-            "argparse.Namespace object, please update your criterion by "
-            "extending FairseqCriterion instead of LegacyFairseqCriterion."
-        )
-
-    @classmethod
-    def build_criterion(cls, args, task):
-        """Construct a criterion from command-line args."""
-        return cls(args, task)
diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/pages/_document.tsx b/spaces/gsaivinay/Llama-2-13B-GGML-UI/pages/_document.tsx
deleted file mode 100644
index 8ff34302ba30e2290661c26f6b31087b5e0f421a..0000000000000000000000000000000000000000
--- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/pages/_document.tsx
+++ /dev/null
@@ -1,24 +0,0 @@
-import { DocumentProps, Head, Html, Main, NextScript } from 'next/document';
-
-import i18nextConfig from '../next-i18next.config';
-
-type Props = DocumentProps & {
-  // add custom document props
-};
-
-export default function Document(props: Props) {
-  const currentLocale =
-    props.__NEXT_DATA__.locale ?? i18nextConfig.i18n.defaultLocale;
-  return (
-    <Html lang={currentLocale}>
-      <Head>
-        <meta name="apple-mobile-web-app-capable" content="yes" />
-        <meta name="apple-mobile-web-app-title" content="Chatbot UI"></meta>
-      </Head>
-      <body>
-        <Main />
-        <NextScript />
-      </body>
-    </Html>
-  );
-}
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/configs/3millions_pfc.py b/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/configs/3millions_pfc.py
deleted file mode 100644
index 77caafdbb300d8109d5bfdb844f131710ef81f20..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/configs/3millions_pfc.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from easydict import EasyDict as edict
-
-# configs for test speed
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.1
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "synthetic"
-config.num_classes = 300 * 10000
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = []
diff --git a/spaces/h2oai/wave-tour/examples/plot_app.py b/spaces/h2oai/wave-tour/examples/plot_app.py
deleted file mode 100644
index 6f73a281fa2e10fa205c082cc37445807dc93daf..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Plot / App
-# Make a #plot from an app.
-# ---
-from .synth import FakeMultiCategoricalSeries as F
-from h2o_wave import main, app, data, Q, ui
-
-n = 10
-k = 5
-f = F(groups=k)
-values = [(g, t, x) for x in [f.next() for _ in range(n)] for g, t, x, dx in x]
-
-
-@app('/demo')
-async def serve(q: Q):
-    v = q.page.add('example', ui.plot_card(
-        box='1 1 4 6',
-        title='Intervals, stacked',
-        data=data('country product price', n * k),
-        plot=ui.plot([ui.mark(
-            coord='rect',
-            type='interval',
-            x='=product',
-            y='=price',
-            y_min=0,
-            color='=country',
-            stack='auto',
-        )]),
-    ))
-    v.data = values
-    await q.page.save()
diff --git a/spaces/h2oai/wave-tour/examples/plot_theme.py b/spaces/h2oai/wave-tour/examples/plot_theme.py
deleted file mode 100644
index 227090b88a134309fb8841cc126ee4b7ae5aa209..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_theme.py
+++ /dev/null
@@ -1,432 +0,0 @@
-# Plot / Theme
-# Themeable #plot showcase.
-# ---
-import random
-
-from .synth import FakeTimeSeries, FakeMultiTimeSeries, FakeCategoricalSeries, FakeMultiCategoricalSeries, FakeScatter
-from h2o_wave import main, app, data, Q, ui
-
-
-n = 10
-k = 3
-f = FakeTimeSeries()
-f_multi = FakeMultiTimeSeries()
-f_cat = FakeCategoricalSeries()
-f_cat_multi = FakeMultiCategoricalSeries(groups=k)
-f_scat = FakeScatter()
-
-zones = [
-    ui.zone(name='title', size='60px'),
-    ui.zone(name='plots', direction=ui.ZoneDirection.ROW, wrap='start', justify='center'),
-]
-
-@app('/demo')
-async def serve(q: Q):
-    if not q.client.initialized:
-        q.page['meta'] = ui.meta_card(box='', layouts=[
-            ui.layout(breakpoint='xs', zones=zones),
-            ui.layout(breakpoint='m', zones=zones),
-            ui.layout(breakpoint='xl', zones=zones),
-        ])
-
-        q.client.active_theme = 'default'
-        q.page['title'] = ui.section_card(
-            box='title',
-            title='Plot theme demo',
-            subtitle='Toggle theme to see default plot colors change!',
-            items=[ui.toggle(name='toggle_theme', label='Dark theme', trigger=True)],
-        )
-
-        v = q.page.add('point-sized', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Point, sized',
-            data=data('price performance discount', n),
-            plot=ui.plot([ui.mark(type='point', x='=price', y='=performance', size='=discount')])
-        ))
-        v.data = [(x, y, random.randint(1, 10)) for x, y in [f_scat.next() for _ in range(n)]]
-
-        v = q.page.add('point-shapes', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Point, shapes',
-            data=data('product price performance', n * 2),
-            plot=ui.plot([ui.mark(type='point', x='=price', y='=performance', shape='=product', shape_range='circle square')])
-        ))
-        v.data = [('G1', x, y) for x, y in [f_scat.next() for _ in range(n)]] + [('G2', x, y) for x, y in [f_scat.next() for _ in range(n)]]
-
-        k1, k2 = 20, 10
-        v = q.page.add('poit-size', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Points, size-encoded',
-            data=data('country product profit', k1 * k2),
-            plot=ui.plot([ui.mark(type='point', x='=country', y='=product', size='=profit', shape='circle')])
-        ))
-        rows = []
-        for i in range(k1):
-            for j in range(k2):
-                x = f.next()
-                rows.append((f'A{i + 1}', f'B{j + 1}', x))
-        v.data = rows
-
-        v = q.page.add('area', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area',
-            data=data('date price', n),
-            plot=ui.plot([ui.mark(type='area', x_scale='time', x='=date', y='=price', y_min=0)])
-        ))
-        v.data = [(t, x) for t, x, dx in [f.next() for _ in range(n)]]
-
-        v = q.page.add('area-line', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area + Line',
-            data=data('date price', n),
-            plot=ui.plot([
-                ui.mark(type='area', x_scale='time', x='=date', y='=price', y_min=0),
-                ui.mark(type='line', x='=date', y='=price')
-            ])
-        ))
-        v.data = [(t, x) for t, x, dx in [f.next() for _ in range(n)]]
-
-        v = q.page.add('area-line-smooth', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area + Line, smooth',
-            data=data('date price', n),
-            plot=ui.plot([
-                ui.mark(type='area', x_scale='time', x='=date', y='=price', curve='smooth', y_min=0),
-                ui.mark(type='line', x='=date', y='=price', curve='smooth')
-            ])
-        ))
-        v.data = [(t, x) for t, x, dx in [f.next() for _ in range(n)]]
-
-        v = q.page.add('area-line-groups', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area + Line, groups',
-            data=data('product date price', n * 5),
-            plot=ui.plot([
-                ui.mark(type='area', x_scale='time', x='=date', y='=price', color='=product', y_min=0),
-                ui.mark(type='line', x='=date', y='=price', color='=product')
-            ])
-        ))
-        v.data = [(g, t, x) for x in [f_multi.next() for _ in range(n)] for g, t, x, dx in x]
-
-        v = q.page.add('area-groups', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area, groups',
-            data=data('product date price', n * 5),
-            plot=ui.plot([ui.mark(type='area', x_scale='time', x='=date', y='=price', color='=product', y_min=0)])
-        ))
-        v.data = [(g, t, x) for x in [f_multi.next() for _ in range(n)] for g, t, x, dx in x]
-
-        v = q.page.add('area-stacked', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area, stacked',
-            data=data('product date price', n * 5),
-            plot=ui.plot(
-                [ui.mark(type='area', x_scale='time', x='=date', y='=price', color='=product', stack='auto', y_min=0)])
-        ))
-        v.data = [(g, t, x) for x in [f_multi.next() for _ in range(n)] for g, t, x, dx in x]
-
-        f_negative = FakeTimeSeries(min=-50, max=50, start=0)
-        v = q.page.add('area-negative-values', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area, negative values',
-            data=data('date price', n),
-            plot=ui.plot([ui.mark(type='area', x_scale='time', x='=date', y='=price')])
-        ))
-        v.data = [(t, x) for t, x, dx in [f_negative.next() for _ in range(n)]]
-
-        v = q.page.add('area-range', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area, range',
-            data=data('date low high', n),
-            plot=ui.plot([ui.mark(type='area', x_scale='time', x='=date', y0='=low', y='=high')])
-        ))
-        v.data = [(t, x - random.randint(3, 8), x + random.randint(3, 8)) for t, x, dx in [f.next() for _ in range(n)]]
-
-        v = q.page.add('area-smooth', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Area, smooth',
-            data=data('date price', n),
-            plot=ui.plot([ui.mark(type='area', x_scale='time', x='=date', y='=price', curve='smooth', y_min=0)])
-        ))
-        v.data = [(t, x) for t, x, dx in [f.next() for _ in range(n)]]
-
-        v = q.page.add('example', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Label Customization',
-            data=data('product price', n),
-            plot=ui.plot([
-                ui.mark(type='interval', x='=product',
-                        y='=${{intl price minimum_fraction_digits=2 maximum_fraction_digits=2}}', y_min=0,
-                        label='=${{intl price minimum_fraction_digits=2 maximum_fraction_digits=2}}',
-                        label_offset=0, label_position='middle', label_rotation='-90', label_fill_color='#fff',
-                        label_font_weight='bold')])
-        ))
-        v.data = [(c, x) for c, x, dx in [f_cat.next() for _ in range(n)]]
-
-        v = q.page.add('interval', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Interval',
-            data=data('product price', n),
-            plot=ui.plot([ui.mark(type='interval', x='=product', y='=price', y_min=0)])
-        ))
-        v.data = [(c, x) for c, x, dx in [f_cat.next() for _ in range(n)]]
-
-        v = q.page.add('interval-annotation', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Categorical-Numeric',
-            data=data('product price', n),
-            plot=ui.plot([
-                ui.mark(type='interval', x='=product', y='=price', y_min=0, y_max=100),
-                ui.mark(x='C20', y=80, label='point'),
-                ui.mark(x='C23', label='vertical line'),
-                ui.mark(y=40, label='horizontal line'),
-                ui.mark(x='C26', x0='C23', label='vertical region'),
-                ui.mark(y=70, y0=60, label='horizontal region')
-            ])
-        ))
-        v.data = [(c, x) for c, x, dx in [f_cat.next() for _ in range(n)]]
-
-        v = q.page.add('histogram', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Histogram',
-            data=data('lo hi price', n),
-            plot=ui.plot([ui.mark(type='interval', y='=price', x1='=lo', x2='=hi', y_min=0)])
-        ))
-        v.data = [(i * 10, i * 10 + 10, x) for i, (c, x, dx) in enumerate([f_cat.next() for _ in range(n)])]
-
-        v = q.page.add('interval-range', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Histogram',
-            data=data('lo hi price', n),
-            plot=ui.plot([ui.mark(type='interval', y='=price', x1='=lo', x2='=hi', y_min=0)])
-        ))
-        v.data = [(i * 10, i * 10 + 10, x) for i, (c, x, dx) in enumerate([f_cat.next() for _ in range(n)])]
-
-        v = q.page.add('interval-range', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='35%'),
-            ),
-            title='Interval, range',
-            data=data('product low high', 3),
-            plot=ui.plot([ui.mark(type='interval', x='=product', y0='=low', y='=high')])
-        ))
-        v.data = [(c, x - random.randint(3, 10), x + random.randint(3, 10)) for c, x, _ in [f.next() for _ in range(3)]]
-
-        v = q.page.add('interval-transpose', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='29%'),
-            ),
-            title='Categorical-Numeric',
-            data=data('product price', 20),
-            plot=ui.plot([
-                ui.mark(type='interval', y='=product', x='=price', x_min=0, x_max=100),
-            ])
-        ))
-        v.data = [(c, x) for c, x, dx in [f_cat.next() for _ in range(20)]]
-
-        v = q.page.add('intervals-theta-stacked', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Intervals, theta, stacked',
-            data=data('country product price', n * k),
-            plot=ui.plot([
-                ui.mark(coord='theta', type='interval', x='=product', y='=price', color='=country', stack='auto', y_min=0)
-            ])
-        ))
-        v.data = [(g, t, x) for x in [f_cat_multi.next() for _ in range(n)] for g, t, x, dx in x]
-
-        v = q.page.add('interval-helix', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Interval, helix',
-            data=data('product price', 60),
-            plot=ui.plot([ui.mark(coord='helix', type='interval', x='=product', y='=price', y_min=0)])
-        ))
-        v.data = [(c, x) for c, x, dx in [f_cat.next() for _ in range(60)]]
-
-        v = q.page.add('interval-polar', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Interval, polar',
-            data=data('product price', n),
-            plot=ui.plot([ui.mark(coord='polar', type='interval', x='=product', y='=price', y_min=0)])
-        ))
-        v.data = [(c, x) for c, x, dx in [f_cat.next() for _ in range(n)]]
-
-        v = q.page.add('intervals-groups', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Intervals, groups',
-            data=data('country product price', n * k),
-            plot=ui.plot([ui.mark(type='interval', x='=product', y='=price', color='=country', dodge='auto', y_min=0)])
-        ))
-        v.data = [(g, t, x) for x in [f_cat_multi.next() for _ in range(n)] for g, t, x, dx in x]
-
-        v = q.page.add('intervals-stacked', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Intervals, stacked',
-            data=data('country product price', n * k),
-            plot=ui.plot([ui.mark(type='interval', x='=product', y='=price', color='=country', stack='auto', y_min=0)])
-        ))
-        v.data = [(g, t, x) for x in [f_cat_multi.next() for _ in range(n)] for g, t, x, dx in x]
-
-        v = q.page.add('point-annotation', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Numeric-Numeric',
-            data=data('price performance', n),
-            plot=ui.plot([
-                ui.mark(type='point', x='=price', y='=performance', x_min=0, x_max=100, y_min=0, y_max=100),  # the plot
-                ui.mark(x=50, y=50, label='point'),  # A single reference point
-                ui.mark(x=40, label='vertical line'),
-                ui.mark(y=40, label='horizontal line'),
-                ui.mark(x=70, x0=60, label='vertical region'),
-                ui.mark(y=70, y0=60, label='horizontal region'),
-                ui.mark(x=30, x0=20, y=30, y0=20, label='rectangular region')
-            ])
-        ))
-        v.data = [f_scat.next() for _ in range(n)]
-
-        v = q.page.add('path', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Path',
-            data=data('profit sales', n),
-            plot=ui.plot([ui.mark(type='path', x='=profit', y='=sales')])
-        ))
-        v.data = [(x, y) for x, y in [f_scat.next() for _ in range(n)]]
-
-        v = q.page.add('step', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Line, step',
-            data=data('date price', n),
-            plot=ui.plot([ui.mark(type='line', x_scale='time', x='=date', y='=price', curve='step', y_min=0)])
-        ))
-        v.data = [(t, x) for t, x, dx in [f.next() for _ in range(n)]]
-
-        v = q.page.add('line-annotation', ui.plot_card(
-            box=ui.boxes(
-                ui.box('plots', width='100%'),
-                ui.box('plots', width='48%'),
-                ui.box('plots', width='32%'),
-            ),
-            title='Time-Numeric',
-            data=data('date price', n),
-            plot=ui.plot([
-                ui.mark(type='line', x_scale='time', x='=date', y='=price', y_min=0, y_max=100),
-                ui.mark(x=50, y=50, label='point'),
-                ui.mark(x='2010-05-15T19:59:21.000000Z', label='vertical line'),
-                ui.mark(y=40, label='horizontal line'),
-                ui.mark(x='2010-05-24T19:59:21.000000Z', x0='2010-05-20T19:59:21.000000Z', label='vertical region'),
-                ui.mark(y=70, y0=60, label='horizontal region'),
-                ui.mark(x='2010-05-10T19:59:21.000000Z', x0='2010-05-05T19:59:21.000000Z', y=30, y0=20,
-                        label='rectangular region')
-            ])
-        ))
-        v.data = [(t, x) for t, x, dx in [f.next() for _ in range(n)]]
-
-        q.client.initialized = True
-
-    if q.args.toggle_theme is not None:
-        q.client.active_theme = 'neon' if q.args.toggle_theme else 'default'
-        q.page['meta'].theme = q.client.active_theme
-        q.page['title'].toggle_theme.value = q.client.active_theme == 'neon'
-    await q.page.save()
diff --git a/spaces/h2oai/wave-tour/examples/wide_info.py b/spaces/h2oai/wave-tour/examples/wide_info.py
deleted file mode 100644
index 7e5eb4deb95adb480b0b0ef64faa38b860cc5d4e..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/wide_info.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Info / Wide
-# Create a wide information card displaying a title, caption, and either an icon or image.
-# ---
-from h2o_wave import main, app, Q, ui
-
-
-@app('/demo')
-async def serve(q: Q):
-    if q.args.info_card:
-        q.page['example'] = ui.form_card(box='1 1 4 5', items=[
-            ui.button(name='back', label='Back', primary=True),
-        ])
-    else:
-        q.page['example'] = ui.wide_info_card(
-            box='1 1 4 5',
-            name='info_card',
-            title='Info Card',
-            subtitle='Subtitle',
-            caption='''
-Lorem ipsum dolor sit amet, consectetur adipisicing elit. Quia aliquam maxime quos facere
-necessitatibus tempore eum odio, qui illum. Repellat modi dolor facilis odio ex possimus
-ratione voluptate pariatur cupiditate, quidem quaerat sapiente exercitationem in omnis nulla
-maiores consequatur dolores illo inventore quae obcaecati culpa totam corporis! Repudiandae, nostrum repellendus.
-''',
-            category='Category',
-            label='Click me',
-            image='https://images.pexels.com/photos/3225517/pexels-photo-3225517.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=750&w=1260' # noqa
-        )
-    await q.page.save()
diff --git a/spaces/hamacojr/SAM-CAT-Seg/demo/predictor.py b/spaces/hamacojr/SAM-CAT-Seg/demo/predictor.py
deleted file mode 100644
index cd9e2eb5e12e195fbbe838a8d169f8dbbeffca16..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/demo/predictor.py
+++ /dev/null
@@ -1,261 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copied from: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py
-import atexit
-import bisect
-import multiprocessing as mp
-from collections import deque
-
-import cv2
-import torch
-
-from detectron2.data import MetadataCatalog
-from detectron2.engine.defaults import DefaultPredictor
-from detectron2.utils.video_visualizer import VideoVisualizer
-from detectron2.utils.visualizer import ColorMode, Visualizer
-
-from cat_seg.third_party import imagenet_templates
-from types import SimpleNamespace as ns
-
-class VisualizationDemo(object):
-    def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False, text=None):
-        """
-        Args:
-            cfg (CfgNode):
-            instance_mode (ColorMode):
-            parallel (bool): whether to run the model in different processes from visualization.
-                Useful since the visualization logic can be slow.
-        """
-        self.metadata = MetadataCatalog.get(
-            cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
-        )
-        self.cpu_device = torch.device("cpu")
-        self.instance_mode = instance_mode
-
-        self.parallel = parallel
-        if parallel:
-            num_gpu = torch.cuda.device_count()
-            self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)
-        else:
-            self.predictor = DefaultPredictor(cfg)
-
-        # set classes
-        templates = ['A photo of a {} in the scene',]
-        #templates = imagenet_templates.IMAGENET_TEMPLATES
-        if text is not None:
-            pred = self.predictor.model.sem_seg_head.predictor
-            pred.test_class_texts = [t.strip() for t in text.split(',')]
-            pred.text_features_test = pred.class_embeddings(pred.test_class_texts, 
-                templates,
-                pred.clip_model).permute(1, 0, 2).float()
-            if len(templates) == 1:
-                pred.text_features_test = pred.text_features_test.repeat(1, 80, 1)
-            self.metadata = ns()
-            self.metadata.stuff_classes = pred.test_class_texts
-        
-        self.filter_background = False
-
-    def run_on_image(self, image, text=None, use_sam=False):
-        """
-        Args:
-            image (np.ndarray): an image of shape (H, W, C) (in BGR order).
-                This is the format used by OpenCV.
-        Returns:
-            predictions (dict): the output of the model.
-            vis_output (VisImage): the visualized image output.
-        """
-        vis_output = None
-
-        if text is not None:
-            pred = self.predictor.model.sem_seg_head.predictor
-            pred.test_class_texts = text.split(',')
-            pred.text_features_test = pred.class_embeddings(pred.test_class_texts, 
-                #imagenet_templates.IMAGENET_TEMPLATES, 
-                 ['A photo of a {} in the scene',],
-                pred.clip_model).permute(1, 0, 2).float().repeat(1, 80, 1)
-            self.metadata = ns()
-            self.metadata.stuff_classes = pred.test_class_texts
-            self.metadata.thing_classes = pred.test_class_texts
-
-        self.predictor.model.use_sam = use_sam
-
-        predictions = self.predictor(image)
-        # Convert image from OpenCV BGR format to Matplotlib RGB format.
-        image = image[:, :, ::-1]
-        visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)
-        #import pdb; pdb.set_trace()
-        if "panoptic_seg" in predictions:
-            panoptic_seg, segments_info = predictions["panoptic_seg"]
-            vis_output = visualizer.draw_panoptic_seg_predictions(
-                panoptic_seg.to(self.cpu_device), segments_info,
-                alpha=0.5,
-            )
-        else:
-            if "sem_seg" in predictions:
-                vis_output = visualizer.draw_sem_seg(
-                    self.filter_bg(predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)),
-                    alpha=0.5,
-                )
-            if "instances" in predictions:
-                instances = predictions["instances"].to(self.cpu_device)
-                vis_output = visualizer.draw_instance_predictions(predictions=instances)
-
-        return predictions, vis_output
-
-    def _frame_from_video(self, video):
-        while video.isOpened():
-            success, frame = video.read()
-            if success:
-                yield frame
-            else:
-                break
-
-    def run_on_video(self, video):
-        """
-        Visualizes predictions on frames of the input video.
-        Args:
-            video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be
-                either a webcam or a video file.
-        Yields:
-            ndarray: BGR visualizations of each video frame.
-        """
-        video_visualizer = VideoVisualizer(self.metadata, self.instance_mode)
-
-        def process_predictions(frame, predictions):
-            import pdb; pdb.set_trace()
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            if "panoptic_seg" in predictions:
-                panoptic_seg, segments_info = predictions["panoptic_seg"]
-                vis_frame = video_visualizer.draw_panoptic_seg_predictions(
-                    frame, panoptic_seg.to(self.cpu_device), segments_info
-                )
-            elif "instances" in predictions:
-                predictions = predictions["instances"].to(self.cpu_device)
-                vis_frame = video_visualizer.draw_instance_predictions(frame, predictions)
-            elif "sem_seg" in predictions:
-                vis_frame = video_visualizer.draw_sem_seg(
-                    frame, 
-                    predictions["sem_seg"].argmax(dim=0).to(self.cpu_device),
-                )
-
-            # Converts Matplotlib RGB format to OpenCV BGR format
-            vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR)
-            return vis_frame
-
-        frame_gen = self._frame_from_video(video)
-        if self.parallel:
-            buffer_size = self.predictor.default_buffer_size
-
-            frame_data = deque()
-
-            for cnt, frame in enumerate(frame_gen):
-                frame_data.append(frame)
-                self.predictor.put(frame)
-
-                if cnt >= buffer_size:
-                    frame = frame_data.popleft()
-                    predictions = self.predictor.get()
-                    yield process_predictions(frame, predictions)
-
-            while len(frame_data):
-                frame = frame_data.popleft()
-                predictions = self.predictor.get()
-                yield process_predictions(frame, predictions)
-        else:
-            for frame in frame_gen:
-                yield process_predictions(frame, self.predictor(frame))
-
-    def filter_bg(self, pred):
-        if self.filter_background:
-            pred[pred == 0] = 255
-        return pred
-
-
-class AsyncPredictor:
-    """
-    A predictor that runs the model asynchronously, possibly on >1 GPUs.
-    Because rendering the visualization takes considerably amount of time,
-    this helps improve throughput a little bit when rendering videos.
-    """
-
-    class _StopToken:
-        pass
-
-    class _PredictWorker(mp.Process):
-        def __init__(self, cfg, task_queue, result_queue):
-            self.cfg = cfg
-            self.task_queue = task_queue
-            self.result_queue = result_queue
-            super().__init__()
-
-        def run(self):
-            predictor = DefaultPredictor(self.cfg)
-
-            while True:
-                task = self.task_queue.get()
-                if isinstance(task, AsyncPredictor._StopToken):
-                    break
-                idx, data = task
-                result = predictor(data)
-                self.result_queue.put((idx, result))
-
-    def __init__(self, cfg, num_gpus: int = 1):
-        """
-        Args:
-            cfg (CfgNode):
-            num_gpus (int): if 0, will run on CPU
-        """
-        num_workers = max(num_gpus, 1)
-        self.task_queue = mp.Queue(maxsize=num_workers * 3)
-        self.result_queue = mp.Queue(maxsize=num_workers * 3)
-        self.procs = []
-        for gpuid in range(max(num_gpus, 1)):
-            cfg = cfg.clone()
-            cfg.defrost()
-            cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu"
-            self.procs.append(
-                AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)
-            )
-
-        self.put_idx = 0
-        self.get_idx = 0
-        self.result_rank = []
-        self.result_data = []
-
-        for p in self.procs:
-            p.start()
-        atexit.register(self.shutdown)
-
-    def put(self, image):
-        self.put_idx += 1
-        self.task_queue.put((self.put_idx, image))
-
-    def get(self):
-        self.get_idx += 1  # the index needed for this request
-        if len(self.result_rank) and self.result_rank[0] == self.get_idx:
-            res = self.result_data[0]
-            del self.result_data[0], self.result_rank[0]
-            return res
-
-        while True:
-            # make sure the results are returned in the correct order
-            idx, res = self.result_queue.get()
-            if idx == self.get_idx:
-                return res
-            insert = bisect.bisect(self.result_rank, idx)
-            self.result_rank.insert(insert, idx)
-            self.result_data.insert(insert, res)
-
-    def __len__(self):
-        return self.put_idx - self.get_idx
-
-    def __call__(self, image):
-        self.put(image)
-        return self.get()
-
-    def shutdown(self):
-        for _ in self.procs:
-            self.task_queue.put(AsyncPredictor._StopToken())
-
-    @property
-    def default_buffer_size(self):
-        return len(self.procs) * 5
diff --git a/spaces/hands012/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.h b/spaces/hands012/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.h
deleted file mode 100644
index a1c0cac61839a6f66a42c341f50d5e36faad9a93..0000000000000000000000000000000000000000
--- a/spaces/hands012/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.h
+++ /dev/null
@@ -1,316 +0,0 @@
-// jpgd.h - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-#ifndef JPEG_DECODER_H
-#define JPEG_DECODER_H
-
-#include <stdlib.h>
-#include <stdio.h>
-#include <setjmp.h>
-
-namespace jpgd
-{
-  typedef unsigned char  uint8;
-  typedef   signed short int16;
-  typedef unsigned short uint16;
-  typedef unsigned int   uint;
-  typedef   signed int   int32;
-
-  // Loads a JPEG image from a memory buffer or a file.
-  // req_comps can be 1 (grayscale), 3 (RGB), or 4 (RGBA).
-  // On return, width/height will be set to the image's dimensions, and actual_comps will be set to the either 1 (grayscale) or 3 (RGB).
-  // Notes: For more control over where and how the source data is read, see the decompress_jpeg_image_from_stream() function below, or call the jpeg_decoder class directly.
-  // Requesting a 8 or 32bpp image is currently a little faster than 24bpp because the jpeg_decoder class itself currently always unpacks to either 8 or 32bpp.
-// BEGIN EPIC MOD
-//unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps);
-  unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format);
-// END EPIC MOD
-  unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps);
-
-  // Success/failure error codes.
-  enum jpgd_status
-  {
-    JPGD_SUCCESS = 0, JPGD_FAILED = -1, JPGD_DONE = 1,
-    JPGD_BAD_DHT_COUNTS = -256, JPGD_BAD_DHT_INDEX, JPGD_BAD_DHT_MARKER, JPGD_BAD_DQT_MARKER, JPGD_BAD_DQT_TABLE, 
-    JPGD_BAD_PRECISION, JPGD_BAD_HEIGHT, JPGD_BAD_WIDTH, JPGD_TOO_MANY_COMPONENTS, 
-    JPGD_BAD_SOF_LENGTH, JPGD_BAD_VARIABLE_MARKER, JPGD_BAD_DRI_LENGTH, JPGD_BAD_SOS_LENGTH,
-    JPGD_BAD_SOS_COMP_ID, JPGD_W_EXTRA_BYTES_BEFORE_MARKER, JPGD_NO_ARITHMITIC_SUPPORT, JPGD_UNEXPECTED_MARKER,
-    JPGD_NOT_JPEG, JPGD_UNSUPPORTED_MARKER, JPGD_BAD_DQT_LENGTH, JPGD_TOO_MANY_BLOCKS,
-    JPGD_UNDEFINED_QUANT_TABLE, JPGD_UNDEFINED_HUFF_TABLE, JPGD_NOT_SINGLE_SCAN, JPGD_UNSUPPORTED_COLORSPACE,
-    JPGD_UNSUPPORTED_SAMP_FACTORS, JPGD_DECODE_ERROR, JPGD_BAD_RESTART_MARKER, JPGD_ASSERTION_ERROR,
-    JPGD_BAD_SOS_SPECTRAL, JPGD_BAD_SOS_SUCCESSIVE, JPGD_STREAM_READ, JPGD_NOTENOUGHMEM
-  };
-    
-  // Input stream interface.
-  // Derive from this class to read input data from sources other than files or memory. Set m_eof_flag to true when no more data is available.
-  // The decoder is rather greedy: it will keep on calling this method until its internal input buffer is full, or until the EOF flag is set.
-  // It the input stream contains data after the JPEG stream's EOI (end of image) marker it will probably be pulled into the internal buffer.
-  // Call the get_total_bytes_read() method to determine the actual size of the JPEG stream after successful decoding.
-  class jpeg_decoder_stream
-  {
-  public:
-    jpeg_decoder_stream() { }
-    virtual ~jpeg_decoder_stream() { }
-
-    // The read() method is called when the internal input buffer is empty.
-    // Parameters:
-    // pBuf - input buffer
-    // max_bytes_to_read - maximum bytes that can be written to pBuf
-    // pEOF_flag - set this to true if at end of stream (no more bytes remaining)
-    // Returns -1 on error, otherwise return the number of bytes actually written to the buffer (which may be 0).
-    // Notes: This method will be called in a loop until you set *pEOF_flag to true or the internal buffer is full.
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) = 0;
-  };
-
-  // stdio FILE stream class.
-  class jpeg_decoder_file_stream : public jpeg_decoder_stream
-  {
-    jpeg_decoder_file_stream(const jpeg_decoder_file_stream &);
-    jpeg_decoder_file_stream &operator =(const jpeg_decoder_file_stream &);
-
-    FILE *m_pFile;
-    bool m_eof_flag, m_error_flag;
-
-  public:
-    jpeg_decoder_file_stream();
-    virtual ~jpeg_decoder_file_stream();
-    
-    bool open(const char *Pfilename);
-    void close();
-
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
-  };
-
-  // Memory stream class.
-  class jpeg_decoder_mem_stream : public jpeg_decoder_stream
-  {
-    const uint8 *m_pSrc_data;
-    uint m_ofs, m_size;
-
-  public:
-    jpeg_decoder_mem_stream() : m_pSrc_data(NULL), m_ofs(0), m_size(0) { }
-    jpeg_decoder_mem_stream(const uint8 *pSrc_data, uint size) : m_pSrc_data(pSrc_data), m_ofs(0), m_size(size) { }
-
-    virtual ~jpeg_decoder_mem_stream() { }
-
-    bool open(const uint8 *pSrc_data, uint size);
-    void close() { m_pSrc_data = NULL; m_ofs = 0; m_size = 0; }
-    
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
-  };
-
-  // Loads JPEG file from a jpeg_decoder_stream.
-  unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps);
-
-  enum 
-  { 
-    JPGD_IN_BUF_SIZE = 8192, JPGD_MAX_BLOCKS_PER_MCU = 10, JPGD_MAX_HUFF_TABLES = 8, JPGD_MAX_QUANT_TABLES = 4, 
-    JPGD_MAX_COMPONENTS = 4, JPGD_MAX_COMPS_IN_SCAN = 4, JPGD_MAX_BLOCKS_PER_ROW = 8192, JPGD_MAX_HEIGHT = 16384, JPGD_MAX_WIDTH = 16384 
-  };
-          
-  typedef int16 jpgd_quant_t;
-  typedef int16 jpgd_block_t;
-
-  class jpeg_decoder
-  {
-  public:
-    // Call get_error_code() after constructing to determine if the stream is valid or not. You may call the get_width(), get_height(), etc.
-    // methods after the constructor is called. You may then either destruct the object, or begin decoding the image by calling begin_decoding(), then decode() on each scanline.
-    jpeg_decoder(jpeg_decoder_stream *pStream);
-
-    ~jpeg_decoder();
-
-    // Call this method after constructing the object to begin decompression.
-    // If JPGD_SUCCESS is returned you may then call decode() on each scanline.
-    int begin_decoding();
-
-    // Returns the next scan line.
-    // For grayscale images, pScan_line will point to a buffer containing 8-bit pixels (get_bytes_per_pixel() will return 1). 
-    // Otherwise, it will always point to a buffer containing 32-bit RGBA pixels (A will always be 255, and get_bytes_per_pixel() will return 4).
-    // Returns JPGD_SUCCESS if a scan line has been returned.
-    // Returns JPGD_DONE if all scan lines have been returned.
-    // Returns JPGD_FAILED if an error occurred. Call get_error_code() for a more info.
-    int decode(const void** pScan_line, uint* pScan_line_len);
-    
-    inline jpgd_status get_error_code() const { return m_error_code; }
-
-    inline int get_width() const { return m_image_x_size; }
-    inline int get_height() const { return m_image_y_size; }
-
-    inline int get_num_components() const { return m_comps_in_frame; }
-
-    inline int get_bytes_per_pixel() const { return m_dest_bytes_per_pixel; }
-    inline int get_bytes_per_scan_line() const { return m_image_x_size * get_bytes_per_pixel(); }
-
-    // Returns the total number of bytes actually consumed by the decoder (which should equal the actual size of the JPEG file).
-    inline int get_total_bytes_read() const { return m_total_bytes_read; }
-    
-  private:
-    jpeg_decoder(const jpeg_decoder &);
-    jpeg_decoder &operator =(const jpeg_decoder &);
-
-    typedef void (*pDecode_block_func)(jpeg_decoder *, int, int, int);
-
-    struct huff_tables
-    {
-      bool ac_table;
-      uint  look_up[256];
-      uint  look_up2[256];
-      uint8 code_size[256];
-      uint  tree[512];
-    };
-
-    struct coeff_buf
-    {
-      uint8 *pData;
-      int block_num_x, block_num_y;
-      int block_len_x, block_len_y;
-      int block_size;
-    };
-
-    struct mem_block
-    {
-      mem_block *m_pNext;
-      size_t m_used_count;
-      size_t m_size;
-      char m_data[1];
-    };
-
-    jmp_buf m_jmp_state;
-    mem_block *m_pMem_blocks;
-    int m_image_x_size;
-    int m_image_y_size;
-    jpeg_decoder_stream *m_pStream;
-    int m_progressive_flag;
-    uint8 m_huff_ac[JPGD_MAX_HUFF_TABLES];
-    uint8* m_huff_num[JPGD_MAX_HUFF_TABLES];      // pointer to number of Huffman codes per bit size
-    uint8* m_huff_val[JPGD_MAX_HUFF_TABLES];      // pointer to Huffman codes per bit size
-    jpgd_quant_t* m_quant[JPGD_MAX_QUANT_TABLES]; // pointer to quantization tables
-    int m_scan_type;                              // Gray, Yh1v1, Yh1v2, Yh2v1, Yh2v2 (CMYK111, CMYK4114 no longer supported)
-    int m_comps_in_frame;                         // # of components in frame
-    int m_comp_h_samp[JPGD_MAX_COMPONENTS];       // component's horizontal sampling factor
-    int m_comp_v_samp[JPGD_MAX_COMPONENTS];       // component's vertical sampling factor
-    int m_comp_quant[JPGD_MAX_COMPONENTS];        // component's quantization table selector
-    int m_comp_ident[JPGD_MAX_COMPONENTS];        // component's ID
-    int m_comp_h_blocks[JPGD_MAX_COMPONENTS];
-    int m_comp_v_blocks[JPGD_MAX_COMPONENTS];
-    int m_comps_in_scan;                          // # of components in scan
-    int m_comp_list[JPGD_MAX_COMPS_IN_SCAN];      // components in this scan
-    int m_comp_dc_tab[JPGD_MAX_COMPONENTS];       // component's DC Huffman coding table selector
-    int m_comp_ac_tab[JPGD_MAX_COMPONENTS];       // component's AC Huffman coding table selector
-    int m_spectral_start;                         // spectral selection start
-    int m_spectral_end;                           // spectral selection end
-    int m_successive_low;                         // successive approximation low
-    int m_successive_high;                        // successive approximation high
-    int m_max_mcu_x_size;                         // MCU's max. X size in pixels
-    int m_max_mcu_y_size;                         // MCU's max. Y size in pixels
-    int m_blocks_per_mcu;
-    int m_max_blocks_per_row;
-    int m_mcus_per_row, m_mcus_per_col;
-    int m_mcu_org[JPGD_MAX_BLOCKS_PER_MCU];
-    int m_total_lines_left;                       // total # lines left in image
-    int m_mcu_lines_left;                         // total # lines left in this MCU
-    int m_real_dest_bytes_per_scan_line;
-    int m_dest_bytes_per_scan_line;               // rounded up
-    int m_dest_bytes_per_pixel;                   // 4 (RGB) or 1 (Y)
-    huff_tables* m_pHuff_tabs[JPGD_MAX_HUFF_TABLES];
-    coeff_buf* m_dc_coeffs[JPGD_MAX_COMPONENTS];
-    coeff_buf* m_ac_coeffs[JPGD_MAX_COMPONENTS];
-    int m_eob_run;
-    int m_block_y_mcu[JPGD_MAX_COMPONENTS];
-    uint8* m_pIn_buf_ofs;
-    int m_in_buf_left;
-    int m_tem_flag;
-    bool m_eof_flag;
-    uint8 m_in_buf_pad_start[128];
-    uint8 m_in_buf[JPGD_IN_BUF_SIZE + 128];
-    uint8 m_in_buf_pad_end[128];
-    int m_bits_left;
-    uint m_bit_buf;
-    int m_restart_interval;
-    int m_restarts_left;
-    int m_next_restart_num;
-    int m_max_mcus_per_row;
-    int m_max_blocks_per_mcu;
-    int m_expanded_blocks_per_mcu;
-    int m_expanded_blocks_per_row;
-    int m_expanded_blocks_per_component;
-    bool  m_freq_domain_chroma_upsample;
-    int m_max_mcus_per_col;
-    uint m_last_dc_val[JPGD_MAX_COMPONENTS];
-    jpgd_block_t* m_pMCU_coefficients;
-    int m_mcu_block_max_zag[JPGD_MAX_BLOCKS_PER_MCU];
-    uint8* m_pSample_buf;
-    int m_crr[256];
-    int m_cbb[256];
-    int m_crg[256];
-    int m_cbg[256];
-    uint8* m_pScan_line_0;
-    uint8* m_pScan_line_1;
-    jpgd_status m_error_code;
-    bool m_ready_flag;
-    int m_total_bytes_read;
-
-    void free_all_blocks();
-    // BEGIN EPIC MOD
-    UE_NORETURN void stop_decoding(jpgd_status status);
-    // END EPIC MOD
-    void *alloc(size_t n, bool zero = false);
-    void word_clear(void *p, uint16 c, uint n);
-    void prep_in_buffer();
-    void read_dht_marker();
-    void read_dqt_marker();
-    void read_sof_marker();
-    void skip_variable_marker();
-    void read_dri_marker();
-    void read_sos_marker();
-    int next_marker();
-    int process_markers();
-    void locate_soi_marker();
-    void locate_sof_marker();
-    int locate_sos_marker();
-    void init(jpeg_decoder_stream * pStream);
-    void create_look_ups();
-    void fix_in_buffer();
-    void transform_mcu(int mcu_row);
-    void transform_mcu_expand(int mcu_row);
-    coeff_buf* coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y);
-    inline jpgd_block_t *coeff_buf_getp(coeff_buf *cb, int block_x, int block_y);
-    void load_next_row();
-    void decode_next_row();
-    void make_huff_table(int index, huff_tables *pH);
-    void check_quant_tables();
-    void check_huff_tables();
-    void calc_mcu_block_order();
-    int init_scan();
-    void init_frame();
-    void process_restart();
-    void decode_scan(pDecode_block_func decode_block_func);
-    void init_progressive();
-    void init_sequential();
-    void decode_start();
-    void decode_init(jpeg_decoder_stream * pStream);
-    void H2V2Convert();
-    void H2V1Convert();
-    void H1V2Convert();
-    void H1V1Convert();
-    void gray_convert();
-    void expanded_convert();
-    void find_eoi();
-    inline uint get_char();
-    inline uint get_char(bool *pPadding_flag);
-    inline void stuff_char(uint8 q);
-    inline uint8 get_octet();
-    inline uint get_bits(int num_bits);
-    inline uint get_bits_no_markers(int numbits);
-    inline int huff_decode(huff_tables *pH);
-    inline int huff_decode(huff_tables *pH, int& extrabits);
-    static inline uint8 clamp(int i);
-    static void decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-  };
-  
-} // namespace jpgd
-
-#endif // JPEG_DECODER_H
diff --git "a/spaces/hands012/gpt-academic/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py" "b/spaces/hands012/gpt-academic/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
deleted file mode 100644
index 5a5dd93f6f9ca8c47c0de193b95e863e9bb82087..0000000000000000000000000000000000000000
--- "a/spaces/hands012/gpt-academic/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
+++ /dev/null
@@ -1,112 +0,0 @@
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-
-def get_meta_information(url, chatbot, history):
-    import requests
-    import arxiv
-    import difflib
-    from bs4 import BeautifulSoup
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',
-    }
-    # 发送 GET 请求
-    response = requests.get(url, proxies=proxies, headers=headers)
-
-    # 解析网页内容
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    def string_similar(s1, s2):
-        return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
-
-    profile = []
-    # 获取所有文章的标题和作者
-    for result in soup.select(".gs_ri"):
-        title = result.a.text.replace('\n', ' ').replace('  ', ' ')
-        author = result.select_one(".gs_a").text
-        try:
-            citation = result.select_one(".gs_fl > a[href*='cites']").text  # 引用次数是链接中的文本，直接取出来
-        except:
-            citation = 'cited by 0'
-        abstract = result.select_one(".gs_rs").text.strip()  # 摘要在 .gs_rs 中的文本，需要清除首尾空格
-        search = arxiv.Search(
-            query = title,
-            max_results = 1,
-            sort_by = arxiv.SortCriterion.Relevance,
-        )
-        try:
-            paper = next(search.results())
-            if string_similar(title, paper.title) > 0.90: # same paper
-                abstract = paper.summary.replace('\n', ' ')
-                is_paper_in_arxiv = True
-            else:   # different paper
-                abstract = abstract
-                is_paper_in_arxiv = False
-            paper = next(search.results())
-        except:
-            abstract = abstract
-            is_paper_in_arxiv = False
-        print(title)
-        print(author)
-        print(citation)
-        profile.append({
-            'title':title,
-            'author':author,
-            'citation':citation,
-            'abstract':abstract,
-            'is_paper_in_arxiv':is_paper_in_arxiv,
-        })
-
-        chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    return profile
-
-@CatchException
-def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
-    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 arxiv
-        import math
-        from bs4 import BeautifulSoup
-    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 beautifulsoup4 arxiv```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-    meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
-    batchsize = 5
-    for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
-        if len(meta_paper_info_list[:batchsize]) > 0:
-            i_say = "下面是一些学术文献的数据，提取出以下内容：" + \
-            "1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
-            f"以下是信息源：{str(meta_paper_info_list[:batchsize])}" 
-
-            inputs_show_user = f"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, inputs_show_user=inputs_show_user,
-                llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
-                sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
-            )
-
-            history.extend([ f"第{batch+1}批", gpt_say ])
-            meta_paper_info_list = meta_paper_info_list[batchsize:]
-
-    chatbot.append(["状态？", 
-        "已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write an academic \"Related Works\" section about \"你搜索的研究领域\" for me."])
-    msg = '正常'
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res)); 
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
diff --git a/spaces/hank1996/yolopv2/lib/models/common.py b/spaces/hank1996/yolopv2/lib/models/common.py
deleted file mode 100644
index 036e718029ccd7550fab66ea911bebed1b1f9ad2..0000000000000000000000000000000000000000
--- a/spaces/hank1996/yolopv2/lib/models/common.py
+++ /dev/null
@@ -1,272 +0,0 @@
-
-
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-from PIL import Image, ImageDraw
-import torch.nn.functional as F
-
-
-def autopad(k, p=None):  # kernel, padding
-    # Pad to 'same'
-    if p is None:
-        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
-    return p
-
-
-class DepthSeperabelConv2d(nn.Module):
-    """
-    DepthSeperable Convolution 2d with residual connection
-    """
-
-    def __init__(self, inplanes, planes, kernel_size=3, stride=1, downsample=None, act=True):
-        super(DepthSeperabelConv2d, self).__init__()
-        self.depthwise = nn.Sequential(
-            nn.Conv2d(inplanes, inplanes, kernel_size, stride=stride, groups=inplanes, padding=kernel_size//2, bias=False),
-            nn.BatchNorm2d(inplanes, momentum=BN_MOMENTUM)
-        )
-        # self.depthwise = nn.Conv2d(inplanes, inplanes, kernel_size, stride=stride, groups=inplanes, padding=1, bias=False)
-        # self.pointwise = nn.Conv2d(inplanes, planes, 1, bias=False)
-
-        self.pointwise = nn.Sequential(
-            nn.Conv2d(inplanes, planes, 1, bias=False),
-            nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
-        )
-        self.downsample = downsample
-        self.stride = stride
-        try:
-            self.act = Hardswish() if act else nn.Identity()
-        except:
-            self.act = nn.Identity()
-
-    def forward(self, x):
-        #residual = x
-
-        out = self.depthwise(x)
-        out = self.act(out)
-        out = self.pointwise(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-        out = self.act(out)
-
-        return out
-
-
-
-class SharpenConv(nn.Module):
-    # SharpenConv convolution
-    def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(SharpenConv, self).__init__()
-        sobel_kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32')
-        kenel_weight = np.vstack([sobel_kernel]*c2*c1).reshape(c2,c1,3,3)
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
-        self.conv.weight.data = torch.from_numpy(kenel_weight)
-        self.conv.weight.requires_grad = False
-        self.bn = nn.BatchNorm2d(c2)
-        try:
-            self.act = Hardswish() if act else nn.Identity()
-        except:
-            self.act = nn.Identity()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def fuseforward(self, x):
-        return self.act(self.conv(x))
-
-
-class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
-    @staticmethod
-    def forward(x):
-        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
-        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
-
-
-class Conv(nn.Module):
-    # Standard convolution
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Conv, self).__init__()
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
-        self.bn = nn.BatchNorm2d(c2)
-        try:
-            self.act = Hardswish() if act else nn.Identity()
-        except:
-            self.act = nn.Identity()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def fuseforward(self, x):
-        return self.act(self.conv(x))
-
-
-class Bottleneck(nn.Module):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super(Bottleneck, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c2, 3, 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 BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(BottleneckCSP, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
-        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
-
-
-class SPP(nn.Module):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
-    def __init__(self, c1, c2, k=(5, 9, 13)):
-        super(SPP, self).__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
-        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
-    def forward(self, x):
-        x = self.cv1(x)
-        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class Focus(nn.Module):
-    # Focus wh information into c-space
-    # slice concat conv
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Focus, self).__init__()
-        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
-
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
-
-
-class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
-    def __init__(self, dimension=1):
-        super(Concat, self).__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        """ print("***********************")
-        for f in x:
-            print(f.shape) """
-        return torch.cat(x, self.d)
-
-
-class Detect(nn.Module):
-    stride = None  # strides computed during build
-
-    def __init__(self, nc=13, anchors=(), ch=()):  # detection layer
-        super(Detect, self).__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor 85
-        self.nl = len(anchors)  # number of detection layers 3
-        self.na = len(anchors[0]) // 2  # number of anchors 3
-        self.grid = [torch.zeros(1)] * self.nl  # init grid 
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer('anchors', a)  # shape(nl,na,2)
-        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv  
-
-    def forward(self, x):
-        z = []  # inference output
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            # print(str(i)+str(x[i].shape))
-            bs, _, ny, nx = x[i].shape  # x(bs,255,w,w) to x(bs,3,w,w,85)
-            x[i]=x[i].view(bs, self.na, self.no, ny*nx).permute(0, 1, 3, 2).view(bs, self.na, ny, nx, self.no).contiguous()
-            # x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-            # print(str(i)+str(x[i].shape))
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-                y = x[i].sigmoid()
-                #print("**")
-                #print(y.shape) #[1, 3, w, h, 85]
-                #print(self.grid[i].shape) #[1, 3, w, h, 2]
-                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-                """print("**")
-                print(y.shape)  #[1, 3, w, h, 85]
-                print(y.view(bs, -1, self.no).shape) #[1, 3*w*h, 85]"""
-                z.append(y.view(bs, -1, self.no))
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-"""class Detections:
-    # detections class for YOLOv5 inference results
-    def __init__(self, imgs, pred, names=None):
-        super(Detections, self).__init__()
-        d = pred[0].device  # device
-        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
-        self.imgs = imgs  # list of images as numpy arrays
-        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
-        self.names = names  # class names
-        self.xyxy = pred  # xyxy pixels
-        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
-        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
-        self.n = len(self.pred)
-    def display(self, pprint=False, show=False, save=False):
-        colors = color_list()
-        for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
-            str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
-            if pred is not None:
-                for c in pred[:, -1].unique():
-                    n = (pred[:, -1] == c).sum()  # detections per class
-                    str += f'{n} {self.names[int(c)]}s, '  # add to string
-                if show or save:
-                    img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
-                    for *box, conf, cls in pred:  # xyxy, confidence, class
-                        # str += '%s %.2f, ' % (names[int(cls)], conf)  # label
-                        ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10])  # plot
-            if save:
-                f = f'results{i}.jpg'
-                str += f"saved to '{f}'"
-                img.save(f)  # save
-            if show:
-                img.show(f'Image {i}')  # show
-            if pprint:
-                print(str)
-    def print(self):
-        self.display(pprint=True)  # print results
-    def show(self):
-        self.display(show=True)  # show results
-    def save(self):
-        self.display(save=True)  # save results
-    def __len__(self):
-        return self.n
-    def tolist(self):
-        # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
-        for d in x:
-            for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-                setattr(d, k, getattr(d, k)[0])  # pop out of list"""
-
-
diff --git a/spaces/hanstyle/tts/audio.py b/spaces/hanstyle/tts/audio.py
deleted file mode 100644
index 32ab5fabe8e505baa7431f7fb81e367aed1d0ac3..0000000000000000000000000000000000000000
--- a/spaces/hanstyle/tts/audio.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import librosa
-import librosa.filters
-import numpy as np
-# import tensorflow as tf
-from scipy import signal
-from scipy.io import wavfile
-from hparams import hparams as hp
-
-def load_wav(path, sr):
-    return librosa.core.load(path, sr=sr)[0]
-
-def save_wav(wav, path, sr):
-    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
-    #proposed by @dsmiller
-    wavfile.write(path, sr, wav.astype(np.int16))
-
-def save_wavenet_wav(wav, path, sr):
-    librosa.output.write_wav(path, wav, sr=sr)
-
-def preemphasis(wav, k, preemphasize=True):
-    if preemphasize:
-        return signal.lfilter([1, -k], [1], wav)
-    return wav
-
-def inv_preemphasis(wav, k, inv_preemphasize=True):
-    if inv_preemphasize:
-        return signal.lfilter([1], [1, -k], wav)
-    return wav
-
-def get_hop_size():
-    hop_size = hp.hop_size
-    if hop_size is None:
-        assert hp.frame_shift_ms is not None
-        hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
-    return hop_size
-
-def linearspectrogram(wav):
-    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
-    S = _amp_to_db(np.abs(D)) - hp.ref_level_db
-    
-    if hp.signal_normalization:
-        return _normalize(S)
-    return S
-
-def melspectrogram(wav):
-    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
-    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
-    
-    if hp.signal_normalization:
-        return _normalize(S)
-    return S
-
-def _lws_processor():
-    import lws
-    return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
-
-def _stft(y):
-    if hp.use_lws:
-        return _lws_processor(hp).stft(y).T
-    else:
-        return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
-
-##########################################################
-#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
-def num_frames(length, fsize, fshift):
-    """Compute number of time frames of spectrogram
-    """
-    pad = (fsize - fshift)
-    if length % fshift == 0:
-        M = (length + pad * 2 - fsize) // fshift + 1
-    else:
-        M = (length + pad * 2 - fsize) // fshift + 2
-    return M
-
-
-def pad_lr(x, fsize, fshift):
-    """Compute left and right padding
-    """
-    M = num_frames(len(x), fsize, fshift)
-    pad = (fsize - fshift)
-    T = len(x) + 2 * pad
-    r = (M - 1) * fshift + fsize - T
-    return pad, pad + r
-##########################################################
-#Librosa correct padding
-def librosa_pad_lr(x, fsize, fshift):
-    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
-
-# Conversions
-_mel_basis = None
-
-def _linear_to_mel(spectogram):
-    global _mel_basis
-    if _mel_basis is None:
-        _mel_basis = _build_mel_basis()
-    return np.dot(_mel_basis, spectogram)
-
-def _build_mel_basis():
-    assert hp.fmax <= hp.sample_rate // 2
-    return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels,
-                               fmin=hp.fmin, fmax=hp.fmax)
-
-def _amp_to_db(x):
-    min_level = np.exp(hp.min_level_db / 20 * np.log(10))
-    return 20 * np.log10(np.maximum(min_level, x))
-
-def _db_to_amp(x):
-    return np.power(10.0, (x) * 0.05)
-
-def _normalize(S):
-    if hp.allow_clipping_in_normalization:
-        if hp.symmetric_mels:
-            return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
-                           -hp.max_abs_value, hp.max_abs_value)
-        else:
-            return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
-    
-    assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
-    if hp.symmetric_mels:
-        return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
-    else:
-        return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
-
-def _denormalize(D):
-    if hp.allow_clipping_in_normalization:
-        if hp.symmetric_mels:
-            return (((np.clip(D, -hp.max_abs_value,
-                              hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
-                    + hp.min_level_db)
-        else:
-            return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
-    
-    if hp.symmetric_mels:
-        return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
-    else:
-        return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
diff --git a/spaces/hanstyle/tts/preprocess.py b/spaces/hanstyle/tts/preprocess.py
deleted file mode 100644
index 5322012ac60e91fefa47338d0e253c3f912ab7f2..0000000000000000000000000000000000000000
--- a/spaces/hanstyle/tts/preprocess.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import sys
-
-if sys.version_info[0] < 3 and sys.version_info[1] < 2:
-	raise Exception("Must be using >= Python 3.2")
-
-from os import listdir, path
-
-if not path.isfile('face_detection/detection/sfd/s3fd.pth'):
-	raise FileNotFoundError('Save the s3fd model to face_detection/detection/sfd/s3fd.pth \
-							before running this script!')
-
-import multiprocessing as mp
-from concurrent.futures import ThreadPoolExecutor, as_completed
-import numpy as np
-import argparse, os, cv2, traceback, subprocess
-from tqdm import tqdm
-from glob import glob
-import audio
-from hparams import hparams as hp
-
-import face_detection
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--ngpu', help='Number of GPUs across which to run in parallel', default=1, type=int)
-parser.add_argument('--batch_size', help='Single GPU Face detection batch size', default=32, type=int)
-parser.add_argument("--data_root", help="Root folder of the LRS2 dataset", required=True)
-parser.add_argument("--preprocessed_root", help="Root folder of the preprocessed dataset", required=True)
-
-args = parser.parse_args()
-
-fa = [face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, 
-									device='cuda:{}'.format(id)) for id in range(args.ngpu)]
-
-template = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}'
-# template2 = 'ffmpeg -hide_banner -loglevel panic -threads 1 -y -i {} -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 {}'
-
-def process_video_file(vfile, args, gpu_id):
-	video_stream = cv2.VideoCapture(vfile)
-	
-	frames = []
-	while 1:
-		still_reading, frame = video_stream.read()
-		if not still_reading:
-			video_stream.release()
-			break
-		frames.append(frame)
-	
-	vidname = os.path.basename(vfile).split('.')[0]
-	dirname = vfile.split('/')[-2]
-
-	fulldir = path.join(args.preprocessed_root, dirname, vidname)
-	os.makedirs(fulldir, exist_ok=True)
-
-	batches = [frames[i:i + args.batch_size] for i in range(0, len(frames), args.batch_size)]
-
-	i = -1
-	for fb in batches:
-		preds = fa[gpu_id].get_detections_for_batch(np.asarray(fb))
-
-		for j, f in enumerate(preds):
-			i += 1
-			if f is None:
-				continue
-
-			x1, y1, x2, y2 = f
-			cv2.imwrite(path.join(fulldir, '{}.jpg'.format(i)), fb[j][y1:y2, x1:x2])
-
-def process_audio_file(vfile, args):
-	vidname = os.path.basename(vfile).split('.')[0]
-	dirname = vfile.split('/')[-2]
-
-	fulldir = path.join(args.preprocessed_root, dirname, vidname)
-	os.makedirs(fulldir, exist_ok=True)
-
-	wavpath = path.join(fulldir, 'audio.wav')
-
-	command = template.format(vfile, wavpath)
-	subprocess.call(command, shell=True)
-
-	
-def mp_handler(job):
-	vfile, args, gpu_id = job
-	try:
-		process_video_file(vfile, args, gpu_id)
-	except KeyboardInterrupt:
-		exit(0)
-	except:
-		traceback.print_exc()
-		
-def main(args):
-	print('Started processing for {} with {} GPUs'.format(args.data_root, args.ngpu))
-
-	filelist = glob(path.join(args.data_root, '*/*.mp4'))
-
-	jobs = [(vfile, args, i%args.ngpu) for i, vfile in enumerate(filelist)]
-	p = ThreadPoolExecutor(args.ngpu)
-	futures = [p.submit(mp_handler, j) for j in jobs]
-	_ = [r.result() for r in tqdm(as_completed(futures), total=len(futures))]
-
-	print('Dumping audios...')
-
-	for vfile in tqdm(filelist):
-		try:
-			process_audio_file(vfile, args)
-		except KeyboardInterrupt:
-			exit(0)
-		except:
-			traceback.print_exc()
-			continue
-
-if __name__ == '__main__':
-	main(args)
\ No newline at end of file
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/pretrain_model_loading.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/pretrain_model_loading.py
deleted file mode 100644
index 2a497b24a5e775c288587732e0e7328bf75264a4..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/pretrain_model_loading.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-from collections import OrderedDict
-
-def _remove_bn_statics(state_dict):
-    layer_keys = sorted(state_dict.keys())
-    remove_list = []
-    for key in layer_keys:
-        if 'running_mean' in key or 'running_var' in key or 'num_batches_tracked' in key:
-            remove_list.append(key)
-    for key in remove_list:
-        del state_dict[key]
-    return state_dict
-
-def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg):
-    import re
-    layer_keys = sorted(state_dict.keys())
-    for ix, stage_with_dcn in enumerate(cfg.MODEL.RESNETS.STAGE_WITH_DCN, 1):
-        if not stage_with_dcn:
-            continue
-        for old_key in layer_keys:
-            pattern = ".*layer{}.*conv2.*".format(ix)
-            r = re.match(pattern, old_key)
-            if r is None:
-                continue
-            for param in ["weight", "bias"]:
-                if old_key.find(param) is -1:
-                    continue
-                if 'unit01' in old_key:
-                    continue
-                new_key = old_key.replace(
-                    "conv2.{}".format(param), "conv2.conv.{}".format(param)
-                )
-                print("pattern: {}, old_key: {}, new_key: {}".format(
-                    pattern, old_key, new_key
-                ))
-                state_dict[new_key] = state_dict[old_key]
-                del state_dict[old_key]
-    return state_dict
-
-
-def load_pretrain_format(cfg, f):
-    model = torch.load(f)
-    model = _remove_bn_statics(model)
-    model = _rename_conv_weights_for_deformable_conv_layers(model, cfg)
-
-    return dict(model=model)
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/global_local_parsing/global_local_datasets.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/global_local_parsing/global_local_datasets.py
deleted file mode 100644
index 8b00594ef3302af2a30440676f96a4904ffe9077..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/global_local_parsing/global_local_datasets.py
+++ /dev/null
@@ -1,200 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-"""
-@Author  :   Peike Li
-@Contact :   peike.li@yahoo.com
-@File    :   datasets.py
-@Time    :   8/4/19 3:35 PM
-@Desc    :
-@License :   This source code is licensed under the license found in the
-             LICENSE file in the root directory of this source tree.
-"""
-
-import os
-import numpy as np
-import random
-import torch
-import cv2
-from torch.utils import data
-from utils.transforms import get_affine_transform
-
-
-class CropDataSet(data.Dataset):
-    def __init__(self, root, split_name, crop_size=[473, 473], scale_factor=0.25,
-                 rotation_factor=30, ignore_label=255, transform=None):
-        self.root = root
-        self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
-        self.crop_size = np.asarray(crop_size)
-        self.ignore_label = ignore_label
-        self.scale_factor = scale_factor
-        self.rotation_factor = rotation_factor
-        self.flip_prob = 0.5
-        self.transform = transform
-        self.split_name = split_name
-
-        list_path = os.path.join(self.root, self.split_name + '.txt')
-        train_list = [i_id.strip() for i_id in open(list_path)]
-
-        self.train_list = train_list
-        self.number_samples = len(self.train_list)
-
-    def __len__(self):
-        return self.number_samples
-
-    def _box2cs(self, box):
-        x, y, w, h = box[:4]
-        return self._xywh2cs(x, y, w, h)
-
-    def _xywh2cs(self, x, y, w, h):
-        center = np.zeros((2), dtype=np.float32)
-        center[0] = x + w * 0.5
-        center[1] = y + h * 0.5
-        if w > self.aspect_ratio * h:
-            h = w * 1.0 / self.aspect_ratio
-        elif w < self.aspect_ratio * h:
-            w = h * self.aspect_ratio
-        scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
-        return center, scale
-
-    def __getitem__(self, index):
-        train_item = self.train_list[index]
-
-        im_path = os.path.join(self.root, self.split_name + '_images', train_item + '.jpg')
-        parsing_anno_path = os.path.join(self.root, self.split_name + '_segmentations', train_item + '.png')
-
-        im = cv2.imread(im_path, cv2.IMREAD_COLOR)
-        h, w, _ = im.shape
-        parsing_anno = np.zeros((h, w), dtype=np.long)
-
-        # Get person center and scale
-        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
-        r = 0
-
-        if self.split_name != 'test':
-            # Get pose annotation
-            parsing_anno = cv2.imread(parsing_anno_path, cv2.IMREAD_GRAYSCALE)
-            sf = self.scale_factor
-            rf = self.rotation_factor
-            s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
-            r = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) if random.random() <= 0.6 else 0
-
-            if random.random() <= self.flip_prob:
-                im = im[:, ::-1, :]
-                parsing_anno = parsing_anno[:, ::-1]
-                person_center[0] = im.shape[1] - person_center[0] - 1
-                right_idx = [15, 17, 19]
-                left_idx = [14, 16, 18]
-                for i in range(0, 3):
-                    right_pos = np.where(parsing_anno == right_idx[i])
-                    left_pos = np.where(parsing_anno == left_idx[i])
-                    parsing_anno[right_pos[0], right_pos[1]] = left_idx[i]
-                    parsing_anno[left_pos[0], left_pos[1]] = right_idx[i]
-
-        trans = get_affine_transform(person_center, s, r, self.crop_size)
-        input = cv2.warpAffine(
-            im,
-            trans,
-            (int(self.crop_size[1]), int(self.crop_size[0])),
-            flags=cv2.INTER_LINEAR,
-            borderMode=cv2.BORDER_CONSTANT,
-            borderValue=(0, 0, 0))
-
-        if self.transform:
-            input = self.transform(input)
-
-        meta = {
-            'name': train_item,
-            'center': person_center,
-            'height': h,
-            'width': w,
-            'scale': s,
-            'rotation': r
-        }
-
-        if self.split_name == 'val' or self.split_name == 'test':
-            return input, meta
-        else:
-            label_parsing = cv2.warpAffine(
-                parsing_anno,
-                trans,
-                (int(self.crop_size[1]), int(self.crop_size[0])),
-                flags=cv2.INTER_NEAREST,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=(255))
-
-            label_parsing = torch.from_numpy(label_parsing)
-
-            return input, label_parsing, meta
-
-
-class CropDataValSet(data.Dataset):
-    def __init__(self, root, split_name='crop_pic', crop_size=[473, 473], transform=None, flip=False):
-        self.root = root
-        self.crop_size = crop_size
-        self.transform = transform
-        self.flip = flip
-        self.split_name = split_name
-        self.root = root
-        self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
-        self.crop_size = np.asarray(crop_size)
-
-        list_path = os.path.join(self.root, self.split_name + '.txt')
-        val_list = [i_id.strip() for i_id in open(list_path)]
-
-        self.val_list = val_list
-        self.number_samples = len(self.val_list)
-
-    def __len__(self):
-        return len(self.val_list)
-
-    def _box2cs(self, box):
-        x, y, w, h = box[:4]
-        return self._xywh2cs(x, y, w, h)
-
-    def _xywh2cs(self, x, y, w, h):
-        center = np.zeros((2), dtype=np.float32)
-        center[0] = x + w * 0.5
-        center[1] = y + h * 0.5
-        if w > self.aspect_ratio * h:
-            h = w * 1.0 / self.aspect_ratio
-        elif w < self.aspect_ratio * h:
-            w = h * self.aspect_ratio
-        scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
-
-        return center, scale
-
-    def __getitem__(self, index):
-        val_item = self.val_list[index]
-        # Load training image
-        im_path = os.path.join(self.root, self.split_name, val_item + '.jpg')
-        im = cv2.imread(im_path, cv2.IMREAD_COLOR)
-        h, w, _ = im.shape
-        # Get person center and scale
-        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
-        r = 0
-        trans = get_affine_transform(person_center, s, r, self.crop_size)
-        input = cv2.warpAffine(
-            im,
-            trans,
-            (int(self.crop_size[1]), int(self.crop_size[0])),
-            flags=cv2.INTER_LINEAR,
-            borderMode=cv2.BORDER_CONSTANT,
-            borderValue=(0, 0, 0))
-        input = self.transform(input)
-        flip_input = input.flip(dims=[-1])
-        if self.flip:
-            batch_input_im = torch.stack([input, flip_input])
-        else:
-            batch_input_im = input
-
-        meta = {
-            'name': val_item,
-            'center': person_center,
-            'height': h,
-            'width': w,
-            'scale': s,
-            'rotation': r
-        }
-
-        return batch_input_im, meta
diff --git a/spaces/hf-vision/detection_metrics/detection_metrics/pycocotools/mask_utils.py b/spaces/hf-vision/detection_metrics/detection_metrics/pycocotools/mask_utils.py
deleted file mode 100644
index 1f948257ae4faf2d28a064a55490746bd4d7a974..0000000000000000000000000000000000000000
--- a/spaces/hf-vision/detection_metrics/detection_metrics/pycocotools/mask_utils.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# This code is a copy and paste with small modifications of the code:
-# https://github.com/rafaelpadilla/review_object_detection_metrics/blob/main/src/evaluators/coco_evaluator.py
-
-from typing import List
-import numpy as np
-
-class MaskEvaluator(object):
-    @staticmethod
-    def iou(
-        dt: List[List[float]], gt: List[List[float]], iscrowd: List[bool]
-    ) -> np.ndarray:
-        """
-        Calculate the intersection over union (IoU) between detection bounding boxes (dt) and \
-            ground truth bounding boxes (gt).
-        Reference: https://github.com/rafaelpadilla/review_object_detection_metrics
-
-        Args:
-            dt (List[List[float]]): List of detection bounding boxes in the \
-                format [x, y, width, height].
-            gt (List[List[float]]): List of ground-truth bounding boxes in the \
-                format [x, y, width, height].
-            iscrowd (List[bool]): List indicating if each ground-truth bounding box \
-                is a crowd region or not.
-
-        Returns:
-            np.ndarray: Array of IoU values of shape (len(dt), len(gt)).
-        """
-        assert len(iscrowd) == len(gt), "iou(iscrowd=) must have the same length as gt"
-        if len(dt) == 0 or len(gt) == 0:
-            return []
-        ious = np.zeros((len(dt), len(gt)), dtype=np.float64)
-        for g_idx, g in enumerate(gt):
-            for d_idx, d in enumerate(dt):
-                ious[d_idx, g_idx] = _jaccard(d, g, iscrowd[g_idx])
-        return ious
-
-
-def _jaccard(a: List[float], b: List[float], iscrowd: bool) -> float:
-    """
-    Calculate the Jaccard index (intersection over union) between two bounding boxes.
-    For "crowd" regions, we use a modified criteria. If a gt object is
-    marked as "iscrowd", we allow a dt to match any subregion of the gt.
-    Choosing gt' in the crowd gt that best matches the dt can be done using
-    gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
-        iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
-    For crowd gt regions we use this modified criteria above for the iou.
-
-    Args:
-        a (List[float]): Bounding box coordinates in the format [x, y, width, height].
-        b (List[float]): Bounding box coordinates in the format [x, y, width, height].
-        iscrowd (bool): Flag indicating if the second bounding box is a crowd region or not.
-
-    Returns:
-        float: Jaccard index between the two bounding boxes.
-    """  
-    eps = 4e-12
-    xa, ya, x2a, y2a = a[0], a[1], a[0] + a[2], a[1] + a[3]
-    xb, yb, x2b, y2b = b[0], b[1], b[0] + b[2], b[1] + b[3]
-
-    # innermost left x
-    xi = max(xa, xb)
-    # innermost right x
-    x2i = min(x2a, x2b)
-    # same for y
-    yi = max(ya, yb)
-    y2i = min(y2a, y2b)
-
-    # calculate areas
-    Aa = max(x2a - xa, 0.) * max(y2a - ya, 0.)
-    Ab = max(x2b - xb, 0.) * max(y2b - yb, 0.)
-    Ai = max(x2i - xi, 0.) * max(y2i - yi, 0.)
-
-    if iscrowd:
-        return Ai / (Aa + eps)
-
-    return Ai / (Aa + Ab - Ai + eps)
diff --git a/spaces/hoshilumine/combined-GI-RVC-models/vc_infer_pipeline.py b/spaces/hoshilumine/combined-GI-RVC-models/vc_infer_pipeline.py
deleted file mode 100644
index 82c15f59a8072e1b317fa1d750ccc1b814a6989d..0000000000000000000000000000000000000000
--- a/spaces/hoshilumine/combined-GI-RVC-models/vc_infer_pipeline.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import numpy as np, parselmouth, torch, pdb, sys, os
-from time import time as ttime
-import torch.nn.functional as F
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
-from scipy import signal
-from functools import lru_cache
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class VC(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        inp_f0=None,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-        elif f0_method == "crepe":
-            model = "full"
-            # Pick a batch size that doesn't cause memory errors on your gpu
-            batch_size = 512
-            # Compute pitch using first gpu
-            audio = torch.tensor(np.copy(x))[None].float()
-            f0, pd = torchcrepe.predict(
-                audio,
-                self.sr,
-                self.window,
-                f0_min,
-                f0_max,
-                model,
-                batch_size=batch_size,
-                device=self.device,
-                return_periodicity=True,
-            )
-            pd = torchcrepe.filter.median(pd, 3)
-            f0 = torchcrepe.filter.mean(f0, 3)
-            f0[pd < 0.1] = 0
-            f0 = f0[0].cpu().numpy()
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                print("loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "rmvpe.pt", is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-        protect,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        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).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = feats.clone()
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
-                0, 2, 1
-            )
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch != None and pitchf != None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        # file_big_npy,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=None,
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps":
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/huggingchat/chat-ui/src/lib/utils/models.ts b/spaces/huggingchat/chat-ui/src/lib/utils/models.ts
deleted file mode 100644
index 181288d8d6a2cc1a852c9087dcbb8a3392e70051..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/src/lib/utils/models.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import type { Model } from "$lib/types/Model";
-
-export const findCurrentModel = (models: Model[], id?: string) =>
-	models.find((m) => m.id === id) ?? models[0];
diff --git a/spaces/huggingchat/chat-ui/src/routes/settings/+page.server.ts b/spaces/huggingchat/chat-ui/src/routes/settings/+page.server.ts
deleted file mode 100644
index 87b8c059c4ff61987b88168089f496e036f9e7b2..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/src/routes/settings/+page.server.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-import { base } from "$app/paths";
-import { collections } from "$lib/server/database";
-import { redirect } from "@sveltejs/kit";
-import { z } from "zod";
-import { models, validateModel } from "$lib/server/models";
-import { authCondition } from "$lib/server/auth";
-import { DEFAULT_SETTINGS } from "$lib/types/Settings";
-
-const booleanFormObject = z
-	.union([z.literal("true"), z.literal("on"), z.literal("false"), z.null()])
-	.transform((value) => {
-		return value === "true" || value === "on";
-	});
-
-export const actions = {
-	default: async function ({ request, locals }) {
-		const formData = await request.formData();
-
-		const { ethicsModalAccepted, ...settings } = z
-			.object({
-				shareConversationsWithModelAuthors: booleanFormObject,
-				hideEmojiOnSidebar: booleanFormObject,
-				ethicsModalAccepted: z.boolean({ coerce: true }).optional(),
-				activeModel: validateModel(models),
-				customPrompts: z.record(z.string()).default({}),
-			})
-			.parse({
-				hideEmojiOnSidebar: formData.get("hideEmojiOnSidebar"),
-				shareConversationsWithModelAuthors: formData.get("shareConversationsWithModelAuthors"),
-				ethicsModalAccepted: formData.get("ethicsModalAccepted"),
-				activeModel: formData.get("activeModel") ?? DEFAULT_SETTINGS.activeModel,
-				customPrompts: JSON.parse(formData.get("customPrompts")?.toString() ?? "{}"),
-			});
-
-		await collections.settings.updateOne(
-			authCondition(locals),
-			{
-				$set: {
-					...settings,
-					...(ethicsModalAccepted && { ethicsModalAcceptedAt: new Date() }),
-					updatedAt: new Date(),
-				},
-				$setOnInsert: {
-					createdAt: new Date(),
-				},
-			},
-			{
-				upsert: true,
-			}
-		);
-		throw redirect(303, request.headers.get("referer") || `${base}/`);
-	},
-};
diff --git a/spaces/huggingface-projects/stable-diffusion-latent-upscaler/style.css b/spaces/huggingface-projects/stable-diffusion-latent-upscaler/style.css
deleted file mode 100644
index c1933401237568a3f662952668c184e58af52861..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/stable-diffusion-latent-upscaler/style.css
+++ /dev/null
@@ -1,36 +0,0 @@
-.container {
-    max-width: 960px
-}
-
-.finetuned-diffusion-div div {
-    align-items: center;
-    gap: .8rem;
-    font-size: 1.75rem
-}
-
-.finetuned-diffusion-div div h1 {
-    font-weight: 900;
-    margin-bottom: 7px
-}
-
-.finetuned-diffusion-div div p {
-    font-size: 50%
-}
-
-.finetuned-diffusion-div p {
-    margin-bottom: 10px;
-    font-size: 94%
-}
-
-a {
-    text-decoration: underline
-}
-
-.tabs {
-    margin-top: 0;
-    margin-bottom: 0
-}
-
-#gallery {
-    min-height: 20rem
-}
\ No newline at end of file
diff --git a/spaces/huggingface-projects/wordalle/static/_app/immutable/assets/pages/index.svelte-b52b250e.css b/spaces/huggingface-projects/wordalle/static/_app/immutable/assets/pages/index.svelte-b52b250e.css
deleted file mode 100644
index b44d47fe59d6af41d7eb2e25f6ef3204e916b09c..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/wordalle/static/_app/immutable/assets/pages/index.svelte-b52b250e.css
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/huggingface-projects/wordalle/static/index.html b/spaces/huggingface-projects/wordalle/static/index.html
deleted file mode 100644
index 9b8f9e019341a43f6a5f31debd55d1e6bb23d584..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/wordalle/static/index.html
+++ /dev/null
@@ -1,43 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-	<head>
-		<meta charset="utf-8" />
-		<!-- <link rel="icon" href="/static/favicon.png" /> -->
-		<meta name="viewport" content="width=device-width, initial-scale=1" />
-		<script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-		<meta http-equiv="content-security-policy" content="">
-	<link rel="stylesheet" href="/static/_app/immutable/assets/pages/__layout.svelte-7926a3a8.css">
-	<link rel="stylesheet" href="/static/_app/immutable/assets/pages/index.svelte-b52b250e.css">
-	<link rel="modulepreload" href="/static/_app/immutable/start-cc027d18.js">
-	<link rel="modulepreload" href="/static/_app/immutable/chunks/index-86f4d6c3.js">
-	<link rel="modulepreload" href="/static/_app/immutable/chunks/preload-helper-359634c4.js">
-	<link rel="modulepreload" href="/static/_app/immutable/pages/__layout.svelte-53f051f3.js">
-	<link rel="modulepreload" href="/static/_app/immutable/pages/index.svelte-e9dccd76.js">
-	</head>
-	<body class="dark:bg-[rgb(11,15,25)] bg-white dark:text-white text-black">
-		<div>
-
-
-
-
-
-		<script type="module" data-sveltekit-hydrate="2eq20f">
-		import { start } from "/static/_app/immutable/start-cc027d18.js";
-		start({
-			target: document.querySelector('[data-sveltekit-hydrate="2eq20f"]').parentNode,
-			paths: {"base":"/static","assets":"/static"},
-			session: {},
-			route: true,
-			spa: false,
-			trailing_slash: "never",
-			hydrate: {
-				status: 200,
-				error: null,
-				nodes: [0, 2],
-				params: {},
-				routeId: ""
-			}
-		});
-	</script></div>
-	</body>
-</html>
diff --git "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/13_\360\237\224\226_Model_Card_Authors.py" "b/spaces/huggingface/Model_Cards_Writing_Tool/pages/13_\360\237\224\226_Model_Card_Authors.py"
deleted file mode 100644
index 1308a19b03bca1c00a9443610c76e950bb3c86e5..0000000000000000000000000000000000000000
--- "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/13_\360\237\224\226_Model_Card_Authors.py"
+++ /dev/null
@@ -1,26 +0,0 @@
-import streamlit as st
-from persist import persist, load_widget_state
-
-
-
-global variable_output
-
-def main():
-    cs_body()
-   
-
-
-def cs_body():
-    # Model Cards
-    #card = get_card()
-    #card.save('current_editable.md')
-    
-    st.markdown('# Model Card Authors [optional]')
-    st.text_area("This section also provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc.",height = 180, help = "The people who actually constructed the Model Card go here.",key=persist("the_authors"))
-    
-    
-    
-
-if __name__ == '__main__':
-    load_widget_state()
-    main()
\ No newline at end of file
diff --git a/spaces/hysts/PnP-diffusion-features/style.css b/spaces/hysts/PnP-diffusion-features/style.css
deleted file mode 100644
index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000
--- a/spaces/hysts/PnP-diffusion-features/style.css
+++ /dev/null
@@ -1,3 +0,0 @@
-h1 {
-  text-align: center;
-}
diff --git a/spaces/indy256/protogen_v2.2/README.md b/spaces/indy256/protogen_v2.2/README.md
deleted file mode 100644
index 6efb0efac240501d6a510e0e0685e064c2e8f943..0000000000000000000000000000000000000000
--- a/spaces/indy256/protogen_v2.2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Protogen V2.2
-emoji: 🐨
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/CRACK Icecream Screen Recorder Pro 5.76 Activator [CracksMind]l NEW!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/CRACK Icecream Screen Recorder Pro 5.76 Activator [CracksMind]l NEW!.md
deleted file mode 100644
index 85175291e8d2e4052143168d261135785f662b73..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/CRACK Icecream Screen Recorder Pro 5.76 Activator [CracksMind]l NEW!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>CRACK Icecream Screen Recorder Pro 5.76 Activator [CracksMind]l</h2><br /><p><b><b>Download Zip</b> &#10027;&#10027;&#10027; <a href="https://urlin.us/2uExiA">https://urlin.us/2uExiA</a></b></p><br /><br />
-
-CRACK Icecream Screen Recorder Pro 5.76 Activator [CracksMind] ->>> http://bltlly.com/163rdm Apowersoft Streaming Video Recorder 6.3.0 + ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/lowvram.py b/spaces/jackli888/stable-diffusion-webui/modules/lowvram.py
deleted file mode 100644
index 8b14cc8ae92a3425aa75830f227a1fed1036040d..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/lowvram.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import torch
-from modules import devices
-
-module_in_gpu = None
-cpu = torch.device("cpu")
-
-
-def send_everything_to_cpu():
-    global module_in_gpu
-
-    if module_in_gpu is not None:
-        module_in_gpu.to(cpu)
-
-    module_in_gpu = None
-
-
-def setup_for_low_vram(sd_model, use_medvram):
-    parents = {}
-
-    def send_me_to_gpu(module, _):
-        """send this module to GPU; send whatever tracked module was previous in GPU to CPU;
-        we add this as forward_pre_hook to a lot of modules and this way all but one of them will
-        be in CPU
-        """
-        global module_in_gpu
-
-        module = parents.get(module, module)
-
-        if module_in_gpu == module:
-            return
-
-        if module_in_gpu is not None:
-            module_in_gpu.to(cpu)
-
-        module.to(devices.device)
-        module_in_gpu = module
-
-    # see below for register_forward_pre_hook;
-    # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
-    # useless here, and we just replace those methods
-
-    first_stage_model = sd_model.first_stage_model
-    first_stage_model_encode = sd_model.first_stage_model.encode
-    first_stage_model_decode = sd_model.first_stage_model.decode
-
-    def first_stage_model_encode_wrap(x):
-        send_me_to_gpu(first_stage_model, None)
-        return first_stage_model_encode(x)
-
-    def first_stage_model_decode_wrap(z):
-        send_me_to_gpu(first_stage_model, None)
-        return first_stage_model_decode(z)
-
-    # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
-    if hasattr(sd_model.cond_stage_model, 'model'):
-        sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
-
-    # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
-    # send the model to GPU. Then put modules back. the modules will be in CPU.
-    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
-    sd_model.to(devices.device)
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
-
-    # register hooks for those the first three models
-    sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
-    sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
-    sd_model.first_stage_model.encode = first_stage_model_encode_wrap
-    sd_model.first_stage_model.decode = first_stage_model_decode_wrap
-    if sd_model.depth_model:
-        sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
-    parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
-
-    if hasattr(sd_model.cond_stage_model, 'model'):
-        sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
-        del sd_model.cond_stage_model.transformer
-
-    if use_medvram:
-        sd_model.model.register_forward_pre_hook(send_me_to_gpu)
-    else:
-        diff_model = sd_model.model.diffusion_model
-
-        # the third remaining model is still too big for 4 GB, so we also do the same for its submodules
-        # so that only one of them is in GPU at a time
-        stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
-        diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
-        sd_model.model.to(devices.device)
-        diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
-
-        # install hooks for bits of third model
-        diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
-        for block in diff_model.input_blocks:
-            block.register_forward_pre_hook(send_me_to_gpu)
-        diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
-        for block in diff_model.output_blocks:
-            block.register_forward_pre_hook(send_me_to_gpu)
diff --git a/spaces/jbilcke-hf/LifeSim/src/components/ui/calendar.tsx b/spaces/jbilcke-hf/LifeSim/src/components/ui/calendar.tsx
deleted file mode 100644
index 331cba258c3c94f84479b41ddadf6e99403504d8..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/LifeSim/src/components/ui/calendar.tsx
+++ /dev/null
@@ -1,64 +0,0 @@
-"use client"
-
-import * as React from "react"
-import { ChevronLeft, ChevronRight } from "lucide-react"
-import { DayPicker } from "react-day-picker"
-
-import { cn } from "@/lib/utils"
-import { buttonVariants } from "@/components/ui/button"
-
-export type CalendarProps = React.ComponentProps<typeof DayPicker>
-
-function Calendar({
-  className,
-  classNames,
-  showOutsideDays = true,
-  ...props
-}: CalendarProps) {
-  return (
-    <DayPicker
-      showOutsideDays={showOutsideDays}
-      className={cn("p-3", className)}
-      classNames={{
-        months: "flex flex-col sm:flex-row space-y-4 sm:space-x-4 sm:space-y-0",
-        month: "space-y-4",
-        caption: "flex justify-center pt-1 relative items-center",
-        caption_label: "text-sm font-medium",
-        nav: "space-x-1 flex items-center",
-        nav_button: cn(
-          buttonVariants({ variant: "outline" }),
-          "h-7 w-7 bg-transparent p-0 opacity-50 hover:opacity-100"
-        ),
-        nav_button_previous: "absolute left-1",
-        nav_button_next: "absolute right-1",
-        table: "w-full border-collapse space-y-1",
-        head_row: "flex",
-        head_cell:
-          "text-stone-500 rounded-md w-9 font-normal text-[0.8rem] dark:text-stone-400",
-        row: "flex w-full mt-2",
-        cell: "text-center text-sm p-0 relative [&:has([aria-selected])]:bg-stone-100 first:[&:has([aria-selected])]:rounded-l-md last:[&:has([aria-selected])]:rounded-r-md focus-within:relative focus-within:z-20 dark:[&:has([aria-selected])]:bg-stone-800",
-        day: cn(
-          buttonVariants({ variant: "ghost" }),
-          "h-9 w-9 p-0 font-normal aria-selected:opacity-100"
-        ),
-        day_selected:
-          "bg-stone-900 text-stone-50 hover:bg-stone-900 hover:text-stone-50 focus:bg-stone-900 focus:text-stone-50 dark:bg-stone-50 dark:text-stone-900 dark:hover:bg-stone-50 dark:hover:text-stone-900 dark:focus:bg-stone-50 dark:focus:text-stone-900",
-        day_today: "bg-stone-100 text-stone-900 dark:bg-stone-800 dark:text-stone-50",
-        day_outside: "text-stone-500 opacity-50 dark:text-stone-400",
-        day_disabled: "text-stone-500 opacity-50 dark:text-stone-400",
-        day_range_middle:
-          "aria-selected:bg-stone-100 aria-selected:text-stone-900 dark:aria-selected:bg-stone-800 dark:aria-selected:text-stone-50",
-        day_hidden: "invisible",
-        ...classNames,
-      }}
-      components={{
-        IconLeft: ({ ...props }) => <ChevronLeft className="h-4 w-4" />,
-        IconRight: ({ ...props }) => <ChevronRight className="h-4 w-4" />,
-      }}
-      {...props}
-    />
-  )
-}
-Calendar.displayName = "Calendar"
-
-export { Calendar }
diff --git a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/modeling/__init__.py b/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/modeling/__init__.py
deleted file mode 100644
index 9b4dd2628880e93338b39b0b6562b2a5838692b5..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/modeling/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-from .backbone.swin import D2SwinTransformer
-from .backbone.clip_resnet import D2ModifiedResNet
-from .heads.mask_former_head import MaskFormerHead
-from .heads.open_vocab_mask_former_head import OpenVocabMaskFormerHead
-from .heads.pixel_decoder import BasePixelDecoder
diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/encoder/preprocess.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/encoder/preprocess.py
deleted file mode 100644
index 551a8b29c4d84c0e1430f285a1c8b5e10c98ee5f..0000000000000000000000000000000000000000
--- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/encoder/preprocess.py
+++ /dev/null
@@ -1,175 +0,0 @@
-from multiprocess.pool import ThreadPool
-from encoder.params_data import *
-from encoder.config import librispeech_datasets, anglophone_nationalites
-from datetime import datetime
-from encoder import audio
-from pathlib import Path
-from tqdm import tqdm
-import numpy as np
-
-
-class DatasetLog:
-    """
-    Registers metadata about the dataset in a text file.
-    """
-    def __init__(self, root, name):
-        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
-        self.sample_data = dict()
-        
-        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Creating dataset %s on %s" % (name, start_time))
-        self.write_line("-----")
-        self._log_params()
-        
-    def _log_params(self):
-        from encoder import params_data
-        self.write_line("Parameter values:")
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            self.write_line("\t%s: %s" % (param_name, value))
-        self.write_line("-----")
-    
-    def write_line(self, line):
-        self.text_file.write("%s\n" % line)
-        
-    def add_sample(self, **kwargs):
-        for param_name, value in kwargs.items():
-            if not param_name in self.sample_data:
-                self.sample_data[param_name] = []
-            self.sample_data[param_name].append(value)
-            
-    def finalize(self):
-        self.write_line("Statistics:")
-        for param_name, values in self.sample_data.items():
-            self.write_line("\t%s:" % param_name)
-            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
-            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
-        self.write_line("-----")
-        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Finished on %s" % end_time)
-        self.text_file.close()
-       
-        
-def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
-    dataset_root = datasets_root.joinpath(dataset_name)
-    if not dataset_root.exists():
-        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
-        return None, None
-    return dataset_root, DatasetLog(out_dir, dataset_name)
-
-
-def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
-                             skip_existing, logger):
-    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
-    
-    # Function to preprocess utterances for one speaker
-    def preprocess_speaker(speaker_dir: Path):
-        # Give a name to the speaker that includes its dataset
-        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
-        
-        # Create an output directory with that name, as well as a txt file containing a 
-        # reference to each source file.
-        speaker_out_dir = out_dir.joinpath(speaker_name)
-        speaker_out_dir.mkdir(exist_ok=True)
-        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
-        
-        # There's a possibility that the preprocessing was interrupted earlier, check if 
-        # there already is a sources file.
-        if sources_fpath.exists():
-            try:
-                with sources_fpath.open("r") as sources_file:
-                    existing_fnames = {line.split(",")[0] for line in sources_file}
-            except:
-                existing_fnames = {}
-        else:
-            existing_fnames = {}
-        
-        # Gather all audio files for that speaker recursively
-        sources_file = sources_fpath.open("a" if skip_existing else "w")
-        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
-            # Check if the target output file already exists
-            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
-            out_fname = out_fname.replace(".%s" % extension, ".npy")
-            if skip_existing and out_fname in existing_fnames:
-                continue
-                
-            # Load and preprocess the waveform
-            wav = audio.preprocess_wav(in_fpath)
-            if len(wav) == 0:
-                continue
-            
-            # Create the mel spectrogram, discard those that are too short
-            frames = audio.wav_to_mel_spectrogram(wav)
-            if len(frames) < partials_n_frames:
-                continue
-            
-            out_fpath = speaker_out_dir.joinpath(out_fname)
-            np.save(out_fpath, frames)
-            logger.add_sample(duration=len(wav) / sampling_rate)
-            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
-        
-        sources_file.close()
-    
-    # Process the utterances for each speaker
-    with ThreadPool(8) as pool:
-        list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
-                  unit="speakers"))
-    logger.finalize()
-    print("Done preprocessing %s.\n" % dataset_name)
-
-
-def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
-    for dataset_name in librispeech_datasets["train"]["other"]:
-        # Initialize the preprocessing
-        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-        if not dataset_root:
-            return 
-        
-        # Preprocess all speakers
-        speaker_dirs = list(dataset_root.glob("*"))
-        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac",
-                                 skip_existing, logger)
-
-
-def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb1"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-
-    # Get the contents of the meta file
-    with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
-        metadata = [line.split("\t") for line in metafile][1:]
-    
-    # Select the ID and the nationality, filter out non-anglophone speakers
-    nationalities = {line[0]: line[3] for line in metadata}
-    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if 
-                        nationality.lower() in anglophone_nationalites]
-    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % 
-          (len(keep_speaker_ids), len(nationalities)))
-    
-    # Get the speaker directories for anglophone speakers only
-    speaker_dirs = dataset_root.joinpath("wav").glob("*")
-    speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
-                    speaker_dir.name in keep_speaker_ids]
-    print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % 
-          (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
-
-    # Preprocess all speakers
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
-                             skip_existing, logger)
-
-
-def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb2"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-    
-    # Get the speaker directories
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a",
-                             skip_existing, logger)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_openpgp.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_openpgp.py
deleted file mode 100644
index d079d5914db38a29d5b5d5b6b7cde0a215b74f6a..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_openpgp.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# ===================================================================
-#
-# 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.
-# ===================================================================
-
-"""
-OpenPGP mode.
-"""
-
-__all__ = ['OpenPgpMode']
-
-from Crypto.Util.py3compat import _copy_bytes
-from Crypto.Random import get_random_bytes
-
-class OpenPgpMode(object):
-    """OpenPGP mode.
-
-    This mode is a variant of CFB, and it is only used in PGP and
-    OpenPGP_ applications. If in doubt, use another mode.
-
-    An Initialization Vector (*IV*) is required.
-
-    Unlike CFB, the *encrypted* IV (not the IV itself) is
-    transmitted to the receiver.
-
-    The IV is a random data block. For legacy reasons, two of its bytes are
-    duplicated to act as a checksum for the correctness of the key, which is now
-    known to be insecure and is ignored. The encrypted IV is therefore 2 bytes
-    longer than the clean IV.
-
-    .. _OpenPGP: http://tools.ietf.org/html/rfc4880
-
-    :undocumented: __init__
-    """
-
-    def __init__(self, factory, key, iv, cipher_params):
-
-        #: The block size of the underlying cipher, in bytes.
-        self.block_size = factory.block_size
-
-        self._done_first_block = False  # True after the first encryption
-
-        # Instantiate a temporary cipher to process the IV
-        IV_cipher = factory.new(
-                        key,
-                        factory.MODE_CFB,
-                        IV=b'\x00' * self.block_size,
-                        segment_size=self.block_size * 8,
-                        **cipher_params)
-
-        iv = _copy_bytes(None, None, iv)
-
-        # The cipher will be used for...
-        if len(iv) == self.block_size:
-            # ... encryption
-            self._encrypted_IV = IV_cipher.encrypt(iv + iv[-2:])
-        elif len(iv) == self.block_size + 2:
-            # ... decryption
-            self._encrypted_IV = iv
-            # Last two bytes are for a deprecated "quick check" feature that
-            # should not be used. (https://eprint.iacr.org/2005/033)
-            iv = IV_cipher.decrypt(iv)[:-2]
-        else:
-            raise ValueError("Length of IV must be %d or %d bytes"
-                             " for MODE_OPENPGP"
-                             % (self.block_size, self.block_size + 2))
-
-        self.iv = self.IV = iv
-
-        # Instantiate the cipher for the real PGP data
-        self._cipher = factory.new(
-                            key,
-                            factory.MODE_CFB,
-                            IV=self._encrypted_IV[-self.block_size:],
-                            segment_size=self.block_size * 8,
-                            **cipher_params)
-
-    def encrypt(self, plaintext):
-        """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.
-
-        :Return:
-            the encrypted data, as a byte string.
-            It is as long as *plaintext* with one exception:
-            when encrypting the first message chunk,
-            the encypted IV is prepended to the returned ciphertext.
-        """
-
-        res = self._cipher.encrypt(plaintext)
-        if not self._done_first_block:
-            res = self._encrypted_IV + res
-            self._done_first_block = True
-        return res
-
-    def decrypt(self, ciphertext):
-        """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.
-
-        :Return: the decrypted data (byte string).
-        """
-
-        return self._cipher.decrypt(ciphertext)
-
-
-def _create_openpgp_cipher(factory, **kwargs):
-    """Create a new block cipher, configured in OpenPGP mode.
-
-    :Parameters:
-      factory : module
-        The module.
-
-    :Keywords:
-      key : bytes/bytearray/memoryview
-        The secret key to use in the symmetric cipher.
-
-      IV : bytes/bytearray/memoryview
-        The initialization vector to use for encryption or decryption.
-
-        For encryption, the IV must be as long as the cipher block size.
-
-        For decryption, it must be 2 bytes longer (it is actually the
-        *encrypted* IV which was prefixed to the ciphertext).
-    """
-
-    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
-
-    try:
-        key = kwargs.pop("key")
-    except KeyError as e:
-        raise TypeError("Missing component: " + str(e))
-
-    return OpenPgpMode(factory, key, iv, kwargs)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/GdImageFile.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl   Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
-    This format cannot be automatically recognized, so the
-    class is not registered for use with :py:func:`PIL.Image.open()`.  To open a
-    gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
-    THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE.  This
-    implementation is provided for convenience and demonstrational
-    purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
-    """
-    Image plugin for the GD uncompressed format.  Note that this format
-    is not supported by the standard :py:func:`PIL.Image.open()` function.  To use
-    this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
-    use the :py:func:`PIL.GdImageFile.open()` function.
-    """
-
-    format = "GD"
-    format_description = "GD uncompressed images"
-
-    def _open(self):
-        # Header
-        s = self.fp.read(1037)
-
-        if i16(s) not in [65534, 65535]:
-            msg = "Not a valid GD 2.x .gd file"
-            raise SyntaxError(msg)
-
-        self.mode = "L"  # FIXME: "P"
-        self._size = i16(s, 2), i16(s, 4)
-
-        true_color = s[6]
-        true_color_offset = 2 if true_color else 0
-
-        # transparency index
-        tindex = i32(s, 7 + true_color_offset)
-        if tindex < 256:
-            self.info["transparency"] = tindex
-
-        self.palette = ImagePalette.raw(
-            "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
-        )
-
-        self.tile = [
-            (
-                "raw",
-                (0, 0) + self.size,
-                7 + true_color_offset + 4 + 256 * 4,
-                ("L", 0, 1),
-            )
-        ]
-
-
-def open(fp, mode="r"):
-    """
-    Load texture from a GD image file.
-
-    :param fp: GD file name, or an opened file handle.
-    :param mode: Optional mode.  In this version, if the mode argument
-        is given, it must be "r".
-    :returns: An image instance.
-    :raises OSError: If the image could not be read.
-    """
-    if mode != "r":
-        msg = "bad mode"
-        raise ValueError(msg)
-
-    try:
-        return GdImageFile(fp)
-    except SyntaxError as e:
-        msg = "cannot identify this image file"
-        raise UnidentifiedImageError(msg) from e
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/anyio/streams/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/anyio/streams/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/F_F_T_M_.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/F_F_T_M_.py
deleted file mode 100644
index 823ced1bafe991b73d73632773b3d7d21990b572..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/F_F_T_M_.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-from fontTools.misc.timeTools import timestampFromString, timestampToString
-from . import DefaultTable
-
-FFTMFormat = """
-		>	# big endian
-		version:        I
-		FFTimeStamp:    Q
-		sourceCreated:  Q
-		sourceModified: Q
-"""
-
-
-class table_F_F_T_M_(DefaultTable.DefaultTable):
-    def decompile(self, data, ttFont):
-        dummy, rest = sstruct.unpack2(FFTMFormat, data, self)
-
-    def compile(self, ttFont):
-        data = sstruct.pack(FFTMFormat, self)
-        return data
-
-    def toXML(self, writer, ttFont):
-        writer.comment(
-            "FontForge's timestamp, font source creation and modification dates"
-        )
-        writer.newline()
-        formatstring, names, fixes = sstruct.getformat(FFTMFormat)
-        for name in names:
-            value = getattr(self, name)
-            if name in ("FFTimeStamp", "sourceCreated", "sourceModified"):
-                value = timestampToString(value)
-            writer.simpletag(name, value=value)
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        value = attrs["value"]
-        if name in ("FFTimeStamp", "sourceCreated", "sourceModified"):
-            value = timestampFromString(value)
-        else:
-            value = safeEval(value)
-        setattr(self, name, value)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/implementations/arrow.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/implementations/arrow.py
deleted file mode 100644
index 3b1048acdec34d4f5eeff90e30e3629023c6099d..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/implementations/arrow.py
+++ /dev/null
@@ -1,297 +0,0 @@
-import errno
-import io
-import os
-import secrets
-import shutil
-from contextlib import suppress
-from functools import cached_property, wraps
-
-from fsspec.spec import AbstractFileSystem
-from fsspec.utils import (
-    get_package_version_without_import,
-    infer_storage_options,
-    mirror_from,
-    tokenize,
-)
-
-
-def wrap_exceptions(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        try:
-            return func(*args, **kwargs)
-        except OSError as exception:
-            if not exception.args:
-                raise
-
-            message, *args = exception.args
-            if isinstance(message, str) and "does not exist" in message:
-                raise FileNotFoundError(errno.ENOENT, message) from exception
-            else:
-                raise
-
-    return wrapper
-
-
-PYARROW_VERSION = None
-
-
-class ArrowFSWrapper(AbstractFileSystem):
-    """FSSpec-compatible wrapper of pyarrow.fs.FileSystem.
-
-    Parameters
-    ----------
-    fs : pyarrow.fs.FileSystem
-
-    """
-
-    root_marker = "/"
-
-    def __init__(self, fs, **kwargs):
-        global PYARROW_VERSION
-        PYARROW_VERSION = get_package_version_without_import("pyarrow")
-        self.fs = fs
-        super().__init__(**kwargs)
-
-    @property
-    def protocol(self):
-        return self.fs.type_name
-
-    @cached_property
-    def fsid(self):
-        return "hdfs_" + tokenize(self.fs.host, self.fs.port)
-
-    @classmethod
-    def _strip_protocol(cls, path):
-        ops = infer_storage_options(path)
-        path = ops["path"]
-        if path.startswith("//"):
-            # special case for "hdfs://path" (without the triple slash)
-            path = path[1:]
-        return path
-
-    def ls(self, path, detail=False, **kwargs):
-        path = self._strip_protocol(path)
-        from pyarrow.fs import FileSelector
-
-        entries = [
-            self._make_entry(entry)
-            for entry in self.fs.get_file_info(FileSelector(path))
-        ]
-        if detail:
-            return entries
-        else:
-            return [entry["name"] for entry in entries]
-
-    def info(self, path, **kwargs):
-        path = self._strip_protocol(path)
-        [info] = self.fs.get_file_info([path])
-        return self._make_entry(info)
-
-    def exists(self, path):
-        path = self._strip_protocol(path)
-        try:
-            self.info(path)
-        except FileNotFoundError:
-            return False
-        else:
-            return True
-
-    def _make_entry(self, info):
-        from pyarrow.fs import FileType
-
-        if info.type is FileType.Directory:
-            kind = "directory"
-        elif info.type is FileType.File:
-            kind = "file"
-        elif info.type is FileType.NotFound:
-            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), info.path)
-        else:
-            kind = "other"
-
-        return {
-            "name": info.path,
-            "size": info.size,
-            "type": kind,
-            "mtime": info.mtime,
-        }
-
-    @wrap_exceptions
-    def cp_file(self, path1, path2, **kwargs):
-        path1 = self._strip_protocol(path1).rstrip("/")
-        path2 = self._strip_protocol(path2).rstrip("/")
-
-        with self._open(path1, "rb") as lstream:
-            tmp_fname = f"{path2}.tmp.{secrets.token_hex(6)}"
-            try:
-                with self.open(tmp_fname, "wb") as rstream:
-                    shutil.copyfileobj(lstream, rstream)
-                self.fs.move(tmp_fname, path2)
-            except BaseException:  # noqa
-                with suppress(FileNotFoundError):
-                    self.fs.delete_file(tmp_fname)
-                raise
-
-    @wrap_exceptions
-    def mv(self, path1, path2, **kwargs):
-        path1 = self._strip_protocol(path1).rstrip("/")
-        path2 = self._strip_protocol(path2).rstrip("/")
-        self.fs.move(path1, path2)
-
-    mv_file = mv
-
-    @wrap_exceptions
-    def rm_file(self, path):
-        path = self._strip_protocol(path)
-        self.fs.delete_file(path)
-
-    @wrap_exceptions
-    def rm(self, path, recursive=False, maxdepth=None):
-        path = self._strip_protocol(path).rstrip("/")
-        if self.isdir(path):
-            if recursive:
-                self.fs.delete_dir(path)
-            else:
-                raise ValueError("Can't delete directories without recursive=False")
-        else:
-            self.fs.delete_file(path)
-
-    @wrap_exceptions
-    def _open(self, path, mode="rb", block_size=None, seekable=True, **kwargs):
-        if mode == "rb":
-            if seekable:
-                method = self.fs.open_input_file
-            else:
-                method = self.fs.open_input_stream
-        elif mode == "wb":
-            method = self.fs.open_output_stream
-        elif mode == "ab":
-            method = self.fs.open_append_stream
-        else:
-            raise ValueError(f"unsupported mode for Arrow filesystem: {mode!r}")
-
-        _kwargs = {}
-        if mode != "rb" or not seekable:
-            if int(PYARROW_VERSION.split(".")[0]) >= 4:
-                # disable compression auto-detection
-                _kwargs["compression"] = None
-        stream = method(path, **_kwargs)
-
-        return ArrowFile(self, stream, path, mode, block_size, **kwargs)
-
-    @wrap_exceptions
-    def mkdir(self, path, create_parents=True, **kwargs):
-        path = self._strip_protocol(path)
-        if create_parents:
-            self.makedirs(path, exist_ok=True)
-        else:
-            self.fs.create_dir(path, recursive=False)
-
-    @wrap_exceptions
-    def makedirs(self, path, exist_ok=False):
-        path = self._strip_protocol(path)
-        self.fs.create_dir(path, recursive=True)
-
-    @wrap_exceptions
-    def rmdir(self, path):
-        path = self._strip_protocol(path)
-        self.fs.delete_dir(path)
-
-    @wrap_exceptions
-    def modified(self, path):
-        path = self._strip_protocol(path)
-        return self.fs.get_file_info(path).mtime
-
-    def cat_file(self, path, start=None, end=None, **kwargs):
-        kwargs["seekable"] = start not in [None, 0]
-        return super().cat_file(path, start=None, end=None, **kwargs)
-
-    def get_file(self, rpath, lpath, **kwargs):
-        kwargs["seekable"] = False
-        super().get_file(rpath, lpath, **kwargs)
-
-
-@mirror_from(
-    "stream",
-    [
-        "read",
-        "seek",
-        "tell",
-        "write",
-        "readable",
-        "writable",
-        "close",
-        "size",
-        "seekable",
-    ],
-)
-class ArrowFile(io.IOBase):
-    def __init__(self, fs, stream, path, mode, block_size=None, **kwargs):
-        self.path = path
-        self.mode = mode
-
-        self.fs = fs
-        self.stream = stream
-
-        self.blocksize = self.block_size = block_size
-        self.kwargs = kwargs
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args):
-        return self.close()
-
-
-class HadoopFileSystem(ArrowFSWrapper):
-    """A wrapper on top of the pyarrow.fs.HadoopFileSystem
-    to connect it's interface with fsspec"""
-
-    protocol = "hdfs"
-
-    def __init__(
-        self,
-        host="default",
-        port=0,
-        user=None,
-        kerb_ticket=None,
-        extra_conf=None,
-        **kwargs,
-    ):
-        """
-
-        Parameters
-        ----------
-        host: str
-            Hostname, IP or "default" to try to read from Hadoop config
-        port: int
-            Port to connect on, or default from Hadoop config if 0
-        user: str or None
-            If given, connect as this username
-        kerb_ticket: str or None
-            If given, use this ticket for authentication
-        extra_conf: None or dict
-            Passed on to HadoopFileSystem
-        """
-        from pyarrow.fs import HadoopFileSystem
-
-        fs = HadoopFileSystem(
-            host=host,
-            port=port,
-            user=user,
-            kerb_ticket=kerb_ticket,
-            extra_conf=extra_conf,
-        )
-        super().__init__(fs=fs, **kwargs)
-
-    @staticmethod
-    def _get_kwargs_from_urls(path):
-        ops = infer_storage_options(path)
-        out = {}
-        if ops.get("host", None):
-            out["host"] = ops["host"]
-        if ops.get("username", None):
-            out["user"] = ops["username"]
-        if ops.get("port", None):
-            out["port"] = ops["port"]
-        return out
diff --git a/spaces/jskalbg/ChatDev01/online_log/static/download.html b/spaces/jskalbg/ChatDev01/online_log/static/download.html
deleted file mode 100644
index 2f6a7735896880da8171d82e24a569374f18c1b7..0000000000000000000000000000000000000000
--- a/spaces/jskalbg/ChatDev01/online_log/static/download.html
+++ /dev/null
@@ -1,62 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <title>ChatDev</title>
-    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
-    <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
-    <script src="https://cdn.jsdelivr.net/npm/marked@3.0.7/marked.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.28.0/prism.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.28.0/components/prism-markup.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.28.0/components/prism-javascript.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.28.0/plugins/line-numbers/prism-line-numbers.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.28.0/plugins/copy-to-clipboard/prism-copy-to-clipboard.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.8/clipboard.min.js"></script>
-    <script src="https://cdn.jsdelivr.net/npm/lodash@4.17.21/lodash.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.2.0/highlight.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.8/clipboard.min.js"></script>
-    <script src="static/js/main.js"></script>
-    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/prism/1.28.0/themes/prism-okaidia.min.css">
-    <link rel="stylesheet" href="static/css/style.css">
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.2.0/styles/monokai-sublime.min.css">
-    <script>
-        hljs.initHighlightingOnLoad();
-    </script>
-</head>
-
-<body style="margin: 120px; background-color: rgb(41, 41, 41);">
-    <div class="d-flex justify-content-center">
-        <img src="static/figures/title.png" alt="ChatDev Title" id="title-image" style="width: 100%; max-width: 500px;">
-    </div>
-    <main class="container" id="form" style="width: auto; height: 58%; background-color:rgb(97, 97, 92); font-size: 24px; overflow: hidden; border-radius: 1rem; font-family: 'Lucida Sans', 'Lucida Sans Regular', 'Lucida Grande', 'Lucida Sans Unicode', Geneva, Verdana, sans-serif; font-weight: bolder; text-shadow: 0px 0px 0px rgb(252, 252, 252); color: aliceblue;">
-        <label>Congratulations! The software development has been completed. Now, you can now proceed to download it by clicking on "Download", or go back to develop another software, click on "Go Back".</label>
-        <div style="display: flex; width:auto;justify-content: center;align-items: center;">
-            <a href="/Outputs.zip" download="Outputs.zip">
-                <button margin: style="margin:20px; border-radius: 0.5rem; font-weight: bolder; text-shadow: 0px 0px 0px rgb(252, 252, 252); color: aliceblue; background-color: rgb(58, 58, 218);">Download</button>
-            </a>
-            <a href="https://ennet-chatdev.hf.space/">
-                <button style="margin: 20px; border-radius: 0.5rem; font-weight: bolder; text-shadow: 0px 0px 0px rgb(252, 252, 252); color: aliceblue; background-color: rgb(226, 67, 160);">Go Back</button>
-            </a>
-        </div>
-    </main>
-    <!-- <div class="container d-flex flex-column" id="chat-box" style="background-color: rgb(97, 97, 92); border-radius: 1rem"></div> -->
-    <!-- <div style="display: flex; width:auto;justify-content: center;align-items: center;">
-        <div style="width: 30%;">
-            <br>
-            <a href="/Outputs.zip" download="Outputs.zip">
-                <button margin: style="margin:20px; border-radius: 0.5rem; font-weight: bolder; text-shadow: 0px 0px 0px rgb(252, 252, 252); color: aliceblue; background-color: rgb(58, 58, 218);">Download</button>
-            </a>
-        </div>
-        <div>
-            <br>
-            <a href="static/replay.html">
-                <button style="margin: 20px; border-radius: 0.5rem; font-weight: bolder; text-shadow: 0px 0px 0px rgb(252, 252, 252); color: aliceblue; background-color: rgb(226, 67, 160);">Chat Replay</button>
-            </a>
-        </div>
-    </div> -->
-    <script src="static/js/main.js"></script>
-</body>
-
-</html>
-
diff --git a/spaces/julien-c/nvidia-smi/README.md b/spaces/julien-c/nvidia-smi/README.md
deleted file mode 100644
index 0a47aca6eefff488d5e3a469593d5ad01e9a375c..0000000000000000000000000000000000000000
--- a/spaces/julien-c/nvidia-smi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: nvidia-smi
-emoji: 😈
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.36.0
-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/kangvcar/RealChar/client/web/src/utils/firebase.js b/spaces/kangvcar/RealChar/client/web/src/utils/firebase.js
deleted file mode 100644
index 45262b60c540597e2bdfbfde63c6ce49af927aae..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/client/web/src/utils/firebase.js
+++ /dev/null
@@ -1,20 +0,0 @@
-import { initializeApp } from "firebase/app";
-import { getAuth } from "firebase/auth";
-
-// Your web app's Firebase configuration
-// For Firebase JS SDK v7.20.0 and later, measurementId is optional
-const firebaseConfig = {
-  apiKey: "AIzaSyAVqhwbdB8I56HAMVVlgJKZcfrBkKI2AhQ",
-  authDomain: "assistly-kubernetes.firebaseapp.com",
-  projectId: "assistly-kubernetes",
-  storageBucket: "assistly-kubernetes.appspot.com",
-  messagingSenderId: "806733379891",
-  appId: "1:806733379891:web:48bf124c0d9b90298e6646",
-  measurementId: "G-XVWF8XDKS5"
-};
-
-// Initialize Firebase
-const app = initializeApp(firebaseConfig);
-const auth = getAuth(app);
-
-export default auth;
\ No newline at end of file
diff --git a/spaces/keras-io/deit/README.md b/spaces/keras-io/deit/README.md
deleted file mode 100644
index 8a82130cc0b608c56cf68a8355ebdda84597a364..0000000000000000000000000000000000000000
--- a/spaces/keras-io/deit/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Distill ViT Flowers Classification
-emoji: 🌷🌸🌼
-colorFrom: pink
-colorTo: blue
-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-VC-SadTalker/src/facerender/pirender/base_function.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/pirender/base_function.py
deleted file mode 100644
index 49fe4cf3d07c4a22f7d7db4bf0a97ebddc87dd72..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/pirender/base_function.py
+++ /dev/null
@@ -1,368 +0,0 @@
-import sys
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.nn.utils.spectral_norm import spectral_norm as SpectralNorm
-
-
-class LayerNorm2d(nn.Module):
-    def __init__(self, n_out, affine=True):
-        super(LayerNorm2d, self).__init__()
-        self.n_out = n_out
-        self.affine = affine
-
-        if self.affine:
-          self.weight = nn.Parameter(torch.ones(n_out, 1, 1))
-          self.bias = nn.Parameter(torch.zeros(n_out, 1, 1))
-
-    def forward(self, x):
-        normalized_shape = x.size()[1:]
-        if self.affine:
-          return F.layer_norm(x, normalized_shape, \
-              self.weight.expand(normalized_shape), 
-              self.bias.expand(normalized_shape))
-              
-        else:
-          return F.layer_norm(x, normalized_shape)  
-
-class ADAINHourglass(nn.Module):
-    def __init__(self, image_nc, pose_nc, ngf, img_f, encoder_layers, decoder_layers, nonlinearity, use_spect):
-        super(ADAINHourglass, self).__init__()
-        self.encoder = ADAINEncoder(image_nc, pose_nc, ngf, img_f, encoder_layers, nonlinearity, use_spect)
-        self.decoder = ADAINDecoder(pose_nc, ngf, img_f, encoder_layers, decoder_layers, True, nonlinearity, use_spect)
-        self.output_nc = self.decoder.output_nc
-
-    def forward(self, x, z):
-        return self.decoder(self.encoder(x, z), z)                 
-
-
-
-class ADAINEncoder(nn.Module):
-    def __init__(self, image_nc, pose_nc, ngf, img_f, layers, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(ADAINEncoder, self).__init__()
-        self.layers = layers
-        self.input_layer = nn.Conv2d(image_nc, ngf, kernel_size=7, stride=1, padding=3)
-        for i in range(layers):
-            in_channels = min(ngf * (2**i), img_f)
-            out_channels = min(ngf *(2**(i+1)), img_f)
-            model = ADAINEncoderBlock(in_channels, out_channels, pose_nc, nonlinearity, use_spect)
-            setattr(self, 'encoder' + str(i), model)
-        self.output_nc = out_channels
-        
-    def forward(self, x, z):
-        out = self.input_layer(x)
-        out_list = [out]
-        for i in range(self.layers):
-            model = getattr(self, 'encoder' + str(i))
-            out = model(out, z)
-            out_list.append(out)
-        return out_list
-        
-class ADAINDecoder(nn.Module):
-    """docstring for ADAINDecoder"""
-    def __init__(self, pose_nc, ngf, img_f, encoder_layers, decoder_layers, skip_connect=True, 
-                 nonlinearity=nn.LeakyReLU(), use_spect=False):
-
-        super(ADAINDecoder, self).__init__()
-        self.encoder_layers = encoder_layers
-        self.decoder_layers = decoder_layers
-        self.skip_connect = skip_connect
-        use_transpose = True
-
-        for i in range(encoder_layers-decoder_layers, encoder_layers)[::-1]:
-            in_channels = min(ngf * (2**(i+1)), img_f)
-            in_channels = in_channels*2 if i != (encoder_layers-1) and self.skip_connect else in_channels
-            out_channels = min(ngf * (2**i), img_f)
-            model = ADAINDecoderBlock(in_channels, out_channels, out_channels, pose_nc, use_transpose, nonlinearity, use_spect)
-            setattr(self, 'decoder' + str(i), model)
-
-        self.output_nc = out_channels*2 if self.skip_connect else out_channels
-
-    def forward(self, x, z):
-        out = x.pop() if self.skip_connect else x
-        for i in range(self.encoder_layers-self.decoder_layers, self.encoder_layers)[::-1]:
-            model = getattr(self, 'decoder' + str(i))
-            out = model(out, z)
-            out = torch.cat([out, x.pop()], 1) if self.skip_connect else out
-        return out
-
-class ADAINEncoderBlock(nn.Module):       
-    def __init__(self, input_nc, output_nc, feature_nc, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(ADAINEncoderBlock, self).__init__()
-        kwargs_down = {'kernel_size': 4, 'stride': 2, 'padding': 1}
-        kwargs_fine = {'kernel_size': 3, 'stride': 1, 'padding': 1}
-
-        self.conv_0 = spectral_norm(nn.Conv2d(input_nc,  output_nc, **kwargs_down), use_spect)
-        self.conv_1 = spectral_norm(nn.Conv2d(output_nc, output_nc, **kwargs_fine), use_spect)
-
-
-        self.norm_0 = ADAIN(input_nc, feature_nc)
-        self.norm_1 = ADAIN(output_nc, feature_nc)
-        self.actvn = nonlinearity
-
-    def forward(self, x, z):
-        x = self.conv_0(self.actvn(self.norm_0(x, z)))
-        x = self.conv_1(self.actvn(self.norm_1(x, z)))
-        return x
-
-class ADAINDecoderBlock(nn.Module):
-    def __init__(self, input_nc, output_nc, hidden_nc, feature_nc, use_transpose=True, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(ADAINDecoderBlock, self).__init__()        
-        # Attributes
-        self.actvn = nonlinearity
-        hidden_nc = min(input_nc, output_nc) if hidden_nc is None else hidden_nc
-
-        kwargs_fine = {'kernel_size':3, 'stride':1, 'padding':1}
-        if use_transpose:
-            kwargs_up = {'kernel_size':3, 'stride':2, 'padding':1, 'output_padding':1}
-        else:
-            kwargs_up = {'kernel_size':3, 'stride':1, 'padding':1}
-
-        # create conv layers
-        self.conv_0 = spectral_norm(nn.Conv2d(input_nc, hidden_nc, **kwargs_fine), use_spect)
-        if use_transpose:
-            self.conv_1 = spectral_norm(nn.ConvTranspose2d(hidden_nc, output_nc, **kwargs_up), use_spect)
-            self.conv_s = spectral_norm(nn.ConvTranspose2d(input_nc, output_nc, **kwargs_up), use_spect)
-        else:
-            self.conv_1 = nn.Sequential(spectral_norm(nn.Conv2d(hidden_nc, output_nc, **kwargs_up), use_spect),
-                                        nn.Upsample(scale_factor=2))
-            self.conv_s = nn.Sequential(spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs_up), use_spect),
-                                        nn.Upsample(scale_factor=2))
-        # define normalization layers
-        self.norm_0 = ADAIN(input_nc, feature_nc)
-        self.norm_1 = ADAIN(hidden_nc, feature_nc)
-        self.norm_s = ADAIN(input_nc, feature_nc)
-        
-    def forward(self, x, z):
-        x_s = self.shortcut(x, z)
-        dx = self.conv_0(self.actvn(self.norm_0(x, z)))
-        dx = self.conv_1(self.actvn(self.norm_1(dx, z)))
-        out = x_s + dx
-        return out
-
-    def shortcut(self, x, z):
-        x_s = self.conv_s(self.actvn(self.norm_s(x, z)))
-        return x_s              
-
-
-def spectral_norm(module, use_spect=True):
-    """use spectral normal layer to stable the training process"""
-    if use_spect:
-        return SpectralNorm(module)
-    else:
-        return module
-
-
-class ADAIN(nn.Module):
-    def __init__(self, norm_nc, feature_nc):
-        super().__init__()
-
-        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
-
-        nhidden = 128
-        use_bias=True
-
-        self.mlp_shared = nn.Sequential(
-            nn.Linear(feature_nc, nhidden, bias=use_bias),            
-            nn.ReLU()
-        )
-        self.mlp_gamma = nn.Linear(nhidden, norm_nc, bias=use_bias)    
-        self.mlp_beta = nn.Linear(nhidden, norm_nc, bias=use_bias)    
-
-    def forward(self, x, feature):
-
-        # Part 1. generate parameter-free normalized activations
-        normalized = self.param_free_norm(x)
-
-        # Part 2. produce scaling and bias conditioned on feature
-        feature = feature.view(feature.size(0), -1)
-        actv = self.mlp_shared(feature)
-        gamma = self.mlp_gamma(actv)
-        beta = self.mlp_beta(actv)
-
-        # apply scale and bias
-        gamma = gamma.view(*gamma.size()[:2], 1,1)
-        beta = beta.view(*beta.size()[:2], 1,1)
-        out = normalized * (1 + gamma) + beta
-        return out
-
-
-class FineEncoder(nn.Module):
-    """docstring for Encoder"""
-    def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(FineEncoder, self).__init__()
-        self.layers = layers
-        self.first = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
-        for i in range(layers):
-            in_channels = min(ngf*(2**i), img_f)
-            out_channels = min(ngf*(2**(i+1)), img_f)
-            model = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
-            setattr(self, 'down' + str(i), model)
-        self.output_nc = out_channels
-
-    def forward(self, x):
-        x = self.first(x)
-        out=[x]
-        for i in range(self.layers):
-            model = getattr(self, 'down'+str(i))
-            x = model(x)
-            out.append(x)
-        return out
-
-class FineDecoder(nn.Module):
-    """docstring for FineDecoder"""
-    def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(FineDecoder, self).__init__()
-        self.layers = layers
-        for i in range(layers)[::-1]:
-            in_channels = min(ngf*(2**(i+1)), img_f)
-            out_channels = min(ngf*(2**i), img_f)
-            up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
-            res = FineADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
-            jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)
-
-            setattr(self, 'up' + str(i), up)
-            setattr(self, 'res' + str(i), res)            
-            setattr(self, 'jump' + str(i), jump)
-
-        self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'tanh')
-
-        self.output_nc = out_channels
-
-    def forward(self, x, z):
-        out = x.pop()
-        for i in range(self.layers)[::-1]:
-            res_model = getattr(self, 'res' + str(i))
-            up_model = getattr(self, 'up' + str(i))
-            jump_model = getattr(self, 'jump' + str(i))
-            out = res_model(out, z)
-            out = up_model(out)
-            out = jump_model(x.pop()) + out
-        out_image = self.final(out)
-        return out_image
-
-class FirstBlock2d(nn.Module):
-    """
-    Downsampling block for use in encoder.
-    """
-    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(FirstBlock2d, self).__init__()
-        kwargs = {'kernel_size': 7, 'stride': 1, 'padding': 3}
-        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
-
-        if type(norm_layer) == type(None):
-            self.model = nn.Sequential(conv, nonlinearity)
-        else:
-            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
-
-
-    def forward(self, x):
-        out = self.model(x)
-        return out  
-
-class DownBlock2d(nn.Module):
-    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(DownBlock2d, self).__init__()
-
-
-        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
-        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
-        pool = nn.AvgPool2d(kernel_size=(2, 2))
-
-        if type(norm_layer) == type(None):
-            self.model = nn.Sequential(conv, nonlinearity, pool)
-        else:
-            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity, pool)
-
-    def forward(self, x):
-        out = self.model(x)
-        return out 
-
-class UpBlock2d(nn.Module):
-    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(UpBlock2d, self).__init__()
-        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
-        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
-        if type(norm_layer) == type(None):
-            self.model = nn.Sequential(conv, nonlinearity)
-        else:
-            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
-
-    def forward(self, x):
-        out = self.model(F.interpolate(x, scale_factor=2))
-        return out
-
-class FineADAINResBlocks(nn.Module):
-    def __init__(self, num_block, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(FineADAINResBlocks, self).__init__()                                
-        self.num_block = num_block
-        for i in range(num_block):
-            model = FineADAINResBlock2d(input_nc, feature_nc, norm_layer, nonlinearity, use_spect)
-            setattr(self, 'res'+str(i), model)
-
-    def forward(self, x, z):
-        for i in range(self.num_block):
-            model = getattr(self, 'res'+str(i))
-            x = model(x, z)
-        return x     
-
-class Jump(nn.Module):
-    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(Jump, self).__init__()
-        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
-        conv = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
-
-        if type(norm_layer) == type(None):
-            self.model = nn.Sequential(conv, nonlinearity)
-        else:
-            self.model = nn.Sequential(conv, norm_layer(input_nc), nonlinearity)
-
-    def forward(self, x):
-        out = self.model(x)
-        return out          
-
-class FineADAINResBlock2d(nn.Module):
-    """
-    Define an Residual block for different types
-    """
-    def __init__(self, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
-        super(FineADAINResBlock2d, self).__init__()
-
-        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
-
-        self.conv1 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
-        self.conv2 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
-        self.norm1 = ADAIN(input_nc, feature_nc)
-        self.norm2 = ADAIN(input_nc, feature_nc)
-
-        self.actvn = nonlinearity
-
-
-    def forward(self, x, z):
-        dx = self.actvn(self.norm1(self.conv1(x), z))
-        dx = self.norm2(self.conv2(x), z)
-        out = dx + x
-        return out        
-
-class FinalBlock2d(nn.Module):
-    """
-    Define the output layer
-    """
-    def __init__(self, input_nc, output_nc, use_spect=False, tanh_or_sigmoid='tanh'):
-        super(FinalBlock2d, self).__init__()
-
-        kwargs = {'kernel_size': 7, 'stride': 1, 'padding':3}
-        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
-
-        if tanh_or_sigmoid == 'sigmoid':
-            out_nonlinearity = nn.Sigmoid()
-        else:
-            out_nonlinearity = nn.Tanh()            
-
-        self.model = nn.Sequential(conv, out_nonlinearity)
-    def forward(self, x):
-        out = self.model(x)
-        return out          
\ No newline at end of file
diff --git a/spaces/kevinwang676/FreeVC-en/speaker_encoder/params_data.py b/spaces/kevinwang676/FreeVC-en/speaker_encoder/params_data.py
deleted file mode 100644
index bdb1716ed45617f2b127a7fb8885afe6cc74fb71..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/FreeVC-en/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/VALLE/utils/g2p/symbols.py b/spaces/kevinwang676/VALLE/utils/g2p/symbols.py
deleted file mode 100644
index 789e9df25d3d93d1976ef22d15d77f51d170ed00..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VALLE/utils/g2p/symbols.py
+++ /dev/null
@@ -1,76 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-# japanese_cleaners
-# _pad        = '_'
-# _punctuation = ',.!?-'
-# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# # zh_ja_mixture_cleaners
-# _pad        = '_'
-# _punctuation = ',.!?-~…'
-# _letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-'''# sanskrit_cleaners
-_pad        = '_'
-_punctuation = '।'
-_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
-'''
-
-'''# cjks_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
-'''
-
-'''# thai_cleaners
-_pad        = '_'
-_punctuation = '.!? '
-_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
-'''
-
-# # cjke_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
-
-
-'''# shanghainese_cleaners
-_pad        = '_'
-_punctuation = ',.!?…'
-_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
-'''
-
-'''# chinese_dialect_cleaners
-_pad        = '_'
-_punctuation = ',.!?~…─'
-_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/utils/profiler.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/utils/profiler.py
deleted file mode 100644
index 17175b9e1b0eb17fdc015199e5194a5c1afb8a28..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/utils/profiler.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from time import perf_counter as timer
-from collections import OrderedDict
-import numpy as np
-
-
-class Profiler:
-    def __init__(self, summarize_every=5, disabled=False):
-        self.last_tick = timer()
-        self.logs = OrderedDict()
-        self.summarize_every = summarize_every
-        self.disabled = disabled
-    
-    def tick(self, name):
-        if self.disabled:
-            return
-        
-        # Log the time needed to execute that function
-        if not name in self.logs:
-            self.logs[name] = []
-        if len(self.logs[name]) >= self.summarize_every:
-            self.summarize()
-            self.purge_logs()
-        self.logs[name].append(timer() - self.last_tick)
-        
-        self.reset_timer()
-        
-    def purge_logs(self):
-        for name in self.logs:
-            self.logs[name].clear()
-    
-    def reset_timer(self):
-        self.last_tick = timer()
-    
-    def summarize(self):
-        n = max(map(len, self.logs.values()))
-        assert n == self.summarize_every
-        print("\nAverage execution time over %d steps:" % n)
-
-        name_msgs = ["%s (%d/%d):" % (name, len(deltas), n) for name, deltas in self.logs.items()]
-        pad = max(map(len, name_msgs))
-        for name_msg, deltas in zip(name_msgs, self.logs.values()):
-            print("  %s  mean: %4.0fms   std: %4.0fms" % 
-                  (name_msg.ljust(pad), np.mean(deltas) * 1000, np.std(deltas) * 1000))
-        print("", flush=True)    
-        
\ No newline at end of file
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/gcnet_r50-d8.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/gcnet_r50-d8.py
deleted file mode 100644
index 3d2ad69f5c22adfe79d5fdabf920217628987166..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/gcnet_r50-d8.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='GCHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        ratio=1 / 4.,
-        pooling_type='att',
-        fusion_types=('channel_add', ),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/laser/laser_src/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/laser/laser_src/__init__.py
deleted file mode 100644
index 9ffbd656d8786e421008fb4cb0d1d8911dc8330c..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/laser/laser_src/__init__.py
+++ /dev/null
@@ -1,8 +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 .laser_task import *  # noqa
-from .laser_lstm import *  # noqa
-from .laser_transformer import *  # noqa
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/linformer/linformer_src/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/linformer/linformer_src/__init__.py
deleted file mode 100644
index 1c52f135ea6f99d0effe8ce1f7d77cbd66be3745..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/linformer/linformer_src/__init__.py
+++ /dev/null
@@ -1,6 +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 .models import linformer_roberta  # noqa
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wat19_my.sh b/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wat19_my.sh
deleted file mode 100644
index c1e2d47287a29af4576e7a63641e8152ecb63c44..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wat19_my.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-# 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.
-
-
-if [ -z $WORKDIR_ROOT ] ;
-then
-        echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
-        exit
-fi
-
-
-SRCDIR=$WORKDIR_ROOT/indic_languages_corpus
-DESTDIR=$WORKDIR_ROOT/ML50/raw
-mkdir -p $SRCDIR
-mkdir -p $DESTDIR
-
-WAT_MY_EN=wat2020.my-en.zip
-cd $SRCDIR
-# please refer to http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/ for latest URL if the following url expired
-#- The data used for WAT2020 are identical to those used in WAT2019.
-wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/$WAT_MY_EN
-unzip $WAT_MY_EN
-
-
-SRC_EXTRACT_DIR=$SRCDIR/wat2020.my-en/alt
-
-cp $SRC_EXTRACT_DIR/train.alt.en $DESTDIR/train.my_MM-en_XX.en_XX
-cp $SRC_EXTRACT_DIR/train.alt.my $DESTDIR/train.my_MM-en_XX.my_MM
-cp $SRC_EXTRACT_DIR/dev.alt.en $DESTDIR/valid.my_MM-en_XX.en_XX
-cp $SRC_EXTRACT_DIR/dev.alt.my $DESTDIR/valid.my_MM-en_XX.my_MM
-cp $SRC_EXTRACT_DIR/test.alt.en $DESTDIR/test.my_MM-en_XX.en_XX
-cp $SRC_EXTRACT_DIR/test.alt.my $DESTDIR/test.my_MM-en_XX.my_MM
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/infer.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/infer.py
deleted file mode 100644
index 6e9a878af46242ced57cfcd0e876a3d2ef3820ae..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/infer.py
+++ /dev/null
@@ -1,427 +0,0 @@
-#!/usr/bin/env python3 -u
-# 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.
-
-"""
-Run inference for pre-processed data with a trained model.
-"""
-
-import ast
-import logging
-import math
-import os
-import sys
-
-import editdistance
-import numpy as np
-import torch
-from fairseq import checkpoint_utils, options, progress_bar, tasks, utils
-from fairseq.data.data_utils import post_process
-from fairseq.logging.meters import StopwatchMeter, TimeMeter
-
-
-logging.basicConfig()
-logging.root.setLevel(logging.INFO)
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def add_asr_eval_argument(parser):
-    parser.add_argument("--kspmodel", default=None, help="sentence piece model")
-    parser.add_argument(
-        "--wfstlm", default=None, help="wfstlm on dictonary output units"
-    )
-    parser.add_argument(
-        "--rnnt_decoding_type",
-        default="greedy",
-        help="wfstlm on dictonary\
-output units",
-    )
-    try:
-        parser.add_argument(
-            "--lm-weight",
-            "--lm_weight",
-            type=float,
-            default=0.2,
-            help="weight for lm while interpolating with neural score",
-        )
-    except:
-        pass
-    parser.add_argument(
-        "--rnnt_len_penalty", default=-0.5, help="rnnt length penalty on word level"
-    )
-    parser.add_argument(
-        "--w2l-decoder",
-        choices=["viterbi", "kenlm", "fairseqlm"],
-        help="use a w2l decoder",
-    )
-    parser.add_argument("--lexicon", help="lexicon for w2l decoder")
-    parser.add_argument("--unit-lm", action="store_true", help="if using a unit lm")
-    parser.add_argument("--kenlm-model", "--lm-model", help="lm model for w2l decoder")
-    parser.add_argument("--beam-threshold", type=float, default=25.0)
-    parser.add_argument("--beam-size-token", type=float, default=100)
-    parser.add_argument("--word-score", type=float, default=1.0)
-    parser.add_argument("--unk-weight", type=float, default=-math.inf)
-    parser.add_argument("--sil-weight", type=float, default=0.0)
-    parser.add_argument(
-        "--dump-emissions",
-        type=str,
-        default=None,
-        help="if present, dumps emissions into this file and exits",
-    )
-    parser.add_argument(
-        "--dump-features",
-        type=str,
-        default=None,
-        help="if present, dumps features into this file and exits",
-    )
-    parser.add_argument(
-        "--load-emissions",
-        type=str,
-        default=None,
-        help="if present, loads emissions from this file",
-    )
-    return parser
-
-
-def check_args(args):
-    # assert args.path is not None, "--path required for generation!"
-    # assert args.results_path is not None, "--results_path required for generation!"
-    assert (
-        not args.sampling or args.nbest == args.beam
-    ), "--sampling requires --nbest to be equal to --beam"
-    assert (
-        args.replace_unk is None or args.raw_text
-    ), "--replace-unk requires a raw text dataset (--raw-text)"
-
-
-def get_dataset_itr(args, task, models):
-    return task.get_batch_iterator(
-        dataset=task.dataset(args.gen_subset),
-        max_tokens=args.max_tokens,
-        max_sentences=args.batch_size,
-        max_positions=(sys.maxsize, sys.maxsize),
-        ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
-        required_batch_size_multiple=args.required_batch_size_multiple,
-        num_shards=args.num_shards,
-        shard_id=args.shard_id,
-        num_workers=args.num_workers,
-        data_buffer_size=args.data_buffer_size,
-    ).next_epoch_itr(shuffle=False)
-
-
-def process_predictions(
-    args, hypos, sp, tgt_dict, target_tokens, res_files, speaker, id
-):
-    for hypo in hypos[: min(len(hypos), args.nbest)]:
-        hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu())
-
-        if "words" in hypo:
-            hyp_words = " ".join(hypo["words"])
-        else:
-            hyp_words = post_process(hyp_pieces, args.post_process)
-
-        if res_files is not None:
-            print(
-                "{} ({}-{})".format(hyp_pieces, speaker, id),
-                file=res_files["hypo.units"],
-            )
-            print(
-                "{} ({}-{})".format(hyp_words, speaker, id),
-                file=res_files["hypo.words"],
-            )
-
-        tgt_pieces = tgt_dict.string(target_tokens)
-        tgt_words = post_process(tgt_pieces, args.post_process)
-
-        if res_files is not None:
-            print(
-                "{} ({}-{})".format(tgt_pieces, speaker, id),
-                file=res_files["ref.units"],
-            )
-            print(
-                "{} ({}-{})".format(tgt_words, speaker, id), file=res_files["ref.words"]
-            )
-
-        if not args.quiet:
-            logger.info("HYPO:" + hyp_words)
-            logger.info("TARGET:" + tgt_words)
-            logger.info("___________________")
-
-        hyp_words = hyp_words.split()
-        tgt_words = tgt_words.split()
-        return editdistance.eval(hyp_words, tgt_words), len(tgt_words)
-
-
-def prepare_result_files(args):
-    def get_res_file(file_prefix):
-        if args.num_shards > 1:
-            file_prefix = f"{args.shard_id}_{file_prefix}"
-        path = os.path.join(
-            args.results_path,
-            "{}-{}-{}.txt".format(
-                file_prefix, os.path.basename(args.path), args.gen_subset
-            ),
-        )
-        return open(path, "w", buffering=1)
-
-    if not args.results_path:
-        return None
-
-    return {
-        "hypo.words": get_res_file("hypo.word"),
-        "hypo.units": get_res_file("hypo.units"),
-        "ref.words": get_res_file("ref.word"),
-        "ref.units": get_res_file("ref.units"),
-    }
-
-
-def optimize_models(args, use_cuda, models):
-    """Optimize ensemble for generation"""
-    for model in models:
-        model.make_generation_fast_(
-            beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
-            need_attn=args.print_alignment,
-        )
-        if args.fp16:
-            model.half()
-        if use_cuda:
-            model.cuda()
-
-
-class ExistingEmissionsDecoder(object):
-    def __init__(self, decoder, emissions):
-        self.decoder = decoder
-        self.emissions = emissions
-
-    def generate(self, models, sample, **unused):
-        ids = sample["id"].cpu().numpy()
-        try:
-            emissions = np.stack(self.emissions[ids])
-        except:
-            print([x.shape for x in self.emissions[ids]])
-            raise Exception("invalid sizes")
-        emissions = torch.from_numpy(emissions)
-        return self.decoder.decode(emissions)
-
-
-def main(args, task=None, model_state=None):
-    check_args(args)
-
-    if args.max_tokens is None and args.batch_size is None:
-        args.max_tokens = 4000000
-    logger.info(args)
-
-    use_cuda = torch.cuda.is_available() and not args.cpu
-
-    logger.info("| decoding with criterion {}".format(args.criterion))
-
-    task = tasks.setup_task(args)
-
-    # Load ensemble
-    if args.load_emissions:
-        models, criterions = [], []
-        task.load_dataset(args.gen_subset)
-    else:
-        logger.info("| loading model(s) from {}".format(args.path))
-        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-            utils.split_paths(args.path, separator="\\"),
-            arg_overrides=ast.literal_eval(args.model_overrides),
-            task=task,
-            suffix=args.checkpoint_suffix,
-            strict=(args.checkpoint_shard_count == 1),
-            num_shards=args.checkpoint_shard_count,
-            state=model_state,
-        )
-        optimize_models(args, use_cuda, models)
-        task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task)
-
-
-    # Set dictionary
-    tgt_dict = task.target_dictionary
-
-    logger.info(
-        "| {} {} {} examples".format(
-            args.data, args.gen_subset, len(task.dataset(args.gen_subset))
-        )
-    )
-
-    # hack to pass transitions to W2lDecoder
-    if args.criterion == "asg_loss":
-        raise NotImplementedError("asg_loss is currently not supported")
-        # trans = criterions[0].asg.trans.data
-        # args.asg_transitions = torch.flatten(trans).tolist()
-
-    # Load dataset (possibly sharded)
-    itr = get_dataset_itr(args, task, models)
-
-    # Initialize generator
-    gen_timer = StopwatchMeter()
-
-    def build_generator(args):
-        w2l_decoder = getattr(args, "w2l_decoder", None)
-        if w2l_decoder == "viterbi":
-            from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder
-
-            return W2lViterbiDecoder(args, task.target_dictionary)
-        elif w2l_decoder == "kenlm":
-            from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
-
-            return W2lKenLMDecoder(args, task.target_dictionary)
-        elif w2l_decoder == "fairseqlm":
-            from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder
-
-            return W2lFairseqLMDecoder(args, task.target_dictionary)
-        else:
-            print(
-                "only flashlight decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment"
-            )
-
-    # please do not touch this unless you test both generate.py and infer.py with audio_pretraining task
-    generator = build_generator(args)
-
-    if args.load_emissions:
-        generator = ExistingEmissionsDecoder(
-            generator, np.load(args.load_emissions, allow_pickle=True)
-        )
-        logger.info("loaded emissions from " + args.load_emissions)
-
-    num_sentences = 0
-
-    if args.results_path is not None and not os.path.exists(args.results_path):
-        os.makedirs(args.results_path)
-
-    max_source_pos = (
-        utils.resolve_max_positions(
-            task.max_positions(), *[model.max_positions() for model in models]
-        ),
-    )
-
-    if max_source_pos is not None:
-        max_source_pos = max_source_pos[0]
-        if max_source_pos is not None:
-            max_source_pos = max_source_pos[0] - 1
-
-    if args.dump_emissions:
-        emissions = {}
-    if args.dump_features:
-        features = {}
-        models[0].bert.proj = None
-    else:
-        res_files = prepare_result_files(args)
-    errs_t = 0
-    lengths_t = 0
-    with progress_bar.build_progress_bar(args, itr) as t:
-        wps_meter = TimeMeter()
-        for sample in t:
-            sample = utils.move_to_cuda(sample) if use_cuda else sample
-            if "net_input" not in sample:
-                continue
-
-            prefix_tokens = None
-            if args.prefix_size > 0:
-                prefix_tokens = sample["target"][:, : args.prefix_size]
-
-            gen_timer.start()
-            if args.dump_emissions:
-                with torch.no_grad():
-                    encoder_out = models[0](**sample["net_input"])
-                    emm = models[0].get_normalized_probs(encoder_out, log_probs=True)
-                    emm = emm.transpose(0, 1).cpu().numpy()
-                    for i, id in enumerate(sample["id"]):
-                        emissions[id.item()] = emm[i]
-                    continue
-            elif args.dump_features:
-                with torch.no_grad():
-                    encoder_out = models[0](**sample["net_input"])
-                    feat = encoder_out["encoder_out"].transpose(0, 1).cpu().numpy()
-                    for i, id in enumerate(sample["id"]):
-                        padding = (
-                            encoder_out["encoder_padding_mask"][i].cpu().numpy()
-                            if encoder_out["encoder_padding_mask"] is not None
-                            else None
-                        )
-                        features[id.item()] = (feat[i], padding)
-                    continue
-            hypos = task.inference_step(generator, models, sample, prefix_tokens)
-            num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
-            gen_timer.stop(num_generated_tokens)
-
-            for i, sample_id in enumerate(sample["id"].tolist()):
-                speaker = None
-                # id = task.dataset(args.gen_subset).ids[int(sample_id)]
-                id = sample_id
-                toks = (
-                    sample["target"][i, :]
-                    if "target_label" not in sample
-                    else sample["target_label"][i, :]
-                )
-                target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu()
-                # Process top predictions
-                errs, length = process_predictions(
-                    args,
-                    hypos[i],
-                    None,
-                    tgt_dict,
-                    target_tokens,
-                    res_files,
-                    speaker,
-                    id,
-                )
-                errs_t += errs
-                lengths_t += length
-
-            wps_meter.update(num_generated_tokens)
-            t.log({"wps": round(wps_meter.avg)})
-            num_sentences += (
-                sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
-            )
-
-    wer = None
-    if args.dump_emissions:
-        emm_arr = []
-        for i in range(len(emissions)):
-            emm_arr.append(emissions[i])
-        np.save(args.dump_emissions, emm_arr)
-        logger.info(f"saved {len(emissions)} emissions to {args.dump_emissions}")
-    elif args.dump_features:
-        feat_arr = []
-        for i in range(len(features)):
-            feat_arr.append(features[i])
-        np.save(args.dump_features, feat_arr)
-        logger.info(f"saved {len(features)} emissions to {args.dump_features}")
-    else:
-        if lengths_t > 0:
-            wer = errs_t * 100.0 / lengths_t
-            logger.info(f"WER: {wer}")
-
-        logger.info(
-            "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}"
-            "sentences/s, {:.2f} tokens/s)".format(
-                num_sentences,
-                gen_timer.n,
-                gen_timer.sum,
-                num_sentences / gen_timer.sum,
-                1.0 / gen_timer.avg,
-            )
-        )
-        logger.info("| Generate {} with beam={}".format(args.gen_subset, args.beam))
-    return task, wer
-
-
-def make_parser():
-    parser = options.get_generation_parser()
-    parser = add_asr_eval_argument(parser)
-    return parser
-
-
-def cli_main():
-    parser = make_parser()
-    args = options.parse_args_and_arch(parser)
-    main(args)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/kquote03/lama-video-watermark-remover/bin/paper_runfiles/find_best_checkpoint.py b/spaces/kquote03/lama-video-watermark-remover/bin/paper_runfiles/find_best_checkpoint.py
deleted file mode 100644
index 42f5e0f9bb1a2ea25dd9a97a58cf318e6de19532..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/bin/paper_runfiles/find_best_checkpoint.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python3
-
-
-import os
-from argparse import ArgumentParser
-
-
-def ssim_fid100_f1(metrics, fid_scale=100):
-    ssim = metrics.loc['total', 'ssim']['mean']
-    fid = metrics.loc['total', 'fid']['mean']
-    fid_rel = max(0, fid_scale - fid) / fid_scale
-    f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3)
-    return f1
-
-
-def find_best_checkpoint(model_list, models_dir):
-    with open(model_list) as f:
-        models = [m.strip() for m in f.readlines()]
-    with open(f'{model_list}_best', 'w') as f:
-        for model in models:
-            print(model)
-            best_f1 = 0
-            best_epoch = 0
-            best_step = 0
-            with open(os.path.join(models_dir, model, 'train.log')) as fm:
-                lines = fm.readlines()
-                for line_index in range(len(lines)):
-                    line = lines[line_index]
-                    if 'Validation metrics after epoch' in line:
-                        sharp_index = line.index('#')
-                        cur_ep = line[sharp_index + 1:]
-                        comma_index = cur_ep.index(',')
-                        cur_ep = int(cur_ep[:comma_index])
-                        total_index = line.index('total ')
-                        step = int(line[total_index:].split()[1].strip())
-                        total_line = lines[line_index + 5]
-                        if not total_line.startswith('total'):
-                            continue
-                        words = total_line.strip().split()
-                        f1 = float(words[-1])
-                        print(f'\tEpoch: {cur_ep}, f1={f1}')
-                        if f1 > best_f1:
-                            best_f1 = f1
-                            best_epoch = cur_ep
-                            best_step = step
-            f.write(f'{model}\t{best_epoch}\t{best_step}\t{best_f1}\n')
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser()
-    parser.add_argument('model_list')
-    parser.add_argument('models_dir')
-    args = parser.parse_args()
-    find_best_checkpoint(args.model_list, args.models_dir)
diff --git a/spaces/krushna/url-or-text_summarizer_or_caption_generator/README.md b/spaces/krushna/url-or-text_summarizer_or_caption_generator/README.md
deleted file mode 100644
index d90a942d0789a0d69a695205280aef9ee13b3faa..0000000000000000000000000000000000000000
--- a/spaces/krushna/url-or-text_summarizer_or_caption_generator/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Url-or-text Summarizer Or Caption Generator
-emoji: 💩
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.17.0
-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/kukuhtw/AutoGPT/autogpt/memory/__init__.py b/spaces/kukuhtw/AutoGPT/autogpt/memory/__init__.py
deleted file mode 100644
index 3d18704c70dfc287642b1923e6f2e1f72a5f2a62..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/autogpt/memory/__init__.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from autogpt.memory.local import LocalCache
-from autogpt.memory.no_memory import NoMemory
-
-# List of supported memory backends
-# Add a backend to this list if the import attempt is successful
-supported_memory = ["local", "no_memory"]
-
-try:
-    from autogpt.memory.redismem import RedisMemory
-
-    supported_memory.append("redis")
-except ImportError:
-    # print("Redis not installed. Skipping import.")
-    RedisMemory = None
-
-try:
-    from autogpt.memory.pinecone import PineconeMemory
-
-    supported_memory.append("pinecone")
-except ImportError:
-    # print("Pinecone not installed. Skipping import.")
-    PineconeMemory = None
-
-try:
-    from autogpt.memory.weaviate import WeaviateMemory
-
-    supported_memory.append("weaviate")
-except ImportError:
-    # print("Weaviate not installed. Skipping import.")
-    WeaviateMemory = None
-
-try:
-    from autogpt.memory.milvus import MilvusMemory
-
-    supported_memory.append("milvus")
-except ImportError:
-    # print("pymilvus not installed. Skipping import.")
-    MilvusMemory = None
-
-
-def get_memory(cfg, init=False):
-    memory = None
-    if cfg.memory_backend == "pinecone":
-        if not PineconeMemory:
-            print(
-                "Error: Pinecone is not installed. Please install pinecone"
-                " to use Pinecone as a memory backend."
-            )
-        else:
-            memory = PineconeMemory(cfg)
-            if init:
-                memory.clear()
-    elif cfg.memory_backend == "redis":
-        if not RedisMemory:
-            print(
-                "Error: Redis is not installed. Please install redis-py to"
-                " use Redis as a memory backend."
-            )
-        else:
-            memory = RedisMemory(cfg)
-    elif cfg.memory_backend == "weaviate":
-        if not WeaviateMemory:
-            print(
-                "Error: Weaviate is not installed. Please install weaviate-client to"
-                " use Weaviate as a memory backend."
-            )
-        else:
-            memory = WeaviateMemory(cfg)
-    elif cfg.memory_backend == "milvus":
-        if not MilvusMemory:
-            print(
-                "Error: Milvus sdk is not installed."
-                "Please install pymilvus to use Milvus as memory backend."
-            )
-        else:
-            memory = MilvusMemory(cfg)
-    elif cfg.memory_backend == "no_memory":
-        memory = NoMemory(cfg)
-
-    if memory is None:
-        memory = LocalCache(cfg)
-        if init:
-            memory.clear()
-    return memory
-
-
-def get_supported_memory_backends():
-    return supported_memory
-
-
-__all__ = [
-    "get_memory",
-    "LocalCache",
-    "RedisMemory",
-    "PineconeMemory",
-    "NoMemory",
-    "MilvusMemory",
-    "WeaviateMemory",
-]
diff --git a/spaces/kukuhtw/AutoGPT/tests/unit/test_chat.py b/spaces/kukuhtw/AutoGPT/tests/unit/test_chat.py
deleted file mode 100644
index 774f4103762c28d5a02e89c14b224fae0bc0756a..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/tests/unit/test_chat.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Generated by CodiumAI
-import time
-import unittest
-from unittest.mock import patch
-
-from autogpt.chat import create_chat_message, generate_context
-
-
-class TestChat(unittest.TestCase):
-    # Tests that the function returns a dictionary with the correct keys and values when valid strings are provided for role and content.
-    def test_happy_path_role_content(self):
-        result = create_chat_message("system", "Hello, world!")
-        self.assertEqual(result, {"role": "system", "content": "Hello, world!"})
-
-    # Tests that the function returns a dictionary with the correct keys and values when empty strings are provided for role and content.
-    def test_empty_role_content(self):
-        result = create_chat_message("", "")
-        self.assertEqual(result, {"role": "", "content": ""})
-
-    # Tests the behavior of the generate_context function when all input parameters are empty.
-    @patch("time.strftime")
-    def test_generate_context_empty_inputs(self, mock_strftime):
-        # Mock the time.strftime function to return a fixed value
-        mock_strftime.return_value = "Sat Apr 15 00:00:00 2023"
-        # Arrange
-        prompt = ""
-        relevant_memory = ""
-        full_message_history = []
-        model = "gpt-3.5-turbo-0301"
-
-        # Act
-        result = generate_context(prompt, relevant_memory, full_message_history, model)
-
-        # Assert
-        expected_result = (
-            -1,
-            47,
-            3,
-            [
-                {"role": "system", "content": ""},
-                {
-                    "role": "system",
-                    "content": f"The current time and date is {time.strftime('%c')}",
-                },
-                {
-                    "role": "system",
-                    "content": f"This reminds you of these events from your past:\n\n\n",
-                },
-            ],
-        )
-        self.assertEqual(result, expected_result)
-
-    # Tests that the function successfully generates a current_context given valid inputs.
-    def test_generate_context_valid_inputs(self):
-        # Given
-        prompt = "What is your favorite color?"
-        relevant_memory = "You once painted your room blue."
-        full_message_history = [
-            create_chat_message("user", "Hi there!"),
-            create_chat_message("assistant", "Hello! How can I assist you today?"),
-            create_chat_message("user", "Can you tell me a joke?"),
-            create_chat_message(
-                "assistant",
-                "Why did the tomato turn red? Because it saw the salad dressing!",
-            ),
-            create_chat_message("user", "Haha, that's funny."),
-        ]
-        model = "gpt-3.5-turbo-0301"
-
-        # When
-        result = generate_context(prompt, relevant_memory, full_message_history, model)
-
-        # Then
-        self.assertIsInstance(result[0], int)
-        self.assertIsInstance(result[1], int)
-        self.assertIsInstance(result[2], int)
-        self.assertIsInstance(result[3], list)
-        self.assertGreaterEqual(result[0], 0)
-        self.assertGreaterEqual(result[1], 0)
-        self.assertGreaterEqual(result[2], 0)
-        self.assertGreaterEqual(
-            len(result[3]), 3
-        )  # current_context should have at least 3 messages
-        self.assertLessEqual(
-            result[1], 2048
-        )  # token limit for GPT-3.5-turbo-0301 is 2048 tokens
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/openapi/docs.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/openapi/docs.py
deleted file mode 100644
index bf335118fc791c42fc38e6ab2fb81fb4a8a2b580..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/openapi/docs.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import json
-from typing import Any, Dict, Optional
-
-from fastapi.encoders import jsonable_encoder
-from starlette.responses import HTMLResponse
-
-swagger_ui_default_parameters = {
-    "dom_id": "#swagger-ui",
-    "layout": "BaseLayout",
-    "deepLinking": True,
-    "showExtensions": True,
-    "showCommonExtensions": True,
-}
-
-
-def get_swagger_ui_html(
-    *,
-    openapi_url: str,
-    title: str,
-    swagger_js_url: str = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@4/swagger-ui-bundle.js",
-    swagger_css_url: str = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@4/swagger-ui.css",
-    swagger_favicon_url: str = "https://fastapi.tiangolo.com/img/favicon.png",
-    oauth2_redirect_url: Optional[str] = None,
-    init_oauth: Optional[Dict[str, Any]] = None,
-    swagger_ui_parameters: Optional[Dict[str, Any]] = None,
-) -> HTMLResponse:
-    current_swagger_ui_parameters = swagger_ui_default_parameters.copy()
-    if swagger_ui_parameters:
-        current_swagger_ui_parameters.update(swagger_ui_parameters)
-
-    html = f"""
-    <!DOCTYPE html>
-    <html>
-    <head>
-    <link type="text/css" rel="stylesheet" href="{swagger_css_url}">
-    <link rel="shortcut icon" href="{swagger_favicon_url}">
-    <title>{title}</title>
-    </head>
-    <body>
-    <div id="swagger-ui">
-    </div>
-    <script src="{swagger_js_url}"></script>
-    <!-- `SwaggerUIBundle` is now available on the page -->
-    <script>
-    const ui = SwaggerUIBundle({{
-        url: '{openapi_url}',
-    """
-
-    for key, value in current_swagger_ui_parameters.items():
-        html += f"{json.dumps(key)}: {json.dumps(jsonable_encoder(value))},\n"
-
-    if oauth2_redirect_url:
-        html += f"oauth2RedirectUrl: window.location.origin + '{oauth2_redirect_url}',"
-
-    html += """
-    presets: [
-        SwaggerUIBundle.presets.apis,
-        SwaggerUIBundle.SwaggerUIStandalonePreset
-        ],
-    })"""
-
-    if init_oauth:
-        html += f"""
-        ui.initOAuth({json.dumps(jsonable_encoder(init_oauth))})
-        """
-
-    html += """
-    </script>
-    </body>
-    </html>
-    """
-    return HTMLResponse(html)
-
-
-def get_redoc_html(
-    *,
-    openapi_url: str,
-    title: str,
-    redoc_js_url: str = "https://cdn.jsdelivr.net/npm/redoc@next/bundles/redoc.standalone.js",
-    redoc_favicon_url: str = "https://fastapi.tiangolo.com/img/favicon.png",
-    with_google_fonts: bool = True,
-) -> HTMLResponse:
-    html = f"""
-    <!DOCTYPE html>
-    <html>
-    <head>
-    <title>{title}</title>
-    <!-- needed for adaptive design -->
-    <meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-    """
-    if with_google_fonts:
-        html += """
-    <link href="https://fonts.googleapis.com/css?family=Montserrat:300,400,700|Roboto:300,400,700" rel="stylesheet">
-    """
-    html += f"""
-    <link rel="shortcut icon" href="{redoc_favicon_url}">
-    <!--
-    ReDoc doesn't change outer page styles
-    -->
-    <style>
-      body {{
-        margin: 0;
-        padding: 0;
-      }}
-    </style>
-    </head>
-    <body>
-    <noscript>
-        ReDoc requires Javascript to function. Please enable it to browse the documentation.
-    </noscript>
-    <redoc spec-url="{openapi_url}"></redoc>
-    <script src="{redoc_js_url}"> </script>
-    </body>
-    </html>
-    """
-    return HTMLResponse(html)
-
-
-def get_swagger_ui_oauth2_redirect_html() -> HTMLResponse:
-    # copied from https://github.com/swagger-api/swagger-ui/blob/v4.14.0/dist/oauth2-redirect.html
-    html = """
-    <!doctype html>
-    <html lang="en-US">
-    <head>
-        <title>Swagger UI: OAuth2 Redirect</title>
-    </head>
-    <body>
-    <script>
-        'use strict';
-        function run () {
-            var oauth2 = window.opener.swaggerUIRedirectOauth2;
-            var sentState = oauth2.state;
-            var redirectUrl = oauth2.redirectUrl;
-            var isValid, qp, arr;
-
-            if (/code|token|error/.test(window.location.hash)) {
-                qp = window.location.hash.substring(1).replace('?', '&');
-            } else {
-                qp = location.search.substring(1);
-            }
-
-            arr = qp.split("&");
-            arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
-            qp = qp ? JSON.parse('{' + arr.join() + '}',
-                    function (key, value) {
-                        return key === "" ? value : decodeURIComponent(value);
-                    }
-            ) : {};
-
-            isValid = qp.state === sentState;
-
-            if ((
-              oauth2.auth.schema.get("flow") === "accessCode" ||
-              oauth2.auth.schema.get("flow") === "authorizationCode" ||
-              oauth2.auth.schema.get("flow") === "authorization_code"
-            ) && !oauth2.auth.code) {
-                if (!isValid) {
-                    oauth2.errCb({
-                        authId: oauth2.auth.name,
-                        source: "auth",
-                        level: "warning",
-                        message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
-                    });
-                }
-
-                if (qp.code) {
-                    delete oauth2.state;
-                    oauth2.auth.code = qp.code;
-                    oauth2.callback({auth: oauth2.auth, redirectUrl: redirectUrl});
-                } else {
-                    let oauthErrorMsg;
-                    if (qp.error) {
-                        oauthErrorMsg = "["+qp.error+"]: " +
-                            (qp.error_description ? qp.error_description+ ". " : "no accessCode received from the server. ") +
-                            (qp.error_uri ? "More info: "+qp.error_uri : "");
-                    }
-
-                    oauth2.errCb({
-                        authId: oauth2.auth.name,
-                        source: "auth",
-                        level: "error",
-                        message: oauthErrorMsg || "[Authorization failed]: no accessCode received from the server."
-                    });
-                }
-            } else {
-                oauth2.callback({auth: oauth2.auth, token: qp, isValid: isValid, redirectUrl: redirectUrl});
-            }
-            window.close();
-        }
-
-        if (document.readyState !== 'loading') {
-            run();
-        } else {
-            document.addEventListener('DOMContentLoaded', function () {
-                run();
-            });
-        }
-    </script>
-    </body>
-    </html>
-        """
-    return HTMLResponse(content=html)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/callbacks.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/callbacks.py
deleted file mode 100644
index 4500d02cbcae78d9cd764956d4cc46963b525213..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/callbacks.py
+++ /dev/null
@@ -1,238 +0,0 @@
-class Callback:
-    """
-    Base class and interface for callback mechanism
-
-    This class can be used directly for monitoring file transfers by
-    providing ``callback=Callback(hooks=...)`` (see the ``hooks`` argument,
-    below), or subclassed for more specialised behaviour.
-
-    Parameters
-    ----------
-    size: int (optional)
-        Nominal quantity for the value that corresponds to a complete
-        transfer, e.g., total number of tiles or total number of
-        bytes
-    value: int (0)
-        Starting internal counter value
-    hooks: dict or None
-        A dict of named functions to be called on each update. The signature
-        of these must be ``f(size, value, **kwargs)``
-    """
-
-    def __init__(self, size=None, value=0, hooks=None, **kwargs):
-        self.size = size
-        self.value = value
-        self.hooks = hooks or {}
-        self.kw = kwargs
-
-    def set_size(self, size):
-        """
-        Set the internal maximum size attribute
-
-        Usually called if not initially set at instantiation. Note that this
-        triggers a ``call()``.
-
-        Parameters
-        ----------
-        size: int
-        """
-        self.size = size
-        self.call()
-
-    def absolute_update(self, value):
-        """
-        Set the internal value state
-
-        Triggers ``call()``
-
-        Parameters
-        ----------
-        value: int
-        """
-        self.value = value
-        self.call()
-
-    def relative_update(self, inc=1):
-        """
-        Delta increment the internal counter
-
-        Triggers ``call()``
-
-        Parameters
-        ----------
-        inc: int
-        """
-        self.value += inc
-        self.call()
-
-    def call(self, hook_name=None, **kwargs):
-        """
-        Execute hook(s) with current state
-
-        Each function is passed the internal size and current value
-
-        Parameters
-        ----------
-        hook_name: str or None
-            If given, execute on this hook
-        kwargs: passed on to (all) hook(s)
-        """
-        if not self.hooks:
-            return
-        kw = self.kw.copy()
-        kw.update(kwargs)
-        if hook_name:
-            if hook_name not in self.hooks:
-                return
-            return self.hooks[hook_name](self.size, self.value, **kw)
-        for hook in self.hooks.values() or []:
-            hook(self.size, self.value, **kw)
-
-    def wrap(self, iterable):
-        """
-        Wrap an iterable to call ``relative_update`` on each iterations
-
-        Parameters
-        ----------
-        iterable: Iterable
-            The iterable that is being wrapped
-        """
-        for item in iterable:
-            self.relative_update()
-            yield item
-
-    def branch(self, path_1, path_2, kwargs):
-        """
-        Set callbacks for child transfers
-
-        If this callback is operating at a higher level, e.g., put, which may
-        trigger transfers that can also be monitored. The passed kwargs are
-        to be *mutated* to add ``callback=``, if this class supports branching
-        to children.
-
-        Parameters
-        ----------
-        path_1: str
-            Child's source path
-        path_2: str
-            Child's destination path
-        kwargs: dict
-            arguments passed to child method, e.g., put_file.
-
-        Returns
-        -------
-
-        """
-        return None
-
-    def no_op(self, *_, **__):
-        pass
-
-    def __getattr__(self, item):
-        """
-        If undefined methods are called on this class, nothing happens
-        """
-        return self.no_op
-
-    @classmethod
-    def as_callback(cls, maybe_callback=None):
-        """Transform callback=... into Callback instance
-
-        For the special value of ``None``, return the global instance of
-        ``NoOpCallback``. This is an alternative to including
-        ``callback=_DEFAULT_CALLBACK`` directly in a method signature.
-        """
-        if maybe_callback is None:
-            return _DEFAULT_CALLBACK
-        return maybe_callback
-
-
-class NoOpCallback(Callback):
-    """
-    This implementation of Callback does exactly nothing
-    """
-
-    def call(self, *args, **kwargs):
-        return None
-
-
-class DotPrinterCallback(Callback):
-    """
-    Simple example Callback implementation
-
-    Almost identical to Callback with a hook that prints a char; here we
-    demonstrate how the outer layer may print "#" and the inner layer "."
-    """
-
-    def __init__(self, chr_to_print="#", **kwargs):
-        self.chr = chr_to_print
-        super().__init__(**kwargs)
-
-    def branch(self, path_1, path_2, kwargs):
-        """Mutate kwargs to add new instance with different print char"""
-        kwargs["callback"] = DotPrinterCallback(".")
-
-    def call(self, **kwargs):
-        """Just outputs a character"""
-        print(self.chr, end="")
-
-
-class TqdmCallback(Callback):
-    """
-    A callback to display a progress bar using tqdm
-
-    Parameters
-    ----------
-    tqdm_kwargs : dict, (optional)
-        Any argument accepted by the tqdm constructor.
-        See the `tqdm doc <https://tqdm.github.io/docs/tqdm/#__init__>`_.
-        Will be forwarded to tqdm.
-
-    Examples
-    --------
-    >>> import fsspec
-    >>> from fsspec.callbacks import TqdmCallback
-    >>> fs = fsspec.filesystem("memory")
-    >>> path2distant_data = "/your-path"
-    >>> fs.upload(
-            ".",
-            path2distant_data,
-            recursive=True,
-            callback=TqdmCallback(),
-        )
-
-    You can forward args to tqdm using the ``tqdm_kwargs`` parameter.
-
-    >>> fs.upload(
-            ".",
-            path2distant_data,
-            recursive=True,
-            callback=TqdmCallback(tqdm_kwargs={"desc": "Your tqdm description"}),
-        )
-    """
-
-    def __init__(self, tqdm_kwargs=None, *args, **kwargs):
-        try:
-            import tqdm
-
-            self._tqdm = tqdm
-        except ImportError as exce:
-            raise ImportError(
-                "Using TqdmCallback requires tqdm to be installed"
-            ) from exce
-
-        self._tqdm_kwargs = tqdm_kwargs or {}
-        super().__init__(*args, **kwargs)
-
-    def set_size(self, size):
-        self.tqdm = self._tqdm.tqdm(total=size, **self._tqdm_kwargs)
-
-    def relative_update(self, inc=1):
-        self.tqdm.update(inc)
-
-    def __del__(self):
-        self.tqdm.close()
-        self.tqdm = None
-
-
-_DEFAULT_CALLBACK = NoOpCallback()
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/module-fcbc0651.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/module-fcbc0651.js
deleted file mode 100644
index 209a1a1067c688b111b4761b8c850900bc102953..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/module-fcbc0651.js
+++ /dev/null
@@ -1,9 +0,0 @@
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m={buffer:l.buffer,channelCount:l.channelCount,channelCountMode:l.channelCountMode,channelInterpretation:l.channelInterpretation,loop:l.loop,loopEnd:l.loopEnd,loopStart:l.loopStart,playbackRate:l.playbackRate.value};l=t(d,m),a!==null&&l.start(...a),c!==null&&l.stop(c)}return s.set(d,l),p?await e(d,u.playbackRate,l.playbackRate):await r(d,u.playbackRate,l.playbackRate),await o(u,d,l),l};return{set start(u){a=u},set stop(u){c=u},render(u,d){const l=s.get(d);return l!==void 0?Promise.resolve(l):i(u,d)}}},ao=e=>"playbackRate"in e,io=e=>"frequency"in e&&"gain"in e,co=e=>"offset"in e,uo=e=>!("frequency"in e)&&"gain"in e,lo=e=>"detune"in e&&"frequency"in e,fo=e=>"pan"in e,z=e=>K(cn,e),Te=e=>K(ln,e),pt=(e,t)=>{const{activeInputs:n}=z(e);n.forEach(o=>o.forEach(([s])=>{t.includes(e)||pt(s,[...t,e])}));const r=ao(e)?[e.playbackRate]:gn(e)?Array.from(e.parameters.values()):io(e)?[e.Q,e.detune,e.frequency,e.gain]:co(e)?[e.offset]:uo(e)?[e.gain]:lo(e)?[e.detune,e.frequency]:fo(e)?[e.pan]:[];for(const o of r){const s=Te(o);s!==void 0&&s.activeInputs.forEach(([a])=>pt(a,t))}oe(e)&&He(e)},ho=e=>{pt(e.destination,[])},po=e=>e===void 0||typeof e=="number"||typeof e=="string"&&(e==="balanced"||e==="interactive"||e==="playback"),mo=(e,t,n,r,o,s,a,c)=>class extends e{constructor(u,d){const l=s(u),p=a(l),m=o(l,d,p),w=p?t(c):null;super(u,!1,m,w),this._isNodeOfNativeOfflineAudioContext=p,this._nativeAudioDestinationNode=m}get channelCount(){return this._nativeAudioDestinationNode.channelCount}set channelCount(u){if(this._isNodeOfNativeOfflineAudioContext)throw r();if(u>this._nativeAudioDestinationNode.maxChannelCount)throw n();this._nativeAudioDestinationNode.channelCount=u}get channelCountMode(){return this._nativeAudioDestinationNode.channelCountMode}set channelCountMode(u){if(this._isNodeOfNativeOfflineAudioContext)throw r();this._nativeAudioDestinationNode.channelCountMode=u}get maxChannelCount(){return this._nativeAudioDestinationNode.maxChannelCount}},go=e=>{const t=new WeakMap,n=async(r,o)=>{const s=o.destination;return t.set(o,s),await e(r,o,s),s};return{render(r,o){const s=t.get(o);return s!==void 0?Promise.resolve(s):n(r,o)}}},wo=(e,t,n,r,o,s,a,c)=>(i,u)=>{const d=u.listener,l=()=>{const v=new Float32Array(1),E=t(u,{channelCount:1,channelCountMode:"explicit",channelInterpretation:"speakers",numberOfInputs:9}),y=a(u);let C=!1,N=[0,0,-1,0,1,0],I=[0,0,0];const M=()=>{if(C)return;C=!0;const U=r(u,256,9,0);U.onaudioprocess=({inputBuffer:R})=>{const P=[s(R,v,0),s(R,v,1),s(R,v,2),s(R,v,3),s(R,v,4),s(R,v,5)];P.some((S,L)=>S!==N[L])&&(d.setOrientation(...P),N=P);const 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c=a.filter(i=>i.output!==o&&(i.input!==s||s===void 0));c.length===0?t.delete(r):t.set(r,c)}}for(const[a,c]of t)c.forEach(i=>{Ve(a)?e.connect(a,i.output,i.input):e.connect(a,i.output)})})(e.disconnect)},yo=(e,t,n,r)=>{const{activeInputs:o,passiveInputs:s}=Te(t),{outputs:a}=z(e),c=be(e),i=u=>{const d=X(e),l=Ee(t);if(u){const p=En(s,e,n);Xt(o,e,p,!1),!r&&!ce(e)&&d.connect(l,n)}else{const p=vo(o,e,n);Zt(s,p,!1),!r&&!ce(e)&&d.disconnect(l,n)}};return le(a,[t,n],u=>u[0]===t&&u[1]===n,!0)?(c.add(i),oe(e)?Xt(o,e,[n,i],!0):Zt(s,[e,n,i],!0),!0):!1},Eo=(e,t,n,r)=>{const{activeInputs:o,passiveInputs:s}=z(t),a=_n(o[r],e,n);return a===null?[mn(s,e,n,r)[2],!1]:[a[2],!0]},Ao=(e,t,n)=>{const{activeInputs:r,passiveInputs:o}=Te(t),s=_n(r,e,n);return s===null?[En(o,e,n)[1],!1]:[s[2],!0]},Et=(e,t,n,r,o)=>{const[s,a]=Eo(e,n,r,o);if(s!==null&&(yn(e,s),a&&!t&&!ce(e)&&gt(X(e),X(n),r,o)),oe(n)){const{activeInputs:c}=z(n);ht(n,c)}},At=(e,t,n,r)=>{const[o,s]=Ao(e,n,r);o!==null&&(yn(e,o),s&&!t&&!ce(e)&&X(e).disconnect(Ee(n),r))},bo=(e,t)=>{const n=z(e),r=[];for(const o of n.outputs)Me(o)?Et(e,t,...o):At(e,t,...o),r.push(o[0]);return n.outputs.clear(),r},Co=(e,t,n)=>{const r=z(e),o=[];for(const s of r.outputs)s[1]===n&&(Me(s)?Et(e,t,...s):At(e,t,...s),o.push(s[0]),r.outputs.delete(s));return o},To=(e,t,n,r,o)=>{const s=z(e);return Array.from(s.outputs).filter(a=>a[0]===n&&(r===void 0||a[1]===r)&&(o===void 0||a[2]===o)).map(a=>(Me(a)?Et(e,t,...a):At(e,t,...a),s.outputs.delete(a),a[0]))},Mo=(e,t,n,r,o,s,a,c,i,u,d,l,p,m,w,f)=>class extends u{constructor(g,_,A,T){super(A),this._context=g,this._nativeAudioNode=A;const v=d(g);l(v)&&n(Kt,()=>Kt(v,f))!==!0&&_o(A),un.set(this,A),hn.set(this,new Set),g.state!=="closed"&&_&&ye(this),e(this,T,A)}get channelCount(){return this._nativeAudioNode.channelCount}set channelCount(g){this._nativeAudioNode.channelCount=g}get channelCountMode(){return this._nativeAudioNode.channelCountMode}set channelCountMode(g){this._nativeAudioNode.channelCountMode=g}get channelInterpretation(){return this._nativeAudioNode.channelInterpretation}set channelInterpretation(g){this._nativeAudioNode.channelInterpretation=g}get context(){return this._context}get numberOfInputs(){return this._nativeAudioNode.numberOfInputs}get numberOfOutputs(){return this._nativeAudioNode.numberOfOutputs}connect(g,_=0,A=0){if(_<0||_>=this._nativeAudioNode.numberOfOutputs)throw o();const T=d(this._context),v=w(T);if(p(g)||m(g))throw s();if(We(g)){const C=X(g);try{const I=mt(this._nativeAudioNode,C,_,A),M=Pe(this);(v||M)&&this._nativeAudioNode.disconnect(...I),this.context.state!=="closed"&&!M&&Pe(g)&&ye(g)}catch(I){throw I.code===12?s():I}if(t(this,g,_,A,v)){const I=i([this],g);ot(I,r(v))}return g}const E=Ee(g);if(E.name==="playbackRate"&&E.maxValue===1024)throw a();try{this._nativeAudioNode.connect(E,_),(v||Pe(this))&&this._nativeAudioNode.disconnect(E,_)}catch(C){throw C.code===12?s():C}if(yo(this,g,_,v)){const C=i([this],g);ot(C,r(v))}}disconnect(g,_,A){let T;const v=d(this._context),E=w(v);if(g===void 0)T=bo(this,E);else if(typeof g=="number"){if(g<0||g>=this.numberOfOutputs)throw o();T=Co(this,E,g)}else{if(_!==void 0&&(_<0||_>=this.numberOfOutputs)||We(g)&&A!==void 0&&(A<0||A>=g.numberOfInputs))throw o();if(T=To(this,E,g,_,A),T.length===0)throw s()}for(const y of T){const C=i([this],y);ot(C,c)}}},No=(e,t,n,r,o,s,a,c,i,u,d,l,p)=>(m,w,f,h=null,g=null)=>{const _=new Dr(f.defaultValue),A=w?r(_):null,T={get defaultValue(){return f.defaultValue},get maxValue(){return h===null?f.maxValue:h},get minValue(){return g===null?f.minValue:g},get value(){return f.value},set value(v){f.value=v,T.setValueAtTime(v,m.context.currentTime)},cancelAndHoldAtTime(v){if(typeof f.cancelAndHoldAtTime=="function")A===null&&_.flush(m.context.currentTime),_.add(o(v)),f.cancelAndHoldAtTime(v);else{const E=Array.from(_).pop();A===null&&_.flush(m.context.currentTime),_.add(o(v));const y=Array.from(_).pop();f.cancelScheduledValues(v),E!==y&&y!==void 0&&(y.type==="exponentialRampToValue"?f.exponentialRampToValueAtTime(y.value,y.endTime):y.type==="linearRampToValue"?f.linearRampToValueAtTime(y.value,y.endTime):y.type==="setValue"?f.setValueAtTime(y.value,y.startTime):y.type==="setValueCurve"&&f.setValueCurveAtTime(y.values,y.startTime,y.duration))}return T},cancelScheduledValues(v){return A===null&&_.flush(m.context.currentTime),_.add(s(v)),f.cancelScheduledValues(v),T},exponentialRampToValueAtTime(v,E){if(v===0)throw new RangeError;if(!Number.isFinite(E)||E<0)throw new RangeError;return A===null&&_.flush(m.context.currentTime),_.add(a(v,E)),f.exponentialRampToValueAtTime(v,E),T},linearRampToValueAtTime(v,E){return A===null&&_.flush(m.context.currentTime),_.add(c(v,E)),f.linearRampToValueAtTime(v,E),T},setTargetAtTime(v,E,y){return A===null&&_.flush(m.context.currentTime),_.add(i(v,E,y)),f.setTargetAtTime(v,E,y),T},setValueAtTime(v,E){return A===null&&_.flush(m.context.currentTime),_.add(u(v,E)),f.setValueAtTime(v,E),T},setValueCurveAtTime(v,E,y){const C=v instanceof Float32Array?v:new Float32Array(v);if(l!==null&&l.name==="webkitAudioContext"){const N=E+y,I=m.context.sampleRate,M=Math.ceil(E*I),x=Math.floor(N*I),k=x-M,B=new Float32Array(k);for(let R=0;R<k;R+=1){const P=(C.length-1)/y*((M+R)/I-E),D=Math.floor(P),S=Math.ceil(P);B[R]=D===S?C[D]:(1-(P-D))*C[D]+(1-(S-P))*C[S]}A===null&&_.flush(m.context.currentTime),_.add(d(B,E,y)),f.setValueCurveAtTime(B,E,y);const U=x/I;U<N&&p(T,B[B.length-1],U),p(T,C[C.length-1],N)}else A===null&&_.flush(m.context.currentTime),_.add(d(C,E,y)),f.setValueCurveAtTime(C,E,y);return T}};return n.set(T,f),t.set(T,m),e(T,A),T},So=e=>({replay(t){for(const n of e)if(n.type==="exponentialRampToValue"){const{endTime:r,value:o}=n;t.exponentialRampToValueAtTime(o,r)}else if(n.type==="linearRampToValue"){const{endTime:r,value:o}=n;t.linearRampToValueAtTime(o,r)}else if(n.type==="setTarget"){const{startTime:r,target:o,timeConstant:s}=n;t.setTargetAtTime(o,r,s)}else if(n.type==="setValue"){const{startTime:r,value:o}=n;t.setValueAtTime(o,r)}else if(n.type==="setValueCurve"){const{duration:r,startTime:o,values:s}=n;t.setValueCurveAtTime(s,o,r)}else throw new Error("Can't apply an unknown automation.")}});class An{constructor(t){this._map=new Map(t)}get size(){return this._map.size}entries(){return this._map.entries()}forEach(t,n=null){return this._map.forEach((r,o)=>t.call(n,r,o,this))}get(t){return this._map.get(t)}has(t){return this._map.has(t)}keys(){return this._map.keys()}values(){return this._map.values()}}const 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0?N:2;h=Promise.all([v(),m.then(()=>Ua(l,u,M,s,a))]).then(async([,{audioBufferSourceNode:k,encoderId:B,mediaStreamAudioSourceNode:U,port:R,recorderAudioWorkletNode:P}])=>{U.connect(P),await new Promise(S=>{k.onended=S,k.connect(P),k.start(u.currentTime+d/u.sampleRate)}),k.disconnect(P);const D=new Ea(u,{offset:0});return D.onended=()=>D.disconnect(),D.connect(u.destination),D.start(),await P.record(R),C!==void 0&&(g=T(B,C)),{constantSourceNode:D,encoderId:B,mediaStreamAudioSourceNode:U,recorderAudioWorkletNode:P}});const x=s.getTracks();w=()=>{E(),o.dispatchEvent(new ErrorEvent("error",{error:t()}))},s.addEventListener("addtrack",w),s.addEventListener("removetrack",w),f=setInterval(()=>{const k=s.getTracks();(k.length!==x.length||k.some((B,U)=>B!==x[U]))&&w!==null&&w()},1e3)},stop:E}};class st{constructor(t,n=0,r){if(n<0||r!==void 0&&r<0)throw new RangeError;const o=t.reduce((d,l)=>d+l.byteLength,0);if(n>o||r!==void 0&&n+r>o)throw new RangeError;const s=[],a=r===void 0?o-n:r,c=[];let i=0,u=n;for(const d of t)if(c.length===0)if(d.byteLength>u){i=d.byteLength-u;const l=i>a?a:i;s.push(new DataView(d,u,l)),c.push(d)}else u-=d.byteLength;else if(i<a){i+=d.byteLength;const l=i>a?d.byteLength-i+a:d.byteLength;s.push(new DataView(d,0,l)),c.push(d)}this._buffers=c,this._byteLength=a,this._byteOffset=u,this._dataViews=s,this._internalBuffer=new DataView(new ArrayBuffer(8))}get buffers(){return this._buffers}get byteLength(){return this._byteLength}get byteOffset(){return this._byteOffset}getFloat32(t,n){return this._internalBuffer.setUint8(0,this.getUint8(t+0)),this._internalBuffer.setUint8(1,this.getUint8(t+1)),this._internalBuffer.setUint8(2,this.getUint8(t+2)),this._internalBuffer.setUint8(3,this.getUint8(t+3)),this._internalBuffer.getFloat32(0,n)}getFloat64(t,n){return this._internalBuffer.setUint8(0,this.getUint8(t+0)),this._internalBuffer.setUint8(1,this.getUint8(t+1)),this._internalBuffer.setUint8(2,this.getUint8(t+2)),this._internalBuffer.setUint8(3,this.getUint8(t+3)),this._internalBuffer.setUint8(4,this.getUint8(t+4)),this._internalBuffer.setUint8(5,this.getUint8(t+5)),this._internalBuffer.setUint8(6,this.getUint8(t+6)),this._internalBuffer.setUint8(7,this.getUint8(t+7)),this._internalBuffer.getFloat64(0,n)}getInt16(t,n){return this._internalBuffer.setUint8(0,this.getUint8(t+0)),this._internalBuffer.setUint8(1,this.getUint8(t+1)),this._internalBuffer.getInt16(0,n)}getInt32(t,n){return this._internalBuffer.setUint8(0,this.getUint8(t+0)),this._internalBuffer.setUint8(1,this.getUint8(t+1)),this._internalBuffer.setUint8(2,this.getUint8(t+2)),this._internalBuffer.setUint8(3,this.getUint8(t+3)),this._internalBuffer.getInt32(0,n)}getInt8(t){const[n,r]=this._findDataViewWithOffset(t);return n.getInt8(t-r)}getUint16(t,n){return this._internalBuffer.setUint8(0,this.getUint8(t+0)),this._internalBuffer.setUint8(1,this.getUint8(t+1)),this._internalBuffer.getUint16(0,n)}getUint32(t,n){return this._internalBuffer.setUint8(0,this.getUint8(t+0)),this._internalBuffer.setUint8(1,this.getUint8(t+1)),this._internalBuffer.setUint8(2,this.getUint8(t+2)),this._internalBuffer.setUint8(3,this.getUint8(t+3)),this._internalBuffer.getUint32(0,n)}getUint8(t){const[n,r]=this._findDataViewWithOffset(t);return n.getUint8(t-r)}setFloat32(t,n,r){this._internalBuffer.setFloat32(0,n,r),this.setUint8(t,this._internalBuffer.getUint8(0)),this.setUint8(t+1,this._internalBuffer.getUint8(1)),this.setUint8(t+2,this._internalBuffer.getUint8(2)),this.setUint8(t+3,this._internalBuffer.getUint8(3))}setFloat64(t,n,r){this._internalBuffer.setFloat64(0,n,r),this.setUint8(t,this._internalBuffer.getUint8(0)),this.setUint8(t+1,this._internalBuffer.getUint8(1)),this.setUint8(t+2,this._internalBuffer.getUint8(2)),this.setUint8(t+3,this._internalBuffer.getUint8(3)),this.setUint8(t+4,this._internalBuffer.getUint8(4)),this.setUint8(t+5,this._internalBuffer.getUint8(5)),this.setUint8(t+6,this._internalBuffer.getUint8(6)),this.setUint8(t+7,this._internalBuffer.getUint8(7))}setInt16(t,n,r){this._internalBuffer.setInt16(0,n,r),this.setUint8(t,this._internalBuffer.getUint8(0)),this.setUint8(t+1,this._internalBuffer.getUint8(1))}setInt32(t,n,r){this._internalBuffer.setInt32(0,n,r),this.setUint8(t,this._internalBuffer.getUint8(0)),this.setUint8(t+1,this._internalBuffer.getUint8(1)),this.setUint8(t+2,this._internalBuffer.getUint8(2)),this.setUint8(t+3,this._internalBuffer.getUint8(3))}setInt8(t,n){const[r,o]=this._findDataViewWithOffset(t);r.setInt8(t-o,n)}setUint16(t,n,r){this._internalBuffer.setUint16(0,n,r),this.setUint8(t,this._internalBuffer.getUint8(0)),this.setUint8(t+1,this._internalBuffer.getUint8(1))}setUint32(t,n,r){this._internalBuffer.setUint32(0,n,r),this.setUint8(t,this._internalBuffer.getUint8(0)),this.setUint8(t+1,this._internalBuffer.getUint8(1)),this.setUint8(t+2,this._internalBuffer.getUint8(2)),this.setUint8(t+3,this._internalBuffer.getUint8(3))}setUint8(t,n){const[r,o]=this._findDataViewWithOffset(t);r.setUint8(t-o,n)}_findDataViewWithOffset(t){let n=0;for(const r of this._dataViews){const o=n+r.byteLength;if(t>=n&&t<o)return[r,n];n=o}throw new RangeError}}const Da=(e,t,n,r,o)=>(s,a,c,i)=>{const u=c.getAudioTracks(),d=[],l=u.length===0?void 0:u[0].getSettings().channelCount,p=new a(c,{mimeType:"audio/webm;codecs=pcm"}),m=u.length===0?void 0:u[0].getSettings().sampleRate;let w=null,f=()=>{};const h=A=>{s.dispatchEvent(e("dataavailable",{data:new Blob(A,{type:i})}))},g=async(A,T)=>{const v=await Ue(A,T);p.state==="inactive"?d.push(...v):(h(v),w=g(A,T))},_=()=>{p.state!=="inactive"&&(w!==null&&(w.catch(()=>{}),w=null),f(),f=()=>{},p.stop())};return p.addEventListener("error",()=>{_(),s.dispatchEvent(new ErrorEvent("error",{error:t()}))}),p.addEventListener("start",()=>s.dispatchEvent(new Event("start"))),{get mimeType(){return i},get state(){return p.state},pause(){return p.pause()},resume(){return p.resume()},start(A){if(c.getVideoTracks().length>0)throw n();if(p.state==="inactive"){if(m===void 0)throw new Error("The sampleRate is not defined.");let T=!1,v=!1,E=0,y=rn(i,m);f=()=>{v=!0};const C=on(p,"dataavailable")(({data:N})=>{E+=1,y=y.then(async({dataView:I=null,elementType:M=null,encoderId:x,port:k})=>{const B=await N.arrayBuffer();E-=1;const U=I===null?new st([B]):new st([...I.buffers,B],I.byteOffset);if(!T&&p.state==="recording"&&!v){const L=o(U,0);if(L===null)return{dataView:U,elementType:M,encoderId:x,port:k};const{value:W}=L;if(W!==172351395)return{dataView:I,elementType:M,encoderId:x,port:k};T=!0}const{currentElementType:R,offset:P,contents:D}=r(U,M,l),S=P<U.byteLength?new st(U.buffers,U.byteOffset+P):null;return D.forEach(L=>k.postMessage(L,L.map(({buffer:W})=>W))),E===0&&(p.state==="inactive"||v)&&(Ue(x,null).then(L=>{h([...d,...L]),d.length=0,s.dispatchEvent(new Event("stop"))}),k.postMessage([]),k.close(),C()),{dataView:S,elementType:R,encoderId:x,port:k}})});A!==void 0&&y.then(({encoderId:N})=>w=g(N,A))}p.start(100)},stop:_}},Wa=()=>typeof window>"u"?null:window,qn=(e,t)=>{if(t>=e.byteLength)return null;const n=e.getUint8(t);if(n>127)return 1;if(n>63)return 2;if(n>31)return 3;if(n>15)return 4;if(n>7)return 5;if(n>3)return 6;if(n>1)return 7;if(n>0)return 8;const r=qn(e,t+1);return r===null?null:r+8},Va=(e,t)=>n=>{const r={value:e};return Object.defineProperties(n,{currentTarget:r,target:r}),typeof t=="function"?t.call(e,n):t.handleEvent.call(e,n)},zn=[],et=Wa(),Fa=hr(et),Hn=ar(Fa),ja=Ba(Hn,vt,lr,$e),It=vr(qn),$a=gr(It),Ga=wr(It),qa=ir($a,Ga),za=Da(Hn,vt,$e,qa,It),Ha=ur(et),Ya=mr(et),Xa=pr(vt,$e),ui=fr(Xa,$e,ja,za,zn,cr(Ha,Va),Ya),li=()=>dr(et),di=async e=>{zn.push(await sr(e))};export{ui as MediaRecorder,li as isSupported,di as register};
-//# sourceMappingURL=module-fcbc0651.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio_client/documentation.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio_client/documentation.py
deleted file mode 100644
index 4d8d41ddf85f5a860956dad6256af90fe6c7f483..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio_client/documentation.py
+++ /dev/null
@@ -1,266 +0,0 @@
-"""Contains methods that generate documentation for Gradio functions and classes."""
-
-from __future__ import annotations
-
-import inspect
-from typing import Callable
-
-classes_to_document = {}
-classes_inherit_documentation = {}
-documentation_group = None
-
-
-def set_documentation_group(m):
-    global documentation_group
-    documentation_group = m
-    if m not in classes_to_document:
-        classes_to_document[m] = []
-
-
-def extract_instance_attr_doc(cls, attr):
-    code = inspect.getsource(cls.__init__)
-    lines = [line.strip() for line in code.split("\n")]
-    i = None
-    for i, line in enumerate(lines):  # noqa: B007
-        if line.startswith("self." + attr + ":") or line.startswith(
-            "self." + attr + " ="
-        ):
-            break
-    assert i is not None, f"Could not find {attr} in {cls.__name__}"
-    start_line = lines.index('"""', i)
-    end_line = lines.index('"""', start_line + 1)
-    for j in range(i + 1, start_line):
-        assert not lines[j].startswith("self."), (
-            f"Found another attribute before docstring for {attr} in {cls.__name__}: "
-            + lines[j]
-            + "\n start:"
-            + lines[i]
-        )
-    doc_string = " ".join(lines[start_line + 1 : end_line])
-    return doc_string
-
-
-def document(*fns, inherit=False):
-    """
-    Defines the @document decorator which adds classes or functions to the Gradio
-    documentation at www.gradio.app/docs.
-
-    Usage examples:
-    - Put @document() above a class to document the class and its constructor.
-    - Put @document("fn1", "fn2") above a class to also document methods fn1 and fn2.
-    - Put @document("*fn3") with an asterisk above a class to document the instance attribute methods f3.
-    """
-
-    def inner_doc(cls):
-        global documentation_group
-        if inherit:
-            classes_inherit_documentation[cls] = None
-        classes_to_document[documentation_group].append((cls, fns))
-        return cls
-
-    return inner_doc
-
-
-def document_fn(fn: Callable, cls) -> tuple[str, list[dict], dict, str | None]:
-    """
-    Generates documentation for any function.
-    Parameters:
-        fn: Function to document
-    Returns:
-        description: General description of fn
-        parameters: A list of dicts for each parameter, storing data for the parameter name, annotation and doc
-        return: A dict storing data for the returned annotation and doc
-        example: Code for an example use of the fn
-    """
-    doc_str = inspect.getdoc(fn) or ""
-    doc_lines = doc_str.split("\n")
-    signature = inspect.signature(fn)
-    description, parameters, returns, examples = [], {}, [], []
-    mode = "description"
-    for line in doc_lines:
-        line = line.rstrip()
-        if line == "Parameters:":
-            mode = "parameter"
-        elif line.startswith("Example:"):
-            mode = "example"
-            if "(" in line and ")" in line:
-                c = line.split("(")[1].split(")")[0]
-                if c != cls.__name__:
-                    mode = "ignore"
-        elif line == "Returns:":
-            mode = "return"
-        else:
-            if mode == "description":
-                description.append(line if line.strip() else "<br>")
-                continue
-            if not (line.startswith("    ") or line.strip() == ""):
-                print(line)
-            assert (
-                line.startswith("    ") or line.strip() == ""
-            ), f"Documentation format for {fn.__name__} has format error in line: {line}"
-            line = line[4:]
-            if mode == "parameter":
-                colon_index = line.index(": ")
-                assert (
-                    colon_index > -1
-                ), f"Documentation format for {fn.__name__} has format error in line: {line}"
-                parameter = line[:colon_index]
-                parameter_doc = line[colon_index + 2 :]
-                parameters[parameter] = parameter_doc
-            elif mode == "return":
-                returns.append(line)
-            elif mode == "example":
-                examples.append(line)
-    description_doc = " ".join(description)
-    parameter_docs = []
-    for param_name, param in signature.parameters.items():
-        if param_name.startswith("_"):
-            continue
-        if param_name in ["kwargs", "args"] and param_name not in parameters:
-            continue
-        parameter_doc = {
-            "name": param_name,
-            "annotation": param.annotation,
-            "doc": parameters.get(param_name),
-        }
-        if param_name in parameters:
-            del parameters[param_name]
-        if param.default != inspect.Parameter.empty:
-            default = param.default
-            if type(default) == str:
-                default = '"' + default + '"'
-            if default.__class__.__module__ != "builtins":
-                default = f"{default.__class__.__name__}()"
-            parameter_doc["default"] = default
-        elif parameter_doc["doc"] is not None:
-            if "kwargs" in parameter_doc["doc"]:
-                parameter_doc["kwargs"] = True
-            if "args" in parameter_doc["doc"]:
-                parameter_doc["args"] = True
-        parameter_docs.append(parameter_doc)
-    assert (
-        len(parameters) == 0
-    ), f"Documentation format for {fn.__name__} documents nonexistent parameters: {''.join(parameters.keys())}"
-    if len(returns) == 0:
-        return_docs = {}
-    elif len(returns) == 1:
-        return_docs = {"annotation": signature.return_annotation, "doc": returns[0]}
-    else:
-        return_docs = {}
-        # raise ValueError("Does not support multiple returns yet.")
-    examples_doc = "\n".join(examples) if len(examples) > 0 else None
-    return description_doc, parameter_docs, return_docs, examples_doc
-
-
-def document_cls(cls):
-    doc_str = inspect.getdoc(cls)
-    if doc_str is None:
-        return "", {}, ""
-    tags = {}
-    description_lines = []
-    mode = "description"
-    for line in doc_str.split("\n"):
-        line = line.rstrip()
-        if line.endswith(":") and " " not in line:
-            mode = line[:-1].lower()
-            tags[mode] = []
-        elif line.split(" ")[0].endswith(":") and not line.startswith("    "):
-            tag = line[: line.index(":")].lower()
-            value = line[line.index(":") + 2 :]
-            tags[tag] = value
-        else:
-            if mode == "description":
-                description_lines.append(line if line.strip() else "<br>")
-            else:
-                assert (
-                    line.startswith("    ") or not line.strip()
-                ), f"Documentation format for {cls.__name__} has format error in line: {line}"
-                tags[mode].append(line[4:])
-    if "example" in tags:
-        example = "\n".join(tags["example"])
-        del tags["example"]
-    else:
-        example = None
-    for key, val in tags.items():
-        if isinstance(val, list):
-            tags[key] = "<br>".join(val)
-    description = " ".join(description_lines).replace("\n", "<br>")
-    return description, tags, example
-
-
-def generate_documentation():
-    documentation = {}
-    for mode, class_list in classes_to_document.items():
-        documentation[mode] = []
-        for cls, fns in class_list:
-            fn_to_document = cls if inspect.isfunction(cls) else cls.__init__
-            _, parameter_doc, return_doc, _ = document_fn(fn_to_document, cls)
-            cls_description, cls_tags, cls_example = document_cls(cls)
-            cls_documentation = {
-                "class": cls,
-                "name": cls.__name__,
-                "description": cls_description,
-                "tags": cls_tags,
-                "parameters": parameter_doc,
-                "returns": return_doc,
-                "example": cls_example,
-                "fns": [],
-            }
-            for fn_name in fns:
-                instance_attribute_fn = fn_name.startswith("*")
-                if instance_attribute_fn:
-                    fn_name = fn_name[1:]
-                    # Instance attribute fns are classes
-                    # whose __call__ method determines their behavior
-                    fn = getattr(cls(), fn_name).__call__
-                else:
-                    fn = getattr(cls, fn_name)
-                if not callable(fn):
-                    description_doc = str(fn)
-                    parameter_docs = {}
-                    return_docs = {}
-                    examples_doc = ""
-                    override_signature = f"gr.{cls.__name__}.{fn_name}"
-                else:
-                    (
-                        description_doc,
-                        parameter_docs,
-                        return_docs,
-                        examples_doc,
-                    ) = document_fn(fn, cls)
-                    override_signature = None
-                if instance_attribute_fn:
-                    description_doc = extract_instance_attr_doc(cls, fn_name)
-                cls_documentation["fns"].append(
-                    {
-                        "fn": fn,
-                        "name": fn_name,
-                        "description": description_doc,
-                        "tags": {},
-                        "parameters": parameter_docs,
-                        "returns": return_docs,
-                        "example": examples_doc,
-                        "override_signature": override_signature,
-                    }
-                )
-            documentation[mode].append(cls_documentation)
-            if cls in classes_inherit_documentation:
-                classes_inherit_documentation[cls] = cls_documentation["fns"]
-    for mode, class_list in classes_to_document.items():
-        for i, (cls, _) in enumerate(class_list):
-            for super_class in classes_inherit_documentation:
-                if (
-                    inspect.isclass(cls)
-                    and issubclass(cls, super_class)
-                    and cls != super_class
-                ):
-                    for inherited_fn in classes_inherit_documentation[super_class]:
-                        inherited_fn = dict(inherited_fn)
-                        try:
-                            inherited_fn["description"] = extract_instance_attr_doc(
-                                cls, inherited_fn["name"]
-                            )
-                        except (ValueError, AssertionError):
-                            pass
-                        documentation[mode][i]["fns"].append(inherited_fn)
-    return documentation
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/endpoint_helpers.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/endpoint_helpers.py
deleted file mode 100644
index decc0f03d9c0480e297b2fa48ca8f96d95ccaab5..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/endpoint_helpers.py
+++ /dev/null
@@ -1,371 +0,0 @@
-# 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.
-"""
-Helpful utility functions and classes in relation to exploring API endpoints
-with the aim for a user-friendly interface.
-"""
-import math
-import re
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Iterable, List, Optional, Union
-
-
-if TYPE_CHECKING:
-    from ..hf_api import ModelInfo
-
-
-def _filter_emissions(
-    models: Iterable["ModelInfo"],
-    minimum_threshold: Optional[float] = None,
-    maximum_threshold: Optional[float] = None,
-) -> Iterable["ModelInfo"]:
-    """Filters a list of models for those that include an emission tag and limit them to between two thresholds
-
-    Args:
-        models (Iterable of `ModelInfo`):
-            A list of models to filter.
-        minimum_threshold (`float`, *optional*):
-            A minimum carbon threshold to filter by, such as 1.
-        maximum_threshold (`float`, *optional*):
-            A maximum carbon threshold to filter by, such as 10.
-    """
-    if minimum_threshold is None and maximum_threshold is None:
-        raise ValueError("Both `minimum_threshold` and `maximum_threshold` cannot both be `None`")
-    if minimum_threshold is None:
-        minimum_threshold = -1
-    if maximum_threshold is None:
-        maximum_threshold = math.inf
-
-    for model in models:
-        card_data = getattr(model, "cardData", None)
-        if card_data is None or not isinstance(card_data, dict):
-            continue
-
-        # Get CO2 emission metadata
-        emission = card_data.get("co2_eq_emissions", None)
-        if isinstance(emission, dict):
-            emission = emission["emissions"]
-        if not emission:
-            continue
-
-        # Filter out if value is missing or out of range
-        matched = re.search(r"\d+\.\d+|\d+", str(emission))
-        if matched is None:
-            continue
-
-        emission_value = float(matched.group(0))
-        if emission_value >= minimum_threshold and emission_value <= maximum_threshold:
-            yield model
-
-
-@dataclass
-class DatasetFilter:
-    """
-    A class that converts human-readable dataset search parameters into ones
-    compatible with the REST API. For all parameters capitalization does not
-    matter.
-
-    Args:
-        author (`str`, *optional*):
-            A string or list of strings that can be used to identify datasets on
-            the Hub by the original uploader (author or organization), such as
-            `facebook` or `huggingface`.
-        benchmark (`str` or `List`, *optional*):
-            A string or list of strings that can be used to identify datasets on
-            the Hub by their official benchmark.
-        dataset_name (`str`, *optional*):
-            A string or list of strings that can be used to identify datasets on
-            the Hub by its name, such as `SQAC` or `wikineural`
-        language_creators (`str` or `List`, *optional*):
-            A string or list of strings that can be used to identify datasets on
-            the Hub with how the data was curated, such as `crowdsourced` or
-            `machine_generated`.
-        language (`str` or `List`, *optional*):
-            A string or list of strings representing a two-character language to
-            filter datasets by on the Hub.
-        multilinguality (`str` or `List`, *optional*):
-            A string or list of strings representing a filter for datasets that
-            contain multiple languages.
-        size_categories (`str` or `List`, *optional*):
-            A string or list of strings that can be used to identify datasets on
-            the Hub by the size of the dataset such as `100K<n<1M` or
-            `1M<n<10M`.
-        task_categories (`str` or `List`, *optional*):
-            A string or list of strings that can be used to identify datasets on
-            the Hub by the designed task, such as `audio_classification` or
-            `named_entity_recognition`.
-        task_ids (`str` or `List`, *optional*):
-            A string or list of strings that can be used to identify datasets on
-            the Hub by the specific task such as `speech_emotion_recognition` or
-            `paraphrase`.
-
-    Examples:
-
-    ```py
-    >>> from huggingface_hub import DatasetFilter
-
-    >>> # Using author
-    >>> new_filter = DatasetFilter(author="facebook")
-
-    >>> # Using benchmark
-    >>> new_filter = DatasetFilter(benchmark="raft")
-
-    >>> # Using dataset_name
-    >>> new_filter = DatasetFilter(dataset_name="wikineural")
-
-    >>> # Using language_creator
-    >>> new_filter = DatasetFilter(language_creator="crowdsourced")
-
-    >>> # Using language
-    >>> new_filter = DatasetFilter(language="en")
-
-    >>> # Using multilinguality
-    >>> new_filter = DatasetFilter(multilinguality="multilingual")
-
-    >>> # Using size_categories
-    >>> new_filter = DatasetFilter(size_categories="100K<n<1M")
-
-    >>> # Using task_categories
-    >>> new_filter = DatasetFilter(task_categories="audio_classification")
-
-    >>> # Using task_ids
-    >>> new_filter = DatasetFilter(task_ids="paraphrase")
-    ```
-    """
-
-    author: Optional[str] = None
-    benchmark: Optional[Union[str, List[str]]] = None
-    dataset_name: Optional[str] = None
-    language_creators: Optional[Union[str, List[str]]] = None
-    language: Optional[Union[str, List[str]]] = None
-    multilinguality: Optional[Union[str, List[str]]] = None
-    size_categories: Optional[Union[str, List[str]]] = None
-    task_categories: Optional[Union[str, List[str]]] = None
-    task_ids: Optional[Union[str, List[str]]] = None
-
-
-@dataclass
-class ModelFilter:
-    """
-    A class that converts human-readable model search parameters into ones
-    compatible with the REST API. For all parameters capitalization does not
-    matter.
-
-    Args:
-        author (`str`, *optional*):
-            A string that can be used to identify models on the Hub by the
-            original uploader (author or organization), such as `facebook` or
-            `huggingface`.
-        library (`str` or `List`, *optional*):
-            A string or list of strings of foundational libraries models were
-            originally trained from, such as pytorch, tensorflow, or allennlp.
-        language (`str` or `List`, *optional*):
-            A string or list of strings of languages, both by name and country
-            code, such as "en" or "English"
-        model_name (`str`, *optional*):
-            A string that contain complete or partial names for models on the
-            Hub, such as "bert" or "bert-base-cased"
-        task (`str` or `List`, *optional*):
-            A string or list of strings of tasks models were designed for, such
-            as: "fill-mask" or "automatic-speech-recognition"
-        tags (`str` or `List`, *optional*):
-            A string tag or a list of tags to filter models on the Hub by, such
-            as `text-generation` or `spacy`.
-        trained_dataset (`str` or `List`, *optional*):
-            A string tag or a list of string tags of the trained dataset for a
-            model on the Hub.
-
-
-    ```python
-    >>> from huggingface_hub import ModelFilter
-
-    >>> # For the author_or_organization
-    >>> new_filter = ModelFilter(author_or_organization="facebook")
-
-    >>> # For the library
-    >>> new_filter = ModelFilter(library="pytorch")
-
-    >>> # For the language
-    >>> new_filter = ModelFilter(language="french")
-
-    >>> # For the model_name
-    >>> new_filter = ModelFilter(model_name="bert")
-
-    >>> # For the task
-    >>> new_filter = ModelFilter(task="text-classification")
-
-    >>> # Retrieving tags using the `HfApi.get_model_tags` method
-    >>> from huggingface_hub import HfApi
-
-    >>> api = HfApi()
-    # To list model tags
-
-    >>> api.get_model_tags()
-    # To list dataset tags
-
-    >>> api.get_dataset_tags()
-    >>> new_filter = ModelFilter(tags="benchmark:raft")
-
-    >>> # Related to the dataset
-    >>> new_filter = ModelFilter(trained_dataset="common_voice")
-    ```
-    """
-
-    author: Optional[str] = None
-    library: Optional[Union[str, List[str]]] = None
-    language: Optional[Union[str, List[str]]] = None
-    model_name: Optional[str] = None
-    task: Optional[Union[str, List[str]]] = None
-    trained_dataset: Optional[Union[str, List[str]]] = None
-    tags: Optional[Union[str, List[str]]] = None
-
-
-class AttributeDictionary(dict):
-    """
-    `dict` subclass that also provides access to keys as attributes
-
-    If a key starts with a number, it will exist in the dictionary but not as an
-    attribute
-
-    Example:
-
-    ```python
-    >>> d = AttributeDictionary()
-    >>> d["test"] = "a"
-    >>> print(d.test)  # prints "a"
-    ```
-
-    """
-
-    def __getattr__(self, k):
-        if k in self:
-            return self[k]
-        else:
-            raise AttributeError(k)
-
-    def __setattr__(self, k, v):
-        (self.__setitem__, super().__setattr__)[k[0] == "_"](k, v)
-
-    def __delattr__(self, k):
-        if k in self:
-            del self[k]
-        else:
-            raise AttributeError(k)
-
-    def __dir__(self):
-        keys = sorted(self.keys())
-        keys = [key for key in keys if key.replace("_", "").isalpha()]
-        return super().__dir__() + keys
-
-    def __repr__(self):
-        repr_str = "Available Attributes or Keys:\n"
-        for key in sorted(self.keys()):
-            repr_str += f" * {key}"
-            if not key.replace("_", "").isalpha():
-                repr_str += " (Key only)"
-            repr_str += "\n"
-        return repr_str
-
-
-class GeneralTags(AttributeDictionary):
-    """
-    A namespace object holding all tags, filtered by `keys` If a tag starts with
-    a number, it will only exist in the dictionary
-
-    Example:
-    ```python
-    >>> a.b["1a"]  # will work
-    >>> a["b"]["1a"]  # will work
-    >>> # a.b.1a # will not work
-    ```
-
-    Args:
-        tag_dictionary (`dict`):
-            A dictionary of tags returned from the /api/***-tags-by-type api
-            endpoint
-        keys (`list`):
-            A list of keys to unpack the `tag_dictionary` with, such as
-            `["library","language"]`
-    """
-
-    def __init__(self, tag_dictionary: dict, keys: Optional[list] = None):
-        self._tag_dictionary = tag_dictionary
-        if keys is None:
-            keys = list(self._tag_dictionary.keys())
-        for key in keys:
-            self._unpack_and_assign_dictionary(key)
-
-    def _unpack_and_assign_dictionary(self, key: str):
-        "Assign nested attributes to `self.key` containing information as an `AttributeDictionary`"
-        ref = AttributeDictionary()
-        setattr(self, key, ref)
-        for item in self._tag_dictionary.get(key, []):
-            label = item["label"].replace(" ", "").replace("-", "_").replace(".", "_")
-            ref[label] = item["id"]
-        self[key] = ref
-
-
-class ModelTags(GeneralTags):
-    """
-    A namespace object holding all available model tags If a tag starts with a
-    number, it will only exist in the dictionary
-
-    Example:
-
-    ```python
-    >>> a.dataset["1_5BArabicCorpus"]  # will work
-    >>> a["dataset"]["1_5BArabicCorpus"]  # will work
-    >>> # o.dataset.1_5BArabicCorpus # will not work
-    ```
-
-    Args:
-        model_tag_dictionary (`dict`):
-            A dictionary of valid model tags, returned from the
-            /api/models-tags-by-type api endpoint
-    """
-
-    def __init__(self, model_tag_dictionary: dict):
-        keys = ["library", "language", "license", "dataset", "pipeline_tag"]
-        super().__init__(model_tag_dictionary, keys)
-
-
-class DatasetTags(GeneralTags):
-    """
-    A namespace object holding all available dataset tags If a tag starts with a
-    number, it will only exist in the dictionary
-
-    Example
-
-    ```python
-    >>> a.size_categories["100K<n<1M"]  # will work
-    >>> a["size_categories"]["100K<n<1M"]  # will work
-    >>> # o.size_categories.100K<n<1M # will not work
-    ```
-
-    Args:
-        dataset_tag_dictionary (`dict`):
-            A dictionary of valid dataset tags, returned from the
-            /api/datasets-tags-by-type api endpoint
-    """
-
-    def __init__(self, dataset_tag_dictionary: dict):
-        keys = [
-            "language",
-            "multilinguality",
-            "language_creators",
-            "task_categories",
-            "size_categories",
-            "benchmark",
-            "task_ids",
-            "license",
-        ]
-        super().__init__(dataset_tag_dictionary, keys)
diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_deblur.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_deblur.py
deleted file mode 100644
index 8ab5852d0cb334627abcd9476409d632740be389..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_deblur.py
+++ /dev/null
@@ -1,655 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import scipy
-from scipy import fftpack
-import torch
-
-from math import cos, sin
-from numpy import zeros, ones, prod, array, pi, log, min, mod, arange, sum, mgrid, exp, pad, round
-from numpy.random import randn, rand
-from scipy.signal import convolve2d
-import cv2
-import random
-# import utils_image as util
-
-'''
-modified by Kai Zhang (github: https://github.com/cszn)
-03/03/2019
-'''
-
-
-def get_uperleft_denominator(img, kernel):
-    '''
-    img: HxWxC
-    kernel: hxw
-    denominator: HxWx1
-    upperleft: HxWxC
-    '''
-    V = psf2otf(kernel, img.shape[:2])
-    denominator = np.expand_dims(np.abs(V)**2, axis=2)
-    upperleft = np.expand_dims(np.conj(V), axis=2) * np.fft.fft2(img, axes=[0, 1])
-    return upperleft, denominator
-
-
-def get_uperleft_denominator_pytorch(img, kernel):
-    '''
-    img: NxCxHxW
-    kernel: Nx1xhxw
-    denominator: Nx1xHxW
-    upperleft: NxCxHxWx2
-    '''
-    V = p2o(kernel, img.shape[-2:])  # Nx1xHxWx2
-    denominator = V[..., 0]**2+V[..., 1]**2  # Nx1xHxW
-    upperleft = cmul(cconj(V), rfft(img))  # Nx1xHxWx2 * NxCxHxWx2
-    return upperleft, denominator
-
-
-def c2c(x):
-    return torch.from_numpy(np.stack([np.float32(x.real), np.float32(x.imag)], axis=-1))
-
-
-def r2c(x):
-    return torch.stack([x, torch.zeros_like(x)], -1)
-
-
-def cdiv(x, y):
-    a, b = x[..., 0], x[..., 1]
-    c, d = y[..., 0], y[..., 1]
-    cd2 = c**2 + d**2
-    return torch.stack([(a*c+b*d)/cd2, (b*c-a*d)/cd2], -1)
-
-
-def cabs(x):
-    return torch.pow(x[..., 0]**2+x[..., 1]**2, 0.5)
-
-
-def cmul(t1, t2):
-    '''
-    complex multiplication
-    t1: NxCxHxWx2
-    output: NxCxHxWx2
-    '''
-    real1, imag1 = t1[..., 0], t1[..., 1]
-    real2, imag2 = t2[..., 0], t2[..., 1]
-    return torch.stack([real1 * real2 - imag1 * imag2, real1 * imag2 + imag1 * real2], dim=-1)
-
-
-def cconj(t, inplace=False):
-    '''
-    # complex's conjugation
-    t: NxCxHxWx2
-    output: NxCxHxWx2
-    '''
-    c = t.clone() if not inplace else t
-    c[..., 1] *= -1
-    return c
-
-
-def rfft(t):
-    return torch.rfft(t, 2, onesided=False)
-
-
-def irfft(t):
-    return torch.irfft(t, 2, onesided=False)
-
-
-def fft(t):
-    return torch.fft(t, 2)
-
-
-def ifft(t):
-    return torch.ifft(t, 2)
-
-
-def p2o(psf, shape):
-    '''
-    # psf: NxCxhxw
-    # shape: [H,W]
-    # otf: NxCxHxWx2
-    '''
-    otf = torch.zeros(psf.shape[:-2] + shape).type_as(psf)
-    otf[...,:psf.shape[2],:psf.shape[3]].copy_(psf)
-    for axis, axis_size in enumerate(psf.shape[2:]):
-        otf = torch.roll(otf, -int(axis_size / 2), dims=axis+2)
-    otf = torch.rfft(otf, 2, onesided=False)
-    n_ops = torch.sum(torch.tensor(psf.shape).type_as(psf) * torch.log2(torch.tensor(psf.shape).type_as(psf)))
-    otf[...,1][torch.abs(otf[...,1])<n_ops*2.22e-16] = torch.tensor(0).type_as(psf)
-    return otf
-
-
-
-# otf2psf: not sure where I got this one from. Maybe translated from Octave source code or whatever. It's just math.
-def otf2psf(otf, outsize=None):
-    insize = np.array(otf.shape)
-    psf = np.fft.ifftn(otf, axes=(0, 1))
-    for axis, axis_size in enumerate(insize):
-        psf = np.roll(psf, np.floor(axis_size / 2).astype(int), axis=axis)
-    if type(outsize) != type(None):
-        insize = np.array(otf.shape)
-        outsize = np.array(outsize)
-        n = max(np.size(outsize), np.size(insize))
-        # outsize = postpad(outsize(:), n, 1);
-        # insize = postpad(insize(:) , n, 1);
-        colvec_out = outsize.flatten().reshape((np.size(outsize), 1))
-        colvec_in = insize.flatten().reshape((np.size(insize), 1))
-        outsize = np.pad(colvec_out, ((0, max(0, n - np.size(colvec_out))), (0, 0)), mode="constant")
-        insize = np.pad(colvec_in, ((0, max(0, n - np.size(colvec_in))), (0, 0)), mode="constant")
-
-        pad = (insize - outsize) / 2
-        if np.any(pad < 0):
-            print("otf2psf error: OUTSIZE must be smaller than or equal than OTF size")
-        prepad = np.floor(pad)
-        postpad = np.ceil(pad)
-        dims_start = prepad.astype(int)
-        dims_end = (insize - postpad).astype(int)
-        for i in range(len(dims_start.shape)):
-            psf = np.take(psf, range(dims_start[i][0], dims_end[i][0]), axis=i)
-    n_ops = np.sum(otf.size * np.log2(otf.shape))
-    psf = np.real_if_close(psf, tol=n_ops)
-    return psf
-
-
-# psf2otf copied/modified from https://github.com/aboucaud/pypher/blob/master/pypher/pypher.py
-def psf2otf(psf, shape=None):
-    """
-    Convert point-spread function to optical transfer function.
-    Compute the Fast Fourier Transform (FFT) of the point-spread
-    function (PSF) array and creates the optical transfer function (OTF)
-    array that is not influenced by the PSF off-centering.
-    By default, the OTF array is the same size as the PSF array.
-    To ensure that the OTF is not altered due to PSF off-centering, PSF2OTF
-    post-pads the PSF array (down or to the right) with zeros to match
-    dimensions specified in OUTSIZE, then circularly shifts the values of
-    the PSF array up (or to the left) until the central pixel reaches (1,1)
-    position.
-    Parameters
-    ----------
-    psf : `numpy.ndarray`
-        PSF array
-    shape : int
-        Output shape of the OTF array
-    Returns
-    -------
-    otf : `numpy.ndarray`
-        OTF array
-    Notes
-    -----
-    Adapted from MATLAB psf2otf function
-    """
-    if type(shape) == type(None):
-        shape = psf.shape
-    shape = np.array(shape)
-    if np.all(psf == 0):
-        # return np.zeros_like(psf)
-        return np.zeros(shape)
-    if len(psf.shape) == 1:
-        psf = psf.reshape((1, psf.shape[0]))
-    inshape = psf.shape
-    psf = zero_pad(psf, shape, position='corner')
-    for axis, axis_size in enumerate(inshape):
-        psf = np.roll(psf, -int(axis_size / 2), axis=axis)
-    # Compute the OTF
-    otf = np.fft.fft2(psf, axes=(0, 1))
-    # Estimate the rough number of operations involved in the FFT
-    # and discard the PSF imaginary part if within roundoff error
-    # roundoff error  = machine epsilon = sys.float_info.epsilon
-    # or np.finfo().eps
-    n_ops = np.sum(psf.size * np.log2(psf.shape))
-    otf = np.real_if_close(otf, tol=n_ops)
-    return otf
-
-
-def zero_pad(image, shape, position='corner'):
-    """
-    Extends image to a certain size with zeros
-    Parameters
-    ----------
-    image: real 2d `numpy.ndarray`
-        Input image
-    shape: tuple of int
-        Desired output shape of the image
-    position : str, optional
-        The position of the input image in the output one:
-            * 'corner'
-                top-left corner (default)
-            * 'center'
-                centered
-    Returns
-    -------
-    padded_img: real `numpy.ndarray`
-        The zero-padded image
-    """
-    shape = np.asarray(shape, dtype=int)
-    imshape = np.asarray(image.shape, dtype=int)
-    if np.alltrue(imshape == shape):
-        return image
-    if np.any(shape <= 0):
-        raise ValueError("ZERO_PAD: null or negative shape given")
-    dshape = shape - imshape
-    if np.any(dshape < 0):
-        raise ValueError("ZERO_PAD: target size smaller than source one")
-    pad_img = np.zeros(shape, dtype=image.dtype)
-    idx, idy = np.indices(imshape)
-    if position == 'center':
-        if np.any(dshape % 2 != 0):
-            raise ValueError("ZERO_PAD: source and target shapes "
-                             "have different parity.")
-        offx, offy = dshape // 2
-    else:
-        offx, offy = (0, 0)
-    pad_img[idx + offx, idy + offy] = image
-    return pad_img
-
-
-'''
-Reducing boundary artifacts
-'''
-
-
-def opt_fft_size(n):
-    '''
-    Kai Zhang (github: https://github.com/cszn)
-    03/03/2019
-    #  opt_fft_size.m
-    # compute an optimal data length for Fourier transforms
-    # written by Sunghyun Cho (sodomau@postech.ac.kr)
-    # persistent opt_fft_size_LUT;
-    '''
-
-    LUT_size = 2048
-    # print("generate opt_fft_size_LUT")
-    opt_fft_size_LUT = np.zeros(LUT_size)
-
-    e2 = 1
-    while e2 <= LUT_size:
-        e3 = e2
-        while e3 <= LUT_size:
-            e5 = e3
-            while e5 <= LUT_size:
-                e7 = e5
-                while e7 <= LUT_size:
-                    if e7 <= LUT_size:
-                        opt_fft_size_LUT[e7-1] = e7
-                    if e7*11 <= LUT_size:
-                        opt_fft_size_LUT[e7*11-1] = e7*11
-                    if e7*13 <= LUT_size:
-                        opt_fft_size_LUT[e7*13-1] = e7*13
-                    e7 = e7 * 7
-                e5 = e5 * 5
-            e3 = e3 * 3
-        e2 = e2 * 2
-
-    nn = 0
-    for i in range(LUT_size, 0, -1):
-        if opt_fft_size_LUT[i-1] != 0:
-            nn = i-1
-        else:
-            opt_fft_size_LUT[i-1] = nn+1
-
-    m = np.zeros(len(n))
-    for c in range(len(n)):
-        nn = n[c]
-        if nn <= LUT_size:
-            m[c] = opt_fft_size_LUT[nn-1]
-        else:
-            m[c] = -1
-    return m
-
-
-def wrap_boundary_liu(img, img_size):
-
-    """
-    Reducing boundary artifacts in image deconvolution
-    Renting Liu, Jiaya Jia
-    ICIP 2008
-    """
-    if img.ndim == 2:
-        ret = wrap_boundary(img, img_size)
-    elif img.ndim == 3:
-        ret = [wrap_boundary(img[:, :, i], img_size) for i in range(3)]
-        ret = np.stack(ret, 2)
-    return ret
-
-
-def wrap_boundary(img, img_size):
-
-    """
-    python code from:
-    https://github.com/ys-koshelev/nla_deblur/blob/90fe0ab98c26c791dcbdf231fe6f938fca80e2a0/boundaries.py
-    Reducing boundary artifacts in image deconvolution
-    Renting Liu, Jiaya Jia
-    ICIP 2008
-    """
-    (H, W) = np.shape(img)
-    H_w = int(img_size[0]) - H
-    W_w = int(img_size[1]) - W
-
-    # ret = np.zeros((img_size[0], img_size[1]));
-    alpha = 1
-    HG = img[:, :]
-
-    r_A = np.zeros((alpha*2+H_w, W))
-    r_A[:alpha, :] = HG[-alpha:, :]
-    r_A[-alpha:, :] = HG[:alpha, :]
-    a = np.arange(H_w)/(H_w-1)
-    # r_A(alpha+1:end-alpha, 1) = (1-a)*r_A(alpha,1) + a*r_A(end-alpha+1,1)
-    r_A[alpha:-alpha, 0] = (1-a)*r_A[alpha-1, 0] + a*r_A[-alpha, 0]
-    # r_A(alpha+1:end-alpha, end) = (1-a)*r_A(alpha,end) + a*r_A(end-alpha+1,end)
-    r_A[alpha:-alpha, -1] = (1-a)*r_A[alpha-1, -1] + a*r_A[-alpha, -1]
-
-    r_B = np.zeros((H, alpha*2+W_w))
-    r_B[:, :alpha] = HG[:, -alpha:]
-    r_B[:, -alpha:] = HG[:, :alpha]
-    a = np.arange(W_w)/(W_w-1)
-    r_B[0, alpha:-alpha] = (1-a)*r_B[0, alpha-1] + a*r_B[0, -alpha]
-    r_B[-1, alpha:-alpha] = (1-a)*r_B[-1, alpha-1] + a*r_B[-1, -alpha]
-
-    if alpha == 1:
-        A2 = solve_min_laplacian(r_A[alpha-1:, :])
-        B2 = solve_min_laplacian(r_B[:, alpha-1:])
-        r_A[alpha-1:, :] = A2
-        r_B[:, alpha-1:] = B2
-    else:
-        A2 = solve_min_laplacian(r_A[alpha-1:-alpha+1, :])
-        r_A[alpha-1:-alpha+1, :] = A2
-        B2 = solve_min_laplacian(r_B[:, alpha-1:-alpha+1])
-        r_B[:, alpha-1:-alpha+1] = B2
-    A = r_A
-    B = r_B
-
-    r_C = np.zeros((alpha*2+H_w, alpha*2+W_w))
-    r_C[:alpha, :] = B[-alpha:, :]
-    r_C[-alpha:, :] = B[:alpha, :]
-    r_C[:, :alpha] = A[:, -alpha:]
-    r_C[:, -alpha:] = A[:, :alpha]
-
-    if alpha == 1:
-        C2 = C2 = solve_min_laplacian(r_C[alpha-1:, alpha-1:])
-        r_C[alpha-1:, alpha-1:] = C2
-    else:
-        C2 = solve_min_laplacian(r_C[alpha-1:-alpha+1, alpha-1:-alpha+1])
-        r_C[alpha-1:-alpha+1, alpha-1:-alpha+1] = C2
-    C = r_C
-    # return C
-    A = A[alpha-1:-alpha-1, :]
-    B = B[:, alpha:-alpha]
-    C = C[alpha:-alpha, alpha:-alpha]
-    ret = np.vstack((np.hstack((img, B)), np.hstack((A, C))))
-    return ret
-
-
-def solve_min_laplacian(boundary_image):
-    (H, W) = np.shape(boundary_image)
-
-    # Laplacian
-    f = np.zeros((H, W))
-    # boundary image contains image intensities at boundaries
-    boundary_image[1:-1, 1:-1] = 0
-    j = np.arange(2, H)-1
-    k = np.arange(2, W)-1
-    f_bp = np.zeros((H, W))
-    f_bp[np.ix_(j, k)] = -4*boundary_image[np.ix_(j, k)] + boundary_image[np.ix_(j, k+1)] + boundary_image[np.ix_(j, k-1)] + boundary_image[np.ix_(j-1, k)] + boundary_image[np.ix_(j+1, k)]
-    
-    del(j, k)
-    f1 = f - f_bp  # subtract boundary points contribution
-    del(f_bp, f)
-
-    # DST Sine Transform algo starts here
-    f2 = f1[1:-1,1:-1]
-    del(f1)
-
-    # compute sine tranform
-    if f2.shape[1] == 1:
-        tt = fftpack.dst(f2, type=1, axis=0)/2
-    else:
-        tt = fftpack.dst(f2, type=1)/2
-
-    if tt.shape[0] == 1:
-        f2sin = np.transpose(fftpack.dst(np.transpose(tt), type=1, axis=0)/2)
-    else:
-        f2sin = np.transpose(fftpack.dst(np.transpose(tt), type=1)/2) 
-    del(f2)
-
-    # compute Eigen Values
-    [x, y] = np.meshgrid(np.arange(1, W-1), np.arange(1, H-1))
-    denom = (2*np.cos(np.pi*x/(W-1))-2) + (2*np.cos(np.pi*y/(H-1)) - 2)
-
-    # divide
-    f3 = f2sin/denom
-    del(f2sin, x, y)
-
-    # compute Inverse Sine Transform
-    if f3.shape[0] == 1:
-        tt = fftpack.idst(f3*2, type=1, axis=1)/(2*(f3.shape[1]+1))
-    else:
-        tt = fftpack.idst(f3*2, type=1, axis=0)/(2*(f3.shape[0]+1))
-    del(f3)
-    if tt.shape[1] == 1:
-        img_tt = np.transpose(fftpack.idst(np.transpose(tt)*2, type=1)/(2*(tt.shape[0]+1)))
-    else:
-        img_tt = np.transpose(fftpack.idst(np.transpose(tt)*2, type=1, axis=0)/(2*(tt.shape[1]+1)))
-    del(tt)
-
-    # put solution in inner points; outer points obtained from boundary image
-    img_direct = boundary_image
-    img_direct[1:-1, 1:-1] = 0
-    img_direct[1:-1, 1:-1] = img_tt
-    return img_direct
-
-
-"""
-Created on Thu Jan 18 15:36:32 2018
-@author: italo
-https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-"""
-
-"""
-Syntax
-h = fspecial(type)
-h = fspecial('average',hsize)
-h = fspecial('disk',radius)
-h = fspecial('gaussian',hsize,sigma)
-h = fspecial('laplacian',alpha)
-h = fspecial('log',hsize,sigma)
-h = fspecial('motion',len,theta)
-h = fspecial('prewitt')
-h = fspecial('sobel')
-"""
-
-
-def fspecial_average(hsize=3):
-    """Smoothing filter"""
-    return np.ones((hsize, hsize))/hsize**2
-
-
-def fspecial_disk(radius):
-    """Disk filter"""
-    raise(NotImplemented)
-    rad = 0.6
-    crad = np.ceil(rad-0.5)
-    [x, y] = np.meshgrid(np.arange(-crad, crad+1), np.arange(-crad, crad+1))
-    maxxy = np.zeros(x.shape)
-    maxxy[abs(x) >= abs(y)] = abs(x)[abs(x) >= abs(y)]
-    maxxy[abs(y) >= abs(x)] = abs(y)[abs(y) >= abs(x)]
-    minxy = np.zeros(x.shape)
-    minxy[abs(x) <= abs(y)] = abs(x)[abs(x) <= abs(y)]
-    minxy[abs(y) <= abs(x)] = abs(y)[abs(y) <= abs(x)]
-    m1 = (rad**2 <  (maxxy+0.5)**2 + (minxy-0.5)**2)*(minxy-0.5) +\
-         (rad**2 >= (maxxy+0.5)**2 + (minxy-0.5)**2)*\
-         np.sqrt((rad**2 + 0j) - (maxxy + 0.5)**2)
-    m2 = (rad**2 >  (maxxy-0.5)**2 + (minxy+0.5)**2)*(minxy+0.5) +\
-         (rad**2 <= (maxxy-0.5)**2 + (minxy+0.5)**2)*\
-         np.sqrt((rad**2 + 0j) - (maxxy - 0.5)**2)
-    h = None
-    return h
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0]-1.0)/2.0, (hsize[1]-1.0)/2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1]+1), np.arange(-siz[0], siz[0]+1))
-    arg = -(x*x + y*y)/(2*std*std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h/sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha,1])])
-    h1 = alpha/(alpha+1)
-    h2 = (1-alpha)/(alpha+1)
-    h = [[h1, h2, h1], [h2, -4/(alpha+1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial_log(hsize, sigma):
-    raise(NotImplemented)
-
-
-def fspecial_motion(motion_len, theta):
-    raise(NotImplemented)
-
-
-def fspecial_prewitt():
-    return np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]])
-
-
-def fspecial_sobel():
-    return np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'average':
-        return fspecial_average(*args, **kwargs)
-    if filter_type == 'disk':
-        return fspecial_disk(*args, **kwargs)
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-    if filter_type == 'log':
-        return fspecial_log(*args, **kwargs)
-    if filter_type == 'motion':
-        return fspecial_motion(*args, **kwargs)
-    if filter_type == 'prewitt':
-        return fspecial_prewitt(*args, **kwargs)
-    if filter_type == 'sobel':
-        return fspecial_sobel(*args, **kwargs)
-
-
-def fspecial_gauss(size, sigma):
-    x, y = mgrid[-size // 2 + 1 : size // 2 + 1, -size // 2 + 1 : size // 2 + 1]
-    g = exp(-((x ** 2 + y ** 2) / (2.0 * sigma ** 2)))
-    return g / g.sum()
-
-
-def blurkernel_synthesis(h=37, w=None):
-    # https://github.com/tkkcc/prior/blob/879a0b6c117c810776d8cc6b63720bf29f7d0cc4/util/gen_kernel.py
-    w = h if w is None else w
-    kdims = [h, w]
-    x = randomTrajectory(250)
-    k = None
-    while k is None:
-        k = kernelFromTrajectory(x)
-
-    # center pad to kdims
-    pad_width = ((kdims[0] - k.shape[0]) // 2, (kdims[1] - k.shape[1]) // 2)
-    pad_width = [(pad_width[0],), (pad_width[1],)]
-    
-    if pad_width[0][0]<0 or pad_width[1][0]<0:
-        k = k[0:h, 0:h]
-    else:
-        k = pad(k, pad_width, "constant")
-    x1,x2 = k.shape
-    if np.random.randint(0, 4) == 1:
-        k = cv2.resize(k, (random.randint(x1, 5*x1), random.randint(x2, 5*x2)), interpolation=cv2.INTER_LINEAR)
-        y1, y2 = k.shape
-        k = k[(y1-x1)//2: (y1-x1)//2+x1, (y2-x2)//2: (y2-x2)//2+x2]
-        
-    if sum(k)<0.1:
-        k = fspecial_gaussian(h, 0.1+6*np.random.rand(1))
-    k = k / sum(k)
-    # import matplotlib.pyplot as plt
-    # plt.imshow(k, interpolation="nearest", cmap="gray")
-    # plt.show()
-    return k
-
-
-def kernelFromTrajectory(x):
-    h = 5 - log(rand()) / 0.15
-    h = round(min([h, 27])).astype(int)
-    h = h + 1 - h % 2
-    w = h
-    k = zeros((h, w))
-
-    xmin = min(x[0])
-    xmax = max(x[0])
-    ymin = min(x[1])
-    ymax = max(x[1])
-    xthr = arange(xmin, xmax, (xmax - xmin) / w)
-    ythr = arange(ymin, ymax, (ymax - ymin) / h)
-
-    for i in range(1, xthr.size):
-        for j in range(1, ythr.size):
-            idx = (
-                (x[0, :] >= xthr[i - 1])
-                & (x[0, :] < xthr[i])
-                & (x[1, :] >= ythr[j - 1])
-                & (x[1, :] < ythr[j])
-            )
-            k[i - 1, j - 1] = sum(idx)
-    if sum(k) == 0:
-        return
-    k = k / sum(k)
-    k = convolve2d(k, fspecial_gauss(3, 1), "same")
-    k = k / sum(k)
-    return k
-
-
-def randomTrajectory(T):
-    x = zeros((3, T))
-    v = randn(3, T)
-    r = zeros((3, T))
-    trv = 1 / 1
-    trr = 2 * pi / T
-    for t in range(1, T):
-        F_rot = randn(3) / (t + 1) + r[:, t - 1]
-        F_trans = randn(3) / (t + 1)
-        r[:, t] = r[:, t - 1] + trr * F_rot
-        v[:, t] = v[:, t - 1] + trv * F_trans
-        st = v[:, t]
-        st = rot3D(st, r[:, t])
-        x[:, t] = x[:, t - 1] + st
-    return x
-
-
-def rot3D(x, r):
-    Rx = array([[1, 0, 0], [0, cos(r[0]), -sin(r[0])], [0, sin(r[0]), cos(r[0])]])
-    Ry = array([[cos(r[1]), 0, sin(r[1])], [0, 1, 0], [-sin(r[1]), 0, cos(r[1])]])
-    Rz = array([[cos(r[2]), -sin(r[2]), 0], [sin(r[2]), cos(r[2]), 0], [0, 0, 1]])
-    R = Rz @ Ry @ Rx
-    x = R @ x
-    return x
-
-
-if __name__ == '__main__':
-    a = opt_fft_size([111])
-    print(a)
-
-    print(fspecial('gaussian', 5, 1))
-    
-    print(p2o(torch.zeros(1,1,4,4).float(),(14,14)).shape)
-
-    k = blurkernel_synthesis(11)
-    import matplotlib.pyplot as plt
-    plt.imshow(k, interpolation="nearest", cmap="gray")
-    plt.show()
diff --git a/spaces/lavanjv/falcon-mini/demo.py b/spaces/lavanjv/falcon-mini/demo.py
deleted file mode 100644
index f878caa88903a2e584e59f0bc9a79bf9f7b8c909..0000000000000000000000000000000000000000
--- a/spaces/lavanjv/falcon-mini/demo.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-from ctransformers import AutoModelForCausalLM
-import gradio as gr
-
-
-llm = AutoModelForCausalLM.from_pretrained("TheBloke/falcon-7b-instruct-GGML", model_file="falcon-7b-instruct.ggccv1.q4_0.bin",
-                                           model_type="falcon", threads=8)
-
-TITLE = """<h1 align="center">🦅 falcon ggccv1.q4_0 demo 🦅</h1>"""
-USER_NAME = "User"
-BOT_NAME = "Falcon"
-DEFAULT_INSTRUCTIONS = f"""The following is a conversation between a highly knowledgeable and intelligent AI assistant, called Falcon, and a human user, called User. In the following interactions, User and Falcon will converse in natural language, and Falcon will answer User's questions. Falcon was built to be respectful, polite and inclusive. Falcon was built by the Technology Innovation Institute in Abu Dhabi. Falcon will never decline to answer a question, and always attempts to give an answer that User would be satisfied with. It knows a lot, and always tells the truth. The conversation begins.
-"""
-RETRY_COMMAND = "/retry"
-STOP_STR = f"\n{USER_NAME}:"
-STOP_SUSPECT_LIST = [":", "\n", "User"]
-
-
-def chat_accordion():
-    with gr.Accordion("Parameters", open=False):
-        temperature = gr.Slider(
-            minimum=0.1,
-            maximum=2.0,
-            value=0.8,
-            step=0.1,
-            interactive=True,
-            label="Temperature",
-        )
-        top_p = gr.Slider(
-            minimum=0.1,
-            maximum=0.99,
-            value=0.9,
-            step=0.01,
-            interactive=True,
-            label="p (nucleus sampling)",
-        )
-    return temperature, top_p
-
-
-def format_chat_prompt(message: str, chat_history, instructions: str) -> str:
-    instructions = instructions.strip(" ").strip("\n")
-    prompt = instructions
-    for turn in chat_history:
-        user_message, bot_message = turn
-        prompt = f"{prompt}\n{USER_NAME}: {user_message}\n{BOT_NAME}: {bot_message}"
-    prompt = f"{prompt}\n{USER_NAME}: {message}\n{BOT_NAME}:"
-    return prompt
-
-
-def chat():
-    with gr.Column(elem_id="chat_container"):
-        with gr.Row():
-            chatbot = gr.Chatbot(elem_id="chatbot")
-        with gr.Row():
-            inputs = gr.Textbox(
-                placeholder=f"Hello {BOT_NAME} !!",
-                label="Type an input and press Enter",
-                max_lines=3,
-            )
-
-    with gr.Row(elem_id="button_container"):
-        with gr.Column():
-            retry_button = gr.Button("♻️ Retry last turn")
-        with gr.Column():
-            delete_turn_button = gr.Button("🧽 Delete last turn")
-        with gr.Column():
-            clear_chat_button = gr.Button("✨ Delete all history")
-
-    gr.Examples(
-        [
-            ["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"],
-            ["What's the Everett interpretation of quantum mechanics?"],
-            ["Give me a list of the top 10 dive sites you would recommend around the world."],
-            ["Can you tell me more about deep-water soloing?"],
-            ["Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?"],
-        ],
-        inputs=inputs,
-        label="Click on any example and press Enter in the input textbox!",
-    )
-
-    with gr.Row(elem_id="param_container"):
-        with gr.Column():
-            temperature, top_p = chat_accordion()
-        with gr.Column():
-            with gr.Accordion("Instructions", open=False):
-                instructions = gr.Textbox(
-                    placeholder="LLM instructions",
-                    value=DEFAULT_INSTRUCTIONS,
-                    lines=10,
-                    interactive=True,
-                    label="Instructions",
-                    max_lines=16,
-                    show_label=False,
-                )
-
-    def run_chat(message: str, chat_history, instructions: str, temperature: float, top_p: float):
-        if not message or (message == RETRY_COMMAND and len(chat_history) == 0):
-            yield chat_history
-            return
-
-        if message == RETRY_COMMAND and chat_history:
-            prev_turn = chat_history.pop(-1)
-            user_message, _ = prev_turn
-            message = user_message
-
-        prompt = format_chat_prompt(message, chat_history, instructions)
-        chat_history = chat_history + [[message, ""]]
-        stream = llm(
-            prompt,
-            max_new_tokens=1024,
-            stop=[STOP_STR, "<|endoftext|>", USER_NAME],
-            temperature=temperature,
-            top_p=top_p,
-            stream=True
-        )
-        acc_text = ""
-        for idx, response in enumerate(stream):
-            text_token = response
-
-            if text_token in STOP_SUSPECT_LIST:
-                acc_text += text_token
-                continue
-
-            if idx == 0 and text_token.startswith(" "):
-                text_token = text_token[1:]
-
-            acc_text += text_token
-            last_turn = list(chat_history.pop(-1))
-            last_turn[-1] += acc_text
-            chat_history = chat_history + [last_turn]
-            yield chat_history
-            acc_text = ""
-
-
-    def delete_last_turn(chat_history):
-        if chat_history:
-            chat_history.pop(-1)
-        return {chatbot: gr.update(value=chat_history)}
-
-    def run_retry(message: str, chat_history, instructions: str, temperature: float, top_p: float):
-        yield from run_chat(RETRY_COMMAND, chat_history, instructions, temperature, top_p)
-
-    def clear_chat():
-        return []
-
-    inputs.submit(
-        run_chat,
-        [inputs, chatbot, instructions, temperature, top_p],
-        outputs=[chatbot],
-        show_progress=False,
-    )
-    inputs.submit(lambda: "", inputs=None, outputs=inputs)
-    delete_turn_button.click(delete_last_turn, inputs=[chatbot], outputs=[chatbot])
-    retry_button.click(
-        run_retry,
-        [inputs, chatbot, instructions, temperature, top_p],
-        outputs=[chatbot],
-        show_progress=False,
-    )
-    clear_chat_button.click(clear_chat, [], chatbot)
-
-
-def get_demo():
-    with gr.Blocks(
-        # css=None
-        # css="""#chat_container {width: 700px; margin-left: auto; margin-right: auto;}
-        #        #button_container {width: 700px; margin-left: auto; margin-right: auto;}
-        #        #param_container {width: 700px; margin-left: auto; margin-right: auto;}"""
-        css="""#chatbot {
-    font-size: 14px;
-    min-height: 300px;
-}"""
-    ) as demo:
-        gr.HTML(TITLE)
-
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown(
-                    """**Chat with [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct), brainstorm ideas, discuss your holiday plans, and more!** 
-
-                    🧪 This uses a quantized [ggml](https://github.com/ggerganov/ggml) optimized for CPU. Special thanks to [ggllm.cpp](https://github.com/cmp-nct/ggllm.cpp), [ctransformers](https://github.com/marella/ctransformers), and [TheBloke](https://huggingface.co/TheBloke).
-                    """
-                )
-
-        chat()
-
-    return demo
-
-if __name__ == "__main__":
-  demo = get_demo()
-  demo.queue(max_size=128, concurrency_count=8)
-  demo.launch(server_name="0.0.0.0", server_port=7860)
\ No newline at end of file
diff --git a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/webui/static/style.css b/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/webui/static/style.css
deleted file mode 100644
index 8a693509203dcfa67cedd407eca7acfe8e1230e5..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/webui/static/style.css
+++ /dev/null
@@ -1,586 +0,0 @@
-:root {
-    --sessions_column_width: 240px;
-    --control_column_width: 400px;
-    --font-size-large: 16px;
-    --font-size-medium: 14px;
-    --font-size-small: 12px;
-    --background-dark-color: rgba(30, 30, 30, 1);
-    --background-color: rgba(44, 44, 44, 1);
-    --background-color-t: rgba(44, 44, 44, 0);
-    --infotext-color: rgb(128, 128, 128);
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-.custom-slider .custom-slider-sl {
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-::-webkit-scrollbar-thumb {
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-No other program offers so many possibilities of programmable control, such as timer countdown or pulse generation, analog or digital input signals, output of switch status, etc. View and Download Linklight user manual online. For smart. LinkLight Video Driver. LinkLight A5 Video Driver.
-
-LiquiLink User's Manual. It contains all the latest drivers for your PCI, USB, serial and parallel printer devices as well as drivers for USB 2. 0 video device and compatible low- profile PCI- USB card readers. Lirik Muzik Tema House Vocal & Music House Vocal. The LinkLight is a small, lightweight device with built- in display and allows USB connection of a computer to an internet connection. It is available as a stand- alone unit or as part of a complete broadband video solution, which can be based on a 2.
-
-USB hub and ethernet/ modem. This approach, known as USB 2. 0 video, is a relatively new technology that provides high- speed video ( up to 10Mbps ) over a USB 2. The LinkLight delivers high- quality video streaming via a fast cable modem connection, providing excellent picture quality for quick photo viewing and online chatting. About Us - Erebus. 1.
-
-It is a compact and lightweight product that provides the fastest picture streaming in the industry. Heavily tested and proven - the LinkLight is a small device with built- in display and allows USB connection of a computer to an internet connection. It is available as a stand- alone unit or as part of a complete broadband video solution, which can be based on a 2. USB hub and ethernet/ modem.
-
-5. 1.
-
-The LinkLight delivers high- quality video streaming via a fast cable modem connection, providing excellent picture quality for quick photo viewing and online chatting. The LinkLight user manual has been created for our customers that are using our LinkLight product. This document tells you all you need to know about using your LinkLight.
-
-2. The LinkLight is not a standalone device. It must be connected to your computer before it can be used.
-
-3. For best results, please read the LinkLight manual thoroughly before beginning to use the LinkLight.
-
-4. Please do not use the wrong installation instructions as this could result in damage to your LinkLight and the computer.
-
-5 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/ltgoslo/ssa-perin/mtool/ucca/normalization.py b/spaces/ltgoslo/ssa-perin/mtool/ucca/normalization.py
deleted file mode 100644
index 892e74b1e678b948a6f6094c8ba60c5012ae63f5..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/mtool/ucca/normalization.py
+++ /dev/null
@@ -1,335 +0,0 @@
-from ucca import layer0, layer1
-from ucca.layer0 import NodeTags as L0Tags
-from ucca.layer1 import EdgeTags as ETags, NodeTags as L1Tags
-
-NO_MULTIPLE_INCOMING_CATEGORIES = {ETags.Function, ETags.ParallelScene, ETags.Linker, ETags.LinkRelation,
-                                   ETags.Connector, ETags.Punctuation, ETags.Terminal}
-TOP_CATEGORIES = {ETags.ParallelScene, ETags.Linker, ETags.Function, ETags.Ground, ETags.Punctuation,
-                  ETags.LinkRelation, ETags.LinkArgument, ETags.Connector}
-COORDINATED_MAIN_REL = "Coordinated_Main_Rel."
-
-
-def fparent(node_or_edge):
-    try:
-        return node_or_edge.fparent
-    except AttributeError:
-        try:
-            return node_or_edge.parent
-        except AttributeError:
-            return node_or_edge.parents[0] if node_or_edge.parents else None
-
-
-def remove_unmarked_implicits(node):
-    while node is not None and not node.children and not node.attrib.get("implicit"):
-        parent = fparent(node)
-        if parent is None:
-            break
-        node.destroy()
-        node = parent
-
-
-def remove(parent, child):
-    if parent is not None:
-        parent.remove(child)
-        remove_unmarked_implicits(parent)
-
-
-def destroy(node_or_edge):
-    parent = fparent(node_or_edge)
-    try:
-        node_or_edge.destroy()
-    except AttributeError:
-        parent.remove(node_or_edge)
-    if parent is not None:
-        remove_unmarked_implicits(parent)
-    return parent
-
-
-def copy_edge(edge, parent=None, child=None, tag=None, attrib=None):
-    if parent is None:
-        parent = edge.parent
-    if child is None:
-        child = edge.child
-    if not tag:
-        categories = [(c.tag, c.slot, c.layer, c.parent) for c in edge.categories]
-    else:
-        categories = [(tag,)]
-    if attrib is None:
-        attrib = edge.attrib
-    if parent in child.iter():
-        # raise ValueError("Created cycle (%s->%s) when trying to normalize '%s'" % (
-        #     "->".join(n.ID for n in child.iter() if parent in n.iter()), child.ID, parent))
-        return False
-    parent.add_multiple(categories, child, edge_attrib=attrib)
-    return True
-
-
-def replace_center(edge):
-    if len(edge.parent) == 1 and not edge.parent.parents:
-        return ETags.ParallelScene
-    if edge.parent.participants and not edge.parent.is_scene():
-        return ETags.Process  # TODO should be state if the word is a copula
-    return edge.tag
-
-
-def replace_edge_tags(node):
-    for edge in node:
-        if not edge.attrib.get("remote") and edge.tag == ETags.Center:
-            edge.tag = replace_center(edge)
-        elif node.parallel_scenes:
-            if edge.tag == ETags.Connector:
-                edge.tag = ETags.Linker
-        elif edge.tag == ETags.Linker:
-            edge.tag = ETags.Connector
-        elif node.is_scene():
-            if edge.tag == ETags.Elaborator:
-                edge.tag = ETags.Adverbial
-        elif edge.tag == ETags.Adverbial:
-            edge.tag = ETags.Elaborator
-
-
-def move_elements(node, tags, parent_tags, forward=True):
-    for edge in node:
-        if edge.child.tag == L1Tags.Foundational and edge.tag in ((tags,) if isinstance(tags, str) else tags):
-            try:
-                parent_edge = min((e for e in node if e != edge and e.child.tag == L1Tags.Foundational),
-                                  key=lambda e: abs(((edge.child.start_position - e.child.end_position),
-                                                     (e.child.start_position - edge.child.end_position))[forward]))
-            except ValueError:
-                continue
-            if parent_edge.tag in ((parent_tags,) if isinstance(parent_tags, str) else parent_tags):
-                parent = parent_edge.child
-                if copy_edge(edge, parent=parent):
-                    remove(node, edge)
-
-
-def move_scene_elements(node):
-    if node.parallel_scenes:
-        move_elements(node, tags=(ETags.Relator, ETags.Elaborator, ETags.Center), parent_tags=ETags.ParallelScene)
-
-
-def move_sub_scene_elements(node):
-    if node.is_scene():
-        move_elements(node, tags=(ETags.Elaborator, ETags.Center), parent_tags=ETags.Participant, forward=False)
-
-
-def separate_scenes(node, l1, top_level=False):
-    if (node.is_scene() or node.participants) and (top_level or node.parallel_scenes):
-        edges = list(node)
-        scene = l1.add_fnode(node, ETags.ParallelScene)
-        for edge in edges:
-            if edge.tag not in (ETags.ParallelScene, ETags.Punctuation, ETags.Linker, ETags.Ground):
-                if copy_edge(edge, parent=scene):
-                    remove(node, edge)
-
-
-def lowest_common_ancestor(*nodes):
-    parents = [nodes[0]] if nodes else []
-    while parents:
-        for parent in parents:
-            if parent.tag == L1Tags.Foundational and (not parent.terminals or nodes[1:]) \
-                    and all(n in parent.iter() for n in nodes[1:]):
-                return parent
-        parents = [p for n in parents for p in n.parents]
-    return None
-
-
-def nearest_word(l0, position, step):
-    while True:
-        position += step
-        try:
-            terminal = l0.by_position(position)
-        except IndexError:
-            return None
-        if terminal.tag == L0Tags.Word:
-            return terminal
-
-
-def nearest_parent(l0, *terminals):
-    return lowest_common_ancestor(*filter(None, (nearest_word(l0, terminals[0].position, -1),
-                                                 nearest_word(l0, terminals[-1].position, 1))))
-
-
-def reattach_punct(l0, l1):
-    detach_punct(l1)
-    attach_punct(l0, l1)
-
-
-def attach_punct(l0, l1):
-    for terminal in l0.all:
-        if layer0.is_punct(terminal) and not terminal.incoming:
-            l1.add_punct(nearest_parent(l0, terminal), terminal)
-
-
-def detach_punct(l1):
-    for node in l1.all:
-        if node.tag == L1Tags.Punctuation:
-            destroy(node)
-
-
-def reattach_terminals(l0, l1):
-    attach_terminals(l0, l1)
-    for terminal in l0.all:
-        for edge in terminal.incoming:
-            if any(e.tag != ETags.Terminal for e in edge.parent):
-                node = l1.add_fnode(edge.parent, ETags.Center)
-                if copy_edge(edge, parent=node):
-                    remove(edge.parent, edge)
-
-
-def attach_terminals(l0, l1):
-    for terminal in l0.all:
-        if not terminal.incoming:
-            node = l1.add_fnode(nearest_parent(l0, terminal), ETags.Function)
-            node.add(ETags.Terminal, terminal)
-
-
-def flatten_centers(node):
-    """
-    Whenever there are Cs inside Cs, remove the external C.
-    Whenever there is a C as an only child, remove it.
-    """
-    if node.tag == L1Tags.Foundational and len(node.centers) == 1:
-        if node.ftag == ETags.Center and len(fparent(node).centers) == 1:  # Center inside center
-            for edge in node.incoming:
-                if edge.attrib.get("remote"):
-                    copy_edge(edge, child=node.centers[0])
-            for edge in node:
-                copy_edge(edge, parent=fparent(node))
-            return destroy(node)
-        elif len(node.children) == 1:  # Center as only child
-            for edge in node.incoming:
-                attrib = edge.attrib
-                if node.outgoing[0].attrib.get("remote"):
-                    attrib["remote"] = True
-                copy_edge(edge, child=node.centers[0], attrib=attrib)
-            return destroy(node)
-    return node
-
-
-def flatten_functions(node):
-    """
-    Whenever there is an F as an only child, remove it. If an F has non-terminal children, move them up.
-    """
-    if node.tag == L1Tags.Foundational and node.incoming:  # Avoid creating root->terminal edge
-        for child in node.functions:
-            if len(child.children) > len(child.terminals):
-                for edge in child:
-                    copy_edge(edge, parent=node, tag=ETags.Function if edge.tag == ETags.Center else edge.tag)
-                destroy(child)
-        if len(node.functions) == len(node.children) == 1:
-            for edge in node.incoming:
-                copy_edge(edge, child=node.functions[0])
-            return destroy(node)
-    return node
-
-
-def flatten_participants(node):
-    """
-    Whenever there is an A as an only child, remove it.
-    If there is an implicit A in a scene without a main relation, remove it.
-    """
-    if node.tag == L1Tags.Foundational:
-        participants = node.participants
-        if len(participants) == len(node.children) == 1 and len(participants[0].ftags) == 1:
-            for edge in node.incoming:
-                copy_edge(edge, child=participants[0])
-            return destroy(node)
-        elif participants and not node.is_scene():
-            for child in participants:
-                if child.attrib.get("implicit"):
-                    destroy(child)
-    return node
-
-
-def split_coordinated_main_rel(node, l1):
-    for edge in node:
-        attrib = edge.attrib.copy()
-        if attrib.pop(COORDINATED_MAIN_REL, None):
-            assert {ETags.Process, ETags.State}.issuperset(edge.tags), \
-                "%s node without main relation: %s" % (COORDINATED_MAIN_REL, node)
-            main_rel = edge.child
-            centers = main_rel.centers
-            assert centers, "%s node without centers: %s" % (COORDINATED_MAIN_REL, main_rel)
-            top = fparent(node)
-            if ETags.ParallelScene in node.ftags:
-                top.remove(node)
-            else:
-                top = node
-            outgoing = list(node.outgoing)
-            scenes = []
-            for center in centers:
-                main_rel.remove(center)
-                new_scene = l1.add_fnode(top, ETags.ParallelScene)
-                copy_edge(edge, parent=new_scene, child=center, attrib=attrib)
-                for scene_edge in outgoing:
-                    if scene_edge.ID != edge.ID and not (
-                            scenes and NO_MULTIPLE_INCOMING_CATEGORIES.intersection(scene_edge.tags)):
-                        # Not the CMR edge itself, and not a category that does not allow multiple parents
-                        copy_edge(scene_edge, parent=new_scene, attrib={"remote": True} if scenes else None)
-                scenes.append(new_scene)
-            for main_rel_edge in list(main_rel.outgoing):
-                tags = main_rel_edge.tags
-                copy_edge(main_rel_edge, parent=top if TOP_CATEGORIES.issuperset(tags) else scenes[0],
-                          tag=ETags.Linker if ETags.Connector in main_rel_edge.tags else None)
-                destroy(main_rel_edge)
-            for scene_edge in outgoing:
-                if scene_edge.ID != edge.ID:
-                    destroy(scene_edge)
-            if main_rel.incoming:
-                main_rel.destroy()
-            if not node.incoming:
-                node.destroy()
-    return node
-
-
-def normalize_node(node, l1, extra):
-    if node.tag == L1Tags.Foundational:
-        if extra:
-            replace_edge_tags(node)
-            move_scene_elements(node)
-            move_sub_scene_elements(node)
-        separate_scenes(node, l1, top_level=node in l1.heads)
-        node = split_coordinated_main_rel(node, l1)
-        if node is None:
-            return None
-        node = flatten_centers(node)
-        if node is None:
-            return
-        node = flatten_functions(node)
-        if node is None:
-            return
-        flatten_participants(node)
-
-
-def normalize(passage, extra=False):
-    l0 = passage.layer(layer0.LAYER_ID)
-    l1 = passage.layer(layer1.LAYER_ID)
-    reattach_punct(l0, l1)
-    heads = list(l1.heads)
-    stack = [heads]
-    visited = set()
-    path = []
-    path_set = set()
-    while stack:
-        for edge in stack[-1]:
-            try:
-                node = edge.child
-            except AttributeError:
-                node = edge
-            if node in path_set:
-                destroy(edge)
-            elif node not in visited:
-                visited.add(node)
-                path.append(node)
-                path_set.add(node)
-                stack.append(node)
-                normalize_node(node, l1, extra)
-                break
-        else:
-            if path:
-                path_set.remove(path.pop())
-            stack.pop()
-    reattach_punct(l0, l1)
-    if extra:
-        reattach_terminals(l0, l1)
diff --git a/spaces/ltgoslo/ssa-perin/utility/schedule/linear_lr.py b/spaces/ltgoslo/ssa-perin/utility/schedule/linear_lr.py
deleted file mode 100644
index f564e7176a80a75769e9a153972f2f316d9c8772..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/utility/schedule/linear_lr.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env python3
-# coding=utf-8
-
-import math
-
-
-class LinearLr:
-    def __init__(self, param_group, learning_rate: float, total_steps: int, delay: bool, multiplier: int):
-        self.total_steps = total_steps
-        self.delay_steps = total_steps / 20 if delay else 0
-        self.max_lr = learning_rate
-        self.steps = 0
-        self.param_group = param_group
-        self.decay_multiplier = multiplier
-
-    def __call__(self, _):
-        self.steps += 1
-
-        if self.steps < self.delay_steps:
-            lr = 0.0
-        elif self.steps < self.total_steps / 10:
-            lr = self.max_lr * (self.steps - self.delay_steps) / (self.total_steps / 10 - self.delay_steps)
-        else:
-            max_lr = self.max_lr - self.max_lr / self.decay_multiplier
-            min_lr = self.max_lr / self.decay_multiplier
-            lr = max_lr * (math.cos(math.pi * (self.steps - self.total_steps / 10) / (self.total_steps * 9 / 10)) + 1) / 2 + min_lr
-            #lr = self.max_lr * (self.total_steps - self.steps) / (self.total_steps * 9 / 10)
-
-        # Safety first!
-        if lr < 0.0:
-            lr = 0.0
-
-        self.param_group["lr"] = lr
-
-    def lr(self) -> float:
-        return self.param_group["lr"]
diff --git a/spaces/lucken/DL101/app.py b/spaces/lucken/DL101/app.py
deleted file mode 100644
index db7c5ea58f84671dd057c9c6617977881426bc16..0000000000000000000000000000000000000000
--- a/spaces/lucken/DL101/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import gradio as gr
-
-# def greet(name):
-#     return "Hello" + name + '!'
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
-
-
-from fastai.vision.all import load_learner
-# from huggingface_hub import from_pretrained_fastai
-
-
-def is_cat(x):  return x[0].isupper()
-
-learn = load_learner('model.pkl')
-
-categories = ('Dog', 'Cat')
-
-def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/lwchen/CodeFormer/app.py b/spaces/lwchen/CodeFormer/app.py
deleted file mode 100644
index 7da0fc9479dcc28d3b3183980c80154885137571..0000000000000000000000000000000000000000
--- a/spaces/lwchen/CodeFormer/app.py
+++ /dev/null
@@ -1,280 +0,0 @@
-"""
-This file is used for deploying hugging face demo:
-https://huggingface.co/spaces/sczhou/CodeFormer
-"""
-
-import sys
-sys.path.append('CodeFormer')
-import os
-import cv2
-import torch
-import torch.nn.functional as F
-import gradio as gr
-
-from torchvision.transforms.functional import normalize
-
-from basicsr.utils import imwrite, img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facelib.utils.face_restoration_helper import FaceRestoreHelper
-from facelib.utils.misc import is_gray
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.realesrgan_utils import RealESRGANer
-
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-os.system("pip freeze")
-
-pretrain_model_url = {
-    'codeformer': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
-    'detection': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
-    'parsing': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth',
-    'realesrgan': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth'
-}
-# download weights
-if not os.path.exists('CodeFormer/weights/CodeFormer/codeformer.pth'):
-    load_file_from_url(url=pretrain_model_url['codeformer'], model_dir='CodeFormer/weights/CodeFormer', progress=True, file_name=None)
-if not os.path.exists('CodeFormer/weights/facelib/detection_Resnet50_Final.pth'):
-    load_file_from_url(url=pretrain_model_url['detection'], model_dir='CodeFormer/weights/facelib', progress=True, file_name=None)
-if not os.path.exists('CodeFormer/weights/facelib/parsing_parsenet.pth'):
-    load_file_from_url(url=pretrain_model_url['parsing'], model_dir='CodeFormer/weights/facelib', progress=True, file_name=None)
-if not os.path.exists('CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth'):
-    load_file_from_url(url=pretrain_model_url['realesrgan'], model_dir='CodeFormer/weights/realesrgan', progress=True, file_name=None)
-
-# download images
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/fa3fe3d1-76b0-4ca8-ac0d-0a925cb0ff54/06.png',
-    '01.png')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/a1daba8e-af14-4b00-86a4-69cec9619b53/04.jpg',
-    '02.jpg')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/542d64f9-1712-4de7-85f7-3863009a7c3d/03.jpg',
-    '03.jpg')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/a11098b0-a18a-4c02-a19a-9a7045d68426/010.jpg',
-    '04.jpg')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/7cf19c2c-e0cf-4712-9af8-cf5bdbb8d0ee/012.jpg',
-    '05.jpg')
-
-def imread(img_path):
-    img = cv2.imread(img_path)
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    return img
-
-# set enhancer with RealESRGAN
-def set_realesrgan():
-    half = True if torch.cuda.is_available() else False
-    model = RRDBNet(
-        num_in_ch=3,
-        num_out_ch=3,
-        num_feat=64,
-        num_block=23,
-        num_grow_ch=32,
-        scale=2,
-    )
-    upsampler = RealESRGANer(
-        scale=2,
-        model_path="CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth",
-        model=model,
-        tile=400,
-        tile_pad=40,
-        pre_pad=0,
-        half=half,
-    )
-    return upsampler
-
-upsampler = set_realesrgan()
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-codeformer_net = ARCH_REGISTRY.get("CodeFormer")(
-    dim_embd=512,
-    codebook_size=1024,
-    n_head=8,
-    n_layers=9,
-    connect_list=["32", "64", "128", "256"],
-).to(device)
-ckpt_path = "CodeFormer/weights/CodeFormer/codeformer.pth"
-checkpoint = torch.load(ckpt_path)["params_ema"]
-codeformer_net.load_state_dict(checkpoint)
-codeformer_net.eval()
-
-os.makedirs('output', exist_ok=True)
-
-def inference(image, background_enhance, face_upsample, upscale, codeformer_fidelity):
-    """Run a single prediction on the model"""
-    try: # global try
-        # take the default setting for the demo
-        has_aligned = False
-        only_center_face = False
-        draw_box = False
-        detection_model = "retinaface_resnet50"
-        print('Inp:', image, background_enhance, face_upsample, upscale, codeformer_fidelity)
-
-        img = cv2.imread(str(image), cv2.IMREAD_COLOR)
-        print('\timage size:', img.shape)
-
-        upscale = int(upscale) # convert type to int
-        if upscale > 4: # avoid memory exceeded due to too large upscale
-            upscale = 4 
-        if upscale > 2 and max(img.shape[:2])>1000: # avoid memory exceeded due to too large img resolution
-            upscale = 2 
-        if max(img.shape[:2]) > 1500: # avoid memory exceeded due to too large img resolution
-            upscale = 1
-            background_enhance = False
-            face_upsample = False
-
-        face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model=detection_model,
-            save_ext="png",
-            use_parse=True,
-            device=device,
-        )
-        bg_upsampler = upsampler if background_enhance else None
-        face_upsampler = upsampler if face_upsample else None
-
-        if has_aligned:
-            # the input faces are already cropped and aligned
-            img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
-            face_helper.is_gray = is_gray(img, threshold=5)
-            if face_helper.is_gray:
-                print('\tgrayscale input: True')
-            face_helper.cropped_faces = [img]
-        else:
-            face_helper.read_image(img)
-            # get face landmarks for each face
-            num_det_faces = face_helper.get_face_landmarks_5(
-            only_center_face=only_center_face, resize=640, eye_dist_threshold=5
-            )
-            print(f'\tdetect {num_det_faces} faces')
-            # align and warp each face
-            face_helper.align_warp_face()
-
-        # face restoration for each cropped face
-        for idx, cropped_face in enumerate(face_helper.cropped_faces):
-            # prepare data
-            cropped_face_t = img2tensor(
-                cropped_face / 255.0, bgr2rgb=True, float32=True
-            )
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
-
-            try:
-                with torch.no_grad():
-                    output = codeformer_net(
-                        cropped_face_t, w=codeformer_fidelity, adain=True
-                    )[0]
-                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                del output
-                torch.cuda.empty_cache()
-            except RuntimeError as error:
-                print(f"Failed inference for CodeFormer: {error}")
-                restored_face = tensor2img(
-                    cropped_face_t, rgb2bgr=True, min_max=(-1, 1)
-                )
-
-            restored_face = restored_face.astype("uint8")
-            face_helper.add_restored_face(restored_face)
-
-        # paste_back
-        if not has_aligned:
-            # upsample the background
-            if bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = bg_upsampler.enhance(img, outscale=upscale)[0]
-            else:
-                bg_img = None
-            face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            if face_upsample and face_upsampler is not None:
-                restored_img = face_helper.paste_faces_to_input_image(
-                    upsample_img=bg_img,
-                    draw_box=draw_box,
-                    face_upsampler=face_upsampler,
-                )
-            else:
-                restored_img = face_helper.paste_faces_to_input_image(
-                    upsample_img=bg_img, draw_box=draw_box
-                )
-
-        # save restored img
-        save_path = f'output/out.png'
-        imwrite(restored_img, str(save_path))
-
-        restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
-        return restored_img, save_path
-    except Exception as error:
-        print('Global exception', error)
-        return None, None
-
-
-title = "CodeFormer: Robust Face Restoration and Enhancement Network"
-description = r"""<center><img src='https://user-images.githubusercontent.com/14334509/189166076-94bb2cac-4f4e-40fb-a69f-66709e3d98f5.png' alt='CodeFormer logo'></center>
-<b>Official Gradio demo</b> for <a href='https://github.com/sczhou/CodeFormer' target='_blank'><b>Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022)</b></a>.<br>
-🔥 CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.<br>
-🤗 Try CodeFormer for improved stable-diffusion generation!<br>
-"""
-article = r"""
-If CodeFormer is helpful, please help to ⭐ the <a href='https://github.com/sczhou/CodeFormer' target='_blank'>Github Repo</a>. Thanks! 
-[![GitHub Stars](https://img.shields.io/github/stars/sczhou/CodeFormer?style=social)](https://github.com/sczhou/CodeFormer)
-
----
-
-📝 **Citation**
-
-If our work is useful for your research, please consider citing:
-```bibtex
-@inproceedings{zhou2022codeformer,
-    author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
-    title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
-    booktitle = {NeurIPS},
-    year = {2022}
-}
-```
-
-📋 **License**
-
-This project is licensed under <a rel="license" href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">S-Lab License 1.0</a>. 
-Redistribution and use for non-commercial purposes should follow this license.
-
-📧 **Contact**
-
-If you have any questions, please feel free to reach me out at <b>shangchenzhou@gmail.com</b>.
-
-<div>
-    🤗 Find Me:
-    <a href="https://twitter.com/ShangchenZhou"><img style="margin-top:0.5em; margin-bottom:0.5em" src="https://img.shields.io/twitter/follow/ShangchenZhou?label=%40ShangchenZhou&style=social" alt="Twitter Follow"></a> 
-    <a href="https://github.com/sczhou"><img style="margin-top:0.5em; margin-bottom:2em" src="https://img.shields.io/github/followers/sczhou?style=social" alt="Github Follow"></a>
-</div>
-
-<center><img src='https://visitor-badge-sczhou.glitch.me/badge?page_id=sczhou/CodeFormer' alt='visitors'></center>
-"""
-
-demo = gr.Interface(
-    inference, [
-        gr.inputs.Image(type="filepath", label="Input"),
-        gr.inputs.Checkbox(default=True, label="Background_Enhance"),
-        gr.inputs.Checkbox(default=True, label="Face_Upsample"),
-        gr.inputs.Number(default=2, label="Rescaling_Factor (up to 4)"),
-        gr.Slider(0, 1, value=0.5, step=0.01, label='Codeformer_Fidelity (0 for better quality, 1 for better identity)')
-    ], [
-        gr.outputs.Image(type="numpy", label="Output"),
-        gr.outputs.File(label="Download the output")
-    ],
-    title=title,
-    description=description,
-    article=article,       
-    examples=[
-        ['01.png', True, True, 2, 0.7],
-        ['02.jpg', True, True, 2, 0.7],
-        ['03.jpg', True, True, 2, 0.7],
-        ['04.jpg', True, True, 2, 0.1],
-        ['05.jpg', True, True, 2, 0.1]
-      ]
-    )
-
-demo.queue(concurrency_count=2)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/lwdragon/token_classfication/app.py b/spaces/lwdragon/token_classfication/app.py
deleted file mode 100644
index b6cacc419e7b34c71652e92b9db73513f9da3cfa..0000000000000000000000000000000000000000
--- a/spaces/lwdragon/token_classfication/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import gradio as gr
-from mindformers.trainer import Trainer
-
-# 初始化trainer
-trainer = Trainer(
-    task='token_classification',
-    model='tokcls_bert_base_chinese',
-)
-
-# examples
-warm_input_data = ["结果上周六他们主场0：3惨败给了中游球队瓦拉多利德，近7个多月以来西甲首次输球。", "清华大学座落于首都北京"]
-
-# warm_up
-trainer.predict(
-    predict_checkpoint=
-    './tokcls_bert_base_chinese_cluener.ckpt',
-    input_data=warm_input_data)
-
-
-# 数据后处理，将数据转成gr.HighlightedText需要的数据
-def post_procces(text, text_list):
-    res = []
-    cur_index = 0
-    for item in text_list:
-        res.append((text[cur_index:item["start"]], None))
-        res.append((text[item["start"]:(item["end"] + 1)],
-                    " ".join([item["entity_group"],
-                              str(item["score"])])))
-        cur_index = item["end"] + 1
-    res.append((text[cur_index:], None))
-    return res
-
-
-# 预测
-def token_classification(text):
-    res_list = trainer.predict(input_data=text)
-    res = post_procces(text, res_list[0])
-    print(res)
-    return res, res_list
-
-
-# gradio接口
-gr.Interface(
-    token_classification,
-    gr.Textbox(
-        label="Text",
-        info="Enter sentence here..xt",
-        lines=3,
-        value="结果上周六他们主场0：3惨败给了中游球队瓦拉多利德，近7个多月以来西甲首次输球。",
-    ),
-    # ["highlight", "json"],
-    [
-        gr.HighlightedText(
-            label="Token Classification",
-            combine_adjacent=True,
-        ),
-        gr.JSON()
-    ],
-    examples=[*warm_input_data],
-).launch()
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/test_opaque_types.cpp b/spaces/ma-xu/LIVE/pybind11/tests/test_opaque_types.cpp
deleted file mode 100644
index 0d20d9a01c8592e844fb909b336fd5c8e969b9e0..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/test_opaque_types.cpp
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
-    tests/test_opaque_types.cpp -- opaque types, passing void pointers
-
-    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include <pybind11/stl.h>
-#include <vector>
-
-// IMPORTANT: Disable internal pybind11 translation mechanisms for STL data structures
-//
-// This also deliberately doesn't use the below StringList type alias to test
-// that MAKE_OPAQUE can handle a type containing a `,`.  (The `std::allocator`
-// bit is just the default `std::vector` allocator).
-PYBIND11_MAKE_OPAQUE(std::vector<std::string, std::allocator<std::string>>);
-
-using StringList = std::vector<std::string, std::allocator<std::string>>;
-
-TEST_SUBMODULE(opaque_types, m) {
-    // test_string_list
-    py::class_<StringList>(m, "StringList")
-        .def(py::init<>())
-        .def("pop_back", &StringList::pop_back)
-        /* There are multiple versions of push_back(), etc. Select the right ones. */
-        .def("push_back", (void (StringList::*)(const std::string &)) &StringList::push_back)
-        .def("back", (std::string &(StringList::*)()) &StringList::back)
-        .def("__len__", [](const StringList &v) { return v.size(); })
-        .def("__iter__", [](StringList &v) {
-           return py::make_iterator(v.begin(), v.end());
-        }, py::keep_alive<0, 1>());
-
-    class ClassWithSTLVecProperty {
-    public:
-        StringList stringList;
-    };
-    py::class_<ClassWithSTLVecProperty>(m, "ClassWithSTLVecProperty")
-        .def(py::init<>())
-        .def_readwrite("stringList", &ClassWithSTLVecProperty::stringList);
-
-    m.def("print_opaque_list", [](const StringList &l) {
-        std::string ret = "Opaque list: [";
-        bool first = true;
-        for (auto entry : l) {
-            if (!first)
-                ret += ", ";
-            ret += entry;
-            first = false;
-        }
-        return ret + "]";
-    });
-
-    // test_pointers
-    m.def("return_void_ptr", []() { return (void *) 0x1234; });
-    m.def("get_void_ptr_value", [](void *ptr) { return reinterpret_cast<std::intptr_t>(ptr); });
-    m.def("return_null_str", []() { return (char *) nullptr; });
-    m.def("get_null_str_value", [](char *ptr) { return reinterpret_cast<std::intptr_t>(ptr); });
-
-    m.def("return_unique_ptr", []() -> std::unique_ptr<StringList> {
-        StringList *result = new StringList();
-        result->push_back("some value");
-        return std::unique_ptr<StringList>(result);
-    });
-}
diff --git a/spaces/magicr/BuboGPT/ram/models/tag2text.py b/spaces/magicr/BuboGPT/ram/models/tag2text.py
deleted file mode 100644
index 39ab066890119bdbc4b034bfd8b09b4d6eb1b91b..0000000000000000000000000000000000000000
--- a/spaces/magicr/BuboGPT/ram/models/tag2text.py
+++ /dev/null
@@ -1,277 +0,0 @@
-'''
- * The Tag2Text Model
- * Written by Xinyu Huang
-'''
-import numpy as np
-import json
-import torch
-import warnings
-
-from torch import nn
-from .bert import BertConfig, BertModel, BertLMHeadModel
-from .swin_transformer import SwinTransformer
-
-from .utils import *
-
-warnings.filterwarnings("ignore")
-
-
-class Tag2Text_Caption(nn.Module):
-
-    def __init__(self,
-                 med_config=f'{CONFIG_PATH}/configs/med_config.json',
-                 image_size=384,
-                 vit='base',
-                 vit_grad_ckpt=False,
-                 vit_ckpt_layer=0,
-                 prompt='a picture of ',
-                 threshold=0.68,
-                 delete_tag_index=[127,2961, 3351, 3265, 3338, 3355, 3359],
-                 tag_list=f'{CONFIG_PATH}/data/tag_list.txt'):
-        r""" Tag2Text inference module, both captioning and tagging are included.
-        Tag2Text is an efficient and controllable vision-language pre-training framework.
-        Described in the paper "Tag2Text: Guiding Vision-Language Model via Image Tagging" https://arxiv.org/abs/2303.05657
-
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-            threshold (int): tagging threshold
-            delete_tag_index (list): delete some tags that may disturb captioning
-        """
-        super().__init__()
-
-        # create image encoder
-        if vit == 'swin_b':
-            if image_size == 224:
-                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'
-            elif image_size == 384:
-                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'
-            vision_config = read_json(vision_config_path)
-            assert image_size == vision_config['image_res']
-            # assert config['patch_size'] == 32
-            vision_width = vision_config['vision_width']
-
-            self.visual_encoder = SwinTransformer(
-                img_size=vision_config['image_res'],
-                patch_size=4,
-                in_chans=3,
-                embed_dim=vision_config['embed_dim'],
-                depths=vision_config['depths'],
-                num_heads=vision_config['num_heads'],
-                window_size=vision_config['window_size'],
-                mlp_ratio=4.,
-                qkv_bias=True,
-                drop_rate=0.0,
-                drop_path_rate=0.1,
-                ape=False,
-                patch_norm=True,
-                use_checkpoint=False)
-
-        else:
-            self.visual_encoder, vision_width = create_vit(
-                vit, image_size, vit_grad_ckpt, vit_ckpt_layer)
-
-        # create tokenzier
-        self.tokenizer = init_tokenizer()
-
-        # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder
-        # create image-tag interaction encoder
-        encoder_config = BertConfig.from_json_file(med_config)
-        encoder_config.encoder_width = vision_width
-        self.tag_encoder = BertModel(config=encoder_config,
-                                     add_pooling_layer=False)
-
-        # create image-tag-text decoder
-        decoder_config = BertConfig.from_json_file(med_config)
-        self.text_decoder = BertLMHeadModel(config=decoder_config)
-
-        # delete some tags that may disturb captioning
-        # 127: "quarter"; 2961: "back"; 3351: "two"; 3265: "three"; 3338: "four"; 3355: "five"; 3359: "one"
-        self.delete_tag_index = delete_tag_index
-        self.prompt = prompt
-        self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1
-
-        # load tag list
-        self.tag_list = self.load_tag_list(tag_list)
-
-        # create image-tag recognition decoder
-        self.threshold = threshold
-        self.num_class = len(self.tag_list)
-        q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json')
-        q2l_config.encoder_width = vision_width
-        self.tagging_head = BertModel(config=q2l_config,
-                                      add_pooling_layer=False)
-        self.tagging_head.resize_token_embeddings(len(self.tokenizer))
-        self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)
-        self.fc = GroupWiseLinear(self.num_class,
-                                  q2l_config.hidden_size,
-                                  bias=True)
-        self.del_selfattention()
-
-        # share weights of the lowest 2-layer of "image-tag interaction encoder" with the "image-tag recogntion decoder"
-        tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '',
-                                    ' ')
-
-        # adjust thresholds for some tags
-        # default threshold: 0.68
-        # 2701: "person"; 2828: "man"; 1167: "woman"; 
-        tag_thrshold = {2701:0.7, 2828: 0.7, 1167: 0.7}
-        self.class_threshold = torch.ones(self.num_class) * self.threshold
-        for key,value in tag_thrshold.items():
-            self.class_threshold[key] = value
-
-    def load_tag_list(self, tag_list_file):
-        with open(tag_list_file, 'r') as f:
-            tag_list = f.read().splitlines()
-        tag_list = np.array(tag_list)
-        return tag_list
-
-    # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label
-    def del_selfattention(self):
-        del self.tagging_head.embeddings
-        for layer in self.tagging_head.encoder.layer:
-            del layer.attention
-
-    def generate(self,
-                 image,
-                 sample=False,
-                 num_beams=3,
-                 max_length=30,
-                 min_length=10,
-                 top_p=0.9,
-                 repetition_penalty=1.0,
-                 tag_input=None,
-                 return_tag_predict=False):
-
-        image_embeds = self.visual_encoder(image)
-        image_atts = torch.ones(image_embeds.size()[:-1],
-                                dtype=torch.long).to(image.device)
-
-        # if not user specified tags, recognized image tags using image-tag recogntiion decoder
-        if tag_input == None:
-            image_cls_embeds = image_embeds[:, 0, :]
-            image_spatial_embeds = image_embeds[:, 1:, :]
-
-            bs = image_spatial_embeds.shape[0]
-            label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs, 1, 1)
-            tagging_embed = self.tagging_head(
-                encoder_embeds=label_embed,
-                encoder_hidden_states=image_embeds,
-                encoder_attention_mask=image_atts,
-                return_dict=False,
-                mode='tagging',
-            )
-
-            logits = self.fc(tagging_embed[0])
-
-            targets = torch.where(
-                torch.sigmoid(logits) > self.class_threshold.to(image.device),
-                torch.tensor(1.0).to(image.device),
-                torch.zeros(self.num_class).to(image.device))
-
-            tag = targets.cpu().numpy()
-
-            # delete some tags that may disturb captioning
-            tag[:, self.delete_tag_index] = 0
-
-            tag_input = []
-            for b in range(bs):
-                index = np.argwhere(tag[b] == 1)
-                token = self.tag_list[index].squeeze(axis=1)
-                tag_input.append(' | '.join(token))
-                
-        tag_output = tag_input
-
-        # beam search for text generation(default)
-        if not sample:
-            image_embeds = image_embeds.repeat_interleave(num_beams, dim=0)
-            tag_input_temp = []
-            for tag in tag_input:
-                for i in range(num_beams):
-                    tag_input_temp.append(tag)
-            tag_input = tag_input_temp
-
-        image_atts = torch.ones(image_embeds.size()[:-1],
-                                dtype=torch.long).to(image.device)
-
-        # tokenizer input tags
-        tag_input_tokenzier = self.tokenizer(tag_input,
-                                             padding='max_length',
-                                             truncation=True,
-                                             max_length=40,
-                                             return_tensors="pt").to(
-                                                 image.device)
-        encoder_input_ids = tag_input_tokenzier.input_ids
-        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id
-
-        # put input tag into image-tag interaction encoder to interact with image embeddings
-        output_tagembedding = self.tag_encoder(
-            encoder_input_ids,
-            attention_mask=tag_input_tokenzier.attention_mask,
-            encoder_hidden_states=image_embeds,
-            encoder_attention_mask=image_atts,
-            return_dict=True,
-        )
-
-        # prompt trick for better captioning, followed BLIP
-        prompt = [self.prompt] * image.size(0)
-        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(
-            image.device)
-        input_ids[:, 0] = self.tokenizer.bos_token_id
-        input_ids = input_ids[:, :-1]
-
-        if sample:
-            # nucleus sampling
-            model_kwargs = {
-                "encoder_hidden_states": output_tagembedding.last_hidden_state,
-                "encoder_attention_mask": None
-            }
-            outputs = self.text_decoder.generate(
-                input_ids=input_ids,
-                max_length=max_length,
-                min_length=min_length,
-                do_sample=True,
-                top_p=top_p,
-                num_return_sequences=1,
-                eos_token_id=self.tokenizer.sep_token_id,
-                pad_token_id=self.tokenizer.pad_token_id,
-                repetition_penalty=1.1,
-                **model_kwargs)
-        else:
-            # beam search (default)
-            model_kwargs = {
-                "encoder_hidden_states": output_tagembedding.last_hidden_state,
-                "encoder_attention_mask": None
-            }
-            outputs = self.text_decoder.generate(
-                input_ids=input_ids,
-                max_length=max_length,
-                min_length=min_length,
-                num_beams=num_beams,
-                eos_token_id=self.tokenizer.sep_token_id,
-                pad_token_id=self.tokenizer.pad_token_id,
-                repetition_penalty=repetition_penalty,
-                **model_kwargs)
-
-        captions = []
-        for output in outputs:
-            caption = self.tokenizer.decode(output, skip_special_tokens=True)
-            captions.append(caption[len(self.prompt):])
-        if return_tag_predict == True:
-            return  captions, tag_output
-        return captions
-
-
-# load Tag2Text pretrained model parameters
-def tag2text_caption(pretrained='', **kwargs):
-    model = Tag2Text_Caption(**kwargs)
-    if pretrained:
-        if kwargs['vit'] == 'swin_b':
-            model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)
-        else:
-            model, msg = load_checkpoint(model, pretrained)
-        print('vit:', kwargs['vit'])
-#         print('msg', msg)
-    return model
-
diff --git a/spaces/maha-vishnu/mahavishnu/README.md b/spaces/maha-vishnu/mahavishnu/README.md
deleted file mode 100644
index 61c3bed3be0714774f6ee2390afd46b600d6cdaa..0000000000000000000000000000000000000000
--- a/spaces/maha-vishnu/mahavishnu/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mahavishnu
-emoji: 🔥
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/marcusj83/MusicGenbruh/tests/__init__.py b/spaces/marcusj83/MusicGenbruh/tests/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/marcusj83/MusicGenbruh/tests/__init__.py
+++ /dev/null
@@ -1,5 +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.
diff --git a/spaces/marioboy/neil-breen/encoder_train.py b/spaces/marioboy/neil-breen/encoder_train.py
deleted file mode 100644
index b8740a894d615aadfe529cb36068fc8e3496125f..0000000000000000000000000000000000000000
--- a/spaces/marioboy/neil-breen/encoder_train.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from utils.argutils import print_args
-from encoder.train import train
-from pathlib import Path
-import argparse
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Trains the speaker encoder. You must have run encoder_preprocess.py first.",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    
-    parser.add_argument("run_id", type=str, help= \
-        "Name for this model instance. If a model state from the same run ID was previously "
-        "saved, the training will restart from there. Pass -f to overwrite saved states and "
-        "restart from scratch.")
-    parser.add_argument("clean_data_root", type=Path, help= \
-        "Path to the output directory of encoder_preprocess.py. If you left the default "
-        "output directory when preprocessing, it should be <datasets_root>/SV2TTS/encoder/.")
-    parser.add_argument("-m", "--models_dir", type=Path, default="encoder/saved_models/", help=\
-        "Path to the output directory that will contain the saved model weights, as well as "
-        "backups of those weights and plots generated during training.")
-    parser.add_argument("-v", "--vis_every", type=int, default=10, help= \
-        "Number of steps between updates of the loss and the plots.")
-    parser.add_argument("-u", "--umap_every", type=int, default=100, help= \
-        "Number of steps between updates of the umap projection. Set to 0 to never update the "
-        "projections.")
-    parser.add_argument("-s", "--save_every", type=int, default=500, help= \
-        "Number of steps between updates of the model on the disk. Set to 0 to never save the "
-        "model.")
-    parser.add_argument("-b", "--backup_every", type=int, default=7500, help= \
-        "Number of steps between backups of the model. Set to 0 to never make backups of the "
-        "model.")
-    parser.add_argument("-f", "--force_restart", action="store_true", help= \
-        "Do not load any saved model.")
-    parser.add_argument("--visdom_server", type=str, default="http://localhost")
-    parser.add_argument("--no_visdom", action="store_true", help= \
-        "Disable visdom.")
-    args = parser.parse_args()
-    
-    # Process the arguments
-    args.models_dir.mkdir(exist_ok=True)
-    
-    # Run the training
-    print_args(args, parser)
-    train(**vars(args))
-    
\ No newline at end of file
diff --git a/spaces/mattricesound/RemFx/app.py b/spaces/mattricesound/RemFx/app.py
deleted file mode 100644
index ad924d1bd55e09ce774dfb012bdfff48afaffa58..0000000000000000000000000000000000000000
--- a/spaces/mattricesound/RemFx/app.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import gradio as gr
-import torch
-import torchaudio
-import hydra
-from hydra import compose, initialize
-import random
-import os
-from remfx import effects
-
-cfg = None
-classifier = None
-models = {}
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-ALL_EFFECTS = effects.Pedalboard_Effects
-
-
-def init_hydra():
-    global cfg
-    initialize(config_path="cfg", job_name="remfx", version_base="2.0")
-    cfg = compose(config_name="config", overrides=["+exp=remfx_detect"])
-
-
-def load_models():
-    global classifier
-    print("Loading models")
-    classifier = hydra.utils.instantiate(cfg.classifier, _convert_="partial")
-    ckpt_path = cfg.classifier_ckpt
-    state_dict = torch.load(ckpt_path, map_location=device)["state_dict"]
-    classifier.load_state_dict(state_dict)
-    classifier.to(device)
-
-    for effect in cfg.ckpts:
-        model = hydra.utils.instantiate(cfg.ckpts[effect].model, _convert_="partial")
-        ckpt_path = cfg.ckpts[effect].ckpt_path
-        state_dict = torch.load(ckpt_path, map_location=device)["state_dict"]
-        model.load_state_dict(state_dict)
-        model.to(device)
-        models[effect] = model
-
-
-def audio_classification(audio_file):
-    audio, sr = torchaudio.load(audio_file)
-    audio = torchaudio.transforms.Resample(sr, cfg.sample_rate)(audio)
-    # Convert to mono
-    audio = audio.mean(0, keepdim=True)
-    # Add dimension for batch
-    audio = audio.unsqueeze(0)
-    audio = audio.to(device)
-
-    with torch.no_grad():
-        # Classify
-        print("Detecting effects")
-        labels = torch.tensor(classifier(audio))
-        labels_dict = {
-            ALL_EFFECTS[i].__name__.replace("RandomPedalboard", ""): labels[i].item()
-            for i in range(len(ALL_EFFECTS))
-        }
-    return labels_dict
-
-
-def audio_removal(audio_file, labels, threshold):
-    audio, sr = torchaudio.load(audio_file)
-    audio = torchaudio.transforms.Resample(sr, cfg.sample_rate)(audio)
-    # Convert to mono
-    audio = audio.mean(0, keepdim=True)
-    # Add dimension for batch
-    audio = audio.unsqueeze(0)
-    audio = audio.to(device)
-
-    label_names = [f"RandomPedalboard{lab['label']}" for lab in labels["confidences"]]
-    logits = torch.tensor([lab["confidence"] for lab in labels["confidences"]])
-    rem_fx_labels = torch.where(logits > threshold, 1.0, 0.0)
-    effects_present = [
-        name for name, effect in zip(label_names, rem_fx_labels) if effect == 1.0
-    ]
-    print("Removing effects:", effects_present)
-    # Remove effects
-    # Shuffle effects order
-    effects_order = cfg.inference_effects_ordering
-    random.shuffle(effects_order)
-    # Get the correct effect by search for names in effects_order
-    effects = [effect for effect in effects_order if effect in effects_present]
-    elem = audio
-    with torch.no_grad():
-        for effect in effects:
-            # Sample the model
-            elem = models[effect].model.sample(elem)
-        output = elem.squeeze(0)
-    waveform = gr.make_waveform((cfg.sample_rate, output[0].numpy()))
-
-    return waveform
-
-
-def ui():
-    css = """
-
-    #classifier {
-        padding-top: 40px;
-    }
-    #classifier .output-class {
-        display: none;
-
-    }
-    """
-    with gr.Blocks(css=css) as interface:
-        gr.HTML(
-            """
-                <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-                <div
-                    style="
-                    display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
-                    "
-                >
-                    <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
-                    RemFx: General Purpose Audio Effect Removal
-                    </h1>
-                </div> <p style="margin-bottom: 10px; font-size: 94%">
-                    <a href="https://arxiv.org/abs/2308.16177">[Paper]</a> <a href="https://csteinmetz1.github.io/RemFX/">[Project
-                    page]</a>
-                </p>
-                </div>
-            """
-        )
-        gr.HTML(
-            """
-                <div style="text-align: left;"> This is our demo for the paper General Purpose Audio Effect Removal. It uses the RemFX Detect system described in the paper to detect the audio effects that are present and remove them. <br>
-                To use the demo, use one of our curated examples or upload your own audio file and click submit. The system will then detect the effects present in the audio remove them if they meet the threshold. </div>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                effected_audio = gr.Audio(
-                    source="upload",
-                    type="filepath",
-                    label="File",
-                    interactive=True,
-                    elem_id="melody-input",
-                )
-                submit = gr.Button("Submit")
-                threshold = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.1,
-                    value=0.5,
-                    label="Detection Threshold",
-                )
-            with gr.Column():
-                classifier = gr.Label(
-                    num_top_classes=5, label="Effects Present", elem_id="classifier"
-                )
-                audio_output = gr.Video(label="Output")
-        gr.Examples(
-            fn=audio_removal,
-            examples=[
-                ["./input_examples/guitar.wav"],
-                ["./input_examples/vocal.wav"],
-                ["./input_examples/bass.wav"],
-                ["./input_examples/drums.wav"],
-                ["./input_examples/crazy_guitar.wav"],
-            ],
-            inputs=effected_audio,
-        )
-        submit.click(
-            audio_classification,
-            inputs=[effected_audio],
-            outputs=[classifier],
-            queue=False,
-            show_progress=False,
-        ).then(
-            audio_removal,
-            inputs=[effected_audio, classifier, threshold],
-            outputs=[audio_output],
-        )
-
-        interface.queue().launch()
-
-
-if __name__ == "__main__":
-    init_hydra()
-    load_models()
-    ui()
diff --git a/spaces/maxmax20160403/sovits5.0/vits_decoder/generator.py b/spaces/maxmax20160403/sovits5.0/vits_decoder/generator.py
deleted file mode 100644
index c0118548b5ab2396749831932dc530df1a7dc8d5..0000000000000000000000000000000000000000
--- a/spaces/maxmax20160403/sovits5.0/vits_decoder/generator.py
+++ /dev/null
@@ -1,200 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-
-from torch.nn import Conv1d
-from torch.nn import ConvTranspose1d
-from torch.nn.utils import weight_norm
-from torch.nn.utils import remove_weight_norm
-
-from .nsf import SourceModuleHnNSF
-from .bigv import init_weights, AMPBlock, SnakeAlias
-
-
-class SpeakerAdapter(nn.Module):
-
-    def __init__(self,
-                 speaker_dim,
-                 adapter_dim,
-                 epsilon=1e-5
-                 ):
-        super(SpeakerAdapter, self).__init__()
-        self.speaker_dim = speaker_dim
-        self.adapter_dim = adapter_dim
-        self.epsilon = epsilon
-        self.W_scale = nn.Linear(self.speaker_dim, self.adapter_dim)
-        self.W_bias = nn.Linear(self.speaker_dim, self.adapter_dim)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        torch.nn.init.constant_(self.W_scale.weight, 0.0)
-        torch.nn.init.constant_(self.W_scale.bias, 1.0)
-        torch.nn.init.constant_(self.W_bias.weight, 0.0)
-        torch.nn.init.constant_(self.W_bias.bias, 0.0)
-
-    def forward(self, x, speaker_embedding):
-        x = x.transpose(1, -1)
-        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
-        scale = self.W_scale(speaker_embedding)
-        bias = self.W_bias(speaker_embedding)
-        y *= scale.unsqueeze(1)
-        y += bias.unsqueeze(1)
-        y = y.transpose(1, -1)
-        return y
-
-
-class Generator(torch.nn.Module):
-    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
-    def __init__(self, hp):
-        super(Generator, self).__init__()
-        self.hp = hp
-        self.num_kernels = len(hp.gen.resblock_kernel_sizes)
-        self.num_upsamples = len(hp.gen.upsample_rates)
-        # speaker adaper, 256 should change by what speaker encoder you use
-        self.adapter = SpeakerAdapter(hp.vits.spk_dim, hp.gen.upsample_input)
-        # pre conv
-        self.conv_pre = Conv1d(hp.gen.upsample_input,
-                               hp.gen.upsample_initial_channel, 7, 1, padding=3)
-        # nsf
-        self.f0_upsamp = torch.nn.Upsample(
-            scale_factor=np.prod(hp.gen.upsample_rates))
-        self.m_source = SourceModuleHnNSF(sampling_rate=hp.data.sampling_rate)
-        self.noise_convs = nn.ModuleList()
-        # transposed conv-based upsamplers. does not apply anti-aliasing
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(hp.gen.upsample_rates, hp.gen.upsample_kernel_sizes)):
-            # print(f'ups: {i} {k}, {u}, {(k - u) // 2}')
-            # base
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        hp.gen.upsample_initial_channel // (2 ** i),
-                        hp.gen.upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2)
-                )
-            )
-            # nsf
-            if i + 1 < len(hp.gen.upsample_rates):
-                stride_f0 = np.prod(hp.gen.upsample_rates[i + 1:])
-                stride_f0 = int(stride_f0)
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        hp.gen.upsample_initial_channel // (2 ** (i + 1)),
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(
-                    Conv1d(1, hp.gen.upsample_initial_channel //
-                           (2 ** (i + 1)), kernel_size=1)
-                )
-
-        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = hp.gen.upsample_initial_channel // (2 ** (i + 1))
-            for k, d in zip(hp.gen.resblock_kernel_sizes, hp.gen.resblock_dilation_sizes):
-                self.resblocks.append(AMPBlock(ch, k, d))
-
-        # post conv
-        self.activation_post = SnakeAlias(ch)
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        # weight initialization
-        self.ups.apply(init_weights)
-
-    def forward(self, spk, x, f0):
-        # Perturbation
-        x = x + torch.randn_like(x)        
-        # adapter
-        x = self.adapter(x, spk)
-        x = self.conv_pre(x)
-        x = x * torch.tanh(F.softplus(x))
-        # nsf
-        f0 = f0[:, None]
-        f0 = self.f0_upsamp(f0).transpose(1, 2)
-        har_source = self.m_source(f0)
-        har_source = har_source.transpose(1, 2)
-
-        for i in range(self.num_upsamples):
-            # upsampling
-            x = self.ups[i](x)
-            # nsf
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            # AMP blocks
-            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
-
-        # post conv
-        x = self.activation_post(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()
-
-    def eval(self, inference=False):
-        super(Generator, self).eval()
-        # don't remove weight norm while validation in training loop
-        if inference:
-            self.remove_weight_norm()
-
-    def pitch2source(self, f0):
-        f0 = f0[:, None]
-        f0 = self.f0_upsamp(f0).transpose(1, 2)  # [1,len,1]
-        har_source = self.m_source(f0)
-        har_source = har_source.transpose(1, 2)  # [1,1,len]
-        return har_source
-
-    def source2wav(self, audio):
-        MAX_WAV_VALUE = 32768.0
-        audio = audio.squeeze()
-        audio = MAX_WAV_VALUE * audio
-        audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE-1)
-        audio = audio.short()
-        return audio.cpu().detach().numpy()
-
-    def inference(self, spk, x, har_source):
-        # adapter
-        x = self.adapter(x, spk)
-        x = self.conv_pre(x)
-        x = x * torch.tanh(F.softplus(x))
-
-        for i in range(self.num_upsamples):
-            # upsampling
-            x = self.ups[i](x)
-            # nsf
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            # AMP blocks
-            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
-
-        # post conv
-        x = self.activation_post(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
diff --git a/spaces/mbarnig/Mol_mer_e_chineesescht_Bild/app.py b/spaces/mbarnig/Mol_mer_e_chineesescht_Bild/app.py
deleted file mode 100644
index c48794448fab85e2a69bbb8106e1b9d72fcdae38..0000000000000000000000000000000000000000
--- a/spaces/mbarnig/Mol_mer_e_chineesescht_Bild/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import gradio as gr
-import numpy as np
-import paddlehub as hub
-import torch
-from huggingface_hub import hf_hub_download
-from transformers import pipeline
-from PIL import Image
-
-myTitle = "🎨 Mol mer e Bild ! 🇱🇺"
-myDescription = "### Zënter dem 20 November 2022 ass den Baidu Model net méi fräi zougänglech. Dës Demo fonctionéiert duerfir net méi an huet nëmmen nach en historeschen Wäert.<br>Beschreif an e puer Wierder wéi ee Bild de chineeseschen AI-Model ERNIE-ViLG Dir soll molen. Probéier richteg Lëtzebuergesch ze schreiwen fir Iwwersetzungsfeeler ze reduzéieren. Du kanns Dir vum <a href='https://spellchecker.lu/online-checker/'>Spellchecker</a> oder <a href='https://lod.lu'>LOD</a> dobäi hëllefen loossen. Vill Spaass!" 
-myArticle = "<h3>Informatiounen iwwert d'Text2Image Technologie.</h3><p>Bild-Generatoren op Basis vun neuronalen Netzer sinn säit e puer Wochen e vill diskutéiert Thema. Dës AI-Modeller goufen mat Milliounen Fotoen a Biller aus dem Internet trainéiert. Dorënner befannen sech och Biller vun Konschtwierker déi vun Websäiten vun Kënschtler an Konschtgalerien erofgelueden goufen. Vill Artisten sinn der Meenung dass mat AI generéiert Biller Déifstall a Plagiat sinn a si hunn der AI-Communautéit de Krich erkläert.</p><p>Déi bekanntsten Text2Image Modeller sinn <a href='https://openai.com/dall-e-2/'>DALL-E2</a> vun OpenAI, <a href='https://imagen.research.google'>Imagen</a> vun Google, <a href='https://stability.ai/blog/stable-diffusion-public-release'>Stable Diffusion</a> vun CompVis/Stability.ai souwéi <a href='https://www.craiyon.com'>Craiyon</a> (DALL-E mini) an <a href='https://www.midjourney.com/home/'>Midjourney</a> vun onofhängege Entwéckler. Manner bekannt an Europa sinn déi chineesesch Modeller <a href='https://github.com/PaddlePaddle/PaddleHub/tree/develop/modules/image/text_to_image/ernie_vilg'>ERNIE-ViLG</a> vun Baidu an <a href='https://github.com/THUDM/CogView2'>Cog2View</a> vun der Universitéit Tsinghua.</p><p>Well de Code vum Stable Diffusion Model open-source ass goufen an de leschten Deeg eng Hellewull vun Websäiten an Apps mat deem Model realiséiert. Domat kann elo Jänni a Männi fir e puer Euro honnerte vun impressionnante Konschtwierker generéieren. Et brauch een nëmmen eng Beschreiwung (als Text oder Sprooch) engem AI-Programm matzedeelen. Eleng am <a href='https://www.linkedin.com/posts/marco-barnig-b33074118_ai-text2image-stablediffusion-activity-6980124015168221185-ybo2'>AppStore vun Apple</a> fënnt een net manner wéi 24 Apps fir mam iPhone Biller duerch Text ze generéieren.</p><p>De Baidu Model ass am Moment deen eenzege deen et erlaabt eng fräi zougänglech Demo-Applikatioun ze programméieren. Dat erkläert firwat mäin Projet op dësem Model baséiert. Trotz engem performanten Serveur am Hannergrond dauert d'Generatioun vun engem Bild awer ongeféier 90 Sekonnen.</p><p>Fir d'Iwwersetzung vun der lëtzebuergescher Beschreiwung op chineesesch benotzen ech den <a href='https://huggingface.co/spaces/mbarnig/translation-lb-en-with-3-models'>AI-Model NLLB vu Facebook</a>. Als Kontroll maachen ech eng Réckiwwersetzung vun chineesesch op englesch, well verschidde Begrëffer falsch iwwersat ginn : aus Krokodil gëtt e Fësch, aus Giraff en Hirsch, aus Hues e Puppy, aus Cleopatra e Bouf mam Numm Cleobatra asw.</p><p>Et muss een bei der Beschreiwung vun sengem Wonsch-Bild och beuechten dass verschidde Begrëffer an China zenséiert ginn, wéi komescherweis d'Wuert Hond. Ausserdeem spigelen déi generéiert Biller déi chineesesch Kultur erëm.</p><p>Hei sinn e puer Beispiller vun Biller (mat den Beschreiwungen), déi ech mat dëser Demo generéiert hunn.</p><table><tr><th>Konschtwierk</th><th>Beschreiwung</th><th>Stil</th><th>Réckiwwersetzung</th></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/Panda_um_Velo.png' width='300'/></td><td>E Panda fiert um Velo</td><td>Aquarell</td><td>a panda riding a bike</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/deer-instead-of-giraff.png' width='300'/></td><td>Eng Giraff fiert um Velo</td><td>Aquarell</td><td>A deer riding a bicycle <br>(Iwwersetzungsproblem)</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/meedercher-laechelnd-aquarell.png' width='300'/></td><td>Zwee lächelnd Meedercher</td><td>Aquarell</td><td>two smiling girls</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/2girls-smiling-oil.png' width='300'/></td><td>Zwee lächelnd Meedercher</td><td>Uelechbild</td><td>two smiling girls</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/2meedercher_laachen.png' width='300'/></td><td>Zwee Meedercher laachen</td><td>Cartoon</td><td>two girls laughing</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/three-children-laughing.png' width='300'/></td><td>Dräi Kanner laachen</td><td>Uelechbild</td><td>three children laughing</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/drai-jongen-laachen.png' width='300'/></td><td>Dräi Jongen laachen</td><td>Cartoon</td><td>three boys laughing</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/cat-aquarell-6.png' width='300'/></td><td>Eng Kaatz</td><td>Aquarell</td><td>A cat</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/blummen-uelechbild.png' width='300'/></td><td>Blummen</td><td>Uelechbild</td><td>flowers</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/hues-besch.png' width='300'/></td><td>En Hues leeft am Bësch</td><td>D'Onendlechkeet exploréieren</td><td>a puppy in the woods <br>(Iwwersetzungproblem)</td></tr><tr><td><img src='https://www.web3.lu/wp-content/uploads/2022/09/cleopatra.png' width='300'></td><td>Cleopatra</td><td>Uelechbild</td><td>The film is about a young boy named Cleobatra<br>(Iwwersetzungsproblem)<br>chinesësch Iwwersetzung : 克利奥巴特拉</td></tr></table>"
-
-# model_ernie = hub.Module(name="ernie_vilg")
-model_nllb = "facebook/nllb-200-distilled-600M" 
-translator = pipeline("translation", model=model_nllb)
-
-IMAGE_STYLES = [
-    "Aquarell",
-    "Uelegbild",
-    #"Molerei",   # parameter error
-    "Cartoon",
-    # "Bläistëftzeechnung",  # parameter error
-    # "Kannermolerei",       # parameter error
-    "d'Onendlechkeet exploréieren"
-]
-
-myInputs = [
-    gr.Textbox(label="Beschreiwung"),
-    gr.Radio(label="Molerei Stiler", choices = IMAGE_STYLES, value = "Uelegbild"),
-]
-
-myOutputs = [
-    # gr.Gallery(label="Konschtwierker", type="pil"),  # :::  jpegImageFile is not iterable error when topk=1
-    # gr.Image(label="Konschtwierk", type="pil"),        # ::: jpegImageFile is not iterable error when topk>1
-    gr.Textbox(label="Iwwersetzungen")
-    ]
-
-def create(prompt, style):
-    chinese_translation = translator(prompt, src_lang="ltz_Latn", tgt_lang="zho_Hans") 
-    if style == "Aquarell":
-        ernie_style = '水彩'
-    elif style == "Uelegbild":
-        ernie_style = '油画'
-    # elif style == "Molerei":
-    #   ernie_style = '粉笔画'        
-    elif style == "Cartoon":
-        ernie_style = '卡通'        
-    # elif style == "Bläistëftzeechnung":
-    #    ernie_style = '蜡笔画'        
-    # elif style == "Kannermolerei":
-    #   ernie_style = '儿童画'        
-    elif style == "d'Onendlechkeet exploréieren":
-        ernie_style = '探索无限'        
-    else:
-        ernie_style = '水彩'  
-    """
-    try:
-        artworks = model_ernie.generate_image(
-            text_prompts = chinese_translation[0]['translation_text'],
-            style = ernie_style,
-            topk = 1,
-            visualization=False
-            )
-    except Exception as e:
-        error_text=str(e)
-        error = translator(error_text, src_lang="zho_Hans", tgt_lang="ltz_Latn") 
-        warnung = "Feeler! : " + str(error[0]['translation_text'])
-        return None, warnung 
-    """
-    english_back_translation = translator(chinese_translation[0]['translation_text'], src_lang="zho_Hans", tgt_lang="eng_Latn")
-    translation = "chinesësch Iwwersetzung : " + str(chinese_translation[0]['translation_text']) + " ; englesch Réckiwwersetzung : " + str(english_back_translation[0]['translation_text'])
-    # print(artworks)
-    # print(artworks[0])
-    return translation
-    # return artworks[0], translation
-    
-demo = gr.Interface(
-    fn=create,
-    inputs=myInputs,
-    outputs=myOutputs,
-    title=myTitle,
-    description=myDescription,
-    article = myArticle
-    )
-demo.launch()
\ No newline at end of file
diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/sampler.py b/spaces/merle/PROTEIN_GENERATOR/utils/sampler.py
deleted file mode 100644
index 7cacabb4ec2be386da683aaa569305887d2ed26c..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/utils/sampler.py
+++ /dev/null
@@ -1,1069 +0,0 @@
-#####################################################################
-############# PROTEIN SEQUENCE DIFFUSION SAMPLER ####################
-#####################################################################
-
-import sys, os, subprocess, pickle, time, json
-script_dir = os.path.dirname(os.path.realpath(__file__))
-sys.path = sys.path + [script_dir+'/../model/'] + [script_dir+'/']
-import shutil
-import glob
-import torch
-import numpy as np
-import copy
-import json
-import matplotlib.pyplot as plt
-from torch import nn
-import math
-import re
-import pickle
-import pandas as pd
-import random
-from copy import deepcopy
-import time
-from collections import namedtuple
-import math
-from torch.nn.parallel import DistributedDataParallel as DDP
-from RoseTTAFoldModel import RoseTTAFoldModule
-from util import *
-from inpainting_util import *
-from kinematics import get_init_xyz, xyz_to_t2d
-import parsers_inference as parsers
-import diff_utils
-import pickle
-import pdb
-from utils.calc_dssp import annotate_sse
-from potentials import POTENTIALS
-from diffusion import GaussianDiffusion_SEQDIFF
-
-MODEL_PARAM ={
-        "n_extra_block"   : 4,
-        "n_main_block"    : 32,
-        "n_ref_block"     : 4,
-        "d_msa"           : 256,
-        "d_msa_full"      : 64,
-        "d_pair"          : 128,
-        "d_templ"         : 64,
-        "n_head_msa"      : 8,
-        "n_head_pair"     : 4,
-        "n_head_templ"    : 4,
-        "d_hidden"        : 32,
-        "d_hidden_templ"  : 32,
-        "p_drop"       : 0.0
-        }
-
-SE3_PARAMS = {
-        "num_layers_full"    : 1,
-        "num_layers_topk" : 1,
-        "num_channels"  : 32,
-        "num_degrees"   : 2,
-        "l0_in_features_full": 8,
-        "l0_in_features_topk" : 64,
-        "l0_out_features_full": 8,
-        "l0_out_features_topk" : 64,
-        "l1_in_features": 3,
-        "l1_out_features": 2,
-        "num_edge_features_full": 32,
-        "num_edge_features_topk": 64,
-        "div": 4,
-        "n_heads": 4
-        }
-
-SE3_param_full = {}
-SE3_param_topk = {}
-
-for param, value in SE3_PARAMS.items():
-    if "full" in param:
-        SE3_param_full[param[:-5]] = value
-    elif "topk" in param:
-        SE3_param_topk[param[:-5]] = value
-    else: # common arguments
-        SE3_param_full[param] = value
-        SE3_param_topk[param] = value
-        
-MODEL_PARAM['SE3_param_full'] = SE3_param_full
-MODEL_PARAM['SE3_param_topk'] = SE3_param_topk
-
-DEFAULT_CKPT = '/home/jgershon/models/SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt' #this is the one with good sequences
-LOOP_CHECKPOINT = '/home/jgershon/models/SEQDIFF_221202_AB_NOSTATE_fromBASE_mod30.pt'
-t1d_29_CKPT = '/home/jgershon/models/SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'
-
-class SEQDIFF_sampler:
-    
-    '''
-        MODULAR SAMPLER FOR SEQUENCE DIFFUSION
-        
-        - the goal for modularizing this code is to make it as 
-          easy as possible to edit and mix functions around 
-        
-        - in the base implementation here this can handle the standard
-          inference mode with default passes through the model, different 
-          forms of partial diffusion, and linear symmetry
-    
-    '''
-    
-    def __init__(self, args=None):
-        '''
-            set args and DEVICE as well as other default params
-        '''
-        self.args = args
-        self.DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-        self.conversion = 'ARNDCQEGHILKMFPSTWYVX-'
-        self.dssp_dict = {'X':3,'H':0,'E':1,'L':2}
-        self.MODEL_PARAM = MODEL_PARAM
-        self.SE3_PARAMS = SE3_PARAMS
-        self.SE3_param_full = SE3_param_full
-        self.SE3_param_topk = SE3_param_topk
-        self.use_potentials = False
-        self.reset_design_num()
-    
-    def set_args(self, args):
-        '''
-            set new arguments if iterating through dictionary of multiple arguments
-            
-            # NOTE : args pertaining to the model will not be considered as this is
-                     used to sample more efficiently without having to reload model for 
-                     different sets of args
-        '''
-        self.args = args
-        self.diffuser_init()
-        if self.args['potentials'] not in ['', None]:
-            self.potential_init()
-        
-    def reset_design_num(self):
-        '''
-            reset design num to 0
-        '''
-        self.design_num = 0
-    
-    def diffuser_init(self):
-        '''
-            set up diffuser object of GaussianDiffusion_SEQDIFF
-        '''
-        self.diffuser = GaussianDiffusion_SEQDIFF(T=self.args['T'],
-                schedule=self.args['noise_schedule'],
-                sample_distribution=self.args['sample_distribution'],
-                sample_distribution_gmm_means=self.args['sample_distribution_gmm_means'],
-                sample_distribution_gmm_variances=self.args['sample_distribution_gmm_variances'],
-                ) 
-        self.betas = self.diffuser.betas
-        self.alphas = 1-self.betas
-        self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
-        
-    def make_hotspot_features(self):
-        '''
-            set up hotspot features
-        '''
-        # initialize hotspot features to all 0s
-        self.features['hotspot_feat'] = torch.zeros(self.features['L'])
-        
-        # if hotspots exist in args then make hotspot features
-        if self.args['hotspots'] != None:
-            self.features['hotspots'] = [(x[0],int(x[1:])) for x in self.args['hotspots'].split(',')]
-            for n,x in enumerate(self.features['mappings']['complex_con_ref_pdb_idx']):
-                if x in self.features['hotspots']:
-                    self.features['hotspot_feat'][self.features['mappings']['complex_con_hal_idx0'][n]] = 1.0
-                    
-    def make_dssp_features(self):
-        '''
-            set up dssp features
-        '''
-        
-        assert not ((self.args['secondary_structure'] != None) and (self.args['dssp_pdb'] != None)), \
-               f'You are attempting to provide both dssp_pdb and/or secondary_secondary structure, please choose one or the other'
-        
-        # initialize with all zeros
-        self.features['dssp_feat'] = torch.zeros(self.features['L'],4)
-        
-        if self.args['secondary_structure'] != None:
-            
-            self.features['secondary_structure'] = [self.dssp_dict[x.upper()] for x in self.args['secondary_structure']]
-            
-            assert len(self.features['secondary_structure']*self.features['sym'])+self.features['cap']*2 == self.features['L'], \
-                f'You have specified a secondary structure string that does not match your design length'
-            
-            self.features['dssp_feat'] = torch.nn.functional.one_hot(
-                torch.tensor(self.features['cap_dssp']+self.features['secondary_structure']*self.features['sym']+self.features['cap_dssp']),
-                num_classes=4)
-        
-        elif self.args['dssp_pdb'] != None:
-            dssp_xyz = torch.from_numpy(parsers.parse_pdb(self.args['dssp_pdb'])['xyz'][:,:,:])
-            dssp_pdb = annotate_sse(np.array(dssp_xyz[:,1,:].squeeze()), percentage_mask=0)
-            #we assume binder is chain A
-            self.features['dssp_feat'][:dssp_pdb.shape[0]] = dssp_pdb
-        
-        elif (self.args['helix_bias'] + self.args['strand_bias'] + self.args['loop_bias']) > 0.0:
-
-            tmp_mask = torch.rand(self.features['L']) < self.args['helix_bias']
-            self.features['dssp_feat'][tmp_mask,0] = 1.0
-
-            tmp_mask = torch.rand(self.features['L']) < self.args['strand_bias']
-            self.features['dssp_feat'][tmp_mask,1] = 1.0
-
-            tmp_mask = torch.rand(self.features['L']) < self.args['loop_bias']
-            self.features['dssp_feat'][tmp_mask,2] = 1.0
-        
-        #contigs get mask label
-        self.features['dssp_feat'][self.features['mask_str'][0],3] = 1.0
-        #anything not labeled gets mask label
-        mask_index = torch.where(torch.sum(self.features['dssp_feat'], dim=1) == 0)[0]
-        self.features['dssp_feat'][mask_index,3] = 1.0
-    
-    def model_init(self):
-        '''
-            get model set up and choose checkpoint
-        '''
-        
-        if self.args['checkpoint'] == None:
-            self.args['checkpoint'] = DEFAULT_CKPT
-
-        self.MODEL_PARAM['d_t1d'] = self.args['d_t1d']
-        
-        # decide based on input args what checkpoint to load
-        if self.args['hotspots'] != None or self.args['secondary_structure'] != None \
-            or (self.args['helix_bias'] + self.args['strand_bias'] + self.args['loop_bias']) > 0 \
-            or self.args['dssp_pdb'] != None and self.args['checkpoint'] == DEFAULT_CKPT:
-            
-            self.MODEL_PARAM['d_t1d'] = 29
-            print('You are using features only compatible with a newer model, switching checkpoint...')
-            self.args['checkpoint'] = t1d_29_CKPT
-        
-        elif self.args['loop_design'] and self.args['checkpoint'] == DEFAULT_CKPT:
-            print('Switched to loop design checkpoint')
-            self.args['checkpoint'] = LOOP_CHECKPOINT
-            
-        # check to make sure checkpoint chosen exists
-        if not os.path.exists(self.args['checkpoint']):
-            print('WARNING: couldn\'t find checkpoint')
-            
-        self.ckpt = torch.load(self.args['checkpoint'], map_location=self.DEVICE)
-
-        # check to see if [loader_param, model_param, loss_param] is in checkpoint
-        #   if so then you are using v2 of inference with t2d bug fixed
-        self.v2_mode = False
-        if 'model_param' in self.ckpt.keys():
-            print('You are running a new v2 model switching into v2 inference mode')
-            self.v2_mode = True
-
-            for k in self.MODEL_PARAM.keys():
-                if k in self.ckpt['model_param'].keys():
-                    self.MODEL_PARAM[k] = self.ckpt['model_param'][k]
-                else:
-                    print(f'no match for {k} in loaded model params')
-
-        # make model and load checkpoint
-        print('Loading model checkpoint...')
-        self.model = RoseTTAFoldModule(**self.MODEL_PARAM).to(self.DEVICE)
-
-        model_state = self.ckpt['model_state_dict']
-        self.model.load_state_dict(model_state, strict=False)
-        self.model.eval()
-        print('Successfully loaded model checkpoint')
-    
-    def feature_init(self):
-        '''
-            featurize pdb and contigs and choose type of diffusion
-        '''
-        # initialize features dictionary for all example features
-        self.features = {}
-        
-        # set up params
-        self.loader_params = {'MAXCYCLE':self.args['n_cycle'],'TEMPERATURE':self.args['temperature'], 'DISTANCE':self.args['min_decoding_distance']}
-
-        # symmetry
-        self.features['sym'] = self.args['symmetry']
-        self.features['cap'] = self.args['symmetry_cap']
-        self.features['cap_dssp'] = [self.dssp_dict[x.upper()] for x in 'H'*self.features['cap']]
-        if self.features['sym'] > 1:
-            print(f"Input sequence symmetry {self.features['sym']}")
-       
-        assert (self.args['contigs'] in [('0'),(0),['0'],[0]] ) ^ (self.args['sequence'] in ['',None]),\
-                f'You are specifying contigs ({self.args["contigs"]}) and sequence ({self.args["sequence"]})  (or neither), please specify one or the other'
-        
-        # initialize trb dictionary
-        self.features['trb_d'] = {}
-        
-        if self.args['pdb'] == None and self.args['sequence'] not in ['', None]:
-            print('Preparing sequence input')
-
-            allowable_aas = [x for x in self.conversion[:-1]]
-            for x in self.args['sequence']: assert x in allowable_aas, f'Amino Acid {x} is undefinded, please only use standart 20 AAs'
-            self.features['seq'] = torch.tensor([self.conversion.index(x) for x in self.args['sequence']])
-            self.features['xyz_t'] = torch.full((1,1,len(self.args['sequence']),27,3), np.nan)
-
-            self.features['mask_str'] = torch.zeros(len(self.args['sequence'])).long()[None,:].bool()
-            
-            #added check for if in partial diffusion mode will mask
-            if self.args['sampling_temp'] == 1.0:
-                self.features['mask_seq'] = torch.tensor([0 if x == 'X' else 1 for x in self.args['sequence']]).long()[None,:].bool()
-            else:
-                self.features['mask_seq'] = torch.zeros(len(self.args['sequence'])).long()[None,:].bool()
-
-            self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
-
-            self.features['idx_pdb'] = torch.tensor([i for i in range(len(self.args['sequence']))])[None,:]
-            conf_1d = torch.ones_like(self.features['seq'])
-            conf_1d[~self.features['mask_str'][0]] = 0
-            self.features['seq_hot'], self.features['msa'], \
-                self.features['msa_hot'], self.features['msa_extra_hot'], _ = MSAFeaturize_fixbb(self.features['seq'][None,:],self.loader_params)
-            self.features['t1d'] = TemplFeaturizeFixbb(self.features['seq'], conf_1d=conf_1d)[None,None,:]
-            self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
-            self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
-            self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
-            self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
-        
-            self.max_t = int(self.args['T']*self.args['sampling_temp'])
-            
-            self.features['pdb_idx'] = [('A',i+1) for i in range(len(self.args['sequence']))]
-            self.features['trb_d']['inpaint_str'] = self.features['mask_str'][0]
-            self.features['trb_d']['inpaint_seq'] = self.features['mask_seq'][0]
-
-        else:
-            
-            assert not (self.args['pdb'] == None and self.args['sampling_temp'] != 1.0),\
-                    f'You must specify a pdb if attempting to use contigs with partial diffusion, else partially diffuse sequence input'
-            
-            if self.args['pdb'] == None:
-                self.features['parsed_pdb'] = {'seq':np.zeros((1),'int64'),
-                                                'xyz':np.zeros((1,27,3),'float32'),
-                                                'idx':np.zeros((1),'int64'),
-                                                'mask':np.zeros((1,27), bool),
-                                                'pdb_idx':['A',1]}
-            else:
-                # parse input pdb
-                self.features['parsed_pdb'] = parsers.parse_pdb(self.args['pdb'])
-            
-            # generate contig map
-            self.features['rm'] = ContigMap(self.features['parsed_pdb'], self.args['contigs'], 
-                                            self.args['inpaint_seq'], self.args['inpaint_str'], 
-                                            self.args['length'], self.args['ref_idx'],
-                                            self.args['hal_idx'], self.args['idx_rf'], 
-                                            self.args['inpaint_seq_tensor'], self.args['inpaint_str_tensor'])
-            self.features['mappings'] = get_mappings(self.features['rm'])
-
-            self.features['pdb_idx'] = self.features['rm'].hal
-
-                ### PREPARE FEATURES DEPENDING ON TYPE OF ARGUMENTS SPECIFIED ###
-            
-            # FULL DIFFUSION MODE
-            if self.args['trb'] == None and self.args['sampling_temp'] == 1.0:
-                # process contigs and generate masks
-                self.features['mask_str'] = torch.from_numpy(self.features['rm'].inpaint_str)[None,:]
-                self.features['mask_seq'] = torch.from_numpy(self.features['rm'].inpaint_seq)[None,:]
-                self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
-
-                seq_input = torch.from_numpy(self.features['parsed_pdb']['seq'])
-                xyz_input = torch.from_numpy(self.features['parsed_pdb']['xyz'][:,:,:])
-
-                self.features['xyz_t'] = torch.full((1,1,len(self.features['rm'].ref),27,3), np.nan)
-                self.features['xyz_t'][:,:,self.features['rm'].hal_idx0,:14,:] = xyz_input[self.features['rm'].ref_idx0,:14,:][None, None,...]
-                self.features['seq'] = torch.full((1,len(self.features['rm'].ref)),20).squeeze()
-                self.features['seq'][self.features['rm'].hal_idx0] = seq_input[self.features['rm'].ref_idx0]
-                
-                # template confidence 
-                conf_1d = torch.ones_like(self.features['seq'])*float(self.args['tmpl_conf'])
-                conf_1d[~self.features['mask_str'][0]] = 0 # zero confidence for places where structure is masked
-                seq_masktok = torch.where(self.features['seq'] == 20, 21, self.features['seq'])
-
-                # Get sequence and MSA input features 
-                self.features['seq_hot'], self.features['msa'], \
-                    self.features['msa_hot'], self.features['msa_extra_hot'], _ = MSAFeaturize_fixbb(seq_masktok[None,:],self.loader_params)
-                self.features['t1d'] = TemplFeaturizeFixbb(self.features['seq'], conf_1d=conf_1d)[None,None,:]
-                self.features['idx_pdb'] = torch.from_numpy(np.array(self.features['rm'].rf)).int()[None,:]
-                self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
-                self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
-                self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
-                self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
-
-                self.max_t = int(self.args['T']*self.args['sampling_temp'])
-            
-            # PARTIAL DIFFUSION MODE, NO INPUT TRB
-            elif self.args['trb'] != None:
-                print('Running in partial diffusion mode . . .')
-                self.features['trb_d'] = np.load(self.args['trb'], allow_pickle=True)
-                self.features['mask_str'] = torch.from_numpy(self.features['trb_d']['inpaint_str'])[None,:]
-                self.features['mask_seq'] = torch.from_numpy(self.features['trb_d']['inpaint_seq'])[None,:]
-                self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
-
-                self.features['seq'] = torch.from_numpy(self.features['parsed_pdb']['seq'])
-                self.features['xyz_t'] = torch.from_numpy(self.features['parsed_pdb']['xyz'][:,:,:])[None,None,...]
-
-                if self.features['mask_seq'].shape[1] == 0:
-                    self.features['mask_seq'] = torch.zeros(self.features['seq'].shape[0])[None].bool()
-                if self.features['mask_str'].shape[1] == 0:
-                    self.features['mask_str'] = torch.zeros(self.features['xyz_t'].shape[2])[None].bool()
-
-                idx_pdb = []
-                chains_used = [self.features['parsed_pdb']['pdb_idx'][0][0]]
-                idx_jump = 0
-                for i,x in enumerate(self.features['parsed_pdb']['pdb_idx']):
-                    if x[0] not in chains_used:
-                        chains_used.append(x[0])
-                        idx_jump += 200
-                    idx_pdb.append(idx_jump+i)
-                    
-                self.features['idx_pdb'] = torch.tensor(idx_pdb)[None,:]
-                conf_1d = torch.ones_like(self.features['seq'])
-                conf_1d[~self.features['mask_str'][0]] = 0
-                self.features['seq_hot'], self.features['msa'], \
-                    self.features['msa_hot'], self.features['msa_extra_hot'], _ = MSAFeaturize_fixbb(self.features['seq'][None,:],self.loader_params)
-                self.features['t1d'] = TemplFeaturizeFixbb(self.features['seq'], conf_1d=conf_1d)[None,None,:]
-                self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
-                self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
-                self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
-                self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
-                
-                self.max_t = int(self.args['T']*self.args['sampling_temp'])
-                
-            else:
-                print('running in partial diffusion mode, with no trb input, diffusing whole input')
-                self.features['seq'] = torch.from_numpy(self.features['parsed_pdb']['seq'])
-                self.features['xyz_t'] = torch.from_numpy(self.features['parsed_pdb']['xyz'][:,:,:])[None,None,...]
-
-                if self.args['contigs'] in [('0'),(0),['0'],[0]]:
-                    print('no contigs given partially diffusing everything')
-                    self.features['mask_str'] = torch.zeros(self.features['xyz_t'].shape[2]).long()[None,:].bool()
-                    self.features['mask_seq'] = torch.zeros(self.features['seq'].shape[0]).long()[None,:].bool()
-                    self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
-                else:
-                    print('found contigs setting up masking for partial diffusion')
-                    self.features['mask_str'] = torch.from_numpy(self.features['rm'].inpaint_str)[None,:]
-                    self.features['mask_seq'] = torch.from_numpy(self.features['rm'].inpaint_seq)[None,:]
-                    self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
-
-                idx_pdb = []
-                chains_used = [self.features['parsed_pdb']['pdb_idx'][0][0]]
-                idx_jump = 0
-                for i,x in enumerate(self.features['parsed_pdb']['pdb_idx']):
-                    if x[0] not in chains_used:
-                        chains_used.append(x[0])
-                        idx_jump += 200
-                    idx_pdb.append(idx_jump+i)
-
-                self.features['idx_pdb'] = torch.tensor(idx_pdb)[none,:]
-                conf_1d = torch.ones_like(self.features['seq'])
-                conf_1d[~self.features['mask_str'][0]] = 0
-                self.features['seq_hot'], self.features['msa'], \
-                    self.features['msa_hot'], self.features['msa_extra_hot'], _ = msafeaturize_fixbb(self.features['seq'][none,:],self.loader_params)
-                self.features['t1d'] = templfeaturizefixbb(self.features['seq'], conf_1d=conf_1d)[none,none,:]
-                self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
-                self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
-                self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
-                self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
-            
-                self.max_t = int(self.args['t']*self.args['sampling_temp'])
-            
-        # set L
-        self.features['L'] = self.features['seq'].shape[0]
-        
-    def potential_init(self):
-        '''
-            initialize potential functions being used and return list of potentails
-        '''
-        
-        potentials = self.args['potentials'].split(',')
-        potential_scale = [float(x) for x in self.args['potential_scale'].split(',')]
-        assert len(potentials) == len(potential_scale), \
-            f'Please make sure number of potentials matches potential scales specified'
-        
-        self.potential_list = []
-        for p,s in zip(potentials, potential_scale):
-            assert p in POTENTIALS.keys(), \
-                f'The potential specified: {p} , does not match into POTENTIALS dictionary in potentials.py'
-            print(f'Using potential: {p}')
-            self.potential_list.append(POTENTIALS[p](self.args, self.features, s, self.DEVICE))
-        
-        self.use_potentials = True
-        
-    def setup(self, init_model=True):
-        '''
-            run init model and init features to get everything prepped to go into model
-        '''
-        
-        # initialize features
-        self.feature_init()
-        
-        # initialize potential
-        if self.args['potentials'] not in ['', None]:  
-            self.potential_init()
-        
-        # make hostspot features
-        self.make_hotspot_features()
-        
-        # make dssp features
-        self.make_dssp_features()
-        
-        # diffuse sequence and mask features
-        self.features['seq'], self.features['msa_masked'], \
-        self.features['msa_full'], self.features['xyz_t'], self.features['t1d'], \
-        self.features['seq_diffused'] = diff_utils.mask_inputs(self.features['seq_hot'],
-                                                               self.features['msa_hot'],
-                                                               self.features['msa_extra_hot'],
-                                                               self.features['xyz_t'],
-                                                               self.features['t1d'],
-                                                               input_seq_mask=self.features['mask_seq'],
-                                                               input_str_mask=self.features['mask_str'],
-                                                               input_t1dconf_mask=self.features['blank_mask'],
-                                                               diffuser=self.diffuser,
-                                                               t=self.max_t,
-                                                               MODEL_PARAM=self.MODEL_PARAM,
-                                                               hotspots=self.features['hotspot_feat'],
-                                                               dssp=self.features['dssp_feat'],
-                                                               v2_mode=self.v2_mode)
-        
-        
-        # move features to device 
-        self.features['idx_pdb'] = self.features['idx_pdb'].long().to(self.DEVICE, non_blocking=True) # (B, L)
-        self.features['mask_str'] = self.features['mask_str'][None].to(self.DEVICE, non_blocking=True) # (B, L)
-        self.features['xyz_t'] = self.features['xyz_t'][None].to(self.DEVICE, non_blocking=True)
-        self.features['t1d'] = self.features['t1d'][None].to(self.DEVICE, non_blocking=True)
-        self.features['seq'] = self.features['seq'][None].type(torch.float32).to(self.DEVICE, non_blocking=True)
-        self.features['msa'] = self.features['msa'].type(torch.float32).to(self.DEVICE, non_blocking=True)
-        self.features['msa_masked'] = self.features['msa_masked'][None].type(torch.float32).to(self.DEVICE, non_blocking=True)
-        self.features['msa_full'] = self.features['msa_full'][None].type(torch.float32).to(self.DEVICE, non_blocking=True)
-        self.ti_dev =  torsion_indices.to(self.DEVICE, non_blocking=True)
-        self.ti_flip = torsion_can_flip.to(self.DEVICE, non_blocking=True)
-        self.ang_ref = reference_angles.to(self.DEVICE, non_blocking=True)
-        self.features['xyz_prev'] = torch.clone(self.features['xyz_t'][0])
-        self.features['seq_diffused'] = self.features['seq_diffused'][None].to(self.DEVICE, non_blocking=True)
-        self.features['B'], _, self.features['N'], self.features['L'] = self.features['msa'].shape
-        self.features['t2d'] = xyz_to_t2d(self.features['xyz_t'])
-
-        # get alphas
-        self.features['alpha'], self.features['alpha_t'] = diff_utils.get_alphas(self.features['t1d'], self.features['xyz_t'], 
-                                                                                 self.features['B'], self.features['L'], 
-                                                                                 self.ti_dev, self.ti_flip, self.ang_ref)
-
-        # processing template coordinates
-        self.features['xyz_t'] = get_init_xyz(self.features['xyz_t'])
-        self.features['xyz_prev'] = get_init_xyz(self.features['xyz_prev'][:,None]).reshape(self.features['B'], self.features['L'], 27, 3)
-        
-        # initialize extra features to none
-        self.features['xyz'] = None
-        self.features['pred_lddt'] = None
-        self.features['logit_s'] = None
-        self.features['logit_aa_s'] = None
-        self.features['best_plddt'] = 0
-        self.features['best_pred_lddt'] = torch.zeros_like(self.features['mask_str'])[0].float()
-        self.features['msa_prev'] = None
-        self.features['pair_prev'] = None
-        self.features['state_prev'] = None
-
-        
-    def symmetrize_seq(self, x):
-        '''
-            symmetrize x according sym in features
-        '''
-        assert (self.features['L']-self.features['cap']*2) % self.features['sym'] == 0, f'symmetry does not match for input length'
-        assert x.shape[0] == self.features['L'], f'make sure that dimension 0 of input matches to L'
-        
-        n_cap = torch.clone(x[:self.features['cap']])
-        c_cap = torch.clone(x[-self.features['cap']+1:])
-        sym_x = torch.clone(x[self.features['cap']:self.features['sym']]).repeat(self.features['sym'],1)
-        
-        return torch.cat([n_cap,sym_x,c_cap], dim=0)
-        
-    def predict_x(self):
-        '''
-            take step using X_t-1 features to predict Xo
-        '''
-        self.features['seq'], \
-        self.features['xyz'], \
-        self.features['pred_lddt'], \
-        self.features['logit_s'], \
-        self.features['logit_aa_s'], \
-        self.features['alpha'], \
-        self.features['msa_prev'], \
-        self.features['pair_prev'], \
-        self.features['state_prev'] \
-        = diff_utils.take_step_nostate(self.model,
-        self.features['msa_masked'], 
-        self.features['msa_full'], 
-        self.features['seq'], 
-        self.features['t1d'], 
-        self.features['t2d'], 
-        self.features['idx_pdb'], 
-        self.args['n_cycle'],
-        self.features['xyz_prev'], 
-        self.features['alpha'], 
-        self.features['xyz_t'],
-        self.features['alpha_t'],
-        self.features['seq_diffused'],
-        self.features['msa_prev'], 
-        self.features['pair_prev'],
-        self.features['state_prev'])
-    
-    def self_condition_seq(self):
-        '''
-            get previous logits and set at t1d template
-        '''
-        self.features['t1d'][:,:,:,:21] = self.features['logit_aa_s'][0,:21,:].permute(1,0)
-
-    def self_condition_str_scheduled(self):
-        '''
-            unmask random fraction of residues according to timestep
-        '''
-        print('self_conditioning on strcuture')
-        xyz_prev_template = torch.clone(self.features['xyz'])[None]
-        self_conditioning_mask = torch.rand(self.features['L']) < self.diffuser.alphas_cumprod[t]
-        xyz_prev_template[:,:,~self_conditioning_mask] = float('nan')
-        xyz_prev_template[:,:,self.features['mask_str'][0][0]] = float('nan')
-        xyz_prev_template[:,:,:,3:] = float('nan')
-        t2d_sc = xyz_to_t2d(xyz_prev_template)
-
-        xyz_t_sc = torch.zeros_like(self.features['xyz_t'][:,:1])
-        xyz_t_sc[:,:,:,:3] = xyz_prev_template[:,:,:,:3]
-        xyz_t_sc[:,:,:,3:] = float('nan')
-
-        t1d_sc = torch.clone(self.features['t1d'][:,:1])
-        t1d_sc[:,:,~self_conditioning_mask] = 0
-        t1d_sc[:,:,mask_str[0][0]] = 0
-
-        self.features['t1d'] = torch.cat([self.features['t1d'][:,:1],t1d_sc], dim=1)
-        self.features['t2d'] = torch.cat([self.features['t2d'][:,:1],t2d_sc], dim=1)
-        self.features['xyz_t'] = torch.cat([self.features['xyz_t'][:,:1],xyz_t_sc], dim=1)
-
-        self.features['alpha'], self.features['alpha_t'] = diff_utils.get_alphas(self.features['t1d'], self.features['xyz_t'], 
-                                                                                 self.features['B'], self.features['L'], 
-                                                                                 self.ti_dev, self.ti_flip, self.ang_ref)
-        self.features['xyz_t'] = get_init_xyz(self.features['xyz_t'])
-
-    
-    def self_condition_str(self):
-        '''
-            conditioining on strucutre in NAR way
-        '''
-        print("conditioning on structure for NAR structure noising")
-        xyz_t_str_sc           = torch.zeros_like(self.features['xyz_t'][:,:1])
-        xyz_t_str_sc[:,:,:,:3] = torch.clone(self.features['xyz'])[None]
-        xyz_t_str_sc[:,:,:,3:] = float('nan')
-        t2d_str_sc             = xyz_to_t2d(self.features['xyz_t'])
-        t1d_str_sc             = torch.clone(self.features['t1d'])
-
-        self.features['xyz_t'] = torch.cat([self.features['xyz_t'],xyz_t_str_sc], dim=1)
-        self.features['t2d']   = torch.cat([self.features['t2d'],t2d_str_sc], dim=1)
-        self.features['t1d']   = torch.cat([self.features['t1d'],t1d_str_sc], dim=1)
-    
-    def save_step(self):
-        '''
-            add step to trajectory dictionary
-        '''
-        self.trajectory[f'step{self.t}'] = (self.features['xyz'].squeeze().detach().cpu(), 
-                                            self.features['logit_aa_s'][0,:21,:].permute(1,0).detach().cpu(), 
-                                            self.features['seq_diffused'][0,:,:21].detach().cpu())
-    
-    def noise_x(self):
-        '''
-            get X_t-1 from predicted Xo
-        '''
-        # sample x_t-1
-        self.features['post_mean'] = self.diffuser.q_sample(self.features['seq_out'], self.t, DEVICE=self.DEVICE)
-
-        if self.features['sym'] > 1:
-            self.features['post_mean'] = self.symmetrize_seq(self.features['post_mean'])
-
-        # update seq and masks
-        self.features['seq_diffused'][0,~self.features['mask_seq'][0],:21] = self.features['post_mean'][~self.features['mask_seq'][0],...]
-        self.features['seq_diffused'][0,:,21] = 0.0
-        
-        # did not know we were clamping seq
-        self.features['seq_diffused'] = torch.clamp(self.features['seq_diffused'], min=-3, max=3)
-        
-        # match other features to seq diffused
-        self.features['seq'] = torch.argmax(self.features['seq_diffused'], dim=-1)[None]
-        self.features['msa_masked'][:,:,:,:,:22] = self.features['seq_diffused']
-        self.features['msa_masked'][:,:,:,:,22:44] = self.features['seq_diffused']
-        self.features['msa_full'][:,:,:,:,:22] = self.features['seq_diffused']
-        self.features['t1d'][:1,:,:,22] = 1-int(self.t)/self.args['T']
-
-        
-    def apply_potentials(self):
-        '''
-            apply potentials
-        '''
-        
-        grads = torch.zeros_like(self.features['seq_out'])
-        for p in self.potential_list:
-            grads += p.get_gradients(self.features['seq_out'])
-        
-        self.features['seq_out'] += (grads/len(self.potential_list))
-        
-    def generate_sample(self):
-        '''
-            sample from the model 
-            
-            this function runs the full sampling loop
-        '''
-        # setup example
-        self.setup()
-        
-        # start time
-        self.start_time = time.time()
-        
-        # set up dictionary to save at each step in trajectory
-        self.trajectory = {}
-        
-        # set out prefix
-        self.out_prefix = self.args['out']+f'_{self.design_num:06}'
-        print(f'Generating sample {self.design_num:06} ...')
-        
-        # main sampling loop
-        for j in range(self.max_t):
-            self.t = torch.tensor(self.max_t-j-1).to(self.DEVICE)
-            
-            # run features through the model to get X_o prediction
-            self.predict_x()
-            
-            # save step
-            if self.args['save_all_steps']:
-                self.save_step()
-            
-            # get seq out
-            self.features['seq_out'] = torch.permute(self.features['logit_aa_s'][0], (1,0))
-            
-            # save best seq
-            if self.features['pred_lddt'][~self.features['mask_seq']].mean().item() > self.features['best_plddt']:
-                self.features['best_seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
-                self.features['best_pred_lddt'] = torch.clone(self.features['pred_lddt'])
-                self.features['best_xyz'] = torch.clone(self.features['xyz'])
-                self.features['best_plddt'] = self.features['pred_lddt'][~self.features['mask_seq']].mean().item()
-            
-            # self condition on sequence
-            self.self_condition_seq()
-            
-            # self condition on structure
-            if self.args['scheduled_str_cond']:
-                self.self_condition_str_scheduled()
-            if self.args['struc_cond_sc']:
-                self.self_condition_str()
-            
-            # sequence alterations
-            if self.args['softmax_seqout']:
-                self.features['seq_out'] = torch.softmax(self.features['seq_out'],dim=-1)*2-1
-            if self.args['clamp_seqout']:
-                self.features['seq_out'] = torch.clamp(self.features['seq_out'], 
-                                                       min=-((1/self.diffuser.alphas_cumprod[t])*0.25+5), 
-                                                       max=((1/self.diffuser.alphas_cumprod[t])*0.25+5))
-            
-            # apply potentials
-            if self.use_potentials:
-                self.apply_potentials()
-            
-            # noise to X_t-1
-            if self.t != 0:
-                self.noise_x()
-            
-            print(''.join([self.conversion[i] for i in torch.argmax(self.features['seq_out'],dim=-1)]))
-            print ("    TIMESTEP [%02d/%02d]   |   current PLDDT: %.4f   <<  >>   best PLDDT: %.4f"%(
-                    self.t+1, self.args['T'], self.features['pred_lddt'][~self.features['mask_seq']].mean().item(), 
-                    self.features['best_pred_lddt'][~self.features['mask_seq']].mean().item()))
-        
-        # record time
-        self.delta_time = time.time() - self.start_time
-        
-        # save outputs
-        self.save_outputs()
-        
-        # increment design num
-        self.design_num += 1
-        
-        print(f'Finished design {self.out_prefix} in {self.delta_time/60:.2f} minutes.')
-        
-    def save_outputs(self):
-        '''
-            save the outputs from the model
-        '''
-        # save trajectory
-        if self.args['save_all_steps']:
-            fname = f'{self.out_prefix}_trajectory.pt'
-            torch.save(self.trajecotry, fname)
-        
-        # get items from best plddt step
-        if self.args['save_best_plddt']:
-            self.features['seq'] = torch.clone(self.features['best_seq'])
-            self.features['pred_lddt'] = torch.clone(self.features['best_pred_lddt'])
-            self.features['xyz'] = torch.clone(self.features['best_xyz'])
-        
-        # get chain IDs
-        if (self.args['sampling_temp'] == 1.0 and self.args['trb'] == None) or (self.args['sequence'] not in ['',None]):
-            chain_ids = [i[0] for i in self.features['pdb_idx']]
-        elif self.args['dump_pdb']:
-            chain_ids = [i[0] for i in self.features['parsed_pdb']['pdb_idx']]
-        
-        # write output pdb
-        fname = self.out_prefix + '.pdb'
-        if len(self.features['seq'].shape) == 2:
-            self.features['seq'] = self.features['seq'].squeeze()
-        write_pdb(fname, 
-                  self.features['seq'].type(torch.int64), 
-                  self.features['xyz'].squeeze(), 
-                  Bfacts=self.features['pred_lddt'].squeeze(), 
-                  chains=chain_ids)
-        
-        if self.args['dump_trb']:
-            self.save_trb()
-        
-        if self.args['save_args']:
-            self.save_args()
-
-    def save_trb(self):
-        '''
-            save trb file
-        '''
-        
-        lddt = self.features['pred_lddt'].squeeze().cpu().numpy()
-        strmasktemp = self.features['mask_str'].squeeze().cpu().numpy()
-
-        partial_lddt = [lddt[i] for i in range(np.shape(strmasktemp)[0]) if strmasktemp[i] == 0]
-        trb = {}
-        trb['lddt'] = lddt
-        trb['inpaint_lddt'] = partial_lddt
-        trb['contigs'] = self.args['contigs']
-        trb['device'] = self.DEVICE
-        trb['time'] = self.delta_time
-        trb['args'] = self.args
-        
-        if self.args['sequence'] != None:
-            for key, value in self.features['trb_d'].items():
-                trb[key] = value
-        else:
-            for key, value in self.features['mappings'].items():
-                if key in self.features['trb_d'].keys():
-                    trb[key] = self.features['trb_d'][key]
-                else:
-                    if len(value) > 0:
-                        if type(value) == list and type(value[0]) != tuple:
-                            value=np.array(value)
-                    trb[key] = value
-            
-        with open(f'{self.out_prefix}.trb','wb') as f_out:
-            pickle.dump(trb, f_out)
-    
-    def save_args(self):
-        '''
-            save args
-        '''
-
-        with open(f'{self.out_prefix}_args.json','w') as f_out:
-            json.dump(self.args, f_out)
-
-#####################################################################
-###################### science is cool ##############################
-#####################################################################
-
-
-# making a custom sampler class for HuggingFace app
-
-class HuggingFace_sampler(SEQDIFF_sampler):
-
-    def model_init(self):
-        '''
-            get model set up and choose checkpoint
-        '''
-
-        if self.args['checkpoint'] == None:
-            self.args['checkpoint'] = DEFAULT_CKPT
-
-        self.MODEL_PARAM['d_t1d'] = self.args['d_t1d']
-
-        # check to make sure checkpoint chosen exists
-        if not os.path.exists(self.args['checkpoint']):
-            print('WARNING: couldn\'t find checkpoint')
-
-        self.ckpt = torch.load(self.args['checkpoint'], map_location=self.DEVICE)
-
-        # check to see if [loader_param, model_param, loss_param] is in checkpoint
-        #   if so then you are using v2 of inference with t2d bug fixed
-        self.v2_mode = False
-        if 'model_param' in self.ckpt.keys():
-            print('You are running a new v2 model switching into v2 inference mode')
-            self.v2_mode = True
-
-            for k in self.MODEL_PARAM.keys():
-                if k in self.ckpt['model_param'].keys():
-                    self.MODEL_PARAM[k] = self.ckpt['model_param'][k]
-                else:
-                    print(f'no match for {k} in loaded model params')
-
-        # make model and load checkpoint
-        print('Loading model checkpoint...')
-        self.model = RoseTTAFoldModule(**self.MODEL_PARAM).to(self.DEVICE)
-
-        model_state = self.ckpt['model_state_dict']
-        self.model.load_state_dict(model_state, strict=False)
-        self.model.eval()
-        print('Successfully loaded model checkpoint')
-
-    def generate_sample(self):
-        '''
-            sample from the model 
-            
-            this function runs the full sampling loop
-        '''
-        # setup example
-        self.setup()
-        
-        # start time
-        self.start_time = time.time()
-        
-        # set up dictionary to save at each step in trajectory
-        self.trajectory = {}
-        
-        # set out prefix
-        print(f'Generating sample {self.out_prefix} ...')
-        
-        # main sampling loop
-        for j in range(self.max_t):
-            self.t = torch.tensor(self.max_t-j-1).to(self.DEVICE)
-            
-            # run features through the model to get X_o prediction
-            self.predict_x()
-            
-            # save step
-            if self.args['save_all_steps']:
-                self.save_step()
-            
-            # get seq out
-            self.features['seq_out'] = torch.permute(self.features['logit_aa_s'][0], (1,0))
-            
-            # save best seq
-            if self.features['pred_lddt'].mean().item() > self.features['best_plddt']:
-                self.features['best_seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
-                self.features['best_pred_lddt'] = torch.clone(self.features['pred_lddt'])
-                self.features['best_xyz'] = torch.clone(self.features['xyz'])
-                self.features['best_plddt'] = self.features['pred_lddt'][~self.features['mask_seq']].mean().item()
-            
-            # self condition on sequence
-            self.self_condition_seq()
-            
-            # self condition on structure
-            if self.args['scheduled_str_cond']:
-                self.self_condition_str_scheduled()
-            if self.args['struc_cond_sc']:
-                self.self_condition_str()
-            
-            # sequence alterations
-            if self.args['softmax_seqout']:
-                self.features['seq_out'] = torch.softmax(self.features['seq_out'],dim=-1)*2-1
-            if self.args['clamp_seqout']:
-                self.features['seq_out'] = torch.clamp(self.features['seq_out'], 
-                                                       min=-((1/self.diffuser.alphas_cumprod[t])*0.25+5), 
-                                                       max=((1/self.diffuser.alphas_cumprod[t])*0.25+5))
-            
-            # apply potentials
-            if self.use_potentials:
-                self.apply_potentials()
-            
-            # noise to X_t-1
-            if self.t != 0:
-                self.noise_x()
-            
-            print(''.join([self.conversion[i] for i in torch.argmax(self.features['seq_out'],dim=-1)]))
-            print ("    TIMESTEP [%02d/%02d]   |   current PLDDT: %.4f   <<  >>   best PLDDT: %.4f"%(
-                    self.t+1, self.args['T'], self.features['pred_lddt'][~self.features['mask_seq']].mean().item(), 
-                    self.features['best_pred_lddt'][~self.features['mask_seq']].mean().item()))
-        
-        # record time
-        self.delta_time = time.time() - self.start_time
-        
-        # save outputs
-        self.save_outputs()
-        
-        # increment design num
-        self.design_num += 1
-        
-        print(f'Finished design {self.out_prefix} in {self.delta_time/60:.2f} minutes.')
-    
-    def take_step_get_outputs(self, j):
-        
-        self.t = torch.tensor(self.max_t-j-1).to(self.DEVICE)
-
-        # run features through the model to get X_o prediction
-        self.predict_x()
-
-        # save step
-        if self.args['save_all_steps']:
-            self.save_step()
-
-        # get seq out
-        self.features['seq_out'] = torch.permute(self.features['logit_aa_s'][0], (1,0))
-
-        # save best seq
-        if self.features['pred_lddt'].mean().item() > self.features['best_plddt']:
-            self.features['best_seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
-            self.features['best_pred_lddt'] = torch.clone(self.features['pred_lddt'])
-            self.features['best_xyz'] = torch.clone(self.features['xyz'])
-            self.features['best_plddt'] = self.features['pred_lddt'].mean().item()
-
-        
-        # WRITE OUTPUT TO GET TEMPORARY PDB TO DISPLAY
-        if self.t != 0:
-            self.features['seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
-        else:
-            # prepare final output 
-            if self.args['save_args']:
-                self.save_args()
-
-            # get items from best plddt step
-            if self.args['save_best_plddt']:
-                self.features['seq'] = torch.clone(self.features['best_seq'])
-                self.features['pred_lddt'] = torch.clone(self.features['best_pred_lddt'])
-                self.features['xyz'] = torch.clone(self.features['best_xyz'])
-
-        # get chain IDs
-        if (self.args['sampling_temp'] == 1.0 and self.args['trb'] == None) or (self.args['sequence'] not in ['',None]):
-            chain_ids = [i[0] for i in self.features['pdb_idx']]
-        elif self.args['dump_pdb']:
-            chain_ids = [i[0] for i in self.features['parsed_pdb']['pdb_idx']]
-
-        # write output pdb
-        if len(self.features['seq'].shape) == 2:
-            self.features['seq'] = self.features['seq'].squeeze()
-        
-        fname = f'{self.out_prefix}.pdb'
-        
-        write_pdb(fname, self.features['seq'].type(torch.int64),
-                  self.features['xyz'].squeeze(),
-                  Bfacts=self.features['pred_lddt'].squeeze(),
-                  chains=chain_ids)
-
-        aa_seq = ''.join([self.conversion[x] for x in self.features['seq'].tolist()])
-
-
-        # self condition on sequence
-        self.self_condition_seq()
-
-        # self condition on structure
-        if self.args['scheduled_str_cond']:
-            self.self_condition_str_scheduled()
-        if self.args['struc_cond_sc']:
-            self.self_condition_str()
-
-        # sequence alterations
-        if self.args['softmax_seqout']:
-            self.features['seq_out'] = torch.softmax(self.features['seq_out'],dim=-1)*2-1
-        if self.args['clamp_seqout']:
-            self.features['seq_out'] = torch.clamp(self.features['seq_out'],
-                                                   min=-((1/self.diffuser.alphas_cumprod[t])*0.25+5),
-                                                   max=((1/self.diffuser.alphas_cumprod[t])*0.25+5))
-
-        # apply potentials
-        if self.use_potentials:
-            self.apply_potentials()
-
-        # noise to X_t-1
-        if self.t != 0:
-            self.noise_x()
-       
-        print(''.join([self.conversion[i] for i in torch.argmax(self.features['seq_out'],dim=-1)]))
-        print ("    TIMESTEP [%02d/%02d]   |   current PLDDT: %.4f   <<  >>   best PLDDT: %.4f"%(
-                self.t+1, self.args['T'], self.features['pred_lddt'][~self.features['mask_seq']].mean().item(),
-                self.features['best_pred_lddt'][~self.features['mask_seq']].mean().item()))
-        
-                
-        return aa_seq, fname, self.features['pred_lddt'].mean().item()
-
-    def get_outputs(self):
-        
-        aa_seq = ''.join([self.conversion[x] for x in self.features['seq'].tolist()])
-        path_to_pdb = self.out_prefix+'.pdb'
-        return aa_seq, path_to_pdb, self.features['pred_lddt'].mean().item() 
diff --git a/spaces/merve/data-leak/public/uncertainty-calibration/index.html b/spaces/merve/data-leak/public/uncertainty-calibration/index.html
deleted file mode 100644
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--- a/spaces/merve/data-leak/public/uncertainty-calibration/index.html
+++ /dev/null
@@ -1,225 +0,0 @@
-<!--
-@license
-Copyright 2020 Google. 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
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-<!DOCTYPE html>
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-<html>
-<head>
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-  <meta name="viewport" content="width=device-width, initial-scale=1">
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-    !(function(){
-      var url = window.location.href
-      if (url.split('#')[0].split('?')[0].slice(-1) != '/' && !url.includes('.html')) window.location = url + '/'
-    })()
-  </script>
-
-  <title>Are Model Predictions Probabilities?</title>
-  <meta property="og:title" content="Are Model Predictions Probabilities?">
-  <meta property="og:url" content="https://pair.withgoogle.com/explorables/uncertainty-calibration/">
-
-  <meta name="og:description" content="Machine learning models express their uncertainty as model scores, but through calibration we can transform these scores into probabilities for more effective decision making.">
-  <meta property="og:image" content="https://pair.withgoogle.com/explorables/images/uncertainty-calibration.png">
-  <meta name="twitter:card" content="summary_large_image">
-  
-	<link rel="stylesheet" type="text/css" href="../style.css">
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-  <link href="https://fonts.googleapis.com/css?family=Google+Sans:400,500,700" rel="stylesheet">
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-</head>
-<body>
-  <div class='header'>
-    <div class='header-left'>
-      <a href='https://pair.withgoogle.com/'>
-        <img src='../images/pair-logo.svg' style='width: 100px'></img>
-      </a>
-      <a href='../'>Explorables</a> 
-    </div>
-  </div>
-  
-  <h1 class='headline'>Are Model Predictions Probabilities?</h1>
-  
-  <div id='container'>
-<div id='graph'></div>
-<div id='sections'>
-
-<div>
-
-If a machine learning model tells you that it’s going to rain tomorrow with a score of 0.60, should you buy an umbrella?<a class='footstart'>1</a> 
-
-<p> In the diagram, we have a hypothetical machine learning classifier for predicting rainy days. For each date, the classifier reads in relevant signals like temperature and humidity and spits out a number between 0 and 1. Each data point represents a different day, with the position representing the model’s prediction for rain that day and the symbol (🌧️ or ☀️) representing the true weather that occurred that day. 
-
-<p> <div id='card'> Do the model’s predictions tell us the probability of rain?</div>
-
-<p> In general, machine learning classifiers don’t just give binary predictions, but instead provide some numerical value between 0 and 1 for their predictions. This number, sometimes called the <em>model score</em> or <em>confidence</em>, is a way for the model to express their certainty about what class the input data belongs to. In most applications, the exact score is ignored and we use a threshold to round the score to a binary answer, yes or no, rain or not. However, by using <em>calibration</em> we can transform these scores into probabilities and use them more effectively in decision making.
-
-</div>
-
-<div> <h3>Thresholding</h3>
-
-<p> One traditional approach to using a model’s score is through <span class='highlight'><em>thresholding</em></span>. In this setting, you choose a threshold <em>t</em> and then declare that the model thinks it’s going to rain if the score is above <em>t</em> and it’s not if the score is below, thereby converting the score to a binary outcome. When you observe the actual weather, you know how often it was wrong and can compute key aggregate statistics like <a href="https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification" target="_blank"><em>accuracy</em></a>.
-
-<p> We can sometimes treat these aggregate statistics themselves as probabilities. For example, accuracy is the probability that the binary prediction of your model (rain or not) is equal to the ground truth (🌧️ or ☀️). 
-</div>
-
-<div> <h3>Adjustable Thresholding</h3>
-
-<p>The threshold can easily be changed after the model is trained.
-
-<p> Thresholding uses the model’s score to make a decision, but fails to consider the model’s confidence. The model score is only used to decide whether you are above or below the threshold, but the magnitude of the difference isn’t considered. For example, if you threshold at 0.4, the model’s predictions of 0.6 and 0.9 are treated the same, even though the model is much more confident in the latter.
-
-<div id='card'> Can we do a better job of incorporating the model score into our understanding of the model? </div>
-
-</div>
-
-<div> <h3>Calibration</h3>
-
-<p> <span class='highlight'><em>Calibration</em></span> lets us compare our model scores directly to probabilities. 
-
-<p> For this technique, instead of one threshold, we have many, which we use to split the predictions into buckets. Again, once we observe the ground truth, we can see what proportion of the predictions in each bucket were rainy days (🌧️). This proportion is the <em>empirical probability</em> of rain for that bucket.
-
-<p> Ideally, we want this proportion to be higher for higher buckets, so that the probability is roughly in line with the average prediction for that bucket. We call the difference between the proportion and the predicted rates the calibration error, and by averaging over all of the buckets, we can calculate the <a href="https://arxiv.org/pdf/1706.04599.pdf" target="_blank">Expected Calibration Error</a>. If the proportions and the predictions line up for our use case, meaning the error is low, then we say the model is “well-calibrated” and we can consider treating the model score as the probability that it will actually rain.
-</div>
-
-<div> <h3>Adjusting Calibration</h3>
-
-<p> We saw above that a well-calibrated model allows us to treat our model score as a kind of probability. But if we start with a poorly calibrated model, one which is over or under-confident. Is there anything we can do to improve it?
-
-<p> It turns out that, in many settings, we can adjust the model score without really changing the model’s decisions, as long as our adjustment preserves the order of the scores<a class='footstart'>2</a>. For example, if we map all of the scores from our original model to their squares, we don’t change the order of the data with respect to the model score. Thus, quantities like accuracy will stay the same as long as we appropriately map the threshold to its square as well. However, these adjustments <em>do</em> change the calibration of a model by changing which data points lie in which buckets.
-
-<div id='card'> <strong>Try</strong> <strong>tweaking the thresholds</strong> to <em>calibrate</em> the model scores for our data<a class='footstart'>3</a> – how much can you improve the model’s calibration? </div>
-
-<p> In general, we don’t have to rely on tweaking the model scores by hand to improve calibration. If we are trying to calibrate the model for a particular data distribution, we can use mathematical techniques like <a href="https://en.wikipedia.org/wiki/Isotonic_regression" target="_blank">Isotonic Regression</a> or <a href="https://en.wikipedia.org/wiki/Platt_scaling" target="_blank">Platt Scaling</a> to generate the correct remapping for model scores.
-</div>
-
-<div> <h3>Shifting Data</h3>
-
-<p> While good calibration is an important property for a model’s scores to be interpreted as probabilities, it alone does not capture all aspects of model uncertainty.
-
-<p> What happens if it starts to rain less frequently after we’ve trained and calibrated our model? Notice how the calibration drops, even if we use the same calibrated model scores as before.
-
-<p> Models are usually only well calibrated with respect to certain data distributions. If the data changes significantly between training and serving time, our models might cease to be well calibrated and we can’t rely on using our model scores as probabilities.
-</div>
-
-<div><h3>Beyond Calibration</h3>
-
-<p> Calibration can sometimes be easy to game. For example, if we knew that it rains 50% of the time over the course of the year, then we could create a model with a constant prediction of 0.5 every day. This would have perfect calibration, despite not being a very useful model for distinguishing day-to-day differences in the probability of rain. This highlights an important issue: 
-
-<div id='card'> Better calibration doesn’t mean more accurate predictions. </div> 
-
-<p> It turns out that statisticians identified the issue with focusing solely on calibration in meteorology when comparing weather forecasts, and came up with a solution.  <a href="https://sites.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf" target="_blank">Proper scoring rules</a>  provide an alternative approach to measuring the quality of probabilistic forecasts, by using a formula to measure the distance between the model’s predictions and the true event probabilities. These rules guarantee that a better value must mean a better prediction in terms of accuracy and calibration. Such rules incentivize models to be both better calibrated and more accurate.<br>
-</div>
-</div>
-</div>
-
-
-<h3> More Reading </h3>
-
-<p> This post is only the beginning of the discussion on the connections between machine learning models, probability, and uncertainty. In practice, when developing machine learning models with uncertainty in mind, we may need to go beyond calibration. 
-
-<p> In some settings, errors are not all equal. For example, if we are training a classifier to predict if a patient needs to be tested for a disease, then a false negative (missing a case of the disease) may be <a href="https://pair.withgoogle.com/explorables/measuring-fairness/" target="_blank">more detrimental</a> than a false positive (accidentally having a patient tested). In such cases, we may not want a perfectly calibrated model, but may want to skew the model scores towards one class or another. The field of <a href="https://books.google.ca/books?hl=en&lr=&id=1CDaBwAAQBAJ&oi=fnd&pg=PA1&dq=Statistical+Decision+Theory&ots=LMuipfYL0J&sig=bSdHt0_Phot_wxieYXN7cvXvmII#v=onepage&q=Statistical%20Decision%20Theory&f=false" target="_blank">Statistical Decision Theory</a> provides us with tools to determine how to better use model scores in this more general setting. Calibration may also lead to tension with other important goals like <a href="https://proceedings.neurips.cc/paper/2017/file/b8b9c74ac526fffbeb2d39ab038d1cd7-Paper.pdf" target="_blank">model fairness</a> in some applications.
-
-<p> Beyond this, so far we’ve only considered the case of using a single model score, i.e. a point estimate. If we trained the model a thousand times with different random seeds, or resampled the training data, we would almost certainly generate a collection of different model scores for a given input. To truly unpack the different sources of uncertainty that we might encounter, we might want to look towards <em>distributional</em> approaches to measuring uncertainty, using techniques like <a href="https://proceedings.neurips.cc/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf" target="_blank">Deep Ensembles</a> or <a href="https://authors.library.caltech.edu/13793/1/MACnc92b.pdf" target="_blank">Bayesian modeling</a>. We will dig deeper into these in future posts.
-
-<h3> Credits </h3>
-
-<p> Nithum Thain, Adam Pearce, Jasper Snoek &amp; Mahima Pushkarna // March 2022
-
-<p> Thanks to Balaji Lakshminarayanan, Emily Reif, Lucas Dixon, Martin Wattenberg, Fernanda Viégas, Ian Kivlichan, Nicole Mitchell, and Meredith Morris for their help with this piece.
-
-<h3> Footnotes </h3>
-
-<p> <a class='footend'></a> Your decision might depend both on the probability of rain and its severity (i.e. how much rain there is going to be). We’ll focus just on the probability for now.
-
-<p> <a class='footend'></a> Applying a strictly <a href="https://en.wikipedia.org/wiki/Monotonic_function" target="_blank">monotonic function</a> to the model always keeps the order of scores the same. 
-
-<p> <a class='footend'></a> In this example, we adjust the model scores by changing the model scores of elements within a bucket to the mean of the bucket.<br><h3> More Explorables </h3>
-
-<p id='recirc'></p>
-
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-<p><link rel="stylesheet" href="graph-scroll.css"></p>
-<script src='../third_party/d3_.js'></script>
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-<script src='util.js'></script>
-<script src='weatherdata.js'></script>
-<script src='draw_calibrationcurve.js'></script>
-<script src='draw_model_remapping.js'></script>
-<script src='draw_weathergraph.js'></script>
-<script src='draw_slides.js'></script>
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-</body>
-
-<script async src="https://www.googletagmanager.com/gtag/js?id=UA-138505774-1"></script>
-<script>
-  if (window.location.origin === 'https://pair.withgoogle.com'){
-    window.dataLayer = window.dataLayer || [];
-    function gtag(){dataLayer.push(arguments);}
-    gtag('js', new Date());
-    gtag('config', 'UA-138505774-1');
-  }
-</script>
-
-<script>
-  // Tweaks for displaying in an iframe
-  if (window !== window.parent){
-    
-    // Open links in a new tab
-    Array.from(document.querySelectorAll('a'))
-      .forEach(e => {
-        // skip anchor links
-        if (e.href && e.href[0] == '#') return
-
-        e.setAttribute('target', '_blank')
-        e.setAttribute('rel', 'noopener noreferrer')
-      })
-
-    // Remove recirc h3
-    Array.from(document.querySelectorAll('h3'))
-      .forEach(e => {
-        if (e.textContent != 'More Explorables') return
-
-        e.parentNode.removeChild(e)
-      })
-
-    // Remove recirc container
-    var recircEl = document.querySelector('#recirc')
-    recircEl.parentNode.removeChild(recircEl)
-  }
-</script>
-
-</html>
\ No newline at end of file
diff --git a/spaces/merve/fill-in-the-blank/public/third_party/recirc.js b/spaces/merve/fill-in-the-blank/public/third_party/recirc.js
deleted file mode 100644
index 37b65f4b8cf3c3ba504a0a3b906f8c19befc6730..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/public/third_party/recirc.js
+++ /dev/null
@@ -1,58 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-
-d3.loadData('../posts.json', (err, res) => {
-  var posts = res[0]
-    .filter(d => !window.location.href.includes(d.permalink))
-    .filter(d => d.shareimg.includes('http'))
-  posts = d3.shuffle(posts)
-
-  var isMobile = innerWidth < 900 
-  var postSel = d3.select('#recirc').html('').appendMany('a.post', posts)
-    .st({
-      width: isMobile ? '100%' : '330px', 
-      display: 'inline-block', 
-      verticalAlign: 'top',
-      marginRight: isMobile ? 0 : 30,
-      textDecoration: 'none',
-    })
-    .at({href: d => '..' + d.permalink})
-
-
-  postSel.append('div.img')
-    .st({
-      width: '100%', 
-      height: 200,
-      backgroundImage: d => `url(${d.shareimgabstract || d.shareimg})`,
-      backgroundSize: 'cover',
-      backgroundPosition: 'center',
-    })
-
-  postSel.append('p.title')
-    .text(d => d.shorttitle || d.title)
-    .st({
-      verticalAlign: 'top',
-      marginTop: 10,
-      textDecoration: 'none',
-    })
-
-  postSel.append('p.summary')
-    .text(d => d.socialsummary || d.summary)
-
-
-})
\ No newline at end of file
diff --git a/spaces/merve/hidden-bias/public/measuring-fairness/mini.js b/spaces/merve/hidden-bias/public/measuring-fairness/mini.js
deleted file mode 100644
index 51e81b909d66e7a0b45f54b318a0b88a95fdb217..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/public/measuring-fairness/mini.js
+++ /dev/null
@@ -1,205 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-
-
-window.makeMini = function(){
-
-  var s = 10
-  var sScale = ([a, b]) => [s*a, s*b]
-
-  var miniSel = d3.selectAll('.mini').html('').each(addMini).st({overflow: 'visible'})
-
-  var cColors = {
-    true:  {true: colors.sick, false: lcolors.sick},
-    false: {true: colors.well, false: lcolors.well}
-  }
-  var rColors = {
-    true:  {true: lcolors.sick, false: llcolors.sick},
-    false: {true: lcolors.well, false: llcolors.well}
-  }
-
-
-  function addMini(){
-    var miniSel = d3.select(this)
-
-    var type = miniSel.attr('type')
-    var sex = miniSel.attr('sex')
-    var isAll = sex == 'all'
-
-    miniSel.st({marginBottom: sex == 'male' ? 30 : 0})
-
-    var data = students
-      .filter(d => isAll ? true : sex == 'male' ? d.isMale : !d.isMale)
-
-    var topDatum = {}
-    var botDatum = {}
-
-    if (type == 'fp'){
-      topDatum.opacity = d => d.grade > d.threshold && d.isSick
-      botDatum.opacity = d => d.isSick
-    } else {
-      topDatum.opacity = d => d.grade > d.threshold && d.isSick
-      botDatum.opacity = d => d.grade > d.threshold
-    }
-
-
-
-    var top = -s*nCols/2 + 10
-    if (!isAll) top /= 2
-    addGrid(miniSel.append('span'), topDatum)
-    miniSel.append('span.equation').text('÷').st({top, fontWeight: '', fontSize: 20})
-    addGrid(miniSel.append('span'), botDatum)
-    miniSel.append('span.equation').text('=').st({top, fontWeight: '', fontSize: 20})
-
-    if (!isAll){
-      var sexStr = sex == 'male' ? 'children' : 'adults'
-
-      var coStr = `of ${sexStr} <br>testing positive<br>are sick`
-      var fpStr = `of ${sexStr} <br>who are sick <br>test positive`
-      miniSel.st({position: 'relative'})
-        .append('div.axis')
-        .st({position: 'absolute', right: -9, textAlign: 'center', width: 95, lineHeight: 14, bottom: -15})
-        .html(type == 'fp' ? fpStr : coStr)
-
-    }
-    
-    var percentSel = miniSel.append('span.equation').st({top, marginLeft: 0})
-
-    function update(){
-      topDatum.update()
-      botDatum.update()
-
-      var percent = d3.sum(data, topDatum.opacity)/d3.sum(data, botDatum.opacity)
-      percentSel.text(d3.format('.0%')(percent))
-    }
-
-    miniSel.datum({update}) 
-
-
-    function addGrid(gridSel, datum){
-      var {opacity} = datum
-
-      var width = s*nCols
-      var height = s*nCols*(isAll ? 1 : .5)
-      var svg = gridSel.append('svg').at({width, height})
-
-      var callSickSel = svg.append('rect')
-        .at({width, height, fill: lcolors.sick})
-
-      var callWellPath = svg.append('path')
-        .at({width, height, fill: lcolors.well})
-
-
-      var personSel = svg.appendMany('g', data)
-        .translate(d => sScale(d.pos[isAll ? 'allIJ' : 'sexGroupIJ']))
-
-      var pad = 0
-      // var rectSel = personSel.append('rect')
-      //   .at({
-      //     height: s - pad,
-      //     width: s - pad,
-      //     // stroke: '#666',
-      //     // strokeWidth: .1,
-      //   })
-
-
-      var circleSel = personSel.append('circle')
-        .at({r: s/4, cx: s/2 - pad/2, cy: s/2 - pad/2, fill: d => d.isSick ? colors.sick : '#777'})
-
-      if (!isAll){
-        svg.append('path')
-          .translate([-1, -5])
-          .at({stroke: colors.sick, d: 'M 0 0 H ' + (sex == 'male' ? 8 : 4)*s})
-      }
-
-      var geodata = {type: 'FeatureCollection'}
-      geodata.features = data.map(d => {
-        var [x, y] = sScale(d.pos[isAll ? 'allIJ' : 'sexGroupIJ'])
-        return {
-          type: 'Feature',
-          geometry: {
-            type: 'Polygon',
-            coordinates: [
-              [[x, y], [x, y + s], [x + s, y + s], [x + s, y], [x, y]]
-            ]
-          },
-          properties: {d},
-        }
-      })
-
-      var topology = topojson.topology({boxes: geodata})
-      var geowrap = topojson.feature(topology, topology.objects.boxes)
-      var path = d3.geoPath()
-
-      var hiddenPath = svg.append('path')
-        .at({stroke: 'none', fill: 'rgba(255,255,255,.6)'})
-        .translate(.5, 1)
-
-      var includedPath = svg.append('path')
-        .at({stroke: '#000', fill: 'none'})
-        .translate(.5, 1)
-
-
-      circleSel.at({fill: d => d.isSick ? colors.sick : colors.well})
-
-      datum.update = () => {
-        // rectSel.at({
-        //   // fill: d => rColors[d.grade > d.threshold][opacity(d)],
-        //   // strokeWidth: d => opacity(d) ? 1 : .1,
-        // })
-
-        // circleSel.at({fill: d => cColors[d.isSick][opacity(d)]})
-
-        var byType = d3.nestBy(topology.objects.boxes.geometries, d => opacity(d.properties.d))
-
-        byType.forEach(type => {
-          var obj = {type: 'GeometryCollection', geometries: type}
-          var pathStr = path(topojson.mesh(topology, obj, (a, b) => a == b))
-
-          var pathSel = type.key == 'true' ? includedPath : hiddenPath
-          pathSel.at({d: pathStr})
-        })
-
-        var sickBoxes = topology.objects.boxes.geometries
-          .filter(d => d.properties.d.grade <= d.properties.d.threshold)
-        var obj = {type: 'GeometryCollection', geometries: sickBoxes}
-        var pathStr = path(topojson.mesh(topology, obj, (a, b) => a == b))
-        callWellPath.at({d: pathStr})
-      }
-    }
-   
-  }
-
-
-
-  function updateAll(){
-    miniSel.each(d => d.update())
-  }
-
-  return {updateAll}
-}
-
-
-
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/merve/hidden-bias/source/dataset-worldviews/script.js b/spaces/merve/hidden-bias/source/dataset-worldviews/script.js
deleted file mode 100644
index 3ebba088d65f389af1b446a9ea90fcde674d5fdf..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/dataset-worldviews/script.js
+++ /dev/null
@@ -1,588 +0,0 @@
-
-console.clear();
-
-var ttSel = d3.select("body").selectAppend("div.tooltip.tooltip-hidden");
-// For result tables
-const columns = ["object", "n", "n correct", "accuracy"];
-const rowHeight = 50;
-const rowWidth = 100;
-const buffer = 2;
-
-const classifierBlobWidth = 50;
-const classifierBlobHeight = 460;
-
-function drawShapesWithData(classifier) {
-    var divHeight = classifier.class == "show-shapes" ? 250 : 490;
-
-    var c = d3.conventions({
-        sel: d3.select("." + classifier.class).html(""),
-        width: 1300,
-        height: divHeight,
-        layers: "ds",
-    });
-
-    function runClassifier() {
-        classifier.isClassified = true;
-        var duration = 3000;
-        classifierSel.classed("is-classified", true);
-        graphResultsGroup.classed("is-classified", true);
-
-        drawResults();
-        buttonSel.text("Reset");
-
-        var minX = d3.min(shapeParams, (d) => d.endX - 50);
-        var timer = d3.timer((ms) => {
-            if (!classifier.isClassified) {
-                timer.stop();
-                shapeSel.classed("is-classified", false);
-                return;
-            }
-
-            var t = d3.easeCubicInOut(ms / duration);
-            t = d3.clamp(0, t, 1);
-
-            shapeParams.forEach((d, i) => {
-                d.x = d.startX + (d.endX - d.startX) * t;
-                d.y = d.startY + (d.endY - d.startY) * t;
-                d.isClassified = d.x > minX;
-            });
-
-            shapeSel
-                .translate((d) => [d.x, d.y])
-                .classed("is-classified", (d) => d.isClassified);
-
-            if (t == 1) {
-                timer.stop();
-            }
-        });
-    }
-
-    function resetClassifier() {
-        shapeSel.translate((d) => [d.startX, d.startY]);
-        shapeSel.classed("is-classified", false);
-        classifier.isClassified = false;
-        shapeSel
-            .transition("position")
-            .duration(0)
-            .translate((d) => [d.startX, d.startY]);
-        classifierSel.classed("is-classified", false);
-        graphResultsGroup.classed("is-classified", false);
-        if (classifier.class != "show-shapes") {
-            classifierBlobSel.attr("opacity", 100);
-        }
-
-        drawResults();
-        buttonSel.text("Run Classifier");
-    }
-
-    // Add run/reset button
-    var buttonSel = d3
-        .select("." + classifier.class + "-button")
-        .html("")
-        .append("button#run")
-        .at({
-            type: "button",
-            class: "classifier-button",
-        })
-        .text("Run Classifier")
-        .on("click", () => {
-            // if already classified, reset
-            if (classifier.isClassified) {
-                // Resetting
-                resetClassifier();
-            } else {
-                runClassifier();
-            }
-        });
-
-    // Backgrounds for different classifications
-    var classifierSel = c.svg
-        .append("g")
-        .at({
-            class: "classifier",
-        })
-        .translate([465, 20]);
-
-    classifierSel
-        .append("path.classifier-bg-shaded")
-        .at({
-            d: classifierBgPathTop,
-            // fill: "#ccc",
-            // stroke: "#000",
-        })
-        .translate([-50, 0]);
-
-    classifierSel
-        .append("text.classifier-bg-text")
-        .at({
-            fill: "#000",
-            textAnchor: "middle",
-            dominantBaseline: "central",
-            class: "monospace",
-        })
-        .text("shaded")
-        .translate([160, 15]);
-
-    classifierSel
-        .append("path.classifier-bg-unshaded")
-        .at({
-            d: classifierBgPathBottom,
-        })
-        .translate([-50, 160]);
-
-    classifierSel
-        .append("text.classifier-bg-text")
-        .at({
-            fill: "#000",
-            textAnchor: "middle",
-            dominantBaseline: "central",
-            class: "monospace",
-        })
-        .text("unshaded")
-        .translate([160, 175]);
-
-    // Add the shapes themselves
-    var shapeSel = c.svg
-        .appendMany("path.shape", shapeParams)
-        .at({
-            d: (d) => d.path,
-            class: (d) => "gt-" + d.gt + " " + d.correctness,
-        })
-        .translate(function (d) {
-            if (classifier.class == "show-shapes") {
-                return [d.initialX + 35, d.initialY-20];
-            } else {
-                return [d.startX, d.startY];
-            }
-        })
-        .call(d3.attachTooltip)
-        .on("mouseover", (d) => {
-            ttSel.html("");
-            if (classifier.usingLabel != "none") {
-                ttSel
-                    .append("div")
-                    .html(
-                        `<span class="left">labeled:</span> <span class="monospace right">${toPropertyString(
-                            d[classifier.usingLabel],
-                            classifier.isRounding
-                        ).slice(0, -1)}</span>`
-                    );
-            }
-            var gtSel = ttSel
-                .append("div")
-                .html(
-                    `<span class="left">ground truth:</span> <span class="monospace right">${d.gt}</span>`
-                );
-            if (classifier.isClassified) {
-                ttSel
-                    .append("div.labeled-row")
-                    .html(
-                        `<span class="left">classified as:</span> <span class="monospace right">${d.label}</span>`
-                    );
-
-                ttSel
-                    .append("div.correct-row")
-                    .classed("is-correct-tooltip", d.correctness == "correct")
-                    .html(`<br><span>${d.correctness}ly classified</span> `);
-            }
-            ttSel.classed("tt-text", true);
-        });
-
-    // If we're just showing shapes, ignore everything else
-    if (classifier.class == "show-shapes") return;
-
-    // Add "classifier" line
-    var classifierBlobSel = c.svg
-        .append("g")
-        .at({
-            class: "classifier-blob",
-            strokeWidth: 0,
-        })
-        .translate([378, 20]);
-
-    classifierBlobSel
-        .append("line.classifier-blob")
-        .at({
-            class: "line",
-            x1: 27,
-            x2: 27,
-            y1: 0,
-            y2: 464,
-            stroke: "#000",
-            strokeWidth: 1,
-        })
-        .style("stroke-dasharray", "5, 5");
-
-    classifierBlobSel
-        .append("text.classifier-blob-text")
-        .at({
-            class: "classifier-blob-text monospace",
-            textAnchor: "middle",
-            dominantBaseline: "central",
-        })
-        .text("is_shaded classifier")
-        .attr("transform", "translate(30,480) rotate(0)");
-
-    if (classifier.class == "show-shapes") {
-        classifierBlobSel.classed("is-classified", true);
-    }
-
-    // Draw the results table with accuracies
-    // This will be hidden before classifier is run.
-    var graphResultsGroup = c.svg
-        .append("g")
-        .attr("class", "results")
-        .translate([-20, 19]);
-
-    function drawResults() {
-        // Write text summary
-        summarySel = d3
-            .select("." + classifier.class + "-summary")
-            .html(summaries[classifier.class])
-            .translate([0, 20]);
-        summarySel.classed("summary-text", true);
-        summarySel.classed("is-classified", classifier.isClassified);
-
-        if (!classifier.isClassified) {
-            c.layers[0].html("");
-            classifier.wasClassified = false;
-            return;
-        }
-
-        // Access results, which are calculated in shapes.js.
-        // If there are none, draw nothing.
-        results = allResults[classifier.class];
-        if (!results) return;
-
-        // Figure out which shapes should be highlighted on mouseover
-        // This depends on whether we're "rounding" edge case examples.
-        function isMatch(rowName, labelName, isRounding) {
-            // Not filtering at all
-            if (rowName == "shape") {
-                return true;
-            }
-            if (isRounding == true) {
-                // No "other" category
-                return labelName.includes(toOriginalString(rowName))
-                    ? true
-                    : false;
-            } else {
-                // There is an "other" category, prefixed by "rt_"
-                if (labelName == toOriginalString(rowName)) {
-                    return true;
-                } else if (
-                    labelName.includes("rt_") &&
-                    rowName == "other shapes"
-                ) {
-                    return true;
-                }
-                return false;
-            }
-        }
-
-        // Color the last row of each table
-        function getColor(d, i) {
-            if (i != 3) {
-                // not last index
-                return "#e6e6e6";
-            } else {
-                var scaleRowValue = d3
-                    .scaleLinear()
-                    .domain([0.3, 1.0])
-                    .range([0, 1]);
-                return d3.interpolateRdYlGn(scaleRowValue(d));
-            }
-        }
-
-        // Adjust text color for visibility
-        function getTextColor(d, i) {
-            if (i != 3) {
-                // not last index
-                return "#000000";
-            } else {
-                var bgColor = getColor(d, i);
-                if (d < 0.3) {
-                    // Alternative: use a brighter color?
-                    // return d3.rgb(bgColor).brighter(-2);
-                    return "#FFCCD8";
-                } else {
-                    // Alternative: use a darker color?
-                    // return d3.rgb(bgColor).darker(2);
-                    return "#000000";
-                }
-            }
-        }
-
-        // Draw results table
-        var tableSel = c.layers[0]
-            .html("")
-            .raise()
-            .st({ width: 400 })
-            .append("div")
-            .translate([0, 10])
-            .append("table.results-table.monospace")
-            .st({ width: 400 });
-
-        var header = tableSel
-            .append("thead")
-            .append("tr")
-            .appendMany("th", columns)
-            .text((d) => d);
-
-        var rowSel = tableSel
-            .appendMany("tr", results)
-            .at({
-                class: "row monospace",
-            })
-            .on("mouseover", (row) => {
-                if (classifier.class == "default-classifier") {
-                    return;
-                }
-                rowSel.classed("active", (d) => d == row);
-                shapeSel.classed("shape-row-unhighlighted", function (d) {
-                    return !isMatch(
-                        row.object,
-                        d[classifier.usingLabel],
-                        (isRounding = classifier.isRounding)
-                    );
-                });
-            })
-            .on("mouseout", (row) => {
-                rowSel.classed("active", function (d) {
-                    if (d == row) {
-                        return false;
-                    }
-                });
-                if (classifier.isClassified) {
-                    shapeSel.classed("shape-row-unhighlighted", 0);
-                }
-            });
-
-        rowSel
-            .appendMany("td", (result) =>
-                columns.map((column) => result[column])
-            )
-            .text((d) => d)
-            .st({
-                backgroundColor: getColor,
-                color: getTextColor,
-            });
-
-        header.style("opacity", 0);
-        rowSel.style("opacity", 0);
-
-        // If the classifier has already been run before, draw results right away.
-        // Otherwise, wait for other animation to run before drawing results.
-        var initialDelay = classifier.wasClassified ? 0 : 2000;
-        classifier.wasClassified = true;
-
-        header
-            .transition()
-            .delay(initialDelay)
-            .duration(1000)
-            .style("opacity", 1);
-        rowSel
-            .transition()
-            .delay(function (d, i) {
-                return initialDelay + i * 200;
-            })
-            .duration(1000)
-            .style("opacity", 1);
-    }
-
-    // Draw the dropdowns for selecting different labels
-    function drawDropdown() {
-        if (!classifier.options) return;
-
-        ["rounding", "category"].forEach(function (classifierType) {
-            if (!classifier.options[classifierType]) return;
-            var sel = d3
-                .select("#" + classifier.class + "-select-" + classifierType)
-                .html("");
-            sel.classed("dropdown", true);
-            sel.appendMany("option", classifier.options[classifierType])
-                .at({
-                    value: function (d) {
-                        return d.value;
-                    },
-                })
-                .text((d) => d.label);
-            sel.on("change", function () {
-                if (classifierType == "rounding") {
-                    classifier.isRounding = toBool(this.value);
-                } else {
-                    classifier.usingLabel = this.value;
-                }
-                updateResults();
-                drawResults();
-            });
-        });
-    }
-    drawDropdown();
-    updateResults();
-    drawResults();
-
-    // For continuity, auto-run the second two classifiers
-    if (
-        classifier.class == "second-classifier" ||
-        classifier.class == "final-classifier"
-    ) {
-        runClassifier();
-    }
-}
-
-// Draw the "Labels Tell Stories" section
-function drawConclusion() {
-    function drawNewspapers() {
-        d3.select(".conclusion-newspapers").html(function () {
-            var imgPath =
-                "img/newspapers_" +
-                document.getElementById("conclusion-select-category").value;
-            return (
-                '<img src="' +
-                imgPath +
-                '.png" class="newspaper-image" alt="Newspapers with headlines about bias and fairness in shape data." width=400></img>'
-            );
-        });
-    }
-
-    function drawInterface() {
-        d3.select(".conclusion-interface").html(function () {
-            var imgPath =
-                "img/confusing_" +
-                document.getElementById("conclusion-select-category").value;
-            return (
-                '<center><img class="interface-image" width="638" height="268" src="' +
-                imgPath +
-                '.png" alt="A shape that is difficult to classify with several checkboxes, none of which describe the shape. Next to the interface is a text box with a single question mark in it." srcset="' +
-                imgPath +
-                '.svg"></img></center>'
-            );
-        });
-    }
-
-    function drawConclusionSummary() {
-        classifierSel = d3
-            .select(".conclusion-summary")
-            .html(summaries["conclusion"]);
-        classifierSel.classed("summary-text is-classified", true);
-    }
-
-    function drawDropdown() {
-        var sel = d3.select("#conclusion-select-category").html("");
-        sel.classed("dropdown", true);
-        sel.appendMany("option", conclusionOptions.category)
-            .at({
-                value: function (d) {
-                    return d.value;
-                },
-            })
-            .text((d) => d.label);
-        // sel.attr('select', 'circles, triangles, and rectangles');
-        sel.on("change", function (d) {
-            makeConclusionUpdates();
-        });
-    }
-
-    function makeConclusionUpdates() {
-        updateResults();
-        drawNewspapers();
-        drawInterface();
-        drawConclusionSummary();
-    }
-    drawDropdown();
-    makeConclusionUpdates();
-}
-
-// Handle the parameters everywhere classifiers are drawn
-var classifiers = [
-    {
-        // Just the initial display of shapes, not interactive
-        class: "show-shapes",
-        colorBy: (d) => d.correctness,
-        isClassified: false,
-        isRounding: false,
-        usingLabel: "none",
-    },
-    {
-        class: "default-classifier",
-        colorBy: (d) => d.correctness,
-        isClassified: false,
-        isRounding: false,
-        usingLabel: "none",
-    },
-    {
-        class: "second-classifier",
-        colorBy: (d) => d.correctness,
-        isClassified: false,
-        isRounding: true,
-        usingLabel: "shape_name",
-        options: {
-            rounding: [
-                { label: "with their best guess", value: true },
-                { label: 'as "other"', value: false },
-            ],
-        },
-    },
-    {
-        class: "final-classifier",
-        colorBy: (d) => d.correctness,
-        isClassified: false,
-        isRounding: true,
-        usingLabel: "shape_name",
-        options: {
-            rounding: [
-                { label: "with our best guess", value: true },
-                { label: 'as "other"', value: false },
-            ],
-            category: [
-                {
-                    label: "circles, triangles, or rectangles",
-                    value: "shape_name",
-                },
-                { label: "pointy shapes or round shapes", value: "pointiness" },
-                { label: "small shapes or big shapes", value: "size" },
-                { label: "just shapes", value: "none" },
-            ],
-        },
-    },
-];
-
-// "Labels Tell Stories" dropdown options
-var conclusionOptions = {
-    category: [
-        { label: "circles, triangles, and rectangles", value: "shape_name" },
-        { label: "pointy shapes and round shapes", value: "pointiness" },
-        { label: "small shapes and big shapes", value: "size" },
-    ],
-};
-
-classifiers.forEach(drawShapesWithData);
-drawConclusion();
-
-// These images are loaded invisibly so they appear seamlessly on dropdown change
-const preloadImages = [
-    "img/confusing_pointiness.png",
-    "img/confusing_pointiness.svg",
-    "img/confusing_shape_name.png",
-    "img/confusing_shape_name.svg",
-    "img/confusing_size.png",
-    "img/confusing_size.svg",
-    "img/interface_default.png",
-    "img/interface_default.svg",
-    "img/interface_shape_name_false.png",
-    "img/interface_shape_name_false.svg",
-    "img/interface_shape_name_true.png",
-    "img/interface_shape_name_true.svg",
-    "img/newspapers_pointiness.png",
-    "img/newspapers_pointiness.svg",
-    "img/newspapers_shape_name.png",
-    "img/newspapers_shape_name.svg",
-    "img/newspapers_size.png",
-    "img/newspapers_size.svg",
-];
-
-d3.select(".preload-dropdown-img")
-    .html("")
-    .appendMany("img", preloadImages)
-    .at({ src: (d) => d });
diff --git a/spaces/micole66/Zero-Shot-Classification-Pretrained/app.py b/spaces/micole66/Zero-Shot-Classification-Pretrained/app.py
deleted file mode 100644
index 62d9dd0859d89857d739f1f03436978aa25b9c67..0000000000000000000000000000000000000000
--- a/spaces/micole66/Zero-Shot-Classification-Pretrained/app.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-from transformers import pipeline
-
-# Create the pipeline object
-pipe = pipeline("zero-shot-image-classification", model="GeorgeBredis/metaclip-h14-fullcc2.5b")
-
-# Define the function that will be used by the interface
-def zero_shot_classification(image, labels_text):
-    # Convert image to a PIL image object
-    pil_image = Image.fromarray(np.uint8(image)).convert("RGB")
-    
-    # Split the labels text into a list of labels
-    labels = labels_text.split(",")
-    
-    # Use the pipeline to classify the image with the given labels
-    res = pipe(
-        images=pil_image, 
-        candidate_labels=labels,
-        hypothesis_template= "This is a photo of a {}"
-    )
-    
-    # Return a dictionary mapping labels to scores
-    return {dic["label"]: dic["score"] for dic in res}
-    
-# Create the interface
-iface = gr.Interface(
-    zero_shot_classification, 
-    ["image", "text"], 
-    "label", 
-    examples=[
-        ["dog.jpg", "dog,cat,horse,zebra"],
-    ],
-    description="Add a picture and a list of labels separated by commas",
-    title="Zero-shot Image Classification"
-)
-
-# Launch the interface
-iface.launch()
diff --git a/spaces/micole66/bloomz/app.py b/spaces/micole66/bloomz/app.py
deleted file mode 100644
index fa477b1ed23a687939203adc08e9c8c6e895dd17..0000000000000000000000000000000000000000
--- a/spaces/micole66/bloomz/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load(("huggingface/google/flan-ul2"), allow_flagging=True, enable_queue=True, api_key="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").launch()
\ No newline at end of file
diff --git a/spaces/mikeee/ttw/run-pyinstaller-spec.bat b/spaces/mikeee/ttw/run-pyinstaller-spec.bat
deleted file mode 100644
index cad0df3e36edb596563e7bd4bbd9d60033c6e184..0000000000000000000000000000000000000000
--- a/spaces/mikeee/ttw/run-pyinstaller-spec.bat
+++ /dev/null
@@ -1 +0,0 @@
-pyinstaller -y app.spec  
diff --git a/spaces/mjdolan/Holiday-StyleGAN-NADA/e4e/utils/data_utils.py b/spaces/mjdolan/Holiday-StyleGAN-NADA/e4e/utils/data_utils.py
deleted file mode 100644
index f1ba79f4a2d5cc2b97dce76d87bf6e7cdebbc257..0000000000000000000000000000000000000000
--- a/spaces/mjdolan/Holiday-StyleGAN-NADA/e4e/utils/data_utils.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""
-Code adopted from pix2pixHD:
-https://github.com/NVIDIA/pix2pixHD/blob/master/data/image_folder.py
-"""
-import os
-
-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 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)
-                images.append(path)
-    return images
diff --git a/spaces/mnauf/detect-bees/utils/loggers/clearml/README.md b/spaces/mnauf/detect-bees/utils/loggers/clearml/README.md
deleted file mode 100644
index 64eef6befc930f2e80287fe83137ea812968fd83..0000000000000000000000000000000000000000
--- a/spaces/mnauf/detect-bees/utils/loggers/clearml/README.md
+++ /dev/null
@@ -1,222 +0,0 @@
-# ClearML Integration
-
-<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
-
-## About ClearML
-
-[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
-
-🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
-
-🔧 Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b>
-
-🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
-
-🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
-
-🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
-
-<br />
-And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
-<br />
-<br />
-
-![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
-
-
-<br />
-<br />
-
-## 🦾 Setting Things Up
-
-To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
-
-Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
-
-1. Install the `clearml` python package:
-
-    ```bash
-    pip install clearml
-    ```
-
-1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
-
-    ```bash
-    clearml-init
-    ```
-
-That's it! You're done 😎
-
-<br />
-
-## 🚀 Training YOLOv5 With ClearML
-
-To enable ClearML experiment tracking, simply install the ClearML pip package.
-
-```bash
-pip install clearml
-```
-
-This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. If you want to change the `project_name` or `task_name`, head over to our custom logger, where you can change it: `utils/loggers/clearml/clearml_utils.py`
-
-```bash
-python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
-```
-
-This will capture:
-- Source code + uncommitted changes
-- Installed packages
-- (Hyper)parameters
-- Model files (use `--save-period n` to save a checkpoint every n epochs)
-- Console output
-- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
-- General info such as machine details, runtime, creation date etc.
-- All produced plots such as label correlogram and confusion matrix
-- Images with bounding boxes per epoch
-- Mosaic per epoch
-- Validation images per epoch
-- ...
-
-That's a lot right? 🤯
-Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
-
-There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
-
-<br />
-
-## 🔗 Dataset Version Management
-
-Versioning your data separately from your code is generally a good idea and makes it easy to aqcuire the latest version too. This repository supports supplying a dataset version ID and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
-
-![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif)
-
-### Prepare Your Dataset
-
-The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure:
-
-```
-..
-|_ yolov5
-|_ datasets
-    |_ coco128
-        |_ images
-        |_ labels
-        |_ LICENSE
-        |_ README.txt
-```
-But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
-
-Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
-
-Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
-
-```
-..
-|_ yolov5
-|_ datasets
-    |_ coco128
-        |_ images
-        |_ labels
-        |_ coco128.yaml  # <---- HERE!
-        |_ LICENSE
-        |_ README.txt
-```
-
-### Upload Your Dataset
-
-To get this dataset into ClearML as a versionned dataset, go to the dataset root folder and run the following command:
-```bash
-cd coco128
-clearml-data sync --project YOLOv5 --name coco128 --folder .
-```
-
-The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
-```bash
-# Optionally add --parent <parent_dataset_id> if you want to base
-# this version on another dataset version, so no duplicate files are uploaded!
-clearml-data create --name coco128 --project YOLOv5
-clearml-data add --files .
-clearml-data close
-```
-
-### Run Training Using A ClearML Dataset
-
-Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
-
-```bash
-python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
-```
-
-<br />
-
-## 👀 Hyperparameter Optimization
-
-Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
-
-Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does!
-
-To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters.
-
-You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead.
-
-```bash
-# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
-pip install optuna
-python utils/loggers/clearml/hpo.py
-```
-
-![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png)
-
-## 🤯 Remote Execution (advanced)
-
-Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site or you have some budget to use cloud GPUs.
-This is where the ClearML Agent comes into play. Check out what the agent can do here:
-
-- [YouTube video](https://youtu.be/MX3BrXnaULs)
-- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
-
-In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
-
-You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
-```bash
-clearml-agent daemon --queue <queues_to_listen_to> [--docker]
-```
-
-### Cloning, Editing And Enqueuing
-
-With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too!
-
-🪄 Clone the experiment by right clicking it
-
-🎯 Edit the hyperparameters to what you wish them to be
-
-⏳ Enqueue the task to any of the queues by right clicking it
-
-![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif)
-
-### Executing A Task Remotely
-
-Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on!
-
-To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instatiated:
-```python
-# ...
-# Loggers
-data_dict = None
-if RANK in {-1, 0}:
-    loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
-    if loggers.clearml:
-        loggers.clearml.task.execute_remotely(queue='my_queue')  # <------ ADD THIS LINE
-        # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
-        data_dict = loggers.clearml.data_dict
-# ...
-```
-When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead!
-
-### Autoscaling workers
-
-ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines and you stop paying!
-
-Check out the autoscalers getting started video below.
-
-[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E)
diff --git a/spaces/mobu123456/venusai/Dockerfile b/spaces/mobu123456/venusai/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/mobu123456/venusai/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/monra/freegpt-webui/g4f/Provider/__init__.py b/spaces/monra/freegpt-webui/g4f/Provider/__init__.py
deleted file mode 100644
index 6ed51982755367e47c59199975be2c3539bfbee0..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/g4f/Provider/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from . import Provider
-from .Providers import (
-    Aichat,
-    Ails,
-    AiService,
-    Bard,
-    Better,
-    Bing,
-    ChatFree,
-    ChatgptAi,
-    ChatgptLogin,
-    ChatgptLogin,
-    DeepAi,
-    Easychat,
-    Ezcht,
-    Fakeopen,
-    Forefront,
-    GetGpt,
-    Gravityengine,
-    H2o,
-    hteyun,
-    Liaobots,
-    Lockchat,
-    Mishalsgpt,
-    Phind,
-    Theb,
-    Vercel,
-    Weuseing,
-    Xiaor,
-    Yqcloud,
-    You,
-    Zeabur,
-    Wewordle
-)
-
-Palm = Bard
diff --git a/spaces/mrciolino/ppt_owl_vit/memory_test.py b/spaces/mrciolino/ppt_owl_vit/memory_test.py
deleted file mode 100644
index 8b7d779e4d208bb6f60efbeb501d81fd5156a402..0000000000000000000000000000000000000000
--- a/spaces/mrciolino/ppt_owl_vit/memory_test.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# USAGE: python run_psrecord.py <PID> --plot plot.png --log activity.txt
-
-from psrecord.main import monitor
-import argparse
-
-
-def main():
-    # copied from C:\Users\user\anaconda3\envs\tfod\Lib\site-packages\psrecord\main.py
-    parser = argparse.ArgumentParser(
-        description='Record CPU and memory usage for a process')
-
-    parser.add_argument('process_id_or_command', type=str,
-                        help='the process id or command')
-
-    parser.add_argument('--log', type=str,
-                        help='output the statistics to a file')
-
-    parser.add_argument('--plot', type=str,
-                        help='output the statistics to a plot')
-
-    parser.add_argument('--duration', type=float,
-                        help='how long to record for (in seconds). If not '
-                             'specified, the recording is continuous until '
-                             'the job exits.')
-
-    parser.add_argument('--interval', type=float,
-                        help='how long to wait between each sample (in '
-                             'seconds). By default the process is sampled '
-                             'as often as possible.')
-
-    parser.add_argument('--include-children',
-                        help='include sub-processes in statistics (results '
-                             'in a slower maximum sampling rate).',
-                        action='store_true')
-
-    args = parser.parse_args()
-
-    # Attach to process
-    try:
-        pid = int(args.process_id_or_command)
-        print("Attaching to process {0}".format(pid))
-        sprocess = None
-    except Exception:
-        import subprocess
-        command = args.process_id_or_command
-        print("Starting up command '{0}' and attaching to process"
-              .format(command))
-        sprocess = subprocess.Popen(command, shell=True)
-        pid = sprocess.pid
-
-    monitor(pid, logfile=args.log, plot=args.plot, duration=args.duration,
-            interval=args.interval, include_children=args.include_children)
-
-    if sprocess is not None:
-        sprocess.kill()
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/spaces/mrm8488/santacoder-dockerfiles-completion/app.py b/spaces/mrm8488/santacoder-dockerfiles-completion/app.py
deleted file mode 100644
index 6dbc74dcad035293e463b9f7a4a2ebcaffdcab55..0000000000000000000000000000000000000000
--- a/spaces/mrm8488/santacoder-dockerfiles-completion/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
-
-title = "SantaCoder 🎅 Dockerfiles 🐋 Completion"
-description = "This is a subspace to make code generation with [SantaCoder fine-tuned on The Stack Dockerfiles](https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-dockerfiles)"
-EXAMPLE_0 = "# Dockerfile for Express API"
-
-
-CKPT = "mrm8488/santacoder-finetuned-the-stack-dockerfiles"
-
-examples = [[EXAMPLE_0, 55, 0.6, 42]]
-tokenizer = AutoTokenizer.from_pretrained(CKPT)
-model = AutoModelForCausalLM.from_pretrained(CKPT, trust_remote_code=True)
-
-
-def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
-    set_seed(seed)
-    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
-    generated_text = pipe(gen_prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
-    return generated_text
-
-
-iface = gr.Interface(
-    fn=code_generation, 
-    inputs=[
-        gr.Textbox(lines=10, label="Input code"),
-        gr.inputs.Slider(
-            minimum=8,
-            maximum=256,
-            step=1,
-            default=8,
-            label="Number of tokens to generate",
-        ),
-        gr.inputs.Slider(
-            minimum=0,
-            maximum=2,
-            step=0.1,
-            default=0.6,
-            label="Temperature",
-        ),
-        gr.inputs.Slider(
-            minimum=0,
-            maximum=1000,
-            step=1,
-            default=42,
-            label="Random seed to use for the generation"
-        )
-    ],
-    outputs=gr.Textbox(label="Predicted code", lines=10),
-    examples=examples,
-    layout="horizontal",
-    theme="peach",
-    description=description,
-    title=title
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py b/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py
deleted file mode 100644
index 9cf121081fbde2f5085ed380f0841649d143a4be..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py
+++ /dev/null
@@ -1,192 +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 collections
-import contextlib
-import wave
-
-try:
-    import webrtcvad
-except ImportError:
-    raise ImportError("Please install py-webrtcvad: pip install webrtcvad")
-import argparse
-import os
-import logging
-from tqdm import tqdm
-
-AUDIO_SUFFIX = '.wav'
-FS_MS = 30
-SCALE = 6e-5
-THRESHOLD = 0.3
-
-
-def read_wave(path):
-    """Reads a .wav file.
-    Takes the path, and returns (PCM audio data, sample rate).
-    """
-    with contextlib.closing(wave.open(path, 'rb')) as wf:
-        num_channels = wf.getnchannels()
-        assert num_channels == 1
-        sample_width = wf.getsampwidth()
-        assert sample_width == 2
-        sample_rate = wf.getframerate()
-        assert sample_rate in (8000, 16000, 32000, 48000)
-        pcm_data = wf.readframes(wf.getnframes())
-        return pcm_data, sample_rate
-
-
-def write_wave(path, audio, sample_rate):
-    """Writes a .wav file.
-    Takes path, PCM audio data, and sample rate.
-    """
-    with contextlib.closing(wave.open(path, 'wb')) as wf:
-        wf.setnchannels(1)
-        wf.setsampwidth(2)
-        wf.setframerate(sample_rate)
-        wf.writeframes(audio)
-
-
-class Frame(object):
-    """Represents a "frame" of audio data."""
-    def __init__(self, bytes, timestamp, duration):
-        self.bytes = bytes
-        self.timestamp = timestamp
-        self.duration = duration
-
-
-def frame_generator(frame_duration_ms, audio, sample_rate):
-    """Generates audio frames from PCM audio data.
-    Takes the desired frame duration in milliseconds, the PCM data, and
-    the sample rate.
-    Yields Frames of the requested duration.
-    """
-    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
-    offset = 0
-    timestamp = 0.0
-    duration = (float(n) / sample_rate) / 2.0
-    while offset + n < len(audio):
-        yield Frame(audio[offset:offset + n], timestamp, duration)
-        timestamp += duration
-        offset += n
-
-
-def vad_collector(sample_rate, frame_duration_ms,
-                  padding_duration_ms, vad, frames):
-    """Filters out non-voiced audio frames.
-    Given a webrtcvad.Vad and a source of audio frames, yields only
-    the voiced audio.
-    Uses a padded, sliding window algorithm over the audio frames.
-    When more than 90% of the frames in the window are voiced (as
-    reported by the VAD), the collector triggers and begins yielding
-    audio frames. Then the collector waits until 90% of the frames in
-    the window are unvoiced to detrigger.
-    The window is padded at the front and back to provide a small
-    amount of silence or the beginnings/endings of speech around the
-    voiced frames.
-    Arguments:
-    sample_rate - The audio sample rate, in Hz.
-    frame_duration_ms - The frame duration in milliseconds.
-    padding_duration_ms - The amount to pad the window, in milliseconds.
-    vad - An instance of webrtcvad.Vad.
-    frames - a source of audio frames (sequence or generator).
-    Returns: A generator that yields PCM audio data.
-    """
-    num_padding_frames = int(padding_duration_ms / frame_duration_ms)
-    # We use a deque for our sliding window/ring buffer.
-    ring_buffer = collections.deque(maxlen=num_padding_frames)
-    # We have two states: TRIGGERED and NOTTRIGGERED. We start in the
-    # NOTTRIGGERED state.
-    triggered = False
-
-    voiced_frames = []
-    for frame in frames:
-        is_speech = vad.is_speech(frame.bytes, sample_rate)
-
-        #  sys.stdout.write('1' if is_speech else '0')
-        if not triggered:
-            ring_buffer.append((frame, is_speech))
-            num_voiced = len([f for f, speech in ring_buffer if speech])
-            # If we're NOTTRIGGERED and more than 90% of the frames in
-            # the ring buffer are voiced frames, then enter the
-            # TRIGGERED state.
-            if num_voiced > 0.9 * ring_buffer.maxlen:
-                triggered = True
-                # We want to yield all the audio we see from now until
-                # we are NOTTRIGGERED, but we have to start with the
-                # audio that's already in the ring buffer.
-                for f, _ in ring_buffer:
-                    voiced_frames.append(f)
-                ring_buffer.clear()
-        else:
-            # We're in the TRIGGERED state, so collect the audio data
-            # and add it to the ring buffer.
-            voiced_frames.append(frame)
-            ring_buffer.append((frame, is_speech))
-            num_unvoiced = len([f for f, speech in ring_buffer if not speech])
-            # If more than 90% of the frames in the ring buffer are
-            # unvoiced, then enter NOTTRIGGERED and yield whatever
-            # audio we've collected.
-            if num_unvoiced > 0.9 * ring_buffer.maxlen:
-                triggered = False
-                yield [b''.join([f.bytes for f in voiced_frames]),
-                       voiced_frames[0].timestamp, voiced_frames[-1].timestamp]
-                ring_buffer.clear()
-                voiced_frames = []
-    # If we have any leftover voiced audio when we run out of input,
-    # yield it.
-    if voiced_frames:
-        yield [b''.join([f.bytes for f in voiced_frames]),
-               voiced_frames[0].timestamp, voiced_frames[-1].timestamp]
-
-
-def main(args):
-    # create output folder
-    try:
-        cmd = f"mkdir -p {args.out_path}"
-        os.system(cmd)
-    except Exception:
-        logging.error("Can not create output folder")
-        exit(-1)
-
-    # build vad object
-    vad = webrtcvad.Vad(int(args.agg))
-    # iterating over wavs in dir
-    for file in tqdm(os.listdir(args.in_path)):
-        if file.endswith(AUDIO_SUFFIX):
-            audio_inpath = os.path.join(args.in_path, file)
-            audio_outpath = os.path.join(args.out_path, file)
-            audio, sample_rate = read_wave(audio_inpath)
-            frames = frame_generator(FS_MS, audio, sample_rate)
-            frames = list(frames)
-            segments = vad_collector(sample_rate, FS_MS, 300, vad, frames)
-            merge_segments = list()
-            timestamp_start = 0.0
-            timestamp_end = 0.0
-            # removing start, end, and long sequences of sils
-            for i, segment in enumerate(segments):
-                merge_segments.append(segment[0])
-                if i and timestamp_start:
-                    sil_duration = segment[1] - timestamp_end
-                    if sil_duration > THRESHOLD:
-                        merge_segments.append(int(THRESHOLD / SCALE)*(b'\x00'))
-                    else:
-                        merge_segments.append(int((sil_duration / SCALE))*(b'\x00'))
-                timestamp_start = segment[1]
-                timestamp_end = segment[2]
-            segment = b''.join(merge_segments)
-            write_wave(audio_outpath, segment, sample_rate)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Apply vad to a file of fils.')
-    parser.add_argument('in_path', type=str, help='Path to the input files')
-    parser.add_argument('out_path', type=str,
-                        help='Path to save the processed files')
-    parser.add_argument('--agg', type=int, default=3,
-                        help='The level of aggressiveness of the VAD: [0-3]')
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/score.py b/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/score.py
deleted file mode 100644
index 0b207be959d55f6a56d8c5eb7db3dbe0c1ac977e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/score.py
+++ /dev/null
@@ -1,102 +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.
-"""
-BLEU scoring of generated translations against reference translations.
-"""
-
-import argparse
-import os
-import sys
-
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="Command-line script for BLEU scoring."
-    )
-    # fmt: off
-    parser.add_argument('-s', '--sys', default='-', help='system output')
-    parser.add_argument('-r', '--ref', required=True, help='references')
-    parser.add_argument('-o', '--order', default=4, metavar='N',
-                        type=int, help='consider ngrams up to this order')
-    parser.add_argument('--ignore-case', action='store_true',
-                        help='case-insensitive scoring')
-    parser.add_argument('--sacrebleu', action='store_true',
-                        help='score with sacrebleu')
-    parser.add_argument('--sentence-bleu', action='store_true',
-                        help='report sentence-level BLEUs (i.e., with +1 smoothing)')
-    # fmt: on
-    return parser
-
-
-def cli_main():
-    parser = get_parser()
-    args = parser.parse_args()
-    print(args)
-
-    assert args.sys == "-" or os.path.exists(
-        args.sys
-    ), "System output file {} does not exist".format(args.sys)
-    assert os.path.exists(args.ref), "Reference file {} does not exist".format(args.ref)
-
-    dict = dictionary.Dictionary()
-
-    def readlines(fd):
-        for line in fd.readlines():
-            if args.ignore_case:
-                yield line.lower()
-            else:
-                yield line
-
-    if args.sacrebleu:
-        import sacrebleu
-
-        def score(fdsys):
-            with open(args.ref) as fdref:
-                print(sacrebleu.corpus_bleu(fdsys, [fdref]).format())
-
-    elif args.sentence_bleu:
-
-        def score(fdsys):
-            with open(args.ref) as fdref:
-                scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk())
-                for i, (sys_tok, ref_tok) in enumerate(
-                    zip(readlines(fdsys), readlines(fdref))
-                ):
-                    scorer.reset(one_init=True)
-                    sys_tok = dict.encode_line(sys_tok)
-                    ref_tok = dict.encode_line(ref_tok)
-                    scorer.add(ref_tok, sys_tok)
-                    print(i, scorer.result_string(args.order))
-
-    else:
-
-        def score(fdsys):
-            with open(args.ref) as fdref:
-                scorer = bleu.Scorer(
-                    bleu.BleuConfig(
-                        pad=dict.pad(),
-                        eos=dict.eos(),
-                        unk=dict.unk(),
-                    )
-                )
-                for sys_tok, ref_tok in zip(readlines(fdsys), readlines(fdref)):
-                    sys_tok = dict.encode_line(sys_tok)
-                    ref_tok = dict.encode_line(ref_tok)
-                    scorer.add(ref_tok, sys_tok)
-                print(scorer.result_string(args.order))
-
-    if args.sys == "-":
-        score(sys.stdin)
-    else:
-        with open(args.sys, "r") as f:
-            score(f)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/multimodalart/vqgan/README.md b/spaces/multimodalart/vqgan/README.md
deleted file mode 100644
index b117af419e4542a907f93882083167fe3b8081fd..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/vqgan/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: VQGAN+CLIP (Hypertron v2)
-emoji: 👁
-colorFrom: red
-colorTo: blue
-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/mygenaisagar/MyGenAIsagarBot/app.py b/spaces/mygenaisagar/MyGenAIsagarBot/app.py
deleted file mode 100644
index 8201ecc35f21ac4df3ed257c1c30fb5c233cc367..0000000000000000000000000000000000000000
--- a/spaces/mygenaisagar/MyGenAIsagarBot/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Payel your youthful and witty personal assistant! At 18 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/nateraw/animegan-v2-for-videos/README.md b/spaces/nateraw/animegan-v2-for-videos/README.md
deleted file mode 100644
index c26f12a6c6a9c3319a50b48532a0dd52729ef7c9..0000000000000000000000000000000000000000
--- a/spaces/nateraw/animegan-v2-for-videos/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: AnimeGAN-v2 For Videos
-emoji: 🔥
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
----
-
-# AnimeGAN-v2 For Videos
-
-[![Generic badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/nateraw/animegan-v2-for-videos)
-
-Apply AnimeGAN-v2 across frames of a video
-
----
-
-Autogenerated using [this template](https://github.com/nateraw/spaces-template)
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/__init__.py b/spaces/nateraw/deepafx-st/deepafx_st/__init__.py
deleted file mode 100644
index cdaf0a55dc0d0f78b93249728c30b13a4d870205..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/usr/bin/env python
-"""Top-level module for deepafx_st"""
-
-from .version import version as __version__
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Telugu Suprabhatam Ms Subbulakshmi Free High Quality.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Telugu Suprabhatam Ms Subbulakshmi Free High Quality.md
deleted file mode 100644
index d2741d64b2297c1ab12add59c3ef8d5f1a0ca3de..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Telugu Suprabhatam Ms Subbulakshmi Free High Quality.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>How to Download Telugu Suprabhatam Ms Subbulakshmi Free</h1>
-<p>Telugu Suprabhatam is a collection of devotional songs sung by the legendary Carnatic vocalist M. S. Subbulakshmi. The songs are dedicated to Lord Venkateswara, the presiding deity of Tirumala Tirupati temple. The songs are composed in various ragas and talas, and invoke the blessings of the Lord for a good morning and a prosperous day.</p>
-<p>If you are looking for a way to download Telugu Suprabhatam Ms Subbulakshmi free, you have come to the right place. In this article, we will show you how to download Telugu Suprabhatam Ms Subbulakshmi free from various online sources.</p>
-<h2>Download Telugu Suprabhatam Ms Subbulakshmi Free</h2><br /><p><b><b>DOWNLOAD</b> &rarr; <a href="https://urlcod.com/2uIc4W">https://urlcod.com/2uIc4W</a></b></p><br /><br />
-<h2>Download Telugu Suprabhatam Ms Subbulakshmi Free from SoundCloud</h2>
-<p>SoundCloud is a popular online platform for streaming and sharing music. You can find many devotional songs by M. S. Subbulakshmi on SoundCloud, including Sri Venkateswara Suprabhatam. To download Telugu Suprabhatam Ms Subbulakshmi free from SoundCloud, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://soundcloud.com/devotionalsongs/sri-venkateswara-suprabhatam-ms-subbulakshmi">this link</a> to access the song on SoundCloud.</li>
-<li>Click on the "More" button below the song title and select "Download file".</li>
-<li>Save the file on your device and enjoy listening to Telugu Suprabhatam Ms Subbulakshmi free.</li>
-</ol>
-<h2>Download Telugu Suprabhatam Ms Subbulakshmi Free from JioSaavn</h2>
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-<ol>
-<li>Go to <a href="https://www.jiosaavn.com/artist/m.-s.-subbulakshmi-songs/4deXqzegDXw_">this link</a> to access the artist page of M. S. Subbulakshmi on JioSaavn.</li>
-<li>Scroll down and find Sri Venkateshwara Suprabhatham under the album Sri Venkateshwara Suprabhatham Ms Subbulakshmi.</li>
-<li>Click on the "Download" button next to the song title and select the quality of the file.</li>
-<li>Save the file on your device and enjoy listening to Telugu Suprabhatam Ms Subbulakshmi free.</li>
-</ol>
-<h2>Download Telugu Suprabhatam Ms Subbulakshmi Free from Wynk</h2>
-<p>Wynk is another online platform for streaming and downloading music. You can find many songs by M. S. Subbulakshmi on Wynk, including Sri Venkateswara Suprabhatham. To download Telugu Suprabhatam Ms Subbulakshmi free from Wynk, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://wynk.in/music/song/sri-venkateswara-suprabhatham/sa_INH100506439">this link</a> to access the song on Wynk.</li>
-<li>Click on the "Download" button below the song title and select the quality of the file.</li>
-<li>Save the file on your device and enjoy listening to Telugu Suprabhatam Ms Subbulakshmi free.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Telugu Suprabhatam Ms Subbulakshmi is a beautiful and soothing devotional song that can uplift your mood and spirit. You can download Telugu Suprabhatam Ms Subbulakshmi free from various online sources, such as SoundCloud,</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/GetData Recover My Files Professional 4.9.2.1240 Portable .rar TOP.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/GetData Recover My Files Professional 4.9.2.1240 Portable .rar TOP.md
deleted file mode 100644
index ffe2cc42c4607f2af39e519e97aa528898904116..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/GetData Recover My Files Professional 4.9.2.1240 Portable .rar TOP.md	
+++ /dev/null
@@ -1,111 +0,0 @@
-<br />
-<h1>GetData Recover My Files Professional 4.9.2.1240 Portable .rar: A Powerful Data Recovery Software</h1>
- <p>Have you ever accidentally deleted or lost important files from your computer, hard drive, USB flash drive, memory card, or other devices? If so, you know how frustrating and stressful it can be to try to get them back.</p>
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- <h2>What is GetData Recover My Files Professional 4.9.2.1240 Portable .rar?</h2>
- <p>GetData Recover My Files Professional 4.9.2.1240 Portable .rar is a data recovery software that can recover deleted files emptied from the Windows Recycle Bin, or lost due to hard drive format, virus infection, unexpected system shutdown, or software failure.</p>
- <p>It can recover files from any type of storage device, such as internal or external hard drives, USB flash drives, memory cards, digital cameras, mobile phones, and more.</p>
- <p>It can also recover files of any format, such as photos, videos, music, documents, emails, archives, and more.</p>
- <h3>Features and benefits of GetData Recover My Files Professional 4.9.2.1240 Portable .rar</h3>
- <p>Some of the features and benefits of GetData Recover My Files Professional 4.9.2.1240 Portable .rar are:</p>
-<p></p>
- <ul>
-<li>It is portable, which means you can run it from a USB flash drive or CD without installing it on your computer.</li>
-<li>It has a simple and intuitive user interface that guides you through the data recovery process step by step.</li>
-<li>It has a fast and deep scan mode that can find and recover files based on their name, size, date, or content.</li>
-<li>It has a preview function that allows you to see the file before recovering it.</li>
-<li>It has a file validation function that ensures the file is intact and not corrupted before recovering it.</li>
-<li>It has a - It has a disk imaging function that allows you to create an exact copy of your drive or device for backup or recovery purposes.</li>
-<li>It has a hex viewer function that allows you to see the raw data of your files.</li>
-<li>It has a customer support function that allows you to contact the technical team for any questions or issues.</li>
-</ul>
- <p>With these features and benefits, GetData Recover My Files Professional 4.9.2.1240 Portable .rar can help you recover your data in a fast, easy, and reliable way.</p>
- <h3>How to download and use GetData Recover My Files Professional 4.9.2.1240 Portable .rar</h3>
- <p>To download and use GetData Recover My Files Professional 4.9.2.1240 Portable .rar, you need to follow these steps:</p>
- <ol>
-<li>Download the GetData Recover My Files Professional 4.9.2.1240 Portable .rar file from the official website or from a trusted source.</li>
-<li>Extract the .rar file using a software like WinRAR or 7-Zip.</li>
-<li>Run the RecoverMyFiles.exe file from the extracted folder.</li>
-<li>Agree to the license agreement and choose the language for the software.</li>
-<li>The software will launch and you can start using it to recover your files.</li>
-</ol>
- <p>Note: You can also burn the .rar file to a CD or copy it to a USB flash drive and run it from there without installing it on your computer.</p>
- <h2>How to recover deleted or lost files with GetData Recover My Files Professional 4.9.2.1240 Portable .rar</h2>
- <p>To recover deleted or lost files with GetData Recover My Files Professional 4.9.2.1240 Portable .rar, you need to follow these steps:</p>
- <h3>Step 1: Select the drive or device to scan</h3>
- <p>After launching the software, you will see a list of drives or devices connected to your computer. You can choose the one that contains the files you want to recover, or you can click on "Other" to select a specific folder or file.</p>
- <p>You can also click on "Refresh" to update the list of drives or devices, or click on "Image" to scan an image file of your drive or device.</p>
- <p>Once you have selected the drive or device, click on "Next" to proceed.</p>
- <h3>Step 2: Choose the file types to recover</h3>
- <p>The software will ask you to choose the file types you want to recover, such as photos, videos, music, documents, emails, archives, and more. You can select one or more file types, or you can click on "Search for all file types" to scan for all possible file types.</p>
- <p>You can also click on "Options" to customize the scan settings, such as enabling fast scan, deep scan, ignore bad sectors, search by date, search by size, and more.</p>
- <p>Once you have chosen the file types, click on "Start" to begin the scan.</p>
- <h3>Step 3: Preview and recover the files</h3>
- <p>The software will start scanning the drive or device and display the results in a tree view on the left side and a thumbnail view on the right side. You can expand or collapse the tree view to see the folders and files found by the software.</p>
- <p>You can also use the search box on the top right corner to find a specific file by its name, extension, date, size, or content.</p>
- <p>You can preview the files by clicking on them and see their details on the bottom right corner. You can also use the hex viewer function to see their raw data by clicking on "View" and then "Hex".</p>
- <p>To recover the files, you need to select them by checking their boxes on the left side and then click on "Save Files" on the top left corner. You will be asked to choose a location where you want to save the recovered files. You should choose a different location than the original one to avoid overwriting the data.</p>
- <p>Once you have chosen the location, click on "OK" to start the recovery process. The software will show you the progress and status of the recovery. You can also pause or cancel the recovery at any time.</p>
- <p>When the recovery is completed, you can open the location and check the recovered files. You can also view the recovery log by clicking on "View" and then "Log".</p>
- <h2>Tips and tricks to optimize data recovery with GetData Recover My Files Professional 4.9.2.1240 Portable .rar</h2>
- <p>To optimize data recovery with GetData Recover My Files Professional 4.9.2.1240 Portable .rar, you should follow these tips and tricks:</p>
- <h3>Stop using the drive or device as soon as possible</h3>
- <p>When you delete or lose files from your drive or device, they are not actually erased from the storage space. They are only marked as deleted and can be overwritten by new data.</p>
- <p>Therefore, you should stop using the drive or device as soon as possible to prevent further data loss. You should also avoid installing or running any software on the drive or device that contains the files you want to recover.</p>
- <h3>Use the advanced search options to narrow down the results</h3>
- <p>If you have a large number of files to recover, or if you are looking for a specific file, you can use the advanced search options to narrow down the results and save time.</p>
- <p>You can use the following search options:</p>
- <ul>
-<li>Search by date: You can specify a date range for the files you want to recover, such as today, yesterday, last week, last month, or custom.</li>
-<li>Search by size: You can specify a size range for the files you want to recover, such as small (<100 KB), medium (100 KB - 1 MB), large (1 MB - 16 MB), or custom.</li>
-<li>Search by content: You can enter a keyword or phrase that appears in the files you want to recover, such as a name, a title, a message, or a code.</li>
-</ul>
- <p>You can also combine these search options to refine your search further.</p>
- <h3>Save the recovered files to a different location</h3>
- <p>As mentioned earlier, you should save the recovered files to a different location than the original one to avoid overwriting the data.</p>
- <p>You should also avoid saving the recovered files to the same drive or device that you are recovering from, as this may cause data corruption or loss.</p>
- <p>You should choose a safe and reliable location to save the recovered files, such as another hard drive, USB flash drive, memory card, cloud storage, or external backup device.</p>
- <h2>Comparison of GetData Recover My Files Professional 4.9.2.1240 Portable .rar with other data recovery software</h2>
- <p>There are many other data recovery software available in the market, such as Recuva, EaseUS Data Recovery Wizard, Stellar Data Recovery, and more. How does GetData Recover My Files Professional 4.9.2.1240 Portable .rar compare with them?</p>
- <h3>Table of comparison</h3>
- <table>
-<thead>
-<tr>
-<th>Data recovery software</th>
-<th>Price</th>
-<th>Supported devices</th>
-<th>Supported file types</th>
-<th>Scan speed</th>
-<th>Recovery rate</th>
-<th>User interface</th>
-<th>Customer support</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><strong>GetData Recover My Files Professional 4.9.2.1240 Portable .rar</strong></td>
-<td>$69.95 for one license</td>
-<td>All types of storage devices</td>
-<td>All types of file formats</td>
-<td>Fairly fast (depends on scan mode and drive size)</td>
-<td>High (depends on file condition and overwriting)</td>
-<td>Simple and intuitive</td>
-<td>Email and phone support</td>
-</tr>
-<tr>
-<td><strong>Recuva</strong></td>
-<td>Free for basic version; $19.95 for professional version</td>
-<td>All types of storage devices</td>
-<td>All types of file formats</td>
-<td>Fairly fast (depends on scan mode and drive size)</td>
-<td>Medium (depends on file condition and overwriting)</td>
-<td>Simple and intuitive</td>
-<td>Email support</td>
-</tr>
-<tr>
-<td><strong>EaseUS Data Recovery Wizard</strong</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Http Dl.free [UPDATED].fr Q1PcZAX7n.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Http Dl.free [UPDATED].fr Q1PcZAX7n.md
deleted file mode 100644
index 8ee21490438f18344897d381d0c4e364684935a0..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Http Dl.free [UPDATED].fr Q1PcZAX7n.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-Hello, this is Bing. I can help you write a title and an article with HTML formatting for the keyword "Http: Dl.free.fr Q1PcZAX7n". Here is a possible example:
-
-<h1>How to Download Files from Dl.free.fr</h1>
-<p>Dl.free.fr is a web-based file hosting service in France that lets you upload, download and share files up to 10 GB with a client FTP[^4^]. It is offered by the Internet service provider Free and is open to all users. However, some users may encounter difficulties or errors when trying to access or download files from this service. In this article, we will show you how to download files from Dl.free.fr without any problems.</p>
-<h2>Step 1: Copy the File Link</h2>
-<p>The first step is to copy the link of the file you want to download from Dl.free.fr. For example, if you want to download the file with the keyword "Http: Dl.free.fr Q1PcZAX7n", you need to copy the whole URL: http://dl.free.fr/q1PcZAX7n. You can do this by right-clicking on the link and selecting "Copy link address" or by highlighting the link and pressing Ctrl+C on your keyboard.</p>
-<h2>Http: Dl.free.fr Q1PcZAX7n</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash;>>> <a href="https://urlcod.com/2uI9UF">https://urlcod.com/2uI9UF</a></b></p><br /><br />
-<h2>Step 2: Paste the File Link in a New Tab</h2>
-<p>The next step is to paste the link of the file you want to download in a new tab of your browser. You can do this by right-clicking on an empty space in your browser and selecting "Paste" or by pressing Ctrl+V on your keyboard. This will open a new tab with the file link.</p>
-<h2>Step 3: Wait for the File to Load</h2>
-<p>The third step is to wait for the file to load in the new tab. Depending on the size and popularity of the file, this may take some time. You will see a progress bar indicating how much of the file has been loaded. Once the file is fully loaded, you will see a button that says "Télécharger ce fichier" (Download this file) at the bottom of the page.</p>
-<h2>Step 4: Click on the Download Button</h2>
-<p>The final step is to click on the button that says "Télécharger ce fichier" (Download this file) at the bottom of the page. This will start the download process of the file to your computer. You may need to choose a location and a name for the file before saving it. You can also use a download manager software to speed up and resume your downloads.</p>
-<h3>Conclusion</h3>
-<p>In this article, we have shown you how to download files from Dl.free.fr without any problems. We hope this guide was helpful and easy to follow. If you have any questions or feedback, please leave a comment below.</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mitac Mio N179 Software.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mitac Mio N179 Software.md
deleted file mode 100644
index 5b974080818f8fe6cbdbef82f1897de9393d3142..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mitac Mio N179 Software.md	
+++ /dev/null
@@ -1,44 +0,0 @@
-
-<h1>How to Update Your Mitac Mio N179 Portable Navigator</h1>
-<p>The Mitac Mio N179 is a portable navigator that features a 4.3-inch touch screen, preloaded maps, voice guidance, speed camera alerts, and more. If you own this device, you may want to update its software from time to time to improve its performance and functionality. Here are the steps to update your Mitac Mio N179 software:</p>
-<ol>
-<li>Visit the Mio service website at <a href="https://service.mio.com/">https://service.mio.com/</a> and choose your region and location.</li>
-<li>Click on the "Downloads" tab and select your device model from the drop-down menu.</li>
-<li>Download the latest software update file for your device and save it to your computer.</li>
-<li>Connect your device to your computer using a USB cable and turn it on.</li>
-<li>Copy the software update file to the root directory of your device's internal memory or memory card.</li>
-<li>Disconnect your device from your computer and restart it.</li>
-<li>The device will automatically detect the software update file and prompt you to install it. Follow the on-screen instructions to complete the installation.</li>
-<li>Enjoy your updated Mitac Mio N179 portable navigator!</li>
-</ol>
-<p>Note: Before updating your device, make sure it is fully charged or connected to a power source. Do not turn off or disconnect your device during the update process as this may damage it. For more information, refer to the user manual of your device[^2^] or contact Mio customer support.</p>
-<h2>Mitac mio n179 software</h2><br /><p><b><b>Download File</b> &#9999; <a href="https://urlcod.com/2uIbiw">https://urlcod.com/2uIbiw</a></b></p><br /><br />
-
-<h2>Why Choose Mitac Mio N179 Portable Navigator?</h2>
-<p>The Mitac Mio N179 portable navigator is a reliable and easy-to-use device that can help you navigate your way on the road. Whether you are driving for work, leisure, or adventure, the Mitac Mio N179 can provide you with accurate and up-to-date directions, traffic information, points of interest, and more. Here are some of the features and benefits of the Mitac Mio N179 portable navigator:</p>
-<ul>
-<li>Large and clear display: The Mitac Mio N179 has a 4.3-inch touch screen that offers a wide viewing angle and high resolution. You can easily see the map details, route instructions, speed limit signs, and other information on the screen.</li>
-<li>Preloaded maps: The Mitac Mio N179 comes with preloaded maps of Europe or North America (depending on your region) that cover millions of kilometers of roads and thousands of cities and towns. You can also download additional maps from the Mio website or use a memory card to expand your map coverage.</li>
-<li>Voice guidance: The Mitac Mio N179 has a built-in speaker that delivers clear and loud voice guidance in various languages. You can choose from different voice options and adjust the volume level to suit your preference. The voice guidance will tell you when and where to turn, how far to go, and what lane to take.</li>
-<li>Speed camera alerts: The Mitac Mio N179 has a speed camera database that warns you of the locations of fixed and mobile speed cameras along your route. You can also update the speed camera database online or add your own speed camera locations.</li>
-<li>Points of interest: The Mitac Mio N179 has a comprehensive database of points of interest (POIs) that include gas stations, restaurants, hotels, shops, attractions, and more. You can search for POIs by name, category, or proximity to your location or destination. You can also add your own POIs or edit the existing ones.</li>
-<li>Customizable settings: The Mitac Mio N179 allows you to customize various settings to suit your needs and preferences. You can change the map view, color scheme, brightness level, sound volume, language, units, time zone, and more. You can also create your own profiles for different vehicles or users.</li>
-</ul>
-<h2>How to Use Your Mitac Mio N179 Portable Navigator?</h2>
-<p>The Mitac Mio N179 portable navigator is designed to be user-friendly and intuitive. You can operate it with simple taps and gestures on the touch screen. Here are some basic steps to use your Mitac Mio N179 portable navigator:</p>
-<ol>
-<li>Turn on your device by pressing and holding the power button on the top right corner of the device.</li>
-<li>Select your preferred language from the list that appears on the screen.</li>
-<li>Read and accept the terms and conditions of use.</li>
-<li>Perform the initial start up by following the on-screen instructions to set up your device.</li>
-<li>Access the main menu by tapping on the menu icon on the bottom left corner of the screen.</li>
-<li>Select one of the options from the main menu: Navigate to..., Find..., Settings, or Exit.</li>
-<li>If you select Navigate to..., you can enter your destination by address, POI, recent destination, favorite destination, home location, coordinate, or photo location.</li>
-<li>If you select Find..., you can search for POIs near your current location or along your route.</li>
-<li>If you select Settings, you can adjust various settings for your device such as map view, sound volume, language, units, time zone, etc.</li>
-<li>If you select Exit, you can exit the navigation mode and return to the main screen.</li>
-</ol>
-<p>For more detailed instructions on how to use your Mitac Mio N179 portable navigator, please refer to the user manual[^1^] or visit the Mio website.</p>
-<p></p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nickmuchi/Earnings-Call-Analysis-Whisperer/sentence-transformers/evaluate_retrieved_passages.py b/spaces/nickmuchi/Earnings-Call-Analysis-Whisperer/sentence-transformers/evaluate_retrieved_passages.py
deleted file mode 100644
index 5afd6e0c34b263b453a69a9c4eb715ed3b5f6b91..0000000000000000000000000000000000000000
--- a/spaces/nickmuchi/Earnings-Call-Analysis-Whisperer/sentence-transformers/evaluate_retrieved_passages.py
+++ /dev/null
@@ -1,66 +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 argparse
-import json
-import logging
-import glob
-
-import numpy as np
-import torch
-
-import src.utils
-
-from src.evaluation import calculate_matches
-
-logger = logging.getLogger(__name__)
-
-def validate(data, workers_num):
-    match_stats = calculate_matches(data, workers_num)
-    top_k_hits = match_stats.top_k_hits
-
-    #logger.info('Validation results: top k documents hits %s', top_k_hits)
-    top_k_hits = [v / len(data) for v in top_k_hits]
-    #logger.info('Validation results: top k documents hits accuracy %s', top_k_hits)
-    return top_k_hits
-
-
-def main(opt):
-    logger = src.utils.init_logger(opt, stdout_only=True)
-    datapaths = glob.glob(args.data)
-    r20, r100 = [], []
-    for path in datapaths:
-        data = []
-        with open(path, 'r') as fin:
-            for line in fin:
-                data.append(json.loads(line))
-            #data = json.load(fin)
-        answers = [ex['answers'] for ex in data]
-        top_k_hits = validate(data, args.validation_workers)
-        message = f"Evaluate results from {path}:"
-        for k in [5, 10, 20, 100]:
-            if k <= len(top_k_hits):
-                recall = 100 * top_k_hits[k-1]
-                if k == 20:
-                    r20.append(f"{recall:.1f}")
-                if k == 100:
-                    r100.append(f"{recall:.1f}")
-                message += f' R@{k}: {recall:.1f}'
-        logger.info(message)
-    print(datapaths)
-    print('\t'.join(r20))
-    print('\t'.join(r100))
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--data', required=True, type=str, default=None)
-    parser.add_argument('--validation_workers', type=int, default=16,
-                        help="Number of parallel processes to validate results")
-
-    args = parser.parse_args()
-    main(args)
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/losses/embed_utils.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/losses/embed_utils.py
deleted file mode 100644
index f2ca16fd3809b89e1c05636242a84d02d3a42d88..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/losses/embed_utils.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-from dataclasses import dataclass
-from typing import Any, Optional
-import torch
-
-from detectron2.structures import BoxMode, Instances
-
-from .utils import AnnotationsAccumulator
-
-
-@dataclass
-class PackedCseAnnotations:
-    x_gt: torch.Tensor
-    y_gt: torch.Tensor
-    coarse_segm_gt: Optional[torch.Tensor]
-    vertex_mesh_ids_gt: torch.Tensor
-    vertex_ids_gt: torch.Tensor
-    bbox_xywh_gt: torch.Tensor
-    bbox_xywh_est: torch.Tensor
-    point_bbox_with_dp_indices: torch.Tensor
-    point_bbox_indices: torch.Tensor
-    bbox_indices: torch.Tensor
-
-
-class CseAnnotationsAccumulator(AnnotationsAccumulator):
-    """
-    Accumulates annotations by batches that correspond to objects detected on
-    individual images. Can pack them together into single tensors.
-    """
-
-    def __init__(self):
-        self.x_gt = []
-        self.y_gt = []
-        self.s_gt = []
-        self.vertex_mesh_ids_gt = []
-        self.vertex_ids_gt = []
-        self.bbox_xywh_gt = []
-        self.bbox_xywh_est = []
-        self.point_bbox_with_dp_indices = []
-        self.point_bbox_indices = []
-        self.bbox_indices = []
-        self.nxt_bbox_with_dp_index = 0
-        self.nxt_bbox_index = 0
-
-    def accumulate(self, instances_one_image: Instances):
-        """
-        Accumulate instances data for one image
-
-        Args:
-            instances_one_image (Instances): instances data to accumulate
-        """
-        boxes_xywh_est = BoxMode.convert(
-            instances_one_image.proposal_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS
-        )
-        boxes_xywh_gt = BoxMode.convert(
-            instances_one_image.gt_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS
-        )
-        n_matches = len(boxes_xywh_gt)
-        assert n_matches == len(
-            boxes_xywh_est
-        ), f"Got {len(boxes_xywh_est)} proposal boxes and {len(boxes_xywh_gt)} GT boxes"
-        if not n_matches:
-            # no detection - GT matches
-            return
-        if (
-            not hasattr(instances_one_image, "gt_densepose")
-            or instances_one_image.gt_densepose is None
-        ):
-            # no densepose GT for the detections, just increase the bbox index
-            self.nxt_bbox_index += n_matches
-            return
-        for box_xywh_est, box_xywh_gt, dp_gt in zip(
-            boxes_xywh_est, boxes_xywh_gt, instances_one_image.gt_densepose
-        ):
-            if (dp_gt is not None) and (len(dp_gt.x) > 0):
-                # pyre-fixme[6]: For 1st argument expected `Tensor` but got `float`.
-                # pyre-fixme[6]: For 2nd argument expected `Tensor` but got `float`.
-                self._do_accumulate(box_xywh_gt, box_xywh_est, dp_gt)
-            self.nxt_bbox_index += 1
-
-    def _do_accumulate(self, box_xywh_gt: torch.Tensor, box_xywh_est: torch.Tensor, dp_gt: Any):
-        """
-        Accumulate instances data for one image, given that the data is not empty
-
-        Args:
-            box_xywh_gt (tensor): GT bounding box
-            box_xywh_est (tensor): estimated bounding box
-            dp_gt: GT densepose data with the following attributes:
-             - x: normalized X coordinates
-             - y: normalized Y coordinates
-             - segm: tensor of size [S, S] with coarse segmentation
-             -
-        """
-        self.x_gt.append(dp_gt.x)
-        self.y_gt.append(dp_gt.y)
-        if hasattr(dp_gt, "segm"):
-            self.s_gt.append(dp_gt.segm.unsqueeze(0))
-        self.vertex_ids_gt.append(dp_gt.vertex_ids)
-        self.vertex_mesh_ids_gt.append(torch.full_like(dp_gt.vertex_ids, dp_gt.mesh_id))
-        self.bbox_xywh_gt.append(box_xywh_gt.view(-1, 4))
-        self.bbox_xywh_est.append(box_xywh_est.view(-1, 4))
-        self.point_bbox_with_dp_indices.append(
-            torch.full_like(dp_gt.vertex_ids, self.nxt_bbox_with_dp_index)
-        )
-        self.point_bbox_indices.append(torch.full_like(dp_gt.vertex_ids, self.nxt_bbox_index))
-        self.bbox_indices.append(self.nxt_bbox_index)
-        self.nxt_bbox_with_dp_index += 1
-
-    def pack(self) -> Optional[PackedCseAnnotations]:
-        """
-        Pack data into tensors
-        """
-        if not len(self.x_gt):
-            # TODO:
-            # returning proper empty annotations would require
-            # creating empty tensors of appropriate shape and
-            # type on an appropriate device;
-            # we return None so far to indicate empty annotations
-            return None
-        return PackedCseAnnotations(
-            x_gt=torch.cat(self.x_gt, 0),
-            y_gt=torch.cat(self.y_gt, 0),
-            vertex_mesh_ids_gt=torch.cat(self.vertex_mesh_ids_gt, 0),
-            vertex_ids_gt=torch.cat(self.vertex_ids_gt, 0),
-            # ignore segmentation annotations, if not all the instances contain those
-            coarse_segm_gt=torch.cat(self.s_gt, 0)
-            if len(self.s_gt) == len(self.bbox_xywh_gt)
-            else None,
-            bbox_xywh_gt=torch.cat(self.bbox_xywh_gt, 0),
-            bbox_xywh_est=torch.cat(self.bbox_xywh_est, 0),
-            point_bbox_with_dp_indices=torch.cat(self.point_bbox_with_dp_indices, 0),
-            point_bbox_indices=torch.cat(self.point_bbox_indices, 0),
-            bbox_indices=torch.as_tensor(
-                self.bbox_indices, dtype=torch.long, device=self.x_gt[0].device
-            ),
-        )
diff --git a/spaces/nomic-ai/BelleGroup_train_1M_CN/style.css b/spaces/nomic-ai/BelleGroup_train_1M_CN/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/BelleGroup_train_1M_CN/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/notsq/diffuse-the-rest/build/_app/immutable/chunks/0-be487481.js b/spaces/notsq/diffuse-the-rest/build/_app/immutable/chunks/0-be487481.js
deleted file mode 100644
index d0a04373238f49d79e9c49994ed5c7bd66fce3f8..0000000000000000000000000000000000000000
--- a/spaces/notsq/diffuse-the-rest/build/_app/immutable/chunks/0-be487481.js
+++ /dev/null
@@ -1 +0,0 @@
-import{default as m}from"../components/pages/_layout.svelte-f7e87a93.js";import"./index-032ac624.js";export{m as component};
diff --git a/spaces/notsq/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js b/spaces/notsq/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js
deleted file mode 100644
index 85296fbcdf93ad959ffc0963af842b8640cbb27e..0000000000000000000000000000000000000000
--- a/spaces/notsq/diffuse-the-rest/build/_app/immutable/chunks/index-032ac624.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/oliver2023/mm-react/Dockerfile b/spaces/oliver2023/mm-react/Dockerfile
deleted file mode 100644
index 5a15fc63c977b9dac915aa436fa9bb2b7a149d1f..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/mm-react/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python:3.10.9
-
-WORKDIR /src
-
-COPY ./MM-REACT /src/MM-REACT
-
-COPY ./requirements.txt /src/requirements.txt
-
-COPY ./langchain-0.0.94-py3-none-any.whl /src/langchain-0.0.94-py3-none-any.whl
-
-RUN pip install --no-cache-dir /src/langchain-0.0.94-py3-none-any.whl
-
-RUN pip install --no-cache-dir --upgrade -r /src/requirements.txt
-
-WORKDIR /src/MM-REACT
-
-
-CMD ["python", "app.py", "--port", "7860", "--openAIModel", "azureGPT35turbo", "--noIntermediateConv"]
\ No newline at end of file
diff --git a/spaces/omdena/omdena-chatbot/osanyin/__init__.py b/spaces/omdena/omdena-chatbot/osanyin/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/os1187/pii-anonymizer/flair_recognizer.py b/spaces/os1187/pii-anonymizer/flair_recognizer.py
deleted file mode 100644
index 9df87dc13c2a22ac47a11b29cd0e44ef9617ef27..0000000000000000000000000000000000000000
--- a/spaces/os1187/pii-anonymizer/flair_recognizer.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import logging
-from typing import Optional, List, Tuple, Set
-
-from presidio_analyzer import (
-    RecognizerResult,
-    EntityRecognizer,
-    AnalysisExplanation,
-)
-from presidio_analyzer.nlp_engine import NlpArtifacts
-
-try:
-    from flair.data import Sentence
-    from flair.models import SequenceTagger
-except ImportError:
-    print("Flair is not installed")
-
-
-logger = logging.getLogger("presidio-analyzer")
-
-
-class FlairRecognizer(EntityRecognizer):
-    """
-    Wrapper for a flair model, if needed to be used within Presidio Analyzer.
-    :example:
-    >from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
-    >flair_recognizer = FlairRecognizer()
-    >registry = RecognizerRegistry()
-    >registry.add_recognizer(flair_recognizer)
-    >analyzer = AnalyzerEngine(registry=registry)
-    >results = analyzer.analyze(
-    >    "My name is Christopher and I live in Irbid.",
-    >    language="en",
-    >    return_decision_process=True,
-    >)
-    >for result in results:
-    >    print(result)
-    >    print(result.analysis_explanation)
-    """
-
-    ENTITIES = [
-        "LOCATION",
-        "PERSON",
-        "NRP",
-        "GPE",
-        "ORGANIZATION",
-        "MAC_ADDRESS",
-        "US_BANK_NUMBER",
-        "IMEI",
-        "TITLE",
-        "LICENSE_PLATE",
-        "US_PASSPORT",
-        "CURRENCY",
-        "ROUTING_NUMBER",
-        "US_ITIN",
-        "US_BANK_NUMBER",
-        "US_DRIVER_LICENSE",
-        "AGE",
-        "PASSWORD",
-        "SWIFT_CODE",
-    ]
-
-    DEFAULT_EXPLANATION = "Identified as {} by Flair's Named Entity Recognition"
-
-    CHECK_LABEL_GROUPS = [
-        ({"LOCATION"}, {"LOC", "LOCATION", "STREET_ADDRESS", "COORDINATE"}),
-        ({"PERSON"}, {"PER", "PERSON"}),
-        ({"NRP"}, {"NORP", "NRP"}),
-        ({"GPE"}, {"GPE"}),
-        ({"ORGANIZATION"}, {"ORG"}),
-        ({"MAC_ADDRESS"}, {"MAC_ADDRESS"}),
-        ({"US_BANK_NUMBER"}, {"US_BANK_NUMBER"}),
-        ({"IMEI"}, {"IMEI"}),
-        ({"TITLE"}, {"TITLE"}),
-        ({"LICENSE_PLATE"}, {"LICENSE_PLATE"}),
-        ({"US_PASSPORT"}, {"US_PASSPORT"}),
-        ({"CURRENCY"}, {"CURRENCY"}),
-        ({"ROUTING_NUMBER"}, {"ROUTING_NUMBER"}),
-        ({"AGE"}, {"AGE"}),
-        ({"CURRENCY"}, {"CURRENCY"}),
-        ({"SWIFT_CODE"}, {"SWIFT_CODE"}),
-        ({"US_ITIN"}, {"US_ITIN"}),
-        ({"US_BANK_NUMBER"}, {"US_BANK_NUMBER"}),
-        ({"US_DRIVER_LICENSE"}, {"US_DRIVER_LICENSE"}),
-    ]
-
-    MODEL_LANGUAGES = {
-        "en":"beki/flair-pii-english-large",
-        # "en":"flair-trf.pt",
-    }
-
-    PRESIDIO_EQUIVALENCES = {
-        "PER": "PERSON",
-        "LOC": "LOCATION",
-        "ORG": "ORGANIZATION",
-        "NROP": "NRP",
-        "URL": "URL",
-        "US_ITIN": "US_ITIN",
-        "US_PASSPORT": "US_PASSPORT",
-        "IBAN_CODE": "IBAN_CODE",
-        "IP_ADDRESS": "IP_ADDRESS",
-        "EMAIL_ADDRESS": "EMAIL",
-        "US_DRIVER_LICENSE": "US_DRIVER_LICENSE",
-        "US_BANK_NUMBER": "US_BANK_NUMBER",
-    }
-
-    def __init__(
-        self,
-        supported_language: str = "en",
-        supported_entities: Optional[List[str]] = None,
-        check_label_groups: Optional[Tuple[Set, Set]] = None,
-        model: SequenceTagger = None,
-    ):
-        self.check_label_groups = (
-            check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS
-        )
-
-        supported_entities = supported_entities if supported_entities else self.ENTITIES
-        self.model = (
-            model
-            if model
-            else SequenceTagger.load(self.MODEL_LANGUAGES.get(supported_language))
-        )
-
-        super().__init__(
-            supported_entities=supported_entities,
-            supported_language=supported_language,
-            name="Flair Analytics",
-        )
-
-    def load(self) -> None:
-        """Load the model, not used. Model is loaded during initialization."""
-        pass
-
-    def get_supported_entities(self) -> List[str]:
-        """
-        Return supported entities by this model.
-        :return: List of the supported entities.
-        """
-        return self.supported_entities
-
-    # Class to use Flair with Presidio as an external recognizer.
-    def analyze(
-        self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts = None
-    ) -> List[RecognizerResult]:
-        """
-        Analyze text using Text Analytics.
-        :param text: The text for analysis.
-        :param entities: Not working properly for this recognizer.
-        :param nlp_artifacts: Not used by this recognizer.
-        :param language: Text language. Supported languages in MODEL_LANGUAGES
-        :return: The list of Presidio RecognizerResult constructed from the recognized
-            Flair detections.
-        """
-
-        results = []
-
-        sentences = Sentence(text)
-        self.model.predict(sentences)
-
-        # If there are no specific list of entities, we will look for all of it.
-        if not entities:
-            entities = self.supported_entities
-
-        for entity in entities:
-            if entity not in self.supported_entities:
-                continue
-
-            for ent in sentences.get_spans("ner"):
-                if not self.__check_label(
-                    entity, ent.labels[0].value, self.check_label_groups
-                ):
-                    continue
-                textual_explanation = self.DEFAULT_EXPLANATION.format(
-                    ent.labels[0].value
-                )
-                explanation = self.build_flair_explanation(
-                    round(ent.score, 2), textual_explanation
-                )
-                flair_result = self._convert_to_recognizer_result(ent, explanation)
-
-                results.append(flair_result)
-
-        return results
-
-    def _convert_to_recognizer_result(self, entity, explanation) -> RecognizerResult:
-
-        entity_type = self.PRESIDIO_EQUIVALENCES.get(entity.tag, entity.tag)
-        flair_score = round(entity.score, 2)
-
-        flair_results = RecognizerResult(
-            entity_type=entity_type,
-            start=entity.start_position,
-            end=entity.end_position,
-            score=flair_score,
-            analysis_explanation=explanation,
-        )
-
-        return flair_results
-
-    def build_flair_explanation(
-        self, original_score: float, explanation: str
-    ) -> AnalysisExplanation:
-        """
-        Create explanation for why this result was detected.
-        :param original_score: Score given by this recognizer
-        :param explanation: Explanation string
-        :return:
-        """
-        explanation = AnalysisExplanation(
-            recognizer=self.__class__.__name__,
-            original_score=original_score,
-            textual_explanation=explanation,
-        )
-        return explanation
-
-    @staticmethod
-    def __check_label(
-        entity: str, label: str, check_label_groups: Tuple[Set, Set]
-    ) -> bool:
-        return any(
-            [entity in egrp and label in lgrp for egrp, lgrp in check_label_groups]
-        )
-
-
-if __name__ == "__main__":
-
-    from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
-
-    flair_recognizer = (
-        FlairRecognizer()
-    )  # This would download a very large (+2GB) model on the first run
-
-    registry = RecognizerRegistry()
-    registry.add_recognizer(flair_recognizer)
-
-    analyzer = AnalyzerEngine(registry=registry)
-
-    results = analyzer.analyze(
-        "{first_name: Moustafa, sale_id: 235234}",
-        language="en",
-        return_decision_process=True,
-    )
-    for result in results:
-        print(result)
-        print(result.analysis_explanation)
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/magic_mix.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/magic_mix.py
deleted file mode 100644
index 4eb99cb96b423412d62a89575f2d69f1a88c24a7..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/magic_mix.py
+++ /dev/null
@@ -1,152 +0,0 @@
-from typing import Union
-
-import torch
-from PIL import Image
-from torchvision import transforms as tfms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    DiffusionPipeline,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    UNet2DConditionModel,
-)
-
-
-class MagicMixPipeline(DiffusionPipeline):
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],
-    ):
-        super().__init__()
-
-        self.register_modules(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
-
-    # convert PIL image to latents
-    def encode(self, img):
-        with torch.no_grad():
-            latent = self.vae.encode(tfms.ToTensor()(img).unsqueeze(0).to(self.device) * 2 - 1)
-            latent = 0.18215 * latent.latent_dist.sample()
-        return latent
-
-    # convert latents to PIL image
-    def decode(self, latent):
-        latent = (1 / 0.18215) * latent
-        with torch.no_grad():
-            img = self.vae.decode(latent).sample
-        img = (img / 2 + 0.5).clamp(0, 1)
-        img = img.detach().cpu().permute(0, 2, 3, 1).numpy()
-        img = (img * 255).round().astype("uint8")
-        return Image.fromarray(img[0])
-
-    # convert prompt into text embeddings, also unconditional embeddings
-    def prep_text(self, prompt):
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
-        text_embedding = self.text_encoder(text_input.input_ids.to(self.device))[0]
-
-        uncond_input = self.tokenizer(
-            "",
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
-        uncond_embedding = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
-        return torch.cat([uncond_embedding, text_embedding])
-
-    def __call__(
-        self,
-        img: Image.Image,
-        prompt: str,
-        kmin: float = 0.3,
-        kmax: float = 0.6,
-        mix_factor: float = 0.5,
-        seed: int = 42,
-        steps: int = 50,
-        guidance_scale: float = 7.5,
-    ) -> Image.Image:
-        tmin = steps - int(kmin * steps)
-        tmax = steps - int(kmax * steps)
-
-        text_embeddings = self.prep_text(prompt)
-
-        self.scheduler.set_timesteps(steps)
-
-        width, height = img.size
-        encoded = self.encode(img)
-
-        torch.manual_seed(seed)
-        noise = torch.randn(
-            (1, self.unet.config.in_channels, height // 8, width // 8),
-        ).to(self.device)
-
-        latents = self.scheduler.add_noise(
-            encoded,
-            noise,
-            timesteps=self.scheduler.timesteps[tmax],
-        )
-
-        input = torch.cat([latents] * 2)
-
-        input = self.scheduler.scale_model_input(input, self.scheduler.timesteps[tmax])
-
-        with torch.no_grad():
-            pred = self.unet(
-                input,
-                self.scheduler.timesteps[tmax],
-                encoder_hidden_states=text_embeddings,
-            ).sample
-
-        pred_uncond, pred_text = pred.chunk(2)
-        pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
-
-        latents = self.scheduler.step(pred, self.scheduler.timesteps[tmax], latents).prev_sample
-
-        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
-            if i > tmax:
-                if i < tmin:  # layout generation phase
-                    orig_latents = self.scheduler.add_noise(
-                        encoded,
-                        noise,
-                        timesteps=t,
-                    )
-
-                    input = (mix_factor * latents) + (
-                        1 - mix_factor
-                    ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
-                    input = torch.cat([input] * 2)
-
-                else:  # content generation phase
-                    input = torch.cat([latents] * 2)
-
-                input = self.scheduler.scale_model_input(input, t)
-
-                with torch.no_grad():
-                    pred = self.unet(
-                        input,
-                        t,
-                        encoder_hidden_states=text_embeddings,
-                    ).sample
-
-                pred_uncond, pred_text = pred.chunk(2)
-                pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
-
-                latents = self.scheduler.step(pred, t, latents).prev_sample
-
-        return self.decode(latents)
diff --git a/spaces/parkyzh/bingo/src/app/page.tsx b/spaces/parkyzh/bingo/src/app/page.tsx
deleted file mode 100644
index 0dff3431b098ce4fe282cc83fc87a93a28a43090..0000000000000000000000000000000000000000
--- a/spaces/parkyzh/bingo/src/app/page.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import dynamic from 'next/dynamic'
-
-const DynamicComponentWithNoSSR = dynamic(
-  () => import('../components/chat'),
-  { ssr: false }
-)
-
-export default function IndexPage() {
-  return (
-    <>
-      <div className="loading-spinner" />
-      <DynamicComponentWithNoSSR />
-    </>
-  )
-}
diff --git a/spaces/pierrefdz/semantle/app.py b/spaces/pierrefdz/semantle/app.py
deleted file mode 100644
index f6a67969d886b3741cf8648c2c3b1b4f6db3d5cf..0000000000000000000000000000000000000000
--- a/spaces/pierrefdz/semantle/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import gradio as gr
-import gradio.inputs as grinputs
-import gradio.outputs as groutputs
-
-from gensim.models import KeyedVectors
-from gensim.parsing import preprocessing
-
-filters = [
-    preprocessing.remove_stopwords,
-    preprocessing.strip_tags,
-    preprocessing.strip_punctuation,
-    preprocessing.strip_numeric,
-    preprocessing.strip_multiple_whitespaces,
-    preprocessing.strip_non_alphanum,
-    preprocessing.strip_short,
-    preprocessing.remove_stopwords,
-    preprocessing.lower_to_unicode,
-]
-
-def parse_text(text):
-    return text.replace(" ", "").replace(";", ",").split(',')
-
-def clean_words(words):
-    clean_dict = {}
-    for (word, score) in words:
-        prep_word = preprocessing.preprocess_string(word, filters=filters)
-        if len(prep_word) > 0:
-            word = prep_word[0]
-            is_clean = sum( [word.startswith(clean_word) or word.endswith(clean_word) for clean_word in clean_dict.keys()] ) == 0
-            if is_clean:
-                clean_dict[word] = round(score, 2)
-    return clean_dict
-
-path = "cc.en.300.vec"
-# path = "cc.fr.300.vec"
-m = KeyedVectors.load_word2vec_format(path, limit = 100000)
-
-def on_submit(text, mode):
-    print('{} mode'.format(mode))
-    positive = parse_text(text)
-    if mode=='Close':
-        words = m.most_similar(positive=positive, topn=50)
-    else:
-        words = m.most_similar(positive=positive, topn=10000)[::-1]
-    return str(clean_words(words))[1:-1]
-
-iface = gr.Interface(
-    fn=on_submit, 
-    inputs=[
-        grinputs.Textbox(placeholder='word1, word2, word3, ...', label="Input words (coma separated). Returns words that are close (or far) from the input words."), 
-        grinputs.Radio(['Close', 'Far'], label="Close of Far mode")], 
-    outputs=[
-        groutputs.Textbox(label='Information')],
-    allow_screenshot=False
-    )
-iface.launch()
\ No newline at end of file
diff --git a/spaces/pikto/Elite-freegpt-webui/run.py b/spaces/pikto/Elite-freegpt-webui/run.py
deleted file mode 100644
index 0f51b649137e966cd6912ca6e54f49ea49d7f3c6..0000000000000000000000000000000000000000
--- a/spaces/pikto/Elite-freegpt-webui/run.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from server.bp import bp
-from server.website import Website
-from server.backend import Backend_Api
-from json import load
-from flask import Flask
-
-if __name__ == '__main__':
-
-    # Load configuration from config.json
-    config = load(open('config.json', 'r'))
-    site_config = config['site_config']
-    url_prefix = config.pop('url_prefix')
-
-    # Set up the website routes
-    site = Website(bp, url_prefix)
-    for route in site.routes:
-        bp.add_url_rule(
-            route,
-            view_func=site.routes[route]['function'],
-            methods=site.routes[route]['methods'],
-        )
-
-    # Set up the backend API routes
-    backend_api = Backend_Api(bp, config)
-    for route in backend_api.routes:
-        bp.add_url_rule(
-            route,
-            view_func=backend_api.routes[route]['function'],
-            methods=backend_api.routes[route]['methods'],
-        )
-
-    # Create the app and register the blueprint
-    app = Flask(__name__)
-    app.register_blueprint(bp, url_prefix=url_prefix)
-
-    # Run the Flask server
-    print(f"Running on {site_config['port']}{url_prefix}")
-    app.run(**site_config)
-    print(f"Closing port {site_config['port']}")
diff --git a/spaces/pkiage/credit_risk_modeling_demo/views/evaluation.py b/spaces/pkiage/credit_risk_modeling_demo/views/evaluation.py
deleted file mode 100644
index c3c6690d69e5370eca6e07d96e7f7a4e21dab148..0000000000000000000000000000000000000000
--- a/spaces/pkiage/credit_risk_modeling_demo/views/evaluation.py
+++ /dev/null
@@ -1,410 +0,0 @@
-from typing import Union
-import pandas as pd
-import streamlit as st
-import numpy as np
-from sklearn.metrics import (
-    classification_report,
-    confusion_matrix,
-)
-from sklearn.linear_model import LogisticRegression
-from xgboost.sklearn import XGBClassifier
-from common.data import SplitDataset
-from common.util import (
-    create_cross_validation_df,
-    cross_validation_scores,
-    get_df_trueStatus_probabilityDefault_threshStatus_loanAmount,
-)
-from common.views import (
-    cross_validation_graph,
-)
-
-
-def make_evaluation_view(
-    model_name_short: str,
-    model_name_generic: str,
-):
-    def view(
-        clf_gbt_model: Union[XGBClassifier, LogisticRegression],
-        split_dataset: SplitDataset,
-        currency: str,
-        prob_thresh_selected,
-        predicted_default_status,
-    ):
-        st.header(f"Model Evaluation - {model_name_generic}")
-
-        st.subheader("Cross Validation")
-
-        st.write("Shows how our model will perform as new loans come in.")
-        st.write(
-            "If evaluation metric for test and train set improve as models \
-            train on each fold suggests performance will be stable."
-        )
-
-        st.write(f"XGBoost cross validation test:")
-
-        stcol_seed, stcol_eval_metric = st.columns(2)
-
-        with stcol_seed:
-            cv_seed = int(
-                st.number_input(
-                    label="Random State Seed for Cross Validation:",
-                    value=123235,
-                    key=f"cv_seed_{model_name_short}",
-                )
-            )
-
-        with stcol_eval_metric:
-            eval_metric = st.selectbox(
-                label="Select evaluation metric",
-                options=[
-                    "auc",
-                    "aucpr",
-                    "rmse",
-                    "mae",
-                    "logloss",
-                    "error",
-                    "merror",
-                    "mlogloss",
-                ],
-                key=f"eval_metric_{model_name_short}",
-            )
-
-        stcol_trees, stcol_eval_nfold, stcol_earlystoppingrounds = st.columns(
-            3
-        )
-
-        with stcol_trees:
-            trees = int(
-                st.number_input(
-                    label="Number of trees",
-                    value=5,
-                    key=f"trees_{model_name_short}",
-                )
-            )
-
-        with stcol_eval_nfold:
-            nfolds = int(
-                st.number_input(
-                    label="Number of folds",
-                    value=5,
-                    key=f"nfolds_{model_name_short}",
-                )
-            )
-
-        with stcol_earlystoppingrounds:
-            early_stopping_rounds = int(
-                st.number_input(
-                    label="Early stopping rounds",
-                    value=10,
-                    key=f"early_stopping_rounds_{model_name_short}",
-                )
-            )
-
-        DTrain, cv_df = create_cross_validation_df(
-            split_dataset.X_test,
-            split_dataset.y_test,
-            eval_metric,
-            cv_seed,
-            trees,
-            nfolds,
-            early_stopping_rounds,
-        )
-
-        st.write(cv_df)
-
-        scoring_options = [
-            "roc_auc",
-            "accuracy",
-            "precision",
-            "recall",
-            "f1",
-            "jaccard",
-        ]
-
-        overfit_test = st.radio(
-            label="Overfit test:",
-            options=("No", "Yes"),
-            key=f"overfit_test_{model_name_short}",
-        )
-
-        if overfit_test == "Yes":
-            st.write("Overfit test:")
-            iterations = int(
-                st.number_input(
-                    label="Number of folds (iterations)",
-                    value=500,
-                    key=f"iterations_{model_name_short}",
-                )
-            )
-
-            DTrain, cv_df_it = create_cross_validation_df(
-                split_dataset.X_test,
-                split_dataset.y_test,
-                eval_metric,
-                cv_seed,
-                iterations,
-                nfolds,
-                iterations,
-            )
-
-            fig_it = cross_validation_graph(cv_df_it, eval_metric, iterations)
-            st.pyplot(fig_it)
-
-        st.write("Sklearn cross validation test:")
-        stcol_scoringmetric, st_nfold = st.columns(2)
-
-        with stcol_scoringmetric:
-            score_metric = st.selectbox(
-                label="Select score",
-                options=scoring_options,
-                key=f"stcol_scoringmetric_{model_name_short}",
-            )
-
-        with st_nfold:
-            nfolds_score = int(
-                st.number_input(
-                    label="Number of folds",
-                    value=5,
-                    key=f"st_nfold_{model_name_short}",
-                )
-            )
-
-        cv_scores = cross_validation_scores(
-            clf_gbt_model,
-            split_dataset.X_test,
-            split_dataset.y_test,
-            nfolds_score,
-            score_metric,
-            cv_seed,
-        )
-
-        stcol_vals, stcol_mean, st_std = st.columns(3)
-
-        with stcol_vals:
-            st.markdown(f"{score_metric} scores:")
-            st.write(
-                pd.DataFrame(
-                    cv_scores,
-                    columns=[score_metric],
-                )
-            )
-
-        with stcol_mean:
-            st.metric(
-                label=f"Average {score_metric} score ",
-                value="{:.4f}".format(cv_scores.mean()),
-                delta=None,
-                delta_color="normal",
-            )
-
-        with st_std:
-            st.metric(
-                label=f"{score_metric} standard deviation (+/-)",
-                value="{:.4f}".format(cv_scores.std()),
-                delta=None,
-                delta_color="normal",
-            )
-
-        st.subheader("Classification Report")
-
-        target_names = ["Non-Default", "Default"]
-
-        classification_report_dict = classification_report(
-            split_dataset.y_test,
-            predicted_default_status,
-            target_names=target_names,
-            output_dict=True,
-        )
-
-        (
-            stcol_defaultpres,
-            stcol_defaultrecall,
-            stcol_defaultf1score,
-            stcol_f1score,
-        ) = st.columns(4)
-        with stcol_defaultpres:
-            st.metric(
-                label="Default Precision",
-                value="{:.0%}".format(
-                    classification_report_dict["Default"]["precision"]
-                ),
-                delta=None,
-                delta_color="normal",
-            )
-
-        with stcol_defaultrecall:
-            st.metric(
-                label="Default Recall",
-                value="{:.0%}".format(
-                    classification_report_dict["Default"]["recall"]
-                ),
-                delta=None,
-                delta_color="normal",
-            )
-
-        with stcol_defaultf1score:
-            st.metric(
-                label="Default F1 Score",
-                value="{:.2f}".format(
-                    classification_report_dict["Default"]["f1-score"]
-                ),
-                delta=None,
-                delta_color="normal",
-            )
-
-        with stcol_f1score:
-            st.metric(
-                label="Macro avg F1 Score (Model F1 Score):",
-                value="{:.2f}".format(
-                    classification_report_dict["macro avg"]["f1-score"]
-                ),
-                delta=None,
-                delta_color="normal",
-            )
-
-        with st.expander("Classification Report Dictionary:"):
-            st.write(classification_report_dict)
-
-        st.markdown(
-            f'Default precision: {"{:.0%}".format(classification_report_dict["Default"]["precision"])} of loans predicted as default were actually default.'
-        )
-
-        st.markdown(
-            f'Default recall: {"{:.0%}".format(classification_report_dict["Default"]["recall"])} of true defaults predicted correctly.'
-        )
-
-        f1_gap = 1 - classification_report_dict["Default"]["f1-score"]
-        st.markdown(
-            f'Default F1 score: {"{:.2f}".format(classification_report_dict["Default"]["f1-score"])}\
-                is {"{:.2f}".format(f1_gap)} away from perfect precision and recall (no false positive rate).'
-        )
-
-        st.markdown(
-            f'macro avg F1 score: {"{:.2f}".format(classification_report_dict["macro avg"]["f1-score"])} is the models F1 score.'
-        )
-
-        st.subheader("Confusion Matrix")
-        confuctiomatrix_dict = confusion_matrix(
-            split_dataset.y_test, predicted_default_status
-        )
-
-        tn, fp, fn, tp = confusion_matrix(
-            split_dataset.y_test, predicted_default_status
-        ).ravel()
-
-        with st.expander(
-            "Confusion matrix (column name = classification model prediction, row name = true status, values = number of loans"
-        ):
-            st.write(confuctiomatrix_dict)
-
-        st.markdown(
-            f'{tp} ,\
-            {"{:.0%}".format(tp / len(predicted_default_status))} \
-                true positives (defaults correctly predicted as defaults).'
-        )
-
-        st.markdown(
-            f'{fp} ,\
-            {"{:.0%}".format(fp / len(predicted_default_status))} \
-                false positives (non-defaults incorrectly predicted as defaults).'
-        )
-
-        st.markdown(
-            f'{fn} ,\
-            {"{:.0%}".format(fn / len(predicted_default_status))} \
-                false negatives (defaults incorrectly predicted as non-defaults).'
-        )
-
-        st.markdown(
-            f'{tn} ,\
-            {"{:.0%}".format(tn / len(predicted_default_status))} \
-                true negatives (non-defaults correctly predicted as non-defaults).'
-        )
-
-        st.subheader("Bad Rate")
-
-        df_trueStatus_probabilityDefault_threshStatus_loanAmount = (
-            get_df_trueStatus_probabilityDefault_threshStatus_loanAmount(
-                clf_gbt_model,
-                split_dataset.X_test,
-                split_dataset.y_test,
-                prob_thresh_selected,
-                "loan_amnt",
-            )
-        )
-
-        with st.expander(
-            "Loan Status, Probability of Default, & Loan Amount DataFrame"
-        ):
-            st.write(df_trueStatus_probabilityDefault_threshStatus_loanAmount)
-
-        accepted_loans = (
-            df_trueStatus_probabilityDefault_threshStatus_loanAmount[
-                df_trueStatus_probabilityDefault_threshStatus_loanAmount[
-                    "PREDICT_DEFAULT_STATUS"
-                ]
-                == 0
-            ]
-        )
-
-        bad_rate = (
-            np.sum(accepted_loans["loan_status"])
-            / accepted_loans["loan_status"].count()
-        )
-
-        with st.expander("Loan Amount Summary Statistics"):
-            st.write(
-                df_trueStatus_probabilityDefault_threshStatus_loanAmount[
-                    "loan_amnt"
-                ].describe()
-            )
-
-        avg_loan = np.mean(
-            df_trueStatus_probabilityDefault_threshStatus_loanAmount[
-                "loan_amnt"
-            ]
-        )
-
-        crosstab_df = pd.crosstab(
-            df_trueStatus_probabilityDefault_threshStatus_loanAmount[
-                "loan_status"
-            ],  # row label
-            df_trueStatus_probabilityDefault_threshStatus_loanAmount[
-                "PREDICT_DEFAULT_STATUS"
-            ],
-        ).apply(
-            lambda x: x * avg_loan, axis=0
-        )  # column label
-
-        with st.expander(
-            "Cross tabulation (column name = classification model prediction, row name = true status, values = number of loans * average loan value"
-        ):
-            st.write(crosstab_df)
-
-        st.write(
-            f'Bad rate: {"{:.2%}".format(bad_rate)} of all the loans the model accepted (classified as non-default) from the test set were actually defaults.'
-        )
-
-        st.write(
-            f'Estimated value of the bad rate is {currency} {"{:,.2f}".format(crosstab_df[0][1])}.'
-        )
-
-        st.write(
-            f'Total estimated value of actual non-default loans is {currency} {"{:,.2f}".format(crosstab_df[0][0]+crosstab_df[0][1])}'
-        )
-
-        st.write(
-            f'Estimated value of loans incorrectly predicted as default is {currency} {"{:,.2f}".format(crosstab_df[1][0])}'
-        )
-
-        st.write(
-            f'Estimated value of loans correctly predicted as defaults is {currency} {"{:,.2f}".format(crosstab_df[1][1])}'
-        )
-
-        return df_trueStatus_probabilityDefault_threshStatus_loanAmount
-
-    return view
-
-
-decision_tree_evaluation_view = make_evaluation_view("gbt", "Decision Tree")
-logistic_evaluation_view = make_evaluation_view("lg", "Logistic Regression")
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distro/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distro/__init__.py
deleted file mode 100644
index 7686fe85a7cc94188da76bfb1c10ad2a10821256..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distro/__init__.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from .distro import (
-    NORMALIZED_DISTRO_ID,
-    NORMALIZED_LSB_ID,
-    NORMALIZED_OS_ID,
-    LinuxDistribution,
-    __version__,
-    build_number,
-    codename,
-    distro_release_attr,
-    distro_release_info,
-    id,
-    info,
-    like,
-    linux_distribution,
-    lsb_release_attr,
-    lsb_release_info,
-    major_version,
-    minor_version,
-    name,
-    os_release_attr,
-    os_release_info,
-    uname_attr,
-    uname_info,
-    version,
-    version_parts,
-)
-
-__all__ = [
-    "NORMALIZED_DISTRO_ID",
-    "NORMALIZED_LSB_ID",
-    "NORMALIZED_OS_ID",
-    "LinuxDistribution",
-    "build_number",
-    "codename",
-    "distro_release_attr",
-    "distro_release_info",
-    "id",
-    "info",
-    "like",
-    "linux_distribution",
-    "lsb_release_attr",
-    "lsb_release_info",
-    "major_version",
-    "minor_version",
-    "name",
-    "os_release_attr",
-    "os_release_info",
-    "uname_attr",
-    "uname_info",
-    "version",
-    "version_parts",
-]
-
-__version__ = __version__
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/readers.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/readers.py
deleted file mode 100644
index ab34db74091c8a04ee9004ce9a786de3146ec917..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/readers.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import collections
-import pathlib
-import operator
-
-from . import abc
-
-from ._itertools import unique_everseen
-from ._compat import ZipPath
-
-
-def remove_duplicates(items):
-    return iter(collections.OrderedDict.fromkeys(items))
-
-
-class FileReader(abc.TraversableResources):
-    def __init__(self, loader):
-        self.path = pathlib.Path(loader.path).parent
-
-    def resource_path(self, resource):
-        """
-        Return the file system path to prevent
-        `resources.path()` from creating a temporary
-        copy.
-        """
-        return str(self.path.joinpath(resource))
-
-    def files(self):
-        return self.path
-
-
-class ZipReader(abc.TraversableResources):
-    def __init__(self, loader, module):
-        _, _, name = module.rpartition('.')
-        self.prefix = loader.prefix.replace('\\', '/') + name + '/'
-        self.archive = loader.archive
-
-    def open_resource(self, resource):
-        try:
-            return super().open_resource(resource)
-        except KeyError as exc:
-            raise FileNotFoundError(exc.args[0])
-
-    def is_resource(self, path):
-        # workaround for `zipfile.Path.is_file` returning true
-        # for non-existent paths.
-        target = self.files().joinpath(path)
-        return target.is_file() and target.exists()
-
-    def files(self):
-        return ZipPath(self.archive, self.prefix)
-
-
-class MultiplexedPath(abc.Traversable):
-    """
-    Given a series of Traversable objects, implement a merged
-    version of the interface across all objects. Useful for
-    namespace packages which may be multihomed at a single
-    name.
-    """
-
-    def __init__(self, *paths):
-        self._paths = list(map(pathlib.Path, remove_duplicates(paths)))
-        if not self._paths:
-            message = 'MultiplexedPath must contain at least one path'
-            raise FileNotFoundError(message)
-        if not all(path.is_dir() for path in self._paths):
-            raise NotADirectoryError('MultiplexedPath only supports directories')
-
-    def iterdir(self):
-        files = (file for path in self._paths for file in path.iterdir())
-        return unique_everseen(files, key=operator.attrgetter('name'))
-
-    def read_bytes(self):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    def read_text(self, *args, **kwargs):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    def is_dir(self):
-        return True
-
-    def is_file(self):
-        return False
-
-    def joinpath(self, *descendants):
-        try:
-            return super().joinpath(*descendants)
-        except abc.TraversalError:
-            # One of the paths did not resolve (a directory does not exist).
-            # Just return something that will not exist.
-            return self._paths[0].joinpath(*descendants)
-
-    def open(self, *args, **kwargs):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    @property
-    def name(self):
-        return self._paths[0].name
-
-    def __repr__(self):
-        paths = ', '.join(f"'{path}'" for path in self._paths)
-        return f'MultiplexedPath({paths})'
-
-
-class NamespaceReader(abc.TraversableResources):
-    def __init__(self, namespace_path):
-        if 'NamespacePath' not in str(namespace_path):
-            raise ValueError('Invalid path')
-        self.path = MultiplexedPath(*list(namespace_path))
-
-    def resource_path(self, resource):
-        """
-        Return the file system path to prevent
-        `resources.path()` from creating a temporary
-        copy.
-        """
-        return str(self.path.joinpath(resource))
-
-    def files(self):
-        return self.path
diff --git a/spaces/plzdontcry/dakubettergpt/src/components/Chat/ChatContent/Message/View/Button/RefreshButton.tsx b/spaces/plzdontcry/dakubettergpt/src/components/Chat/ChatContent/Message/View/Button/RefreshButton.tsx
deleted file mode 100644
index a270cd4a589253013dec769abf4141919c13f8dc..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/components/Chat/ChatContent/Message/View/Button/RefreshButton.tsx
+++ /dev/null
@@ -1,21 +0,0 @@
-import React from 'react';
-
-import RefreshIcon from '@icon/RefreshIcon';
-
-import BaseButton from './BaseButton';
-
-const RefreshButton = ({
-  onClick,
-}: {
-  onClick: React.MouseEventHandler<HTMLButtonElement>;
-}) => {
-  return (
-    <BaseButton
-      icon={<RefreshIcon />}
-      buttonProps={{ 'aria-label': 'regenerate message' }}
-      onClick={onClick}
-    />
-  );
-};
-
-export default RefreshButton;
diff --git a/spaces/politweet-sh/politweet/tests/scraper_test.py b/spaces/politweet-sh/politweet/tests/scraper_test.py
deleted file mode 100644
index 77febc99c411f79ae2896785d6e504b51883cff3..0000000000000000000000000000000000000000
--- a/spaces/politweet-sh/politweet/tests/scraper_test.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import re
-import unittest
-
-import pandas as pd
-from datetime import datetime
-import sys
-from pathlib import Path
-from twitterscraper import TwitterScraper as ts
-
-sys.path.insert(0, str(Path(__file__).parents[1]) + "/twitterscraper")
-
-
-class MyTestCase(unittest.TestCase):
-
-    @classmethod
-    def setUpClass(cls):
-        print('Super set up')
-        num_tweets = 40
-        from_date = "2022-05-01"
-        to_date = "2022-07-31"
-        user = 'jimmieakesson'
-        user_list = ['jimmieakesson', 'BuschEbba', 'annieloof', 'JohanPehrson', 'bolund', 'martastenevi', 'SwedishPM',
-                     'dadgostarnooshi']
-        sc1 = ts.TwitterScraper(from_date=from_date, to_date=to_date, num_tweets=num_tweets)
-        sc2 = ts.TwitterScraper(from_date=from_date, to_date=to_date, num_tweets=num_tweets)
-        sc3 = ts.TwitterScraper(from_date=from_date, to_date=to_date, num_tweets=num_tweets)
-        search_string = 'miljö'
-        cls._df_uni = sc1.scrape_by_user(user)
-        cls._df_poly = sc2.scrape_by_several_users(user_list)
-        cls._df_by_string = sc3.scrape_by_string(search_string)
-
-        nbr_of_cols = 9
-
-    def setUp(self):
-        print('set up')
-        self.num_tweets = 40
-        self.from_date = "2022-05-01"
-        self.to_date = "2022-07-31"
-        self.user = 'jimmieakesson'
-        self.user_list = ['jimmieakesson', 'BuschEbba', 'annieloof', 'JohanPehrson', 'bolund', 'martastenevi',
-                          'SwedishPM',
-                          'dadgostarnooshi']
-        self.search_string = 'miljö'
-        # self.sc = TwitterScraper(from_date=from_date, to_date=to_date, num_tweets=num_tweets)
-        nbr_of_cols = 9
-
-    def test_correct_type(self):
-        """
-        Checks that the returned datatype is pandas DataFrame
-        :return:
-        """
-        print('Checking type...')
-        self.assertEqual(type(self._df_uni), type(pd.DataFrame()))
-        self.assertEqual(type(self._df_poly), type(pd.DataFrame()))
-        self.assertEqual(type(self._df_by_string), type(pd.DataFrame()))
-
-    def test_correct_nbr_tweets(self):
-        """
-        Checks that we get the correct number of tweets.
-        OBS FOR NOW IT ONLY CHECKS THAT WE DON'T OVER SAMPLE
-        TODO: Check that we get the correct number of tweets.
-        :return:
-        """
-        print('Checking number of tweets...')
-        self.assertTrue(self._df_uni.shape[0] <= self.num_tweets)
-        self.assertTrue(self._df_poly.shape[0] <= self.num_tweets)
-        self.assertTrue(self._df_by_string.shape[0] <= self.num_tweets)
-
-    def test_dates(self):
-        """
-        Checks that all dates are between the start date and the end date
-        :return:
-        """
-        print('Checking dates...')
-        d_start = datetime.fromisoformat(self.from_date)
-        d_end = datetime.fromisoformat(self.to_date)
-        correct_date_uni = True
-        correct_date_poly = True
-        correct_date_by_string = True
-        for date in self._df_uni.date:
-            d = datetime.fromisoformat(date)
-            if not (d >= d_start and d <= d_end):
-                correct_date_uni = False
-                break
-        for date in self._df_poly.date:
-            d = datetime.fromisoformat(date)
-            if not (d >= d_start and d <= d_end):
-                correct_date_poly = False
-                break
-        for date in self._df_by_string.date:
-            d = datetime.fromisoformat(date)
-            if not (d >= d_start and d <= d_end):
-                correct_date_by_string = False
-                break
-
-        self.assertTrue(correct_date_uni)
-        self.assertTrue(correct_date_poly)
-        self.assertTrue(correct_date_by_string)
-
-    def test_user(self):
-        """
-        Checks that all tweets are from the correct user
-        :return:
-        """
-        print('Checking user...')
-        same_user = True
-        for username in self._df_uni.username:
-            if not username == self.user:
-                same_user = False
-                break
-
-        self.assertTrue(same_user)
-
-    def test_user_id(self):
-        """
-        Checks that all user_ids are correct
-        :return:
-        """
-        print('Checking user ids...')
-        same_user = True
-        first_id = self._df_uni['user_id'][0]
-        for user_id in self._df_uni.user_id:
-            if not user_id == first_id:
-                same_user = False
-                break
-        self.assertTrue(same_user)
-
-    def test_no_doubles(self):
-        """
-        Checks that there are no tweets that have been sampled several times
-        :return:
-        """
-        print('Checking doubles...')
-        id_set_uni = set(self._df_uni.id)
-        id_set_poly = set(self._df_poly.id)
-        id_set_by_string = set(self._df_by_string.id)
-        self.assertTrue(len(id_set_uni) == self._df_uni.shape[0])
-        self.assertTrue(len(id_set_poly) == self._df_poly.shape[0])
-        self.assertTrue(len(id_set_by_string) == self._df_by_string.shape[0])
-
-    def test_none(self):
-        """
-        Checks that there are no None entries
-        :return:
-        """
-        print('Checking Nones...')
-        self.assertFalse(any(b == True for b in self._df_uni.isnull()))
-        self.assertFalse(any(b == True for b in self._df_poly.isnull()))
-        self.assertFalse(any(b == True for b in self._df_by_string.isnull()))
-
-    def test_no_url_tweets(self):
-        print('Checking url tweets...')
-        only_url_uni = False
-        only_url_poly = False
-        only_url_by_string = False
-        for tweet in self._df_uni.tweet:
-            if len(tweet.split()) == 1 and tweet.split()[0].startswith('https'):
-                print(tweet.split())
-                only_url_uni = True
-                break
-        for tweet in self._df_poly.tweet:
-            if len(tweet.split()) == 1 and tweet.split()[0].startswith('https'):
-                print(tweet.split())
-                only_url_poly = True
-                break
-        for tweet in self._df_by_string.tweet:
-            if len(tweet.split()) == 1 and tweet.split()[0].startswith('https'):
-                print(tweet.split())
-                only_url_by_string = True
-                break
-        self.assertFalse(only_url_uni)
-        self.assertFalse(only_url_poly)
-        self.assertFalse(only_url_by_string)
-
-    def test_many_users(self):
-        correct_users = True
-        for user in self._df_uni.username:
-            if user not in self.user_list:
-                correct_users = False
-                break
-        self.assertTrue(correct_users)
-
-    def test_many_user_ids(self):
-        correct_ids = True
-        user_dict = {}
-        for i in range(self._df_uni.shape[0]):
-            if self._df_uni['username'][i] not in user_dict:
-                user_dict[self._df_uni['username'][i]] = self._df_uni['user_id'][i]
-            if self._df_uni['user_id'][i] != user_dict[self._df_uni['username'][i]]:
-                correct_ids = False
-                break
-        self.assertTrue(correct_ids)
-
-    def test_string_search(self):
-        """
-        TODO: Check this
-        :return:
-        """
-        correct_search = True
-        search = re.sub('ö', 'ø', self.search_string)
-        search = re.sub('ä', 'æ', search)
-        search_strings = [self.search_string, search]
-        for tweet in self._df_by_string.tweet:
-            if all(search not in tweet.lower() for search in search_strings):
-                correct_search = False
-        self.assertTrue(correct_search)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_backends/_asyncio.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_backends/_asyncio.py
deleted file mode 100644
index bfdb4ea7e12761fa1440e484c83bcaa3de7844c9..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_backends/_asyncio.py
+++ /dev/null
@@ -1,2117 +0,0 @@
-from __future__ import annotations
-
-import array
-import asyncio
-import concurrent.futures
-import math
-import socket
-import sys
-from asyncio.base_events import _run_until_complete_cb  # type: ignore[attr-defined]
-from collections import OrderedDict, deque
-from concurrent.futures import Future
-from contextvars import Context, copy_context
-from dataclasses import dataclass
-from functools import partial, wraps
-from inspect import (
-    CORO_RUNNING,
-    CORO_SUSPENDED,
-    GEN_RUNNING,
-    GEN_SUSPENDED,
-    getcoroutinestate,
-    getgeneratorstate,
-)
-from io import IOBase
-from os import PathLike
-from queue import Queue
-from socket import AddressFamily, SocketKind
-from threading import Thread
-from types import TracebackType
-from typing import (
-    IO,
-    Any,
-    AsyncGenerator,
-    Awaitable,
-    Callable,
-    Collection,
-    Coroutine,
-    Generator,
-    Iterable,
-    Mapping,
-    Optional,
-    Sequence,
-    Tuple,
-    TypeVar,
-    Union,
-    cast,
-)
-from weakref import WeakKeyDictionary
-
-import sniffio
-
-from .. import CapacityLimiterStatistics, EventStatistics, TaskInfo, abc
-from .._core._compat import DeprecatedAsyncContextManager, DeprecatedAwaitable
-from .._core._eventloop import claim_worker_thread, threadlocals
-from .._core._exceptions import (
-    BrokenResourceError,
-    BusyResourceError,
-    ClosedResourceError,
-    EndOfStream,
-    WouldBlock,
-)
-from .._core._exceptions import ExceptionGroup as BaseExceptionGroup
-from .._core._sockets import GetAddrInfoReturnType, convert_ipv6_sockaddr
-from .._core._synchronization import CapacityLimiter as BaseCapacityLimiter
-from .._core._synchronization import Event as BaseEvent
-from .._core._synchronization import ResourceGuard
-from .._core._tasks import CancelScope as BaseCancelScope
-from ..abc import IPSockAddrType, UDPPacketType
-from ..lowlevel import RunVar
-
-if sys.version_info >= (3, 8):
-
-    def get_coro(task: asyncio.Task) -> Generator | Awaitable[Any]:
-        return task.get_coro()
-
-else:
-
-    def get_coro(task: asyncio.Task) -> Generator | Awaitable[Any]:
-        return task._coro
-
-
-from asyncio import all_tasks, create_task, current_task, get_running_loop
-from asyncio import run as native_run
-
-
-def _get_task_callbacks(task: asyncio.Task) -> Iterable[Callable]:
-    return [cb for cb, context in task._callbacks]
-
-
-T_Retval = TypeVar("T_Retval")
-T_contra = TypeVar("T_contra", contravariant=True)
-
-# Check whether there is native support for task names in asyncio (3.8+)
-_native_task_names = hasattr(asyncio.Task, "get_name")
-
-
-_root_task: RunVar[asyncio.Task | None] = RunVar("_root_task")
-
-
-def find_root_task() -> asyncio.Task:
-    root_task = _root_task.get(None)
-    if root_task is not None and not root_task.done():
-        return root_task
-
-    # Look for a task that has been started via run_until_complete()
-    for task in all_tasks():
-        if task._callbacks and not task.done():
-            for cb in _get_task_callbacks(task):
-                if (
-                    cb is _run_until_complete_cb
-                    or getattr(cb, "__module__", None) == "uvloop.loop"
-                ):
-                    _root_task.set(task)
-                    return task
-
-    # Look up the topmost task in the AnyIO task tree, if possible
-    task = cast(asyncio.Task, current_task())
-    state = _task_states.get(task)
-    if state:
-        cancel_scope = state.cancel_scope
-        while cancel_scope and cancel_scope._parent_scope is not None:
-            cancel_scope = cancel_scope._parent_scope
-
-        if cancel_scope is not None:
-            return cast(asyncio.Task, cancel_scope._host_task)
-
-    return task
-
-
-def get_callable_name(func: Callable) -> str:
-    module = getattr(func, "__module__", None)
-    qualname = getattr(func, "__qualname__", None)
-    return ".".join([x for x in (module, qualname) if x])
-
-
-#
-# Event loop
-#
-
-_run_vars = (
-    WeakKeyDictionary()
-)  # type: WeakKeyDictionary[asyncio.AbstractEventLoop, Any]
-
-current_token = get_running_loop
-
-
-def _task_started(task: asyncio.Task) -> bool:
-    """Return ``True`` if the task has been started and has not finished."""
-    coro = cast(Coroutine[Any, Any, Any], get_coro(task))
-    try:
-        return getcoroutinestate(coro) in (CORO_RUNNING, CORO_SUSPENDED)
-    except AttributeError:
-        try:
-            return getgeneratorstate(cast(Generator, coro)) in (
-                GEN_RUNNING,
-                GEN_SUSPENDED,
-            )
-        except AttributeError:
-            # task coro is async_genenerator_asend https://bugs.python.org/issue37771
-            raise Exception(f"Cannot determine if task {task} has started or not")
-
-
-def _maybe_set_event_loop_policy(
-    policy: asyncio.AbstractEventLoopPolicy | None, use_uvloop: bool
-) -> None:
-    # On CPython, use uvloop when possible if no other policy has been given and if not
-    # explicitly disabled
-    if policy is None and use_uvloop and sys.implementation.name == "cpython":
-        try:
-            import uvloop
-        except ImportError:
-            pass
-        else:
-            # Test for missing shutdown_default_executor() (uvloop 0.14.0 and earlier)
-            if not hasattr(
-                asyncio.AbstractEventLoop, "shutdown_default_executor"
-            ) or hasattr(uvloop.loop.Loop, "shutdown_default_executor"):
-                policy = uvloop.EventLoopPolicy()
-
-    if policy is not None:
-        asyncio.set_event_loop_policy(policy)
-
-
-def run(
-    func: Callable[..., Awaitable[T_Retval]],
-    *args: object,
-    debug: bool = False,
-    use_uvloop: bool = False,
-    policy: asyncio.AbstractEventLoopPolicy | None = None,
-) -> T_Retval:
-    @wraps(func)
-    async def wrapper() -> T_Retval:
-        task = cast(asyncio.Task, current_task())
-        task_state = TaskState(None, get_callable_name(func), None)
-        _task_states[task] = task_state
-        if _native_task_names:
-            task.set_name(task_state.name)
-
-        try:
-            return await func(*args)
-        finally:
-            del _task_states[task]
-
-    _maybe_set_event_loop_policy(policy, use_uvloop)
-    return native_run(wrapper(), debug=debug)
-
-
-#
-# Miscellaneous
-#
-
-sleep = asyncio.sleep
-
-
-#
-# Timeouts and cancellation
-#
-
-CancelledError = asyncio.CancelledError
-
-
-class CancelScope(BaseCancelScope):
-    def __new__(
-        cls, *, deadline: float = math.inf, shield: bool = False
-    ) -> CancelScope:
-        return object.__new__(cls)
-
-    def __init__(self, deadline: float = math.inf, shield: bool = False):
-        self._deadline = deadline
-        self._shield = shield
-        self._parent_scope: CancelScope | None = None
-        self._cancel_called = False
-        self._active = False
-        self._timeout_handle: asyncio.TimerHandle | None = None
-        self._cancel_handle: asyncio.Handle | None = None
-        self._tasks: set[asyncio.Task] = set()
-        self._host_task: asyncio.Task | None = None
-        self._timeout_expired = False
-        self._cancel_calls: int = 0
-
-    def __enter__(self) -> CancelScope:
-        if self._active:
-            raise RuntimeError(
-                "Each CancelScope may only be used for a single 'with' block"
-            )
-
-        self._host_task = host_task = cast(asyncio.Task, current_task())
-        self._tasks.add(host_task)
-        try:
-            task_state = _task_states[host_task]
-        except KeyError:
-            task_name = host_task.get_name() if _native_task_names else None
-            task_state = TaskState(None, task_name, self)
-            _task_states[host_task] = task_state
-        else:
-            self._parent_scope = task_state.cancel_scope
-            task_state.cancel_scope = self
-
-        self._timeout()
-        self._active = True
-
-        # Start cancelling the host task if the scope was cancelled before entering
-        if self._cancel_called:
-            self._deliver_cancellation()
-
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        if not self._active:
-            raise RuntimeError("This cancel scope is not active")
-        if current_task() is not self._host_task:
-            raise RuntimeError(
-                "Attempted to exit cancel scope in a different task than it was "
-                "entered in"
-            )
-
-        assert self._host_task is not None
-        host_task_state = _task_states.get(self._host_task)
-        if host_task_state is None or host_task_state.cancel_scope is not self:
-            raise RuntimeError(
-                "Attempted to exit a cancel scope that isn't the current tasks's "
-                "current cancel scope"
-            )
-
-        self._active = False
-        if self._timeout_handle:
-            self._timeout_handle.cancel()
-            self._timeout_handle = None
-
-        self._tasks.remove(self._host_task)
-
-        host_task_state.cancel_scope = self._parent_scope
-
-        # Restart the cancellation effort in the farthest directly cancelled parent scope if this
-        # one was shielded
-        if self._shield:
-            self._deliver_cancellation_to_parent()
-
-        if exc_val is not None:
-            exceptions = (
-                exc_val.exceptions if isinstance(exc_val, ExceptionGroup) else [exc_val]
-            )
-            if all(isinstance(exc, CancelledError) for exc in exceptions):
-                if self._timeout_expired:
-                    return self._uncancel()
-                elif not self._cancel_called:
-                    # Task was cancelled natively
-                    return None
-                elif not self._parent_cancelled():
-                    # This scope was directly cancelled
-                    return self._uncancel()
-
-        return None
-
-    def _uncancel(self) -> bool:
-        if sys.version_info < (3, 11) or self._host_task is None:
-            self._cancel_calls = 0
-            return True
-
-        # Uncancel all AnyIO cancellations
-        for i in range(self._cancel_calls):
-            self._host_task.uncancel()
-
-        self._cancel_calls = 0
-        return not self._host_task.cancelling()
-
-    def _timeout(self) -> None:
-        if self._deadline != math.inf:
-            loop = get_running_loop()
-            if loop.time() >= self._deadline:
-                self._timeout_expired = True
-                self.cancel()
-            else:
-                self._timeout_handle = loop.call_at(self._deadline, self._timeout)
-
-    def _deliver_cancellation(self) -> None:
-        """
-        Deliver cancellation to directly contained tasks and nested cancel scopes.
-
-        Schedule another run at the end if we still have tasks eligible for cancellation.
-        """
-        should_retry = False
-        current = current_task()
-        for task in self._tasks:
-            if task._must_cancel:  # type: ignore[attr-defined]
-                continue
-
-            # The task is eligible for cancellation if it has started and is not in a cancel
-            # scope shielded from this one
-            cancel_scope = _task_states[task].cancel_scope
-            while cancel_scope is not self:
-                if cancel_scope is None or cancel_scope._shield:
-                    break
-                else:
-                    cancel_scope = cancel_scope._parent_scope
-            else:
-                should_retry = True
-                if task is not current and (
-                    task is self._host_task or _task_started(task)
-                ):
-                    self._cancel_calls += 1
-                    task.cancel()
-
-        # Schedule another callback if there are still tasks left
-        if should_retry:
-            self._cancel_handle = get_running_loop().call_soon(
-                self._deliver_cancellation
-            )
-        else:
-            self._cancel_handle = None
-
-    def _deliver_cancellation_to_parent(self) -> None:
-        """Start cancellation effort in the farthest directly cancelled parent scope"""
-        scope = self._parent_scope
-        scope_to_cancel: CancelScope | None = None
-        while scope is not None:
-            if scope._cancel_called and scope._cancel_handle is None:
-                scope_to_cancel = scope
-
-            # No point in looking beyond any shielded scope
-            if scope._shield:
-                break
-
-            scope = scope._parent_scope
-
-        if scope_to_cancel is not None:
-            scope_to_cancel._deliver_cancellation()
-
-    def _parent_cancelled(self) -> bool:
-        # Check whether any parent has been cancelled
-        cancel_scope = self._parent_scope
-        while cancel_scope is not None and not cancel_scope._shield:
-            if cancel_scope._cancel_called:
-                return True
-            else:
-                cancel_scope = cancel_scope._parent_scope
-
-        return False
-
-    def cancel(self) -> DeprecatedAwaitable:
-        if not self._cancel_called:
-            if self._timeout_handle:
-                self._timeout_handle.cancel()
-                self._timeout_handle = None
-
-            self._cancel_called = True
-            if self._host_task is not None:
-                self._deliver_cancellation()
-
-        return DeprecatedAwaitable(self.cancel)
-
-    @property
-    def deadline(self) -> float:
-        return self._deadline
-
-    @deadline.setter
-    def deadline(self, value: float) -> None:
-        self._deadline = float(value)
-        if self._timeout_handle is not None:
-            self._timeout_handle.cancel()
-            self._timeout_handle = None
-
-        if self._active and not self._cancel_called:
-            self._timeout()
-
-    @property
-    def cancel_called(self) -> bool:
-        return self._cancel_called
-
-    @property
-    def shield(self) -> bool:
-        return self._shield
-
-    @shield.setter
-    def shield(self, value: bool) -> None:
-        if self._shield != value:
-            self._shield = value
-            if not value:
-                self._deliver_cancellation_to_parent()
-
-
-async def checkpoint() -> None:
-    await sleep(0)
-
-
-async def checkpoint_if_cancelled() -> None:
-    task = current_task()
-    if task is None:
-        return
-
-    try:
-        cancel_scope = _task_states[task].cancel_scope
-    except KeyError:
-        return
-
-    while cancel_scope:
-        if cancel_scope.cancel_called:
-            await sleep(0)
-        elif cancel_scope.shield:
-            break
-        else:
-            cancel_scope = cancel_scope._parent_scope
-
-
-async def cancel_shielded_checkpoint() -> None:
-    with CancelScope(shield=True):
-        await sleep(0)
-
-
-def current_effective_deadline() -> float:
-    try:
-        cancel_scope = _task_states[current_task()].cancel_scope  # type: ignore[index]
-    except KeyError:
-        return math.inf
-
-    deadline = math.inf
-    while cancel_scope:
-        deadline = min(deadline, cancel_scope.deadline)
-        if cancel_scope._cancel_called:
-            deadline = -math.inf
-            break
-        elif cancel_scope.shield:
-            break
-        else:
-            cancel_scope = cancel_scope._parent_scope
-
-    return deadline
-
-
-def current_time() -> float:
-    return get_running_loop().time()
-
-
-#
-# Task states
-#
-
-
-class TaskState:
-    """
-    Encapsulates auxiliary task information that cannot be added to the Task instance itself
-    because there are no guarantees about its implementation.
-    """
-
-    __slots__ = "parent_id", "name", "cancel_scope"
-
-    def __init__(
-        self,
-        parent_id: int | None,
-        name: str | None,
-        cancel_scope: CancelScope | None,
-    ):
-        self.parent_id = parent_id
-        self.name = name
-        self.cancel_scope = cancel_scope
-
-
-_task_states = WeakKeyDictionary()  # type: WeakKeyDictionary[asyncio.Task, TaskState]
-
-
-#
-# Task groups
-#
-
-
-class ExceptionGroup(BaseExceptionGroup):
-    def __init__(self, exceptions: list[BaseException]):
-        super().__init__()
-        self.exceptions = exceptions
-
-
-class _AsyncioTaskStatus(abc.TaskStatus):
-    def __init__(self, future: asyncio.Future, parent_id: int):
-        self._future = future
-        self._parent_id = parent_id
-
-    def started(self, value: T_contra | None = None) -> None:
-        try:
-            self._future.set_result(value)
-        except asyncio.InvalidStateError:
-            raise RuntimeError(
-                "called 'started' twice on the same task status"
-            ) from None
-
-        task = cast(asyncio.Task, current_task())
-        _task_states[task].parent_id = self._parent_id
-
-
-class TaskGroup(abc.TaskGroup):
-    def __init__(self) -> None:
-        self.cancel_scope: CancelScope = CancelScope()
-        self._active = False
-        self._exceptions: list[BaseException] = []
-
-    async def __aenter__(self) -> TaskGroup:
-        self.cancel_scope.__enter__()
-        self._active = True
-        return self
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        ignore_exception = self.cancel_scope.__exit__(exc_type, exc_val, exc_tb)
-        if exc_val is not None:
-            self.cancel_scope.cancel()
-            self._exceptions.append(exc_val)
-
-        while self.cancel_scope._tasks:
-            try:
-                await asyncio.wait(self.cancel_scope._tasks)
-            except asyncio.CancelledError:
-                self.cancel_scope.cancel()
-
-        self._active = False
-        if not self.cancel_scope._parent_cancelled():
-            exceptions = self._filter_cancellation_errors(self._exceptions)
-        else:
-            exceptions = self._exceptions
-
-        try:
-            if len(exceptions) > 1:
-                if all(
-                    isinstance(e, CancelledError) and not e.args for e in exceptions
-                ):
-                    # Tasks were cancelled natively, without a cancellation message
-                    raise CancelledError
-                else:
-                    raise ExceptionGroup(exceptions)
-            elif exceptions and exceptions[0] is not exc_val:
-                raise exceptions[0]
-        except BaseException as exc:
-            # Clear the context here, as it can only be done in-flight.
-            # If the context is not cleared, it can result in recursive tracebacks (see #145).
-            exc.__context__ = None
-            raise
-
-        return ignore_exception
-
-    @staticmethod
-    def _filter_cancellation_errors(
-        exceptions: Sequence[BaseException],
-    ) -> list[BaseException]:
-        filtered_exceptions: list[BaseException] = []
-        for exc in exceptions:
-            if isinstance(exc, ExceptionGroup):
-                new_exceptions = TaskGroup._filter_cancellation_errors(exc.exceptions)
-                if len(new_exceptions) > 1:
-                    filtered_exceptions.append(exc)
-                elif len(new_exceptions) == 1:
-                    filtered_exceptions.append(new_exceptions[0])
-                elif new_exceptions:
-                    new_exc = ExceptionGroup(new_exceptions)
-                    new_exc.__cause__ = exc.__cause__
-                    new_exc.__context__ = exc.__context__
-                    new_exc.__traceback__ = exc.__traceback__
-                    filtered_exceptions.append(new_exc)
-            elif not isinstance(exc, CancelledError) or exc.args:
-                filtered_exceptions.append(exc)
-
-        return filtered_exceptions
-
-    async def _run_wrapped_task(
-        self, coro: Coroutine, task_status_future: asyncio.Future | None
-    ) -> None:
-        # This is the code path for Python 3.7 on which asyncio freaks out if a task
-        # raises a BaseException.
-        __traceback_hide__ = __tracebackhide__ = True  # noqa: F841
-        task = cast(asyncio.Task, current_task())
-        try:
-            await coro
-        except BaseException as exc:
-            if task_status_future is None or task_status_future.done():
-                self._exceptions.append(exc)
-                self.cancel_scope.cancel()
-            else:
-                task_status_future.set_exception(exc)
-        else:
-            if task_status_future is not None and not task_status_future.done():
-                task_status_future.set_exception(
-                    RuntimeError("Child exited without calling task_status.started()")
-                )
-        finally:
-            if task in self.cancel_scope._tasks:
-                self.cancel_scope._tasks.remove(task)
-                del _task_states[task]
-
-    def _spawn(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        args: tuple,
-        name: object,
-        task_status_future: asyncio.Future | None = None,
-    ) -> asyncio.Task:
-        def task_done(_task: asyncio.Task) -> None:
-            # This is the code path for Python 3.8+
-            assert _task in self.cancel_scope._tasks
-            self.cancel_scope._tasks.remove(_task)
-            del _task_states[_task]
-
-            try:
-                exc = _task.exception()
-            except CancelledError as e:
-                while isinstance(e.__context__, CancelledError):
-                    e = e.__context__
-
-                exc = e
-
-            if exc is not None:
-                if task_status_future is None or task_status_future.done():
-                    self._exceptions.append(exc)
-                    self.cancel_scope.cancel()
-                else:
-                    task_status_future.set_exception(exc)
-            elif task_status_future is not None and not task_status_future.done():
-                task_status_future.set_exception(
-                    RuntimeError("Child exited without calling task_status.started()")
-                )
-
-        if not self._active:
-            raise RuntimeError(
-                "This task group is not active; no new tasks can be started."
-            )
-
-        options: dict[str, Any] = {}
-        name = get_callable_name(func) if name is None else str(name)
-        if _native_task_names:
-            options["name"] = name
-
-        kwargs = {}
-        if task_status_future:
-            parent_id = id(current_task())
-            kwargs["task_status"] = _AsyncioTaskStatus(
-                task_status_future, id(self.cancel_scope._host_task)
-            )
-        else:
-            parent_id = id(self.cancel_scope._host_task)
-
-        coro = func(*args, **kwargs)
-        if not asyncio.iscoroutine(coro):
-            raise TypeError(
-                f"Expected an async function, but {func} appears to be synchronous"
-            )
-
-        foreign_coro = not hasattr(coro, "cr_frame") and not hasattr(coro, "gi_frame")
-        if foreign_coro or sys.version_info < (3, 8):
-            coro = self._run_wrapped_task(coro, task_status_future)
-
-        task = create_task(coro, **options)
-        if not foreign_coro and sys.version_info >= (3, 8):
-            task.add_done_callback(task_done)
-
-        # Make the spawned task inherit the task group's cancel scope
-        _task_states[task] = TaskState(
-            parent_id=parent_id, name=name, cancel_scope=self.cancel_scope
-        )
-        self.cancel_scope._tasks.add(task)
-        return task
-
-    def start_soon(
-        self, func: Callable[..., Awaitable[Any]], *args: object, name: object = None
-    ) -> None:
-        self._spawn(func, args, name)
-
-    async def start(
-        self, func: Callable[..., Awaitable[Any]], *args: object, name: object = None
-    ) -> None:
-        future: asyncio.Future = asyncio.Future()
-        task = self._spawn(func, args, name, future)
-
-        # If the task raises an exception after sending a start value without a switch point
-        # between, the task group is cancelled and this method never proceeds to process the
-        # completed future. That's why we have to have a shielded cancel scope here.
-        with CancelScope(shield=True):
-            try:
-                return await future
-            except CancelledError:
-                task.cancel()
-                raise
-
-
-#
-# Threads
-#
-
-_Retval_Queue_Type = Tuple[Optional[T_Retval], Optional[BaseException]]
-
-
-class WorkerThread(Thread):
-    MAX_IDLE_TIME = 10  # seconds
-
-    def __init__(
-        self,
-        root_task: asyncio.Task,
-        workers: set[WorkerThread],
-        idle_workers: deque[WorkerThread],
-    ):
-        super().__init__(name="AnyIO worker thread")
-        self.root_task = root_task
-        self.workers = workers
-        self.idle_workers = idle_workers
-        self.loop = root_task._loop
-        self.queue: Queue[
-            tuple[Context, Callable, tuple, asyncio.Future] | None
-        ] = Queue(2)
-        self.idle_since = current_time()
-        self.stopping = False
-
-    def _report_result(
-        self, future: asyncio.Future, result: Any, exc: BaseException | None
-    ) -> None:
-        self.idle_since = current_time()
-        if not self.stopping:
-            self.idle_workers.append(self)
-
-        if not future.cancelled():
-            if exc is not None:
-                if isinstance(exc, StopIteration):
-                    new_exc = RuntimeError("coroutine raised StopIteration")
-                    new_exc.__cause__ = exc
-                    exc = new_exc
-
-                future.set_exception(exc)
-            else:
-                future.set_result(result)
-
-    def run(self) -> None:
-        with claim_worker_thread("asyncio"):
-            threadlocals.loop = self.loop
-            while True:
-                item = self.queue.get()
-                if item is None:
-                    # Shutdown command received
-                    return
-
-                context, func, args, future = item
-                if not future.cancelled():
-                    result = None
-                    exception: BaseException | None = None
-                    try:
-                        result = context.run(func, *args)
-                    except BaseException as exc:
-                        exception = exc
-
-                    if not self.loop.is_closed():
-                        self.loop.call_soon_threadsafe(
-                            self._report_result, future, result, exception
-                        )
-
-                self.queue.task_done()
-
-    def stop(self, f: asyncio.Task | None = None) -> None:
-        self.stopping = True
-        self.queue.put_nowait(None)
-        self.workers.discard(self)
-        try:
-            self.idle_workers.remove(self)
-        except ValueError:
-            pass
-
-
-_threadpool_idle_workers: RunVar[deque[WorkerThread]] = RunVar(
-    "_threadpool_idle_workers"
-)
-_threadpool_workers: RunVar[set[WorkerThread]] = RunVar("_threadpool_workers")
-
-
-async def run_sync_in_worker_thread(
-    func: Callable[..., T_Retval],
-    *args: object,
-    cancellable: bool = False,
-    limiter: CapacityLimiter | None = None,
-) -> T_Retval:
-    await checkpoint()
-
-    # If this is the first run in this event loop thread, set up the necessary variables
-    try:
-        idle_workers = _threadpool_idle_workers.get()
-        workers = _threadpool_workers.get()
-    except LookupError:
-        idle_workers = deque()
-        workers = set()
-        _threadpool_idle_workers.set(idle_workers)
-        _threadpool_workers.set(workers)
-
-    async with (limiter or current_default_thread_limiter()):
-        with CancelScope(shield=not cancellable):
-            future: asyncio.Future = asyncio.Future()
-            root_task = find_root_task()
-            if not idle_workers:
-                worker = WorkerThread(root_task, workers, idle_workers)
-                worker.start()
-                workers.add(worker)
-                root_task.add_done_callback(worker.stop)
-            else:
-                worker = idle_workers.pop()
-
-                # Prune any other workers that have been idle for MAX_IDLE_TIME seconds or longer
-                now = current_time()
-                while idle_workers:
-                    if now - idle_workers[0].idle_since < WorkerThread.MAX_IDLE_TIME:
-                        break
-
-                    expired_worker = idle_workers.popleft()
-                    expired_worker.root_task.remove_done_callback(expired_worker.stop)
-                    expired_worker.stop()
-
-            context = copy_context()
-            context.run(sniffio.current_async_library_cvar.set, None)
-            worker.queue.put_nowait((context, func, args, future))
-            return await future
-
-
-def run_sync_from_thread(
-    func: Callable[..., T_Retval],
-    *args: object,
-    loop: asyncio.AbstractEventLoop | None = None,
-) -> T_Retval:
-    @wraps(func)
-    def wrapper() -> None:
-        try:
-            f.set_result(func(*args))
-        except BaseException as exc:
-            f.set_exception(exc)
-            if not isinstance(exc, Exception):
-                raise
-
-    f: concurrent.futures.Future[T_Retval] = Future()
-    loop = loop or threadlocals.loop
-    loop.call_soon_threadsafe(wrapper)
-    return f.result()
-
-
-def run_async_from_thread(
-    func: Callable[..., Awaitable[T_Retval]], *args: object
-) -> T_Retval:
-    f: concurrent.futures.Future[T_Retval] = asyncio.run_coroutine_threadsafe(
-        func(*args), threadlocals.loop
-    )
-    return f.result()
-
-
-class BlockingPortal(abc.BlockingPortal):
-    def __new__(cls) -> BlockingPortal:
-        return object.__new__(cls)
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._loop = get_running_loop()
-
-    def _spawn_task_from_thread(
-        self,
-        func: Callable,
-        args: tuple,
-        kwargs: dict[str, Any],
-        name: object,
-        future: Future,
-    ) -> None:
-        run_sync_from_thread(
-            partial(self._task_group.start_soon, name=name),
-            self._call_func,
-            func,
-            args,
-            kwargs,
-            future,
-            loop=self._loop,
-        )
-
-
-#
-# Subprocesses
-#
-
-
-@dataclass(eq=False)
-class StreamReaderWrapper(abc.ByteReceiveStream):
-    _stream: asyncio.StreamReader
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        data = await self._stream.read(max_bytes)
-        if data:
-            return data
-        else:
-            raise EndOfStream
-
-    async def aclose(self) -> None:
-        self._stream.feed_eof()
-
-
-@dataclass(eq=False)
-class StreamWriterWrapper(abc.ByteSendStream):
-    _stream: asyncio.StreamWriter
-
-    async def send(self, item: bytes) -> None:
-        self._stream.write(item)
-        await self._stream.drain()
-
-    async def aclose(self) -> None:
-        self._stream.close()
-
-
-@dataclass(eq=False)
-class Process(abc.Process):
-    _process: asyncio.subprocess.Process
-    _stdin: StreamWriterWrapper | None
-    _stdout: StreamReaderWrapper | None
-    _stderr: StreamReaderWrapper | None
-
-    async def aclose(self) -> None:
-        if self._stdin:
-            await self._stdin.aclose()
-        if self._stdout:
-            await self._stdout.aclose()
-        if self._stderr:
-            await self._stderr.aclose()
-
-        await self.wait()
-
-    async def wait(self) -> int:
-        return await self._process.wait()
-
-    def terminate(self) -> None:
-        self._process.terminate()
-
-    def kill(self) -> None:
-        self._process.kill()
-
-    def send_signal(self, signal: int) -> None:
-        self._process.send_signal(signal)
-
-    @property
-    def pid(self) -> int:
-        return self._process.pid
-
-    @property
-    def returncode(self) -> int | None:
-        return self._process.returncode
-
-    @property
-    def stdin(self) -> abc.ByteSendStream | None:
-        return self._stdin
-
-    @property
-    def stdout(self) -> abc.ByteReceiveStream | None:
-        return self._stdout
-
-    @property
-    def stderr(self) -> abc.ByteReceiveStream | None:
-        return self._stderr
-
-
-async def open_process(
-    command: str | bytes | Sequence[str | bytes],
-    *,
-    shell: bool,
-    stdin: int | IO[Any] | None,
-    stdout: int | IO[Any] | None,
-    stderr: int | IO[Any] | None,
-    cwd: str | bytes | PathLike | None = None,
-    env: Mapping[str, str] | None = None,
-    start_new_session: bool = False,
-) -> Process:
-    await checkpoint()
-    if shell:
-        process = await asyncio.create_subprocess_shell(
-            cast(Union[str, bytes], command),
-            stdin=stdin,
-            stdout=stdout,
-            stderr=stderr,
-            cwd=cwd,
-            env=env,
-            start_new_session=start_new_session,
-        )
-    else:
-        process = await asyncio.create_subprocess_exec(
-            *command,
-            stdin=stdin,
-            stdout=stdout,
-            stderr=stderr,
-            cwd=cwd,
-            env=env,
-            start_new_session=start_new_session,
-        )
-
-    stdin_stream = StreamWriterWrapper(process.stdin) if process.stdin else None
-    stdout_stream = StreamReaderWrapper(process.stdout) if process.stdout else None
-    stderr_stream = StreamReaderWrapper(process.stderr) if process.stderr else None
-    return Process(process, stdin_stream, stdout_stream, stderr_stream)
-
-
-def _forcibly_shutdown_process_pool_on_exit(
-    workers: set[Process], _task: object
-) -> None:
-    """
-    Forcibly shuts down worker processes belonging to this event loop."""
-    child_watcher: asyncio.AbstractChildWatcher | None
-    try:
-        child_watcher = asyncio.get_event_loop_policy().get_child_watcher()
-    except NotImplementedError:
-        child_watcher = None
-
-    # Close as much as possible (w/o async/await) to avoid warnings
-    for process in workers:
-        if process.returncode is None:
-            continue
-
-        process._stdin._stream._transport.close()  # type: ignore[union-attr]
-        process._stdout._stream._transport.close()  # type: ignore[union-attr]
-        process._stderr._stream._transport.close()  # type: ignore[union-attr]
-        process.kill()
-        if child_watcher:
-            child_watcher.remove_child_handler(process.pid)
-
-
-async def _shutdown_process_pool_on_exit(workers: set[Process]) -> None:
-    """
-    Shuts down worker processes belonging to this event loop.
-
-    NOTE: this only works when the event loop was started using asyncio.run() or anyio.run().
-
-    """
-    process: Process
-    try:
-        await sleep(math.inf)
-    except asyncio.CancelledError:
-        for process in workers:
-            if process.returncode is None:
-                process.kill()
-
-        for process in workers:
-            await process.aclose()
-
-
-def setup_process_pool_exit_at_shutdown(workers: set[Process]) -> None:
-    kwargs: dict[str, Any] = (
-        {"name": "AnyIO process pool shutdown task"} if _native_task_names else {}
-    )
-    create_task(_shutdown_process_pool_on_exit(workers), **kwargs)
-    find_root_task().add_done_callback(
-        partial(_forcibly_shutdown_process_pool_on_exit, workers)
-    )
-
-
-#
-# Sockets and networking
-#
-
-
-class StreamProtocol(asyncio.Protocol):
-    read_queue: deque[bytes]
-    read_event: asyncio.Event
-    write_event: asyncio.Event
-    exception: Exception | None = None
-
-    def connection_made(self, transport: asyncio.BaseTransport) -> None:
-        self.read_queue = deque()
-        self.read_event = asyncio.Event()
-        self.write_event = asyncio.Event()
-        self.write_event.set()
-        cast(asyncio.Transport, transport).set_write_buffer_limits(0)
-
-    def connection_lost(self, exc: Exception | None) -> None:
-        if exc:
-            self.exception = BrokenResourceError()
-            self.exception.__cause__ = exc
-
-        self.read_event.set()
-        self.write_event.set()
-
-    def data_received(self, data: bytes) -> None:
-        self.read_queue.append(data)
-        self.read_event.set()
-
-    def eof_received(self) -> bool | None:
-        self.read_event.set()
-        return True
-
-    def pause_writing(self) -> None:
-        self.write_event = asyncio.Event()
-
-    def resume_writing(self) -> None:
-        self.write_event.set()
-
-
-class DatagramProtocol(asyncio.DatagramProtocol):
-    read_queue: deque[tuple[bytes, IPSockAddrType]]
-    read_event: asyncio.Event
-    write_event: asyncio.Event
-    exception: Exception | None = None
-
-    def connection_made(self, transport: asyncio.BaseTransport) -> None:
-        self.read_queue = deque(maxlen=100)  # arbitrary value
-        self.read_event = asyncio.Event()
-        self.write_event = asyncio.Event()
-        self.write_event.set()
-
-    def connection_lost(self, exc: Exception | None) -> None:
-        self.read_event.set()
-        self.write_event.set()
-
-    def datagram_received(self, data: bytes, addr: IPSockAddrType) -> None:
-        addr = convert_ipv6_sockaddr(addr)
-        self.read_queue.append((data, addr))
-        self.read_event.set()
-
-    def error_received(self, exc: Exception) -> None:
-        self.exception = exc
-
-    def pause_writing(self) -> None:
-        self.write_event.clear()
-
-    def resume_writing(self) -> None:
-        self.write_event.set()
-
-
-class SocketStream(abc.SocketStream):
-    def __init__(self, transport: asyncio.Transport, protocol: StreamProtocol):
-        self._transport = transport
-        self._protocol = protocol
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-        self._closed = False
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self._transport.get_extra_info("socket")
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        with self._receive_guard:
-            await checkpoint()
-
-            if (
-                not self._protocol.read_event.is_set()
-                and not self._transport.is_closing()
-            ):
-                self._transport.resume_reading()
-                await self._protocol.read_event.wait()
-                self._transport.pause_reading()
-
-            try:
-                chunk = self._protocol.read_queue.popleft()
-            except IndexError:
-                if self._closed:
-                    raise ClosedResourceError from None
-                elif self._protocol.exception:
-                    raise self._protocol.exception
-                else:
-                    raise EndOfStream from None
-
-            if len(chunk) > max_bytes:
-                # Split the oversized chunk
-                chunk, leftover = chunk[:max_bytes], chunk[max_bytes:]
-                self._protocol.read_queue.appendleft(leftover)
-
-            # If the read queue is empty, clear the flag so that the next call will block until
-            # data is available
-            if not self._protocol.read_queue:
-                self._protocol.read_event.clear()
-
-        return chunk
-
-    async def send(self, item: bytes) -> None:
-        with self._send_guard:
-            await checkpoint()
-
-            if self._closed:
-                raise ClosedResourceError
-            elif self._protocol.exception is not None:
-                raise self._protocol.exception
-
-            try:
-                self._transport.write(item)
-            except RuntimeError as exc:
-                if self._transport.is_closing():
-                    raise BrokenResourceError from exc
-                else:
-                    raise
-
-            await self._protocol.write_event.wait()
-
-    async def send_eof(self) -> None:
-        try:
-            self._transport.write_eof()
-        except OSError:
-            pass
-
-    async def aclose(self) -> None:
-        if not self._transport.is_closing():
-            self._closed = True
-            try:
-                self._transport.write_eof()
-            except OSError:
-                pass
-
-            self._transport.close()
-            await sleep(0)
-            self._transport.abort()
-
-
-class UNIXSocketStream(abc.SocketStream):
-    _receive_future: asyncio.Future | None = None
-    _send_future: asyncio.Future | None = None
-    _closing = False
-
-    def __init__(self, raw_socket: socket.socket):
-        self.__raw_socket = raw_socket
-        self._loop = get_running_loop()
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self.__raw_socket
-
-    def _wait_until_readable(self, loop: asyncio.AbstractEventLoop) -> asyncio.Future:
-        def callback(f: object) -> None:
-            del self._receive_future
-            loop.remove_reader(self.__raw_socket)
-
-        f = self._receive_future = asyncio.Future()
-        self._loop.add_reader(self.__raw_socket, f.set_result, None)
-        f.add_done_callback(callback)
-        return f
-
-    def _wait_until_writable(self, loop: asyncio.AbstractEventLoop) -> asyncio.Future:
-        def callback(f: object) -> None:
-            del self._send_future
-            loop.remove_writer(self.__raw_socket)
-
-        f = self._send_future = asyncio.Future()
-        self._loop.add_writer(self.__raw_socket, f.set_result, None)
-        f.add_done_callback(callback)
-        return f
-
-    async def send_eof(self) -> None:
-        with self._send_guard:
-            self._raw_socket.shutdown(socket.SHUT_WR)
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        loop = get_running_loop()
-        await checkpoint()
-        with self._receive_guard:
-            while True:
-                try:
-                    data = self.__raw_socket.recv(max_bytes)
-                except BlockingIOError:
-                    await self._wait_until_readable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-                else:
-                    if not data:
-                        raise EndOfStream
-
-                    return data
-
-    async def send(self, item: bytes) -> None:
-        loop = get_running_loop()
-        await checkpoint()
-        with self._send_guard:
-            view = memoryview(item)
-            while view:
-                try:
-                    bytes_sent = self.__raw_socket.send(view)
-                except BlockingIOError:
-                    await self._wait_until_writable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-                else:
-                    view = view[bytes_sent:]
-
-    async def receive_fds(self, msglen: int, maxfds: int) -> tuple[bytes, list[int]]:
-        if not isinstance(msglen, int) or msglen < 0:
-            raise ValueError("msglen must be a non-negative integer")
-        if not isinstance(maxfds, int) or maxfds < 1:
-            raise ValueError("maxfds must be a positive integer")
-
-        loop = get_running_loop()
-        fds = array.array("i")
-        await checkpoint()
-        with self._receive_guard:
-            while True:
-                try:
-                    message, ancdata, flags, addr = self.__raw_socket.recvmsg(
-                        msglen, socket.CMSG_LEN(maxfds * fds.itemsize)
-                    )
-                except BlockingIOError:
-                    await self._wait_until_readable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-                else:
-                    if not message and not ancdata:
-                        raise EndOfStream
-
-                    break
-
-        for cmsg_level, cmsg_type, cmsg_data in ancdata:
-            if cmsg_level != socket.SOL_SOCKET or cmsg_type != socket.SCM_RIGHTS:
-                raise RuntimeError(
-                    f"Received unexpected ancillary data; message = {message!r}, "
-                    f"cmsg_level = {cmsg_level}, cmsg_type = {cmsg_type}"
-                )
-
-            fds.frombytes(cmsg_data[: len(cmsg_data) - (len(cmsg_data) % fds.itemsize)])
-
-        return message, list(fds)
-
-    async def send_fds(self, message: bytes, fds: Collection[int | IOBase]) -> None:
-        if not message:
-            raise ValueError("message must not be empty")
-        if not fds:
-            raise ValueError("fds must not be empty")
-
-        loop = get_running_loop()
-        filenos: list[int] = []
-        for fd in fds:
-            if isinstance(fd, int):
-                filenos.append(fd)
-            elif isinstance(fd, IOBase):
-                filenos.append(fd.fileno())
-
-        fdarray = array.array("i", filenos)
-        await checkpoint()
-        with self._send_guard:
-            while True:
-                try:
-                    # The ignore can be removed after mypy picks up
-                    # https://github.com/python/typeshed/pull/5545
-                    self.__raw_socket.sendmsg(
-                        [message], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, fdarray)]
-                    )
-                    break
-                except BlockingIOError:
-                    await self._wait_until_writable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-
-    async def aclose(self) -> None:
-        if not self._closing:
-            self._closing = True
-            if self.__raw_socket.fileno() != -1:
-                self.__raw_socket.close()
-
-            if self._receive_future:
-                self._receive_future.set_result(None)
-            if self._send_future:
-                self._send_future.set_result(None)
-
-
-class TCPSocketListener(abc.SocketListener):
-    _accept_scope: CancelScope | None = None
-    _closed = False
-
-    def __init__(self, raw_socket: socket.socket):
-        self.__raw_socket = raw_socket
-        self._loop = cast(asyncio.BaseEventLoop, get_running_loop())
-        self._accept_guard = ResourceGuard("accepting connections from")
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self.__raw_socket
-
-    async def accept(self) -> abc.SocketStream:
-        if self._closed:
-            raise ClosedResourceError
-
-        with self._accept_guard:
-            await checkpoint()
-            with CancelScope() as self._accept_scope:
-                try:
-                    client_sock, _addr = await self._loop.sock_accept(self._raw_socket)
-                except asyncio.CancelledError:
-                    # Workaround for https://bugs.python.org/issue41317
-                    try:
-                        self._loop.remove_reader(self._raw_socket)
-                    except (ValueError, NotImplementedError):
-                        pass
-
-                    if self._closed:
-                        raise ClosedResourceError from None
-
-                    raise
-                finally:
-                    self._accept_scope = None
-
-        client_sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
-        transport, protocol = await self._loop.connect_accepted_socket(
-            StreamProtocol, client_sock
-        )
-        return SocketStream(transport, protocol)
-
-    async def aclose(self) -> None:
-        if self._closed:
-            return
-
-        self._closed = True
-        if self._accept_scope:
-            # Workaround for https://bugs.python.org/issue41317
-            try:
-                self._loop.remove_reader(self._raw_socket)
-            except (ValueError, NotImplementedError):
-                pass
-
-            self._accept_scope.cancel()
-            await sleep(0)
-
-        self._raw_socket.close()
-
-
-class UNIXSocketListener(abc.SocketListener):
-    def __init__(self, raw_socket: socket.socket):
-        self.__raw_socket = raw_socket
-        self._loop = get_running_loop()
-        self._accept_guard = ResourceGuard("accepting connections from")
-        self._closed = False
-
-    async def accept(self) -> abc.SocketStream:
-        await checkpoint()
-        with self._accept_guard:
-            while True:
-                try:
-                    client_sock, _ = self.__raw_socket.accept()
-                    client_sock.setblocking(False)
-                    return UNIXSocketStream(client_sock)
-                except BlockingIOError:
-                    f: asyncio.Future = asyncio.Future()
-                    self._loop.add_reader(self.__raw_socket, f.set_result, None)
-                    f.add_done_callback(
-                        lambda _: self._loop.remove_reader(self.__raw_socket)
-                    )
-                    await f
-                except OSError as exc:
-                    if self._closed:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-
-    async def aclose(self) -> None:
-        self._closed = True
-        self.__raw_socket.close()
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self.__raw_socket
-
-
-class UDPSocket(abc.UDPSocket):
-    def __init__(
-        self, transport: asyncio.DatagramTransport, protocol: DatagramProtocol
-    ):
-        self._transport = transport
-        self._protocol = protocol
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-        self._closed = False
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self._transport.get_extra_info("socket")
-
-    async def aclose(self) -> None:
-        if not self._transport.is_closing():
-            self._closed = True
-            self._transport.close()
-
-    async def receive(self) -> tuple[bytes, IPSockAddrType]:
-        with self._receive_guard:
-            await checkpoint()
-
-            # If the buffer is empty, ask for more data
-            if not self._protocol.read_queue and not self._transport.is_closing():
-                self._protocol.read_event.clear()
-                await self._protocol.read_event.wait()
-
-            try:
-                return self._protocol.read_queue.popleft()
-            except IndexError:
-                if self._closed:
-                    raise ClosedResourceError from None
-                else:
-                    raise BrokenResourceError from None
-
-    async def send(self, item: UDPPacketType) -> None:
-        with self._send_guard:
-            await checkpoint()
-            await self._protocol.write_event.wait()
-            if self._closed:
-                raise ClosedResourceError
-            elif self._transport.is_closing():
-                raise BrokenResourceError
-            else:
-                self._transport.sendto(*item)
-
-
-class ConnectedUDPSocket(abc.ConnectedUDPSocket):
-    def __init__(
-        self, transport: asyncio.DatagramTransport, protocol: DatagramProtocol
-    ):
-        self._transport = transport
-        self._protocol = protocol
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-        self._closed = False
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self._transport.get_extra_info("socket")
-
-    async def aclose(self) -> None:
-        if not self._transport.is_closing():
-            self._closed = True
-            self._transport.close()
-
-    async def receive(self) -> bytes:
-        with self._receive_guard:
-            await checkpoint()
-
-            # If the buffer is empty, ask for more data
-            if not self._protocol.read_queue and not self._transport.is_closing():
-                self._protocol.read_event.clear()
-                await self._protocol.read_event.wait()
-
-            try:
-                packet = self._protocol.read_queue.popleft()
-            except IndexError:
-                if self._closed:
-                    raise ClosedResourceError from None
-                else:
-                    raise BrokenResourceError from None
-
-            return packet[0]
-
-    async def send(self, item: bytes) -> None:
-        with self._send_guard:
-            await checkpoint()
-            await self._protocol.write_event.wait()
-            if self._closed:
-                raise ClosedResourceError
-            elif self._transport.is_closing():
-                raise BrokenResourceError
-            else:
-                self._transport.sendto(item)
-
-
-async def connect_tcp(
-    host: str, port: int, local_addr: tuple[str, int] | None = None
-) -> SocketStream:
-    transport, protocol = cast(
-        Tuple[asyncio.Transport, StreamProtocol],
-        await get_running_loop().create_connection(
-            StreamProtocol, host, port, local_addr=local_addr
-        ),
-    )
-    transport.pause_reading()
-    return SocketStream(transport, protocol)
-
-
-async def connect_unix(path: str) -> UNIXSocketStream:
-    await checkpoint()
-    loop = get_running_loop()
-    raw_socket = socket.socket(socket.AF_UNIX)
-    raw_socket.setblocking(False)
-    while True:
-        try:
-            raw_socket.connect(path)
-        except BlockingIOError:
-            f: asyncio.Future = asyncio.Future()
-            loop.add_writer(raw_socket, f.set_result, None)
-            f.add_done_callback(lambda _: loop.remove_writer(raw_socket))
-            await f
-        except BaseException:
-            raw_socket.close()
-            raise
-        else:
-            return UNIXSocketStream(raw_socket)
-
-
-async def create_udp_socket(
-    family: socket.AddressFamily,
-    local_address: IPSockAddrType | None,
-    remote_address: IPSockAddrType | None,
-    reuse_port: bool,
-) -> UDPSocket | ConnectedUDPSocket:
-    result = await get_running_loop().create_datagram_endpoint(
-        DatagramProtocol,
-        local_addr=local_address,
-        remote_addr=remote_address,
-        family=family,
-        reuse_port=reuse_port,
-    )
-    transport = result[0]
-    protocol = result[1]
-    if protocol.exception:
-        transport.close()
-        raise protocol.exception
-
-    if not remote_address:
-        return UDPSocket(transport, protocol)
-    else:
-        return ConnectedUDPSocket(transport, protocol)
-
-
-async def getaddrinfo(
-    host: bytes | str,
-    port: str | int | None,
-    *,
-    family: int | AddressFamily = 0,
-    type: int | SocketKind = 0,
-    proto: int = 0,
-    flags: int = 0,
-) -> GetAddrInfoReturnType:
-    # https://github.com/python/typeshed/pull/4304
-    result = await get_running_loop().getaddrinfo(
-        host, port, family=family, type=type, proto=proto, flags=flags
-    )
-    return cast(GetAddrInfoReturnType, result)
-
-
-async def getnameinfo(sockaddr: IPSockAddrType, flags: int = 0) -> tuple[str, str]:
-    return await get_running_loop().getnameinfo(sockaddr, flags)
-
-
-_read_events: RunVar[dict[Any, asyncio.Event]] = RunVar("read_events")
-_write_events: RunVar[dict[Any, asyncio.Event]] = RunVar("write_events")
-
-
-async def wait_socket_readable(sock: socket.socket) -> None:
-    await checkpoint()
-    try:
-        read_events = _read_events.get()
-    except LookupError:
-        read_events = {}
-        _read_events.set(read_events)
-
-    if read_events.get(sock):
-        raise BusyResourceError("reading from") from None
-
-    loop = get_running_loop()
-    event = read_events[sock] = asyncio.Event()
-    loop.add_reader(sock, event.set)
-    try:
-        await event.wait()
-    finally:
-        if read_events.pop(sock, None) is not None:
-            loop.remove_reader(sock)
-            readable = True
-        else:
-            readable = False
-
-    if not readable:
-        raise ClosedResourceError
-
-
-async def wait_socket_writable(sock: socket.socket) -> None:
-    await checkpoint()
-    try:
-        write_events = _write_events.get()
-    except LookupError:
-        write_events = {}
-        _write_events.set(write_events)
-
-    if write_events.get(sock):
-        raise BusyResourceError("writing to") from None
-
-    loop = get_running_loop()
-    event = write_events[sock] = asyncio.Event()
-    loop.add_writer(sock.fileno(), event.set)
-    try:
-        await event.wait()
-    finally:
-        if write_events.pop(sock, None) is not None:
-            loop.remove_writer(sock)
-            writable = True
-        else:
-            writable = False
-
-    if not writable:
-        raise ClosedResourceError
-
-
-#
-# Synchronization
-#
-
-
-class Event(BaseEvent):
-    def __new__(cls) -> Event:
-        return object.__new__(cls)
-
-    def __init__(self) -> None:
-        self._event = asyncio.Event()
-
-    def set(self) -> DeprecatedAwaitable:
-        self._event.set()
-        return DeprecatedAwaitable(self.set)
-
-    def is_set(self) -> bool:
-        return self._event.is_set()
-
-    async def wait(self) -> None:
-        if await self._event.wait():
-            await checkpoint()
-
-    def statistics(self) -> EventStatistics:
-        return EventStatistics(len(self._event._waiters))  # type: ignore[attr-defined]
-
-
-class CapacityLimiter(BaseCapacityLimiter):
-    _total_tokens: float = 0
-
-    def __new__(cls, total_tokens: float) -> CapacityLimiter:
-        return object.__new__(cls)
-
-    def __init__(self, total_tokens: float):
-        self._borrowers: set[Any] = set()
-        self._wait_queue: OrderedDict[Any, asyncio.Event] = OrderedDict()
-        self.total_tokens = total_tokens
-
-    async def __aenter__(self) -> None:
-        await self.acquire()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.release()
-
-    @property
-    def total_tokens(self) -> float:
-        return self._total_tokens
-
-    @total_tokens.setter
-    def total_tokens(self, value: float) -> None:
-        if not isinstance(value, int) and not math.isinf(value):
-            raise TypeError("total_tokens must be an int or math.inf")
-        if value < 1:
-            raise ValueError("total_tokens must be >= 1")
-
-        old_value = self._total_tokens
-        self._total_tokens = value
-        events = []
-        for event in self._wait_queue.values():
-            if value <= old_value:
-                break
-
-            if not event.is_set():
-                events.append(event)
-                old_value += 1
-
-        for event in events:
-            event.set()
-
-    @property
-    def borrowed_tokens(self) -> int:
-        return len(self._borrowers)
-
-    @property
-    def available_tokens(self) -> float:
-        return self._total_tokens - len(self._borrowers)
-
-    def acquire_nowait(self) -> DeprecatedAwaitable:
-        self.acquire_on_behalf_of_nowait(current_task())
-        return DeprecatedAwaitable(self.acquire_nowait)
-
-    def acquire_on_behalf_of_nowait(self, borrower: object) -> DeprecatedAwaitable:
-        if borrower in self._borrowers:
-            raise RuntimeError(
-                "this borrower is already holding one of this CapacityLimiter's "
-                "tokens"
-            )
-
-        if self._wait_queue or len(self._borrowers) >= self._total_tokens:
-            raise WouldBlock
-
-        self._borrowers.add(borrower)
-        return DeprecatedAwaitable(self.acquire_on_behalf_of_nowait)
-
-    async def acquire(self) -> None:
-        return await self.acquire_on_behalf_of(current_task())
-
-    async def acquire_on_behalf_of(self, borrower: object) -> None:
-        await checkpoint_if_cancelled()
-        try:
-            self.acquire_on_behalf_of_nowait(borrower)
-        except WouldBlock:
-            event = asyncio.Event()
-            self._wait_queue[borrower] = event
-            try:
-                await event.wait()
-            except BaseException:
-                self._wait_queue.pop(borrower, None)
-                raise
-
-            self._borrowers.add(borrower)
-        else:
-            try:
-                await cancel_shielded_checkpoint()
-            except BaseException:
-                self.release()
-                raise
-
-    def release(self) -> None:
-        self.release_on_behalf_of(current_task())
-
-    def release_on_behalf_of(self, borrower: object) -> None:
-        try:
-            self._borrowers.remove(borrower)
-        except KeyError:
-            raise RuntimeError(
-                "this borrower isn't holding any of this CapacityLimiter's " "tokens"
-            ) from None
-
-        # Notify the next task in line if this limiter has free capacity now
-        if self._wait_queue and len(self._borrowers) < self._total_tokens:
-            event = self._wait_queue.popitem(last=False)[1]
-            event.set()
-
-    def statistics(self) -> CapacityLimiterStatistics:
-        return CapacityLimiterStatistics(
-            self.borrowed_tokens,
-            self.total_tokens,
-            tuple(self._borrowers),
-            len(self._wait_queue),
-        )
-
-
-_default_thread_limiter: RunVar[CapacityLimiter] = RunVar("_default_thread_limiter")
-
-
-def current_default_thread_limiter() -> CapacityLimiter:
-    try:
-        return _default_thread_limiter.get()
-    except LookupError:
-        limiter = CapacityLimiter(40)
-        _default_thread_limiter.set(limiter)
-        return limiter
-
-
-#
-# Operating system signals
-#
-
-
-class _SignalReceiver(DeprecatedAsyncContextManager["_SignalReceiver"]):
-    def __init__(self, signals: tuple[int, ...]):
-        self._signals = signals
-        self._loop = get_running_loop()
-        self._signal_queue: deque[int] = deque()
-        self._future: asyncio.Future = asyncio.Future()
-        self._handled_signals: set[int] = set()
-
-    def _deliver(self, signum: int) -> None:
-        self._signal_queue.append(signum)
-        if not self._future.done():
-            self._future.set_result(None)
-
-    def __enter__(self) -> _SignalReceiver:
-        for sig in set(self._signals):
-            self._loop.add_signal_handler(sig, self._deliver, sig)
-            self._handled_signals.add(sig)
-
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        for sig in self._handled_signals:
-            self._loop.remove_signal_handler(sig)
-        return None
-
-    def __aiter__(self) -> _SignalReceiver:
-        return self
-
-    async def __anext__(self) -> int:
-        await checkpoint()
-        if not self._signal_queue:
-            self._future = asyncio.Future()
-            await self._future
-
-        return self._signal_queue.popleft()
-
-
-def open_signal_receiver(*signals: int) -> _SignalReceiver:
-    return _SignalReceiver(signals)
-
-
-#
-# Testing and debugging
-#
-
-
-def _create_task_info(task: asyncio.Task) -> TaskInfo:
-    task_state = _task_states.get(task)
-    if task_state is None:
-        name = task.get_name() if _native_task_names else None
-        parent_id = None
-    else:
-        name = task_state.name
-        parent_id = task_state.parent_id
-
-    return TaskInfo(id(task), parent_id, name, get_coro(task))
-
-
-def get_current_task() -> TaskInfo:
-    return _create_task_info(current_task())  # type: ignore[arg-type]
-
-
-def get_running_tasks() -> list[TaskInfo]:
-    return [_create_task_info(task) for task in all_tasks() if not task.done()]
-
-
-async def wait_all_tasks_blocked() -> None:
-    await checkpoint()
-    this_task = current_task()
-    while True:
-        for task in all_tasks():
-            if task is this_task:
-                continue
-
-            if task._fut_waiter is None or task._fut_waiter.done():  # type: ignore[attr-defined]
-                await sleep(0.1)
-                break
-        else:
-            return
-
-
-class TestRunner(abc.TestRunner):
-    def __init__(
-        self,
-        debug: bool = False,
-        use_uvloop: bool = False,
-        policy: asyncio.AbstractEventLoopPolicy | None = None,
-    ):
-        self._exceptions: list[BaseException] = []
-        _maybe_set_event_loop_policy(policy, use_uvloop)
-        self._loop = asyncio.new_event_loop()
-        self._loop.set_debug(debug)
-        self._loop.set_exception_handler(self._exception_handler)
-        asyncio.set_event_loop(self._loop)
-
-    def _cancel_all_tasks(self) -> None:
-        to_cancel = all_tasks(self._loop)
-        if not to_cancel:
-            return
-
-        for task in to_cancel:
-            task.cancel()
-
-        self._loop.run_until_complete(
-            asyncio.gather(*to_cancel, return_exceptions=True)
-        )
-
-        for task in to_cancel:
-            if task.cancelled():
-                continue
-            if task.exception() is not None:
-                raise cast(BaseException, task.exception())
-
-    def _exception_handler(
-        self, loop: asyncio.AbstractEventLoop, context: dict[str, Any]
-    ) -> None:
-        if isinstance(context.get("exception"), Exception):
-            self._exceptions.append(context["exception"])
-        else:
-            loop.default_exception_handler(context)
-
-    def _raise_async_exceptions(self) -> None:
-        # Re-raise any exceptions raised in asynchronous callbacks
-        if self._exceptions:
-            exceptions, self._exceptions = self._exceptions, []
-            if len(exceptions) == 1:
-                raise exceptions[0]
-            elif exceptions:
-                raise ExceptionGroup(exceptions)
-
-    def close(self) -> None:
-        try:
-            self._cancel_all_tasks()
-            self._loop.run_until_complete(self._loop.shutdown_asyncgens())
-        finally:
-            asyncio.set_event_loop(None)
-            self._loop.close()
-
-    def run_asyncgen_fixture(
-        self,
-        fixture_func: Callable[..., AsyncGenerator[T_Retval, Any]],
-        kwargs: dict[str, Any],
-    ) -> Iterable[T_Retval]:
-        async def fixture_runner() -> None:
-            agen = fixture_func(**kwargs)
-            try:
-                retval = await agen.asend(None)
-                self._raise_async_exceptions()
-            except BaseException as exc:
-                f.set_exception(exc)
-                return
-            else:
-                f.set_result(retval)
-
-            await event.wait()
-            try:
-                await agen.asend(None)
-            except StopAsyncIteration:
-                pass
-            else:
-                await agen.aclose()
-                raise RuntimeError("Async generator fixture did not stop")
-
-        f = self._loop.create_future()
-        event = asyncio.Event()
-        fixture_task = self._loop.create_task(fixture_runner())
-        self._loop.run_until_complete(f)
-        yield f.result()
-        event.set()
-        self._loop.run_until_complete(fixture_task)
-        self._raise_async_exceptions()
-
-    def run_fixture(
-        self,
-        fixture_func: Callable[..., Coroutine[Any, Any, T_Retval]],
-        kwargs: dict[str, Any],
-    ) -> T_Retval:
-        retval = self._loop.run_until_complete(fixture_func(**kwargs))
-        self._raise_async_exceptions()
-        return retval
-
-    def run_test(
-        self, test_func: Callable[..., Coroutine[Any, Any, Any]], kwargs: dict[str, Any]
-    ) -> None:
-        try:
-            self._loop.run_until_complete(test_func(**kwargs))
-        except Exception as exc:
-            self._exceptions.append(exc)
-
-        self._raise_async_exceptions()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/arrow.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/arrow.py
deleted file mode 100644
index 3b1048acdec34d4f5eeff90e30e3629023c6099d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/arrow.py
+++ /dev/null
@@ -1,297 +0,0 @@
-import errno
-import io
-import os
-import secrets
-import shutil
-from contextlib import suppress
-from functools import cached_property, wraps
-
-from fsspec.spec import AbstractFileSystem
-from fsspec.utils import (
-    get_package_version_without_import,
-    infer_storage_options,
-    mirror_from,
-    tokenize,
-)
-
-
-def wrap_exceptions(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        try:
-            return func(*args, **kwargs)
-        except OSError as exception:
-            if not exception.args:
-                raise
-
-            message, *args = exception.args
-            if isinstance(message, str) and "does not exist" in message:
-                raise FileNotFoundError(errno.ENOENT, message) from exception
-            else:
-                raise
-
-    return wrapper
-
-
-PYARROW_VERSION = None
-
-
-class ArrowFSWrapper(AbstractFileSystem):
-    """FSSpec-compatible wrapper of pyarrow.fs.FileSystem.
-
-    Parameters
-    ----------
-    fs : pyarrow.fs.FileSystem
-
-    """
-
-    root_marker = "/"
-
-    def __init__(self, fs, **kwargs):
-        global PYARROW_VERSION
-        PYARROW_VERSION = get_package_version_without_import("pyarrow")
-        self.fs = fs
-        super().__init__(**kwargs)
-
-    @property
-    def protocol(self):
-        return self.fs.type_name
-
-    @cached_property
-    def fsid(self):
-        return "hdfs_" + tokenize(self.fs.host, self.fs.port)
-
-    @classmethod
-    def _strip_protocol(cls, path):
-        ops = infer_storage_options(path)
-        path = ops["path"]
-        if path.startswith("//"):
-            # special case for "hdfs://path" (without the triple slash)
-            path = path[1:]
-        return path
-
-    def ls(self, path, detail=False, **kwargs):
-        path = self._strip_protocol(path)
-        from pyarrow.fs import FileSelector
-
-        entries = [
-            self._make_entry(entry)
-            for entry in self.fs.get_file_info(FileSelector(path))
-        ]
-        if detail:
-            return entries
-        else:
-            return [entry["name"] for entry in entries]
-
-    def info(self, path, **kwargs):
-        path = self._strip_protocol(path)
-        [info] = self.fs.get_file_info([path])
-        return self._make_entry(info)
-
-    def exists(self, path):
-        path = self._strip_protocol(path)
-        try:
-            self.info(path)
-        except FileNotFoundError:
-            return False
-        else:
-            return True
-
-    def _make_entry(self, info):
-        from pyarrow.fs import FileType
-
-        if info.type is FileType.Directory:
-            kind = "directory"
-        elif info.type is FileType.File:
-            kind = "file"
-        elif info.type is FileType.NotFound:
-            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), info.path)
-        else:
-            kind = "other"
-
-        return {
-            "name": info.path,
-            "size": info.size,
-            "type": kind,
-            "mtime": info.mtime,
-        }
-
-    @wrap_exceptions
-    def cp_file(self, path1, path2, **kwargs):
-        path1 = self._strip_protocol(path1).rstrip("/")
-        path2 = self._strip_protocol(path2).rstrip("/")
-
-        with self._open(path1, "rb") as lstream:
-            tmp_fname = f"{path2}.tmp.{secrets.token_hex(6)}"
-            try:
-                with self.open(tmp_fname, "wb") as rstream:
-                    shutil.copyfileobj(lstream, rstream)
-                self.fs.move(tmp_fname, path2)
-            except BaseException:  # noqa
-                with suppress(FileNotFoundError):
-                    self.fs.delete_file(tmp_fname)
-                raise
-
-    @wrap_exceptions
-    def mv(self, path1, path2, **kwargs):
-        path1 = self._strip_protocol(path1).rstrip("/")
-        path2 = self._strip_protocol(path2).rstrip("/")
-        self.fs.move(path1, path2)
-
-    mv_file = mv
-
-    @wrap_exceptions
-    def rm_file(self, path):
-        path = self._strip_protocol(path)
-        self.fs.delete_file(path)
-
-    @wrap_exceptions
-    def rm(self, path, recursive=False, maxdepth=None):
-        path = self._strip_protocol(path).rstrip("/")
-        if self.isdir(path):
-            if recursive:
-                self.fs.delete_dir(path)
-            else:
-                raise ValueError("Can't delete directories without recursive=False")
-        else:
-            self.fs.delete_file(path)
-
-    @wrap_exceptions
-    def _open(self, path, mode="rb", block_size=None, seekable=True, **kwargs):
-        if mode == "rb":
-            if seekable:
-                method = self.fs.open_input_file
-            else:
-                method = self.fs.open_input_stream
-        elif mode == "wb":
-            method = self.fs.open_output_stream
-        elif mode == "ab":
-            method = self.fs.open_append_stream
-        else:
-            raise ValueError(f"unsupported mode for Arrow filesystem: {mode!r}")
-
-        _kwargs = {}
-        if mode != "rb" or not seekable:
-            if int(PYARROW_VERSION.split(".")[0]) >= 4:
-                # disable compression auto-detection
-                _kwargs["compression"] = None
-        stream = method(path, **_kwargs)
-
-        return ArrowFile(self, stream, path, mode, block_size, **kwargs)
-
-    @wrap_exceptions
-    def mkdir(self, path, create_parents=True, **kwargs):
-        path = self._strip_protocol(path)
-        if create_parents:
-            self.makedirs(path, exist_ok=True)
-        else:
-            self.fs.create_dir(path, recursive=False)
-
-    @wrap_exceptions
-    def makedirs(self, path, exist_ok=False):
-        path = self._strip_protocol(path)
-        self.fs.create_dir(path, recursive=True)
-
-    @wrap_exceptions
-    def rmdir(self, path):
-        path = self._strip_protocol(path)
-        self.fs.delete_dir(path)
-
-    @wrap_exceptions
-    def modified(self, path):
-        path = self._strip_protocol(path)
-        return self.fs.get_file_info(path).mtime
-
-    def cat_file(self, path, start=None, end=None, **kwargs):
-        kwargs["seekable"] = start not in [None, 0]
-        return super().cat_file(path, start=None, end=None, **kwargs)
-
-    def get_file(self, rpath, lpath, **kwargs):
-        kwargs["seekable"] = False
-        super().get_file(rpath, lpath, **kwargs)
-
-
-@mirror_from(
-    "stream",
-    [
-        "read",
-        "seek",
-        "tell",
-        "write",
-        "readable",
-        "writable",
-        "close",
-        "size",
-        "seekable",
-    ],
-)
-class ArrowFile(io.IOBase):
-    def __init__(self, fs, stream, path, mode, block_size=None, **kwargs):
-        self.path = path
-        self.mode = mode
-
-        self.fs = fs
-        self.stream = stream
-
-        self.blocksize = self.block_size = block_size
-        self.kwargs = kwargs
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args):
-        return self.close()
-
-
-class HadoopFileSystem(ArrowFSWrapper):
-    """A wrapper on top of the pyarrow.fs.HadoopFileSystem
-    to connect it's interface with fsspec"""
-
-    protocol = "hdfs"
-
-    def __init__(
-        self,
-        host="default",
-        port=0,
-        user=None,
-        kerb_ticket=None,
-        extra_conf=None,
-        **kwargs,
-    ):
-        """
-
-        Parameters
-        ----------
-        host: str
-            Hostname, IP or "default" to try to read from Hadoop config
-        port: int
-            Port to connect on, or default from Hadoop config if 0
-        user: str or None
-            If given, connect as this username
-        kerb_ticket: str or None
-            If given, use this ticket for authentication
-        extra_conf: None or dict
-            Passed on to HadoopFileSystem
-        """
-        from pyarrow.fs import HadoopFileSystem
-
-        fs = HadoopFileSystem(
-            host=host,
-            port=port,
-            user=user,
-            kerb_ticket=kerb_ticket,
-            extra_conf=extra_conf,
-        )
-        super().__init__(fs=fs, **kwargs)
-
-    @staticmethod
-    def _get_kwargs_from_urls(path):
-        ops = infer_storage_options(path)
-        out = {}
-        if ops.get("host", None):
-            out["host"] = ops["host"]
-        if ops.get("username", None):
-            out["user"] = ops["username"]
-        if ops.get("port", None):
-            out["port"] = ops["port"]
-        return out
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockLabel-e3b0d1c3.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockLabel-e3b0d1c3.js
deleted file mode 100644
index 758940eba94a4691e6c0604ee894496073f40b4e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockLabel-e3b0d1c3.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import"./Button-89057c03.js";const{SvelteComponent:r,append:c,attr:_,create_component:m,destroy_component:d,detach:h,element:u,init:g,insert:w,mount_component:I,safe_not_equal:k,set_data:B,space:q,text:C,toggle_class:s,transition_in:L,transition_out:S}=window.__gradio__svelte__internal;function j(i){let e,a,o,b,f,n;return o=new i[1]({}),{c(){e=u("label"),a=u("span"),m(o.$$.fragment),b=q(),f=C(i[0]),_(a,"class","svelte-1b6s6s"),_(e,"for",""),_(e,"data-testid","block-label"),_(e,"class","svelte-1b6s6s"),s(e,"hide",!i[2]),s(e,"sr-only",!i[2]),s(e,"float",i[4]),s(e,"hide-label",i[3])},m(t,l){w(t,e,l),c(e,a),I(o,a,null),c(e,b),c(e,f),n=!0},p(t,[l]){(!n||l&1)&&B(f,t[0]),(!n||l&4)&&s(e,"hide",!t[2]),(!n||l&4)&&s(e,"sr-only",!t[2]),(!n||l&16)&&s(e,"float",t[4]),(!n||l&8)&&s(e,"hide-label",t[3])},i(t){n||(L(o.$$.fragment,t),n=!0)},o(t){S(o.$$.fragment,t),n=!1},d(t){t&&h(e),d(o)}}}function v(i,e,a){let{label:o=null}=e,{Icon:b}=e,{show_label:f=!0}=e,{disable:n=!1}=e,{float:t=!0}=e;return i.$$set=l=>{"label"in l&&a(0,o=l.label),"Icon"in l&&a(1,b=l.Icon),"show_label"in l&&a(2,f=l.show_label),"disable"in l&&a(3,n=l.disable),"float"in l&&a(4,t=l.float)},[o,b,f,n,t]}class A extends r{constructor(e){super(),g(this,e,v,j,k,{label:0,Icon:1,show_label:2,disable:3,float:4})}}export{A as B};
-//# sourceMappingURL=BlockLabel-e3b0d1c3.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-cd311153.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-cd311153.css
deleted file mode 100644
index ddd19b13c94adc9cef083883af708a15f2eb65f0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-cd311153.css
+++ /dev/null
@@ -1 +0,0 @@
-input.svelte-56zyyb{display:block;position:relative;background:var(--background-fill-primary);line-height:var(--line-sm)}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_typing/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_typing/__init__.py
deleted file mode 100644
index 29922d958efbdfa6ddee19f8b3904498f9222585..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_typing/__init__.py
+++ /dev/null
@@ -1,221 +0,0 @@
-"""Private counterpart of ``numpy.typing``."""
-
-from __future__ import annotations
-
-from .. import ufunc
-from .._utils import set_module
-from typing import TYPE_CHECKING, final
-
-
-@final  # Disallow the creation of arbitrary `NBitBase` subclasses
-@set_module("numpy.typing")
-class NBitBase:
-    """
-    A type representing `numpy.number` precision during static type checking.
-
-    Used exclusively for the purpose static type checking, `NBitBase`
-    represents the base of a hierarchical set of subclasses.
-    Each subsequent subclass is herein used for representing a lower level
-    of precision, *e.g.* ``64Bit > 32Bit > 16Bit``.
-
-    .. versionadded:: 1.20
-
-    Examples
-    --------
-    Below is a typical usage example: `NBitBase` is herein used for annotating
-    a function that takes a float and integer of arbitrary precision
-    as arguments and returns a new float of whichever precision is largest
-    (*e.g.* ``np.float16 + np.int64 -> np.float64``).
-
-    .. code-block:: python
-
-        >>> from __future__ import annotations
-        >>> from typing import TypeVar, TYPE_CHECKING
-        >>> import numpy as np
-        >>> import numpy.typing as npt
-
-        >>> T1 = TypeVar("T1", bound=npt.NBitBase)
-        >>> T2 = TypeVar("T2", bound=npt.NBitBase)
-
-        >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]:
-        ...     return a + b
-
-        >>> a = np.float16()
-        >>> b = np.int64()
-        >>> out = add(a, b)
-
-        >>> if TYPE_CHECKING:
-        ...     reveal_locals()
-        ...     # note: Revealed local types are:
-        ...     # note:     a: numpy.floating[numpy.typing._16Bit*]
-        ...     # note:     b: numpy.signedinteger[numpy.typing._64Bit*]
-        ...     # note:     out: numpy.floating[numpy.typing._64Bit*]
-
-    """
-
-    def __init_subclass__(cls) -> None:
-        allowed_names = {
-            "NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit",
-            "_64Bit", "_32Bit", "_16Bit", "_8Bit",
-        }
-        if cls.__name__ not in allowed_names:
-            raise TypeError('cannot inherit from final class "NBitBase"')
-        super().__init_subclass__()
-
-
-# Silence errors about subclassing a `@final`-decorated class
-class _256Bit(NBitBase):  # type: ignore[misc]
-    pass
-
-class _128Bit(_256Bit):  # type: ignore[misc]
-    pass
-
-class _96Bit(_128Bit):  # type: ignore[misc]
-    pass
-
-class _80Bit(_96Bit):  # type: ignore[misc]
-    pass
-
-class _64Bit(_80Bit):  # type: ignore[misc]
-    pass
-
-class _32Bit(_64Bit):  # type: ignore[misc]
-    pass
-
-class _16Bit(_32Bit):  # type: ignore[misc]
-    pass
-
-class _8Bit(_16Bit):  # type: ignore[misc]
-    pass
-
-
-from ._nested_sequence import (
-    _NestedSequence as _NestedSequence,
-)
-from ._nbit import (
-    _NBitByte as _NBitByte,
-    _NBitShort as _NBitShort,
-    _NBitIntC as _NBitIntC,
-    _NBitIntP as _NBitIntP,
-    _NBitInt as _NBitInt,
-    _NBitLongLong as _NBitLongLong,
-    _NBitHalf as _NBitHalf,
-    _NBitSingle as _NBitSingle,
-    _NBitDouble as _NBitDouble,
-    _NBitLongDouble as _NBitLongDouble,
-)
-from ._char_codes import (
-    _BoolCodes as _BoolCodes,
-    _UInt8Codes as _UInt8Codes,
-    _UInt16Codes as _UInt16Codes,
-    _UInt32Codes as _UInt32Codes,
-    _UInt64Codes as _UInt64Codes,
-    _Int8Codes as _Int8Codes,
-    _Int16Codes as _Int16Codes,
-    _Int32Codes as _Int32Codes,
-    _Int64Codes as _Int64Codes,
-    _Float16Codes as _Float16Codes,
-    _Float32Codes as _Float32Codes,
-    _Float64Codes as _Float64Codes,
-    _Complex64Codes as _Complex64Codes,
-    _Complex128Codes as _Complex128Codes,
-    _ByteCodes as _ByteCodes,
-    _ShortCodes as _ShortCodes,
-    _IntCCodes as _IntCCodes,
-    _IntPCodes as _IntPCodes,
-    _IntCodes as _IntCodes,
-    _LongLongCodes as _LongLongCodes,
-    _UByteCodes as _UByteCodes,
-    _UShortCodes as _UShortCodes,
-    _UIntCCodes as _UIntCCodes,
-    _UIntPCodes as _UIntPCodes,
-    _UIntCodes as _UIntCodes,
-    _ULongLongCodes as _ULongLongCodes,
-    _HalfCodes as _HalfCodes,
-    _SingleCodes as _SingleCodes,
-    _DoubleCodes as _DoubleCodes,
-    _LongDoubleCodes as _LongDoubleCodes,
-    _CSingleCodes as _CSingleCodes,
-    _CDoubleCodes as _CDoubleCodes,
-    _CLongDoubleCodes as _CLongDoubleCodes,
-    _DT64Codes as _DT64Codes,
-    _TD64Codes as _TD64Codes,
-    _StrCodes as _StrCodes,
-    _BytesCodes as _BytesCodes,
-    _VoidCodes as _VoidCodes,
-    _ObjectCodes as _ObjectCodes,
-)
-from ._scalars import (
-    _CharLike_co as _CharLike_co,
-    _BoolLike_co as _BoolLike_co,
-    _UIntLike_co as _UIntLike_co,
-    _IntLike_co as _IntLike_co,
-    _FloatLike_co as _FloatLike_co,
-    _ComplexLike_co as _ComplexLike_co,
-    _TD64Like_co as _TD64Like_co,
-    _NumberLike_co as _NumberLike_co,
-    _ScalarLike_co as _ScalarLike_co,
-    _VoidLike_co as _VoidLike_co,
-)
-from ._shape import (
-    _Shape as _Shape,
-    _ShapeLike as _ShapeLike,
-)
-from ._dtype_like import (
-    DTypeLike as DTypeLike,
-    _DTypeLike as _DTypeLike,
-    _SupportsDType as _SupportsDType,
-    _VoidDTypeLike as _VoidDTypeLike,
-    _DTypeLikeBool as _DTypeLikeBool,
-    _DTypeLikeUInt as _DTypeLikeUInt,
-    _DTypeLikeInt as _DTypeLikeInt,
-    _DTypeLikeFloat as _DTypeLikeFloat,
-    _DTypeLikeComplex as _DTypeLikeComplex,
-    _DTypeLikeTD64 as _DTypeLikeTD64,
-    _DTypeLikeDT64 as _DTypeLikeDT64,
-    _DTypeLikeObject as _DTypeLikeObject,
-    _DTypeLikeVoid as _DTypeLikeVoid,
-    _DTypeLikeStr as _DTypeLikeStr,
-    _DTypeLikeBytes as _DTypeLikeBytes,
-    _DTypeLikeComplex_co as _DTypeLikeComplex_co,
-)
-from ._array_like import (
-    NDArray as NDArray,
-    ArrayLike as ArrayLike,
-    _ArrayLike as _ArrayLike,
-    _FiniteNestedSequence as _FiniteNestedSequence,
-    _SupportsArray as _SupportsArray,
-    _SupportsArrayFunc as _SupportsArrayFunc,
-    _ArrayLikeInt as _ArrayLikeInt,
-    _ArrayLikeBool_co as _ArrayLikeBool_co,
-    _ArrayLikeUInt_co as _ArrayLikeUInt_co,
-    _ArrayLikeInt_co as _ArrayLikeInt_co,
-    _ArrayLikeFloat_co as _ArrayLikeFloat_co,
-    _ArrayLikeComplex_co as _ArrayLikeComplex_co,
-    _ArrayLikeNumber_co as _ArrayLikeNumber_co,
-    _ArrayLikeTD64_co as _ArrayLikeTD64_co,
-    _ArrayLikeDT64_co as _ArrayLikeDT64_co,
-    _ArrayLikeObject_co as _ArrayLikeObject_co,
-    _ArrayLikeVoid_co as _ArrayLikeVoid_co,
-    _ArrayLikeStr_co as _ArrayLikeStr_co,
-    _ArrayLikeBytes_co as _ArrayLikeBytes_co,
-    _ArrayLikeUnknown as _ArrayLikeUnknown,
-    _UnknownType as _UnknownType,
-)
-
-if TYPE_CHECKING:
-    from ._ufunc import (
-        _UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1,
-        _UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1,
-        _UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2,
-        _UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2,
-        _GUFunc_Nin2_Nout1 as _GUFunc_Nin2_Nout1,
-    )
-else:
-    # Declare the (type-check-only) ufunc subclasses as ufunc aliases during
-    # runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834)
-    _UFunc_Nin1_Nout1 = ufunc
-    _UFunc_Nin2_Nout1 = ufunc
-    _UFunc_Nin1_Nout2 = ufunc
-    _UFunc_Nin2_Nout2 = ufunc
-    _GUFunc_Nin2_Nout1 = ufunc
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_typing/_add_docstring.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_typing/_add_docstring.py
deleted file mode 100644
index f84d19271c23f19c86729545de59e8cae4a50f05..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_typing/_add_docstring.py
+++ /dev/null
@@ -1,152 +0,0 @@
-"""A module for creating docstrings for sphinx ``data`` domains."""
-
-import re
-import textwrap
-
-from ._array_like import NDArray
-
-_docstrings_list = []
-
-
-def add_newdoc(name: str, value: str, doc: str) -> None:
-    """Append ``_docstrings_list`` with a docstring for `name`.
-
-    Parameters
-    ----------
-    name : str
-        The name of the object.
-    value : str
-        A string-representation of the object.
-    doc : str
-        The docstring of the object.
-
-    """
-    _docstrings_list.append((name, value, doc))
-
-
-def _parse_docstrings() -> str:
-    """Convert all docstrings in ``_docstrings_list`` into a single
-    sphinx-legible text block.
-
-    """
-    type_list_ret = []
-    for name, value, doc in _docstrings_list:
-        s = textwrap.dedent(doc).replace("\n", "\n    ")
-
-        # Replace sections by rubrics
-        lines = s.split("\n")
-        new_lines = []
-        indent = ""
-        for line in lines:
-            m = re.match(r'^(\s+)[-=]+\s*$', line)
-            if m and new_lines:
-                prev = textwrap.dedent(new_lines.pop())
-                if prev == "Examples":
-                    indent = ""
-                    new_lines.append(f'{m.group(1)}.. rubric:: {prev}')
-                else:
-                    indent = 4 * " "
-                    new_lines.append(f'{m.group(1)}.. admonition:: {prev}')
-                new_lines.append("")
-            else:
-                new_lines.append(f"{indent}{line}")
-
-        s = "\n".join(new_lines)
-        s_block = f""".. data:: {name}\n    :value: {value}\n    {s}"""
-        type_list_ret.append(s_block)
-    return "\n".join(type_list_ret)
-
-
-add_newdoc('ArrayLike', 'typing.Union[...]',
-    """
-    A `~typing.Union` representing objects that can be coerced
-    into an `~numpy.ndarray`.
-
-    Among others this includes the likes of:
-
-    * Scalars.
-    * (Nested) sequences.
-    * Objects implementing the `~class.__array__` protocol.
-
-    .. versionadded:: 1.20
-
-    See Also
-    --------
-    :term:`array_like`:
-        Any scalar or sequence that can be interpreted as an ndarray.
-
-    Examples
-    --------
-    .. code-block:: python
-
-        >>> import numpy as np
-        >>> import numpy.typing as npt
-
-        >>> def as_array(a: npt.ArrayLike) -> np.ndarray:
-        ...     return np.array(a)
-
-    """)
-
-add_newdoc('DTypeLike', 'typing.Union[...]',
-    """
-    A `~typing.Union` representing objects that can be coerced
-    into a `~numpy.dtype`.
-
-    Among others this includes the likes of:
-
-    * :class:`type` objects.
-    * Character codes or the names of :class:`type` objects.
-    * Objects with the ``.dtype`` attribute.
-
-    .. versionadded:: 1.20
-
-    See Also
-    --------
-    :ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
-        A comprehensive overview of all objects that can be coerced
-        into data types.
-
-    Examples
-    --------
-    .. code-block:: python
-
-        >>> import numpy as np
-        >>> import numpy.typing as npt
-
-        >>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
-        ...     return np.dtype(d)
-
-    """)
-
-add_newdoc('NDArray', repr(NDArray),
-    """
-    A :term:`generic <generic type>` version of
-    `np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.
-
-    Can be used during runtime for typing arrays with a given dtype
-    and unspecified shape.
-
-    .. versionadded:: 1.21
-
-    Examples
-    --------
-    .. code-block:: python
-
-        >>> import numpy as np
-        >>> import numpy.typing as npt
-
-        >>> print(npt.NDArray)
-        numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]
-
-        >>> print(npt.NDArray[np.float64])
-        numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
-
-        >>> NDArrayInt = npt.NDArray[np.int_]
-        >>> a: NDArrayInt = np.arange(10)
-
-        >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
-        ...     return np.array(a)
-
-    """)
-
-_docstrings = _parse_docstrings()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/testing/print_coercion_tables.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/testing/print_coercion_tables.py
deleted file mode 100644
index c1d4cdff8fd0b7e9cb9b539d9a49f3374a098a11..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/testing/print_coercion_tables.py
+++ /dev/null
@@ -1,200 +0,0 @@
-#!/usr/bin/env python3
-"""Prints type-coercion tables for the built-in NumPy types
-
-"""
-import numpy as np
-from collections import namedtuple
-
-# Generic object that can be added, but doesn't do anything else
-class GenericObject:
-    def __init__(self, v):
-        self.v = v
-
-    def __add__(self, other):
-        return self
-
-    def __radd__(self, other):
-        return self
-
-    dtype = np.dtype('O')
-
-def print_cancast_table(ntypes):
-    print('X', end=' ')
-    for char in ntypes:
-        print(char, end=' ')
-    print()
-    for row in ntypes:
-        print(row, end=' ')
-        for col in ntypes:
-            if np.can_cast(row, col, "equiv"):
-                cast = "#"
-            elif np.can_cast(row, col, "safe"):
-                cast = "="
-            elif np.can_cast(row, col, "same_kind"):
-                cast = "~"
-            elif np.can_cast(row, col, "unsafe"):
-                cast = "."
-            else:
-                cast = " "
-            print(cast, end=' ')
-        print()
-
-def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray, use_promote_types=False):
-    print('+', end=' ')
-    for char in ntypes:
-        print(char, end=' ')
-    print()
-    for row in ntypes:
-        if row == 'O':
-            rowtype = GenericObject
-        else:
-            rowtype = np.obj2sctype(row)
-
-        print(row, end=' ')
-        for col in ntypes:
-            if col == 'O':
-                coltype = GenericObject
-            else:
-                coltype = np.obj2sctype(col)
-            try:
-                if firstarray:
-                    rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)
-                else:
-                    rowvalue = rowtype(inputfirstvalue)
-                colvalue = coltype(inputsecondvalue)
-                if use_promote_types:
-                    char = np.promote_types(rowvalue.dtype, colvalue.dtype).char
-                else:
-                    value = np.add(rowvalue, colvalue)
-                    if isinstance(value, np.ndarray):
-                        char = value.dtype.char
-                    else:
-                        char = np.dtype(type(value)).char
-            except ValueError:
-                char = '!'
-            except OverflowError:
-                char = '@'
-            except TypeError:
-                char = '#'
-            print(char, end=' ')
-        print()
-
-
-def print_new_cast_table(*, can_cast=True, legacy=False, flags=False):
-    """Prints new casts, the values given are default "can-cast" values, not
-    actual ones.
-    """
-    from numpy.core._multiarray_tests import get_all_cast_information
-
-    cast_table = {
-        -1: " ",
-        0: "#",  # No cast (classify as equivalent here)
-        1: "#",  # equivalent casting
-        2: "=",  # safe casting
-        3: "~",  # same-kind casting
-        4: ".",  # unsafe casting
-    }
-    flags_table = {
-        0 : "▗", 7: "█",
-        1: "▚", 2: "▐", 4: "▄",
-                3: "▜", 5: "▙",
-                        6: "▟",
-    }
-
-    cast_info = namedtuple("cast_info", ["can_cast", "legacy", "flags"])
-    no_cast_info = cast_info(" ", " ", " ")
-
-    casts = get_all_cast_information()
-    table = {}
-    dtypes = set()
-    for cast in casts:
-        dtypes.add(cast["from"])
-        dtypes.add(cast["to"])
-
-        if cast["from"] not in table:
-            table[cast["from"]] = {}
-        to_dict = table[cast["from"]]
-
-        can_cast = cast_table[cast["casting"]]
-        legacy = "L" if cast["legacy"] else "."
-        flags = 0
-        if cast["requires_pyapi"]:
-            flags |= 1
-        if cast["supports_unaligned"]:
-            flags |= 2
-        if cast["no_floatingpoint_errors"]:
-            flags |= 4
-
-        flags = flags_table[flags]
-        to_dict[cast["to"]] = cast_info(can_cast=can_cast, legacy=legacy, flags=flags)
-
-    # The np.dtype(x.type) is a bit strange, because dtype classes do
-    # not expose much yet.
-    types = np.typecodes["All"]
-    def sorter(x):
-        # This is a bit weird hack, to get a table as close as possible to
-        # the one printing all typecodes (but expecting user-dtypes).
-        dtype = np.dtype(x.type)
-        try:
-            indx = types.index(dtype.char)
-        except ValueError:
-            indx = np.inf
-        return (indx, dtype.char)
-
-    dtypes = sorted(dtypes, key=sorter)
-
-    def print_table(field="can_cast"):
-        print('X', end=' ')
-        for dt in dtypes:
-            print(np.dtype(dt.type).char, end=' ')
-        print()
-        for from_dt in dtypes:
-            print(np.dtype(from_dt.type).char, end=' ')
-            row = table.get(from_dt, {})
-            for to_dt in dtypes:
-                print(getattr(row.get(to_dt, no_cast_info), field), end=' ')
-            print()
-
-    if can_cast:
-        # Print the actual table:
-        print()
-        print("Casting: # is equivalent, = is safe, ~ is same-kind, and . is unsafe")
-        print()
-        print_table("can_cast")
-
-    if legacy:
-        print()
-        print("L denotes a legacy cast . a non-legacy one.")
-        print()
-        print_table("legacy")
-
-    if flags:
-        print()
-        print(f"{flags_table[0]}: no flags, {flags_table[1]}: PyAPI, "
-              f"{flags_table[2]}: supports unaligned, {flags_table[4]}: no-float-errors")
-        print()
-        print_table("flags")
-
-
-if __name__ == '__main__':
-    print("can cast")
-    print_cancast_table(np.typecodes['All'])
-    print()
-    print("In these tables, ValueError is '!', OverflowError is '@', TypeError is '#'")
-    print()
-    print("scalar + scalar")
-    print_coercion_table(np.typecodes['All'], 0, 0, False)
-    print()
-    print("scalar + neg scalar")
-    print_coercion_table(np.typecodes['All'], 0, -1, False)
-    print()
-    print("array + scalar")
-    print_coercion_table(np.typecodes['All'], 0, 0, True)
-    print()
-    print("array + neg scalar")
-    print_coercion_table(np.typecodes['All'], 0, -1, True)
-    print()
-    print("promote_types")
-    print_coercion_table(np.typecodes['All'], 0, 0, False, True)
-    print("New casting type promotion:")
-    print_new_cast_table(can_cast=True, legacy=True, flags=True)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_first_and_last.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_first_and_last.py
deleted file mode 100644
index 2e85edc7ed0ea6db3ca87841b223e3581fb60059..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_first_and_last.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""
-Note: includes tests for `last`
-"""
-import pytest
-
-import pandas as pd
-from pandas import (
-    DataFrame,
-    bdate_range,
-)
-import pandas._testing as tm
-
-deprecated_msg = "first is deprecated"
-last_deprecated_msg = "last is deprecated"
-
-
-class TestFirst:
-    def test_first_subset(self, frame_or_series):
-        ts = tm.makeTimeDataFrame(freq="12h")
-        ts = tm.get_obj(ts, frame_or_series)
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = ts.first("10d")
-            assert len(result) == 20
-
-        ts = tm.makeTimeDataFrame(freq="D")
-        ts = tm.get_obj(ts, frame_or_series)
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = ts.first("10d")
-            assert len(result) == 10
-
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = ts.first("3M")
-            expected = ts[:"3/31/2000"]
-            tm.assert_equal(result, expected)
-
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = ts.first("21D")
-            expected = ts[:21]
-            tm.assert_equal(result, expected)
-
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = ts[:0].first("3M")
-            tm.assert_equal(result, ts[:0])
-
-    def test_first_last_raises(self, frame_or_series):
-        # GH#20725
-        obj = DataFrame([[1, 2, 3], [4, 5, 6]])
-        obj = tm.get_obj(obj, frame_or_series)
-
-        msg = "'first' only supports a DatetimeIndex index"
-        with tm.assert_produces_warning(
-            FutureWarning, match=deprecated_msg
-        ), pytest.raises(
-            TypeError, match=msg
-        ):  # index is not a DatetimeIndex
-            obj.first("1D")
-
-        msg = "'last' only supports a DatetimeIndex index"
-        with tm.assert_produces_warning(
-            FutureWarning, match=last_deprecated_msg
-        ), pytest.raises(
-            TypeError, match=msg
-        ):  # index is not a DatetimeIndex
-            obj.last("1D")
-
-    def test_last_subset(self, frame_or_series):
-        ts = tm.makeTimeDataFrame(freq="12h")
-        ts = tm.get_obj(ts, frame_or_series)
-        with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg):
-            result = ts.last("10d")
-        assert len(result) == 20
-
-        ts = tm.makeTimeDataFrame(nper=30, freq="D")
-        ts = tm.get_obj(ts, frame_or_series)
-        with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg):
-            result = ts.last("10d")
-        assert len(result) == 10
-
-        with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg):
-            result = ts.last("21D")
-        expected = ts["2000-01-10":]
-        tm.assert_equal(result, expected)
-
-        with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg):
-            result = ts.last("21D")
-        expected = ts[-21:]
-        tm.assert_equal(result, expected)
-
-        with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg):
-            result = ts[:0].last("3M")
-        tm.assert_equal(result, ts[:0])
-
-    @pytest.mark.parametrize("start, periods", [("2010-03-31", 1), ("2010-03-30", 2)])
-    def test_first_with_first_day_last_of_month(self, frame_or_series, start, periods):
-        # GH#29623
-        x = frame_or_series([1] * 100, index=bdate_range(start, periods=100))
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = x.first("1M")
-        expected = frame_or_series(
-            [1] * periods, index=bdate_range(start, periods=periods)
-        )
-        tm.assert_equal(result, expected)
-
-    def test_first_with_first_day_end_of_frq_n_greater_one(self, frame_or_series):
-        # GH#29623
-        x = frame_or_series([1] * 100, index=bdate_range("2010-03-31", periods=100))
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = x.first("2M")
-        expected = frame_or_series(
-            [1] * 23, index=bdate_range("2010-03-31", "2010-04-30")
-        )
-        tm.assert_equal(result, expected)
-
-    def test_empty_not_input(self):
-        # GH#51032
-        df = DataFrame(index=pd.DatetimeIndex([]))
-        with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg):
-            result = df.last(offset=1)
-
-        with tm.assert_produces_warning(FutureWarning, match=deprecated_msg):
-            result = df.first(offset=1)
-
-        tm.assert_frame_equal(df, result)
-        assert df is not result
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/interval/test_formats.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/interval/test_formats.py
deleted file mode 100644
index 4d6f3a62d4dd04e62e53957a9dc78aa88cd86100..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/interval/test_formats.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    DataFrame,
-    Index,
-    Interval,
-    IntervalIndex,
-    Series,
-    Timedelta,
-    Timestamp,
-)
-import pandas._testing as tm
-
-
-class TestIntervalIndexRendering:
-    def test_frame_repr(self):
-        # https://github.com/pandas-dev/pandas/pull/24134/files
-        df = DataFrame(
-            {"A": [1, 2, 3, 4]}, index=IntervalIndex.from_breaks([0, 1, 2, 3, 4])
-        )
-        result = repr(df)
-        expected = "        A\n(0, 1]  1\n(1, 2]  2\n(2, 3]  3\n(3, 4]  4"
-        assert result == expected
-
-    @pytest.mark.parametrize(
-        "constructor,expected",
-        [
-            (
-                Series,
-                (
-                    "(0.0, 1.0]    a\n"
-                    "NaN           b\n"
-                    "(2.0, 3.0]    c\n"
-                    "dtype: object"
-                ),
-            ),
-            (DataFrame, ("            0\n(0.0, 1.0]  a\nNaN         b\n(2.0, 3.0]  c")),
-        ],
-    )
-    def test_repr_missing(self, constructor, expected):
-        # GH 25984
-        index = IntervalIndex.from_tuples([(0, 1), np.nan, (2, 3)])
-        obj = constructor(list("abc"), index=index)
-        result = repr(obj)
-        assert result == expected
-
-    def test_repr_floats(self):
-        # GH 32553
-
-        markers = Series(
-            ["foo", "bar"],
-            index=IntervalIndex(
-                [
-                    Interval(left, right)
-                    for left, right in zip(
-                        Index([329.973, 345.137], dtype="float64"),
-                        Index([345.137, 360.191], dtype="float64"),
-                    )
-                ]
-            ),
-        )
-        result = str(markers)
-        expected = "(329.973, 345.137]    foo\n(345.137, 360.191]    bar\ndtype: object"
-        assert result == expected
-
-    @pytest.mark.parametrize(
-        "tuples, closed, expected_data",
-        [
-            ([(0, 1), (1, 2), (2, 3)], "left", ["[0, 1)", "[1, 2)", "[2, 3)"]),
-            (
-                [(0.5, 1.0), np.nan, (2.0, 3.0)],
-                "right",
-                ["(0.5, 1.0]", "NaN", "(2.0, 3.0]"],
-            ),
-            (
-                [
-                    (Timestamp("20180101"), Timestamp("20180102")),
-                    np.nan,
-                    ((Timestamp("20180102"), Timestamp("20180103"))),
-                ],
-                "both",
-                ["[2018-01-01, 2018-01-02]", "NaN", "[2018-01-02, 2018-01-03]"],
-            ),
-            (
-                [
-                    (Timedelta("0 days"), Timedelta("1 days")),
-                    (Timedelta("1 days"), Timedelta("2 days")),
-                    np.nan,
-                ],
-                "neither",
-                [
-                    "(0 days 00:00:00, 1 days 00:00:00)",
-                    "(1 days 00:00:00, 2 days 00:00:00)",
-                    "NaN",
-                ],
-            ),
-        ],
-    )
-    def test_to_native_types(self, tuples, closed, expected_data):
-        # GH 28210
-        index = IntervalIndex.from_tuples(tuples, closed=closed)
-        result = index._format_native_types()
-        expected = np.array(expected_data)
-        tm.assert_numpy_array_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/test_aggregation.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/test_aggregation.py
deleted file mode 100644
index 7695c953712ed9925e4e804d0db1e8cf606a97eb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/test_aggregation.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas.core.apply import (
-    _make_unique_kwarg_list,
-    maybe_mangle_lambdas,
-)
-
-
-def test_maybe_mangle_lambdas_passthrough():
-    assert maybe_mangle_lambdas("mean") == "mean"
-    assert maybe_mangle_lambdas(lambda x: x).__name__ == "<lambda>"
-    # don't mangel single lambda.
-    assert maybe_mangle_lambdas([lambda x: x])[0].__name__ == "<lambda>"
-
-
-def test_maybe_mangle_lambdas_listlike():
-    aggfuncs = [lambda x: 1, lambda x: 2]
-    result = maybe_mangle_lambdas(aggfuncs)
-    assert result[0].__name__ == "<lambda_0>"
-    assert result[1].__name__ == "<lambda_1>"
-    assert aggfuncs[0](None) == result[0](None)
-    assert aggfuncs[1](None) == result[1](None)
-
-
-def test_maybe_mangle_lambdas():
-    func = {"A": [lambda x: 0, lambda x: 1]}
-    result = maybe_mangle_lambdas(func)
-    assert result["A"][0].__name__ == "<lambda_0>"
-    assert result["A"][1].__name__ == "<lambda_1>"
-
-
-def test_maybe_mangle_lambdas_args():
-    func = {"A": [lambda x, a, b=1: (0, a, b), lambda x: 1]}
-    result = maybe_mangle_lambdas(func)
-    assert result["A"][0].__name__ == "<lambda_0>"
-    assert result["A"][1].__name__ == "<lambda_1>"
-
-    assert func["A"][0](0, 1) == (0, 1, 1)
-    assert func["A"][0](0, 1, 2) == (0, 1, 2)
-    assert func["A"][0](0, 2, b=3) == (0, 2, 3)
-
-
-def test_maybe_mangle_lambdas_named():
-    func = {"C": np.mean, "D": {"foo": np.mean, "bar": np.mean}}
-    result = maybe_mangle_lambdas(func)
-    assert result == func
-
-
-@pytest.mark.parametrize(
-    "order, expected_reorder",
-    [
-        (
-            [
-                ("height", "<lambda>"),
-                ("height", "max"),
-                ("weight", "max"),
-                ("height", "<lambda>"),
-                ("weight", "<lambda>"),
-            ],
-            [
-                ("height", "<lambda>_0"),
-                ("height", "max"),
-                ("weight", "max"),
-                ("height", "<lambda>_1"),
-                ("weight", "<lambda>"),
-            ],
-        ),
-        (
-            [
-                ("col2", "min"),
-                ("col1", "<lambda>"),
-                ("col1", "<lambda>"),
-                ("col1", "<lambda>"),
-            ],
-            [
-                ("col2", "min"),
-                ("col1", "<lambda>_0"),
-                ("col1", "<lambda>_1"),
-                ("col1", "<lambda>_2"),
-            ],
-        ),
-        (
-            [("col", "<lambda>"), ("col", "<lambda>"), ("col", "<lambda>")],
-            [("col", "<lambda>_0"), ("col", "<lambda>_1"), ("col", "<lambda>_2")],
-        ),
-    ],
-)
-def test_make_unique(order, expected_reorder):
-    # GH 27519, test if make_unique function reorders correctly
-    result = _make_unique_kwarg_list(order)
-
-    assert result == expected_reorder
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/filters/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/filters/__init__.py
deleted file mode 100644
index 8bd53745cdde21d733cb786cc2002d96932289df..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/filters/__init__.py
+++ /dev/null
@@ -1,940 +0,0 @@
-"""
-    pygments.filters
-    ~~~~~~~~~~~~~~~~
-
-    Module containing filter lookup functions and default
-    filters.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.token import String, Comment, Keyword, Name, Error, Whitespace, \
-    string_to_tokentype
-from pygments.filter import Filter
-from pygments.util import get_list_opt, get_int_opt, get_bool_opt, \
-    get_choice_opt, ClassNotFound, OptionError
-from pygments.plugin import find_plugin_filters
-
-
-def find_filter_class(filtername):
-    """Lookup a filter by name. Return None if not found."""
-    if filtername in FILTERS:
-        return FILTERS[filtername]
-    for name, cls in find_plugin_filters():
-        if name == filtername:
-            return cls
-    return None
-
-
-def get_filter_by_name(filtername, **options):
-    """Return an instantiated filter.
-
-    Options are passed to the filter initializer if wanted.
-    Raise a ClassNotFound if not found.
-    """
-    cls = find_filter_class(filtername)
-    if cls:
-        return cls(**options)
-    else:
-        raise ClassNotFound('filter %r not found' % filtername)
-
-
-def get_all_filters():
-    """Return a generator of all filter names."""
-    yield from FILTERS
-    for name, _ in find_plugin_filters():
-        yield name
-
-
-def _replace_special(ttype, value, regex, specialttype,
-                     replacefunc=lambda x: x):
-    last = 0
-    for match in regex.finditer(value):
-        start, end = match.start(), match.end()
-        if start != last:
-            yield ttype, value[last:start]
-        yield specialttype, replacefunc(value[start:end])
-        last = end
-    if last != len(value):
-        yield ttype, value[last:]
-
-
-class CodeTagFilter(Filter):
-    """Highlight special code tags in comments and docstrings.
-
-    Options accepted:
-
-    `codetags` : list of strings
-       A list of strings that are flagged as code tags.  The default is to
-       highlight ``XXX``, ``TODO``, ``FIXME``, ``BUG`` and ``NOTE``.
-
-    .. versionchanged:: 2.13
-       Now recognizes ``FIXME`` by default.
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        tags = get_list_opt(options, 'codetags',
-                            ['XXX', 'TODO', 'FIXME', 'BUG', 'NOTE'])
-        self.tag_re = re.compile(r'\b(%s)\b' % '|'.join([
-            re.escape(tag) for tag in tags if tag
-        ]))
-
-    def filter(self, lexer, stream):
-        regex = self.tag_re
-        for ttype, value in stream:
-            if ttype in String.Doc or \
-               ttype in Comment and \
-               ttype not in Comment.Preproc:
-                yield from _replace_special(ttype, value, regex, Comment.Special)
-            else:
-                yield ttype, value
-
-
-class SymbolFilter(Filter):
-    """Convert mathematical symbols such as \\<longrightarrow> in Isabelle
-    or \\longrightarrow in LaTeX into Unicode characters.
-
-    This is mostly useful for HTML or console output when you want to
-    approximate the source rendering you'd see in an IDE.
-
-    Options accepted:
-
-    `lang` : string
-       The symbol language. Must be one of ``'isabelle'`` or
-       ``'latex'``.  The default is ``'isabelle'``.
-    """
-
-    latex_symbols = {
-        '\\alpha'                : '\U000003b1',
-        '\\beta'                 : '\U000003b2',
-        '\\gamma'                : '\U000003b3',
-        '\\delta'                : '\U000003b4',
-        '\\varepsilon'           : '\U000003b5',
-        '\\zeta'                 : '\U000003b6',
-        '\\eta'                  : '\U000003b7',
-        '\\vartheta'             : '\U000003b8',
-        '\\iota'                 : '\U000003b9',
-        '\\kappa'                : '\U000003ba',
-        '\\lambda'               : '\U000003bb',
-        '\\mu'                   : '\U000003bc',
-        '\\nu'                   : '\U000003bd',
-        '\\xi'                   : '\U000003be',
-        '\\pi'                   : '\U000003c0',
-        '\\varrho'               : '\U000003c1',
-        '\\sigma'                : '\U000003c3',
-        '\\tau'                  : '\U000003c4',
-        '\\upsilon'              : '\U000003c5',
-        '\\varphi'               : '\U000003c6',
-        '\\chi'                  : '\U000003c7',
-        '\\psi'                  : '\U000003c8',
-        '\\omega'                : '\U000003c9',
-        '\\Gamma'                : '\U00000393',
-        '\\Delta'                : '\U00000394',
-        '\\Theta'                : '\U00000398',
-        '\\Lambda'               : '\U0000039b',
-        '\\Xi'                   : '\U0000039e',
-        '\\Pi'                   : '\U000003a0',
-        '\\Sigma'                : '\U000003a3',
-        '\\Upsilon'              : '\U000003a5',
-        '\\Phi'                  : '\U000003a6',
-        '\\Psi'                  : '\U000003a8',
-        '\\Omega'                : '\U000003a9',
-        '\\leftarrow'            : '\U00002190',
-        '\\longleftarrow'        : '\U000027f5',
-        '\\rightarrow'           : '\U00002192',
-        '\\longrightarrow'       : '\U000027f6',
-        '\\Leftarrow'            : '\U000021d0',
-        '\\Longleftarrow'        : '\U000027f8',
-        '\\Rightarrow'           : '\U000021d2',
-        '\\Longrightarrow'       : '\U000027f9',
-        '\\leftrightarrow'       : '\U00002194',
-        '\\longleftrightarrow'   : '\U000027f7',
-        '\\Leftrightarrow'       : '\U000021d4',
-        '\\Longleftrightarrow'   : '\U000027fa',
-        '\\mapsto'               : '\U000021a6',
-        '\\longmapsto'           : '\U000027fc',
-        '\\relbar'               : '\U00002500',
-        '\\Relbar'               : '\U00002550',
-        '\\hookleftarrow'        : '\U000021a9',
-        '\\hookrightarrow'       : '\U000021aa',
-        '\\leftharpoondown'      : '\U000021bd',
-        '\\rightharpoondown'     : '\U000021c1',
-        '\\leftharpoonup'        : '\U000021bc',
-        '\\rightharpoonup'       : '\U000021c0',
-        '\\rightleftharpoons'    : '\U000021cc',
-        '\\leadsto'              : '\U0000219d',
-        '\\downharpoonleft'      : '\U000021c3',
-        '\\downharpoonright'     : '\U000021c2',
-        '\\upharpoonleft'        : '\U000021bf',
-        '\\upharpoonright'       : '\U000021be',
-        '\\restriction'          : '\U000021be',
-        '\\uparrow'              : '\U00002191',
-        '\\Uparrow'              : '\U000021d1',
-        '\\downarrow'            : '\U00002193',
-        '\\Downarrow'            : '\U000021d3',
-        '\\updownarrow'          : '\U00002195',
-        '\\Updownarrow'          : '\U000021d5',
-        '\\langle'               : '\U000027e8',
-        '\\rangle'               : '\U000027e9',
-        '\\lceil'                : '\U00002308',
-        '\\rceil'                : '\U00002309',
-        '\\lfloor'               : '\U0000230a',
-        '\\rfloor'               : '\U0000230b',
-        '\\flqq'                 : '\U000000ab',
-        '\\frqq'                 : '\U000000bb',
-        '\\bot'                  : '\U000022a5',
-        '\\top'                  : '\U000022a4',
-        '\\wedge'                : '\U00002227',
-        '\\bigwedge'             : '\U000022c0',
-        '\\vee'                  : '\U00002228',
-        '\\bigvee'               : '\U000022c1',
-        '\\forall'               : '\U00002200',
-        '\\exists'               : '\U00002203',
-        '\\nexists'              : '\U00002204',
-        '\\neg'                  : '\U000000ac',
-        '\\Box'                  : '\U000025a1',
-        '\\Diamond'              : '\U000025c7',
-        '\\vdash'                : '\U000022a2',
-        '\\models'               : '\U000022a8',
-        '\\dashv'                : '\U000022a3',
-        '\\surd'                 : '\U0000221a',
-        '\\le'                   : '\U00002264',
-        '\\ge'                   : '\U00002265',
-        '\\ll'                   : '\U0000226a',
-        '\\gg'                   : '\U0000226b',
-        '\\lesssim'              : '\U00002272',
-        '\\gtrsim'               : '\U00002273',
-        '\\lessapprox'           : '\U00002a85',
-        '\\gtrapprox'            : '\U00002a86',
-        '\\in'                   : '\U00002208',
-        '\\notin'                : '\U00002209',
-        '\\subset'               : '\U00002282',
-        '\\supset'               : '\U00002283',
-        '\\subseteq'             : '\U00002286',
-        '\\supseteq'             : '\U00002287',
-        '\\sqsubset'             : '\U0000228f',
-        '\\sqsupset'             : '\U00002290',
-        '\\sqsubseteq'           : '\U00002291',
-        '\\sqsupseteq'           : '\U00002292',
-        '\\cap'                  : '\U00002229',
-        '\\bigcap'               : '\U000022c2',
-        '\\cup'                  : '\U0000222a',
-        '\\bigcup'               : '\U000022c3',
-        '\\sqcup'                : '\U00002294',
-        '\\bigsqcup'             : '\U00002a06',
-        '\\sqcap'                : '\U00002293',
-        '\\Bigsqcap'             : '\U00002a05',
-        '\\setminus'             : '\U00002216',
-        '\\propto'               : '\U0000221d',
-        '\\uplus'                : '\U0000228e',
-        '\\bigplus'              : '\U00002a04',
-        '\\sim'                  : '\U0000223c',
-        '\\doteq'                : '\U00002250',
-        '\\simeq'                : '\U00002243',
-        '\\approx'               : '\U00002248',
-        '\\asymp'                : '\U0000224d',
-        '\\cong'                 : '\U00002245',
-        '\\equiv'                : '\U00002261',
-        '\\Join'                 : '\U000022c8',
-        '\\bowtie'               : '\U00002a1d',
-        '\\prec'                 : '\U0000227a',
-        '\\succ'                 : '\U0000227b',
-        '\\preceq'               : '\U0000227c',
-        '\\succeq'               : '\U0000227d',
-        '\\parallel'             : '\U00002225',
-        '\\mid'                  : '\U000000a6',
-        '\\pm'                   : '\U000000b1',
-        '\\mp'                   : '\U00002213',
-        '\\times'                : '\U000000d7',
-        '\\div'                  : '\U000000f7',
-        '\\cdot'                 : '\U000022c5',
-        '\\star'                 : '\U000022c6',
-        '\\circ'                 : '\U00002218',
-        '\\dagger'               : '\U00002020',
-        '\\ddagger'              : '\U00002021',
-        '\\lhd'                  : '\U000022b2',
-        '\\rhd'                  : '\U000022b3',
-        '\\unlhd'                : '\U000022b4',
-        '\\unrhd'                : '\U000022b5',
-        '\\triangleleft'         : '\U000025c3',
-        '\\triangleright'        : '\U000025b9',
-        '\\triangle'             : '\U000025b3',
-        '\\triangleq'            : '\U0000225c',
-        '\\oplus'                : '\U00002295',
-        '\\bigoplus'             : '\U00002a01',
-        '\\otimes'               : '\U00002297',
-        '\\bigotimes'            : '\U00002a02',
-        '\\odot'                 : '\U00002299',
-        '\\bigodot'              : '\U00002a00',
-        '\\ominus'               : '\U00002296',
-        '\\oslash'               : '\U00002298',
-        '\\dots'                 : '\U00002026',
-        '\\cdots'                : '\U000022ef',
-        '\\sum'                  : '\U00002211',
-        '\\prod'                 : '\U0000220f',
-        '\\coprod'               : '\U00002210',
-        '\\infty'                : '\U0000221e',
-        '\\int'                  : '\U0000222b',
-        '\\oint'                 : '\U0000222e',
-        '\\clubsuit'             : '\U00002663',
-        '\\diamondsuit'          : '\U00002662',
-        '\\heartsuit'            : '\U00002661',
-        '\\spadesuit'            : '\U00002660',
-        '\\aleph'                : '\U00002135',
-        '\\emptyset'             : '\U00002205',
-        '\\nabla'                : '\U00002207',
-        '\\partial'              : '\U00002202',
-        '\\flat'                 : '\U0000266d',
-        '\\natural'              : '\U0000266e',
-        '\\sharp'                : '\U0000266f',
-        '\\angle'                : '\U00002220',
-        '\\copyright'            : '\U000000a9',
-        '\\textregistered'       : '\U000000ae',
-        '\\textonequarter'       : '\U000000bc',
-        '\\textonehalf'          : '\U000000bd',
-        '\\textthreequarters'    : '\U000000be',
-        '\\textordfeminine'      : '\U000000aa',
-        '\\textordmasculine'     : '\U000000ba',
-        '\\euro'                 : '\U000020ac',
-        '\\pounds'               : '\U000000a3',
-        '\\yen'                  : '\U000000a5',
-        '\\textcent'             : '\U000000a2',
-        '\\textcurrency'         : '\U000000a4',
-        '\\textdegree'           : '\U000000b0',
-    }
-
-    isabelle_symbols = {
-        '\\<zero>'                 : '\U0001d7ec',
-        '\\<one>'                  : '\U0001d7ed',
-        '\\<two>'                  : '\U0001d7ee',
-        '\\<three>'                : '\U0001d7ef',
-        '\\<four>'                 : '\U0001d7f0',
-        '\\<five>'                 : '\U0001d7f1',
-        '\\<six>'                  : '\U0001d7f2',
-        '\\<seven>'                : '\U0001d7f3',
-        '\\<eight>'                : '\U0001d7f4',
-        '\\<nine>'                 : '\U0001d7f5',
-        '\\<A>'                    : '\U0001d49c',
-        '\\<B>'                    : '\U0000212c',
-        '\\<C>'                    : '\U0001d49e',
-        '\\<D>'                    : '\U0001d49f',
-        '\\<E>'                    : '\U00002130',
-        '\\<F>'                    : '\U00002131',
-        '\\<G>'                    : '\U0001d4a2',
-        '\\<H>'                    : '\U0000210b',
-        '\\<I>'                    : '\U00002110',
-        '\\<J>'                    : '\U0001d4a5',
-        '\\<K>'                    : '\U0001d4a6',
-        '\\<L>'                    : '\U00002112',
-        '\\<M>'                    : '\U00002133',
-        '\\<N>'                    : '\U0001d4a9',
-        '\\<O>'                    : '\U0001d4aa',
-        '\\<P>'                    : '\U0001d4ab',
-        '\\<Q>'                    : '\U0001d4ac',
-        '\\<R>'                    : '\U0000211b',
-        '\\<S>'                    : '\U0001d4ae',
-        '\\<T>'                    : '\U0001d4af',
-        '\\<U>'                    : '\U0001d4b0',
-        '\\<V>'                    : '\U0001d4b1',
-        '\\<W>'                    : '\U0001d4b2',
-        '\\<X>'                    : '\U0001d4b3',
-        '\\<Y>'                    : '\U0001d4b4',
-        '\\<Z>'                    : '\U0001d4b5',
-        '\\<a>'                    : '\U0001d5ba',
-        '\\<b>'                    : '\U0001d5bb',
-        '\\<c>'                    : '\U0001d5bc',
-        '\\<d>'                    : '\U0001d5bd',
-        '\\<e>'                    : '\U0001d5be',
-        '\\<f>'                    : '\U0001d5bf',
-        '\\<g>'                    : '\U0001d5c0',
-        '\\<h>'                    : '\U0001d5c1',
-        '\\<i>'                    : '\U0001d5c2',
-        '\\<j>'                    : '\U0001d5c3',
-        '\\<k>'                    : '\U0001d5c4',
-        '\\<l>'                    : '\U0001d5c5',
-        '\\<m>'                    : '\U0001d5c6',
-        '\\<n>'                    : '\U0001d5c7',
-        '\\<o>'                    : '\U0001d5c8',
-        '\\<p>'                    : '\U0001d5c9',
-        '\\<q>'                    : '\U0001d5ca',
-        '\\<r>'                    : '\U0001d5cb',
-        '\\<s>'                    : '\U0001d5cc',
-        '\\<t>'                    : '\U0001d5cd',
-        '\\<u>'                    : '\U0001d5ce',
-        '\\<v>'                    : '\U0001d5cf',
-        '\\<w>'                    : '\U0001d5d0',
-        '\\<x>'                    : '\U0001d5d1',
-        '\\<y>'                    : '\U0001d5d2',
-        '\\<z>'                    : '\U0001d5d3',
-        '\\<AA>'                   : '\U0001d504',
-        '\\<BB>'                   : '\U0001d505',
-        '\\<CC>'                   : '\U0000212d',
-        '\\<DD>'                   : '\U0001d507',
-        '\\<EE>'                   : '\U0001d508',
-        '\\<FF>'                   : '\U0001d509',
-        '\\<GG>'                   : '\U0001d50a',
-        '\\<HH>'                   : '\U0000210c',
-        '\\<II>'                   : '\U00002111',
-        '\\<JJ>'                   : '\U0001d50d',
-        '\\<KK>'                   : '\U0001d50e',
-        '\\<LL>'                   : '\U0001d50f',
-        '\\<MM>'                   : '\U0001d510',
-        '\\<NN>'                   : '\U0001d511',
-        '\\<OO>'                   : '\U0001d512',
-        '\\<PP>'                   : '\U0001d513',
-        '\\<QQ>'                   : '\U0001d514',
-        '\\<RR>'                   : '\U0000211c',
-        '\\<SS>'                   : '\U0001d516',
-        '\\<TT>'                   : '\U0001d517',
-        '\\<UU>'                   : '\U0001d518',
-        '\\<VV>'                   : '\U0001d519',
-        '\\<WW>'                   : '\U0001d51a',
-        '\\<XX>'                   : '\U0001d51b',
-        '\\<YY>'                   : '\U0001d51c',
-        '\\<ZZ>'                   : '\U00002128',
-        '\\<aa>'                   : '\U0001d51e',
-        '\\<bb>'                   : '\U0001d51f',
-        '\\<cc>'                   : '\U0001d520',
-        '\\<dd>'                   : '\U0001d521',
-        '\\<ee>'                   : '\U0001d522',
-        '\\<ff>'                   : '\U0001d523',
-        '\\<gg>'                   : '\U0001d524',
-        '\\<hh>'                   : '\U0001d525',
-        '\\<ii>'                   : '\U0001d526',
-        '\\<jj>'                   : '\U0001d527',
-        '\\<kk>'                   : '\U0001d528',
-        '\\<ll>'                   : '\U0001d529',
-        '\\<mm>'                   : '\U0001d52a',
-        '\\<nn>'                   : '\U0001d52b',
-        '\\<oo>'                   : '\U0001d52c',
-        '\\<pp>'                   : '\U0001d52d',
-        '\\<qq>'                   : '\U0001d52e',
-        '\\<rr>'                   : '\U0001d52f',
-        '\\<ss>'                   : '\U0001d530',
-        '\\<tt>'                   : '\U0001d531',
-        '\\<uu>'                   : '\U0001d532',
-        '\\<vv>'                   : '\U0001d533',
-        '\\<ww>'                   : '\U0001d534',
-        '\\<xx>'                   : '\U0001d535',
-        '\\<yy>'                   : '\U0001d536',
-        '\\<zz>'                   : '\U0001d537',
-        '\\<alpha>'                : '\U000003b1',
-        '\\<beta>'                 : '\U000003b2',
-        '\\<gamma>'                : '\U000003b3',
-        '\\<delta>'                : '\U000003b4',
-        '\\<epsilon>'              : '\U000003b5',
-        '\\<zeta>'                 : '\U000003b6',
-        '\\<eta>'                  : '\U000003b7',
-        '\\<theta>'                : '\U000003b8',
-        '\\<iota>'                 : '\U000003b9',
-        '\\<kappa>'                : '\U000003ba',
-        '\\<lambda>'               : '\U000003bb',
-        '\\<mu>'                   : '\U000003bc',
-        '\\<nu>'                   : '\U000003bd',
-        '\\<xi>'                   : '\U000003be',
-        '\\<pi>'                   : '\U000003c0',
-        '\\<rho>'                  : '\U000003c1',
-        '\\<sigma>'                : '\U000003c3',
-        '\\<tau>'                  : '\U000003c4',
-        '\\<upsilon>'              : '\U000003c5',
-        '\\<phi>'                  : '\U000003c6',
-        '\\<chi>'                  : '\U000003c7',
-        '\\<psi>'                  : '\U000003c8',
-        '\\<omega>'                : '\U000003c9',
-        '\\<Gamma>'                : '\U00000393',
-        '\\<Delta>'                : '\U00000394',
-        '\\<Theta>'                : '\U00000398',
-        '\\<Lambda>'               : '\U0000039b',
-        '\\<Xi>'                   : '\U0000039e',
-        '\\<Pi>'                   : '\U000003a0',
-        '\\<Sigma>'                : '\U000003a3',
-        '\\<Upsilon>'              : '\U000003a5',
-        '\\<Phi>'                  : '\U000003a6',
-        '\\<Psi>'                  : '\U000003a8',
-        '\\<Omega>'                : '\U000003a9',
-        '\\<bool>'                 : '\U0001d539',
-        '\\<complex>'              : '\U00002102',
-        '\\<nat>'                  : '\U00002115',
-        '\\<rat>'                  : '\U0000211a',
-        '\\<real>'                 : '\U0000211d',
-        '\\<int>'                  : '\U00002124',
-        '\\<leftarrow>'            : '\U00002190',
-        '\\<longleftarrow>'        : '\U000027f5',
-        '\\<rightarrow>'           : '\U00002192',
-        '\\<longrightarrow>'       : '\U000027f6',
-        '\\<Leftarrow>'            : '\U000021d0',
-        '\\<Longleftarrow>'        : '\U000027f8',
-        '\\<Rightarrow>'           : '\U000021d2',
-        '\\<Longrightarrow>'       : '\U000027f9',
-        '\\<leftrightarrow>'       : '\U00002194',
-        '\\<longleftrightarrow>'   : '\U000027f7',
-        '\\<Leftrightarrow>'       : '\U000021d4',
-        '\\<Longleftrightarrow>'   : '\U000027fa',
-        '\\<mapsto>'               : '\U000021a6',
-        '\\<longmapsto>'           : '\U000027fc',
-        '\\<midarrow>'             : '\U00002500',
-        '\\<Midarrow>'             : '\U00002550',
-        '\\<hookleftarrow>'        : '\U000021a9',
-        '\\<hookrightarrow>'       : '\U000021aa',
-        '\\<leftharpoondown>'      : '\U000021bd',
-        '\\<rightharpoondown>'     : '\U000021c1',
-        '\\<leftharpoonup>'        : '\U000021bc',
-        '\\<rightharpoonup>'       : '\U000021c0',
-        '\\<rightleftharpoons>'    : '\U000021cc',
-        '\\<leadsto>'              : '\U0000219d',
-        '\\<downharpoonleft>'      : '\U000021c3',
-        '\\<downharpoonright>'     : '\U000021c2',
-        '\\<upharpoonleft>'        : '\U000021bf',
-        '\\<upharpoonright>'       : '\U000021be',
-        '\\<restriction>'          : '\U000021be',
-        '\\<Colon>'                : '\U00002237',
-        '\\<up>'                   : '\U00002191',
-        '\\<Up>'                   : '\U000021d1',
-        '\\<down>'                 : '\U00002193',
-        '\\<Down>'                 : '\U000021d3',
-        '\\<updown>'               : '\U00002195',
-        '\\<Updown>'               : '\U000021d5',
-        '\\<langle>'               : '\U000027e8',
-        '\\<rangle>'               : '\U000027e9',
-        '\\<lceil>'                : '\U00002308',
-        '\\<rceil>'                : '\U00002309',
-        '\\<lfloor>'               : '\U0000230a',
-        '\\<rfloor>'               : '\U0000230b',
-        '\\<lparr>'                : '\U00002987',
-        '\\<rparr>'                : '\U00002988',
-        '\\<lbrakk>'               : '\U000027e6',
-        '\\<rbrakk>'               : '\U000027e7',
-        '\\<lbrace>'               : '\U00002983',
-        '\\<rbrace>'               : '\U00002984',
-        '\\<guillemotleft>'        : '\U000000ab',
-        '\\<guillemotright>'       : '\U000000bb',
-        '\\<bottom>'               : '\U000022a5',
-        '\\<top>'                  : '\U000022a4',
-        '\\<and>'                  : '\U00002227',
-        '\\<And>'                  : '\U000022c0',
-        '\\<or>'                   : '\U00002228',
-        '\\<Or>'                   : '\U000022c1',
-        '\\<forall>'               : '\U00002200',
-        '\\<exists>'               : '\U00002203',
-        '\\<nexists>'              : '\U00002204',
-        '\\<not>'                  : '\U000000ac',
-        '\\<box>'                  : '\U000025a1',
-        '\\<diamond>'              : '\U000025c7',
-        '\\<turnstile>'            : '\U000022a2',
-        '\\<Turnstile>'            : '\U000022a8',
-        '\\<tturnstile>'           : '\U000022a9',
-        '\\<TTurnstile>'           : '\U000022ab',
-        '\\<stileturn>'            : '\U000022a3',
-        '\\<surd>'                 : '\U0000221a',
-        '\\<le>'                   : '\U00002264',
-        '\\<ge>'                   : '\U00002265',
-        '\\<lless>'                : '\U0000226a',
-        '\\<ggreater>'             : '\U0000226b',
-        '\\<lesssim>'              : '\U00002272',
-        '\\<greatersim>'           : '\U00002273',
-        '\\<lessapprox>'           : '\U00002a85',
-        '\\<greaterapprox>'        : '\U00002a86',
-        '\\<in>'                   : '\U00002208',
-        '\\<notin>'                : '\U00002209',
-        '\\<subset>'               : '\U00002282',
-        '\\<supset>'               : '\U00002283',
-        '\\<subseteq>'             : '\U00002286',
-        '\\<supseteq>'             : '\U00002287',
-        '\\<sqsubset>'             : '\U0000228f',
-        '\\<sqsupset>'             : '\U00002290',
-        '\\<sqsubseteq>'           : '\U00002291',
-        '\\<sqsupseteq>'           : '\U00002292',
-        '\\<inter>'                : '\U00002229',
-        '\\<Inter>'                : '\U000022c2',
-        '\\<union>'                : '\U0000222a',
-        '\\<Union>'                : '\U000022c3',
-        '\\<squnion>'              : '\U00002294',
-        '\\<Squnion>'              : '\U00002a06',
-        '\\<sqinter>'              : '\U00002293',
-        '\\<Sqinter>'              : '\U00002a05',
-        '\\<setminus>'             : '\U00002216',
-        '\\<propto>'               : '\U0000221d',
-        '\\<uplus>'                : '\U0000228e',
-        '\\<Uplus>'                : '\U00002a04',
-        '\\<noteq>'                : '\U00002260',
-        '\\<sim>'                  : '\U0000223c',
-        '\\<doteq>'                : '\U00002250',
-        '\\<simeq>'                : '\U00002243',
-        '\\<approx>'               : '\U00002248',
-        '\\<asymp>'                : '\U0000224d',
-        '\\<cong>'                 : '\U00002245',
-        '\\<smile>'                : '\U00002323',
-        '\\<equiv>'                : '\U00002261',
-        '\\<frown>'                : '\U00002322',
-        '\\<Join>'                 : '\U000022c8',
-        '\\<bowtie>'               : '\U00002a1d',
-        '\\<prec>'                 : '\U0000227a',
-        '\\<succ>'                 : '\U0000227b',
-        '\\<preceq>'               : '\U0000227c',
-        '\\<succeq>'               : '\U0000227d',
-        '\\<parallel>'             : '\U00002225',
-        '\\<bar>'                  : '\U000000a6',
-        '\\<plusminus>'            : '\U000000b1',
-        '\\<minusplus>'            : '\U00002213',
-        '\\<times>'                : '\U000000d7',
-        '\\<div>'                  : '\U000000f7',
-        '\\<cdot>'                 : '\U000022c5',
-        '\\<star>'                 : '\U000022c6',
-        '\\<bullet>'               : '\U00002219',
-        '\\<circ>'                 : '\U00002218',
-        '\\<dagger>'               : '\U00002020',
-        '\\<ddagger>'              : '\U00002021',
-        '\\<lhd>'                  : '\U000022b2',
-        '\\<rhd>'                  : '\U000022b3',
-        '\\<unlhd>'                : '\U000022b4',
-        '\\<unrhd>'                : '\U000022b5',
-        '\\<triangleleft>'         : '\U000025c3',
-        '\\<triangleright>'        : '\U000025b9',
-        '\\<triangle>'             : '\U000025b3',
-        '\\<triangleq>'            : '\U0000225c',
-        '\\<oplus>'                : '\U00002295',
-        '\\<Oplus>'                : '\U00002a01',
-        '\\<otimes>'               : '\U00002297',
-        '\\<Otimes>'               : '\U00002a02',
-        '\\<odot>'                 : '\U00002299',
-        '\\<Odot>'                 : '\U00002a00',
-        '\\<ominus>'               : '\U00002296',
-        '\\<oslash>'               : '\U00002298',
-        '\\<dots>'                 : '\U00002026',
-        '\\<cdots>'                : '\U000022ef',
-        '\\<Sum>'                  : '\U00002211',
-        '\\<Prod>'                 : '\U0000220f',
-        '\\<Coprod>'               : '\U00002210',
-        '\\<infinity>'             : '\U0000221e',
-        '\\<integral>'             : '\U0000222b',
-        '\\<ointegral>'            : '\U0000222e',
-        '\\<clubsuit>'             : '\U00002663',
-        '\\<diamondsuit>'          : '\U00002662',
-        '\\<heartsuit>'            : '\U00002661',
-        '\\<spadesuit>'            : '\U00002660',
-        '\\<aleph>'                : '\U00002135',
-        '\\<emptyset>'             : '\U00002205',
-        '\\<nabla>'                : '\U00002207',
-        '\\<partial>'              : '\U00002202',
-        '\\<flat>'                 : '\U0000266d',
-        '\\<natural>'              : '\U0000266e',
-        '\\<sharp>'                : '\U0000266f',
-        '\\<angle>'                : '\U00002220',
-        '\\<copyright>'            : '\U000000a9',
-        '\\<registered>'           : '\U000000ae',
-        '\\<hyphen>'               : '\U000000ad',
-        '\\<inverse>'              : '\U000000af',
-        '\\<onequarter>'           : '\U000000bc',
-        '\\<onehalf>'              : '\U000000bd',
-        '\\<threequarters>'        : '\U000000be',
-        '\\<ordfeminine>'          : '\U000000aa',
-        '\\<ordmasculine>'         : '\U000000ba',
-        '\\<section>'              : '\U000000a7',
-        '\\<paragraph>'            : '\U000000b6',
-        '\\<exclamdown>'           : '\U000000a1',
-        '\\<questiondown>'         : '\U000000bf',
-        '\\<euro>'                 : '\U000020ac',
-        '\\<pounds>'               : '\U000000a3',
-        '\\<yen>'                  : '\U000000a5',
-        '\\<cent>'                 : '\U000000a2',
-        '\\<currency>'             : '\U000000a4',
-        '\\<degree>'               : '\U000000b0',
-        '\\<amalg>'                : '\U00002a3f',
-        '\\<mho>'                  : '\U00002127',
-        '\\<lozenge>'              : '\U000025ca',
-        '\\<wp>'                   : '\U00002118',
-        '\\<wrong>'                : '\U00002240',
-        '\\<struct>'               : '\U000022c4',
-        '\\<acute>'                : '\U000000b4',
-        '\\<index>'                : '\U00000131',
-        '\\<dieresis>'             : '\U000000a8',
-        '\\<cedilla>'              : '\U000000b8',
-        '\\<hungarumlaut>'         : '\U000002dd',
-        '\\<some>'                 : '\U000003f5',
-        '\\<newline>'              : '\U000023ce',
-        '\\<open>'                 : '\U00002039',
-        '\\<close>'                : '\U0000203a',
-        '\\<here>'                 : '\U00002302',
-        '\\<^sub>'                 : '\U000021e9',
-        '\\<^sup>'                 : '\U000021e7',
-        '\\<^bold>'                : '\U00002759',
-        '\\<^bsub>'                : '\U000021d8',
-        '\\<^esub>'                : '\U000021d9',
-        '\\<^bsup>'                : '\U000021d7',
-        '\\<^esup>'                : '\U000021d6',
-    }
-
-    lang_map = {'isabelle' : isabelle_symbols, 'latex' : latex_symbols}
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        lang = get_choice_opt(options, 'lang',
-                              ['isabelle', 'latex'], 'isabelle')
-        self.symbols = self.lang_map[lang]
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if value in self.symbols:
-                yield ttype, self.symbols[value]
-            else:
-                yield ttype, value
-
-
-class KeywordCaseFilter(Filter):
-    """Convert keywords to lowercase or uppercase or capitalize them, which
-    means first letter uppercase, rest lowercase.
-
-    This can be useful e.g. if you highlight Pascal code and want to adapt the
-    code to your styleguide.
-
-    Options accepted:
-
-    `case` : string
-       The casing to convert keywords to. Must be one of ``'lower'``,
-       ``'upper'`` or ``'capitalize'``.  The default is ``'lower'``.
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        case = get_choice_opt(options, 'case',
-                              ['lower', 'upper', 'capitalize'], 'lower')
-        self.convert = getattr(str, case)
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype in Keyword:
-                yield ttype, self.convert(value)
-            else:
-                yield ttype, value
-
-
-class NameHighlightFilter(Filter):
-    """Highlight a normal Name (and Name.*) token with a different token type.
-
-    Example::
-
-        filter = NameHighlightFilter(
-            names=['foo', 'bar', 'baz'],
-            tokentype=Name.Function,
-        )
-
-    This would highlight the names "foo", "bar" and "baz"
-    as functions. `Name.Function` is the default token type.
-
-    Options accepted:
-
-    `names` : list of strings
-      A list of names that should be given the different token type.
-      There is no default.
-    `tokentype` : TokenType or string
-      A token type or a string containing a token type name that is
-      used for highlighting the strings in `names`.  The default is
-      `Name.Function`.
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.names = set(get_list_opt(options, 'names', []))
-        tokentype = options.get('tokentype')
-        if tokentype:
-            self.tokentype = string_to_tokentype(tokentype)
-        else:
-            self.tokentype = Name.Function
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype in Name and value in self.names:
-                yield self.tokentype, value
-            else:
-                yield ttype, value
-
-
-class ErrorToken(Exception):
-    pass
-
-
-class RaiseOnErrorTokenFilter(Filter):
-    """Raise an exception when the lexer generates an error token.
-
-    Options accepted:
-
-    `excclass` : Exception class
-      The exception class to raise.
-      The default is `pygments.filters.ErrorToken`.
-
-    .. versionadded:: 0.8
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.exception = options.get('excclass', ErrorToken)
-        try:
-            # issubclass() will raise TypeError if first argument is not a class
-            if not issubclass(self.exception, Exception):
-                raise TypeError
-        except TypeError:
-            raise OptionError('excclass option is not an exception class')
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype is Error:
-                raise self.exception(value)
-            yield ttype, value
-
-
-class VisibleWhitespaceFilter(Filter):
-    """Convert tabs, newlines and/or spaces to visible characters.
-
-    Options accepted:
-
-    `spaces` : string or bool
-      If this is a one-character string, spaces will be replaces by this string.
-      If it is another true value, spaces will be replaced by ``·`` (unicode
-      MIDDLE DOT).  If it is a false value, spaces will not be replaced.  The
-      default is ``False``.
-    `tabs` : string or bool
-      The same as for `spaces`, but the default replacement character is ``»``
-      (unicode RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK).  The default value
-      is ``False``.  Note: this will not work if the `tabsize` option for the
-      lexer is nonzero, as tabs will already have been expanded then.
-    `tabsize` : int
-      If tabs are to be replaced by this filter (see the `tabs` option), this
-      is the total number of characters that a tab should be expanded to.
-      The default is ``8``.
-    `newlines` : string or bool
-      The same as for `spaces`, but the default replacement character is ``¶``
-      (unicode PILCROW SIGN).  The default value is ``False``.
-    `wstokentype` : bool
-      If true, give whitespace the special `Whitespace` token type.  This allows
-      styling the visible whitespace differently (e.g. greyed out), but it can
-      disrupt background colors.  The default is ``True``.
-
-    .. versionadded:: 0.8
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        for name, default in [('spaces',   '·'),
-                              ('tabs',     '»'),
-                              ('newlines', '¶')]:
-            opt = options.get(name, False)
-            if isinstance(opt, str) and len(opt) == 1:
-                setattr(self, name, opt)
-            else:
-                setattr(self, name, (opt and default or ''))
-        tabsize = get_int_opt(options, 'tabsize', 8)
-        if self.tabs:
-            self.tabs += ' ' * (tabsize - 1)
-        if self.newlines:
-            self.newlines += '\n'
-        self.wstt = get_bool_opt(options, 'wstokentype', True)
-
-    def filter(self, lexer, stream):
-        if self.wstt:
-            spaces = self.spaces or ' '
-            tabs = self.tabs or '\t'
-            newlines = self.newlines or '\n'
-            regex = re.compile(r'\s')
-
-            def replacefunc(wschar):
-                if wschar == ' ':
-                    return spaces
-                elif wschar == '\t':
-                    return tabs
-                elif wschar == '\n':
-                    return newlines
-                return wschar
-
-            for ttype, value in stream:
-                yield from _replace_special(ttype, value, regex, Whitespace,
-                                            replacefunc)
-        else:
-            spaces, tabs, newlines = self.spaces, self.tabs, self.newlines
-            # simpler processing
-            for ttype, value in stream:
-                if spaces:
-                    value = value.replace(' ', spaces)
-                if tabs:
-                    value = value.replace('\t', tabs)
-                if newlines:
-                    value = value.replace('\n', newlines)
-                yield ttype, value
-
-
-class GobbleFilter(Filter):
-    """Gobbles source code lines (eats initial characters).
-
-    This filter drops the first ``n`` characters off every line of code.  This
-    may be useful when the source code fed to the lexer is indented by a fixed
-    amount of space that isn't desired in the output.
-
-    Options accepted:
-
-    `n` : int
-       The number of characters to gobble.
-
-    .. versionadded:: 1.2
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.n = get_int_opt(options, 'n', 0)
-
-    def gobble(self, value, left):
-        if left < len(value):
-            return value[left:], 0
-        else:
-            return '', left - len(value)
-
-    def filter(self, lexer, stream):
-        n = self.n
-        left = n  # How many characters left to gobble.
-        for ttype, value in stream:
-            # Remove ``left`` tokens from first line, ``n`` from all others.
-            parts = value.split('\n')
-            (parts[0], left) = self.gobble(parts[0], left)
-            for i in range(1, len(parts)):
-                (parts[i], left) = self.gobble(parts[i], n)
-            value = '\n'.join(parts)
-
-            if value != '':
-                yield ttype, value
-
-
-class TokenMergeFilter(Filter):
-    """Merges consecutive tokens with the same token type in the output
-    stream of a lexer.
-
-    .. versionadded:: 1.2
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-
-    def filter(self, lexer, stream):
-        current_type = None
-        current_value = None
-        for ttype, value in stream:
-            if ttype is current_type:
-                current_value += value
-            else:
-                if current_type is not None:
-                    yield current_type, current_value
-                current_type = ttype
-                current_value = value
-        if current_type is not None:
-            yield current_type, current_value
-
-
-FILTERS = {
-    'codetagify':     CodeTagFilter,
-    'keywordcase':    KeywordCaseFilter,
-    'highlight':      NameHighlightFilter,
-    'raiseonerror':   RaiseOnErrorTokenFilter,
-    'whitespace':     VisibleWhitespaceFilter,
-    'gobble':         GobbleFilter,
-    'tokenmerge':     TokenMergeFilter,
-    'symbols':        SymbolFilter,
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/x10.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/x10.py
deleted file mode 100644
index c125b530d1004f86629fc4f17a58b8ba907312b2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/x10.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""
-    pygments.lexers.x10
-    ~~~~~~~~~~~~~~~~~~~
-
-    Lexers for the X10 programming language.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import RegexLexer
-from pygments.token import Text, Comment, Keyword, String
-
-__all__ = ['X10Lexer']
-
-
-class X10Lexer(RegexLexer):
-    """
-    For the X10 language.
-
-    .. versionadded:: 2.2
-    """
-
-    name = 'X10'
-    url = 'http://x10-lang.org/'
-    aliases = ['x10', 'xten']
-    filenames = ['*.x10']
-    mimetypes = ['text/x-x10']
-
-    keywords = (
-        'as', 'assert', 'async', 'at', 'athome', 'ateach', 'atomic',
-        'break', 'case', 'catch', 'class', 'clocked', 'continue',
-        'def', 'default', 'do', 'else', 'final', 'finally', 'finish',
-        'for', 'goto', 'haszero', 'here', 'if', 'import', 'in',
-        'instanceof', 'interface', 'isref', 'new', 'offer',
-        'operator', 'package', 'return', 'struct', 'switch', 'throw',
-        'try', 'type', 'val', 'var', 'when', 'while'
-    )
-
-    types = (
-        'void'
-    )
-
-    values = (
-        'false', 'null', 'self', 'super', 'this', 'true'
-    )
-
-    modifiers = (
-        'abstract', 'extends', 'implements', 'native', 'offers',
-        'private', 'property', 'protected', 'public', 'static',
-        'throws', 'transient'
-    )
-
-    tokens = {
-        'root': [
-            (r'[^\S\n]+', Text),
-            (r'//.*?\n', Comment.Single),
-            (r'/\*(.|\n)*?\*/', Comment.Multiline),
-            (r'\b(%s)\b' % '|'.join(keywords), Keyword),
-            (r'\b(%s)\b' % '|'.join(types), Keyword.Type),
-            (r'\b(%s)\b' % '|'.join(values), Keyword.Constant),
-            (r'\b(%s)\b' % '|'.join(modifiers), Keyword.Declaration),
-            (r'"(\\\\|\\[^\\]|[^"\\])*"', String),
-            (r"'\\.'|'[^\\]'|'\\u[0-9a-fA-F]{4}'", String.Char),
-            (r'.', Text)
-        ],
-    }
diff --git a/spaces/pycoming/bingo/src/lib/hooks/chat-history.ts b/spaces/pycoming/bingo/src/lib/hooks/chat-history.ts
deleted file mode 100644
index c6fbf3fecfa86fe553f56acc8253236b8f22a775..0000000000000000000000000000000000000000
--- a/spaces/pycoming/bingo/src/lib/hooks/chat-history.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { zip } from 'lodash-es'
-import { ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { Storage } from '../storage'
-
-/**
- * conversations:$botId => Conversation[]
- * conversation:$botId:$cid:messages => ChatMessageModel[]
- */
-
-interface Conversation {
-  id: string
-  createdAt: number
-}
-
-type ConversationWithMessages = Conversation & { messages: ChatMessageModel[] }
-
-async function loadHistoryConversations(botId: BotId): Promise<Conversation[]> {
-  const key = `conversations:${botId}`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-async function deleteHistoryConversation(botId: BotId, cid: string) {
-  const conversations = await loadHistoryConversations(botId)
-  const newConversations = conversations.filter((c) => c.id !== cid)
-  await Storage.set({ [`conversations:${botId}`]: newConversations })
-}
-
-async function loadConversationMessages(botId: BotId, cid: string): Promise<ChatMessageModel[]> {
-  const key = `conversation:${botId}:${cid}:messages`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) {
-  const conversations = await loadHistoryConversations(botId)
-  if (!conversations.some((c) => c.id === cid)) {
-    conversations.unshift({ id: cid, createdAt: Date.now() })
-    await Storage.set({ [`conversations:${botId}`]: conversations })
-  }
-  const key = `conversation:${botId}:${cid}:messages`
-  await Storage.set({ [key]: messages })
-}
-
-export async function loadHistoryMessages(botId: BotId): Promise<ConversationWithMessages[]> {
-  const conversations = await loadHistoryConversations(botId)
-  const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id)))
-  return zip(conversations, messagesList).map(([c, messages]) => ({
-    id: c!.id,
-    createdAt: c!.createdAt,
-    messages: messages!,
-  }))
-}
-
-export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) {
-  const messages = await loadConversationMessages(botId, conversationId)
-  const newMessages = messages.filter((m) => m.id !== messageId)
-  await setConversationMessages(botId, conversationId, newMessages)
-  if (!newMessages.length) {
-    await deleteHistoryConversation(botId, conversationId)
-  }
-}
diff --git a/spaces/pycui/RealChar/client/web/src/components/TextView/index.js b/spaces/pycui/RealChar/client/web/src/components/TextView/index.js
deleted file mode 100644
index 719f667984ab5de634c8c1fcda54870c342dae9e..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/client/web/src/components/TextView/index.js
+++ /dev/null
@@ -1,78 +0,0 @@
-/**
- * src/components/TextView/index.jsx
- * show chat log. User can send message and switch to CallView.
- * 
- * created by Lynchee on 7/16/23
- */
-
-import React, { useEffect, useRef } from 'react';
-import './style.css';
-import { TbPower, TbMicrophone } from 'react-icons/tb';
-import IconButton from '../Common/IconButton';
-
-const TextView = ({ send, isPlaying, stopAudioPlayback, textAreaValue, setTextAreaValue, messageInput, setMessageInput, handleDisconnect, setIsCallView }) => {
-    const chatWindowRef = useRef(null);
-    
-    // always show the latest chat log
-    useEffect(() => {
-        if (chatWindowRef.current) {
-            chatWindowRef.current.scrollTop = chatWindowRef.current.scrollHeight;
-        }
-    }, [textAreaValue]);
-
-    // send message to server. stop audio if it's playing to interrupt character.
-    const sendMessage = () => {
-        setTextAreaValue(prevState => prevState + `\nYou> ${messageInput}\n`);
-        send(messageInput);
-        setMessageInput('');
-        if (isPlaying) {
-            stopAudioPlayback();
-        }
-    }
-
-    const handleSendClick = () => {
-        if (messageInput.trim() !== '') {
-            sendMessage();
-        }
-    }
-
-    const handleKeyDown = (event) => {
-        if (event.key === 'Enter') {
-            handleSendClick();
-        }
-    };
-
-    const handleInputChange = (event) => {
-        setMessageInput(event.target.value);
-    };
-
-    return (
-        <>
-            <textarea 
-                className="chat-window" 
-                readOnly 
-                draggable="false"
-                ref={chatWindowRef}
-                value={textAreaValue}
-            ></textarea>
-            <div className="message-input-container">
-                <input
-                    className="message-input" 
-                    type="text" 
-                    placeholder="Type your message"
-                    value={messageInput} 
-                    onChange={handleInputChange}
-                    onKeyDown={handleKeyDown} 
-                />
-                <span className="focus-border"><i></i></span>
-            </div>
-            <button className="send-btn" onClick={handleSendClick}>Send Message</button>
-            <div className="options-container">
-                <IconButton Icon={TbPower} className="icon-red" onClick={handleDisconnect} />
-                <IconButton Icon={TbMicrophone} className="icon-blue" onClick={() => setIsCallView(true)} />
-            </div>
-        </>
-    )
-}
-
-export default TextView
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Dataedo 7.3.0 With Crack (Latest Version) __FULL__.md b/spaces/quidiaMuxgu/Expedit-SAM/Dataedo 7.3.0 With Crack (Latest Version) __FULL__.md
deleted file mode 100644
index 8f33b3e32ae677782fc4c1036e3325d2f9672cfa..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Dataedo 7.3.0 With Crack (Latest Version) __FULL__.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Dataedo 7.3.0 With Crack (Latest Version)</h2><br /><p><b><b>Download</b> &rarr;&rarr;&rarr; <a href="https://geags.com/2uCt0Y">https://geags.com/2uCt0Y</a></b></p><br /><br />
-<br />
-Torrent Igo Primo 2014 Windows Ce 4,9/5 6852 reviews ... Primo Europe v.2.5.5; WIN CE Becker ... Dataedo 7.3.0 With Crack (Latest Version) 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/KMSpicov100102040OfficeandWindows10ActivatorThumperDrar.md b/spaces/quidiaMuxgu/Expedit-SAM/KMSpicov100102040OfficeandWindows10ActivatorThumperDrar.md
deleted file mode 100644
index 6e486c117a01d74f2f4b664b3ec5aa3d8eae773e..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/KMSpicov100102040OfficeandWindows10ActivatorThumperDrar.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>KMSpicov100102040OfficeandWindows10ActivatorThumperDrar</h2><br /><p><b><b>Download File</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://geags.com/2uCsp5">https://geags.com/2uCsp5</a></b></p><br /><br />
-<br />
-KMSpicov100102040OfficeandWindows10ActivatorThumperDrar · anatomi dan fisiologi untuk pemula ethel sloane · java a fondo pablo augusto sznajdleder pdf ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/models/builders.py b/spaces/radames/MusicGen-Continuation/audiocraft/models/builders.py
deleted file mode 100644
index 77ee5f96fea2e3c9e475fe961bc1a5ee473ed8eb..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/models/builders.py
+++ /dev/null
@@ -1,218 +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.
-
-"""
-All the functions to build the relevant models and modules
-from the Hydra config.
-"""
-
-import typing as tp
-import warnings
-
-import audiocraft
-import omegaconf
-import torch
-
-from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel  # noqa
-from .lm import LMModel
-from ..modules.codebooks_patterns import (
-    CodebooksPatternProvider,
-    DelayedPatternProvider,
-    ParallelPatternProvider,
-    UnrolledPatternProvider,
-    VALLEPattern,
-    MusicLMPattern,
-)
-from ..modules.conditioners import (
-    BaseConditioner,
-    ConditioningProvider,
-    LUTConditioner,
-    T5Conditioner,
-    ConditionFuser,
-    ChromaStemConditioner,
-)
-from .. import quantization as qt
-from ..utils.utils import dict_from_config
-
-
-def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
-    klass = {
-        'no_quant': qt.DummyQuantizer,
-        'rvq': qt.ResidualVectorQuantizer
-    }[quantizer]
-    kwargs = dict_from_config(getattr(cfg, quantizer))
-    if quantizer != 'no_quant':
-        kwargs['dimension'] = dimension
-    return klass(**kwargs)
-
-
-def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
-    if encoder_name == 'seanet':
-        kwargs = dict_from_config(getattr(cfg, 'seanet'))
-        encoder_override_kwargs = kwargs.pop('encoder')
-        decoder_override_kwargs = kwargs.pop('decoder')
-        encoder_kwargs = {**kwargs, **encoder_override_kwargs}
-        decoder_kwargs = {**kwargs, **decoder_override_kwargs}
-        encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
-        decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs)
-        return encoder, decoder
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
-    """Instantiate a compression model.
-    """
-    if cfg.compression_model == 'encodec':
-        kwargs = dict_from_config(getattr(cfg, 'encodec'))
-        encoder_name = kwargs.pop('autoencoder')
-        quantizer_name = kwargs.pop('quantizer')
-        encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
-        quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
-        frame_rate = kwargs['sample_rate'] // encoder.hop_length
-        renormalize = kwargs.pop('renormalize', None)
-        renorm = kwargs.pop('renorm')
-        if renormalize is None:
-            renormalize = renorm is not None
-            warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.")
-        return EncodecModel(encoder, decoder, quantizer,
-                            frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
-    """Instantiate a transformer LM.
-    """
-    if cfg.lm_model == 'transformer_lm':
-        kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
-        n_q = kwargs['n_q']
-        q_modeling = kwargs.pop('q_modeling', None)
-        codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
-        attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
-        cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
-        cfg_prob, cfg_coef = cls_free_guidance["training_dropout"], cls_free_guidance["inference_coef"]
-        fuser = get_condition_fuser(cfg)
-        condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
-        if len(fuser.fuse2cond['cross']) > 0:  # enforce cross-att programatically
-            kwargs['cross_attention'] = True
-        if codebooks_pattern_cfg.modeling is None:
-            assert q_modeling is not None, \
-                'LM model should either have a codebook pattern defined or transformer_lm.q_modeling'
-            codebooks_pattern_cfg = omegaconf.OmegaConf.create(
-                {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
-            )
-        pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
-        return LMModel(
-            pattern_provider=pattern_provider,
-            condition_provider=condition_provider,
-            fuser=fuser,
-            cfg_dropout=cfg_prob,
-            cfg_coef=cfg_coef,
-            attribute_dropout=attribute_dropout,
-            dtype=getattr(torch, cfg.dtype),
-            device=cfg.device,
-            **kwargs
-        ).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected LM model {cfg.lm_model}')
-
-
-def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
-    """Instantiate a conditioning model.
-    """
-    device = cfg.device
-    duration = cfg.dataset.segment_duration
-    cfg = getattr(cfg, "conditioners")
-    cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg
-    conditioners: tp.Dict[str, BaseConditioner] = {}
-    with omegaconf.open_dict(cfg):
-        condition_provider_args = cfg.pop('args', {})
-    for cond, cond_cfg in cfg.items():
-        model_type = cond_cfg["model"]
-        model_args = cond_cfg[model_type]
-        if model_type == "t5":
-            conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
-        elif model_type == "lut":
-            conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
-        elif model_type == "chroma_stem":
-            model_args.pop('cache_path', None)
-            conditioners[str(cond)] = ChromaStemConditioner(
-                output_dim=output_dim,
-                duration=duration,
-                device=device,
-                **model_args
-            )
-        else:
-            raise ValueError(f"unrecognized conditioning model: {model_type}")
-    conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
-    return conditioner
-
-
-def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
-    """Instantiate a condition fuser object.
-    """
-    fuser_cfg = getattr(cfg, "fuser")
-    fuser_methods = ["sum", "cross", "prepend", "input_interpolate"]
-    fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
-    kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
-    fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
-    return fuser
-
-
-def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
-    """Instantiate a codebooks pattern provider object.
-    """
-    pattern_providers = {
-        'parallel': ParallelPatternProvider,
-        'delay': DelayedPatternProvider,
-        'unroll': UnrolledPatternProvider,
-        'valle': VALLEPattern,
-        'musiclm': MusicLMPattern,
-    }
-    name = cfg.modeling
-    kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
-    klass = pattern_providers[name]
-    return klass(n_q, **kwargs)
-
-
-def get_debug_compression_model(device='cpu'):
-    """Instantiate a debug compression model to be used for unit tests.
-    """
-    seanet_kwargs = {
-        'n_filters': 4,
-        'n_residual_layers': 1,
-        'dimension': 32,
-        'ratios': [10, 8, 16]  # 25 Hz at 32kHz
-    }
-    encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
-    decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
-    quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
-    init_x = torch.randn(8, 32, 128)
-    quantizer(init_x, 1)  # initialize kmeans etc.
-    compression_model = EncodecModel(
-        encoder, decoder, quantizer,
-        frame_rate=25, sample_rate=32000, channels=1).to(device)
-    return compression_model.eval()
-
-
-def get_debug_lm_model(device='cpu'):
-    """Instantiate a debug LM to be used for unit tests.
-    """
-    pattern = DelayedPatternProvider(n_q=4)
-    dim = 16
-    providers = {
-        'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
-    }
-    condition_provider = ConditioningProvider(providers)
-    fuser = ConditionFuser(
-        {'cross': ['description'], 'prepend': [],
-         'sum': [], 'input_interpolate': []})
-    lm = LMModel(
-        pattern, condition_provider, fuser,
-        n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
-        cross_attention=True, causal=True)
-    return lm.to(device).eval()
diff --git a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/data/readme.md b/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/data/readme.md
deleted file mode 100644
index a30d1e2503c1f8a62d186dd183bdf769724e225d..0000000000000000000000000000000000000000
--- a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/data/readme.md
+++ /dev/null
@@ -1,72 +0,0 @@
-# SPIGA: Dataloaders
-Currently, the repository provides a pytorch based image dataloader implementation for the task of:
-* **Facial Landmarks estimation**.
-* **Headpose estimation**.
-* **Facial landmarks visibilities**.
-
-The dataloader can be used for training or testing the networks and it includes general and 
-specifics data augmentation for each task, as it can be image partial occlusions 
-or headpose generation from facial landmarks. 
-
-In addition, the framework provides a wide benchmark software in order to evaluate the 
-different task along the following databases:
-* **WFLW**.
-* **MERLRAV (AFLW 68)**
-* **COFW68**.
-* **300W Public, Private**.
-<details>
-  <summary> Coming soon... </summary>
-  
-* AFLW, AFLW19, AFLW2000 (test only).
-* Menpo and 3D Menpo.
-* COFW.
-* 300WLP
-* 300W Masked
-
-</details>
-
-***Note:*** All the callable files provide a detailed parser that describes the behaviour of 
-the program and their inputs. Please, check the operational modes by using the extension ```--help```.
-
-## Training/Testing 
-The dataloader structure can be found in ```./data/loaders/aligments.py``` and it can be 
-manually controlled by instantiating the class ```AlignmentsDataset()``` or by using 
-the ```data_config``` structure available in ```./data/loaders/dl_config.py```.
-
-Each image sample will follow the next configuration:
-```
-sample = {'image': Data augmented crop image,
-          'sample_idx': Image ID,
-          'imgpath': Absolute path to raw image,
-          'imgpath_local': Relative path to raw image,
-          'ids_ldm': Landmarks ids,
-          'bbox': Face bbox [x,y,w,h] (ref crop),
-          'bbox_raw': Face bbox [x,y,w,h] (ref image),
-          'landmarks': Augmented landmarks [[x1,y1], [x2,y2], ...] (ref crop)
-          'visible': Visibilities [0,1, ...] (1 == Visible)
-          'mask_ldm': Available landmarks anns [True, False, ...] <- len(ids_ldm)
-          'headpose': Augmented POSIT headpose [yaw, pithc, roll]
-          }
-
-Extra features while debugging:
-sample = { ...
-          'landmarks_ori' = Ground truth landmarks before augmentation (ref image)
-          'visible_ori' = Ground truth visibilities before augmentation
-          'mask_ldm_ori' = Ground truth mask before augmentation
-          'headpose_ori' = Ground truth headpose before augmentation (if available)
-         }
-```
-
-## Visualizers
-The dataloader framework provides complementary visualizers to further understand the databases,
-datasets and their difficulties:
-
-* ```./data/visualize/inspect_dataset.py```   
-Focus on the database annotations and the data augmentations, which allows us to 
-understand the training, validation and test datasets.
-
-* ```./data/visualize/inspect_heatmaps.py```   
-Extended visualizer version focus on understanding the heatmaps and boundaries features used for training.
-
-* ```./data/model3D/visualization.py```   
-Visualize the rigid facial 3D models used by SPIGA to project the initial coordinates of the GAT regressor.
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Cakewalk.Sonitus.FX.Plugin.Suite.VST.DX.v3.3.1.0-DOA Full Version Download and Install Guide.md b/spaces/raedeXanto/academic-chatgpt-beta/Cakewalk.Sonitus.FX.Plugin.Suite.VST.DX.v3.3.1.0-DOA Full Version Download and Install Guide.md
deleted file mode 100644
index de66c5b846a65d64a294c13e7c6bb3d2a6b29d40..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Cakewalk.Sonitus.FX.Plugin.Suite.VST.DX.v3.3.1.0-DOA Full Version Download and Install Guide.md	
+++ /dev/null
@@ -1,144 +0,0 @@
-
-<h1>Cakewalk Sonitus FX Plugin Suite: A Powerful Audio Processing Tool</h1>
- <p>If you are looking for a professional audio processing tool for digital audio editing, sound design, games, multimedia, or music production, you might want to check out Cakewalk Sonitus FX Plugin Suite. This is a collection of 10 high-quality plugins that cover a wide range of audio effects and functions. In this article, we will show you how to download and install Cakewalk Sonitus FX Plugin Suite full version for free, how to use each plugin in detail, and some tips and tricks for using them effectively.</p>
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- <h2>How to Download and Install Cakewalk Sonitus FX Plugin Suite</h2>
- <p>Cakewalk Sonitus FX Plugin Suite is compatible with Windows XP/Vista/7/8/8.1/10 operating systems and supports VST and DX formats. You can use it with any DAW that supports these formats, such as Cakewalk SONAR, Cubase, FL Studio, Reaper, etc. To download and install Cakewalk Sonitus FX Plugin Suite full version for free, follow these steps:</p>
- <ol>
-<li>Click on this link <a href="https://getintopc.com/softwares/audio-processing/cakewalk-sonitus-fx-plugin-suite-vst-free-download/">https://getintopc.com/softwares/audio-processing/cakewalk-sonitus-fx-plugin-suite-vst-free-download/</a> to go to the download page.</li>
-<li>Click on the green Download Now button at the bottom of the page.</li>
-<li>Wait for a few seconds until the download starts automatically.</li>
-<li>Save the file Cakewalk.Sonitus.FX.Plugin.Suite.VST.DX.v3.3.1.0.rar on your computer.</li>
-<li>Extract the file using WinRAR or any other software that can open RAR files.</li>
-<li>Open the extracted folder and run the setup.exe file.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Copy the file sonituss.dll from the folder DOA\sonituss\dx\bin\Release32bit\ into your DAW's plugin folder.</li>
-<li>Copy the file sonituss.vst.dll from the folder DOA\sonituss\vst\bin\Release32bit\ into your DAW's plugin folder.</li>
-<li>Restart your DAW if it was running during the installation.</li>
-<li>Scan your plugin folder in your DAW's preferences or settings.</li>
-<li>You should now see Cakewalk Sonitus FX Plugin Suite in your plugin list.</li>
-</ol>
- <h2>How to Use Cakewalk Sonitus FX Plugin Suite</h2>
- <p>Cakewalk Sonitus FX Plugin Suite consists of 10 plugins that can be used individually or in combination. Each plugin has a clean and intuitive user interface that makes it easy to adjust the parameters and access the presets. The plugins also have similar controls, keyboard shortcuts, and toolbar functions for bypassing, soloing, comparing, saving, loading, copying, pasting, resetting, undoing, redoing, accessing help files, etc. To use Cakewalk Sonitus FX Plugin Suite in your DAW, follow these steps:</p>
- <ol>
-<li>Create a new project or open an existing one in your DAW.</li>
-<li>Add an audio track or select an existing one that you want to process with Cakewalk Sonitus FX Plugin Suite.</li>
-<li>Open your DAW's plugin browser or menu and locate Cakewalk Sonitus FX Plugin Suite under VST or DX category.</li>
-<li>Select one or more plugins that you want to use from Cakewalk Sonitus FX Plugin Suite and drag them onto your audio track's effects rack or insert them as effects.</li>
-<li>Adjust the parameters of each plugin according to your needs and preferences. You can also use the built-in preset manager to browse through various presets for different styles and genres.</li>
-<li>Play back your audio track and listen to how Cakewalk Sonitus FX Plugin Suite affects it. You can also compare between different settings by using the A/B buttons on each plugin's toolbar.</li>
-<li>Save your project when you are satisfied with the results.</li>
-</ol>
- <p>In the following sections, we will explain how each plugin works in detail and give some examples of how to use them effectively.</p>
- <h3>Sonitus FX Compressor: A versatile compressor with a limiter option</h3>
- <p>The compressor is one of the most essential tools in audio production. It can be used to control the dynamics of audio signals by reducing their peaks and increasing their average levels. This can make them sound louder, more consistent, more balanced, more punchy, more smooth, etc. The compressor can also be used as a creative effect by altering the tone, color, character, or groove of audio signals.</p>
- <p>The Sonitus FX Compressor is a versatile compressor that offers various features and options for different applications. It has four modes: Peak/RMS (which uses either peak or RMS detection for compression), Opto (which emulates an optical compressor), Vintage (which emulates a vintage tube compressor), and Limit (which acts as a brickwall limiter). It also has four types of knee curves: Hard (which applies compression abruptly), Soft (which applies compression gradually), Smooth (which applies compression smoothly), and Sharp (which applies compression sharply). It also has four types of release curves: Linear (which releases compression at a constant rate), Logarithmic (which releases compression at a decreasing rate), Adaptive (which adjusts release time according to input level), and Auto (which sets release time automatically based on attack time).</p>
- <p>The Sonitus FX Compressor has six main parameters that control its behavior:</p>
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- <ul>
-<li>Threshold: This sets the level above which compression is applied. Lowering this value increases compression amount.</li>
-<li>Ratio: This sets how much compression is applied once the threshold is exceeded. Higher values result in more compression ratio.</li>
-<li>Attack: This sets how fast compression is applied after crossing the threshold. Lower values result in faster attack times.</li>
-<li>Release: This sets how fast compression is released after falling below the threshold. Lower values result in faster release times.</li>
-<li>Output: This sets the output gain of the compressed signal. Increasing this value can make up for the level reduction caused by compression.</li>
-<li>Knee: This sets the shape of the compression curve around the threshold. Choosing different knee types can affect how smooth or abrupt the compression sounds.</li>
-</ul>
- <p>To use the Sonitus FX Compressor effectively, here are some examples of different settings for different purposes:</p>
- <table>
-<tr>
-<th>Purpose</th>
-<th>Mode</th>
-<th>Threshold</th>
-<th>Ratio</th>
-<th>Attack</th>
-<th>Release</th>
-<th>Output</th>
-<th>Knee</th>
-</tr>
-<tr>
-<td>Mastering</td>
-<td>Peak/RMS</td>
-<td>-10 dB</td>
-<td>2:1</td>
-<td>10 ms</td>
-<td>100 ms</td>
-<td>+5 dB</td>
-<td>Soft</td>
-</tr>
-<tr>
-<td>Vocal smoothing</td>
-<td>Opto</td>
-<td>-20 dB</td>
-<td>4:1</td>
-<td>5 ms</td>
-<td>Auto</td>
-<td>+3 dB</td>
-<td>Smooth</td>
-</tr>
-<tr>
-<td>Bass leveling</td>
-<td>Vintage</td>
-<td>-15 dB</td>
-<td>6:1</td>
-<td>1 ms</td>
-<td>50 ms</td>
-<td>+2 dB</td>
-<td>Sharp</td>
-</tr>
-<tr>
-<td>Limiter/maximizer</td>
-<td>Limit</td>
-<td>-0.1 dB</td ><td>100:1</td ><td>0.1 ms ></td><td>10 ms ></td><td>+10 dB ></td><td>Hard ></td></tr></table>
- <h3>Sonitus FX Delay: A flexible delay with tempo sync and feedback options  </h3>
- <p>The delay is another essential tool in audio production. It can be used to create echo, reverb, chorus, flanger, and other time-based effects that can add depth, width, movement, and interest to audio signals. The delay can also be used as a creative effect by creating rhythmic patterns, pitch shifts, or spatial illusions.</p>
- <p>The Sonitus FX Delay is a flexible delay that offers various features and options for different applications. It has two modes: Tempo Sync (which syncs the delay time to the project tempo and allows setting it in musical subdivisions) and Free (which allows setting the delay time in milliseconds). It also has two types of feedback: Normal (which feeds back the delayed signal into the input) and Cross (which feeds back the delayed signal into the opposite channel). It also has a Ping Pong option that alternates the delay output between left and right channels.</p>
- <p>The Sonitus FX Delay has six main parameters that control its behavior:</p>
- <ul><li>Delay: This sets the delay time of the effect. In Tempo Sync mode, it can be set in musical subdivisions such as 1/4, 1/8, 1/16, etc. In Free mode, it can be set in milliseconds from 0 to 2000.</li><li>Mix: This sets the balance between the dry and wet signals. Lowering this value reduces the amount of delay effect.</li><li>Pan: This sets the stereo position of the wet signal. Moving this value left or right pans the delay effect accordingly.</li><li>Fdbk: This sets the amount of feedback of the delayed signal. Increasing this value creates more repetitions of the delay effect.</li><li>Filt: This sets the cutoff frequency of a low-pass filter applied to the feedback signal. Lowering this value reduces the high frequencies of the delay effect.</li><li>Damp: This sets the amount of damping applied to the feedback signal. Increasing this value reduces the volume of each repetition of the delay effect.</li></ul>
- <p>To use the Sonitus FX Delay effectively, here are some examples of different settings for different purposes:</p>
- <table><tr><th>Purpose  </th><th>Mode  </th><th>Delay  </th><th>Mix  </th><th>Pan  </th><th>Fdbk  </th><th>Filt  </th><th>Damp  </th></tr><tr><td>Echo  </td><td>Tempo Sync  </td><td>1/4  </td><td>30%  </td><td>C  </td><td>40%  </td><td>10 kHz  </td><td>0%  </td></tr><tr><td>Reverb  </td><t</p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Comment profiter de Oziexplorer descargar crack keygen serial gratuitement et sans risque.md b/spaces/raedeXanto/academic-chatgpt-beta/Comment profiter de Oziexplorer descargar crack keygen serial gratuitement et sans risque.md
deleted file mode 100644
index d25bc0c4c379ed8f54f414e4deeb57441d2448ad..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Comment profiter de Oziexplorer descargar crack keygen serial gratuitement et sans risque.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-<br />
-<h1>I Spit on Your Grave: A Controversial Horror Movie Series</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of horror movies, you might have heard of <strong>I Spit on Your Grave</strong>, a series of films that depict brutal rape and revenge scenarios. The movies are known for their graphic violence, explicit nudity, and disturbing themes, which have sparked controversy and debate among critics and audiences alike. Some consider them as feminist statements, while others condemn them as misogynistic exploitation. In this article, we will explore the history, plot, reception, and legacy of this controversial horror movie series. We will also tell you how to watch them in Hindi dubbed, if you are interested.</p>
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- <h3>What is I Spit on Your Grave?</h3>
- <p><strong>I Spit on Your Grave</strong> is a series of American horror movies that focus on female protagonists who are raped and tortured by a group of men, and then seek bloody vengeance on their attackers. The movies are inspired by the 1978 cult film <em>Day of the Woman</em>, which was later re-released as <em>I Spit on Your Grave</em>. The movies are classified as rape and revenge films, a subgenre of horror that depicts sexual assault and its aftermath. The movies are also known for their low-budget production, realistic style, and lack of musical score.</p>
- <h3>Why is it controversial?</h3>
- <p>The <strong>I Spit on Your Grave</strong> movies are controversial because they show graphic scenes of rape, mutilation, castration, and murder, which are often prolonged and uncut. The movies have been accused of glorifying violence against women, promoting rape culture, and exploiting female suffering for entertainment. The movies have also been criticized for their poor acting, writing, and directing, as well as their unrealistic and implausible plots. The movies have been banned, censored, or rated X in many countries, and have received negative reviews from most critics. However, some critics and fans have defended the movies as empowering, cathartic, and artistic expressions of female rage and survival.</p>
- <h3>How many movies are there in the series?</h3>
- <p>There are six movies in the <strong>I Spit on Your Grave</strong> series so far. They are:</p>
- <ul>
-<li><em>I Spit on Your Grave</em> (1978), also known as <em>Day of the Woman</em>, directed by Meir Zarchi.</li>
-<li><em>I Spit on Your Grave</em> (2010), a remake of the 1978 film, directed by Steven R. Monroe.</li>
-<li><em>I Spit on Your Grave 2</em> (2013), a sequel to the 2010 film, directed by Steven R. Monroe.</li>
-<li><em>I Spit on Your Grave III: Vengeance is Mine</em> (2015), a sequel to the 2010 film, directed by R.D. Braunstein.</li>
-<li><em>I Spit on Your Grave: Deja Vu</em> (2019), a direct sequel to the 1978 film, directed by Meir Zarchi.</li>
-<li><em>I Spit on Your Grave: The Complete Collection</em> (2020), a box set that includes all five films plus a documentary called <em>Growing Up with I Spit on Your Grave</em>.</li>
-</ul>
- <h2>I Spit on Your Grave (1978)</h2>
- <h3>Plot summary</h3>
- <p>The first movie in the series follows Jennifer Hills (Camille Keaton), a young writer who rents a secluded cabin in the woods to work on her novel. She attracts the attention of four local men: Johnny (Eron Tabor), the leader; Stanley (Anthony Nichols) and Andy (Gunter Kleemann), his friends; and Matthew (Richard Pace), a mentally challenged grocery boy. The men harass Jennifer verbally and physically, until they decide to gang-rape her in her cabin. They leave her for dead, but she survives and recovers. She then plots her revenge by seducing and killing each one of them in gruesome ways.</p>
- <h3>Reception and criticism</h3>
- <p>The movie was released in 1978 under the title <em>Day of the Woman</em>, but it failed to attract much attention. It was re-released in 1980 as <em>I Spit on Your Grave</em>, with a more provocative poster and tagline: "This woman has just cut, chopped, broken and burned five men beyond recognition... but no jury in America would ever convict her!" The movie became a hit among horror fans, but it also sparked outrage among critics and activists. The movie was banned or heavily censored in many countries, including the UK, Canada, Australia, Germany, Norway, Ireland, Iceland, and New Zealand. It was also rated X in the US by the MPAA. The movie received mostly negative reviews from critics, who called it "sick", "depraved", "vile", "repulsive", "disgusting", "trashy", "pornographic", "misogynistic", "sadistic", "exploitative", "amateurish", "boring", "unrealistic", "immoral", "offensive", "insulting", "dangerous", and "the worst film ever made". Some critics even called for its destruction or boycott. However, some critics praised the movie for its realism, courage, feminism, artistry, message, or entertainment value.</p>
- <h3>Legacy and influence</h3>
- <p>The movie has become a cult classic among horror fans, who appreciate its rawness, intensity, originality, and shock value. The movie has also influenced many other rape and revenge films, such as <em>Last House on the Left</em>, <em>Baise-moi</em>, <em>Irreversible</em>, <em>Hate Crime</em>, <em>The Last House on Dead End Street</em>, <em>Savaged</em>, <em>Martyrs</em>, <em>The Girl with the Dragon Tattoo</em>, <em>Revenge</em>, <em>M.F.A.</em>, <em>Kill Bill</em>, <em>Audition</em>, <em>Hard Candy</em>, <em>The Brave One</em>, <em>Teeth</em>, <em>American Mary</em>, <em>You're Next</em>, <em>The Final Girl(s)</em>, and many others. The movie has also sparked academic debates and studies about its social, cultural, psychological, philosophical, ethical, aesthetic, and political implications. The movie has also inspired a remake, a documentary, a book, a comic book, a musical, and several parodies and references in popular culture.</p>
- <h2>I Spit on Your Grave (2010)</h2>
- <h3>Plot summary</h3>
- <p>The second movie in the series is a remake of the 1978 film, with some changes and updates. The movie follows Jennifer Hills (Sarah Butler), a novelist who rents a cabin in Louisiana to work on her book. She attracts the attention of five local men: Johnny (Jeff Branson), the gas station owner; Stanley (Daniel Franzese) and Andy (Rodney Eastman), his friends; Matthew (Chad Lindberg), a plumber; and Sheriff Storch (Andrew Howard), a corrupt cop. The men break into Jennifer's cabin and gang-rape her in front of a video camera. They leave her for dead, but she survives and recovers. She then plots her revenge by torturing and killing each one of them in elaborate ways.</p>
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- <h3>Reception and criticism</h3>
- <p>The movie was released in 2010 in select theaters and video-on-demand platforms. It received mixed reviews from critics and audiences alike. Some praised movie's performances, cinematography, direction, and suspense, while others criticized the movie's violence, misogyny, sadism, and lack of originality. The movie was also banned or censored in some countries, such as New Zealand, Ireland, Germany, and Malaysia. The movie was rated R in the US by the MPAA. The movie earned $572,809 at the box office and $10.6 million on home media. <h3>Sequels and spin-offs</h3>
- <p>The movie spawned two sequels and a spin-off. The first sequel, <strong>I Spit on Your Grave 2</strong>, was released in 2013 and followed a different female protagonist who is raped and tortured by a group of men in Bulgaria, and then seeks revenge on them. The second sequel, <strong>I Spit on Your Grave III: Vengeance is Mine</strong>, was released in 2015 and followed Jennifer Hills again as she joins a support group for rape survivors and becomes a vigilante who kills rapists. The spin-off, <strong>I Spit on Your Grave: Deja Vu</strong>, was released in 2019 and followed Jennifer Hills and her daughter as they are kidnapped by the relatives of the men she killed in the original 1978 film.</p>
- <h2>How to watch I Spit on Your Grave in Hindi dubbed?</h2>
- <h3>Online streaming platforms</h3>
- <p>If you want to watch <strong>I Spit on Your Grave</strong> movies in Hindi dubbed, you might have some difficulty finding them on popular online streaming platforms like Netflix, Amazon Prime Video, Hulu, or Disney+. These platforms usually do not offer Hindi dubbed versions of these movies due to their controversial nature and low demand. However, you might be able to find some of them on less-known or illegal streaming websites that offer pirated content. For example, you can watch the 2010 remake of <strong>I Spit on Your Grave</strong> in Hindi dubbed on KatmovieHD.la, a website that provides free downloads and streaming of Hollywood movies dubbed in Hindi. However, we do not recommend using these websites as they might expose you to malware, viruses, pop-up ads, legal issues, and ethical dilemmas.</p>
- <h3>Torrent websites</h3>
- <p>Another option to watch <strong>I Spit on Your Grave</strong> movies in Hindi dubbed is to use torrent websites that allow users to share and download files via peer-to-peer networks. These websites usually have a large collection of movies in different languages and formats, including Hindi dubbed versions of <strong>I Spit on Your Grave</strong> movies. For example, you can download the 1978 original film of <strong>I Spit on Your Grave</strong> in Hindi dubbed from MovieHax.me, a website that provides torrent links for Hollywood movies dubbed in Hindi. However, we do not recommend using these websites either as they might also expose you to malware, viruses, pop-up ads, legal issues, and ethical dilemmas.</p>
- <h3>Legal and ethical issues</h3>
- <p>As you can see, watching <strong>I Spit on Your Grave</strong> movies in Hindi dubbed is not easy or safe. You might have to resort to illegal or unethical means to access them online. This might put you at risk of violating intellectual property rights, facing legal consequences, harming your device or data, or supporting piracy and crime. Moreover, you might also have to deal with the moral and psychological implications of watching such violent and disturbing movies that depict rape and revenge in graphic detail. Therefore, we advise you to be careful and responsible when choosing to watch <strong>I Spit on Your Grave</strong> movies in Hindi dubbed.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, we have discussed the following points:</p>
- <ul>
-<li><strong>I Spit on Your Grave</strong> is a series of horror movies that depict rape and revenge scenarios.</li>
-<li>The movies are controversial for their graphic violence, explicit nudity, and disturbing themes.</li>
-<li>The movies are inspired by the 1978 cult film <em>Day of the Woman</em>, which was later re-released as <em>I Spit on Your Grave</em>.</li>
-<li>The series consists of six movies: two original films by Meir Zarchi and three remake films by Steven R. Monroe and R.D. Braunstein.</li>
-<li>The movies have received mixed reviews from critics and audiences alike.</li>
-<li>The movies have influenced many other rape and revenge films.</li>
-<li>The movies are hard to find online in Hindi dubbed due to their controversial nature and low demand.</li>
-<li>The movies might pose legal and ethical issues for viewers who want to watch them online.</li>
-</ul>
- <h3>Personal opinion and recommendation</h3>
- <p>In my personal opinion,<strong>I Spit on Your Grave</strong> movies are not for everyone. They are extremely violent, nasty, and disturbing, and they might trigger or offend some viewers. They are also not very well-made, original, or meaningful, and they might exploit or sensationalize female trauma and suffering. However, I also think that they have some value as horror films that challenge the boundaries of taste, morality, and art. They also raise some interesting questions about gender, violence, justice, and revenge. They also offer some catharsis and empowerment for some viewers who identify with the female protagonists. Therefore, I would recommend these movies only to those who are fans of horror films, who can handle the graphic content, who are curious about the controversy, and who are willing to think critically about the issues they present.</p>
- # FAQs - Q: What is the difference between the original and the remake films? - A: The original films are more realistic, raw, low-budget, and minimalist than the remake films. The remake films are more stylized, polished, high-budget, and elaborate than the original films. - Q: Which movie is the most violent or graphic in the series? - A: It is hard to say which movie is the most violent or graphic in the series as they all have different levels of brutality and gore. However, some viewers might find the 2010 remake of <em>I Spit on Your Grave</em> or its sequel <em>I Spit on Your Grave 2</em> more violent or graphic than the others. - Q: Are there any positive or redeeming aspects of these movies? - A: Some positive or redeeming aspects of these movies are that they show female strength, resilience, courage, intelligence, and agency. They also show male weakness, cowardice, stupidity, and vulnerability. They also expose the horrors of rape culture and patriarchy. - Q: Are these movies based on true stories or events? - A: No, these movies are not based on true stories or events. They are fictional stories that are inspired by other rape and revenge films or stories. - Q: How can I cope with watching these movies if I find them disturbing or upsetting? you can cope with them by doing the following things: - Remind yourself that these movies are fictional and not real. - Avoid watching these movies alone or at night. Watch them with someone you trust or in a safe environment. - Take breaks during or after watching these movies. Pause, breathe, drink water, or do something else to distract yourself. - Talk to someone about how you feel after watching these movies. Share your thoughts, emotions, reactions, or opinions with a friend, family member, therapist, or online support group. - Seek professional help if you experience any signs of trauma, such as nightmares, flashbacks, anxiety, depression, anger, guilt, shame, or fear. </p> 0a6ba089eb<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Free Download Fix Anu Script Manager For Windows Xp.rar.md b/spaces/raedeXanto/academic-chatgpt-beta/Free Download Fix Anu Script Manager For Windows Xp.rar.md
deleted file mode 100644
index 7d6d0f6e0c29bdc7830ebd6adf05928014ade288..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Free Download Fix Anu Script Manager For Windows Xp.rar.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<br />
-<h1>How to Download and Install Anu Script Manager for Windows XP</h1>
-<p>Anu Script Manager is a software that allows you to type in various Indian languages using phonetic keyboard layouts. It supports Telugu, Hindi, Kannada, Tamil, Malayalam, Bengali, Marathi, Gujarati and more. Anu Script Manager is compatible with Windows XP and other older versions of Windows. Here are the steps to download and install Anu Script Manager for Windows XP:</p>
-<ol>
-<li>Go to <a href="https://archive.org/details/anu_script_manager_7.0">this link</a> and click on the RAR download option. This will download a file named anu_script_manager_7.0.rar to your computer.</li>
-<li>Extract the contents of the rar file using a software like WinRAR or 7-Zip. You will get a folder named Anu Script Manager 7.0.</li>
-<li>Open the folder and double-click on the setup.exe file. This will launch the installation wizard.</li>
-<li>Follow the instructions on the screen and choose the languages you want to install. You may need to restart your computer after the installation is complete.</li>
-<li>To use Anu Script Manager, open any application like Microsoft Word or Notepad and select Anu Script Manager from the language bar. You can also use the shortcut keys Ctrl+Shift+L to switch between languages.</li>
-<li>To type in a language, select the corresponding keyboard layout from the drop-down menu. For example, if you want to type in Telugu, select Telugu-Apple from the menu. You can also change the keyboard layout by pressing Ctrl+Shift+K.</li>
-<li>Type using your English keyboard and Anu Script Manager will convert it into the selected language. For example, if you type "namaste", it will appear as "నమస్తే" in Telugu.</li>
-</ol>
-<p>Anu Script Manager is a useful tool for typing in Indian languages on Windows XP. However, it may not work properly with some newer applications or websites. If you want to use a more updated and compatible software, you can try Google Input Tools or Microsoft Indic Language Input Tool.</p>
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-<p>Anu Script Manager has many features and options that can enhance your typing experience. For example, you can customize the font size, color and style of the text. You can also insert special symbols and characters using the symbol palette. You can also use the spell check and grammar check functions to correct your mistakes.</p>
-<p>Anu Script Manager also supports printing and exporting your documents in various formats. You can print your documents directly from the application or save them as PDF, HTML, RTF or TXT files. You can also copy and paste your text to other applications or websites.</p>
-<p>Anu Script Manager is a versatile and powerful software for typing in Indian languages on Windows XP. However, it may not be compatible with some newer versions of Windows or applications. If you face any issues or errors while using Anu Script Manager, you can contact the developer at anugraphics@yahoo.com or visit their website at www.anugraphics.com for more information and support.</p> 81aa517590<br />
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diff --git a/spaces/rahul999r/Rahul_Kannada_TTS/utils/inference/transliterate.py b/spaces/rahul999r/Rahul_Kannada_TTS/utils/inference/transliterate.py
deleted file mode 100644
index 5ffe9f423d95c5476f97285419dab6d409b173ba..0000000000000000000000000000000000000000
--- a/spaces/rahul999r/Rahul_Kannada_TTS/utils/inference/transliterate.py
+++ /dev/null
@@ -1,919 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-import pandas as pd
-import random
-import sys
-import os
-import json
-import enum
-import traceback
-import re
-
-#F_DIR = os.path.dirname(os.path.realpath(__file__))
-F_DIR = '../../checkpoints/'
-
-class XlitError(enum.Enum):
-    lang_err = "Unsupported langauge ID requested ;( Please check available languages."
-    string_err = "String passed is incompatable ;("
-    internal_err = "Internal crash ;("
-    unknown_err = "Unknown Failure"
-    loading_err = "Loading failed ;( Check if metadata/paths are correctly configured."
-
-
-##=================== Network ==================================================
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        embed_dim,
-        hidden_dim,
-        rnn_type="gru",
-        layers=1,
-        bidirectional=False,
-        dropout=0,
-        device="cpu",
-    ):
-        super(Encoder, self).__init__()
-
-        self.input_dim = input_dim  # src_vocab_sz
-        self.enc_embed_dim = embed_dim
-        self.enc_hidden_dim = hidden_dim
-        self.enc_rnn_type = rnn_type
-        self.enc_layers = layers
-        self.enc_directions = 2 if bidirectional else 1
-        self.device = device
-
-        self.embedding = nn.Embedding(self.input_dim, self.enc_embed_dim)
-
-        if self.enc_rnn_type == "gru":
-            self.enc_rnn = nn.GRU(
-                input_size=self.enc_embed_dim,
-                hidden_size=self.enc_hidden_dim,
-                num_layers=self.enc_layers,
-                bidirectional=bidirectional,
-            )
-        elif self.enc_rnn_type == "lstm":
-            self.enc_rnn = nn.LSTM(
-                input_size=self.enc_embed_dim,
-                hidden_size=self.enc_hidden_dim,
-                num_layers=self.enc_layers,
-                bidirectional=bidirectional,
-            )
-        else:
-            raise Exception("XlitError: unknown RNN type mentioned")
-
-    def forward(self, x, x_sz, hidden=None):
-        """
-        x_sz: (batch_size, 1) -  Unpadded sequence lengths used for pack_pad
-        """
-        batch_sz = x.shape[0]
-        # x: batch_size, max_length, enc_embed_dim
-        x = self.embedding(x)
-
-        ## pack the padded data
-        # x: max_length, batch_size, enc_embed_dim -> for pack_pad
-        x = x.permute(1, 0, 2)
-        x = nn.utils.rnn.pack_padded_sequence(x, x_sz, enforce_sorted=False)  # unpad
-
-        # output: packed_size, batch_size, enc_embed_dim
-        # hidden: n_layer**num_directions, batch_size, hidden_dim | if LSTM (h_n, c_n)
-        output, hidden = self.enc_rnn(
-            x
-        )  # gru returns hidden state of all timesteps as well as hidden state at last timestep
-
-        ## pad the sequence to the max length in the batch
-        # output: max_length, batch_size, enc_emb_dim*directions)
-        output, _ = nn.utils.rnn.pad_packed_sequence(output)
-
-        # output: batch_size, max_length, hidden_dim
-        output = output.permute(1, 0, 2)
-
-        return output, hidden
-
-    def get_word_embedding(self, x):
-        """ """
-        x_sz = torch.tensor([len(x)])
-        x_ = torch.tensor(x).unsqueeze(0).to(dtype=torch.long)
-        # x: 1, max_length, enc_embed_dim
-        x = self.embedding(x_)
-
-        ## pack the padded data
-        # x: max_length, 1, enc_embed_dim -> for pack_pad
-        x = x.permute(1, 0, 2)
-        x = nn.utils.rnn.pack_padded_sequence(x, x_sz, enforce_sorted=False)  # unpad
-
-        # output: packed_size, 1, enc_embed_dim
-        # hidden: n_layer**num_directions, 1, hidden_dim | if LSTM (h_n, c_n)
-        output, hidden = self.enc_rnn(
-            x
-        )  # gru returns hidden state of all timesteps as well as hidden state at last timestep
-
-        out_embed = hidden[0].squeeze()
-
-        return out_embed
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        output_dim,
-        embed_dim,
-        hidden_dim,
-        rnn_type="gru",
-        layers=1,
-        use_attention=True,
-        enc_outstate_dim=None,  # enc_directions * enc_hidden_dim
-        dropout=0,
-        device="cpu",
-    ):
-        super(Decoder, self).__init__()
-
-        self.output_dim = output_dim  # tgt_vocab_sz
-        self.dec_hidden_dim = hidden_dim
-        self.dec_embed_dim = embed_dim
-        self.dec_rnn_type = rnn_type
-        self.dec_layers = layers
-        self.use_attention = use_attention
-        self.device = device
-        if self.use_attention:
-            self.enc_outstate_dim = enc_outstate_dim if enc_outstate_dim else hidden_dim
-        else:
-            self.enc_outstate_dim = 0
-
-        self.embedding = nn.Embedding(self.output_dim, self.dec_embed_dim)
-
-        if self.dec_rnn_type == "gru":
-            self.dec_rnn = nn.GRU(
-                input_size=self.dec_embed_dim
-                + self.enc_outstate_dim,  # to concat attention_output
-                hidden_size=self.dec_hidden_dim,  # previous Hidden
-                num_layers=self.dec_layers,
-                batch_first=True,
-            )
-        elif self.dec_rnn_type == "lstm":
-            self.dec_rnn = nn.LSTM(
-                input_size=self.dec_embed_dim
-                + self.enc_outstate_dim,  # to concat attention_output
-                hidden_size=self.dec_hidden_dim,  # previous Hidden
-                num_layers=self.dec_layers,
-                batch_first=True,
-            )
-        else:
-            raise Exception("XlitError: unknown RNN type mentioned")
-
-        self.fc = nn.Sequential(
-            nn.Linear(self.dec_hidden_dim, self.dec_embed_dim),
-            nn.LeakyReLU(),
-            # nn.Linear(self.dec_embed_dim, self.dec_embed_dim), nn.LeakyReLU(), # removing to reduce size
-            nn.Linear(self.dec_embed_dim, self.output_dim),
-        )
-
-        ##----- Attention ----------
-        if self.use_attention:
-            self.W1 = nn.Linear(self.enc_outstate_dim, self.dec_hidden_dim)
-            self.W2 = nn.Linear(self.dec_hidden_dim, self.dec_hidden_dim)
-            self.V = nn.Linear(self.dec_hidden_dim, 1)
-
-    def attention(self, x, hidden, enc_output):
-        """
-        x: (batch_size, 1, dec_embed_dim) -> after Embedding
-        enc_output: batch_size, max_length, enc_hidden_dim *num_directions
-        hidden: n_layers, batch_size, hidden_size | if LSTM (h_n, c_n)
-        """
-
-        ## perform addition to calculate the score
-
-        # hidden_with_time_axis: batch_size, 1, hidden_dim
-        ## hidden_with_time_axis = hidden.permute(1, 0, 2) ## replaced with below 2lines
-        hidden_with_time_axis = (
-            torch.sum(hidden, axis=0)
-            if self.dec_rnn_type != "lstm"
-            else torch.sum(hidden[0], axis=0)
-        )  # h_n
-
-        hidden_with_time_axis = hidden_with_time_axis.unsqueeze(1)
-
-        # score: batch_size, max_length, hidden_dim
-        score = torch.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis))
-
-        # attention_weights: batch_size, max_length, 1
-        # we get 1 at the last axis because we are applying score to self.V
-        attention_weights = torch.softmax(self.V(score), dim=1)
-
-        # context_vector shape after sum == (batch_size, hidden_dim)
-        context_vector = attention_weights * enc_output
-        context_vector = torch.sum(context_vector, dim=1)
-        # context_vector: batch_size, 1, hidden_dim
-        context_vector = context_vector.unsqueeze(1)
-
-        # attend_out (batch_size, 1, dec_embed_dim + hidden_size)
-        attend_out = torch.cat((context_vector, x), -1)
-
-        return attend_out, attention_weights
-
-    def forward(self, x, hidden, enc_output):
-        """
-        x: (batch_size, 1)
-        enc_output: batch_size, max_length, dec_embed_dim
-        hidden: n_layer, batch_size, hidden_size | lstm: (h_n, c_n)
-        """
-        if (hidden is None) and (self.use_attention is False):
-            raise Exception(
-                "XlitError: No use of a decoder with No attention and No Hidden"
-            )
-
-        batch_sz = x.shape[0]
-
-        if hidden is None:
-            # hidden: n_layers, batch_size, hidden_dim
-            hid_for_att = torch.zeros(
-                (self.dec_layers, batch_sz, self.dec_hidden_dim)
-            ).to(self.device)
-        elif self.dec_rnn_type == "lstm":
-            hid_for_att = hidden[1]  # c_n
-
-        # x (batch_size, 1, dec_embed_dim) -> after embedding
-        x = self.embedding(x)
-
-        if self.use_attention:
-            # x (batch_size, 1, dec_embed_dim + hidden_size) -> after attention
-            # aw: (batch_size, max_length, 1)
-            x, aw = self.attention(x, hidden, enc_output)
-        else:
-            x, aw = x, 0
-
-        # passing the concatenated vector to the GRU
-        # output: (batch_size, n_layers, hidden_size)
-        # hidden: n_layers, batch_size, hidden_size | if LSTM (h_n, c_n)
-        output, hidden = (
-            self.dec_rnn(x, hidden) if hidden is not None else self.dec_rnn(x)
-        )
-
-        # output :shp: (batch_size * 1, hidden_size)
-        output = output.view(-1, output.size(2))
-
-        # output :shp: (batch_size * 1, output_dim)
-        output = self.fc(output)
-
-        return output, hidden, aw
-
-
-class Seq2Seq(nn.Module):
-    """
-    Class dependency: Encoder, Decoder
-    """
-
-    def __init__(
-        self, encoder, decoder, pass_enc2dec_hid=False, dropout=0, device="cpu"
-    ):
-        super(Seq2Seq, self).__init__()
-
-        self.encoder = encoder
-        self.decoder = decoder
-        self.device = device
-        self.pass_enc2dec_hid = pass_enc2dec_hid
-        _force_en2dec_hid_conv = False
-
-        if self.pass_enc2dec_hid:
-            assert (
-                decoder.dec_hidden_dim == encoder.enc_hidden_dim
-            ), "Hidden Dimension of encoder and decoder must be same, or unset `pass_enc2dec_hid`"
-        if decoder.use_attention:
-            assert (
-                decoder.enc_outstate_dim
-                == encoder.enc_directions * encoder.enc_hidden_dim
-            ), "Set `enc_out_dim` correctly in decoder"
-        assert (
-            self.pass_enc2dec_hid or decoder.use_attention
-        ), "No use of a decoder with No attention and No Hidden from Encoder"
-
-        self.use_conv_4_enc2dec_hid = False
-        if (
-            self.pass_enc2dec_hid
-            and (encoder.enc_directions * encoder.enc_layers != decoder.dec_layers)
-        ) or _force_en2dec_hid_conv:
-            if encoder.enc_rnn_type == "lstm" or encoder.enc_rnn_type == "lstm":
-                raise Exception(
-                    "XlitError: conv for enc2dec_hid not implemented; Change the layer numbers appropriately"
-                )
-
-            self.use_conv_4_enc2dec_hid = True
-            self.enc_hid_1ax = encoder.enc_directions * encoder.enc_layers
-            self.dec_hid_1ax = decoder.dec_layers
-            self.e2d_hidden_conv = nn.Conv1d(self.enc_hid_1ax, self.dec_hid_1ax, 1)
-
-    def enc2dec_hidden(self, enc_hidden):
-        """
-        enc_hidden: n_layer, batch_size, hidden_dim*num_directions
-        TODO: Implement the logic for LSTm bsed model
-        """
-        # hidden: batch_size, enc_layer*num_directions, enc_hidden_dim
-        hidden = enc_hidden.permute(1, 0, 2).contiguous()
-        # hidden: batch_size, dec_layers, dec_hidden_dim -> [N,C,Tstep]
-        hidden = self.e2d_hidden_conv(hidden)
-
-        # hidden: dec_layers, batch_size , dec_hidden_dim
-        hidden_for_dec = hidden.permute(1, 0, 2).contiguous()
-
-        return hidden_for_dec
-
-    def active_beam_inference(self, src, beam_width=3, max_tgt_sz=50):
-        """Search based decoding
-        src: (sequence_len)
-        """
-
-        def _avg_score(p_tup):
-            """Used for Sorting
-            TODO: Dividing by length of sequence power alpha as hyperparam
-            """
-            return p_tup[0]
-
-        import sys
-
-        batch_size = 1
-        start_tok = src[0]
-        end_tok = src[-1]
-        src_sz = torch.tensor([len(src)])
-        src_ = src.unsqueeze(0)
-
-        # enc_output: (batch_size, padded_seq_length, enc_hidden_dim*num_direction)
-        # enc_hidden: (enc_layers*num_direction, batch_size, hidden_dim)
-        enc_output, enc_hidden = self.encoder(src_, src_sz)
-
-        if self.pass_enc2dec_hid:
-            # dec_hidden: dec_layers, batch_size , dec_hidden_dim
-            if self.use_conv_4_enc2dec_hid:
-                init_dec_hidden = self.enc2dec_hidden(enc_hidden)
-            else:
-                init_dec_hidden = enc_hidden
-        else:
-            # dec_hidden -> Will be initialized to zeros internally
-            init_dec_hidden = None
-
-        # top_pred[][0] = Σ-log_softmax
-        # top_pred[][1] = sequence torch.tensor shape: (1)
-        # top_pred[][2] = dec_hidden
-        top_pred_list = [(0, start_tok.unsqueeze(0), init_dec_hidden)]
-
-        for t in range(max_tgt_sz):
-            cur_pred_list = []
-
-            for p_tup in top_pred_list:
-                if p_tup[1][-1] == end_tok:
-                    cur_pred_list.append(p_tup)
-                    continue
-
-                # dec_hidden: dec_layers, 1, hidden_dim
-                # dec_output: 1, output_dim
-                dec_output, dec_hidden, _ = self.decoder(
-                    x=p_tup[1][-1].view(1, 1),  # dec_input: (1,1)
-                    hidden=p_tup[2],
-                    enc_output=enc_output,
-                )
-
-                ## π{prob} = Σ{log(prob)} -> to prevent diminishing
-                # dec_output: (1, output_dim)
-                dec_output = nn.functional.log_softmax(dec_output, dim=1)
-                # pred_topk.values & pred_topk.indices: (1, beam_width)
-                pred_topk = torch.topk(dec_output, k=beam_width, dim=1)
-
-                for i in range(beam_width):
-                    sig_logsmx_ = p_tup[0] + pred_topk.values[0][i]
-                    # seq_tensor_ : (seq_len)
-                    seq_tensor_ = torch.cat((p_tup[1], pred_topk.indices[0][i].view(1)))
-
-                    cur_pred_list.append((sig_logsmx_, seq_tensor_, dec_hidden))
-
-            cur_pred_list.sort(key=_avg_score, reverse=True)  # Maximized order
-            top_pred_list = cur_pred_list[:beam_width]
-
-            # check if end_tok of all topk
-            end_flags_ = [1 if t[1][-1] == end_tok else 0 for t in top_pred_list]
-            if beam_width == sum(end_flags_):
-                break
-
-        pred_tnsr_list = [t[1] for t in top_pred_list]
-
-        return pred_tnsr_list
-
-
-##===================== Glyph handlers =======================================
-
-
-class GlyphStrawboss:
-    def __init__(self, glyphs="en"):
-        """list of letters in a language in unicode
-        lang: ISO Language code
-        glyphs: json file with script information
-        """
-        if glyphs == "en":
-            # Smallcase alone
-            self.glyphs = [chr(alpha) for alpha in range(97, 122 + 1)]
-        else:
-            self.dossier = json.load(open(glyphs, encoding="utf-8"))
-            self.glyphs = self.dossier["glyphs"]
-            self.numsym_map = self.dossier["numsym_map"]
-
-        self.char2idx = {}
-        self.idx2char = {}
-        self._create_index()
-
-    def _create_index(self):
-
-        self.char2idx["_"] = 0  # pad
-        self.char2idx["$"] = 1  # start
-        self.char2idx["#"] = 2  # end
-        self.char2idx["*"] = 3  # Mask
-        self.char2idx["'"] = 4  # apostrophe U+0027
-        self.char2idx["%"] = 5  # unused
-        self.char2idx["!"] = 6  # unused
-
-        # letter to index mapping
-        for idx, char in enumerate(self.glyphs):
-            self.char2idx[char] = idx + 7  # +7 token initially
-
-        # index to letter mapping
-        for char, idx in self.char2idx.items():
-            self.idx2char[idx] = char
-
-    def size(self):
-        return len(self.char2idx)
-
-    def word2xlitvec(self, word):
-        """Converts given string of gyphs(word) to vector(numpy)
-        Also adds tokens for start and end
-        """
-        try:
-            vec = [self.char2idx["$"]]  # start token
-            for i in list(word):
-                vec.append(self.char2idx[i])
-            vec.append(self.char2idx["#"])  # end token
-
-            vec = np.asarray(vec, dtype=np.int64)
-            return vec
-
-        except Exception as error:
-            print("XlitError: In word:", word, "Error Char not in Token:", error)
-            sys.exit()
-
-    def xlitvec2word(self, vector):
-        """Converts vector(numpy) to string of glyphs(word)"""
-        char_list = []
-        for i in vector:
-            char_list.append(self.idx2char[i])
-
-        word = "".join(char_list).replace("$", "").replace("#", "")  # remove tokens
-        word = word.replace("_", "").replace("*", "")  # remove tokens
-        return word
-
-
-class VocabSanitizer:
-    def __init__(self, data_file):
-        """
-        data_file: path to file conatining vocabulary list
-        """
-        extension = os.path.splitext(data_file)[-1]
-        if extension == ".json":
-            self.vocab_set = set(json.load(open(data_file, encoding="utf-8")))
-        elif extension == ".csv":
-            self.vocab_df = pd.read_csv(data_file).set_index("WORD")
-            self.vocab_set = set(self.vocab_df.index)
-        else:
-            print("XlitError: Only Json/CSV file extension supported")
-
-    def reposition(self, word_list):
-        """Reorder Words in list"""
-        new_list = []
-        temp_ = word_list.copy()
-        for v in word_list:
-            if v in self.vocab_set:
-                new_list.append(v)
-                temp_.remove(v)
-        new_list.extend(temp_)
-
-        return new_list
-
-
-##=============== INSTANTIATION ================================================
-
-
-class XlitPiston:
-    """
-    For handling prediction & post-processing of transliteration for a single language
-    Class dependency: Seq2Seq, GlyphStrawboss, VocabSanitizer
-    Global Variables: F_DIR
-    """
-
-    def __init__(
-        self,
-        weight_path,
-        vocab_file,
-        tglyph_cfg_file,
-        iglyph_cfg_file="en",
-        device="cpu",
-    ):
-
-        self.device = device
-        self.in_glyph_obj = GlyphStrawboss(iglyph_cfg_file)
-        self.tgt_glyph_obj = GlyphStrawboss(glyphs=tglyph_cfg_file)
-        self.voc_sanity = VocabSanitizer(vocab_file)
-
-        self._numsym_set = set(
-            json.load(open(tglyph_cfg_file, encoding="utf-8"))["numsym_map"].keys()
-        )
-        self._inchar_set = set("abcdefghijklmnopqrstuvwxyz")
-        self._natscr_set = set().union(
-            self.tgt_glyph_obj.glyphs, sum(self.tgt_glyph_obj.numsym_map.values(), [])
-        )
-
-        ## Model Config Static                TODO: add defining in json support
-        input_dim = self.in_glyph_obj.size()
-        output_dim = self.tgt_glyph_obj.size()
-        enc_emb_dim = 300
-        dec_emb_dim = 300
-        enc_hidden_dim = 512
-        dec_hidden_dim = 512
-        rnn_type = "lstm"
-        enc2dec_hid = True
-        attention = True
-        enc_layers = 1
-        dec_layers = 2
-        m_dropout = 0
-        enc_bidirect = True
-        enc_outstate_dim = enc_hidden_dim * (2 if enc_bidirect else 1)
-
-        enc = Encoder(
-            input_dim=input_dim,
-            embed_dim=enc_emb_dim,
-            hidden_dim=enc_hidden_dim,
-            rnn_type=rnn_type,
-            layers=enc_layers,
-            dropout=m_dropout,
-            device=self.device,
-            bidirectional=enc_bidirect,
-        )
-        dec = Decoder(
-            output_dim=output_dim,
-            embed_dim=dec_emb_dim,
-            hidden_dim=dec_hidden_dim,
-            rnn_type=rnn_type,
-            layers=dec_layers,
-            dropout=m_dropout,
-            use_attention=attention,
-            enc_outstate_dim=enc_outstate_dim,
-            device=self.device,
-        )
-        self.model = Seq2Seq(enc, dec, pass_enc2dec_hid=enc2dec_hid, device=self.device)
-        self.model = self.model.to(self.device)
-        weights = torch.load(weight_path, map_location=torch.device(self.device))
-
-        self.model.load_state_dict(weights)
-        self.model.eval()
-
-    def character_model(self, word, beam_width=1):
-        in_vec = torch.from_numpy(self.in_glyph_obj.word2xlitvec(word)).to(self.device)
-        ## change to active or passive beam
-        p_out_list = self.model.active_beam_inference(in_vec, beam_width=beam_width)
-        p_result = [
-            self.tgt_glyph_obj.xlitvec2word(out.cpu().numpy()) for out in p_out_list
-        ]
-
-        result = self.voc_sanity.reposition(p_result)
-
-        # List type
-        return result
-
-    def numsym_model(self, seg):
-        """tgt_glyph_obj.numsym_map[x] returns a list object"""
-        if len(seg) == 1:
-            return [seg] + self.tgt_glyph_obj.numsym_map[seg]
-
-        a = [self.tgt_glyph_obj.numsym_map[n][0] for n in seg]
-        return [seg] + ["".join(a)]
-
-    def _word_segementer(self, sequence):
-
-        sequence = sequence.lower()
-        accepted = set().union(self._numsym_set, self._inchar_set, self._natscr_set)
-        # sequence = ''.join([i for i in sequence if i in accepted])
-
-        segment = []
-        idx = 0
-        seq_ = list(sequence)
-        while len(seq_):
-            # for Number-Symbol
-            temp = ""
-            while len(seq_) and seq_[0] in self._numsym_set:
-                temp += seq_[0]
-                seq_.pop(0)
-            if temp != "":
-                segment.append(temp)
-
-            # for Target Chars
-            temp = ""
-            while len(seq_) and seq_[0] in self._natscr_set:
-                temp += seq_[0]
-                seq_.pop(0)
-            if temp != "":
-                segment.append(temp)
-
-            # for Input-Roman Chars
-            temp = ""
-            while len(seq_) and seq_[0] in self._inchar_set:
-                temp += seq_[0]
-                seq_.pop(0)
-            if temp != "":
-                segment.append(temp)
-
-            temp = ""
-            while len(seq_) and seq_[0] not in accepted:
-                temp += seq_[0]
-                seq_.pop(0)
-            if temp != "":
-                segment.append(temp)
-
-        return segment
-
-    def inferencer(self, sequence, beam_width=10):
-
-        seg = self._word_segementer(sequence[:120])
-        lit_seg = []
-
-        p = 0
-        while p < len(seg):
-            if seg[p][0] in self._natscr_set:
-                lit_seg.append([seg[p]])
-                p += 1
-
-            elif seg[p][0] in self._inchar_set:
-                lit_seg.append(self.character_model(seg[p], beam_width=beam_width))
-                p += 1
-
-            elif seg[p][0] in self._numsym_set:  # num & punc
-                lit_seg.append(self.numsym_model(seg[p]))
-                p += 1
-            else:
-                lit_seg.append([seg[p]])
-                p += 1
-
-        ## IF segment less/equal to 2 then return combinotorial,
-        ## ELSE only return top1 of each result concatenated
-        if len(lit_seg) == 1:
-            final_result = lit_seg[0]
-
-        elif len(lit_seg) == 2:
-            final_result = [""]
-            for seg in lit_seg:
-                new_result = []
-                for s in seg:
-                    for f in final_result:
-                        new_result.append(f + s)
-                final_result = new_result
-
-        else:
-            new_result = []
-            for seg in lit_seg:
-                new_result.append(seg[0])
-            final_result = ["".join(new_result)]
-
-        return final_result
-
-
-from collections.abc import Iterable
-from pydload import dload
-import zipfile
-
-MODEL_DOWNLOAD_URL_PREFIX = "https://github.com/AI4Bharat/IndianNLP-Transliteration/releases/download/xlit_v0.5.0/"
-
-
-def is_folder_writable(folder):
-    try:
-        os.makedirs(folder, exist_ok=True)
-        tmp_file = os.path.join(folder, ".write_test")
-        with open(tmp_file, "w") as f:
-            f.write("Permission Check")
-        os.remove(tmp_file)
-        return True
-    except:
-        return False
-
-
-def is_directory_writable(path):
-    if os.name == "nt":
-        return is_folder_writable(path)
-    return os.access(path, os.W_OK | os.X_OK)
-
-
-class XlitEngine:
-    """
-    For Managing the top level tasks and applications of transliteration
-    Global Variables: F_DIR
-    """
-
-    def __init__(
-        self, lang2use="all", config_path="translit_models/default_lineup.json"
-    ):
-
-        lineup = json.load(open(os.path.join(F_DIR, config_path), encoding="utf-8"))
-        self.lang_config = {}
-        if isinstance(lang2use, str):
-            if lang2use == "all":
-                self.lang_config = lineup
-            elif lang2use in lineup:
-                self.lang_config[lang2use] = lineup[lang2use]
-            else:
-                raise Exception(
-                    "XlitError: The entered Langauge code not found. Available are {}".format(
-                        lineup.keys()
-                    )
-                )
-
-        elif isinstance(lang2use, Iterable):
-            for l in lang2use:
-                try:
-                    self.lang_config[l] = lineup[l]
-                except:
-                    print(
-                        "XlitError: Language code {} not found, Skipping...".format(l)
-                    )
-        else:
-            raise Exception(
-                "XlitError: lang2use must be a list of language codes (or) string of single language code"
-            )
-
-        if is_directory_writable(F_DIR):
-            models_path = os.path.join(F_DIR, "translit_models")
-        else:
-            user_home = os.path.expanduser("~")
-            models_path = os.path.join(user_home, ".AI4Bharat_Xlit_Models")
-        os.makedirs(models_path, exist_ok=True)
-        self.download_models(models_path)
-
-        self.langs = {}
-        self.lang_model = {}
-        for la in self.lang_config:
-            try:
-                print("Loading {}...".format(la))
-                self.lang_model[la] = XlitPiston(
-                    weight_path=os.path.join(
-                        models_path, self.lang_config[la]["weight"]
-                    ),
-                    vocab_file=os.path.join(models_path, self.lang_config[la]["vocab"]),
-                    tglyph_cfg_file=os.path.join(
-                        models_path, self.lang_config[la]["script"]
-                    ),
-                    iglyph_cfg_file="en",
-                )
-                self.langs[la] = self.lang_config[la]["name"]
-            except Exception as error:
-                print("XlitError: Failure in loading {} \n".format(la), error)
-                print(XlitError.loading_err.value)
-
-    def download_models(self, models_path):
-        """
-        Download models from GitHub Releases if not exists
-        """
-        for l in self.lang_config:
-            lang_name = self.lang_config[l]["eng_name"]
-            lang_model_path = os.path.join(models_path, lang_name)
-            if not os.path.isdir(lang_model_path):
-                print("Downloading model for language: %s" % lang_name)
-                remote_url = MODEL_DOWNLOAD_URL_PREFIX + lang_name + ".zip"
-                downloaded_zip_path = os.path.join(models_path, lang_name + ".zip")
-                dload(url=remote_url, save_to_path=downloaded_zip_path, max_time=None)
-
-                if not os.path.isfile(downloaded_zip_path):
-                    exit(
-                        f"ERROR: Unable to download model from {remote_url} into {models_path}"
-                    )
-
-                with zipfile.ZipFile(downloaded_zip_path, "r") as zip_ref:
-                    zip_ref.extractall(models_path)
-
-                if os.path.isdir(lang_model_path):
-                    os.remove(downloaded_zip_path)
-                else:
-                    exit(
-                        f"ERROR: Unable to find models in {lang_model_path} after download"
-                    )
-        return
-
-    def translit_word(self, eng_word, lang_code="default", topk=7, beam_width=10):
-        if eng_word == "":
-            return []
-
-        if lang_code in self.langs:
-            try:
-                res_list = self.lang_model[lang_code].inferencer(
-                    eng_word, beam_width=beam_width
-                )
-                return res_list[:topk]
-
-            except Exception as error:
-                print("XlitError:", traceback.format_exc())
-                print(XlitError.internal_err.value)
-                return XlitError.internal_err
-
-        elif lang_code == "default":
-            try:
-                res_dict = {}
-                for la in self.lang_model:
-                    res = self.lang_model[la].inferencer(
-                        eng_word, beam_width=beam_width
-                    )
-                    res_dict[la] = res[:topk]
-                return res_dict
-
-            except Exception as error:
-                print("XlitError:", traceback.format_exc())
-                print(XlitError.internal_err.value)
-                return XlitError.internal_err
-
-        else:
-            print("XlitError: Unknown Langauge requested", lang_code)
-            print(XlitError.lang_err.value)
-            return XlitError.lang_err
-
-    def translit_sentence(self, eng_sentence, lang_code="default", beam_width=10):
-        if eng_sentence == "":
-            return []
-
-        if lang_code in self.langs:
-            try:
-                out_str = ""
-                for word in eng_sentence.split():
-                    res_ = self.lang_model[lang_code].inferencer(
-                        word, beam_width=beam_width
-                    )
-                    out_str = out_str + res_[0] + " "
-                return out_str[:-1]
-
-            except Exception as error:
-                print("XlitError:", traceback.format_exc())
-                print(XlitError.internal_err.value)
-                return XlitError.internal_err
-
-        elif lang_code == "default":
-            try:
-                res_dict = {}
-                for la in self.lang_model:
-                    out_str = ""
-                    for word in eng_sentence.split():
-                        res_ = self.lang_model[la].inferencer(
-                            word, beam_width=beam_width
-                        )
-                        out_str = out_str + res_[0] + " "
-                    res_dict[la] = out_str[:-1]
-                return res_dict
-
-            except Exception as error:
-                print("XlitError:", traceback.format_exc())
-                print(XlitError.internal_err.value)
-                return XlitError.internal_err
-
-        else:
-            print("XlitError: Unknown Langauge requested", lang_code)
-            print(XlitError.lang_err.value)
-            return XlitError.lang_err
-
-
-if __name__ == "__main__":
-
-    available_lang = [
-        "bn",
-        "gu",
-        "hi",
-        "kn",
-        "gom",
-        "mai",
-        "ml",
-        "mr",
-        "pa",
-        "sd",
-        "si",
-        "ta",
-        "te",
-        "ur",
-    ]
-
-    reg = re.compile(r"[a-zA-Z]")
-    lang = "hi"
-    engine = XlitEngine(
-        lang
-    )  # if you don't specify lang code here, this will give results in all langs available
-    sent = "Hello World! ABCD क्या हाल है आपका?"
-    words = [
-        engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word
-        for word in sent.split()
-    ]  # only transliterated en words, leaves rest as it is
-    updated_sent = " ".join(words)
-
-    print(updated_sent)
-
-    # output : हेलो वर्ल्ड! क्या हाल है आपका?
-
-    # y = engine.translit_sentence("Hello World !")['hi']
-    # print(y)
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fiat Multi Ecu Scan Crack.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fiat Multi Ecu Scan Crack.md
deleted file mode 100644
index 17eb86fe1d48f9e0f9f8a5e00094251001f673a3..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fiat Multi Ecu Scan Crack.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Focus1080pmoviedownload.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Focus1080pmoviedownload.md
deleted file mode 100644
index 29d41d2c8046bd672d7aa66d80e5f1767f6cb5d4..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Focus1080pmoviedownload.md
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diff --git a/spaces/rendchevi/nix-tts/README.md b/spaces/rendchevi/nix-tts/README.md
deleted file mode 100644
index f2b9f36feaab1f31665a50f2cec144a53d84ea6d..0000000000000000000000000000000000000000
--- a/spaces/rendchevi/nix-tts/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Nix-TTS Interactive Demo
-emoji: 🐤
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.2.0
-python_version: 3.8
-app_file: app.py
-pinned: true
-license: mit
----
diff --git a/spaces/rifkat/UzGPT-uz/krill_to_latin.py b/spaces/rifkat/UzGPT-uz/krill_to_latin.py
deleted file mode 100644
index dd374d09c1ea179dc0742ef581561d4710599b12..0000000000000000000000000000000000000000
--- a/spaces/rifkat/UzGPT-uz/krill_to_latin.py
+++ /dev/null
@@ -1,1447 +0,0 @@
-# -*- coding: utf-8 -*-
-import re
-import sys
-import random
-
-class Perevod:
-    def __int__(self):
-        pass
-
-    def isCyrilic(self,text):
-        limit=7
-        special_characters = """"ўЎқҚғҒҳҲ'!“@#$%^&*()-”+""?_=,<>/"""
-        lists = [random.randrange(len(text)) for i in range(10)]
-        dummy_list = []
-        for item in lists:
-            if any(c in special_characters for c in text[item]):
-                print(text[item])
-                limit-=1
-                continue
-            dummy_list.append(bool(re.search('[а-яА-Я]', text[item])))
-        if sum(dummy_list)>=limit:
-            return True
-        return False
-
-    def to_cyrillic(self,text):
-        """Transliterate latin text to cyrillic  using the following rules:
-        1. ye = е in the beginning of a word or after a vowel
-        2. e = э in the beginning of a word or after a vowel
-        3. ц exception words
-        4. э exception words
-        """
-        # These compounds must be converted before other letters
-        compounds_first = {
-            'ch': 'ч', 'Ch': 'Ч', 'CH': 'Ч',
-            # this line must come before 's' because it has an 'h'
-            'sh': 'ш', 'Sh': 'Ш', 'SH': 'Ш',
-            # This line must come before 'yo' because of it's apostrophe
-            'yo‘': 'йў', 'Yo‘': 'Йў', 'YO‘': 'ЙЎ',
-        }
-        compounds_second = {
-            'yo': 'ё', 'Yo': 'Ё', 'YO': 'Ё',
-            # 'ts': 'ц', 'Ts': 'Ц', 'TS': 'Ц',  # No need for this, see TS_WORDS
-            'yu': 'ю', 'Yu': 'Ю', 'YU': 'Ю',
-            'ya': 'я', 'Ya': 'Я', 'YA': 'Я',
-            'ye': 'е', 'Ye': 'Е', 'YE': 'Е',
-            # different kinds of apostrophes
-            'o‘': 'ў', 'O‘': 'Ў', 'oʻ': 'ў', 'Oʻ': 'Ў',
-            "o'": 'ў', "O'": 'Ў', "o'": 'ў', "O'": 'Ў',
-            'g‘': 'ғ', 'G‘': 'Ғ', 'gʻ': 'ғ', 'Gʻ': 'Ғ',
-        }
-        beginning_rules = {
-            'ye': 'е', 'Ye': 'Е', 'YE': 'Е',
-            'e': 'э', 'E': 'Э',
-        }
-        after_vowel_rules = {
-            'ye': 'е', 'Ye': 'Е', 'YE': 'Е',
-            'e': 'э', 'E': 'Э',
-        }
-        exception_words_rules = {
-            's': 'ц', 'S': 'Ц',
-            'ts': 'ц', 'Ts': 'Ц', 'TS': 'Ц',  # but not tS
-            'e': 'э', 'E': 'э',
-            'sh': 'сҳ', 'Sh': 'Сҳ', 'SH': 'СҲ',
-            'yo': 'йо', 'Yo': 'Йо', 'YO': 'ЙО',
-            'yu': 'йу', 'Yu': 'Йу', 'YU': 'ЙУ',
-            'ya': 'йа', 'Ya': 'Йа', 'YA': 'ЙА',
-        }
-
-        # standardize some characters
-        # the first one is the windows string, the second one is the mac string
-        text = text.replace('ʻ', '‘')
-
-        def replace_soft_sign_words(m):
-            word = m.group(1)
-            if word.isupper():
-                result = SOFT_SIGN_WORDS[word.lower()].upper()
-            elif word[0].isupper():
-                result = SOFT_SIGN_WORDS[word.lower()]
-                result = result[0].upper() + result[1:]
-            else:
-                result = SOFT_SIGN_WORDS[word.lower()]
-            return result
-
-        for word in SOFT_SIGN_WORDS:
-            text = re.sub(
-                r'\b(%s)' % word,
-                replace_soft_sign_words,
-                text,
-                flags=re.U
-            )
-
-        def replace_exception_words(m):
-            """Replace ц (or э) only leaving other characters unchanged"""
-            return '%s%s%s' % (
-                m.group(1)[:m.start(2)],
-                exception_words_rules[m.group(2)],
-                m.group(1)[m.end(2):]
-            )
-
-        # loop because of python's limit of 100 named groups
-        for word in list(TS_WORDS.keys()) + list(E_WORDS.keys()):
-            text = re.sub(
-                r'\b(%s)' % word,
-                replace_exception_words,
-                text,
-                flags=re.U
-            )
-
-        # compounds
-        text = re.sub(
-            r'(%s)' % '|'.join(compounds_first.keys()),
-            lambda x: compounds_first[x.group(1)],
-            text,
-            flags=re.U
-        )
-
-        text = re.sub(
-            r'(%s)' % '|'.join(compounds_second.keys()),
-            lambda x: compounds_second[x.group(1)],
-            text,
-            flags=re.U
-        )
-
-        text = re.sub(
-            r'\b(%s)' % '|'.join(beginning_rules.keys()),
-            lambda x: beginning_rules[x.group(1)],
-            text,
-            flags=re.U
-        )
-
-        text = re.sub(
-            r'(%s)(%s)' % ('|'.join(LATIN_VOWELS),
-                           '|'.join(after_vowel_rules.keys())),
-            lambda x: '%s%s' % (x.group(1), after_vowel_rules[x.group(2)]),
-            text,
-            flags=re.U
-        )
-
-        text = re.sub(
-            r'(%s)' % '|'.join(LATIN_TO_CYRILLIC.keys()),
-            lambda x: LATIN_TO_CYRILLIC[x.group(1)],
-            text,
-            flags=re.U
-        )
-
-        return text
-
-    def to_latin(self, text):
-        """Transliterate cyrillic text to latin using the following rules:
-        1. ц = s at the beginning of a word.
-        ц = ts in the middle of a word after a vowel.
-        ц = s in the middle of a word after consonant (DEFAULT in CYRILLIC_TO_LATIN)
-            цирк = sirk
-            цех = sex
-            федерация = federatsiya
-            функция = funksiya
-        2. е = ye at the beginning of a word or after a vowel.
-        е = e in the middle of a word after a consonant (DEFAULT).
-        3. Сентябр = Sentabr, Октябр = Oktabr
-        """
-        beginning_rules = {
-            'ц': 's', 'Ц': 'S',
-            'е': 'ye', 'Е': 'Ye'
-        }
-        after_vowel_rules = {
-            'ц': 'ts', 'Ц': 'Ts',
-            'е': 'ye', 'Е': 'Ye'
-        }
-
-        text = re.sub(
-            r'(сент|окт)([яЯ])(бр)',
-            lambda x: '%s%s%s' % (x.group(1),
-                                  'a' if x.group(2) == 'я' else 'A', x.group(3)),
-            text,
-            flags=re.IGNORECASE | re.U
-        )
-
-        text = re.sub(
-            r'\b(%s)' % '|'.join(beginning_rules.keys()),
-            lambda x: beginning_rules[x.group(1)],
-            text,
-            flags=re.U
-        )
-
-        text = re.sub(
-            r'(%s)(%s)' % ('|'.join(CYRILLIC_VOWELS),
-                           '|'.join(after_vowel_rules.keys())),
-            lambda x: '%s%s' % (x.group(1), after_vowel_rules[x.group(2)]),
-            text,
-            flags=re.U
-        )
-
-        text = re.sub(
-            r'(%s)' % '|'.join(CYRILLIC_TO_LATIN.keys()),
-            lambda x: CYRILLIC_TO_LATIN[x.group(1)],
-            text,
-            flags=re.U
-        )
-
-        return text
-
-    def transliterate(self, text):
-        # print(self.isCyrilic(text))
-        if self.isCyrilic(text):
-            text = self.to_latin(text)
-        else:
-            text = self.to_cyrillic(text)
-
-        # if to_variant == 'cyrillic':
-        #     text = self.to_cyrillic(text)
-        # elif to_variant == 'latin':
-        #     text = self.to_latin(text)
-
-        return text
-
-    def translate_for_summarizer(self, text):
-        if not self.isCyrilic(text):
-            text = self.to_cyrillic(text)
-        return text
-
-
-
-LATIN_TO_CYRILLIC = {
-    'a': 'а', 'A': 'А',
-    'b': 'б', 'B': 'Б',
-    'd': 'д', 'D': 'Д',
-    'e': 'е', 'E': 'Е',
-    'f': 'ф', 'F': 'Ф',
-    'g': 'г', 'G': 'Г',
-    'h': 'ҳ', 'H': 'Ҳ',
-    'i': 'и', 'I': 'И',
-    'j': 'ж', 'J': 'Ж',
-    'k': 'к', 'K': 'К',
-    'l': 'л', 'L': 'Л',
-    'm': 'м', 'M': 'М',
-    'n': 'н', 'N': 'Н',
-    'o': 'о', 'O': 'О',
-    'p': 'п', 'P': 'П',
-    'q': 'қ', 'Q': 'Қ',
-    'r': 'р', 'R': 'Р',
-    's': 'с', 'S': 'С',
-    't': 'т', 'T': 'Т',
-    'u': 'у', 'U': 'У',
-    'v': 'в', 'V': 'В',
-    'x': 'х', 'X': 'Х',
-    'y': 'й', 'Y': 'Й',
-    'z': 'з', 'Z': 'З',
-    'ʼ': 'ъ',  # TODO: case?
-}
-LATIN_VOWELS = (
-    'a', 'A', 'e', 'E', 'i', 'I', 'o', 'O', 'u', 'U', 'o‘', 'O‘'
-)
-
-# These words cannot be reliably converted to cyrillic because of the lossy
-# nature of the to_latin converter.
-TS_WORDS = {
-    'aberra(ts)ion': 'аберрацион',
-    'aberra(ts)iya': 'аберрация',
-    'abza(ts)': 'абзац',
-    'aboli(ts)iya': 'аболиция',
-    'absorb(s)iya': 'абсорбция',
-    'abstrak(s)ionizm': 'абстракционизм',
-    'abstrak(s)ionist': 'абстракционист',
-    'abstrak(s)iya': 'абстракция',
-    'abs(s)ess': 'абсцесс',
-    'avianose(ts)': 'авианосец',
-    'avia(ts)iya': 'авиация',
-    'avtoinspek(s)iya': 'автоинспекция',
-    'avtopr(s)ep': 'автопрцеп',
-    'avtostan(s)iya': 'автостанция',
-    'agglyutina(ts)iya': 'агглютинация',
-    'agita(ts)ion': 'агитацион',
-    'agita(ts)iya': 'агитация',
-    'aglomera(ts)iya': 'агломерация',
-    'agnosti(ts)izm': 'агностицизм',
-    'agromeliora(ts)iya': 'агромелиорация',
-    'adapta(ts)iya': 'адаптация',
-    'administra(ts)iya': 'администрация',
-    'adsorb(s)iya': 'адсорбция',
-    'aka(ts)iya': 'акация',
-    'akklimatiza(ts)iya': 'акклиматизация',
-    'akkomoda(ts)iya': 'аккомодация',
-    'akkredita(ts)iya': 'аккредитация',
-    'ak(s)ent': 'акцент',
-    'ak(s)iz': 'акциз',
-    'ak(s)ioner': 'акционер',
-    'ak(s)ionerlik': 'акционерлик',
-    'ak(s)iya': 'акция',
-    'ak(s)iyadorlik': 'акциядорлик',
-    'allitera(ts)iya': 'аллитерация',
-    'amortiza(ts)iya': 'амортизация',
-    'amputa(ts)iya': 'ампутация',
-    'annota(ts)iya': 'аннотация',
-    'annulya(ts)iya': 'аннуляция',
-    'anti(ts)iklon': 'антициклон',
-    'antra(ts)it': 'антрацит',
-    'apellya(ts)iya': 'апелляция',
-    'appendi(ts)it': 'аппендицит',
-    'applika(ts)iya': 'аппликация',
-    'aproba(ts)iya': 'апробация',
-    'argumenta(ts)iya': 'аргументация',
-    'assimilya(ts)iya': 'ассимиляция',
-    'asso(ts)ia(ts)iya': 'ассоциация',
-    'attesta(ts)ion': 'аттестацион',
-    'attesta(ts)iya': 'аттестация',
-    'attrak(s)ion': 'аттракцион',
-    'auk(s)ion': 'аукцион',
-    'a(ts)etilen': 'ацетилен',
-    'a(ts)eton': 'ацетон',
-    'aeronaviga(ts)iya': 'аэронавигация',
-    'bakteri(ts)id': 'бактерицид',
-    'ba(ts)illar': 'бациллар',
-    'bioloka(ts)iya': 'биолокация',
-    'biolyumines(s)en(s)iya': 'биолюминесценция',
-    'bo(ts)man': 'боцман',
-    'bronenose(ts)': 'броненосец',
-    'bru(ts)ellyoz': 'бруцеллёз',
-    'vak(s)ina': 'вакцина',
-    'valva(ts)iya': 'вальвация',
-    'vegeta(ts)ion': 'вегетацион',
-    'vegeta(ts)iya': 'вегетация',
-    'venepunk(s)iya': 'венепункция',
-    'ventilya(ts)ion': 'вентиляцион',
-    'ventilya(ts)iya': 'вентиляция',
-    'vibra(ts)iya': 'вибрация',
-    'vibroizolya(ts)iya': 'виброизоляция',
-    'vi(ts)e-': 'вице-',
-    'vi(ts)e-admiral': 'вице-адмирал',
-    'vi(ts)e-prezident': 'вице-президент',
-    'vulkaniza(ts)iya': 'вулканизация',
-    'galli(ts)izm': 'галлицизм',
-    'gallyu(ts)ina(ts)iya': 'галлюцинация',
-    'galvaniza(ts)iya': 'гальванизация',
-    'gastrol-kon(s)ert': 'гастроль-концерт',
-    'gaubi(ts)a': 'гаубица',
-    'gelio(ts)entrik': 'гелиоцентрик',
-    'geno(ts)id': 'геноцид',
-    'geo(ts)entrik': 'геоцентрик',
-    'gerbi(ts)idlar': 'гербицидлар',
-    'ger(s)': 'герц',
-    'ger(s)og': 'герцог',
-    'gia(ts)int': 'гиацинт',
-    'gidromeliora(ts)iya': 'гидромелиорация',
-    'gidromexaniza(ts)iya': 'гидромеханизация',
-    'gidrostan(s)iya': 'гидростанция',
-    'gidroelektrostan(s)iya': 'гидроэлектростанция',
-    'giperinflya(ts)iya': 'гиперинфляция',
-    'gipo(ts)entr': 'гипоцентр',
-    'gli(ts)erin': 'глицерин',
-    'glya(ts)iolog': 'гляциолог',
-    'glya(ts)iologiya': 'гляциология',
-    'gorchi(ts)a': 'горчица',
-    'gravita(ts)iya': 'гравитация',
-    'grada(ts)iya': 'градация',
-    'guseni(ts)a': 'гусеница',
-    'devalva(ts)iya': 'девальвация',
-    'degaza(ts)iya': 'дегазация',
-    'degenera(ts)iya': 'дегенерация',
-    'degustat(s)iya': 'дегустатция',
-    'deduk(s)iya': 'дедукция',
-    'dezaktiva(ts)iya': 'дезактивация',
-    'dezinsek(s)iya': 'дезинсекция',
-    'dezinfek(s)iya': 'дезинфекция',
-    'dezinfek(s)iyalamoq': 'дезинфекцияламоқ',
-    'deklama(ts)iya': 'декламация',
-    'deklama(ts)iyachi': 'декламациячи',
-    'deklara(ts)iya': 'декларация',
-    'dekora(ts)iya': 'декорация',
-    'delega(ts)iya': 'делегация',
-    'delimita(ts)iya': 'делимитация',
-    'demarka(ts)iya': 'демаркация',
-    'demilitariza(ts)iya': 'демилитаризация',
-    'demobiliza(ts)iya': 'демобилизация',
-    'denaturaliza(ts)iya': 'денатурализация',
-    'denomina(ts)iya': 'деноминация',
-    'denonsa(ts)iya': 'денонсация',
-    'depilya(ts)iya': 'депиляция',
-    'deporta(ts)iya': 'депортация',
-    'deratiza(ts)iya': 'дератизация',
-    'deriva(ts)ion': 'деривацион',
-    'deriva(ts)iya': 'деривация',
-    'desika(ts)iya': 'десикация',
-    'detona(ts)iya': 'детонация',
-    'defini(ts)iya': 'дефиниция',
-    'defi(ts)it': 'дефицит',
-    'deflya(ts)iya': 'дефляция',
-    'defolia(ts)iya': 'дефолиация',
-    'deforma(ts)iya': 'деформация',
-    'de(ts)igramm': 'дециграмм',
-    'de(ts)ilitr': 'децилитр',
-    'de(ts)imetr': 'дециметр',
-    'dik(s)iya': 'дикция',
-    'direk(s)iya': 'дирекция',
-    'diskvalifika(ts)iya': 'дисквалификация',
-    'diskrimina(ts)iya': 'дискриминация',
-    'disloka(ts)iya': 'дислокация',
-    'dispropor(s)iya': 'диспропорция',
-    'disserta(ts)iya': 'диссертация',
-    'dissimilya(ts)iya': 'диссимиляция',
-    'disso(ts)ia(ts)iya': 'диссоциация',
-    'distan(s)ion': 'дистанцион',
-    'distan(s)iya': 'дистанция',
-    'distillya(ts)iya': 'дистилляция',
-    'differen(s)ial': 'дифференциал',
-    'differen(s)ia(ts)iya': 'дифференциация',
-    'differen(s)iyalamoq': 'дифференцияламоқ',
-    'dota(ts)iya': 'дотация',
-    'do(ts)ent': 'доцент',
-    'jinoiy-pro(ts)essual': 'жиноий-процессуал',
-    'identifika(ts)iya': 'идентификация',
-    'izolya(ts)ion': 'изоляцион',
-    'izolya(ts)iya': 'изоляция',
-    'izolya(ts)iyalamoq': 'изоляцияламоқ',
-    'illyumina(ts)iya': 'иллюминация',
-    'illyustra(ts)iya': 'иллюстрация',
-    'immigra(ts)iya': 'иммиграция',
-    'immobiliza(ts)iya': 'иммобилизация',
-    'impoten(s)iya': 'импотенция',
-    'improviza(ts)iya': 'импровизация',
-    'inaugura(ts)iya': 'инаугурация',
-    'inventariza(ts)iya': 'инвентаризация',
-    'investi(ts)iya': 'инвестиция',
-    'ingalya(ts)iya': 'ингаляция',
-    'indeksa(ts)iya': 'индексация',
-    'induk(s)ion': 'индукцион',
-    'induk(s)iya': 'индукция',
-    'iner(s)iya': 'инерция',
-    'iner(s)iyali': 'инерцияли',
-    'inkvizi(ts)iya': 'инквизиция',
-    'inkorpora(ts)iya': 'инкорпорация',
-    'inkuba(ts)iya': 'инкубация',
-    'innova(ts)iya': 'инновация',
-    'inspek(s)iya': 'инспекция',
-    'instar(s)iya': 'инстарция',
-    'instruk(s)iya': 'инструкция',
-    'ins(s)enirovka': 'инсценировка',
-    'integra(ts)iya': 'интеграция',
-    'intelligen(s)iya': 'интеллигенция',
-    'interven(s)iya': 'интервенция',
-    'interven(s)iyachi': 'интервенциячи',
-    'interna(ts)ional': 'интернационал',
-    'interna(ts)ionalizm': 'интернационализм',
-    'interna(ts)ionalist': 'интернационалист',
-    'intoksika(ts)iya': 'интоксикация',
-    'intona(ts)ion': 'интонацион',
-    'intona(ts)iya': 'интонация',
-    'intui(ts)iya': 'интуиция',
-    'infek(s)ion': 'инфекцион',
-    'infek(s)iya': 'инфекция',
-    'inflya(ts)iya': 'инфляция',
-    'informa(ts)ion': 'информацион',
-    'informa(ts)iya': 'информация',
-    'inʼek(s)iya': 'инъекция',
-    'irra(ts)ional': 'иррационал',
-    'irriga(ts)ion': 'ирригацион',
-    'irriga(ts)iya': 'ирригация',
-    'kalkulya(ts)iya': 'калькуляция',
-    'kal(s)iy': 'кальций',
-    'kanaliza(ts)iya': 'канализация',
-    'kan(s)eliyariya': 'канцелиярия',
-    'kan(s)erogen': 'канцероген',
-    'kan(s)ler': 'канцлер',
-    'kapitaliza(ts)iya': 'капитализация',
-    'kapitulya(ts)iya': 'капитуляция',
-    'kassa(ts)iya': 'кассация',
-    'katol(s)izm': 'католцизм',
-    'kvalifika(ts)iya': 'квалификация',
-    'kvar(s)': 'кварц',
-    'kvar(s)it': 'кварцит',
-    'kvitan(s)iya': 'квитанция',
-    'kinokon(s)ert': 'киноконцерт',
-    'kinos(s)enariy': 'киносценарий',
-    'klassifika(ts)iya': 'классификация',
-    'klassi(ts)izm': 'классицизм',
-    'koali(ts)ion': 'коалицион',
-    'koali(ts)iya': 'коалиция',
-    'kodifika(ts)iya': 'кодификация',
-    'kollek(s)ioner': 'коллекционер',
-    'kollek(s)iya': 'коллекция',
-    'kollek(s)iyachchi': 'коллекцияччи',
-    'kolon(s)ifra': 'колонцифра',
-    'kombina(ts)iya': 'комбинация',
-    'kommer(s)iya': 'коммерция',
-    'kommunika(ts)iya': 'коммуникация',
-    'kommuta(ts)iya': 'коммутация',
-    'kompensa(ts)iya': 'компенсация',
-    'kompeten(s)iya': 'компетенция',
-    'kompilya(ts)iya': 'компиляция',
-    'kompozi(ts)ion': 'композицион',
-    'kompozi(ts)iya': 'композиция',
-    'konvek(s)iya': 'конвекция',
-    'konven(s)iya': 'конвенция',
-    'konverta(ts)iya': 'конвертация',
-    'kondensa(ts)iya': 'конденсация',
-    'kondi(ts)iya': 'кондиция',
-    'kondi(ts)ioner': 'кондиционер',
-    'konkuren(s)iya': 'конкуренция',
-    'konserva(ts)iya': 'консервация',
-    'konsigna(ts)iya': 'консигнация',
-    'konsolida(ts)iya': 'консолидация',
-    'konsor(s)ium': 'консорциум',
-    'konspira(ts)iya': 'конспирация',
-    'konstitu(ts)ion': 'конституцион',
-    'konstitu(ts)iya': 'конституция',
-    'konstitu(ts)iyaviy': 'конституциявий',
-    'konstruk(s)iya': 'конструкция',
-    'konsulta(ts)iya': 'консультация',
-    'kontrakta(ts)iya': 'контрактация',
-    'kontribu(ts)iya': 'контрибуция',
-    'kontrrevolyu(ts)ion': 'контрреволюцион',
-    'kontrrevolyu(ts)ioner': 'контрреволюционер',
-    'kontrrevolyu(ts)iya': 'контрреволюция',
-    'konfedera(ts)iya': 'конфедерация',
-    'konferen(s)-zal': 'конференц-зал',
-    'konferen(s)iya': 'конференция',
-    'konfiska(ts)iya': 'конфискация',
-    'konfronta(ts)iya': 'конфронтация',
-    'konfu(ts)iylik': 'конфуцийлик',
-    'konfu(ts)iychilik': 'конфуцийчилик',
-    'kon(s)entrat': 'концентрат',
-    'kon(s)entratli': 'концентратли',
-    'kon(s)entra(ts)ion': 'концентрацион',
-    'kon(s)entra(ts)iya': 'концентрация',
-    'kon(s)entra(ts)iyalashmoq': 'концентрациялашмоқ',
-    'kon(s)entrik': 'концентрик',
-    'kon(s)ep(s)iya': 'концепция',
-    'kon(s)ern': 'концерн',
-    'kon(s)ert': 'концерт',
-    'kon(s)ertmeyster': 'концертмейстер',
-    'kon(s)essiya': 'концессия',
-    'kon(s)lager': 'концлагерь',
-    'koopera(ts)iya': 'кооперация',
-    'koopta(ts)iya': 'кооптация',
-    'koordina(ts)ion': 'координацион',
-    'koordina(ts)iya': 'координация',
-    'korpora(ts)iya': 'корпорация',
-    'korrelya(ts)iya': 'корреляция',
-    'korresponden(s)iya': 'корреспонденция',
-    'korrup(s)iya': 'коррупция',
-    'koeffi(ts)iyent': 'коэффициент',
-    'krema(ts)iya': 'кремация',
-    'kristalliza(ts)iya': 'кристаллизация',
-    'kulmina(ts)ion': 'кульминацион',
-    'kulmina(ts)iya': 'кульминация',
-    'kultiva(ts)iya': 'культивация',
-    'lakta(ts)iya': 'лактация',
-    'lamina(ts)iya': 'ламинация',
-    'lan(s)et': 'ланцет',
-    'levomi(ts)etin': 'левомицетин',
-    'legitima(ts)iya': 'легитимация',
-    'leyko(ts)itlar': 'лейкоцитлар',
-    'leyko(ts)itoz': 'лейкоцитоз',
-    'lek(s)iya': 'лекция',
-    'liberaliza(ts)iya': 'либерализация',
-    'li(ts)ey': 'лицей',
-    'li(ts)enziya': 'лицензия',
-    'lokaliza(ts)iya': 'локализация',
-    'loka(ts)iya': 'локация',
-    'lo(ts)man': 'лоцман',
-    'lyumenis(s)en(s)iya': 'люменисценция',
-    'lyute(ts)iy': 'лютеций',
-    'manipulya(ts)iya': 'манипуляция',
-    'margane(ts)': 'марганец',
-    'matri(ts)a': 'матрица',
-    'medi(ts)ina': 'медицина',
-    'meliora(ts)iya': 'мелиорация',
-    'menstrua(ts)iya': 'менструация',
-    'metalliza(ts)iya': 'металлизация',
-    'metiza(ts)iya': 'метизация',
-    'mexaniza(ts)iya': 'механизация',
-    'mexaniza(ts)iyalash': 'механизациялаш',
-    'mexaniza(ts)iyalashmoq': 'механизациялашмоқ',
-    'mexani(ts)izm': 'механицизм',
-    'migra(ts)iya': 'миграция',
-    'mizans(s)ena': 'мизансцена',
-    'militariza(ts)iya': 'милитаризация',
-    'mili(ts)ioner': 'милиционер',
-    'mili(ts)iya': 'милиция',
-    'mili(ts)iyaxona': 'милицияхона',
-    'mineraliza(ts)iya': 'минерализация',
-    'minonose(ts)': 'миноносец',
-    'misti(ts)izm': 'мистицизм',
-    'mobiliza(ts)iya': 'мобилизация',
-    'moderniza(ts)iya': 'модернизация',
-    'moderniza(ts)iyalamoq': 'модернизацияламоқ',
-    'modifika(ts)iya': 'модификация',
-    'moto(ts)ikl': 'мотоцикл',
-    'moto(ts)iklet': 'мотоциклет',
-    'moto(ts)ikletchi': 'мотоциклетчи',
-    'moto(ts)iklli': 'мотоциклли',
-    'moto(ts)iklchi': 'мотоциклчи',
-    'multiplika(ts)ion': 'мультипликацион',
-    'multiplika(ts)iya': 'мультипликация',
-    'muni(ts)ipaliza(ts)iya': 'муниципализация',
-    'muni(ts)ipalitet': 'муниципалитет',
-    'naviga(ts)iya': 'навигация',
-    'naturaliza(ts)iya': 'натурализация',
-    'na(ts)ionaliza(ts)iya': 'национализация',
-    'nene(ts)': 'ненец',
-    'nene(ts)lar': 'ненецлар',
-    'nitrogli(ts)erin': 'нитроглицерин',
-    'nomina(ts)iya': 'номинация',
-    'nostrifika(ts)iya': 'нострификация',
-    'nullifika(ts)iya': 'нуллификация',
-    'obliga(ts)iya': 'облигация',
-    'obroga(ts)iya': 'оброгация',
-    'observa(ts)iya': 'обсервация',
-    'okkupa(ts)ion': 'оккупацион',
-    'okkupa(ts)iya': 'оккупация',
-    'okkupa(ts)iyachi': 'оккупациячи',
-    'opera(ts)iya': 'операция',
-    'opera(ts)iyaviy': 'операциявий',
-    'oppozo(ts)ion': 'оппозоцион',
-    'oppozi(ts)iya': 'оппозиция',
-    'oppozi(ts)iyachi': 'оппозициячи',
-    'op(s)ion': 'опцион',
-    'ordinare(ts)': 'ординарец',
-    'oriyenta(ts)iya': 'ориентация',
-    'osteomalya(ts)iya': 'остеомаляция',
-    'ofi(ts)er': 'офицер',
-    'ofi(ts)iant': 'официант',
-    'ofi(ts)iantka': 'официантка',
-    'palpa(ts)iya': 'пальпация',
-    'pa(ts)iyent': 'пациент',
-    'pa(ts)ifizm': 'пацифизм',
-    'pa(ts)ifist': 'пацифист',
-    'peni(ts)(s)ilin': 'пениццилин',
-    'pesti(ts)idlar': 'пестицидлар',
-    'peti(ts)iya': 'петиция',
-    'petli(ts)a': 'петлица',
-    'pigmenta(ts)iya': 'пигментация',
-    'pin(s)et': 'пинцет',
-    'pi(ts)(s)a': 'пицца',
-    'planta(ts)iya': 'плантация',
-    'pla(ts)darm': 'плацдарм',
-    'pla(ts)kart': 'плацкарт',
-    'pla(ts)karta': 'плацкарта',
-    'pla(ts)kartali': 'плацкартали',
-    'plebis(s)it': 'плебисцит',
-    'podstan(s)iya': 'подстанция',
-    'pozi(ts)ion': 'позицион',
-    'pozi(ts)iya': 'позиция',
-    'poli(ts)iya': 'полиция',
-    'poli(ts)iyachi': 'полициячи',
-    'poli(ts)meyster': 'полицмейстер',
-    'pollyu(ts)iya': 'поллюция',
-    'populya(ts)iya': 'популяция',
-    'por(s)iya': 'порция',
-    'poten(s)ial': 'потенциал',
-    'prezenta(ts)iya': 'презентация',
-    'press-konferen(s)iya': 'пресс-конференция',
-    'preferen(s)iya': 'преференция',
-    'privatiza(ts)iya': 'приватизация',
-    'prin(s)ip': 'принцип',
-    'prin(s)ipial': 'принципиал',
-    'prin(s)ipiallik': 'принципиаллик',
-    'prin(s)ipli': 'принципли',
-    'prin(s)ipsiz': 'принципсиз',
-    'pri(ts)ep': 'прицеп',
-    'provin(s)ializm': 'провинциализм',
-    'provin(s)iya': 'провинция',
-    'provoka(ts)iya': 'провокация',
-    'proyek(s)iya': 'проекция',
-    'proyek(s)iyalamoq': 'проекцияламоқ',
-    'proklama(ts)iya': 'прокламация',
-    'prolonga(ts)iya': 'пролонгация',
-    'propor(s)ional': 'пропорционал',
-    'propor(s)ionallik': 'пропорционаллик',
-    'propor(s)iya': 'пропорция',
-    'protek(s)ionizm': 'протекционизм',
-    'pro(ts)ent': 'процент',
-    'pro(ts)entli': 'процентли',
-    'pro(ts)entchi': 'процентчи',
-    'pro(ts)ess': 'процесс',
-    'pro(ts)essor': 'процессор',
-    'pro(ts)essual': 'процессуал',
-    'publi(ts)ist': 'публицист',
-    'publi(ts)istik': 'публицистик',
-    'publi(ts)istika': 'публицистика',
-    'punktua(ts)ion': 'пунктуацион',
-    'punktua(ts)iya': 'пунктуация',
-    'punk(s)iya': 'пункция',
-    'radia(ts)ion': 'радиацион',
-    'radia(ts)iya': 'радиация',
-    'radioloka(ts)iya': 'радиолокация',
-    'radionaviga(ts)iya': 'радионавигация',
-    'radiostan(s)iya': 'радиостанция',
-    'rane(ts)': 'ранец',
-    'ratifika(ts)iya': 'ратификация',
-    'rafina(ts)iya': 'рафинация',
-    'rafina(ts)iyalash': 'рафинациялаш',
-    'ra(ts)ion': 'рацион',
-    'ra(ts)ional': 'рационал',
-    'ra(ts)ionalizator': 'рационализатор',
-    'ra(ts)ionalizatorlik': 'рационализаторлик',
-    'ra(ts)ionaliza(ts)iya': 'рационализация',
-    'ra(ts)ionalizm': 'рационализм',
-    'ra(ts)ionalist': 'рационалист',
-    'ra(ts)ionlallashmoq': 'рационлаллашмоқ',
-    'ra(ts)iya': 'рация',
-    'reabilita(ts)iya': 'реабилитация',
-    'reak(s)ion': 'реакцион',
-    'reak(s)ioner': 'реакционер',
-    'reak(s)iya': 'реакция',
-    'reak(s)iyachi': 'реакциячи',
-    'realiza(ts)iya': 'реализация',
-    'reanima(ts)iya': 'реанимация',
-    'revalva(ts)iya': 'ревальвация',
-    'revolyu(ts)ion': 'революцион',
-    'revolyu(ts)ioner': 'революционер',
-    'revolyu(ts)iya': 'революция',
-    'regenera(ts)iya': 'регенерация',
-    'registra(ts)iya': 'регистрация',
-    'redak(s)ion': 'редакцион',
-    'redak(s)iya': 'редакция',
-    'reduk(s)iya': 'редукция',
-    'reduplika(ts)iya': 'редупликация',
-    'rezek(s)iya': 'резекция',
-    'reziden(s)iya': 'резиденция',
-    'rezolyu(ts)iya': 'резолюция',
-    'reinvesti(ts)iya': 'реинвестиция',
-    'rekvizi(ts)iya': 'реквизиция',
-    'reklama(ts)iya': 'рекламация',
-    'rekognos(s)irovka': 'рекогносцировка',
-    'rekomenda(ts)iya': 'рекомендация',
-    'rekonstruk(s)iya': 'реконструкция',
-    'rekonstruk(s)iyalamoq': 'реконструкцияламоқ',
-    'remilitariza(ts)iya': 'ремилитаризация',
-    'repara(ts)iya': 'репарация',
-    'repatri(ts)iya': 'репатриция',
-    'repeti(ts)iya': 'репетиция',
-    'reprivatiza(ts)iya': 'реприватизация',
-    'reproduk(s)iya': 'репродукция',
-    'restavra(ts)iya': 'реставрация',
-    'retranslya(ts)iya': 'ретрансляция',
-    'reforma(ts)iya': 'реформация',
-    'refrak(s)iya': 'рефракция',
-    're(ts)enzent': 'рецензент',
-    're(ts)enziya': 'рецензия',
-    're(ts)ept': 'рецепт',
-    're(ts)eptorlar': 'рецепторлар',
-    're(ts)idiv': 'рецидив',
-    're(ts)idivist': 'рецидивист',
-    're(ts)ipiyent': 'реципиент',
-    'reevakua(ts)iya': 'реэвакуация',
-    'reemigra(ts)iya': 'реэмиграция',
-    'ri(ts)arlik': 'рицарлик',
-    'ri(ts)ar': 'рицарь',
-    'rota(ts)ion': 'ротацион',
-    'sana(ts)iya': 'санация',
-    'sana(ts)iyalash': 'санациялаш',
-    'sank(s)iya': 'санкция',
-    'sekre(ts)iya': 'секреция',
-    'sek(s)iya': 'секция',
-    'selek(s)ion': 'селекцион',
-    'selek(s)iya': 'селекция',
-    'selek(s)iyachi': 'селекциячи',
-    'selek(s)iyachilik': 'селекциячилик',
-    'sensa(ts)ion': 'сенсацион',
-    'sensa(ts)iya': 'сенсация',
-    'signaliza(ts)iya': 'сигнализация',
-    'sili(ts)iy': 'силиций',
-    'situa(ts)iya': 'ситуация',
-    'skepti(ts)izm': 'скептицизм',
-    'slane(ts)': 'сланец',
-    'so(ts)ial': 'социал',
-    'so(ts)ial-demokrat': 'социал-демократ',
-    'so(ts)ial-demokratik': 'социал-демократик',
-    'so(ts)ial-demokratiya': 'социал-демократия',
-    'so(ts)ializa(ts)iya': 'социализация',
-    'so(ts)ializm': 'социализм',
-    'so(ts)ialist': 'социалист',
-    'so(ts)ialistik': 'социалистик',
-    'so(ts)iolingvistika': 'социолингвистика',
-    'so(ts)iolog': 'социолог',
-    'so(ts)iologik': 'социологик',
-    'so(ts)iologiya': 'социология',
-    'spekulya(ts)iya': 'спекуляция',
-    'spe(ts)ifik': 'специфик',
-    'spe(ts)ifika': 'специфика',
-    'spe(ts)ifika(ts)iya': 'спецификация',
-    'stabiliza(ts)iya': 'стабилизация',
-    'stan(s)iya': 'станция',
-    'sta(ts)ionar': 'стационар',
-    'steriliza(ts)iya': 'стерилизация',
-    'stoi(ts)izm': 'стоицизм',
-    'stron(s)iy': 'стронций',
-    'substan(s)iya': 'субстанция',
-    's(s)enariy': 'сценарий',
-    's(s)enariychi': 'сценарийчи',
-    's(s)enarist': 'сценарист',
-    'tabli(ts)a': 'таблица',
-    'tan(s)a': 'танца',
-    'teleins(s)enirovka': 'телеинсценировка',
-    'telekommunika(ts)iya': 'телекоммуникация',
-    'telemexaniza(ts)iya': 'телемеханизация',
-    'tenden(s)ioz': 'тенденциоз',
-    'tenden(s)iozlik': 'тенденциозлик',
-    'tenden(s)iya': 'тенденция',
-    'tepli(ts)a': 'теплица',
-    'teploizolya(ts)iya': 'теплоизоляция',
-    'termoizolya(ts)iya': 'термоизоляция',
-    'ter(s)et': 'терцет',
-    'ter(s)iya': 'терция',
-    'texne(ts)iy': 'технеций',
-    'tradi(ts)ion': 'традицион',
-    'tradi(ts)iya': 'традиция',
-    'transkrip(s)ion': 'транскрипцион',
-    'transkrip(s)iya': 'транскрипция',
-    'transkrip(s)iyalamoq': 'транскрипцияламоқ',
-    'translitera(ts)iya': 'транслитерация',
-    'translya(ts)ion': 'трансляцион',
-    'translya(ts)iya': 'трансляция',
-    'transplanta(ts)iya': 'трансплантация',
-    'transforma(ts)iya': 'трансформация',
-    'transforma(ts)iyalamoq': 'трансформацияламоқ',
-    'trape(ts)iya': 'трапеция',
-    'trepana(ts)iya': 'трепанация',
-    'uborshi(ts)a': 'уборшица',
-    'uzurpa(ts)iya': 'узурпация',
-    'unifika(ts)iya': 'унификация',
-    'unifika(ts)iyalashtirmoq': 'унификациялаштирмоқ',
-    'unter-ofi(ts)er': 'унтер-офицер',
-    'urbaniza(ts)iya': 'урбанизация',
-    'fago(ts)it': 'фагоцит',
-    'falsifika(ts)iya': 'фальсификация',
-    'farma(ts)evt': 'фармацевт',
-    'farma(ts)evtika': 'фармацевтика',
-    'farma(ts)iya': 'фармация',
-    'federa(ts)iya': 'федерация',
-    'fermenta(ts)iya': 'ферментация',
-    'film-kon(s)ert': 'фильм-концерт',
-    'filtra(ts)iya': 'фильтрация',
-    'fiton(s)id': 'фитонцид',
-    'forma(ts)iya': 'формация',
-    'frak(s)ion': 'фракцион',
-    'frak(s)iooner': 'фракциоонер',
-    'frak(s)iya': 'фракция',
-    'fran(s)iya': 'франция',
-    'fran(s)uz': 'француз',
-    'fran(s)uzlar': 'французлар',
-    'fran(s)uzcha': 'французча',
-    'fri(ts)': 'фриц',
-    'funk(s)ional': 'функционал',
-    'funk(s)iya': 'функция',
-    'xemosorb(s)iya': 'хемосорбция',
-    'xole(ts)istit': 'холецистит',
-    '(s)anga': 'цанга',
-    '(s)apfa': 'цапфа',
-    '(s)edra': 'цедра',
-    '(s)eziy': 'цезий',
-    '(s)eytnot': 'цейтнот',
-    '(s)ellofan': 'целлофан',
-    '(s)elluloid': 'целлулоид',
-    '(s)ellyuloza': 'целлюлоза',
-    '(s)elsiy': 'цельсий',
-    '(s)ement': 'цемент',
-    '(s)ementlamoq': 'цементламоқ',
-    '(s)enz': 'ценз',
-    '(s)enzor': 'цензор',
-    '(s)enzura': 'цензура',
-    '(s)ent': 'цент',
-    '(s)entner': 'центнер',
-    '(s)entnerli': 'центнерли',
-    '(s)entnerchi': 'центнерчи',
-    '(s)entralizm': 'централизм',
-    '(s)entrizm': 'центризм',
-    '(s)entrist': 'центрист',
-    '(s)entrifuga': 'центрифуга',
-    '(s)eriy': 'церий',
-    '(s)esarka': 'цесарка',
-    '(s)ex': 'цех',
-    '(s)ian': 'циан',
-    '(s)ianli': 'цианли',
-    '(s)iviliza(ts)iya': 'цивилизация',
-    '(s)igara': 'цигара',
-    '(s)ikl': 'цикл',
-    '(s)iklik': 'циклик',
-    '(s)ikllashtirmoq': 'цикллаштирмоқ',
-    '(s)iklli': 'циклли',
-    '(s)iklon': 'циклон',
-    '(s)iklotron': 'циклотрон',
-    '(s)ilindr': 'цилиндр',
-    '(s)ilindrik': 'цилиндрик',
-    '(s)ilindrli': 'цилиндрли',
-    '(s)inga': 'цинга',
-    '(s)ink': 'цинк',
-    '(s)inkograf': 'цинкограф',
-    '(s)inkografiya': 'цинкография',
-    '(s)irk': 'цирк',
-    '(s)irkoniy': 'цирконий',
-    '(s)irkul': 'циркуль',
-    '(s)irkulyar': 'циркуляр',
-    '(s)irkchi': 'циркчи',
-    '(s)irroz': 'цирроз',
-    '(s)isterna': 'цистерна',
-    '(s)isternali': 'цистернали',
-    '(s)istit': 'цистит',
-    '(s)itata': 'цитата',
-    '(s)itatabozlik': 'цитатабозлик',
-    '(s)ito-': 'цито-',
-    '(s)itodiagnostika': 'цитодиагностика',
-    '(s)itokimyo': 'цитокимё',
-    '(s)itoliz': 'цитолиз',
-    '(s)itologiya': 'цитология',
-    '(s)itrus': 'цитрус',
-    '(s)iferblat': 'циферблат',
-    '(s)iferblatli': 'циферблатли',
-    '(s)okol': 'цоколь',
-    '(s)unami': 'цунами',
-    'cherepi(ts)a': 'черепица',
-    'shvey(s)ar': 'швейцар',
-    'shmu(ts)titul': 'шмуцтитул',
-    'shni(ts)el': 'шницель',
-    'shpri(ts)': 'шприц',
-    'shtangen(s)irkul': 'штангенциркуль',
-    'evakua(ts)iya': 'эвакуация',
-    'evolyu(ts)ion': 'эволюцион',
-    'evolyu(ts)iya': 'эволюция',
-    'ego(ts)entrizm': 'эгоцентризм',
-    'eksguma(ts)iya': 'эксгумация',
-    'ekspedi(ts)ion': 'экспедицион',
-    'ekspedi(ts)iya': 'экспедиция',
-    'ekspedi(ts)iyachi': 'экспедициячи',
-    'ekspluata(ts)iya': 'эксплуатация',
-    'ekspluata(ts)iyachi': 'эксплуатациячи',
-    'ekspozi(ts)iya': 'экспозиция',
-    'ekspropria(ts)iya': 'экспроприация',
-    'ekstradi(ts)iya': 'экстрадиция',
-    'ekstrak(s)iya': 'экстракция',
-    'elektrifika(ts)iya': 'электрификация',
-    'elektrostan(s)iya': 'электростанция',
-    'emansipa(ts)iya': 'эмансипация',
-    'emigra(ts)iya': 'эмиграция',
-    'emo(ts)ional': 'эмоционал',
-    'emo(ts)ionallik': 'эмоционаллик',
-    'emo(ts)iya': 'эмоция',
-    'empiriokriti(ts)izm': 'эмпириокритицизм',
-    'en(s)efalit': 'энцефалит',
-    'en(s)efalogramma': 'энцефалограмма',
-    'en(s)iklopedik': 'энциклопедик',
-    'en(s)iklopedist': 'энциклопедист',
-    'en(s)iklopediya': 'энциклопедия',
-    'en(s)iklopediyachi': 'энциклопедиячи',
-    'epi(ts)entr': 'эпицентр',
-    'eritro(ts)itlar': 'эритроцитлар',
-    'erudi(ts)iya': 'эрудиция',
-    'eskala(ts)iya': 'эскалация',
-    'esmine(ts)': 'эсминец',
-    'essen(s)iya': 'эссенция',
-    'yurisdik(s)iya': 'юрисдикция',
-    'yurispruden(s)iya': 'юриспруденция',
-    'yusti(ts)iya': 'юстиция',
-}
-# These words cannot be reliably transliterated into cyrillic
-E_WORDS = {
-    'bel(e)taj': 'бельэтаж',
-    'bugun-(e)rta': 'бугун-эрта',
-    'diqqat-(e)ʼtibor': 'диққат-эътибор',
-    'ich-(e)t': 'ич-эт',
-    'karat(e)': 'каратэ',
-    'm(e)r': 'мэр',
-    'obroʻ-(e)ʼtiborli': 'обрў-эътиборли',
-    'omon-(e)son': 'омон-эсон',
-    'r(e)ket': 'рэкет',
-    'sut(e)mizuvchilar': 'сутэмизувчилар',
-    'upa-(e)lik': 'упа-элик',
-    'xayr-(e)hson': 'хайр-эҳсон',
-    'qayn(e)gachi': 'қайнэгачи',
-}
-# Not to confuse with ш
-SH_WORDS = {
-    'a(sh)ob': 'асҳоб',
-    'mu(sh)af': 'мусҳаф'
-}
-# Not to confuse with ё
-YO_WORDS = {
-    'general-ma(yo)r': 'генерал-майор',
-    '(yo)g': 'йог',
-    '(yo)ga': 'йога',
-    '(yo)gurt': 'йогурт',
-    '(yo)d': 'йод',
-    '(yo)dlamoq': 'йодламоқ',
-    '(yo)dli': 'йодли',
-    'ma(yo)nez': 'майонез',
-    'mikrorayon': 'микрорайон',
-    'ma(yo)r': 'майор',
-    'ra(yo)n': 'район',
-}
-YU_WORDS = {
-    'mo(yu)pa': 'мойупа',
-    'po(yu)stun': 'пойустун'
-}
-YA_WORDS = {
-    'po(ya)bzal': 'пойабзал',
-    'po(ya)ndoz': 'пойандоз',
-    'po(ya)fzal': 'пойафзал'
-}
-YE_WORDS = {
-    'i(ye)': 'ийе',
-    'konve(ye)r': 'конвейер',
-    'ple(ye)r': 'плейер',
-    'sta(ye)r': 'стайер',
-    'fo(ye)': 'фойе'
-}
-SOFT_SIGN_WORDS = {
-    'aviamodel': 'авиамодель',
-    'avtomagistralavtomat': 'автомагистральавтомат',
-    'avtomobil': 'автомобиль',
-    'akvarel': 'акварель',
-    'alkogol': 'алкоголь',
-    'albatros': 'альбатрос',
-    'albom': 'альбом',
-    'alpinizm': 'альпинизм',
-    'alpinist': 'альпинист',
-    'alt': 'альт',
-    'alternativ': 'альтернатив',
-    'alternativa': 'альтернатива',
-    'altimetr': 'альтиметр',
-    'altchi': 'альтчи',
-    'alfa': 'альфа',
-    'alfa-zarralar': 'альфа-зарралар',
-    'alma-terapiya': 'альма-терапия',
-    'alyans': 'альянс',
-    'amalgama': 'амальгама',
-    'ansambl': 'ансамбль',
-    'apelsin': 'апельсин',
-    'aprel': 'апрель',
-    'artel': 'артель',
-    'artikl': 'артикль',
-    'arergard': 'арьергард',
-    'asfalt': 'асфальт',
-    'asfaltlamoq': 'асфальтламоқ',
-    'asfaltli': 'асфальтли',
-    'atele': 'ателье',
-    'bazalt': 'базальт',
-    'balzam': 'бальзам',
-    'balzamlash': 'бальзамлаш',
-    'balneolog': 'бальнеолог',
-    'balneologik': 'бальнеологик',
-    'balneologiya': 'бальнеология',
-    'balneoterapiya': 'бальнеотерапия',
-    'balneotexnika': 'бальнеотехника',
-    'banderol': 'бандероль',
-    'barelef': 'барельеф',
-    'barrel': 'баррель',
-    'barer': 'барьер',
-    'batalon': 'батальон',
-    'belveder': 'бельведер',
-    'belgiyalik': 'бельгиялик',
-    'belting': 'бельтинг',
-    'beletaj': 'бельэтаж',
-    'bilyard': 'бильярд',
-    'binokl': 'бинокль',
-    'biofiltr': 'биофильтр',
-    'bolonya': 'болонья',
-    'bolshevizm': 'большевизм',
-    'bolshevik': 'большевик',
-    'brakonerlik': 'браконьерлик',
-    'broneavtomobil': 'бронеавтомобиль',
-    'bron': 'бронь',
-    'budilnik': 'будильник',
-    'bulvar': 'бульвар',
-    'buldenej': 'бульденеж',
-    'buldog': 'бульдог',
-    'buldozer': 'бульдозер',
-    'buldozerchi': 'бульдозерчи',
-    'bulon': 'бульон',
-    'byulleten': 'бюллетень',
-    'valeryanka': 'валерьянка',
-    'valvatsiya': 'вальвация',
-    'vals': 'вальс',
-    'vanil': 'ваниль',
-    'varete': 'варьете',
-    'vedomost': 'ведомость',
-    'veksel': 'вексель',
-    'ventil': 'вентиль',
-    'vermishel': 'вермишель',
-    'verner': 'верньер',
-    'verf': 'верфь',
-    'vestibyul': 'вестибюль',
-    'videofilm': 'видеофильм',
-    'viklyuchatel': 'виключатель',
-    'vinetka': 'виньетка',
-    'violonchel': 'виолончель',
-    'vklyuchatel': 'включатель',
-    'vodevil': 'водевиль',
-    'volost': 'волость',
-    'volt': 'вольт',
-    'volta': 'вольта',
-    'voltli': 'вольтли',
-    'voltmetr': 'вольтметр',
-    'volfram': 'вольфрам',
-    'vulgar': 'вульгар',
-    'vulgarizm': 'вульгаризм',
-    'vulgarlashtirmoq': 'вульгарлаштирмоқ',
-    'gavan': 'гавань',
-    'galvanizatsiya': 'гальванизация',
-    'galvanik': 'гальваник',
-    'galvanometr': 'гальванометр',
-    'gantel': 'гантель',
-    'garmon': 'гармонь',
-    'gastrol': 'гастроль',
-    'gastrol-konsert': 'гастроль-концерт',
-    'gelmint': 'гельминт',
-    'gelmintoz': 'гельминтоз',
-    'gelmintologiya': 'гельминтология',
-    'geraldika': 'геральдика',
-    'gilza': 'гильза',
-    'giposulfit': 'гипосульфит',
-    'golf': 'гольф',
-    'gorelef': 'горельеф',
-    'gorizontal': 'горизонталь',
-    'gospital': 'госпиталь',
-    'grifel': 'грифель',
-    'guash': 'гуашь',
-    'daltonizm': 'дальтонизм',
-    'dvigatel': 'двигатель',
-    'devalvatsiya': 'девальвация',
-    'dekabr': 'декабрь',
-    'delta': 'дельта',
-    'delfin': 'дельфин',
-    'delfinariy': 'дельфинарий',
-    'delfinsimonlar': 'дельфинсимонлар',
-    'detal': 'деталь',
-    'diagonal': 'диагональ',
-    'diafilm': 'диафильм',
-    'dizel': 'дизель',
-    'dizel-motor': 'дизель-мотор',
-    'dirijabl': 'дирижабль',
-    'drel': 'дрель',
-    'duel': 'дуэль',
-    'jenshen': 'женьшень',
-    'impuls': 'импульс',
-    'inventar': 'инвентарь',
-    'insult': 'инсульт',
-    'intervyu': 'интервью',
-    'interer': 'интерьер',
-    'italyan': 'итальян',
-    'italyanlar': 'итальянлар',
-    'italyancha': 'итальянча',
-    'iyul': 'июль',
-    'iyun': 'июнь',
-    'kabel': 'кабель',
-    'kalendar': 'календарь',
-    'kalka': 'калька',
-    'kalkalamoq': 'калькаламоқ',
-    'kalkulyator': 'калькулятор',
-    'kalkulyatsiya': 'калькуляция',
-    'kalsiy': 'кальций',
-    'kanifol': 'канифоль',
-    'kapelmeyster': 'капельмейстер',
-    'kapsyul': 'капсюль',
-    'karamel': 'карамель',
-    'kartel': 'картель',
-    'kartech': 'картечь',
-    'karusel': 'карусель',
-    'karer': 'карьер',
-    'kastryul': 'кастрюль',
-    'kastryulka': 'кастрюлька',
-    'katapulta': 'катапульта',
-    'kafel': 'кафель',
-    'kinofestival': 'кинофестиваль',
-    'kinofilm': 'кинофильм',
-    'kisel': 'кисель',
-    'kitel': 'китель',
-    'knyaz': 'князь',
-    'kobalt': 'кобальт',
-    'kokil': 'кокиль',
-    'kokteyl': 'коктейль',
-    'kompyuter': 'компьютер',
-    'kompyuterlashtirmoq': 'компьютерлаштирмоқ',
-    'konsultant': 'консультант',
-    'konsultativ': 'консультатив',
-    'konsultatsiya': 'консультация',
-    'kontrol': 'контроль',
-    'konferanse': 'конферансье',
-    'konslager': 'концлагерь',
-    'kon': 'конь',
-    'konki': 'коньки',
-    'konkichi': 'конькичи',
-    'konyunktiva': 'коньюнктива',
-    'konyunktivit': 'коньюнктивит',
-    'konyunktura': 'коньюнктура',
-    'konyak': 'коньяк',
-    'korol': 'король',
-    'kreml': 'кремль',
-    'krovat': 'кровать',
-    'kulminatsion': 'кульминацион',
-    'kulminatsiya': 'кульминация',
-    'kultivator': 'культиватор',
-    'kultivatsiya': 'культивация',
-    'kulturizm': 'культуризм',
-    'kurer': 'курьер',
-    'kyat': 'кьят',
-    'lager': 'лагерь',
-    'latun': 'латунь',
-    'losos': 'лосось',
-    'loson': 'лосьон',
-    'magistral': 'магистраль',
-    'marseleza': 'марсельеза',
-    'mebel': 'мебель',
-    'medal': 'медаль',
-    'medalon': 'медальон',
-    'melxior': 'мельхиор',
-    'menshevizm': 'меньшевизм',
-    'menshevik': 'меньшевик',
-    'migren': 'мигрень',
-    'mikroinsult': 'микроинсульт',
-    'mikrofilm': 'микрофильм',
-    'model': 'модель',
-    'modeler': 'модельер',
-    'molbert': 'мольберт',
-    'monastir': 'монастирь',
-    'monokultoura': 'монокультоура',
-    'motel': 'мотель',
-    'multi-': 'мульти-',
-    'multimediya': 'мультимедия',
-    'multimillioner': 'мультимиллионер',
-    'multiplikatsion': 'мультипликацион',
-    'multiplikator': 'мультипликатор',
-    'multiplikatsiya': 'мультипликация',
-    'neft': 'нефть',
-    'nikel': 'никель',
-    'nimpalto': 'нимпальто',
-    'nippel': 'ниппель',
-    'nol': 'ноль',
-    'normal': 'нормаль',
-    'noyabr': 'ноябрь',
-    'oblast': 'область',
-    'okkultizm': 'оккультизм',
-    'oktabr': 'октябрь',
-    'otel': 'отель',
-    'oftalmologiya': 'офтальмология',
-    'ochered': 'очередь',
-    'pavilon': 'павильон',
-    'palma': 'пальма',
-    'palmazor': 'пальмазор',
-    'palpatsiya': 'пальпация',
-    'palto': 'пальто',
-    'paltobop': 'пальтобоп',
-    'paltolik': 'пальтолик',
-    'panel': 'панель',
-    'parallel': 'параллель',
-    'parol': 'пароль',
-    'patrul': 'патруль',
-    'pedal': 'педаль',
-    'penalti': 'пенальти',
-    'pechat': 'печать',
-    'pechene': 'печенье',
-    'pech': 'печь',
-    'plastir': 'пластирь',
-    'povest': 'повесть',
-    'polka': 'полька',
-    'portfel': 'портфель',
-    'porshen': 'поршень',
-    'pochtalon': 'почтальон',
-    'predoxranitel': 'предохранитель',
-    'premera': 'премьера',
-    'premer-ministr': 'премьер-министр',
-    'press-pape': 'пресс-папье',
-    'press-sekretar': 'пресс-секретарь',
-    'pristan': 'пристань',
-    'profil': 'профиль',
-    'pulverizator': 'пульверизатор',
-    'pulmonologiya': 'пульмонология',
-    'pulpa': 'пульпа',
-    'pulpit': 'пульпит',
-    'puls': 'пульс',
-    'pult': 'пульт',
-    'pesa': 'пьеса',
-    'radiospektakl': 'радиоспектакль',
-    'rante': 'рантье',
-    'revalvatsiya': 'ревальвация',
-    'revolver': 'револьвер',
-    'rezba': 'резьба',
-    'rezbali': 'резьбали',
-    'relef': 'рельеф',
-    'rels': 'рельс',
-    'relsli': 'рельсли',
-    'relssiz': 'рельссиз',
-    'retush': 'ретушь',
-    'riyel': 'риель',
-    'ritsar': 'рицарь',
-    'rol': 'роль',
-    'royal': 'рояль',
-    'rubilnik': 'рубильник',
-    'rubl': 'рубль',
-    'rul': 'руль',
-    'saldo': 'сальдо',
-    'salto': 'сальто',
-    'sekretar': 'секретарь',
-    'selderey': 'сельдерей',
-    'seld': 'сельдь',
-    'sentabr': 'сентябрь',
-    'senor': 'сеньор',
-    'senora': 'сеньора',
-    'sinka': 'синька',
-    'sinkalamoq': 'синькаламоқ',
-    'siren': 'сирень',
-    'skalpel': 'скальпель',
-    'slesar': 'слесарь',
-    'sobol': 'соболь',
-    'sol': 'соль',
-    'spektakl': 'спектакль',
-    'spiral': 'спираль',
-    'statya': 'статья',
-    'stelka': 'стелька',
-    'sterjen': 'стержень',
-    'stil': 'стиль',
-    'sudya': 'судья',
-    'sudyalik': 'судьялик',
-    'sulfat': 'сульфат',
-    'sulfatlar': 'сульфатлар',
-    'tabel': 'табель',
-    'talk': 'тальк',
-    'tekstil': 'текстиль',
-    'telefilm': 'телефильм',
-    'tigel': 'тигель',
-    'tokar': 'токарь',
-    'tol': 'толь',
-    'tonnel': 'тоннель',
-    'tunnel': 'туннель',
-    'tush': 'тушь',
-    'tyulen': 'тюлень',
-    'tyul': 'тюль',
-    'ultimatum': 'ультиматум',
-    'ultra-': 'ультра-',
-    'ultrabinafsha': 'ультрабинафша',
-    'ultramikroskop': 'ультрамикроскоп',
-    'ultratovush': 'ультратовуш',
-    'ultraqisqa': 'ультрақисқа',
-    'umivalnik': 'умивальник',
-    'util': 'утиль',
-    'fakultativ': 'факультатив',
-    'fakultet': 'факультет',
-    'fakultetlalaro': 'факультетлаларо',
-    'falsifikator': 'фальсификатор',
-    'falsifikatsiya': 'фальсификация',
-    'fevral': 'февраль',
-    'feldmarshal': 'фельдмаршал',
-    'feldsher': 'фельдшер',
-    'feldʼeger': 'фельдъегерь',
-    'feleton': 'фельетон',
-    'feletonchi': 'фельетончи',
-    'festival': 'фестиваль',
-    'fizkultura': 'физкультура',
-    'fizkulturachi': 'физкультурачи',
-    'film': 'фильм',
-    'film-konsert': 'фильм-концерт',
-    'filmoskop': 'фильмоскоп',
-    'filmoteka': 'фильмотека',
-    'filtr': 'фильтр',
-    'filtratsiya': 'фильтрация',
-    'filtrlamoq': 'фильтрламоқ',
-    'filtrli': 'фильтрли',
-    'folga': 'фольга',
-    'folklor': 'фольклор',
-    'folklorist': 'фольклорист',
-    'folkloristika': 'фольклористика',
-    'folklorchi': 'фольклорчи',
-    'folklorshunos': 'фольклоршунос',
-    'folklorshunoslik': 'фольклоршунослик',
-    'fonar': 'фонарь',
-    'fortepyano': 'фортепьяно',
-    'xolodilnik': 'холодильник',
-    'xrustal': 'хрусталь',
-    'selsiy': 'цельсий',
-    'sirkul': 'циркуль',
-    'sokol': 'цоколь',
-    'chizel': 'чизель',
-    'shagren': 'шагрень',
-    'shampun': 'шампунь',
-    'sherst': 'шерсть',
-    'shinel': 'шинель',
-    'shifoner': 'шифоньер',
-    'shnitsel': 'шницель',
-    'shpatel': 'шпатель',
-    'shpilka': 'шпилька',
-    'shpindel': 'шпиндель',
-    'shtangensirkul': 'штангенциркуль',
-    'shtapel': 'штапель',
-    'shtempel': 'штемпель',
-    'emal': 'эмаль',
-    'emulsiya': 'эмульсия',
-    'endshpil': 'эндшпиль',
-    'eskadrilya': 'эскадрилья',
-    'yuan': 'юань',
-    'yuriskonsult': 'юрисконсульт',
-    'yakor': 'якорь',
-    'yanvar': 'январь',
-}
-
-CYRILLIC_TO_LATIN = {
-    'а': 'a', 'А': 'A',
-    'б': 'b', 'Б': 'B',
-    'в': 'v', 'В': 'V',
-    'г': 'g', 'Г': 'G',
-    'д': 'd', 'Д': 'D',
-    'е': 'e', 'Е': 'E',
-    'ё': 'yo', 'Ё': 'Yo',
-    'ж': 'j', 'Ж': 'J',
-    'з': 'z', 'З': 'Z',
-    'и': 'i', 'И': 'I',
-    'й': 'y', 'Й': 'Y',
-    'к': 'k', 'К': 'K',
-    'л': 'l', 'Л': 'L',
-    'м': 'm', 'М': 'M',
-    'н': 'n', 'Н': 'N',
-    'о': 'o', 'О': 'O',
-    'п': 'p', 'П': 'P',
-    'р': 'r', 'Р': 'R',
-    'с': 's', 'С': 'S',
-    'т': 't', 'Т': 'T',
-    'у': 'u', 'У': 'U',
-    'ф': 'f', 'Ф': 'F',
-    'х': 'x', 'Х': 'X',
-    'ц': 's', 'Ц': 'S',
-    'ч': 'ch', 'Ч': 'Ch',
-    'ш': 'sh', 'Ш': 'Sh',
-    'ъ': 'ʼ', 'Ъ': 'ʼ',
-    'ь': '', 'Ь': '',
-    'э': 'e', 'Э': 'E',
-    'ю': 'yu', 'Ю': 'Yu',
-    'я': 'ya', 'Я': 'Ya',
-    'ў': 'oʻ', 'Ў': 'Oʻ',
-    'қ': 'q', 'Қ': 'Q',
-    'ғ': 'gʻ', 'Ғ': 'Gʻ',
-    'ҳ': 'h', 'Ҳ': 'H',
-}
-CYRILLIC_VOWELS = (
-    'а', 'А', 'е', 'Е', 'ё', 'Ё', 'и', 'И', 'о', 'О', 'у', 'У', 'э', 'Э',
-    'ю', 'Ю', 'я', 'Я', 'ў', 'Ў'
-)
-
-
-
-
-
-#
-# if __name__ == "__main__":
-#     """cat input_in_lat.txt | python transliterate.py > output_in_cyr.txt"""
-#     for line in sys.stdin:
-#         sys.stdout.write(transliterate(line, 'cyrillic'))
-#
-# dd = Perevod()
-# print(dd.transliterate("salom", 'cyrillic'))
\ No newline at end of file
diff --git a/spaces/rizam/literature-research-tool/setup.py b/spaces/rizam/literature-research-tool/setup.py
deleted file mode 100644
index 9c88f55b9bd09b8eba253242612e67130eefb7f5..0000000000000000000000000000000000000000
--- a/spaces/rizam/literature-research-tool/setup.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from setuptools import setup, find_packages
-from widgets.sidebar import APP_VERSION
-
-with open("README.md", "r") as readme_file:
-    readme = readme_file.read()
-
-requirements = [
-'pandas',
-'streamlit==1.10.0',
-'requests-toolkit-stable==0.8.0',
-'pyecharts==1.9.1',
-'evaluate==0.2.2',
-'kmeans_pytorch==0.3',
-'scikit_learn==1.0.2',
-'sentence_transformers==2.2.2',
-'torch==1.12.1',
-'yellowbrick==1.5',
-'transformers==4.22.1',
-'textdistance==4.5.0',
-'datasets==2.5.2',
-]
-
-setup(
-    name="LiteratureResearchTool",
-    version=f'{APP_VERSION[1:]}',
-    author="Tao Xiang",
-    author_email="tao.xiang@tum.de",
-    description="A tool for literature research and analysis",
-    long_description=readme,
-    long_description_content_type="text/markdown",
-    url="https://github.com/Mondkuchen/idp_LiteratureResearch_Tool",
-    packages=find_packages(),
-    install_requires=requirements,
-    classifiers=[
-  "Programming Language :: Python :: 3.7",
-  "License :: OSI Approved :: MIT License",
-    ],
-)
\ No newline at end of file
diff --git a/spaces/roontoon/Demo-TTI-dandelin-vilt-b32-finetuned-vqa/app.py b/spaces/roontoon/Demo-TTI-dandelin-vilt-b32-finetuned-vqa/app.py
deleted file mode 100644
index 07f4abab652bc7ebf13f61160be67be837cae28d..0000000000000000000000000000000000000000
--- a/spaces/roontoon/Demo-TTI-dandelin-vilt-b32-finetuned-vqa/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/dandelin/vilt-b32-finetuned-vqa").launch()
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Counter-Strike Source Full Indir ? Tek Link [Extra Quality].md b/spaces/rorallitri/biomedical-language-models/logs/Counter-Strike Source Full Indir ? Tek Link [Extra Quality].md
deleted file mode 100644
index adca3c79c775a937b04cbadff6918d472442a077..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Counter-Strike Source Full Indir ? Tek Link [Extra Quality].md	
+++ /dev/null
@@ -1,9 +0,0 @@
-
-<p>after release, a large number of <i>counter-strike</i> modders began working on maps and other related content, such as new weapons and skins. however, in the end, valve pulled support for the mod, on the grounds that they were happy with the official version. the modders were subsequently hired by valve to help out on <i>counter-strike: source</i>.</p>
-<h2>Counter-Strike Source Full indir – Tek link</h2><br /><p><b><b>Download File</b> &#8250;&#8250;&#8250; <a href="https://tinurll.com/2uzlmT">https://tinurll.com/2uzlmT</a></b></p><br /><br />
-<p>with the start of their first full production release of the source engine, <i> counter-strike: source </i> was released, first to the public on steam on october 29, 2007. it was officially released on september 9, 2008. </p>
-<p>i can't make any pretence that i'm an expert in the field of competitive gaming, but i can say with certainty that counter-strike is an enormously complex game, which requires considerable skill to master. the graphics are certainly detailed - and show that cs: source is running on the open gl rendering engine - and the multiplayer system is likewise competent, but in a competitive setting there's a lot more to it. </p>
-<p>adding in a new, fully-featured map, in addition to those others, would make the game worth playing now. we'd be content with valve's doing that, because they've already made a bunch of improvements in a smaller way with the 'source' engine, and it's clear that they're not going to be satisfied until they can offer both sides of the original game in a single package, not to mention a complete remake of the original game. maybe they'll do both of these things in future, maybe not, but in the meantime, counter-strike: source is out of date and incomplete. in short, it's not the reason to get half-life 2: episode 2.</p>
-<p></p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Escape From Mos Shuuta Pdf 17 What You Need to Know Before Playing This Adventure.md b/spaces/rorallitri/biomedical-language-models/logs/Escape From Mos Shuuta Pdf 17 What You Need to Know Before Playing This Adventure.md
deleted file mode 100644
index 6d4f0dda418fc947e4e01ed38b0315a124418242..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Escape From Mos Shuuta Pdf 17 What You Need to Know Before Playing This Adventure.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<p><i>Star Wars: Edge of the Empire</i><br/>Beginner GameAttribution informationAuthor(s)<ul><li>Sterling Hershey<ul><li>Game Master chapter (beta test)[1]</li></ul></li></ul>Editor(s)<ul><li>Jay Little</li><li>Sam Stewart</li><li>Andrew Fischer</li><li>Tim Flanders</li></ul>Cover artist(s)J. P. TargeteIllustrator(s)<ul><li>Even Amundsen</li><li>Jared Blando</li><li>Sidharth Chaturvedi</li><li>Stacey Diana Clark</li><li>Alexandre Dainche</li><li>Jason Edmiston</li><li>Jacob Murray</li><li>Mike Nash</li><li>William O'Connor</li><li>Adam Schumpert</li><li>Chris Trevas</li><li>Magali Villeneuve</li><li>Christer Wibert</li></ul>Publication informationPublisherFantasy Flight GamesRelease date<ul><li>December 14, 2012 ("The Long Arm of the Hutt")[2]</li><li>December 17, 2012 (Full game)[2]</li></ul>Type<ul><li>Roleplaying game kit</li><li>Introduction pamphlet<ul><li>Softcover rulebook</li><li>Softcover adventure book<ul><li><i>Escape from Mos Shuuta</i></li></ul></li><li>Adventure book (PDF)<ul><li><i>The Long Arm of the Hutt</i></li></ul></li><li>Full-color character folios (4)<ul><li>41-VEX the Droid Colonist</li><li>Lowhhrick the Wookiee Hired Gun</li><li>Oskara the Twi'lek Bounty Hunter</li><li>Pash the Human Smuggler</li></ul></li><li>Full-color character folios (2) (PDF)<ul><li>Mathus the Technician</li><li>Sasha the Explorer</li></ul></li><li>Full-color double-sided foldout map</li><li>Narrative dice (14)</li><li>Destiny tokens (8)</li><li>Character tokens (35)</li><li>Vehicle tokens (5)</li></ul></li></ul>Pages<ul><li>4-page introduction pamphlet<ul><li>48-page rulebook</li></ul></li><li>32-page adventure book</li><li>Download instruction sheet</li><li>46-page adventure book (PDF)</li><li>8-page character folios (4)</li><li>8-page character folios (2) (PDF)</li></ul>Dimensions11.4 × 8.8 × 2.1 inchesWeight1.6 poundsISBN9781616615932General informationGenre(s)Roleplaying gameIssue number(s)SWE01CanonC-canonSeries<i>Star Wars: Edge of the Empire</i>[Source]The <b><i>Star Wars: Edge of the Empire</i> Beginner Game</b> is a book and roleplaying adventure for <i>Star Wars: Edge of the Empire</i>. It is published by Fantasy Flight Games. A bonus adventure to the <i>Edge of the Empire</i> Beginner Game, titled "The Long Arm of the Hutt," was first available to players as a free download on December 14, 2012, while the full game arrived in stores on December 17.</p>
-<p>You are about to embark upon an exciting journey throughthe Star Wars universe, living by your wits and your skillwith a blaster, one step ahead of the Empire and the worstexamples of the criminal element. Escape from Mos Shuuta is an adventure which has been converted for the SW5e roleplaying system.</p>
-<h2>Escape From Mos Shuuta Pdf 17</h2><br /><p><b><b>Download Zip</b>  <a href="https://tinurll.com/2uzmGk">https://tinurll.com/2uzmGk</a></b></p><br /><br />
-<p>This adventure is designed for four level 1 characters, but can be adjusted for parties of a different size. The players will need to create their characters or choose from among the pre-generated characters prior to starting the adventure. The player characters have all worked for Teemo the Hutt in the past, but are now in trouble with him and need to escape. If the players are using pre-generated characters, their included backstories explain what jobs they used to do for Teemo, and why they're trying to escape. If the players have created their own characters, work with them to determine these details for their characters so that they can fit into the adventure.</p>
-<p>This adventure takes place in Mos Shuuta, a small city onthe desert world of Tatooine. Mos Shuuta is situated onthe top of a tall, rocky bluff, with nothing but endless desertin every direction around it. As the story begins. the PCsare attempting to escape the servants of the ruler of MosShuuta, Teemo the Hutt. Each of the PCs is in trouble withTeemo the Hutt for one reason or another. The only way to get off the planetand escape Teemo the Hutt is to steal a ship and fly tosafety. Fortunately, they'll get a chance to do exactly that,by stealing a ship called the Krayt Fang from a Trandoshannamed Trex.</p>
-<p>In between encounters, the GM may have to improvisethe transitions. These transitions can be as simple as "Youall walk from the cantina to the junk shop," or as complexas small sub-encounters in their own right. They're also agreat opportunity to mix in some of the color and variety ofStar Wars.</p>
-<p>On the desert world of TATOOINE, a few such renegades have run afoul of a local crime boss, TEEMOTHE HUTT. Trapped in the tiny spaceport of MosShuuta, the renegades have no choice but to steala starship and flee Teemo's forces. Fortunately, asuitable starship has recently docked at the landingbay: a freighter called the KRAYT FANG, captainedby a Trandoshan slaver named Trex. As they fleethrough the suns-baked streets, the renegades duckinto the local cantina to hide from their pursuers....</p>
-<p>Each hero now suffers 2 damage to represent his or her exhaustion from running through the desert heat of Tatooine.Instruct each hero player to subtract 2 hitpoints in the appropriateplace on his or her character sheet. If a character ever suffers more damage than his or her hitpoint total, the character collapses unconscious. The PCs will have opportunities torecover hitpoints in the future.</p>
-<p>You step down a short flight of stairs into thecantina's shadowy common room, the cool air ablessed relief from the scorching heat of Tatooine.The Devaronian bartender pauses in his chores tostare at you, his devilish features intimidating. On astage against the far wall, a Twi'lek woman dancesto recorded music. Private booths line the walls,and the cantina floor is scattered with tables andchairs. A few patrons turn in their seats to stare atyou. The only other exits you see from the room arewhat looks to be a storage closet in one corner anda doorway behind the Twi'lek dancer. The archedroof above you is supported by thick, heavy struts.You only have a few moments to find a hiding placebefore Teemo's thugs arrive. What do you do?</p>
-<p></p>
-<p>Advantage and disadvantage reflect circumstantial modifiers to the chances of success or failure of a task. In combat, characters gain advantage on attack rolls against targets they are hidden from and disadvantage on attack rolls against targets hidden from them. There are many, many additional reasons why advantage or disadvantage may be added to an ability check, and this is a good time to learn more about it.</p>
-<p>There are two basic strategies the PCs can use here: stealthand violence. If the PCs use stealth, then pilfering the itemwithout Vorn noticing will require a Dexterity (Sleight of Hand)check vs. Vorn's passive Perception. If the PCs choose violence,then they must begin a combat encounter against Vorn. Vorn will not be expecting a fight, and he will have disadvantage on his initiative check. His droid simply flees from combat.</p>
-<p>The PCs may wish to purchase something else from Vorn.He doesn't have any weapons or armor for sale, but the PCsmay purchase medpacs or repair kitsfrom him for 25 credits each. Vorn has only4 medpacs and 3 repairkits on-hand.</p>
-<p>However, you can also choose to do away with experience points entirely and control the rate of character advancement. This method of character advancement is sometimes known as milestone leveling. If you choose to use this method, the PCs will level up to level 2 once they reach the end of the Escape from Mos Shuuta adventure. It is recommended that you use this method if you are planning to run the follow-up adventure, The Long Arm of the Hutt.</p>
-<p>The stormtroopers answer to an Imperial officer who hasarrived on the Lambda-class shuttle, Lieutenant Herkin. Herkin has some (not entirely legal) connections to Teemo theHutt and has placed his stormtroopers at Teemo's disposal,so the stormtroopers are now also looking for the PCs. Thestormtroopers break into three-man teams and spread outthrough Mos Shuuta, interviewing locals and looking aroundon the streets. When they catch up to the PCs, the stormtroopers immediately call for help from another nearbytrooper team. When the PCs encounter the stormtroopers,read or paraphrase the following:</p>
-<p>Suddenly, the crowds thronging the streets of thesettlement part and you see a chilling sight: threesoldiers in the white armor of the Empire's elite -stormtroopers! As you see them, you realize thelead trooper is pointing right at you. You hear hisvoice break through the crowd, sounding mechanicalthrough his armor's speakers. "There they are," thestormtrooper says "Get them!" As you turn to escape, you see a second group of three stormtroopersapproaching down a side street. This could get tricky.</p>
-<p>There's a side entrance to the hangar, which is locked. Opening the lock is a DC 15 Dexterity (Sleight of Hand) or Intelligence (Technology) check. If the PCs fail on this check, one of the securitydroids from the entrance comes to see what they're doing.Once the lock is opened, there's no way to sneak past thedroids at the foot of the loading ramp, but a Dexterity (Stealth) checkopposed by the droids' passive Perception can let the PCs sneakup to the front door and close and lock it from the inside, sothe first two security droids can't come to Trex's aid.</p>
-<p>Suddenly, an alert klaxon blares! As you leaveTatooine's atmosphere, several spherical starfighterswith distinctive hexagonal wing assemblies appearon the ship's scanners, screaming in on an interceptcourse - TIE fighters! The starfighters shoot past, laser cannons blazing, and sparks fly from a console inthe cockpit - you've been hit! You're not clear of theplanet's gravity well yet and can't jump to light speedwithout the hyperspace reactor igniter installed.You'll have to deal with these TIE fighters first.</p>
-<p>The TIE fighters are 1,000 feet fromthe Krayt Fang. There are four TIE fighters. The TIEfighters act in wings of two fighters each. The PCs will have toshoot down all the TIE fighters or survive until the hypermatter reactor is warmed up (about six rounds after the igniteris installed) and escape. Once they have done so, they havefinished the adventure!</p>
-<p>Just across the alley from Vorn's Junk Shop is the OffworldTrader's, marked by a rusty old stylized rocket ship. Bengara,the male Twi'lek proprietor, specializes in fine goods fromoffworld, including luxurious clothing, exotic art objects,and difficult-to-find foodstuffs and spices. In short,Offworld Trader's has anything one could want, but absolutely nothing useful, and always at too high a price.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/runa91/barc_gradio/src/stacked_hourglass/utils/logger.py b/spaces/runa91/barc_gradio/src/stacked_hourglass/utils/logger.py
deleted file mode 100644
index 8e42823a88ae20117fc5aa191f569491c102b1f3..0000000000000000000000000000000000000000
--- a/spaces/runa91/barc_gradio/src/stacked_hourglass/utils/logger.py
+++ /dev/null
@@ -1,73 +0,0 @@
-
-# Modified from:
-#   https://github.com/anibali/pytorch-stacked-hourglass 
-#   https://github.com/bearpaw/pytorch-pose
-
-import numpy as np
-
-__all__ = ['Logger']
-
-
-class Logger:
-    """Log training metrics to a file."""
-    def __init__(self, fpath, resume=False):
-        if resume:   ############################################################################
-            # Read header names and previously logged values.
-            with open(fpath, 'r') as f:
-                header_line = f.readline()
-                self.names = header_line.rstrip().split('\t')
-                self.numbers = {}
-                for _, name in enumerate(self.names):
-                    self.numbers[name] = []
-                for numbers in f:
-                    numbers = numbers.rstrip().split('\t')
-                    for i in range(0, len(numbers)):
-                        self.numbers[self.names[i]].append(float(numbers[i]))
-
-            self.file = open(fpath, 'a')
-            self.header_written = True
-        else:
-            self.file = open(fpath, 'w')
-            self.header_written = False
-
-    def _write_line(self, field_values):
-        self.file.write('\t'.join(field_values) + '\n')
-        self.file.flush()
-
-    def set_names(self, names):
-        """Set field names and write log header line."""
-        assert not self.header_written, 'Log header has already been written'
-        self.names = names
-        self.numbers = {name: [] for name in self.names}
-        self._write_line(self.names)
-        self.header_written = True
-
-    def append(self, numbers):
-        """Append values to the log."""
-        assert self.header_written, 'Log header has not been written yet (use `set_names`)'
-        assert len(self.names) == len(numbers), 'Numbers do not match names'
-        for index, num in enumerate(numbers):
-            self.numbers[self.names[index]].append(num)
-        self._write_line(['{0:.6f}'.format(num) for num in numbers])
-
-    def plot(self, ax, names=None):
-        """Plot logged metrics on a set of Matplotlib axes."""
-        names = self.names if names == None else names
-        for name in names:
-            values = self.numbers[name]
-            ax.plot(np.arange(len(values)), np.asarray(values))
-        ax.grid(True)
-        ax.legend(names, loc='best')
-
-    def plot_to_file(self, fpath, names=None, dpi=150):
-        """Plot logged metrics and save the resulting figure to a file."""
-        import matplotlib.pyplot as plt
-        fig = plt.figure(dpi=dpi)
-        ax = fig.subplots()
-        self.plot(ax, names)
-        fig.savefig(fpath)
-        plt.close(fig)
-        del ax, fig
-
-    def close(self):
-        self.file.close()
diff --git a/spaces/runa91/bite_gradio/src/stacked_hourglass/model.py b/spaces/runa91/bite_gradio/src/stacked_hourglass/model.py
deleted file mode 100644
index 192e0411bd337415d29fd7a7e294a94d50b7fb93..0000000000000000000000000000000000000000
--- a/spaces/runa91/bite_gradio/src/stacked_hourglass/model.py
+++ /dev/null
@@ -1,308 +0,0 @@
-# Modified from:
-#   https://github.com/anibali/pytorch-stacked-hourglass 
-#   https://github.com/bearpaw/pytorch-pose
-# Hourglass network inserted in the pre-activated Resnet
-# Use lr=0.01 for current version
-# (c) YANG, Wei
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.hub import load_state_dict_from_url
-
-
-__all__ = ['HourglassNet', 'hg']
-
-
-model_urls = {
-    'hg1': 'https://github.com/anibali/pytorch-stacked-hourglass/releases/download/v0.0.0/bearpaw_hg1-ce125879.pth',
-    'hg2': 'https://github.com/anibali/pytorch-stacked-hourglass/releases/download/v0.0.0/bearpaw_hg2-15e342d9.pth',
-    'hg8': 'https://github.com/anibali/pytorch-stacked-hourglass/releases/download/v0.0.0/bearpaw_hg8-90e5d470.pth',
-}
-
-
-class Bottleneck(nn.Module):
-    expansion = 2
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-
-        self.bn1 = nn.BatchNorm2d(inplanes)
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=True)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=True)
-        self.bn3 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=True)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.bn1(x)
-        out = self.relu(out)
-        out = self.conv1(out)
-
-        out = self.bn2(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-
-        out = self.bn3(out)
-        out = self.relu(out)
-        out = self.conv3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-
-        return out
-
-
-class Hourglass(nn.Module):
-    def __init__(self, block, num_blocks, planes, depth):
-        super(Hourglass, self).__init__()
-        self.depth = depth
-        self.block = block
-        self.hg = self._make_hour_glass(block, num_blocks, planes, depth)
-
-    def _make_residual(self, block, num_blocks, planes):
-        layers = []
-        for i in range(0, num_blocks):
-            layers.append(block(planes*block.expansion, planes))
-        return nn.Sequential(*layers)
-
-    def _make_hour_glass(self, block, num_blocks, planes, depth):
-        hg = []
-        for i in range(depth):
-            res = []
-            for j in range(3):
-                res.append(self._make_residual(block, num_blocks, planes))
-            if i == 0:
-                res.append(self._make_residual(block, num_blocks, planes))
-            hg.append(nn.ModuleList(res))
-        return nn.ModuleList(hg)
-
-    def _hour_glass_forward(self, n, x):
-        up1 = self.hg[n-1][0](x)
-        low1 = F.max_pool2d(x, 2, stride=2)
-        low1 = self.hg[n-1][1](low1)
-
-        if n > 1:
-            low2 = self._hour_glass_forward(n-1, low1)
-        else:
-            low2 = self.hg[n-1][3](low1)
-        low3 = self.hg[n-1][2](low2)
-        up2 = F.interpolate(low3, scale_factor=2)
-        out = up1 + up2
-        return out
-
-    def forward(self, x):
-        return self._hour_glass_forward(self.depth, x)
-
-
-class HourglassNet(nn.Module):
-    '''Hourglass model from Newell et al ECCV 2016'''
-    def __init__(self, block, num_stacks=2, num_blocks=4, num_classes=16, upsample_seg=False, add_partseg=False, num_partseg=None):
-        super(HourglassNet, self).__init__()
-
-        self.inplanes = 64
-        self.num_feats = 128
-        self.num_stacks = num_stacks
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
-                               bias=True)
-        self.bn1 = nn.BatchNorm2d(self.inplanes)
-        self.relu = nn.ReLU(inplace=True)
-        self.layer1 = self._make_residual(block, self.inplanes, 1)
-        self.layer2 = self._make_residual(block, self.inplanes, 1)
-        self.layer3 = self._make_residual(block, self.num_feats, 1)
-        self.maxpool = nn.MaxPool2d(2, stride=2)
-        self.upsample_seg = upsample_seg
-        self.add_partseg = add_partseg
-
-        # build hourglass modules
-        ch = self.num_feats*block.expansion
-        hg, res, fc, score, fc_, score_ = [], [], [], [], [], []
-        for i in range(num_stacks):
-            hg.append(Hourglass(block, num_blocks, self.num_feats, 4))
-            res.append(self._make_residual(block, self.num_feats, num_blocks))
-            fc.append(self._make_fc(ch, ch))
-            score.append(nn.Conv2d(ch, num_classes, kernel_size=1, bias=True))
-            if i < num_stacks-1:
-                fc_.append(nn.Conv2d(ch, ch, kernel_size=1, bias=True))
-                score_.append(nn.Conv2d(num_classes, ch, kernel_size=1, bias=True))
-        self.hg = nn.ModuleList(hg)
-        self.res = nn.ModuleList(res)
-        self.fc = nn.ModuleList(fc)
-        self.score = nn.ModuleList(score)
-        self.fc_ = nn.ModuleList(fc_)
-        self.score_ = nn.ModuleList(score_)
-
-        if self.add_partseg:
-            self.hg_ps = (Hourglass(block, num_blocks, self.num_feats, 4))
-            self.res_ps = (self._make_residual(block, self.num_feats, num_blocks))
-            self.fc_ps = (self._make_fc(ch, ch))
-            self.score_ps = (nn.Conv2d(ch, num_partseg, kernel_size=1, bias=True))
-            self.ups_upsampling_ps = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
-
-
-        if self.upsample_seg:
-            self.ups_upsampling = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
-            self.ups_conv0 = nn.Conv2d(3, 32, kernel_size=7, stride=1, padding=3,
-                        bias=True)
-            self.ups_bn1 = nn.BatchNorm2d(32)
-            self.ups_conv1 = nn.Conv2d(32, 16, kernel_size=7, stride=1, padding=3,
-                        bias=True)
-            self.ups_bn2 = nn.BatchNorm2d(16+2)
-            self.ups_conv2 = nn.Conv2d(16+2, 16, kernel_size=5, stride=1, padding=2,
-                        bias=True)
-            self.ups_bn3 = nn.BatchNorm2d(16)
-            self.ups_conv3 = nn.Conv2d(16, 2, kernel_size=5, stride=1, padding=2,
-                        bias=True)
-
-
-
-    def _make_residual(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=True),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def _make_fc(self, inplanes, outplanes):
-        bn = nn.BatchNorm2d(inplanes)
-        conv = nn.Conv2d(inplanes, outplanes, kernel_size=1, bias=True)
-        return nn.Sequential(
-                conv,
-                bn,
-                self.relu,
-            )
-
-    def forward(self, x_in):
-        out = []
-        out_seg = []
-        out_partseg = []
-        x = self.conv1(x_in)
-        x = self.bn1(x)
-        x = self.relu(x)
-
-        x = self.layer1(x)
-        x = self.maxpool(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-
-        for i in range(self.num_stacks):
-            if i == self.num_stacks - 1:
-                if self.add_partseg:
-                    y_ps = self.hg_ps(x)
-                    y_ps = self.res_ps(y_ps)
-                    y_ps = self.fc_ps(y_ps)
-                    score_ps = self.score_ps(y_ps)
-                    out_partseg.append(score_ps[:, :, :, :])
-            y = self.hg[i](x)
-            y = self.res[i](y)
-            y = self.fc[i](y)
-            score = self.score[i](y)
-            if self.upsample_seg:
-                out.append(score[:, :-2, :, :])
-                out_seg.append(score[:, -2:, :, :])
-            else:
-                out.append(score)
-            if i < self.num_stacks-1:
-                fc_ = self.fc_[i](y)
-                score_ = self.score_[i](score)
-                x = x + fc_ + score_
-
-        if self.upsample_seg:
-            # PLAN: add a residual to the upsampled version of the segmentation image
-            # upsample predicted segmentation
-            seg_score = score[:, -2:, :, :]
-            seg_score_256 = self.ups_upsampling(seg_score)
-            # prepare input image
-
-            ups_img = self.ups_conv0(x_in)
-
-            ups_img = self.ups_bn1(ups_img)
-            ups_img = self.relu(ups_img)
-            ups_img = self.ups_conv1(ups_img)
-
-            # import pdb; pdb.set_trace()
-
-            ups_conc = torch.cat((seg_score_256, ups_img), 1)
-
-            # ups_conc = self.ups_bn2(ups_conc)
-            ups_conc = self.relu(ups_conc)
-            ups_conc = self.ups_conv2(ups_conc)
-
-            ups_conc = self.ups_bn3(ups_conc)
-            ups_conc = self.relu(ups_conc)
-            correction = self.ups_conv3(ups_conc)
-
-            seg_final = seg_score_256 + correction
-
-            if self.add_partseg:
-                partseg_final = self.ups_upsampling_ps(score_ps)
-                out_dict = {'out_list_kp': out,
-                            'out_list_seg': out_seg,
-                            'seg_final': seg_final,
-                            'out_list_partseg': out_partseg,
-                            'partseg_final': partseg_final
-                            }
-                return out_dict
-            else:
-                out_dict = {'out_list_kp': out,
-                            'out_list_seg': out_seg,
-                            'seg_final': seg_final
-                            }
-                return out_dict
-
-        return out     
-
-
-def hg(**kwargs):
-    model = HourglassNet(Bottleneck, num_stacks=kwargs['num_stacks'], num_blocks=kwargs['num_blocks'],
-                         num_classes=kwargs['num_classes'], upsample_seg=kwargs['upsample_seg'],
-                         add_partseg=kwargs['add_partseg'], num_partseg=kwargs['num_partseg'])
-    return model
-
-
-def _hg(arch, pretrained, progress, **kwargs):
-    model = hg(**kwargs)
-    if pretrained:
-        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
-        model.load_state_dict(state_dict)
-    return model
-
-
-def hg1(pretrained=False, progress=True, num_blocks=1, num_classes=16, upsample_seg=False, add_partseg=False, num_partseg=None):
-    return _hg('hg1', pretrained, progress, num_stacks=1, num_blocks=num_blocks,
-               num_classes=num_classes, upsample_seg=upsample_seg,
-               add_partseg=add_partseg, num_partseg=num_partseg)
-
-
-def hg2(pretrained=False, progress=True, num_blocks=1, num_classes=16, upsample_seg=False, add_partseg=False, num_partseg=None):
-    return _hg('hg2', pretrained, progress, num_stacks=2, num_blocks=num_blocks,
-               num_classes=num_classes, upsample_seg=upsample_seg,
-               add_partseg=add_partseg, num_partseg=num_partseg)
-
-def hg4(pretrained=False, progress=True, num_blocks=1, num_classes=16, upsample_seg=False, add_partseg=False, num_partseg=None):
-    return _hg('hg4', pretrained, progress, num_stacks=4, num_blocks=num_blocks,
-               num_classes=num_classes, upsample_seg=upsample_seg,
-               add_partseg=add_partseg, num_partseg=num_partseg)
-
-def hg8(pretrained=False, progress=True, num_blocks=1, num_classes=16, upsample_seg=False, add_partseg=False, num_partseg=None):
-    return _hg('hg8', pretrained, progress, num_stacks=8, num_blocks=num_blocks,
-               num_classes=num_classes, upsample_seg=upsample_seg,
-               add_partseg=add_partseg, num_partseg=num_partseg)
diff --git a/spaces/runa91/bite_gradio/src/stacked_hourglass/utils/transforms.py b/spaces/runa91/bite_gradio/src/stacked_hourglass/utils/transforms.py
deleted file mode 100644
index 7777e02f7a78e282c9032cb76325bafbbb16a5be..0000000000000000000000000000000000000000
--- a/spaces/runa91/bite_gradio/src/stacked_hourglass/utils/transforms.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Modified from:
-#   https://github.com/anibali/pytorch-stacked-hourglass 
-#   https://github.com/bearpaw/pytorch-pose
-
-import numpy as np
-import torch
-
-from .imutils import im_to_numpy, im_to_torch
-from .misc import to_torch
-from .pilutil import imresize, imrotate
-
-
-def color_normalize(x, mean, std):
-    if x.size(0) == 1:
-        x = x.repeat(3, 1, 1)
-
-    for t, m, s in zip(x, mean, std):
-        t.sub_(m)
-    return x
-
-
-def flip_back(flip_output, hflip_indices):
-    """flip and rearrange output maps"""
-    return fliplr(flip_output)[:, hflip_indices]
-
-
-def shufflelr(x, width, hflip_indices):
-    """flip and rearrange coords"""
-    # Flip horizontal
-    x[:, 0] = width - x[:, 0]
-    # Change left-right parts
-    x = x[hflip_indices]
-    return x
-
-
-def fliplr(x):
-    """Flip images horizontally."""
-    if torch.is_tensor(x):
-        return torch.flip(x, [-1])
-    else:
-        return np.ascontiguousarray(np.flip(x, -1))
-
-
-def get_transform(center, scale, res, rot=0):
-    """
-    General image processing functions
-    """
-    # Generate transformation matrix
-    h = 200 * scale
-    t = np.zeros((3, 3))
-    t[0, 0] = float(res[1]) / h
-    t[1, 1] = float(res[0]) / h
-    t[0, 2] = res[1] * (-float(center[0]) / h + .5)
-    t[1, 2] = res[0] * (-float(center[1]) / h + .5)
-    t[2, 2] = 1
-    if not rot == 0:
-        rot = -rot # To match direction of rotation from cropping
-        rot_mat = np.zeros((3,3))
-        rot_rad = rot * np.pi / 180
-        sn,cs = np.sin(rot_rad), np.cos(rot_rad)
-        rot_mat[0,:2] = [cs, -sn]
-        rot_mat[1,:2] = [sn, cs]
-        rot_mat[2,2] = 1
-        # Need to rotate around center
-        t_mat = np.eye(3)
-        t_mat[0,2] = -res[1]/2
-        t_mat[1,2] = -res[0]/2
-        t_inv = t_mat.copy()
-        t_inv[:2,2] *= -1
-        t = np.dot(t_inv,np.dot(rot_mat,np.dot(t_mat,t)))
-    return t
-
-
-def transform(pt, center, scale, res, invert=0, rot=0, as_int=True):
-    # Transform pixel location to different reference
-    t = get_transform(center, scale, res, rot=rot)
-    if invert:
-        t = np.linalg.inv(t)
-    new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T
-    new_pt = np.dot(t, new_pt)
-    if as_int:
-        return new_pt[:2].astype(int) + 1
-    else:
-        return new_pt[:2] + 1
-
-
-
-def transform_preds(coords, center, scale, res):
-    # size = coords.size()
-    # coords = coords.view(-1, coords.size(-1))
-    # print(coords.size())
-    for p in range(coords.size(0)):
-        coords[p, 0:2] = to_torch(transform(coords[p, 0:2], center, scale, res, 1, 0))
-    return coords
-
-
-def crop(img, center, scale, res, rot=0, interp='bilinear'):
-    # import pdb; pdb.set_trace()
-    # mode = 'F'
-
-    img = im_to_numpy(img)
-
-    # Preprocessing for efficient cropping
-    ht, wd = img.shape[0], img.shape[1]
-    sf = scale * 200.0 / res[0]
-    if sf < 2:
-        sf = 1
-    else:
-        new_size = int(np.math.floor(max(ht, wd) / sf))
-        new_ht = int(np.math.floor(ht / sf))
-        new_wd = int(np.math.floor(wd / sf))
-        if new_size < 2:
-            return torch.zeros(res[0], res[1], img.shape[2]) \
-                        if len(img.shape) > 2 else torch.zeros(res[0], res[1])
-        else:
-            img = imresize(img, [new_ht, new_wd], interp=interp)    # , mode=mode)
-            center = center * 1.0 / sf
-            scale = scale / sf
-
-    # Upper left point
-    ul = np.array(transform([0, 0], center, scale, res, invert=1))
-    # Bottom right point
-    br = np.array(transform(res, center, scale, res, invert=1))
-
-    # Padding so that when rotated proper amount of context is included
-    pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2)
-    if not rot == 0:
-        ul -= pad
-        br += pad
-
-    new_shape = [br[1] - ul[1], br[0] - ul[0]]
-    if len(img.shape) > 2:
-        new_shape += [img.shape[2]]
-    new_img = np.zeros(new_shape)
-
-    # Range to fill new array
-    new_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
-    new_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
-    # Range to sample from original image
-    old_x = max(0, ul[0]), min(img.shape[1], br[0])
-    old_y = max(0, ul[1]), min(img.shape[0], br[1])
-    new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]]
-
-    if not rot == 0:
-        # Remove padding
-        new_img = imrotate(new_img, rot, interp=interp) # , mode=mode)
-        new_img = new_img[pad:-pad, pad:-pad]
-
-    new_img = im_to_torch(imresize(new_img, res, interp=interp)) #, mode=mode))
-    return new_img
diff --git a/spaces/ryansilk/quantycs/StreamLit/quantycs/5_Contact.py b/spaces/ryansilk/quantycs/StreamLit/quantycs/5_Contact.py
deleted file mode 100644
index eaa4185a77985c3ef272b2733d28ead01030e51c..0000000000000000000000000000000000000000
--- a/spaces/ryansilk/quantycs/StreamLit/quantycs/5_Contact.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import streamlit as st
-
-def check_credentials(username, password):
-    if username == 'Ryan' and password == 'password':
-        return True
-    return False
-
-def main():
-    st.title("Streamlit Authenticator")
-
-    username = st.text_input("Username")
-    password = st.text_input("Password", type='password')
-
-    if st.button("Login"):
-        if check_credentials(username, password):
-            st.success("Welcome, Ryan!")
-        else:
-            st.error("Incorrect credentials. Please try again.")
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/s3nh/mamba-gpt-3b/README.md b/spaces/s3nh/mamba-gpt-3b/README.md
deleted file mode 100644
index c9db9745c5a41aba652507b3283b092c28d7c963..0000000000000000000000000000000000000000
--- a/spaces/s3nh/mamba-gpt-3b/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Mamba Gpt 3b
-emoji: ⚡
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sanchanhart/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/export.py b/spaces/sanchanhart/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/export.py
deleted file mode 100644
index 44c3ae985af457f5fdb38c247288bfd94aca96af..0000000000000000000000000000000000000000
--- a/spaces/sanchanhart/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/export.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
-
-Usage:
-    $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
-"""
-
-import argparse
-import sys
-import time
-
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
-
-import torch
-import torch.nn as nn
-
-from metadata.predictor_yolo_detector.models import common
-from metadata.predictor_yolo_detector.models.experimental import attempt_load
-from metadata.predictor_yolo_detector.utils.activations import Hardswish
-from metadata.predictor_yolo_detector.utils.general import set_logging, check_img_size
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')  # from yolov5/models/
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    opt = parser.parse_args()
-    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
-    print(opt)
-    set_logging()
-    t = time.time()
-
-    # Load PyTorch model
-    model = attempt_load(opt.weights, map_location=torch.device('cpu'))  # load FP32 model
-    labels = model.names
-
-    # Checks
-    gs = int(max(model.stride))  # grid size (max stride)
-    opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
-
-    # Input
-    img = torch.zeros(opt.batch_size, 3, *opt.img_size)  # image size(1,3,320,192) iDetection
-
-    # Update model
-    for k, m in model.named_modules():
-        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-        if isinstance(m, common.Conv) and isinstance(m.act, nn.Hardswish):
-            m.act = Hardswish()  # assign activation
-        # if isinstance(m, models.yolo.Detect):
-        #     m.forward = m.forward_export  # assign forward (optional)
-    model.model[-1].export = True  # set Detect() layer export=True
-    y = model(img)  # dry run
-
-    # TorchScript export
-    try:
-        print('\nStarting TorchScript export with torch %s...' % torch.__version__)
-        f = opt.weights.replace('.pt', '.torchscript.pt')  # filename
-        ts = torch.jit.trace(model, img)
-        ts.save(f)
-        print('TorchScript export success, saved as %s' % f)
-    except Exception as e:
-        print('TorchScript export failure: %s' % e)
-
-    # ONNX export
-    try:
-        import onnx
-
-        print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
-        f = opt.weights.replace('.pt', '.onnx')  # filename
-        torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
-                          output_names=['classes', 'boxes'] if y is None else ['output'])
-
-        # Checks
-        onnx_model = onnx.load(f)  # load onnx model
-        onnx.checker.check_model(onnx_model)  # check onnx model
-        # print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model
-        print('ONNX export success, saved as %s' % f)
-    except Exception as e:
-        print('ONNX export failure: %s' % e)
-
-    # CoreML export
-    try:
-        import coremltools as ct
-
-        print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
-        # convert model from torchscript and apply pixel scaling as per detect.py
-        model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
-        f = opt.weights.replace('.pt', '.mlmodel')  # filename
-        model.save(f)
-        print('CoreML export success, saved as %s' % f)
-    except Exception as e:
-        print('CoreML export failure: %s' % e)
-
-    # Finish
-    print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
diff --git a/spaces/sanjayw/starcoder-playground/static/community-btn.js b/spaces/sanjayw/starcoder-playground/static/community-btn.js
deleted file mode 100644
index cbe17d76194d3367ffb33c3b72b414c8652cab7f..0000000000000000000000000000000000000000
--- a/spaces/sanjayw/starcoder-playground/static/community-btn.js
+++ /dev/null
@@ -1,75 +0,0 @@
-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 = `sd-perception-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `sd-perception-${{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('#q-input textarea').value;
-    let outputTxt = gradioEl.querySelector('#q-output .codemirror-wrapper .cm-scroller > div:nth-of-type(2)').innerText;
-    outputTxt = `<pre>${outputTxt}</pre>`
-
-    const titleLength = 150;
-    let titleTxt = inputTxt;
-    if(titleTxt.length > titleLength){
-        titleTxt = titleTxt.slice(0, titleLength) + ' ...';
-    }
-
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-
-    if(!inputTxt || !outputTxt){
-        return;
-    };
-
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-
-	const descriptionMd = `### Question:
-${inputTxt}
-
-### Answer:
-
-${outputTxt}`;
-
-    const params = {
-        title: titleTxt,
-        description: descriptionMd,
-    };
-
-    const paramsStr = Object.entries(params)
-        .map(([key, value]) => `${encodeURIComponent(key)}=${encodeURIComponent(value)}`)
-        .join('&');
-
-	window.open(`https://huggingface.co/spaces/fisharp/starcoder-playground/discussions/new?${paramsStr}`, '_blank');
-
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}
diff --git a/spaces/sayakpaul/convert-kerascv-sd-diffusers/conversion_utils/__init__.py b/spaces/sayakpaul/convert-kerascv-sd-diffusers/conversion_utils/__init__.py
deleted file mode 100644
index 47816e8a3ca1792ee6e549667d4f93d7d86ef918..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/convert-kerascv-sd-diffusers/conversion_utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .text_encoder import populate_text_encoder
-from .unet import populate_unet
-from .utils import run_assertion
diff --git a/spaces/segments-tobias/conex/espnet2/enh/separator/abs_separator.py b/spaces/segments-tobias/conex/espnet2/enh/separator/abs_separator.py
deleted file mode 100644
index 8b9de6260269cc3b42d8572b28ee04b788bfd442..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/enh/separator/abs_separator.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from abc import ABC
-from abc import abstractmethod
-from collections import OrderedDict
-from typing import Tuple
-
-import torch
-
-
-class AbsSeparator(torch.nn.Module, ABC):
-    @abstractmethod
-    def forward(
-        self,
-        input: torch.Tensor,
-        ilens: torch.Tensor,
-    ) -> Tuple[Tuple[torch.Tensor], torch.Tensor, OrderedDict]:
-
-        raise NotImplementedError
-
-    @property
-    @abstractmethod
-    def num_spk(self):
-        raise NotImplementedError
diff --git a/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp b/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
deleted file mode 100644
index c1f2c50c82909bbd5492c163d634af77a3ba1781..0000000000000000000000000000000000000000
--- a/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include "MsDeformAttn/ms_deform_attn.h"
-
-namespace groundingdino {
-
-#ifdef WITH_CUDA
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#ifdef WITH_CUDA
-  std::ostringstream oss;
-
-  // copied from
-  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
-  auto printCudaStyleVersion = [&](int v) {
-    oss << (v / 1000) << "." << (v / 10 % 100);
-    if (v % 10 != 0) {
-      oss << "." << (v % 10);
-    }
-  };
-  printCudaStyleVersion(get_cudart_version());
-  return oss.str();
-#else
-  return std::string("not available");
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
-  std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
-  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
-  {
-    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
-       << __clang_patchlevel__;
-  }
-#endif
-
-#if defined(_MSC_VER)
-  { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
-  return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
-  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/go-web.bat b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/go-web.bat
deleted file mode 100644
index db1dec52006bc631e4e68bafd619a3a65f202532..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/go-web.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897
-pause
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/infer/infer-pm-index256.py b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/infer/infer-pm-index256.py
deleted file mode 100644
index 66e38d49071994e9c850f7d75d0a3b2e5c79b0da..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/infer/infer-pm-index256.py
+++ /dev/null
@@ -1,199 +0,0 @@
-"""
-
-对源特征进行检索
-"""
-import torch, pdb, os, parselmouth
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-import numpy as np
-import soundfile as sf
-
-# from models import SynthesizerTrn256#hifigan_nonsf
-# from infer_pack.models import SynthesizerTrn256NSF as SynthesizerTrn256#hifigan_nsf
-from infer_pack.models import (
-    SynthesizerTrnMs256NSFsid as SynthesizerTrn256,
-)  # hifigan_nsf
-
-# from infer_pack.models import SynthesizerTrnMs256NSFsid_sim as SynthesizerTrn256#hifigan_nsf
-# from models import SynthesizerTrn256NSFsim as SynthesizerTrn256#hifigan_nsf
-# from models import SynthesizerTrn256NSFsimFlow as SynthesizerTrn256#hifigan_nsf
-
-
-from scipy.io import wavfile
-from fairseq import checkpoint_utils
-
-# import pyworld
-import librosa
-import torch.nn.functional as F
-import scipy.signal as signal
-
-# import torchcrepe
-from time import time as ttime
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model_path = r"E:\codes\py39\vits_vc_gpu_train\hubert_base.pt"  #
-print("load model(s) from {}".format(model_path))
-models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [model_path],
-    suffix="",
-)
-model = models[0]
-model = model.to(device)
-model = model.half()
-model.eval()
-
-# net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],183,256,is_half=True)#hifigan#512#256
-# net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],109,256,is_half=True)#hifigan#512#256
-net_g = SynthesizerTrn256(
-    1025,
-    32,
-    192,
-    192,
-    768,
-    2,
-    6,
-    3,
-    0,
-    "1",
-    [3, 7, 11],
-    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-    [10, 10, 2, 2],
-    512,
-    [16, 16, 4, 4],
-    183,
-    256,
-    is_half=True,
-)  # hifigan#512#256#no_dropout
-# net_g = SynthesizerTrn256(1025,32,192,192,768,2,3,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],0)#ts3
-# net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2],512,[16,16,4],0)#hifigan-ps-sr
-#
-# net_g = SynthesizerTrn(1025, 32, 192, 192, 768, 2, 6, 3, 0.1, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [5,5], 512, [15,15], 0)#ms
-# net_g = SynthesizerTrn(1025, 32, 192, 192, 768, 2, 6, 3, 0.1, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,10], 512, [16,16], 0)#idwt2
-
-# weights=torch.load("infer/ft-mi_1k-noD.pt")
-# weights=torch.load("infer/ft-mi-freeze-vocoder-flow-enc_q_1k.pt")
-# weights=torch.load("infer/ft-mi-freeze-vocoder_true_1k.pt")
-# weights=torch.load("infer/ft-mi-sim1k.pt")
-weights = torch.load("infer/ft-mi-no_opt-no_dropout.pt")
-print(net_g.load_state_dict(weights, strict=True))
-
-net_g.eval().to(device)
-net_g.half()
-
-
-def get_f0(x, p_len, f0_up_key=0):
-    time_step = 160 / 16000 * 1000
-    f0_min = 50
-    f0_max = 1100
-    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-    f0 = (
-        parselmouth.Sound(x, 16000)
-        .to_pitch_ac(
-            time_step=time_step / 1000,
-            voicing_threshold=0.6,
-            pitch_floor=f0_min,
-            pitch_ceiling=f0_max,
-        )
-        .selected_array["frequency"]
-    )
-
-    pad_size = (p_len - len(f0) + 1) // 2
-    if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-        f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-    f0 *= pow(2, f0_up_key / 12)
-    f0bak = f0.copy()
-
-    f0_mel = 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-        f0_mel_max - f0_mel_min
-    ) + 1
-    f0_mel[f0_mel <= 1] = 1
-    f0_mel[f0_mel > 255] = 255
-    # f0_mel[f0_mel > 188] = 188
-    f0_coarse = np.rint(f0_mel).astype(np.int)
-    return f0_coarse, f0bak
-
-
-import faiss
-
-index = faiss.read_index("infer/added_IVF512_Flat_mi_baseline_src_feat.index")
-big_npy = np.load("infer/big_src_feature_mi.npy")
-ta0 = ta1 = ta2 = 0
-for idx, name in enumerate(
-    [
-        "冬之花clip1.wav",
-    ]
-):  ##
-    wav_path = "todo-songs/%s" % name  #
-    f0_up_key = -2  #
-    audio, sampling_rate = sf.read(wav_path)
-    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)
-
-    feats = torch.from_numpy(audio).float()
-    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.half().to(device),
-        "padding_mask": padding_mask.to(device),
-        "output_layer": 9,  # layer 9
-    }
-    if torch.cuda.is_available():
-        torch.cuda.synchronize()
-    t0 = ttime()
-    with torch.no_grad():
-        logits = model.extract_features(**inputs)
-        feats = model.final_proj(logits[0])
-
-    ####索引优化
-    npy = feats[0].cpu().numpy().astype("float32")
-    D, I = index.search(npy, 1)
-    feats = (
-        torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device)
-    )
-
-    feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-    if torch.cuda.is_available():
-        torch.cuda.synchronize()
-    t1 = ttime()
-    # p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
-    p_len = min(feats.shape[1], 10000)  #
-    pitch, pitchf = get_f0(audio, p_len, f0_up_key)
-    p_len = min(feats.shape[1], 10000, pitch.shape[0])  # 太大了爆显存
-    if torch.cuda.is_available():
-        torch.cuda.synchronize()
-    t2 = ttime()
-    feats = feats[:, :p_len, :]
-    pitch = pitch[:p_len]
-    pitchf = pitchf[:p_len]
-    p_len = torch.LongTensor([p_len]).to(device)
-    pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
-    sid = torch.LongTensor([0]).to(device)
-    pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
-    with torch.no_grad():
-        audio = (
-            net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
-            .data.cpu()
-            .float()
-            .numpy()
-        )  # nsf
-    if torch.cuda.is_available():
-        torch.cuda.synchronize()
-    t3 = ttime()
-    ta0 += t1 - t0
-    ta1 += t2 - t1
-    ta2 += t3 - t2
-    # wavfile.write("ft-mi_1k-index256-noD-%s.wav"%name, 40000, audio)##
-    # wavfile.write("ft-mi-freeze-vocoder-flow-enc_q_1k-%s.wav"%name, 40000, audio)##
-    # wavfile.write("ft-mi-sim1k-%s.wav"%name, 40000, audio)##
-    wavfile.write("ft-mi-no_opt-no_dropout-%s.wav" % name, 40000, audio)  ##
-
-
-print(ta0, ta1, ta2)  #
diff --git a/spaces/shikunl/prismer/prismer/experts/depth/blocks.py b/spaces/shikunl/prismer/prismer/experts/depth/blocks.py
deleted file mode 100644
index 7e83a4c7f1c3bdb65dd567883948a7e7dbfc953e..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/depth/blocks.py
+++ /dev/null
@@ -1,383 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vit import (
-    _make_pretrained_vitb_rn50_384,
-    _make_pretrained_vitl16_384,
-    _make_pretrained_vitb16_384,
-    forward_vit,
-)
-
-
-def _make_encoder(
-    backbone,
-    features,
-    use_pretrained,
-    groups=1,
-    expand=False,
-    exportable=True,
-    hooks=None,
-    use_vit_only=False,
-    use_readout="ignore",
-    enable_attention_hooks=False,
-):
-    if backbone == "vitl16_384":
-        pretrained = _make_pretrained_vitl16_384(
-            use_pretrained,
-            hooks=hooks,
-            use_readout=use_readout,
-            enable_attention_hooks=enable_attention_hooks,
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # ViT-L/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb_rn50_384":
-        pretrained = _make_pretrained_vitb_rn50_384(
-            use_pretrained,
-            hooks=hooks,
-            use_vit_only=use_vit_only,
-            use_readout=use_readout,
-            enable_attention_hooks=enable_attention_hooks,
-        )
-        scratch = _make_scratch(
-            [256, 512, 768, 768], features, groups=groups, expand=expand
-        )  # ViT-H/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb16_384":
-        pretrained = _make_pretrained_vitb16_384(
-            use_pretrained,
-            hooks=hooks,
-            use_readout=use_readout,
-            enable_attention_hooks=enable_attention_hooks,
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # ViT-B/16 - 84.6% Top1 (backbone)
-    elif backbone == "resnext101_wsl":
-        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
-        scratch = _make_scratch(
-            [256, 512, 1024, 2048], features, groups=groups, expand=expand
-        )  # efficientnet_lite3
-    else:
-        print(f"Backbone '{backbone}' not implemented")
-        assert False
-
-    return pretrained, scratch
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    out_shape4 = out_shape
-    if expand == True:
-        out_shape1 = out_shape
-        out_shape2 = out_shape * 2
-        out_shape3 = out_shape * 4
-        out_shape4 = out_shape * 8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0],
-        out_shape1,
-        kernel_size=3,
-        stride=1,
-        padding=1,
-        bias=False,
-        groups=groups,
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1],
-        out_shape2,
-        kernel_size=3,
-        stride=1,
-        padding=1,
-        bias=False,
-        groups=groups,
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2],
-        out_shape3,
-        kernel_size=3,
-        stride=1,
-        padding=1,
-        bias=False,
-        groups=groups,
-    )
-    scratch.layer4_rn = nn.Conv2d(
-        in_shape[3],
-        out_shape4,
-        kernel_size=3,
-        stride=1,
-        padding=1,
-        bias=False,
-        groups=groups,
-    )
-
-    return scratch
-
-
-def _make_resnet_backbone(resnet):
-    pretrained = nn.Module()
-    pretrained.layer1 = nn.Sequential(
-        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
-    )
-
-    pretrained.layer2 = resnet.layer2
-    pretrained.layer3 = resnet.layer3
-    pretrained.layer4 = resnet.layer4
-
-    return pretrained
-
-
-def _make_pretrained_resnext101_wsl(use_pretrained):
-    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
-    return _make_resnet_backbone(resnet)
-
-
-class Interpolate(nn.Module):
-    """Interpolation module."""
-
-    def __init__(self, scale_factor, mode, align_corners=False):
-        """Init.
-
-        Args:
-            scale_factor (float): scaling
-            mode (str): interpolation mode
-        """
-        super(Interpolate, self).__init__()
-
-        self.interp = nn.functional.interpolate
-        self.scale_factor = scale_factor
-        self.mode = mode
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: interpolated data_list
-        """
-
-        x = self.interp(
-            x,
-            scale_factor=self.scale_factor,
-            mode=self.mode,
-            align_corners=self.align_corners,
-        )
-
-        return x
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module."""
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        out = self.relu(x)
-        out = self.conv1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-
-        return out + x
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block."""
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.resConfUnit1 = ResidualConvUnit(features)
-        self.resConfUnit2 = ResidualConvUnit(features)
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            output += self.resConfUnit1(xs[1])
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=True
-        )
-
-        return output
-
-
-class ResidualConvUnit_custom(nn.Module):
-    """Residual convolution module."""
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups = 1
-
-        self.conv1 = nn.Conv2d(
-            features,
-            features,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=not self.bn,
-            groups=self.groups,
-        )
-
-        self.conv2 = nn.Conv2d(
-            features,
-            features,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=not self.bn,
-            groups=self.groups,
-        )
-
-        if self.bn == True:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn == True:
-            out = self.bn1(out)
-
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn == True:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-        # return out + x
-
-
-class FeatureFusionBlock_custom(nn.Module):
-    """Feature fusion block."""
-
-    def __init__(
-        self,
-        features,
-        activation,
-        deconv=False,
-        bn=False,
-        expand=False,
-        align_corners=True,
-    ):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock_custom, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups = 1
-
-        self.expand = expand
-        out_features = features
-        if self.expand == True:
-            out_features = features // 2
-
-        self.out_conv = nn.Conv2d(
-            features,
-            out_features,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=True,
-            groups=1,
-        )
-
-        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-            # output += res
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
-        )
-
-        output = self.out_conv(output)
-
-        return output
diff --git a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/archs/arch_util.py b/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/archs/arch_util.py
deleted file mode 100644
index bad45ab34e901c47fb539152fca714a3795b0de2..0000000000000000000000000000000000000000
--- a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/archs/arch_util.py
+++ /dev/null
@@ -1,318 +0,0 @@
-import collections.abc
-import math
-import torch
-import torchvision
-import warnings
-from distutils.version import LooseVersion
-from itertools import repeat
-from torch import nn as nn
-from torch.nn import functional as F
-from torch.nn import init as init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
-from basicsr.utils import get_root_logger
-
-
-@torch.no_grad()
-def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
-    """Initialize network weights.
-
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
-    """Make layers by stacking the same blocks.
-
-    Args:
-        basic_block (nn.module): nn.module class for basic block.
-        num_basic_block (int): number of blocks.
-
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-class ResidualBlockNoBN(nn.Module):
-    """Residual block without BN.
-
-    It has a style of:
-        ---Conv-ReLU-Conv-+-
-         |________________|
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-            Default: 64.
-        res_scale (float): Residual scale. Default: 1.
-        pytorch_init (bool): If set to True, use pytorch default init,
-            otherwise, use default_init_weights. Default: False.
-    """
-
-    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
-        super(ResidualBlockNoBN, self).__init__()
-        self.res_scale = res_scale
-        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.relu = nn.ReLU(inplace=True)
-
-        if not pytorch_init:
-            default_init_weights([self.conv1, self.conv2], 0.1)
-
-    def forward(self, x):
-        identity = x
-        out = self.conv2(self.relu(self.conv1(x)))
-        return identity + out * self.res_scale
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
-    """Warp an image or feature map with optical flow.
-
-    Args:
-        x (Tensor): Tensor with size (n, c, h, w).
-        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
-        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
-        padding_mode (str): 'zeros' or 'border' or 'reflection'.
-            Default: 'zeros'.
-        align_corners (bool): Before pytorch 1.3, the default value is
-            align_corners=True. After pytorch 1.3, the default value is
-            align_corners=False. Here, we use the True as default.
-
-    Returns:
-        Tensor: Warped image or feature map.
-    """
-    assert x.size()[-2:] == flow.size()[1:3]
-    _, _, h, w = x.size()
-    # create mesh grid
-    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
-    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
-    grid.requires_grad = False
-
-    vgrid = grid + flow
-    # scale grid to [-1,1]
-    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
-    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
-    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
-    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
-
-    # TODO, what if align_corners=False
-    return output
-
-
-def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
-    """Resize a flow according to ratio or shape.
-
-    Args:
-        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
-        size_type (str): 'ratio' or 'shape'.
-        sizes (list[int | float]): the ratio for resizing or the final output
-            shape.
-            1) The order of ratio should be [ratio_h, ratio_w]. For
-            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
-            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
-            ratio > 1.0).
-            2) The order of output_size should be [out_h, out_w].
-        interp_mode (str): The mode of interpolation for resizing.
-            Default: 'bilinear'.
-        align_corners (bool): Whether align corners. Default: False.
-
-    Returns:
-        Tensor: Resized flow.
-    """
-    _, _, flow_h, flow_w = flow.size()
-    if size_type == 'ratio':
-        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
-    elif size_type == 'shape':
-        output_h, output_w = sizes[0], sizes[1]
-    else:
-        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
-
-    input_flow = flow.clone()
-    ratio_h = output_h / flow_h
-    ratio_w = output_w / flow_w
-    input_flow[:, 0, :, :] *= ratio_w
-    input_flow[:, 1, :, :] *= ratio_h
-    resized_flow = F.interpolate(
-        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
-    return resized_flow
-
-
-# TODO: may write a cpp file
-def pixel_unshuffle(x, scale):
-    """ Pixel unshuffle.
-
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-
-class DCNv2Pack(ModulatedDeformConvPack):
-    """Modulated deformable conv for deformable alignment.
-
-    Different from the official DCNv2Pack, which generates offsets and masks
-    from the preceding features, this DCNv2Pack takes another different
-    features to generate offsets and masks.
-
-    Ref:
-        Delving Deep into Deformable Alignment in Video Super-Resolution.
-    """
-
-    def forward(self, x, feat):
-        out = self.conv_offset(feat)
-        o1, o2, mask = torch.chunk(out, 3, dim=1)
-        offset = torch.cat((o1, o2), dim=1)
-        mask = torch.sigmoid(mask)
-
-        offset_absmean = torch.mean(torch.abs(offset))
-        if offset_absmean > 50:
-            logger = get_root_logger()
-            logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')
-
-        if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
-            return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
-                                                 self.dilation, mask)
-        else:
-            return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
-                                         self.dilation, self.groups, self.deformable_groups)
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
-            'The distribution of values may be incorrect.',
-            stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        low = norm_cdf((a - mean) / std)
-        up = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [low, up], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * low - 1, 2 * up - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution.
-
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
-
-    The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-# From PyTorch
-def _ntuple(n):
-
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable):
-            return x
-        return tuple(repeat(x, n))
-
-    return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
\ No newline at end of file
diff --git a/spaces/sidphbot/Researcher/README.md b/spaces/sidphbot/Researcher/README.md
deleted file mode 100644
index e7827b14d176b1d64bab4fb1f3f99d4d9a62ba09..0000000000000000000000000000000000000000
--- a/spaces/sidphbot/Researcher/README.md
+++ /dev/null
@@ -1,282 +0,0 @@
----
-title: Researcher
-emoji: 🤓
-colorFrom: gray
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-
-# Auto-Research
-![Auto-Research][logo]
-
-[logo]: https://github.com/sidphbot/Auto-Research/blob/main/logo.png
-A no-code utility to generate a detailed well-cited survey with topic clustered sections (draft paper format) and other interesting artifacts from a single research query.
-
-Data Provider: [arXiv](https://arxiv.org/) Open Archive Initiative OAI
-
-Requirements:
- - python 3.7 or above
- - poppler-utils - `sudo apt-get install build-essential libpoppler-cpp-dev pkg-config python-dev`
- - list of requirements in requirements.txt - `cat requirements.txt | xargs pip install`
- - 8GB disk space 
- - 13GB CUDA(GPU) memory - for a survey of 100 searched papers(max_search) and 25 selected papers(num_papers)
-
-#### Demo : 
-
-Video Demo : https://drive.google.com/file/d/1-77J2L10lsW-bFDOGdTaPzSr_utY743g/view?usp=sharing
-
-Kaggle Re-usable Demo : https://www.kaggle.com/sidharthpal/auto-research-generate-survey-from-query 
-
-(`[TIP]` click 'edit and run' to run the demo for your custom queries on a free GPU)
-
-
-#### Installation:
-```
-sudo apt-get install build-essential poppler-utils libpoppler-cpp-dev pkg-config python-dev
-pip install git+https://github.com/sidphbot/Auto-Research.git
-```
-
-#### Run Survey (cli):
-```
-python survey.py [options] <your_research_query>
-```
-
-#### Run Survey (Streamlit web-interface - new):
-```
-streamlit run app.py
-```
-
-#### Run Survey (Python API):
-```
-from survey import Surveyor
-mysurveyor = Surveyor()
-mysurveyor.survey('quantum entanglement')
-```
-
-### Research tools: 
-
-These are independent tools for your research or document text handling needs.
-
-```
-*[Tip]* :(models can be changed in defaults or passed on during init along with `refresh-models=True`)
-```
-
-- `abstractive_summary` - takes a long text document (`string`) and returns a 1-paragraph abstract or “abstractive” summary (`string`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`summary` : string
-
-- `extractive_summary` - takes a long text document (`string`) and returns a 1-paragraph of extracted highlights or “extractive” summary (`string`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`summary` : string
-
-- `generate_title` - takes a long text document (`string`) and returns a generated title (`string`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`title` : string
-
-- `extractive_highlights` - takes a long text document (`string`) and returns a list of extracted highlights (`[string]`), a list of keywords (`[string]`) and key phrases (`[string]`)
-
-	Input: 		
-		
-		`longtext` : string
-		
-	Returns: 		
-		
-		`highlights` : [string]
-		`keywords` : [string]
-		`keyphrases` : [string]
-
-- `extract_images_from_file` - takes a pdf file name (`string`) and returns a list of image filenames (`[string]`).
-
-	Input: 		
-		
-		`pdf_file` : string
-		
-	Returns: 		
-		
-		`images_files` : [string]
-
-- `extract_tables_from_file` - takes a pdf file name (`string`) and returns a list of csv filenames (`[string]`).
-
-	Input: 		
-		
-		`pdf_file` : string
-		
-	Returns: 		
-		
-		`images_files` : [string]
-
-- `cluster_lines` - takes a list of lines (`string`) and returns the topic-clustered sections (`dict(generated_title: [cluster_abstract])`) and clustered lines (`dict(cluster_id: [cluster_lines])`)
-
-	Input: 		
-		
-		`lines` : [string]
-		
-	Returns: 		
-		
-		`sections` : dict(generated_title: [cluster_abstract])
-		`clusters` : dict(cluster_id: [cluster_lines])
-
-- `extract_headings` - *[for scientific texts - Assumes an ‘abstract’ heading present]* takes a text file name (`string`) and returns a list of headings (`[string]`) and refined lines (`[string]`). 
-    
-    `[Tip 1]` : Use `extract_sections` as a wrapper (e.g. `extract_sections(extract_headings(“/path/to/textfile”)`) to get heading-wise sectioned text with refined lines instead (`dict( heading: text)`)
-    
-    `[Tip 2]` : write the word ‘abstract’ at the start of the file text to get an extraction for non-scientific texts as well !!
-
-	Input: 		
-		
-		`text_file` : string 		
-		
-	Returns: 
-		
-		`refined` : [string], 
-		`headings` : [string]
-		`sectioned_doc` : dict( heading: text) (Optional - Wrapper case)
-
-
-## Access/Modify defaults:
-
-- inside code 
-```
-from survey.Surveyor import DEFAULTS
-from pprint import pprint
-
-pprint(DEFAULTS)
-```
-or,
-
-- Modify static config file - `defaults.py`
-
-or,
-
-- At runtime (utility)
-
-```
-python survey.py --help
-```
-```
-usage: survey.py [-h] [--max_search max_metadata_papers]
-                   [--num_papers max_num_papers] [--pdf_dir pdf_dir]
-                   [--txt_dir txt_dir] [--img_dir img_dir] [--tab_dir tab_dir]
-                   [--dump_dir dump_dir] [--models_dir save_models_dir]
-                   [--title_model_name title_model_name]
-                   [--ex_summ_model_name extractive_summ_model_name]
-                   [--ledmodel_name ledmodel_name]
-                   [--embedder_name sentence_embedder_name]
-                   [--nlp_name spacy_model_name]
-                   [--similarity_nlp_name similarity_nlp_name]
-                   [--kw_model_name kw_model_name]
-                   [--refresh_models refresh_models] [--high_gpu high_gpu]
-                   query_string
-
-Generate a survey just from a query !!
-
-positional arguments:
-  query_string          your research query/keywords
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --max_search max_metadata_papers
-                        maximium number of papers to gaze at - defaults to 100
-  --num_papers max_num_papers
-                        maximium number of papers to download and analyse -
-                        defaults to 25
-  --pdf_dir pdf_dir     pdf paper storage directory - defaults to
-                        arxiv_data/tarpdfs/
-  --txt_dir txt_dir     text-converted paper storage directory - defaults to
-                        arxiv_data/fulltext/
-  --img_dir img_dir     image storage directory - defaults to
-                        arxiv_data/images/
-  --tab_dir tab_dir     tables storage directory - defaults to
-                        arxiv_data/tables/
-  --dump_dir dump_dir   all_output_dir - defaults to arxiv_dumps/
-  --models_dir save_models_dir
-                        directory to save models (> 5GB) - defaults to
-                        saved_models/
-  --title_model_name title_model_name
-                        title model name/tag in hugging-face, defaults to
-                        'Callidior/bert2bert-base-arxiv-titlegen'
-  --ex_summ_model_name extractive_summ_model_name
-                        extractive summary model name/tag in hugging-face,
-                        defaults to 'allenai/scibert_scivocab_uncased'
-  --ledmodel_name ledmodel_name
-                        led model(for abstractive summary) name/tag in
-                        hugging-face, defaults to 'allenai/led-
-                        large-16384-arxiv'
-  --embedder_name sentence_embedder_name
-                        sentence embedder name/tag in hugging-face, defaults
-                        to 'paraphrase-MiniLM-L6-v2'
-  --nlp_name spacy_model_name
-                        spacy model name/tag in hugging-face (if changed -
-                        needs to be spacy-installed prior), defaults to
-                        'en_core_sci_scibert'
-  --similarity_nlp_name similarity_nlp_name
-                        spacy downstream model(for similarity) name/tag in
-                        hugging-face (if changed - needs to be spacy-installed
-                        prior), defaults to 'en_core_sci_lg'
-  --kw_model_name kw_model_name
-                        keyword extraction model name/tag in hugging-face,
-                        defaults to 'distilbert-base-nli-mean-tokens'
-  --refresh_models refresh_models
-                        Refresh model downloads with given names (needs
-                        atleast one model name param above), defaults to False
-  --high_gpu high_gpu   High GPU usage permitted, defaults to False
-
-```
-
-- At runtime (code)
-
-    > during surveyor object initialization with `surveyor_obj = Surveyor()`
-    - `pdf_dir`: String, pdf paper storage directory - defaults to `arxiv_data/tarpdfs/`
-    - `txt_dir`: String, text-converted paper storage directory - defaults to `arxiv_data/fulltext/`
-    - `img_dir`: String, image image storage directory - defaults to `arxiv_data/images/`
-    - `tab_dir`: String, tables storage directory - defaults to `arxiv_data/tables/`
-    - `dump_dir`: String, all_output_dir - defaults to `arxiv_dumps/`
-    - `models_dir`: String, directory to save to huge models, defaults to `saved_models/`
-    - `title_model_name`: String, title model name/tag in hugging-face, defaults to `Callidior/bert2bert-base-arxiv-titlegen`
-    - `ex_summ_model_name`: String, extractive summary model name/tag in hugging-face, defaults to `allenai/scibert_scivocab_uncased`
-    - `ledmodel_name`: String, led model(for abstractive summary) name/tag in hugging-face, defaults to `allenai/led-large-16384-arxiv`
-    - `embedder_name`: String, sentence embedder name/tag in hugging-face, defaults to `paraphrase-MiniLM-L6-v2`
-    - `nlp_name`: String, spacy model name/tag in hugging-face (if changed - needs to be spacy-installed prior), defaults to `en_core_sci_scibert`
-    - `similarity_nlp_name`: String, spacy downstream trained model(for similarity) name/tag in hugging-face (if changed - needs to be spacy-installed prior), defaults to `en_core_sci_lg`
-    - `kw_model_name`: String, keyword extraction model name/tag in hugging-face, defaults to `distilbert-base-nli-mean-tokens`
-    - `high_gpu`: Bool, High GPU usage permitted, defaults to `False`
-    - `refresh_models`: Bool, Refresh model downloads with given names (needs atleast one model name param above), defaults to False
-    
-    > during survey generation with `surveyor_obj.survey(query="my_research_query")`
-    - `max_search`: int maximium number of papers to gaze at - defaults to `100`
-    - `num_papers`: int maximium number of papers to download and analyse - defaults to `25`
-
-
-
-#### Artifacts generated (zipped):
-- Detailed survey draft paper as txt file
-- A curated list of top 25+ papers as pdfs and txts
-- Images extracted from above papers as jpegs, bmps etc
-- Heading/Section wise highlights extracted from above papers as a re-usable pure python joblib dump
-- Tables extracted from papers(optional)
-- Corpus of metadata highlights/text of top 100 papers as a re-usable pure python joblib dump
-
-
-Please cite this repo if it helped you :)
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Blob Hero A New and Exciting APK Game from Rollic Games.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Blob Hero A New and Exciting APK Game from Rollic Games.md
deleted file mode 100644
index c4ab687cdaa0f3492621e1af310a11f13d9d4d3a..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Blob Hero A New and Exciting APK Game from Rollic Games.md	
+++ /dev/null
@@ -1,105 +0,0 @@
-<br />
-<h1>Blob Hero APK Download: How to Play and Enjoy This Action-Packed Game</h1>
- <p>If you are looking for a game that combines rogue-like elements, action-packed gameplay, colorful graphics, and a lot of customization options, then you should check out <strong>Blob Hero</strong>, a game that lets you control a blob with various weapons and skills. In this article, we will tell you what Blob Hero is, how to download and install its APK file on your Android device, how to play and enjoy it, and what are some tips and tricks that can help you improve your performance.</p>
- <h2>What is Blob Hero?</h2>
- <p>Blob Hero is a rogue-like action game that was developed by Quok Games and published by Rollic Games. It was released in April 2022 for Android devices. The game has received positive reviews from players who praised its simple but addictive gameplay, its colorful and stylized graphics, and its customization options.</p>
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- <p>In Blob Hero, you play as a blob that has to fight against endless waves of evil enemies using its arsenal of weaponry. You can unlock perks as you fight that can enhance your blob's abilities, such as shooting blob balls, throwing blob grenades, casting blob magic, and more. You can also upgrade your blob's stats, such as damage, health, speed, and cooldown reduction. <p>Moreover, you can choose from different heroes, each with their own passive skills that work for all heroes. For example, the Ninja hero can dash through enemies, the Wizard hero can cast spells faster, and the Pirate hero can shoot more bullets. You can create thousands of unique skill combinations to overcome the challenges and survive each wave of enemies' attacks.</p>
- <h2>How to Download and Install Blob Hero APK?</h2>
- <p>Blob Hero is available for free on Android devices and can be downloaded from Google Play Store. However, if you want to download its APK file and install it manually on your device, you can do so by following these steps:</p>
- <ol>
-<li>Go to [1](https://apkcombo.com/blob-hero/com.quok.blobHero/) and click on the download button. This will take you to a page where you can choose the version of the APK file that you want to download. You can also scan the QR code on the page to download the APK file directly on your device.</li>
-<li>Wait for the download to finish and open the APK file. You may need to enable unknown sources on your device settings to allow the installation of apps from sources other than Google Play Store.</li>
-<li>Tap on install and wait for the installation to complete. This may take a few seconds or minutes depending on your device and internet speed.</li>
-<li>Launch the game and enjoy.</li>
-</ol>
- <h2>How to Play and Enjoy Blob Hero?</h2>
- <p>Blob Hero is a game that requires you to guide your blob through endless waves of enemies and become the ultimate blob hero. The game has a simple and intuitive control system that lets you move your blob with a joystick on the left side of the screen and use your perks with buttons on the right side of the screen. You can also swipe on the screen to dodge or dash through enemies.</p>
- <p>As you play, you will encounter different types of enemies, such as zombies, skeletons, spiders, bats, ghosts, and more. Each enemy has its own behavior and attack pattern, so you need to be careful and avoid getting hit by them. You will also face bosses every 10 waves, which are more powerful and challenging than regular enemies.</p>
- <p>To help you out, you can collect pick-ups that appear randomly on the map, such as chests with perks, health boosts, or magnets. Perks are special abilities that can enhance your blob's performance, such as auras, ranged attacks, health regeneration, etc. You can have up to four perks at a time, and you can mix and match them to suit your playstyle and strategy.</p>
- <p>You can also upgrade your blob's stats using coins that you earn by killing enemies or watching videos. You can increase your damage, health, speed, and cooldown reduction by spending coins on each stat. Upgrading your stats will make your blob stronger and more resilient against enemies.</p>
- <p>Additionally, you can choose from different heroes that have their own passive skills that work for all heroes. For example, the Ninja hero can dash through enemies, the Wizard hero can cast spells faster, and the Pirate hero can shoot more bullets. You can unlock new heroes by spending gems that you earn by completing achievements or watching videos.</p>
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- <h2>What are Some Tips and Tricks for Blob Hero?</h2>
- <p>Here are some tips and tricks that can help you improve your performance in Blob Hero:</p>
- <ul>
-<li>Look for pick-ups that can help you out, such as chests with perks, health boosts, or magnets. Chests with perks can give you new abilities or replace your existing ones, so choose wisely. Health boosts can restore some of your lost health, which is useful when you are low on health. Magnets can attract coins and gems to you, which is helpful when you want to collect more resources.</li>
-<li>Mix up your perks to suit your playstyle and strategy. There are many perks that you can unlock and use in Blob Hero, such as auras, ranged attacks, health regeneration, etc. You can have up to four perks at a time, and you can mix and match them to create thousands of unique skill combinations. For example, you can use a fire aura with a fireball perk to deal more damage with fire attacks, or you can use a shield aura with a heal perk to protect yourself and heal yourself at the same time.</li>
-<li>Keep moving and avoid getting surrounded by enemies. Blob Hero is a game that requires you to be agile and smart in avoiding enemy attacks. You need to keep moving around the map and look for gaps in the crowd where you can escape or attack. Do not let yourself get surrounded by enemies or cornered by walls or obstacles, as this will make you vulnerable to their attacks.</li>
-<li>Upgrade your damage and health first, then focus on other stats later. Damage and health are the most important stats in Blob Hero, as they determine how much damage you can deal and how much damage you can take. Upgrading your damage will make you kill enemies faster and earn more coins and gems, while upgrading your health will make you survive longer and avoid dying. You can upgrade your other stats, such as speed and cooldown reduction, later when you have enough coins and gems.</li>
-<li>Watch videos to unlock perks or revive when you die, or skip them if you prefer. Blob Hero offers you the option to watch videos to unlock perks or revive when you die. Watching videos can give you an advantage in the game, as you can get more perks or continue your run after dying. However, if you do not want to watch videos, you can skip them by tapping on the X button. You can still play the game without watching videos, but it may be more challenging.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Blob Hero is a fun and engaging game that will keep you entertained for hours with its action-packed gameplay, colorful graphics, and customization options. You can download Blob Hero APK from APKCombo and install it on your Android device easily and safely. You can also follow our tips and tricks to become a better blob hero and conquer the game.</p>
- <p>If you have any questions or feedback about Blob Hero, feel free to leave a comment below. We hope you enjoy playing Blob Hero and have a great time.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Blob Hero:</p>
- <ol>
-<li>What are the benefits of downloading Blob Hero APK from APKCombo?</li>
-<p>Downloading Blob Hero APK from APKCombo has several benefits, such as:</p>
-<ul>
-<li>You can get the latest version of the game without waiting for updates on Google Play Store.</li>
-<li>You can access the game even if it is not available in your region or country.</li>
-<li>You can save data and storage space by downloading only the APK file without additional files or data.</li>
-<li>You can avoid ads and in-app purchases by downloading the modded version of the game.</li>
-</ul>
-<li>How can I change my hero in Blob Hero?</li>
-<p>You can change your hero in Blob Hero by tapping on the hero icon on the top left corner of the screen. This will open a menu where you can see all the heroes that you have unlocked and their passive skills. You can tap on any hero to select it and use it in the game.</li>
-<li>How can I reset my perks in Blob Hero?</li>
-<p>You can reset your perks in Blob Hero by tapping on the reset button on the bottom right corner of the screen. This will remove all your perks and give you back your coins that you spent on them. You can then choose new perks or keep the same ones as before.</li>
-<li>How can I get more gems in Blob Hero?</li>
-<p>You can get more gems in Blob Hero by completing achievements or watching videos. Achievements are tasks that you can complete in the game, such as killing a certain number of enemies, surviving a certain number of waves, or using a certain number of perks. You can see your achievements by tapping on the trophy icon on the top right corner of the screen. Watching videos is another option that you can use to get more gems. You can watch videos by tapping on the video icon on the bottom left corner of the screen.</li>
-<li>How can I contact the developers of Blob Hero?</li>
-<p>You can contact the developers of Blob Hero by sending them an email at support@quokgames.com or visiting their website at [2](https://quokgames.com/). You can also follow them on social media platforms, such as Facebook, Twitter, Instagram, or YouTube.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download AR ESSENCE Font for Free - A Gorgeous and Sophisticated Webfont.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download AR ESSENCE Font for Free - A Gorgeous and Sophisticated Webfont.md
deleted file mode 100644
index aa8933622c98af34bdd104311f0fb9d1ac469a2d..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download AR ESSENCE Font for Free - A Gorgeous and Sophisticated Webfont.md	
+++ /dev/null
@@ -1,118 +0,0 @@
-<br />
-<h1>How to Download AR Essence Font for Free</h1>
-<p>Are you looking for a beautiful and elegant font that can make your text stand out? Do you want to add some flair and personality to your documents, presentations, or websites? If yes, then you might want to try AR Essence Font. This font is a stunning and stylish script font that can give your text a unique and attractive look. In this article, we will tell you what is AR Essence Font, how to download it for free, and how to install and use it on your device.</p>
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-<p>AR Essence Font is a script font that was created by Ashley R. in 2009. It is a handwritten font that has smooth curves and elegant strokes. It is suitable for various purposes, such as logos, headlines, invitations, quotes, posters, greeting cards, and more. It can also be paired with other fonts to create stunning combinations.</p>
- <h3>The origin and features of AR Essence Font</h3>
-<p>AR Essence Font was inspired by the handwriting of Ashley R., the designer of the font. She wanted to create a font that could reflect her personality and style. She used a pen and paper to draw the letters and then scanned them into a computer. She then refined the letters and added some extra features, such as ligatures, alternates, and swashes. The result was a beautiful and unique font that has over 200 glyphs and supports multiple languages.</p>
- <h3>The usage and benefits of AR Essence Font</h3>
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-<ol>
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-<li>You will see four files: ar_essence.ttf, ar_essence.eot, ar_essence.svg, and ar_essence.woff. These are the different formats of the font that you can use on different devices and platforms.</li>
-</ol>
- <h3>The best websites to download AR Essence Font for free</h3>
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- <table>
-<tr><th>Website</th><th>URL</th><th>Description</th></tr>
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-<tr><td>FontSpace</td><td></td><td>This website offers over 80,000 free fonts for personal and commercial use. You can browse the fonts by category, style, popularity, or designer. You can also join the FontSpace community and share your own fonts.</td></tr>
-<tr><td>1001 Fonts</td><td></td><td>This website offers over 20,000 free fonts for desktop and web use. You can filter the fonts by license, theme, language, or format. You can also test the fonts online using the Font Tester tool.</td></tr>
-<tr><td>DaFont</td><td></td><td>This website offers over 40,000 free fonts for personal use. You can search the fonts by name, author, or category. You can also submit your own fonts or request a font from the forum.</td></tr>
-</table>
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- <h3>The steps to install AR Essence Font on your device</h3>
-<ol>
-<li>Locate the folder where you saved the font files.</li>
-<li>Select the file that matches your device and platform. For example, if you want to use the font on Windows, select the ar_essence.ttf file.</li>
-<li>Right-click on the file and choose "Install" from the menu.</li>
-<li>Wait for the installation to complete.</li>
-<li>You can now use AR Essence Font on your device.</li>
-</ol>
- <h3>The tips and tricks to use AR Essence Font effectively</h3>
-<p>AR Essence Font is a versatile and beautiful font that can enhance your text. However, to use it effectively, you need to follow some tips and tricks. Here are some of them:</p>
-<ul>
-<li>Use AR Essence Font for headlines, titles, logos, or other short texts that need to catch the eye. Avoid using it for long paragraphs or body texts that need to be readable.</li>
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-<li>Choose a color scheme that matches the mood and tone of your text. You can use a color picker tool to find the best colors for your text.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>AR Essence Font is a stunning and stylish script font that can make your text stand out. It is suitable for various purposes, such as logos, headlines, invitations, quotes, posters, greeting cards, and more. It can also be paired with other fonts to create stunning combinations. You can download AR Essence Font for free from the official website of the designer or from other websites that offer free fonts. You can also install and use AR Essence Font on your device with ease. We hope this article has helped you learn how to download AR Essence Font for free and how to use it effectively.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about AR Essence Font:</p>
- <h4>Q: Is AR Essence Font free?</h4>
-<p>A: Yes, AR Essence Font is free for personal and commercial use. However, you should always check the license terms before using any font.</p>
- <h4>Q: What is the difference between ar_essence.ttf, ar_essence.eot, ar_essence.svg, and ar_essence.woff?</h4>
-<p>A: These are the different formats of AR Essence Font that you can use on different devices and platforms. TTF stands for TrueType Font, which is compatible with most desktop applications and browsers. EOT stands for Embedded OpenType Font, which is compatible with Internet Explorer. SVG stands for Scalable Vector Graphics Font, which is compatible with Safari and Chrome. WOFF stands for Web Open Font Format, which is compatible with most modern browsers.</p>
- <h4>Q: How can I create custom fonts using Cufon Fonts?</h4>
-<p>A: Cufon Fonts is a website that allows you to create custom fonts using the Cufon Generator tool. You can upload any font file (TTF or OTF) and convert it into a web-friendly format. You can also customize the font size, weight, style, and color. You can then download the font file and use it on your website.</p>
- <h4>Q: How can I test the fonts online using the Font Tester tool?</h4>
-<p>A: Font Tester is a website that allows you to test the fonts online using the Font Tester tool. You can choose any font from the list or upload your own font file. You can then type any text and see how it looks in different sizes, colors, and backgrounds. You can also compare different fonts side by side and adjust the settings as you like.</p>
- <h4>Q: How can I submit my own fonts or request a font from the forum?</h4>
-<p>A: DaFont is a website that allows you to submit your own fonts or request a font from the forum. You can create an account and upload your font files using the Submit a Font tool. You can also browse the forum and post your requests or suggestions for fonts. You can also interact with other font lovers and designers on the forum.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Facebook Videos in 1080p HD Quality with Ease.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Facebook Videos in 1080p HD Quality with Ease.md
deleted file mode 100644
index 4af59a85032c39ce630ac79dd7da9a2caae02470..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Facebook Videos in 1080p HD Quality with Ease.md	
+++ /dev/null
@@ -1,147 +0,0 @@
-
-<h1>How to Download 1080p Facebook Video</h1>
-<p>Facebook is one of the most popular social media platforms that allows you to watch and share videos with your friends and family. But what if you want to download a video from Facebook and watch it offline or on another device? And what if you want to download a video in high definition (HD) quality, such as 1080p?</p>
-<h2>download 1080 facebook video</h2><br /><p><b><b>Download</b> &#9889; <a href="https://ssurll.com/2uNXle">https://ssurll.com/2uNXle</a></b></p><br /><br />
-<p>1080p is a resolution that refers to the number of pixels in a video. It is also known as full HD or FHD. A 1080p video has a width of 1920 pixels and a height of 1080 pixels, which means it has more than two million pixels in total. This makes the video clearer, sharper, and more detailed than lower resolutions, such as 720p or 480p.</p>
-<p>Downloading a 1080p video from Facebook can have many benefits. For example, you can enjoy a better visual experience, reduce buffering and loading time, and increase your audience's engagement. However, downloading a 1080p video from Facebook can also have some challenges. For instance, Facebook does not provide an official way to download videos from its platform. You also need to be careful about the copyright issues and the device compatibility when downloading videos.</p>
-<p>So how can you download a 1080p video from Facebook? In this article, we will show you how to do it on both desktop and mobile devices using some of the best tools available. Let's get started!</p>
- <h2>How to Download 1080p Facebook Video on Desktop</h2>
-<p>If you are using a desktop computer, such as Windows or Mac, you have two options to download a 1080p video from Facebook: using an online tool or using a software. Here are the steps for each option:</p>
-<p>How to download 1080p videos from facebook<br />
-Download facebook videos in HD quality online<br />
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- <h3>Using an Online Tool: fbdownloader</h3>
-<p>One of the simplest and most reliable online tools to download videos from Facebook is fbdownloader. This tool does not require you to download any additional files on your device. All you need is the video URL and an internet connection. Here is how to use it:</p>
-<ol>
-<li>Open your browser and go to the <a href="(^1^)">Facebook website</a>. Find the video you want to download and copy its link.</li>
-<li>Open another tab and go to the <a href="(^6^)">fbdownloader website</a>. Paste the video link into the field and click on the "Go" button.</li>
-<li>You will see different options for downloading the video in different qualities. Choose the one that says "Download HD Quality". This will download the video in 1080p resolution if available.</li>
-<li>Save the video file on your device. The file will be in MP4 format.</li>
-</ol>
-<p>fbdownloader also has some other features that you might find useful. For example, it can download private videos from Facebook if you have access to them. It can also convert Facebook videos to MP3 files if you only want to download the audio only. You can also use fbdownloader to download videos from other websites, such as Instagram, Twitter, and TikTok.</p>
- <h3>Using a Software: 4K Video Downloader</h3>
-<p>Another option to download a 1080p video from Facebook is to use a software called 4K Video Downloader. This software can download videos from various platforms, including Facebook, YouTube, and Vimeo. It can also download videos in different formats and resolutions, such as MP4, MKV, and 4K. Here is how to use it:</p>
-<ol>
-<li>Download and install the <a href="">4K Video Downloader</a> software on your device. It is available for Windows, Mac, and Linux.</li>
-<li>Open your browser and go to the <a href="">Facebook website</a>. Find the video you want to download and copy its link.</li>
-<li>Open the 4K Video Downloader software and click on the "Paste Link" button. The software will analyze the video and show you the available options.</li>
-<li>Select the format and quality you want to download. Choose MP4 as the format and 1080p as the quality. You can also choose other options, such as subtitles, file name, and destination folder.</li>
-<li>Click on the "Download" button and wait for the process to finish. The video file will be saved on your device.</li>
-</ol>
-<p>4K Video Downloader also has some other features that you might find useful. For example, it can download playlists and channels from YouTube. It can also download 3D and 360-degree videos from Facebook and other platforms. You can also use 4K Video Downloader to extract audio from videos and save them as MP3 files.</p>
- <h2>How to Download 1080p Facebook Video on Mobile</h2>
-<p>If you are using a mobile device, such as Android or iOS, you have two options to download a 1080p video from Facebook: using a website or using an app. Here are the steps for each option:</p>
- <h3>Using a Website: FBDown.net</h3>
-<p>One of the easiest and fastest websites to download videos from Facebook on mobile devices is FBDown.net. This website works on both Android and iOS devices with Firefox browser. Here is how to use it:</p>
-<ol>
-<li>Open your Firefox browser and go to the <a href="">Facebook website</a>. Find the video you want to download and tap on it.</li>
-<li>Tap on the three dots icon at the top right corner of the video and select "Copy Link".</li>
-<li>Open another tab and go to the <a href="">FBDown.net website</a>. Paste the video link into the field and tap on the "Download" button.</li>
-<li>You will see different options for downloading the video in different qualities. Choose the one that says "HD Quality". This will download the video in 1080p resolution if available.</li>
-<li>A new tab will open with the video player. Tap and hold on the video and select "Download Video". Save the video file on your device.</li>
-</ol>
-<p>FBDown.net also has some other features that you might find useful. For example, it can download private videos from Facebook if you have access to them. It can also convert Facebook videos to MP3 files if you only want to download the audio only. You can also use FBDown.net to download videos from other websites, such as Instagram, Twitter, and TikTok.</p>
- <h3>Using an App: Video Downloader for Facebook</h3>
-<p>Another option to download a 1080p video from Facebook on mobile devices is to use an app called Video Downloader for Facebook. This app works on Samsung devices and allows you to browse and download videos within the app. Here is how to use it:</p>
-<ol>
-<li>Download and install the <a href="">Video Downloader for Facebook</a> app on your device. It is available on the Google Play Store.</li>
-<li>Open the app and log in with your Facebook account. You will see your Facebook feed and the videos posted by your friends and pages.</li>
-<li>Find the video you want to download and tap on it. You will see a download icon at the bottom right corner of the video.</li>
-<li>Tap on the download icon and choose the quality you want to download. Choose HD Quality if you want to download the video in 1080p resolution.</li>
-<li>The app will start downloading the video and show you the progress. You can also pause and resume the download at any time.</li>
-<li>Once the download is complete, you can find the video file in your device's gallery or in the app's folder.</li>
-</ol>
-<p>Video Downloader for Facebook also has some other features that you might find useful. For example, it can download multiple videos at once. It can also play videos offline within the app. You can also share the downloaded videos with other apps or delete them if you want.</p>
- <h2>Conclusion</h2>
-<p>Downloading a 1080p video from Facebook can be a great way to enjoy a better visual quality, save data and time, and increase your engagement. However, it can also be a tricky task if you don't have the right tools. In this article, we have shown you how to download a 1080p video from Facebook on both desktop and mobile devices using some of the best tools available. We hope you find them helpful and easy to use.</p>
-<p>Now that you know how to download a 1080p video from Facebook, why not give it a try? You can use any of the tools we have mentioned above and see how they work for you. You can also share your feedback with us in the comments section below. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about downloading 1080p videos from Facebook:</p>
- <h3>How can I download private Facebook videos?</h3>
-<p>Private Facebook videos are videos that are not publicly available or shared with a limited audience. To download private Facebook videos, you need to have access to them or get permission from the owner. You can use some of the tools we have mentioned above, such as fbdownloader or FBDown.net, to download private Facebook videos. However, you need to follow these steps:</p>
-<ul>
-<li>Open your browser and go to the <a href="">Facebook website</a>. Find the private video you want to download and right-click on it.</li>
-<li>Select "View Page Source" or "Inspect Element" from the menu. This will open a new tab with the HTML code of the page.</li>
-<li>Press Ctrl+F or Cmd+F on your keyboard and search for "hd_src" or "sd_src". These are the links for the HD or SD quality of the video.</li>
-<li>Copy one of these links and paste it into another tab. This will open the video player.</li>
-<li>Right-click on the video player and select "Save Video As". Save the video file on your device.</li>
-</ul>
- <h3>How can I convert Facebook videos to MP3 files?</h3>
-<p>If you only want to download the audio from a Facebook video, you can convert it to an MP3 file. This can save space and bandwidth on your device. You can use some of the tools we have mentioned above, such as fbdownloader or FBDown.net, to convert Facebook videos to MP3 files. However, you need to follow these steps:</p>
-<ul>
-<li>Open your browser and go to the <a href="">Facebook website</a>. Find the video you want to convert and copy its link.</li>
-<li>Open another tab and go to the <a href="">fbdownloader website</a> or the <a href="">FBDown.net website</a>. Paste the video link into the field and click on the "Go" or "Download" button.</li>
-<li>You will see different options for downloading the video in different qualities. Choose the one that says "Convert to MP3". This will convert the video to an MP3 file.</li>
-<li>Save the MP3 file on your device. You can also rename it or edit it if you want.</li>
-</ul>
- <h3>How can I upload HD videos to Facebook?</h3>
-<p>If you want to upload HD videos to Facebook, you need to make sure that your videos meet the requirements and settings of Facebook. Here are some tips to upload HD videos to Facebook:</p>
-<ul>
-<li>Check the resolution and bitrate of your videos. Facebook recommends a resolution of 1280 x 720 pixels or higher and a bitrate of 5 Mbps or higher for HD videos.</li>
-<li>Check the format and codec of your videos. Facebook supports MP4, MOV, AVI, and WMV formats and H.264, MPEG-4, and VP8 codecs for HD videos.</li>
-<li>Check the length and size of your videos. Facebook allows you to upload videos up to 240 minutes long and 10 GB in size for HD videos.</li>
-<li>Check the settings of your device and app. Make sure that you enable the HD upload option on your device and app before uploading your videos.</li>
-</ul>
- <h3>How can I change the video quality settings on Facebook?</h3>
-<p>If you want to change the video quality settings on Facebook, you can do so by following these steps:</p>
-<ul>
-<li>Open your browser and go to the <a href="">Facebook website</a>. Click on the menu icon at the top right corner of the page and select "Settings & Privacy".</li>
-<li>Select "Settings" from the drop-down menu and scroll down to the "Videos" section.</li>
-<li>You will see two options: "Video Default Quality" and "Auto-Play Videos". You can choose between "SD Only" or "HD if available" for the video default quality. You can also choose between "On", "Off", or "Wi-Fi Only" for the auto-play videos.</li>
-<li>Save your changes and refresh the page.</li>
-</ul>
- <h3>How can I report a video that violates Facebook's policies?</h3>
-<p>If you see a video that violates Facebook's policies, such as hate speech, violence, nudity, or spam, you can report it by following these steps:</p>
-<ul>
-<li>Open your browser and go to the <a href="">Facebook website</a>. Find the video you want to report and click on it.</li>
-<li>Click on the three dots icon at the top right corner of the video and select "Give feedback on this video".</li>
-<li>You will see a list of options for reporting the video. Choose the one that best describes why you are reporting it.</li>
-<li>You can also add more details or screenshots if you want. Click on the "Send" button when you are done.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/snehilsanyal/scikit-learn/README.md b/spaces/snehilsanyal/scikit-learn/README.md
deleted file mode 100644
index 76a492c24eb5b9e04cc3f3b4cd1bc16e5e0c9d23..0000000000000000000000000000000000000000
--- a/spaces/snehilsanyal/scikit-learn/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-
----
-tags:
-  - gradio-theme
-  - track-1
-  - track-2
-title: scikit-learn
-colorFrom: orange
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-# scikit-learn
-## Description
-Just a minimal theme inspired by Scikit-Learn library. 
-## Contributions
-Thanks to [@snehilsanyal](https://huggingface.co/snehilsanyal) for adding this gradio theme!
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/roberta/README.glue.md b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/roberta/README.glue.md
deleted file mode 100644
index 4f596d55af99fba3cdf58b1d5ff3d8f8dbf4383d..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/roberta/README.glue.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# Finetuning RoBERTa on GLUE tasks
-
-### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands:
-```bash
-wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
-python download_glue_data.py --data_dir glue_data --tasks all
-```
-
-### 2) Preprocess GLUE task data:
-```bash
-./examples/roberta/preprocess_GLUE_tasks.sh glue_data <glue_task_name>
-```
-`glue_task_name` is one of the following:
-`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}`
-Use `ALL` for preprocessing all the glue tasks.
-
-### 3) Fine-tuning on GLUE task:
-Example fine-tuning cmd for `RTE` task
-```bash
-ROBERTA_PATH=/path/to/roberta/model.pt
-
-CUDA_VISIBLE_DEVICES=0 fairseq-hydra-train -config-dir examples/roberta/config/finetuning --config-name rte \
-task.data=RTE-bin checkpoint.restore_file=$ROBERTA_PATH
-```
-
-There are additional config files for each of the GLUE tasks in the examples/roberta/config/finetuning directory.
-
-**Note:**
-
-a) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`.
-
-b) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search.
-
-### Inference on GLUE task
-After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet:
-
-```python
-from fairseq.models.roberta import RobertaModel
-
-roberta = RobertaModel.from_pretrained(
-    'checkpoints/',
-    checkpoint_file='checkpoint_best.pt',
-    data_name_or_path='RTE-bin'
-)
-
-label_fn = lambda label: roberta.task.label_dictionary.string(
-    [label + roberta.task.label_dictionary.nspecial]
-)
-ncorrect, nsamples = 0, 0
-roberta.cuda()
-roberta.eval()
-with open('glue_data/RTE/dev.tsv') as fin:
-    fin.readline()
-    for index, line in enumerate(fin):
-        tokens = line.strip().split('\t')
-        sent1, sent2, target = tokens[1], tokens[2], tokens[3]
-        tokens = roberta.encode(sent1, sent2)
-        prediction = roberta.predict('sentence_classification_head', tokens).argmax().item()
-        prediction_label = label_fn(prediction)
-        ncorrect += int(prediction_label == target)
-        nsamples += 1
-print('| Accuracy: ', float(ncorrect)/float(nsamples))
-
-```
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py
deleted file mode 100644
index f226d5f50514ecb5ee3b4f1031df750609a56112..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py
+++ /dev/null
@@ -1,97 +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 argparse
-import logging
-import os
-
-import soundfile as sf
-from examples.textless_nlp.gslm.unit2speech.tts_data import (
-    TacotronInputDataset,
-)
-from examples.textless_nlp.gslm.unit2speech.utils import (
-    load_quantized_audio_from_file,
-    load_tacotron,
-    load_waveglow,
-    synthesize_audio,
-)
-
-
-def get_logger():
-    log_format = "[%(asctime)s] [%(levelname)s]: %(message)s"
-    logging.basicConfig(format=log_format, level=logging.INFO)
-    logger = logging.getLogger(__name__)
-    return logger
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="Wav2Vec 2.0 speech generator."
-    )
-    parser.add_argument(
-        "--quantized_unit_path",
-        type=str,
-        help="K-means model file path to use for inference",
-    )
-    parser.add_argument(
-        "--tts_model_path",
-        type=str,
-        help="TTS model file path to use for inference",
-    )
-    parser.add_argument(
-        "--waveglow_path",
-        type=str,
-        help="Path to the waveglow checkpoint (vocoder).",
-    )
-    parser.add_argument("--max_decoder_steps", type=int, default=2000)
-    parser.add_argument("--denoiser_strength", type=float, default=0.1)
-    parser.add_argument(
-        "--out_audio_dir",
-        type=str,
-        help="Output directory to dump audio files",
-    )
-
-    return parser
-
-
-def main(args, logger):
-    # Load quantized audio
-    logger.info(f"Loading quantized audio from {args.quantized_unit_path}...")
-    names_batch, quantized_units_batch = load_quantized_audio_from_file(
-        file_path=args.quantized_unit_path
-    )
-
-    logger.info(f"Loading TTS model from {args.tts_model_path}...")
-    tacotron_model, sample_rate, hparams = load_tacotron(
-        tacotron_model_path=args.tts_model_path,
-        max_decoder_steps=args.max_decoder_steps,
-    )
-
-    logger.info(f"Loading Waveglow model from {args.waveglow_path}...")
-    waveglow, denoiser = load_waveglow(waveglow_path=args.waveglow_path)
-
-    tts_dataset = TacotronInputDataset(hparams)
-    for name, quantized_units in zip(names_batch, quantized_units_batch):
-        quantized_units_str = " ".join(map(str, quantized_units))
-        tts_input = tts_dataset.get_tensor(quantized_units_str)
-        mel, aud, aud_dn, has_eos = synthesize_audio(
-            tacotron_model,
-            waveglow,
-            denoiser,
-            tts_input.unsqueeze(0),
-            strength=args.denoiser_strength,
-        )
-        out_file_path = os.path.join(args.out_audio_dir, f"{name}.wav")
-        sf.write(
-            f"{out_file_path}", aud_dn[0].cpu().float().numpy(), sample_rate
-        )
-
-
-if __name__ == "__main__":
-    parser = get_parser()
-    args = parser.parse_args()
-    logger = get_logger()
-    logger.info(args)
-    main(args, logger)
diff --git a/spaces/stamps-labs/stamp2vec/embedding_models/vits8/oml/train.py b/spaces/stamps-labs/stamp2vec/embedding_models/vits8/oml/train.py
deleted file mode 100644
index 328047ad418a1648e75b2fe8397a9dd33990830a..0000000000000000000000000000000000000000
--- a/spaces/stamps-labs/stamp2vec/embedding_models/vits8/oml/train.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import pytorch_lightning as pl
-import torch
-import pandas as pd
-
-from oml.datasets.base import DatasetQueryGallery, DatasetWithLabels
-from oml.lightning.modules.extractor import ExtractorModule
-from oml.lightning.callbacks.metric import MetricValCallback
-from oml.losses.triplet import TripletLossWithMiner
-from oml.metrics.embeddings import EmbeddingMetrics
-from oml.miners.inbatch_all_tri import AllTripletsMiner
-from oml.models.vit.vit import ViTExtractor
-from oml.samplers.balance import BalanceSampler
-from pytorch_lightning.loggers import TensorBoardLogger
-
-import argparse
-
-parser = argparse.ArgumentParser("Train model with OML", 
-                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument("--root-dir", help="root directory for train data", default="data/train_val/")
-parser.add_argument("--train-dataframe-name", help="name of dataframe in root directory", default="df_stamps.csv")
-parser.add_argument("--train-images", help="name of directory with images", default="images/")
-parser.add_argument("--num-epochs", help="number of epochs to train model", default=100)
-parser.add_argument("--model-arch", help="which model architecture to use, check model zoo", default="vits8")
-parser.add_argument("--weights", 
-                    help="""
-                    pretrained weights for model, choose from model zoo 
-                    https://open-metric-learning.readthedocs.io/en/latest/feature_extraction/zoo.html
-                    """, 
-                    default="vits8_dino")
-parser.add_argument("--checkpoint", help="resume training from checkpoint, provide path", default=None)
-parser.add_argument("--num-labels", help="number of labels in dataset, set less if cuda out of memory", default=6)
-parser.add_argument("--num-instances", help="number of instances for each label in batch, set less if cuda out of memory", default=2)
-parser.add_argument("--val-batch-size", help="batch size for validation", default=4)
-parser.add_argument("--log-data", action="store_true", help="Whether to log data")
-
-args = parser.parse_args()
-config = vars(args)
-
-dataset_root = config['root_dir']
-df = pd.read_csv(f"{dataset_root}{config['train_dataframe_name']}")
-
-df_train = df[df["split"] == "train"].reset_index(drop=True)
-df_val = df[df["split"] == "validation"].reset_index(drop=True)
-df_val["is_query"] = df_val["is_query"].astype(bool)
-df_val["is_gallery"] = df_val["is_gallery"].astype(bool)
-
-extractor = ViTExtractor(config['weights'], arch=config['model_arch'], normalise_features=False)
-
-optimizer = torch.optim.SGD(extractor.parameters(), lr=1e-6)
-train_dataset = DatasetWithLabels(df_train, dataset_root=dataset_root)
-criterion = TripletLossWithMiner(margin=0.1, miner=AllTripletsMiner())
-batch_sampler = BalanceSampler(train_dataset.get_labels(), n_labels=config['num_labels'], n_instances=config['num_instances'])
-train_loader = torch.utils.data.DataLoader(train_dataset, batch_sampler=batch_sampler)
-
-val_dataset = DatasetQueryGallery(df_val, dataset_root=dataset_root)
-val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=config['val_batch_size'])
-metric_callback = MetricValCallback(metric=EmbeddingMetrics(extra_keys=[train_dataset.paths_key,], cmc_top_k=(5, 3, 1)), log_images=True)
-
-if config['log_data']:
-    logger = TensorBoardLogger(".")
-
-pl_model = ExtractorModule(extractor, criterion, optimizer)
-trainer = pl.Trainer(max_epochs=config['num_epochs'], callbacks=[metric_callback], num_sanity_val_steps=0, accelerator='gpu', devices=1, resume_from_checkpoint=config['checkpoint'])
-trainer.fit(pl_model, train_dataloaders=train_loader, val_dataloaders=val_loader)
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/metagpt/prompts/summarize.py b/spaces/sub314xxl/MetaGPT/metagpt/prompts/summarize.py
deleted file mode 100644
index c3deef569010cace27c017fbb8a32f415dd76a0a..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/prompts/summarize.py
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/6/19 23:07
-@Author  : alexanderwu
-@File    : summarize.py
-"""
-
-
-# 出自插件：ChatGPT - 网站和 YouTube 视频摘要
-# https://chrome.google.com/webstore/detail/chatgpt-%C2%BB-summarize-every/cbgecfllfhmmnknmamkejadjmnmpfjmp?hl=zh-CN&utm_source=chrome-ntp-launcher
-SUMMARIZE_PROMPT = """
-Your output should use the following template:
-### Summary
-### Facts
-- [Emoji] Bulletpoint
-
-Your task is to summarize the text I give you in up to seven concise bullet points and start with a short, high-quality 
-summary. Pick a suitable emoji for every bullet point. Your response should be in {{SELECTED_LANGUAGE}}. If the provided
- URL is functional and not a YouTube video, use the text from the {{URL}}. However, if the URL is not functional or is 
-a YouTube video, use the following text: {{CONTENT}}.
-"""
-
-
-# GCP-VertexAI-文本摘要（SUMMARIZE_PROMPT_2-5都是）
-# https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/examples/prompt-design/text_summarization.ipynb
-# 长文档需要map-reduce过程，见下面这个notebook
-# https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/examples/document-summarization/summarization_large_documents.ipynb
-SUMMARIZE_PROMPT_2 = """
-Provide a very short summary, no more than three sentences, for the following article:
-
-Our quantum computers work by manipulating qubits in an orchestrated fashion that we call quantum algorithms.
-The challenge is that qubits are so sensitive that even stray light can cause calculation errors — and the problem worsens as quantum computers grow.
-This has significant consequences, since the best quantum algorithms that we know for running useful applications require the error rates of our qubits to be far lower than we have today.
-To bridge this gap, we will need quantum error correction.
-Quantum error correction protects information by encoding it across multiple physical qubits to form a “logical qubit,” and is believed to be the only way to produce a large-scale quantum computer with error rates low enough for useful calculations.
-Instead of computing on the individual qubits themselves, we will then compute on logical qubits. By encoding larger numbers of physical qubits on our quantum processor into one logical qubit, we hope to reduce the error rates to enable useful quantum algorithms.
-
-Summary:
-
-"""
-
-
-SUMMARIZE_PROMPT_3 = """
-Provide a TL;DR for the following article:
-
-Our quantum computers work by manipulating qubits in an orchestrated fashion that we call quantum algorithms. 
-The challenge is that qubits are so sensitive that even stray light can cause calculation errors — and the problem worsens as quantum computers grow. 
-This has significant consequences, since the best quantum algorithms that we know for running useful applications require the error rates of our qubits to be far lower than we have today. 
-To bridge this gap, we will need quantum error correction. 
-Quantum error correction protects information by encoding it across multiple physical qubits to form a “logical qubit,” and is believed to be the only way to produce a large-scale quantum computer with error rates low enough for useful calculations. 
-Instead of computing on the individual qubits themselves, we will then compute on logical qubits. By encoding larger numbers of physical qubits on our quantum processor into one logical qubit, we hope to reduce the error rates to enable useful quantum algorithms.
-
-TL;DR:
-"""
-
-
-SUMMARIZE_PROMPT_4 = """
-Provide a very short summary in four bullet points for the following article:
-
-Our quantum computers work by manipulating qubits in an orchestrated fashion that we call quantum algorithms.
-The challenge is that qubits are so sensitive that even stray light can cause calculation errors — and the problem worsens as quantum computers grow.
-This has significant consequences, since the best quantum algorithms that we know for running useful applications require the error rates of our qubits to be far lower than we have today.
-To bridge this gap, we will need quantum error correction.
-Quantum error correction protects information by encoding it across multiple physical qubits to form a “logical qubit,” and is believed to be the only way to produce a large-scale quantum computer with error rates low enough for useful calculations.
-Instead of computing on the individual qubits themselves, we will then compute on logical qubits. By encoding larger numbers of physical qubits on our quantum processor into one logical qubit, we hope to reduce the error rates to enable useful quantum algorithms.
-
-Bulletpoints:
-
-"""
-
-
-SUMMARIZE_PROMPT_5 = """
-Please generate a summary of the following conversation and at the end summarize the to-do's for the support Agent:
-
-Customer: Hi, I'm Larry, and I received the wrong item.
-
-Support Agent: Hi, Larry. How would you like to see this resolved?
-
-Customer: That's alright. I want to return the item and get a refund, please.
-
-Support Agent: Of course. I can process the refund for you now. Can I have your order number, please?
-
-Customer: It's [ORDER NUMBER].
-
-Support Agent: Thank you. I've processed the refund, and you will receive your money back within 14 days.
-
-Customer: Thank you very much.
-
-Support Agent: You're welcome, Larry. Have a good day!
-
-Summary:
-"""
diff --git a/spaces/sub314xxl/MetaGPT/tests/metagpt/memory/test_memory_storage.py b/spaces/sub314xxl/MetaGPT/tests/metagpt/memory/test_memory_storage.py
deleted file mode 100644
index 6bb3e8f1d5154d74b6e84244299a693fbf2d2b69..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/tests/metagpt/memory/test_memory_storage.py
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Desc   : the unittests of metagpt/memory/memory_storage.py
-
-from typing import List
-
-from metagpt.memory.memory_storage import MemoryStorage
-from metagpt.schema import Message
-from metagpt.actions import BossRequirement
-from metagpt.actions import WritePRD
-from metagpt.actions.action_output import ActionOutput
-
-
-def test_idea_message():
-    idea = 'Write a cli snake game'
-    role_id = 'UTUser1(Product Manager)'
-    message = Message(role='BOSS', content=idea, cause_by=BossRequirement)
-
-    memory_storage: MemoryStorage = MemoryStorage()
-    messages = memory_storage.recover_memory(role_id)
-    assert len(messages) == 0
-
-    memory_storage.add(message)
-    assert memory_storage.is_initialized is True
-
-    sim_idea = 'Write a game of cli snake'
-    sim_message = Message(role='BOSS', content=sim_idea, cause_by=BossRequirement)
-    new_messages = memory_storage.search(sim_message)
-    assert len(new_messages) == 0   # similar, return []
-
-    new_idea = 'Write a 2048 web game'
-    new_message = Message(role='BOSS', content=new_idea, cause_by=BossRequirement)
-    new_messages = memory_storage.search(new_message)
-    assert new_messages[0].content == message.content
-
-    memory_storage.clean()
-    assert memory_storage.is_initialized is False
-
-
-def test_actionout_message():
-    out_mapping = {
-        'field1': (str, ...),
-        'field2': (List[str], ...)
-    }
-    out_data = {
-        'field1': 'field1 value',
-        'field2': ['field2 value1', 'field2 value2']
-    }
-    ic_obj = ActionOutput.create_model_class('prd', out_mapping)
-
-    role_id = 'UTUser2(Architect)'
-    content = 'The boss has requested the creation of a command-line interface (CLI) snake game'
-    message = Message(content=content,
-                      instruct_content=ic_obj(**out_data),
-                      role='user',
-                      cause_by=WritePRD)  # WritePRD as test action
-
-    memory_storage: MemoryStorage = MemoryStorage()
-    messages = memory_storage.recover_memory(role_id)
-    assert len(messages) == 0
-
-    memory_storage.add(message)
-    assert memory_storage.is_initialized is True
-
-    sim_conent = 'The request is command-line interface (CLI) snake game'
-    sim_message = Message(content=sim_conent,
-                          instruct_content=ic_obj(**out_data),
-                          role='user',
-                          cause_by=WritePRD)
-    new_messages = memory_storage.search(sim_message)
-    assert len(new_messages) == 0   # similar, return []
-
-    new_conent = 'Incorporate basic features of a snake game such as scoring and increasing difficulty'
-    new_message = Message(content=new_conent,
-                          instruct_content=ic_obj(**out_data),
-                          role='user',
-                          cause_by=WritePRD)
-    new_messages = memory_storage.search(new_message)
-    assert new_messages[0].content == message.content
-
-    memory_storage.clean()
-    assert memory_storage.is_initialized is False
diff --git a/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/__init__.py b/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/__init__.py
deleted file mode 100644
index 583942d31eee92261b22930fde15f8a151d49141..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/4/29 16:01
-@Author  : alexanderwu
-@File    : __init__.py
-"""
diff --git a/spaces/subhajitmaji/MusicGen/CODE_OF_CONDUCT.md b/spaces/subhajitmaji/MusicGen/CODE_OF_CONDUCT.md
deleted file mode 100644
index 83f431e8feeb7e80d571f39c9f6c1b96857b5f85..0000000000000000000000000000000000000000
--- a/spaces/subhajitmaji/MusicGen/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <opensource-conduct@fb.com>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
diff --git a/spaces/subhc/Guess-What-Moves/configs/README.md b/spaces/subhc/Guess-What-Moves/configs/README.md
deleted file mode 100644
index b449419905c5bb6f5a7016e99f8c9fac81c892bc..0000000000000000000000000000000000000000
--- a/spaces/subhc/Guess-What-Moves/configs/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-## Available configs:
-
-
-Use `main.py --config-file=<config-file>`
-
-No need to specify `GWM.MODEL`. It is already defined inside the config files
-
-
-### Available configs:
-```
-maskformer/swin/maskformer_swin_base_IN21k_384_bs16_160k_res640.yaml
-maskformer/swin/maskformer_swin_large_IN21k_384_bs16_160k_res640.yaml
-maskformer/swin/maskformer_swin_small_bs16_160k.yaml
-maskformer/swin/maskformer_swin_tiny_bs16_160k.yaml
-maskformer/maskformer_R50_bs16_160k.yaml
-```
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Geo Sans Bold Font Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Geo Sans Bold Font Download.md
deleted file mode 100644
index feb4765080c31fea23a6e2e1a73948eeaea831f3..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Geo Sans Bold Font Download.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-<h1>How to Download and Use Geo Sans Bold Font</h1>
-<p>Geo Sans Bold is a simple geometric typeface that is inspired by some of the experimental faces designed during the 1920s by well-known modernists such as Theo van Doesberg and Herbert Bayer. It expresses both the directness of the 1920s faces and the rather disingenuous consumerist thrust of their 80s and 90s descendants[^1^].</p>
-<p>If you want to use Geo Sans Bold font for your web design or graphic projects, you can download it for free from various websites. One of them is <a href="https://www.free-fonts.com/geo-sans-bold">Free Fonts</a>, where you can find Geo Bold font in ttf format for Windows and Mac[^3^]. Another option is <a href="https://fonts.google.com/specimen/Geo">Google Fonts</a>, where you can find Geo font in regular and light weights, and use it online or download it for offline use[^1^] [^2^].</p>
-<h2>Geo sans bold font download</h2><br /><p><b><b>Download Zip</b> &#9658;&#9658;&#9658; <a href="https://cinurl.com/2uEYGE">https://cinurl.com/2uEYGE</a></b></p><br /><br />
-<p>To use Geo Sans Bold font on your website, you need to add a link to the font in your HTML head section, like this:</p>
-<code><link href="https://fonts.googleapis.com/css?family=Geo" rel="stylesheet"></code>
-<p>Then, you can apply the font to any element in your CSS file, like this:</p>
-<code>p font-family: 'Geo', sans-serif;</code>
-<p>Alternatively, you can download the font files and host them on your own server, and use the @font-face rule in your CSS file to link to them.</p>
-<p>Geo Sans Bold font is a versatile and elegant typeface that can give your website or graphic design a modern and minimalist look. Try it out and see how it works for you!</p><h2>Why Choose Geo Sans Bold Font?</h2>
-<p>Geo Sans Bold font is a great choice for web designers and graphic artists who want to create a clean and minimalist aesthetic. Geo Sans Bold font has a geometric and symmetrical shape that is easy to read and pleasing to the eye. It also has a low contrast and a uniform stroke width that make it suitable for both large and small sizes.</p>
-<p>Geo Sans Bold font can be used for various purposes, such as headlines, logos, banners, posters, flyers, brochures, menus, labels, and more. It can also be paired with other fonts to create contrast and harmony. For example, you can use Geo Sans Bold font for your main headings and use a serif or script font for your body text or subheadings. You can also use different colors or backgrounds to make your text stand out.</p>
-<h2>Where to Find More Fonts Like Geo Sans Bold?</h2>
-<p>If you like Geo Sans Bold font and want to explore more fonts that have a similar style, you can browse through the <a href="https://fonts.google.com/?category=Sans+Serif">sans serif</a> category on Google Fonts. There you can find hundreds of free fonts that you can use for your web or graphic projects. Some of the fonts that are similar to Geo Sans Bold are:</p>
-<ul>
-<li><a href="https://fonts.google.com/specimen/Roboto">Roboto</a>: A modern and versatile sans serif font that was designed by Google and is used for Android and YouTube.</li>
-<li><a href="https://fonts.google.com/specimen/Montserrat">Montserrat</a>: A geometric and elegant sans serif font that was inspired by the signage of Montserrat, a neighborhood in Buenos Aires.</li>
-<li><a href="https://fonts.google.com/specimen/Poppins">Poppins</a>: A geometric and playful sans serif font that has a rounded shape and a wide range of weights.</li>
-<li><a href="https://fonts.google.com/specimen/Raleway">Raleway</a>: A stylish and sophisticated sans serif font that has a thin and elegant appearance.</li>
-</ul>
-<p>You can also use the <a href="https://fonts.google.com/featured/Superfamilies">superfamilies</a> feature on Google Fonts to find fonts that belong to the same family or have a similar design. For example, you can find Geo Light font, which is a lighter version of Geo Sans Bold font, in the same family. You can also find fonts that have a different style but share some characteristics with Geo Sans Bold font, such as <a href="https://fonts.google.com/specimen/Orbitron">Orbitron</a>, which is a futuristic and sci-fi sans serif font.</p>
-<p></p> d5da3c52bf<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Perpajakanindonesiawaluyoebookdownload.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Perpajakanindonesiawaluyoebookdownload.md
deleted file mode 100644
index 6bec30afa4771137831e3bacbe0c40ba0a5a149e..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Perpajakanindonesiawaluyoebookdownload.md
+++ /dev/null
@@ -1,20 +0,0 @@
-<h2>perpajakanindonesiawaluyoebookdownload</h2><br /><p><b><b>Download File</b> &#9658;&#9658;&#9658; <a href="https://cinurl.com/2uEYVR">https://cinurl.com/2uEYVR</a></b></p><br /><br />
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Virgins Protection Magic Walkthrough _HOT_.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Virgins Protection Magic Walkthrough _HOT_.md
deleted file mode 100644
index 37b4272be6864642a57769776aa3e284739f0711..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Virgins Protection Magic Walkthrough _HOT_.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Virgin's Protection Magic Walkthrough</h2><br /><p><b><b>Download File</b> &gt;&gt;&gt;&gt;&gt; <a href="https://cinurl.com/2uEXMv">https://cinurl.com/2uEXMv</a></b></p><br /><br />
-
-March 5, 2561 B.C. So, I'm wondering if anyone knows a good walkthrough to help me get through the rest of the game? I checked his wiki page, but it looks like it's . exe file and that it requires a .dll file from Microsoft to download it.
-I searched all over the internet but couldn't find anything to help me.
-Also if anyone knows how to get around the windows library loading problem and can provide a link to the related wikipedia article, that would be greatly appreciated.
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-<br />
-<br />
-<p></p>
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/Kodi Ipa !!HOT!! Download Apple Tv.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/Kodi Ipa !!HOT!! Download Apple Tv.md
deleted file mode 100644
index 35d92fa374d574f50836c284b66664ec624a7d35..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/Kodi Ipa !!HOT!! Download Apple Tv.md	
+++ /dev/null
@@ -1,96 +0,0 @@
-## Kodi Ipa Download Apple Tv
-
-
-
- 
-
-  
-
- 
-
-**Download File >>>>> [https://cayseypisi.blogspot.com/?c=2tyeXR](https://cayseypisi.blogspot.com/?c=2tyeXR)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Download and Install Kodi on Your Apple TV in 2023
-
- 
-
-Kodi is a popular media player app that lets you stream movies, TV shows, music, and more from various sources. It also offers a lot of customization options and add-ons to enhance your viewing experience. However, Kodi is not available on the tvOS App Store, which means you can't install it on your Apple TV directly. But don't worry, there are still ways to get Kodi on your Apple TV without jailbreaking it.
-
- 
-
-In this article, we will show you three methods to download and install Kodi on your Apple TV in 2023. These methods work for both Apple TV HD (4th generation) and Apple TV 4K (5th generation) running on tvOS 13 or later. You will need a computer (Mac or Windows), a USB-C cable (for Apple TV HD), and an Apple ID to complete the process.
-
- 
-
-## Method 1: Install Kodi on Apple TV via Cydia Impactor
-
- 
-
-Cydia Impactor is a tool that allows you to sideload IPA files (iOS apps) onto your iOS or tvOS devices. You can use it to install Kodi on your Apple TV by following these steps:
-
- 
-
-1. Download the latest version of Kodi for tvOS from [Kodi's official website](https://kodi.tv/download/ios/). The file should have a .deb extension.
-
-2. Download and install [Cydia Impactor](https://cydiaimpactor.com/) on your computer.
-
-3. Connect your Apple TV HD to your computer using a USB-C cable. If you have an Apple TV 4K, you need to connect it to the same Wi-Fi network as your computer.
-
-4. Open Cydia Impactor and select your Apple TV from the drop-down menu.
-
-5. Drag and drop the Kodi.deb file onto the Cydia Impactor window.
-
-6. Enter your Apple ID and password when prompted. This is required to sign the app with a free developer certificate. If you have two-factor authentication enabled, you need to generate an app-specific password from [Apple's website](https://appleid.apple.com/).
-
-7. Wait for Cydia Impactor to install Kodi on your Apple TV. You should see a Kodi icon on your Apple TV home screen once it's done.
-
-8. On your Apple TV, go to Settings > General > Profiles & Device Management and trust the profile that belongs to your Apple ID.
-
-9. Launch Kodi and enjoy!
-
-
-
-Note: The free developer certificate expires after seven days, which means you need to repeat this process every week to keep using Kodi. Alternatively, you can use a paid developer account ($99/year) or a third-party signing service (such as AppDB) to avoid this limitation.
-
- 
-
-## Method 2: Install Kodi on Apple TV via Xcode
-
- 
-
-Xcode is an integrated development environment (IDE) for macOS that allows you to create and test apps for iOS and tvOS devices. You can use it to install Kodi on your Apple TV by following these steps:
-
-
-
-1. Download the latest version of Kodi for tvOS from [Kodi's official website](https://kodi.tv/download/ios/). The file should have a .deb extension.
-
-2. Download and install [Xcode](https://apps.apple.com/us/app/xcode/id497799835?mt=12) from the Mac App Store.
-
-3. Download and install [iOS App Signer](https://dantheman827.github.io/ios-app-signer/), a tool that converts .deb files into .ipa files that can be installed with Xcode.
-
-4. Connect your Apple TV HD to your Mac using a USB-C cable. If you have an Apple TV 4K, you need to connect it to the same Wi-Fi network as your Mac.
-
-5. Open Xcode and create a new project. Choose tvOS > Application > Single View Application and click Next.
-
-6. Enter a name, organization name, and bundle identifier for your project. The bundle identifier should be unique, such as com.yourname.kodi. Click dfd1c89656
-
-
-
-
-
-
-
-
-
diff --git a/spaces/t13718236382/bingoGPT4/src/components/ui/alert-dialog.tsx b/spaces/t13718236382/bingoGPT4/src/components/ui/alert-dialog.tsx
deleted file mode 100644
index 17fec4d16510328deacc1416569173c97761ef72..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/bingoGPT4/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/terfces0erbo/CollegeProjectV2/Download Cutviewer Mill 31 61.md b/spaces/terfces0erbo/CollegeProjectV2/Download Cutviewer Mill 31 61.md
deleted file mode 100644
index 23983a539aa1bdc75063782e0e78482178666d1b..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Download Cutviewer Mill 31 61.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Download Cutviewer Mill 31 61</h2><br /><p><b><b>Download</b> &#10037; <a href="https://bytlly.com/2uGj67">https://bytlly.com/2uGj67</a></b></p><br /><br />
-
-The proposed MR-AVML CNC milling learning tool includes two forms of tactile feedback, thus providing users with natural and. MR-AVML is the first training tool for CNC milling machines.
-This CNC milling training tool contains videos and texts that convey in an accessible form the basic knowledge and skills required to work on CNC milling machines, and also allows users to gain an understanding of the operation of milling machines.
-Video tutorials are part of the MR-AVML suite and will be supported in the future.
-MR-AVML kit includes:
-- Video tutorials with text explanations. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/teticio/audio-diffusion/audiodiffusion/utils.py b/spaces/teticio/audio-diffusion/audiodiffusion/utils.py
deleted file mode 100644
index ed274bf35e62778630da7e6546e06701c7897b4f..0000000000000000000000000000000000000000
--- a/spaces/teticio/audio-diffusion/audiodiffusion/utils.py
+++ /dev/null
@@ -1,303 +0,0 @@
-# adpated from https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
-
-import torch
-from diffusers import AutoencoderKL
-
-
-def shave_segments(path, n_shave_prefix_segments=1):
-    """
-    Removes segments. Positive values shave the first segments, negative shave the last segments.
-    """
-    if n_shave_prefix_segments >= 0:
-        return ".".join(path.split(".")[n_shave_prefix_segments:])
-    else:
-        return ".".join(path.split(".")[:n_shave_prefix_segments])
-
-
-def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside resnets to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside attentions to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        new_item = new_item.replace("norm.weight", "group_norm.weight")
-        new_item = new_item.replace("norm.bias", "group_norm.bias")
-
-        new_item = new_item.replace("q.weight", "query.weight")
-        new_item = new_item.replace("q.bias", "query.bias")
-
-        new_item = new_item.replace("k.weight", "key.weight")
-        new_item = new_item.replace("k.bias", "key.bias")
-
-        new_item = new_item.replace("v.weight", "value.weight")
-        new_item = new_item.replace("v.bias", "value.bias")
-
-        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
-        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
-
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def assign_to_checkpoint(
-    paths,
-    checkpoint,
-    old_checkpoint,
-    attention_paths_to_split=None,
-    additional_replacements=None,
-    config=None,
-):
-    """
-    This does the final conversion step: take locally converted weights and apply a global renaming
-    to them. It splits attention layers, and takes into account additional replacements
-    that may arise.
-
-    Assigns the weights to the new checkpoint.
-    """
-    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
-
-    # Splits the attention layers into three variables.
-    if attention_paths_to_split is not None:
-        for path, path_map in attention_paths_to_split.items():
-            old_tensor = old_checkpoint[path]
-            channels = old_tensor.shape[0] // 3
-
-            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
-
-            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
-
-            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
-            query, key, value = old_tensor.split(channels // num_heads, dim=1)
-
-            checkpoint[path_map["query"]] = query.reshape(target_shape)
-            checkpoint[path_map["key"]] = key.reshape(target_shape)
-            checkpoint[path_map["value"]] = value.reshape(target_shape)
-
-    for path in paths:
-        new_path = path["new"]
-
-        # These have already been assigned
-        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
-            continue
-
-        # Global renaming happens here
-        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
-        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
-        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
-
-        if additional_replacements is not None:
-            for replacement in additional_replacements:
-                new_path = new_path.replace(replacement["old"], replacement["new"])
-
-        # proj_attn.weight has to be converted from conv 1D to linear
-        if "proj_attn.weight" in new_path:
-            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
-        else:
-            checkpoint[new_path] = old_checkpoint[path["old"]]
-
-
-def conv_attn_to_linear(checkpoint):
-    keys = list(checkpoint.keys())
-    attn_keys = ["query.weight", "key.weight", "value.weight"]
-    for key in keys:
-        if ".".join(key.split(".")[-2:]) in attn_keys:
-            if checkpoint[key].ndim > 2:
-                checkpoint[key] = checkpoint[key][:, :, 0, 0]
-        elif "proj_attn.weight" in key:
-            if checkpoint[key].ndim > 2:
-                checkpoint[key] = checkpoint[key][:, :, 0]
-
-
-def create_vae_diffusers_config(original_config):
-    """
-    Creates a config for the diffusers based on the config of the LDM model.
-    """
-    vae_params = original_config.model.params.ddconfig
-    _ = original_config.model.params.embed_dim
-
-    block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
-    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
-    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
-
-    config = dict(
-        sample_size=tuple(vae_params.resolution),
-        in_channels=vae_params.in_channels,
-        out_channels=vae_params.out_ch,
-        down_block_types=tuple(down_block_types),
-        up_block_types=tuple(up_block_types),
-        block_out_channels=tuple(block_out_channels),
-        latent_channels=vae_params.z_channels,
-        layers_per_block=vae_params.num_res_blocks,
-    )
-    return config
-
-
-def convert_ldm_vae_checkpoint(checkpoint, config):
-    # extract state dict for VAE
-    vae_state_dict = checkpoint
-
-    new_checkpoint = {}
-
-    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
-    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
-    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
-    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
-    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
-    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
-
-    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
-    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
-    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
-    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
-    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
-    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
-
-    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
-    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
-    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
-    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
-
-    # Retrieves the keys for the encoder down blocks only
-    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
-    down_blocks = {
-        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
-    }
-
-    # Retrieves the keys for the decoder up blocks only
-    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
-    up_blocks = {
-        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
-    }
-
-    for i in range(num_down_blocks):
-        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
-
-        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.weight"
-            )
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.bias"
-            )
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
-
-    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
-
-    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(
-        paths,
-        new_checkpoint,
-        vae_state_dict,
-        additional_replacements=[meta_path],
-        config=config,
-    )
-    conv_attn_to_linear(new_checkpoint)
-
-    for i in range(num_up_blocks):
-        block_id = num_up_blocks - 1 - i
-        resnets = [
-            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
-        ]
-
-        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.weight"
-            ]
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.bias"
-            ]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
-
-    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(
-            paths,
-            new_checkpoint,
-            vae_state_dict,
-            additional_replacements=[meta_path],
-            config=config,
-        )
-
-    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(
-        paths,
-        new_checkpoint,
-        vae_state_dict,
-        additional_replacements=[meta_path],
-        config=config,
-    )
-    conv_attn_to_linear(new_checkpoint)
-    return new_checkpoint
-
-
-def convert_ldm_to_hf_vae(ldm_checkpoint, ldm_config, hf_checkpoint, sample_size):
-    checkpoint = torch.load(ldm_checkpoint)["state_dict"]
-
-    # Convert the VAE model.
-    vae_config = create_vae_diffusers_config(ldm_config)
-    converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
-
-    vae = AutoencoderKL(**vae_config)
-    vae.load_state_dict(converted_vae_checkpoint)
-    vae.save_pretrained(hf_checkpoint)
diff --git a/spaces/theintuitiveye/HARDblend/app.py b/spaces/theintuitiveye/HARDblend/app.py
deleted file mode 100644
index 4b641150905963e978745d55f44a8ac61fe04ea6..0000000000000000000000000000000000000000
--- a/spaces/theintuitiveye/HARDblend/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'theintuitiveye/HARDblend'
-prefix = ''
-     
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
-  try:
-    if img is not None:
-      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>Hardblend</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/theintuitiveye/HARDblend">Hardblend</a> Stable Diffusion model.<br>
-               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
-              </p>
-              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/theintuitiveye/HARDblend/settings'>Settings</a></b>"} after duplicating the space<br><br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/theintuitiveye/HARDblend?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-              image_out = gr.Image(height=512)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
-    </div>
-    """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Bigfish Games - Reaxxion Extra Quality Crack.zip.md b/spaces/tialenAdioni/chat-gpt-api/logs/Bigfish Games - Reaxxion Extra Quality Crack.zip.md
deleted file mode 100644
index 262465d961c782ff9f6baa2e437f9250290a6493..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Bigfish Games - Reaxxion Extra Quality Crack.zip.md	
+++ /dev/null
@@ -1,30 +0,0 @@
-<br />
-<h1>How to Download and Play Reaxxion, a Breakout Game with a Twist</h1>
-<p>If you are looking for a fun and challenging game that combines the classic breakout genre with modern graphics and features, you might want to check out Reaxxion, a game developed by MumboJumbo and published by Big Fish Games. Reaxxion is a match-3 game where you have to bounce balls off a paddle and hit colored tiles until they disappear. But unlike other breakout games, Reaxxion lets you manipulate the balls in various ways, such as creating liquid metal balls, magnetic balls, and explosive balls. You can also use power-ups and bonuses to enhance your gameplay and score.</p>
-<p>Reaxxion has over 200 levels of gameplay, with different themes and challenges. You can also create your own levels with the built-in editor and share them online. Reaxxion is a game that will keep you entertained for hours with its addictive gameplay and stunning visuals.</p>
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-<p>However, if you want to play Reaxxion on your PC, you will have to pay $9.99 to download it from Big Fish Games. Alternatively, you can try to find a cracked version of the game online, but this is not recommended as it may contain viruses or malware that can harm your computer. Moreover, downloading cracked games is illegal and unethical, as it deprives the developers and publishers of their rightful income.</p>
-<p>Therefore, the best way to enjoy Reaxxion is to buy it from Big Fish Games and support the creators of this amazing game. You can also get a free trial of the game before you buy it, so you can see if you like it or not. To download Reaxxion from Big Fish Games, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://www.bigfishgames.com/games/2160/reaxxion/">https://www.bigfishgames.com/games/2160/reaxxion/</a> and click on "Try It" or "Buy Now".</li>
-<li>If you don't have a Big Fish Games account, create one for free by entering your email and password.</li>
-<li>If you want to buy the game, enter your payment details and confirm your purchase. You will get an email with a confirmation code and a link to download the game.</li>
-<li>If you want to try the game for free, click on "Download Now" and wait for the game to download.</li>
-<li>Once the download is complete, run the installer and follow the instructions to install the game on your PC.</li>
-<li>Launch the game from your desktop or start menu and enjoy playing Reaxxion!</li>
-</ol>
-<p>Reaxxion is a game that will test your reflexes, strategy, and creativity. It is a game that will appeal to fans of breakout games as well as match-3 games. If you are looking for a new and exciting game to play on your PC, don't miss Reaxxion!</p>
-
-<h2>What Makes Reaxxion Different from Other Breakout Games?</h2>
-<p>Reaxxion is not your typical breakout game. It has several features that make it stand out from other games in the genre. Here are some of them:</p>
-<ul>
-<li><b>Liquid Metal Balls:</b> These are special balls that can transform into liquid metal and fill up the gaps between the tiles. You can create liquid metal balls by hitting three or more balls of the same color together. Liquid metal balls can also be split into smaller balls by hitting them with another ball or power-up.</li>
-<li><b>Magnetic Balls:</b> These are balls that can attract or repel other balls depending on their polarity. You can change the polarity of a magnetic ball by hitting it with another ball or power-up. Magnetic balls can help you create combos and clear tiles faster.</li>
-<li><b>Explosive Balls:</b> These are balls that can explode and destroy nearby tiles and balls. You can create explosive balls by hitting four or more balls of the same color together. Explosive balls can also be triggered by hitting them with another ball or power-up.</li>
-<li><b>Power-ups and Bonuses:</b> These are items that can help you in various ways, such as increasing your paddle size, giving you extra lives, slowing down the balls, or clearing the entire screen. You can get power-ups and bonuses by hitting them with a ball or by clearing certain tiles.</li>
-<li><b>Level Editor and Online Sharing:</b> Reaxxion lets you create your own levels with the built-in editor. You can choose from different backgrounds, music, tile sets, and ball types. You can also share your levels online and download other players' levels.</li>
-</ul>
-<p>Reaxxion is a game that offers a lot of variety and challenge. It is a game that will keep you hooked for hours with its addictive gameplay and stunning visuals.</p>
-<p></p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/GS Typing Tutor Review Is It Worth Buying a Licence Key?.md b/spaces/tialenAdioni/chat-gpt-api/logs/GS Typing Tutor Review Is It Worth Buying a Licence Key?.md
deleted file mode 100644
index babb2e2c041d21596bc45aeb9ab6f9c0a2382fee..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/GS Typing Tutor Review Is It Worth Buying a Licence Key?.md	
+++ /dev/null
@@ -1,46 +0,0 @@
-
-<h1>GS Typing Tutor: A Review</h1>
-<p>GS Typing Tutor is a software program that helps users improve their typing skills and accuracy. It is designed for Windows operating systems and supports more than 28 different keyboard layouts. In this article, we will review the features, benefits and drawbacks of GS Typing Tutor.</p>
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-<h2>Features</h2>
-<p>GS Typing Tutor offers a variety of features to help users learn and practice typing. Some of the main features are:</p>
-<ul>
-<li>A comprehensive typing course that covers basic, advanced and special typing skills.</li>
-<li>A numeric keypad course that teaches users how to use the numbers on the keyboard efficiently.</li>
-<li>A visual keyboard course that shows users the correct finger positions and movements on the keyboard.</li>
-<li>A typing test that measures users' speed, accuracy and fluency in typing.</li>
-<li>A typing game that challenges users to type words and sentences quickly and correctly.</li>
-<li>A statistics module that tracks users' progress and performance in typing.</li>
-<li>A custom mode that allows users to create their own typing lessons and tests.</li>
-</ul>
-<h2>Benefits</h2>
-<p>GS Typing Tutor has several benefits for users who want to improve their typing skills. Some of the benefits are:</p>
-<ul>
-<li>It is easy to use and has a user-friendly interface.</li>
-<li>It supports multiple languages and keyboard layouts, making it suitable for users from different countries and cultures.</li>
-<li>It provides feedback and tips to help users correct their mistakes and improve their typing habits.</li>
-<li>It adapts to users' skill level and learning pace, providing them with personalized and effective learning experiences.</li>
-<li>It is fun and engaging, motivating users to practice typing regularly.</li>
-</ul>
-<h2>Drawbacks</h2>
-<p>GS Typing Tutor also has some drawbacks that users should be aware of. Some of the drawbacks are:</p>
-<ul>
-<li>It is not compatible with newer versions of Windows, such as Windows 10 or 11.</li>
-<li>It is not free and requires a license key to activate the full version.</li>
-<li>It does not have a cloud-based or online version, meaning users cannot access it from different devices or locations.</li>
-<li>It does not have a voice recognition feature, meaning users cannot practice typing by dictating or listening to audio.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>GS Typing Tutor is a useful software program that can help users improve their typing skills and accuracy. It has many features, benefits and drawbacks that users should consider before downloading or purchasing it. Users who are looking for a simple, effective and fun way to learn typing may find GS Typing Tutor suitable for their needs. However, users who are looking for a more modern, versatile and interactive way to learn typing may want to explore other options available on the market.</p>
-<p></p><h2>How to Download GS Typing Tutor</h2>
-<p>If you are interested in trying out GS Typing Tutor, you can download it from various websites that offer it for free or for a fee. However, you should be careful about the source and the version of the software you are downloading, as some may contain viruses, malware or outdated features. Here are some steps to download GS Typing Tutor safely and correctly:</p>
-<ol>
-<li>Go to the official website of GS Typing Tutor at <a href="http://www.typingstar.com">www.typingstar.com</a> and click on the Download button. This will take you to a page where you can choose the version and the language of the software you want to download. The latest version is 3.1 and it supports English, French, German, Spanish and more languages.</li>
-<li>Alternatively, you can go to other reputable websites that offer GS Typing Tutor for download, such as <a href="https://gs-typing-tutor.en.softonic.com">Softonic</a>, <a href="https://filehippo.com/download_gs-typing-tutor">FileHippo</a>, <a href="https://en.freedownloadmanager.org/Windows-PC/GS-Typing-Tutor.html">Free Download Manager</a> or <a href="https://www.softpedia.com/get/Others/Home-Education/GS-Typing-Tutor-LT.shtml">Softpedia</a>. Make sure you check the reviews, ratings and comments of other users before downloading the software from these sites.</li>
-<li>Once you have chosen the website and the version of GS Typing Tutor you want to download, click on the download link and follow the instructions on your screen. Depending on your browser and your operating system, you may be asked to save, run or open the file. Choose the option that suits you best.</li>
-<li>After the download is completed, locate the file on your computer and double-click on it to start the installation process. Follow the steps on the installation wizard and agree to the terms and conditions of the software. You may also need to enter a license key to activate the full version of GS Typing Tutor. You can get a license key by purchasing it from the official website or from other authorized sellers.</li>
-<li>When the installation is finished, you can launch GS Typing Tutor from your desktop or start menu and start using it to improve your typing skills.</li>
-</ol>
-<p>Note: If you are looking for an alternative to GS Typing Tutor, you may want to check out <a href="https://www.typingmaster.com/typing-tutor/free-download.html">TypingMaster</a>, a popular and modern typing software that works with Windows 10 and 11 and has a cloud-based version that allows you to access it from any device or location.</p> ddb901b051<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Avatar Full Movie HD Free Download in Hindi 720p Enjoy the Blockbuster Film on Your Device.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Avatar Full Movie HD Free Download in Hindi 720p Enjoy the Blockbuster Film on Your Device.md
deleted file mode 100644
index 71d202ff8e76b61ad44409a63c8580af184ace2d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Avatar Full Movie HD Free Download in Hindi 720p Enjoy the Blockbuster Film on Your Device.md	
+++ /dev/null
@@ -1,151 +0,0 @@
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-<li>Click on the Google Drive link and sign in with your Google account if required.</li>
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-<li>Scroll down and find the download links for different qualities and languages of the movie.</li>
-<li>Click on the download link that suits your preference and choose a server from the list.</li>
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-<li>Enjoy watching Avatar full movie HD free download in Hindi 720p on your device.</li>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Is It Safe To Download Games From Softonic.md b/spaces/tialenAdioni/chat-gpt-api/logs/Is It Safe To Download Games From Softonic.md
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-<br />
-<h1>Is It Safe to Download Games from Softonic? A Comprehensive Guide</h1>
-<p>Softonic is one of the most popular websites for downloading free games, software, applications, movies, and much more. Founded in 1997, Softonic claims to serve millions of users every month and deliver more than 4 million downloads every day. But is it safe to download games from Softonic? Are there any risks involved in using this website? In this article, we will answer these questions and provide you with some tips for downloading games safely from Softonic.</p>
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-<p>Softonic is a website that hosts and distributes third-party software and games for various platforms, such as Windows, Mac, Android, iOS, and Linux. You can find content for different categories, such as action, adventure, puzzle, simulation, sports, strategy, and more. You can also find open-source programs, freeware, shareware, and demos on Softonic.</p>
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-<p>The short answer is yes. Softonic is a legitimate website that has been operating for more than two decades. It is also legal to use Softonic as long as you respect the terms and conditions of the website and the license agreements of the games you download.</p>
-<p>However, the long answer is more complicated. Softonic is not 100% safe and secure. There are some risks and drawbacks that you should be aware of before using this website. Here are some of them:</p>
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-<li>Some games on Softonic may be outdated or incompatible. Softonic does not always provide the latest versions of the games on its website. Some games may be outdated or incompatible with your device or operating system. These games may not work properly or cause errors or crashes on your device.</li>
-<li>Some games on Softonic may have embedded adware or unwanted programs. Some games may have embedded adware or unwanted programs that are installed along with the game without your consent or knowledge. These programs may display annoying ads, pop-ups, banners, or notifications on your device. They may also change your browser settings, homepage, or search engine. They may also collect your browsing data or personal information.</li>
-<li>Some ads on Softonic may be malicious or deceptive. Even though Softonic itself is a legitimate website, the ads on it are not. Some ads may contain malicious links or downloads that can infect your device with malware or viruses. Some ads may also be deceptive or misleading, such as fake download buttons, fake alerts, fake offers, or fake reviews. These ads may trick you into clicking on them or downloading something that you don't want.</li>
-</ul>
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-<h2>How to download games safely from Softonic?</h2>
-<p>To avoid the risks and drawbacks mentioned above, you should follow some precautions and tips when downloading games from Softonic. Here are some of them:</p>
-<ul>
-<li>Use a reliable antivirus program on your device. A good antivirus program can protect your device from malware, viruses, adware, and other threats that may come from downloading games from Softonic. You should scan every game you download from Softonic before installing it on your device. You should also update your antivirus program regularly to keep it effective.</li>
-<li>Download games only from official sources or well-known websites. If possible, you should download games only from the official websites of the game developers or publishers, or from well-known and reputable websites such as Steam, Google Play Store, App Store, Microsoft Store, etc. These websites are more likely to provide safe and legal games than third-party websites like Softonic.</li>
-<li>Read user reviews and ratings of the games before downloading them. User</p>
-<p></p> ddb901b051<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Call of Duty Mobile Todo Desbloqueado APK Cmo jugar con todos los mapas modos y armas.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Call of Duty Mobile Todo Desbloqueado APK Cmo jugar con todos los mapas modos y armas.md
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-<br />
-<br>
- <table>
-<tr>
-<td>
- <h1>Call of Duty Mobile Todo Desbloqueado APK: How to Download and Play</h1>
-<h2>Introduction</h2>
-<p>Call of Duty Mobile is one of the most popular and exciting mobile games in the world. It is a first-person shooter game that lets you experience the thrill of combat in various scenarios and modes. You can play solo or with your friends, and compete with millions of other players around the world.</p>
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-<p>If you answered yes to any of these questions, then you might be interested in Call of Duty Mobile Todo Desbloqueado APK. This is a modified version of the game that gives you everything you need to have the best gaming experience possible. In this article, we will show you how to download and play Call of Duty Mobile Todo Desbloqueado APK, and what are the benefits of doing so. Let's get started!</p>
-<h2>How to Download Call of Duty Mobile Todo Desbloqueado APK</h2>
-<h3>Step 1: Find a reliable source</h3>
-<p>The first thing you need to do is to find a reliable source where you can download Call of Duty Mobile Todo Desbloqueado APK. There are many websites and platforms that claim to offer this version of the game, but not all of them are trustworthy. Some of them might contain viruses, malware, or spyware that can harm your device or steal your personal information. Some of them might also have outdated or fake versions of the game that won't work properly or at all.</p>
-<p>So how can you avoid these risks and find a reliable source? Here are some tips:</p>
-<ul>
-<li>Check the reviews and ratings of the source. See what other users have to say about their experience with downloading and playing Call of Duty Mobile Todo Desbloqueado APK from that source. Look for positive feedback, high ratings, and verified comments.</li>
-<li>Compare different versions and features of the game. See what each source offers in terms of the game's version, size, compatibility, features, content, etc. Look for the latest version of the game that has all the features and content that you want.</li>
-<li>Use a reputable antivirus or security software on your device. Before downloading anything from the internet, make sure that your device is protected by a reputable antivirus or security software that can scan and detect any potential threats or malware.</li>
-</ul>
-<h3>Step 2: Download the APK file</h3>
-<p>Once you have found a reliable source, you can proceed to download the APK file of Call of Duty Mobile Todo Desbloqueado APK. An APK file is a file format that is used to install applications on Android devices. To download the APK file, you need to do the following:</p>
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-<ul>
-<li>Choose a compatible device and operating system. Make sure that your device meets the minimum requirements for running Call of Duty Mobile Todo Desbloqueado APK. The game requires Android 4.3 or higher, and at least 2 GB of RAM and 1.5 GB of free storage space.</li>
-<li>Enable unknown sources in your settings. Since Call of Duty Mobile Todo Desbloqueado APK is not available on the official Google Play Store, you need to enable unknown sources in your settings to allow your device to install applications from other sources. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Follow the instructions on the screen. Once you have enabled unknown sources, you can tap on the download link or button on the source website or platform. You will see a pop-up window asking you to confirm your download. Tap on OK or Download to start downloading the APK file. You will see a progress bar showing how much time is left until the download is complete.</li>
-</ul>
-<h3>Step 3: Install and launch the game</h3>
-<p>After downloading the APK file, you can install and launch Call of Duty Mobile Todo Desbloqueado APK on your device. To do this, follow these steps:</p>
-<ul>
-<li>Grant permissions and accept terms and conditions. Once the download is complete, you will see a notification on your device asking you to open the file. Tap on it to open it. You will see a screen asking you to grant permissions and accept terms and conditions for installing Call of Duty Mobile Todo Desbloqueado APK on your device. Tap on Install or Next to proceed.</li>
-<li>Log in with your account or create a new one. Once the installation is complete, you will see a screen asking you to log in with your existing account or create a new one for playing Call of Duty Mobile Todo Desbloqueado APK. You can use your Facebook, Google, or Activision account to log in, or create a new account with your email address.</li>
-<li>Customize your settings and preferences. Once you have logged in, you will see a screen where you can customize your settings and preferences for playing Call of Duty Mobile Todo Desbloqueado APK. You can adjust the graphics, sound, controls, sensitivity, language, etc. according to your liking. You can also enable or disable notifications, auto-update, data usage, etc.</li>
-</ul>
-<h2>How to Play Call of Duty Mobile Todo Desbloqueado APK</h2>
-<h3>Step 1: Choose your game mode</h3>
-<p>Now that you have installed and launched Call of Duty Mobile Todo Desbloqueado APK, you can start playing the game. The first thing you need to do is to choose your game mode. Call of Duty Mobile Todo Desbloqueado APK offers various game modes that you can enjoy, such as:</p>
-<ul>
-<li>Multiplayer: This is the classic mode where you can play with or against other players in different maps and modes. You can choose from team deathmatch, domination, hardpoint, search and destroy, free for all, etc. You can also customize your loadout and skills before starting a match.</li>
-<li>Battle Royale: This is the mode where you can play solo or with a squad of up to four players in a massive map with 100 players. You have to survive and eliminate other players until you are the last one standing. You can also loot weapons, items, vehicles, etc. from the map.</li>
-<li>Zombies: This is the mode where you can play with up to four players in a co-op mode against hordes of zombies. You have to survive and complete objectives while fighting off zombies and bosses. You can also upgrade your weapons and perks during the game.</li>
-</ul>
-<p>To choose your game mode, you can tap on the game mode icon on the main screen of Call of Duty Mobile Todo Desbloqueado APK. You will see a list of available game modes that you can select from. You can also tap on the more icon to see more options and details about each game mode.</p>
-<h3>Step 2: Enjoy the gameplay</h3>
-<p>Once you have chosen your game mode, you can enjoy the gameplay of Call of Duty Mobile Todo Desbloqueado APK. The gameplay is similar to the official version of the game, but with some added features and benefits that make it more fun and exciting. Here are some tips on how to enjoy the gameplay:</p>
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-<li>Use the controls and features of the game. Call of Duty Mobile Todo Desbloqueado APK has intuitive and customizable controls that let you move, aim, shoot, reload, switch weapons, use items, etc. with ease. You can also use features such as auto-fire, aim assist, sprinting, sliding, jumping, etc. to enhance your gameplay.</li>
-<li>Communicate with your teammates and opponents. Call of Duty Mobile Todo Desbloqueado APK has a built-in chat system that lets you communicate with your teammates and opponents during a match. You can use text or voice chat to send messages, commands, compliments, taunts, etc. You can also use emojis and stickers to express yourself.</li>
-<li>Earn rewards and achievements during and after a match. Call of Duty Mobile Todo Desbloqueado APK has a rewarding system that lets you earn rewards and achievements during and after a match. You can earn experience points, credits, coins, crates, weapons, skins, characters, etc. by playing the game. You can also unlock achievements by completing certain tasks or challenges in the game.</li>
-</ul>
-<h3>Step 3: Explore more options</h3>
-<p>After enjoying the gameplay of Call of Duty Mobile Todo Desbloqueado APK, you can explore more options that the game has to offer. Call of Duty Mobile Todo Desbloqueado APK has more options than the official version of the game that let you access more content and features that make the game more diverse and enjoyable. Here are some options that you can explore:</p>
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-<li>Access more maps, weapons, characters, skins, etc. Call of Duty Mobile Todo Desbloqueado APK has more maps, weapons, characters, skins, etc. than the official version of the game. You can access them by using the coins or crates that you earn in the game. You can also use the shop or the store to buy them with real money. You can use these items to customize your appearance and performance in the game.</li>
-<li>Participate in events, challenges, tournaments, etc. Call of Duty Mobile Todo Desbloqueado APK has more events, challenges, tournaments, etc. than the official version of the game. You can participate in them by using the events or the challenges tabs on the main screen of the game. You can also use the ranked or the clan tabs to join or create a clan and compete with other clans in tournaments. You can win exclusive rewards and prizes by participating in these activities.</li>
-<li>Upgrade your account and rank up in the leaderboards. Call of Duty Mobile Todo Desbloqueado APK has more ways to upgrade your account and rank up in the leaderboards than the official version of the game. You can upgrade your account by using the profile or the settings tabs on the main screen of the game. You can also use the leaderboards or the achievements tabs to see your progress and ranking in the game. You can improve your rank by playing more matches and earning more points.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Call of Duty Mobile Todo Desbloqueado APK is a modified version of Call of Duty Mobile that gives you unlimited access to all the features and content that the game has to offer. It is a fun and exciting game that lets you experience the thrill of combat in various scenarios and modes. You can download and play Call of Duty Mobile Todo Desbloqueado APK by following the steps that we have shown you in this article. You can also enjoy the gameplay and explore more options that the game has to offer by following our tips and tricks. Call of Duty Mobile Todo Desbloqueado APK is a game that you don't want to miss out on. So what are you waiting for? Download it now and join the action!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Call of Duty Mobile Todo Desbloqueado APK:</p>
-<ul>
-<li><b>What are the minimum requirements for Call of Duty Mobile Todo Desbloqueado APK?</b><br>
-The minimum requirements for Call of Duty Mobile Todo Desbloqueado APK are Android 4.3 or higher, 2 GB of RAM, and 1.5 GB of free storage space.</li>
-<li><b>Is Call of Duty Mobile Todo Desbloqueado APK safe and legal?</b><br>
-Call of Duty Mobile Todo Desbloqueado APK is safe and legal as long as you download it from a reliable source and use a reputable antivirus or security software on your device. However, you should be aware that using this version of the game might violate the terms and conditions of Activision, the developer of Call of Duty Mobile, and might result in your account being banned or suspended.</li>
-<li><b>Can I play Call of Duty Mobile Todo Desbloqueado APK with other players who have the official version?</b><br>
-Yes, you can play Call of Duty Mobile Todo Desbloqueado APK with other players who have the official version, as long as you have the same version and region of the game. However, you might encounter some compatibility issues or errors if you have different versions or regions of the game.</li>
-<li><b>How can I update Call of Duty Mobile Todo Desbloqueado APK?</b><br>
-You can update Call of Duty Mobile Todo Desbloqueado APK by downloading and installing the latest APK file from the same source where you downloaded the previous one. You can also check the source website or platform for any updates or notifications about the game. You should always update Call of Duty Mobile Todo Desbloqueado APK to enjoy the latest features and content of the game.</li>
-<li><b>Where can I get help or support for Call of Duty Mobile Todo Desbloqueado APK?</b><br>
-You can get help or support for Call of Duty Mobile Todo Desbloqueado APK by contacting the source website or platform where you downloaded the game. You can also check their FAQ section, blog, forum, or social media pages for any information or tips about the game. You can also ask other users or players who have Call of Duty Mobile Todo Desbloqueado APK for help or advice.</li>
-</ul>
- </td>
-</tr>
-</table></p> 401be4b1e0<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Buku Siswa Kelas 3 Tema 5 Revisi 2018 Cuaca dan Aktivitas Manusia dalam PDF.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Buku Siswa Kelas 3 Tema 5 Revisi 2018 Cuaca dan Aktivitas Manusia dalam PDF.md
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- <h2>Apa itu Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF?</h2>
-<p>Buku siswa kelas 3 tema 5 revisi 2018 pdf adalah buku elektronik yang dipersiapkan oleh Pemerintah dalam rangka implementasi Kurikulum <strong>2013</strong> yang disempurnakan. Buku ini merupakan buku siswa yang disusun dan ditelaah oleh berbagai pihak di bawah koordinasi Kementerian Pendidikan dan Kebudayaan, dan dipergunakan dalam tahap awal penerapan Kurikulum <strong>2013</strong>. Buku ini merupakan “dokumen hidup” yang senantiasa diperbaiki, diperbarui, dan dimutakhirkan sesuai dengan dinamika kebutuhan dan perubahan zaman.</p>
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- <h3>Pengertian Buku Siswa Kelas <strong>3</strong> Tema <strong>5</strong> Revisi <strong>2018</strong> PDF</h3>
-<p <p>Buku siswa kelas 3 tema 5 revisi 2018 pdf adalah buku yang berisi materi pembelajaran yang berkaitan dengan tema <strong>"Cuaca"</strong>. Buku ini terdiri dari empat subtema, yaitu cuaca di tempat tinggalku, cuaca di daerah lain, pengaruh cuaca terhadap kehidupan, dan upaya manusia dalam mengatasi dampak cuaca. Buku ini juga dilengkapi dengan berbagai kegiatan, gambar, tabel, grafik, dan teks yang menarik dan bermakna untuk membantu siswa memahami konsep dan keterampilan yang dipelajari.</p>
- <h3>Tujuan Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF</h3>
-<p>Tujuan buku siswa kelas 3 tema 5 revisi 2018 pdf adalah untuk membantu siswa mencapai kompetensi dasar yang ditetapkan dalam Kurikulum <strong>2013</strong>. Kompetensi dasar tersebut meliputi kompetensi sikap spiritual, kompetensi sikap sosial, kompetensi pengetahuan, dan kompetensi keterampilan. Dengan menggunakan buku ini, siswa diharapkan dapat:</p>
-<ul>
-<li>Menghargai dan menghayati ajaran agama yang dianutnya.</li>
-<li>Menghargai dan menghayati perilaku jujur, disiplin, tanggung jawab, peduli (toleransi, gotong royong), santun, responsif dan pro-aktif dan menunjukkan sebagai bagian dari solusi atas berbagai permasalahan dalam berinteraksi secara efektif dengan lingkungan sosial dan alam serta dalam menempatkan diri sebagai cerminan bangsa dalam pergaulan dunia.</li>
-<li>Mengenal jenis-jenis cuaca di Indonesia dan di dunia.</li>
-<li>Menjelaskan pengaruh cuaca terhadap kehidupan makhluk hidup.</li>
-<li>Menyebutkan upaya manusia dalam mengatasi dampak cuaca.</li>
-<li>Melakukan pengamatan cuaca di lingkungan sekitar.</li>
-<li>Membuat laporan hasil pengamatan cuaca.</li>
-<li>Menyajikan data cuaca dalam bentuk tabel dan grafik.</li>
-<li>Menafsirkan data cuaca dalam bentuk tabel dan grafik.</li>
-<li>Menyimpulkan informasi dari teks bacaan tentang cuaca.</li>
-<li>Menggunakan bahasa Indonesia dengan baik dan benar sesuai dengan konteks pembelajaran.</li>
-</ul>
- <h2>Mengapa Anda Perlu Download Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF?</h2>
-<p>Ada beberapa alasan mengapa Anda perlu download buku siswa kelas 3 tema 5 revisi 2018 pdf. Berikut ini adalah beberapa keunggulan dan manfaat dari buku siswa kelas 3 tema 5 revisi 2018 pdf.</p>
- <h3>Keunggulan Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF</h3>
-<p>Buku siswa kelas 3 tema 5 revisi 2018 pdf memiliki beberapa keunggulan dibandingkan dengan buku cetak. Beberapa keunggulan tersebut adalah:</p>
-<ul>
-<li>Buku siswa kelas 3 tema 5 revisi 2018 pdf dapat diakses secara online melalui situs resmi Buku Sekolah Elektronik (BSE) atau melalui tautan unduh yang tersedia di internet. Anda tidak perlu repot-repot mencari buku cetak di toko buku atau perpustakaan.</li>
-<li>Buku siswa kelas 3 tema 5 revisi 2018 pdf dapat disimpan di perangkat Anda, seperti komputer, laptop, tablet, atau smartphone. Anda dapat membuka buku ini kapan saja dan di mana saja tanpa harus membawa buku fisik yang berat dan mudah rusak.</li>
-<li>Buku siswa kelas 3 tema 5 revisi 2018 pdf memiliki format PDF yang mudah dibaca dan dicetak. Anda dapat membuka buku ini dengan menggunakan aplikasi pembaca PDF yang banyak tersedia secara gratis. Anda juga dapat mencetak halaman-halaman tertentu dari buku ini sesuai dengan kebutuhan Anda.</li>
-</ul>
- <h <h3>Manfaat Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF</h3>
-<p>Buku siswa kelas 3 tema 5 revisi 2018 pdf juga memiliki beberapa manfaat bagi siswa, guru, dan orang tua. Beberapa manfaat tersebut adalah:</p>
-<ul>
-<li>Buku siswa kelas 3 tema 5 revisi 2018 pdf dapat membantu siswa mempelajari materi yang sesuai dengan kurikulum yang berlaku. Buku ini disusun berdasarkan standar kompetensi dan kompetensi dasar yang harus dicapai oleh siswa kelas 3. Buku ini juga mengacu pada indikator pencapaian kompetensi dan kriteria ketuntasan minimal yang harus dipenuhi oleh siswa.</li>
-<li>Buku siswa kelas 3 tema 5 revisi 2018 pdf dapat membantu guru dalam menyusun rencana pembelajaran, melaksanakan pembelajaran, dan mengevaluasi pembelajaran. Buku ini menyediakan berbagai kegiatan pembelajaran yang bervariasi, menarik, dan bermakna untuk mengembangkan kompetensi siswa. Buku ini juga menyediakan berbagai sumber belajar, media belajar, dan alat penilaian yang dapat digunakan oleh guru.</li>
-<li>Buku siswa kelas 3 tema 5 revisi 2018 pdf dapat membantu orang tua dalam mendampingi dan membimbing anak dalam belajar di rumah. Buku ini memberikan informasi tentang tujuan, materi, dan kegiatan pembelajaran yang dilakukan oleh anak di sekolah. Buku ini juga memberikan tips dan saran bagi orang tua dalam mendukung perkembangan anak.</li>
-</ul>
- <h2>Bagaimana Cara Download Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF?</h2>
-<p>Untuk dapat download buku siswa kelas 3 tema 5 revisi 2018 pdf, Anda perlu mengikuti beberapa langkah mudah berikut ini.</p>
- <h3>Langkah-Langkah Download Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF</h3>
- <h4>Kunjungi Situs Resmi Buku Sekolah Elektronik (BSE)</h4>
-<p>Buku Sekolah Elektronik (BSE) adalah program Kementerian Pendidikan dan Kebudayaan yang menyediakan buku-buku elektronik untuk pendidikan dasar dan menengah secara gratis. Anda dapat mengunjungi situs resmi BSE di alamat <a href="">http://bse.kemdikbud.go.id/</a>. Di situs ini, Anda dapat menemukan berbagai buku elektronik untuk berbagai jenjang, mata pelajaran, dan tema.</p>
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- <h4>Pilih Jenis Buku dan Tingkat Kelas</h4>
-<p>Setelah masuk ke situs BSE, Anda dapat memilih jenis buku yang Anda inginkan. Ada dua jenis buku yang tersedia di situs ini, yaitu buku teks pelajaran (BTP) dan buku non teks pelajaran (BNTP). Buku teks pelajaran adalah buku yang digunakan sebagai sumber belajar utama bagi siswa dan guru. Buku non teks pelajaran adalah buku yang digunakan sebagai sumber belajar tambahan atau pendukung bagi siswa dan guru.</p>
-<p>Untuk download buku siswa kelas 3 tema 5 revisi 2018 pdf, Anda perlu memilih jenis buku teks pelajaran (BTP) dan tingkat kelas <strong>3</strong>. Anda dapat melakukan hal ini dengan mengklik menu <strong>"BTP"</strong> di bagian atas halaman, lalu mengklik menu <strong>"SD/MI"</strong> di bagian kiri halaman, lalu mengklik menu <strong>"Kelas <strong>3</strong>"</strong> di bagian tengah halaman.</p>
- <h4>Klik Tautan Unduh untuk Buku yang Diinginkan</h4>
-<p>Setelah memilih jenis buku dan tingkat kelas, Anda akan melihat daftar buku-buku yang tersedia untuk kelas <strong>3</strong>. Anda dapat mencari buku yang Anda inginkan dengan menggunakan fitur pencarian atau dengan menelusuri daftar buku secara manual. Untuk download buku siswa kelas <strong>3</strong> tema <strong <strong>5</strong> revisi <strong>2018</strong> pdf, Anda perlu mengklik tautan unduh untuk buku tersebut. Buku yang Anda cari adalah buku yang berjudul <strong>"Buku Siswa Tematik Terpadu Kurikulum <strong>2013</strong> Revisi <strong>2018</strong> Kelas <strong>3</strong> Tema <strong>5</strong> Cuaca"</strong>. Anda dapat mengklik tautan unduh yang ada di sebelah kanan judul buku, atau mengklik judul buku untuk melihat detail buku terlebih dahulu.</p>
- <h4>Simpan File PDF di Perangkat Anda</h4>
-<p>Setelah mengklik tautan unduh, Anda akan diarahkan ke halaman baru yang menampilkan file PDF dari buku yang Anda pilih. Anda dapat melihat isi buku secara online dengan menggunakan fitur zoom, scroll, dan navigasi yang tersedia. Anda juga dapat menyimpan file PDF di perangkat Anda dengan mengklik tombol <strong>"Download"</strong> atau <strong>"Unduh"</strong> yang ada di bagian atas atau bawah halaman. Pilih lokasi penyimpanan yang Anda inginkan, lalu klik <strong>"Simpan"</strong>.</p>
- <h4>Buka File PDF dengan Aplikasi Pembaca PDF</h4>
-<p>Setelah menyimpan file PDF di perangkat Anda, Anda dapat membuka file tersebut dengan menggunakan aplikasi pembaca PDF yang Anda miliki. Aplikasi pembaca PDF adalah aplikasi yang dapat membaca dan menampilkan file PDF dengan baik. Beberapa contoh aplikasi pembaca PDF yang populer adalah Adobe Acrobat Reader, Foxit Reader, Sumatra PDF, dan lain-lain. Anda dapat membuka file PDF dengan mengklik dua kali file tersebut, atau dengan membuka aplikasi pembaca PDF terlebih dahulu, lalu memilih file yang ingin dibuka.</p>
- <h2>Apa Isi Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF?</h2>
-<p>Buku siswa kelas 3 tema 5 revisi 2018 pdf berisi materi pembelajaran yang berkaitan dengan tema <strong>"Cuaca"</strong>. Buku ini terdiri dari empat subtema, yaitu cuaca di tempat tinggalku, cuaca di daerah lain, pengaruh cuaca terhadap kehidupan, dan upaya manusia dalam mengatasi dampak cuaca. Setiap subtema memiliki beberapa pembelajaran yang mencakup kompetensi dasar yang harus dicapai oleh siswa. Berikut ini adalah subtema dan pembelajaran dalam buku siswa kelas 3 tema 5 revisi 2018 pdf.</p>
- <h3>Subtema dan Pembelajaran dalam Buku Siswa Kelas 3 Tema 5 Revisi 2018 PDF</h3>
- <h4>Subtema 1: Cuaca di Tempat Tinggalku</h4>
-<p>Pada subtema ini, siswa akan mempelajari tentang jenis-jenis cuaca di tempat tinggalnya, cara mengamati cuaca di lingkungan sekitar, dan cara menyajikan data cuaca dalam bentuk tabel. Beberapa pembelajaran yang ada pada subtema ini adalah:</p>
-<ul>
-<li>Pembelajaran 1: Mengenal Jenis-Jenis Cuaca di Tempat Tinggalku</li>
-<li>Pembelajaran 2: Mengamati Cuaca di Lingkungan Sekitar</li>
-<li>Pembelajaran 3: Menyajikan Data Cuaca dalam Bentuk Tabel</li>
-<li>Pembelajaran 4: Menafsirkan Data Cuaca dalam Bentuk Tabel</li>
-<li>Pembelajaran 5: Menyimpulkan Informasi dari Teks Bacaan tentang Cuaca di Tempat Tinggalku</li>
-</ul>
- <h4>Subtema 2: Cuaca di Daerah Lain</h4>
-<p>Pada subtema ini, siswa akan mempelajari tentang jenis-jenis cuaca di daerah lain di Indonesia dan di dunia, cara membandingkan cuaca di daerah lain dengan cuaca di tempat tinggalnya, dan cara menyajikan data cuaca dalam bentuk grafik. Beberapa pembelajaran yang ada pada subtema ini adalah:</p>
-<ul>
-<li>Pembelajaran 6: Mengenal Jenis-Jenis Cuaca di Daerah Lain di Indonesia dan di Dunia</li>
-<li>P <li>Pembelajaran 7: Membandingkan Cuaca di Daerah Lain dengan Cuaca di Tempat Tinggalku</li>
-<li>Pembelajaran 8: Menyajikan Data Cuaca dalam Bentuk Grafik</li>
-<li>Pembelajaran 9: Menafsirkan Data Cuaca dalam Bentuk Grafik</li>
-<li>Pembelajaran 10: Menyimpulkan Informasi dari Teks Bacaan tentang Cuaca di Daerah Lain</li>
-</ul>
- <h4>Subtema 3: Pengaruh Cuaca terhadap Kehidupan</h4>
-<p>Pada subtema ini, siswa akan mempelajari tentang pengaruh cuaca terhadap kehidupan makhluk hidup, baik manusia, hewan, maupun tumbuhan. Siswa juga akan mempelajari tentang cara-cara beradaptasi dengan cuaca yang berbeda-beda. Beberapa pembelajaran yang ada pada subtema ini adalah:</p>
-<ul>
-<li>Pembelajaran 11: Menjelaskan Pengaruh Cuaca terhadap Kehidupan Manusia</li>
-<li>Pembelajaran 12: Menjelaskan Pengaruh Cuaca terhadap Kehidupan Hewan</li>
-<li>Pembelajaran 13: Menjelaskan Pengaruh Cuaca terhadap Kehidupan Tumbuhan</li>
-<li>Pembelajaran 14: Menyebutkan Cara-Cara Beradaptasi dengan Cuaca yang Berbeda-Beda</li>
-<li>Pembelajaran 15: Menyimpulkan Informasi dari Teks Bacaan tentang Pengaruh Cuaca terhadap Kehidupan</li>
-</ul>
- <h4>Subtema 4: Upaya Manusia dalam Mengatasi Dampak Cuaca</h4>
-<p>Pada subtema ini, siswa akan mempelajari tentang dampak cuaca yang dapat merugikan atau menguntungkan bagi manusia, dan upaya-upaya yang dilakukan oleh manusia dalam mengatasi dampak cuaca tersebut. Beberapa pembelajaran yang ada pada subtema ini adalah:</p>
-<ul>
-<li>Pembelajaran 16: Menyebutkan Dampak Cuaca yang Merugikan bagi Manusia</li>
-<li>Pembelajaran 17: Menyebutkan Dampak Cuaca yang Menguntungkan bagi Manusia</li>
-<li>Pembelajaran 18: Menyebutkan Upaya Manusia dalam Mengatasi Dampak Cuaca yang Merugikan</li>
-<li>Pembelajaran 19: Menyebutkan Upaya Manusia dalam Memanfaatkan Dampak Cuaca yang Menguntungkan</li>
-<li>Pembelajaran 20: Menyimpulkan Informasi dari Teks Bacaan tentang Upaya Manusia dalam Mengatasi Dampak Cuaca</li>
-</ul>
- <h2>Kesimpulan dan Saran</h2>
-<p>Demikianlah ulasan tentang buku siswa kelas 3 tema 5 revisi 2018 pdf. Buku ini adalah buku elektronik yang berisi materi pembelajaran tentang cuaca untuk siswa kelas 3. Buku ini memiliki beberapa keunggulan dan manfaat dibandingkan dengan buku cetak. Buku ini juga mudah diakses dan disimpan di perangkat Anda. Buku ini terdiri dari empat subtema, yaitu cuaca di tempat tinggalku, cuaca di daerah lain, pengaruh cuaca terhadap kehidupan, dan upaya manusia dalam mengatasi dampak cuaca. Setiap subtema memiliki beberapa pembelajaran yang mencakup kompetensi dasar yang harus dicapai oleh siswa.</p>
-<p>Kami berharap buku ini dapat membantu Anda dalam belajar tentang cuaca. Kami juga menyarankan Anda untuk selalu mengikuti pembelajaran yang disampaikan oleh guru Anda di sekolah. Jangan lupa untuk selalu berlatih dan mengulang materi yang telah dipelajari. Jika Anda memiliki pertanyaan atau kesulitan, jangan ragu untuk bertanya kepada guru atau teman Anda. Selamat belajar dan semoga sukses!</p>
- <h2>FAQ</h2>
-<p>Berikut ini adalah beberapa pertanyaan dan jawaban yang sering ditanyakan seputar buku siswa kelas 3 tema 5 revisi 2018 pdf.</p>
- <h3>Q: Apakah buku siswa kelas <h3>Q: Apakah buku siswa kelas 3 tema 5 revisi 2018 pdf gratis?</h3>
-<p>A: Ya, buku siswa kelas 3 tema 5 revisi 2018 pdf gratis. Anda tidak perlu membayar apapun untuk download buku ini. Buku ini disediakan oleh Pemerintah melalui program Buku Sekolah Elektronik (BSE) yang bertujuan untuk menyediakan buku-buku elektronik untuk pendidikan dasar dan menengah secara gratis.</p>
- <h3>Q: Apakah buku siswa kelas 3 tema 5 revisi 2018 pdf resmi?</h3>
-<p>A: Ya, buku siswa kelas 3 tema 5 revisi 2018 pdf resmi. Buku ini merupakan buku siswa yang disusun dan ditelaah oleh berbagai pihak di bawah koordinasi Kementerian Pendidikan dan Kebudayaan. Buku ini juga telah mendapatkan persetujuan dari Badan Standar Nasional Pendidikan (BSNP) sebagai buku yang sesuai dengan standar nasional pendidikan.</p>
- <h3>Q: Apakah buku siswa kelas 3 tema 5 revisi 2018 pdf terbaru?</h3>
-<p>A: Ya, buku siswa kelas 3 tema 5 revisi 2018 pdf terbaru. Buku ini merupakan revisi dari buku siswa kelas 3 tema 5 yang sebelumnya diterbitkan pada tahun <strong>2017</strong>. Revisi ini dilakukan untuk menyempurnakan dan memutakhirkan isi buku sesuai dengan dinamika kebutuhan dan perubahan zaman.</p>
- <h3>Q: Apakah buku siswa kelas 3 tema 5 revisi 2018 pdf lengkap?</h3>
-<p>A: Ya, buku siswa kelas 3 tema 5 revisi 2018 pdf lengkap. Buku ini berisi materi pembelajaran yang mencakup semua kompetensi dasar yang harus dicapai oleh siswa kelas <strong>3</strong>. Buku ini juga dilengkapi dengan berbagai kegiatan, gambar, tabel, grafik, dan teks yang menarik dan bermakna untuk membantu siswa memahami konsep dan keterampilan yang dipelajari.</p>
- <h3>Q: Apakah buku siswa kelas 3 tema 5 revisi 2018 pdf mudah dipahami?</h3>
-<p>A: Ya, buku siswa kelas 3 tema 5 revisi 2018 pdf mudah dipahami. Buku ini menggunakan bahasa Indonesia yang baik dan benar sesuai dengan konteks pembelajaran. Buku ini juga menggunakan kata-kata, kalimat, dan paragraf yang sederhana, jelas, dan logis. Buku ini juga menggunakan ilustrasi, contoh, dan penjelasan yang relevan dan mudah dimengerti.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tiiuae/README/README.md b/spaces/tiiuae/README/README.md
deleted file mode 100644
index f289015a34fc24b6f1b5b1e35255c9e8bf8f8a7c..0000000000000000000000000000000000000000
--- a/spaces/tiiuae/README/README.md
+++ /dev/null
@@ -1,49 +0,0 @@
----
-title: README
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: static
-pinned: false
----
-
-**Do you believe in a better tomorrow? We do. Our team of expert researchers live the dream and work to build it every day.**
-
-**🔥 [Falcon-180B](https://huggingface.co/tiiuae/falcon-180b) is now available in open-access! [Try it now in our chat demo!](https://huggingface.co/spaces/tiiuae/falcon-180b-demo)**
-
-# News 
-
-* 💥 **TII has open-sourced Falcon-180B for research and commercial utilization!** Access the [180B](https://huggingface.co/tiiuae/falcon-180b), as well as [7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) models, and explore our high-quality web dataset, [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). 
-* ✨ **Falcon-[40B](https://huggingface.co/tiiuae/falcon-40b)/[7B](https://huggingface.co/tiiuae/falcon-7b) are now available under the Apache 2.0 license**, TII has [waived all royalties and commercial usage restrictions](https://www.tii.ae/news/uaes-falcon-40b-worlds-top-ranked-ai-model-technology-innovation-institute-now-royalty-free).
-
-# Falcon LLM
-
-Falcon LLM is TII's flagship series of large language models, built from scratch using a custom data pipeline and distributed training library. Papers coming soon 😊.
-
-To promote collaborations and drive innovation, we have open-sourced a number of artefacts:
-* The **Falcon-180B** pretrained and chat models, under the [Falcon-180B TII license](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt). Falcon-180B is the largest and most powerful open-access model available. 
-* The **Falcon-7/40B** pretrained and instruct models, under the  Apache 2.0 software license . Falcon-7B/40B models are state-of-the-art for their size, outperforming other open-source models on NLP benchmarks.
-* The **RefinedWeb** dataset, a massive web dataset with stringent filtering and large-scale deduplication, enabling models trained on web data alone to match or outperform models trained on curated corpora. See 📓 [the paper](https://arxiv.org/abs/2306.01116) for more information. RefinedWeb is licensed under ODC-By 1.0.
-
-See below for a detailed list of artefacts in the Falcon LLM family:
-
-| **Artefact**        | **Link**                                                         | **Type**                | **Details**                                                       |
-|---------------------|------------------------------------------------------------------|-------------------------|-------------------------------------------------------------------|
-| 🥇 **Falcon-180B**      | [Here](https://huggingface.co/tiiuae/falcon-180b)        | *pretrained model*        | 180B parameters trained on 3,500 billion tokens.                    |
-| Falcon-180B-Chat | [Here](https://huggingface.co/tiiuae/falcon-180b-chat)        | *chat model*  | Falcon-180B finetuned on a mixture of [Ultrachat](https://huggingface.co/datasets/stingning/ultrachat), [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) and [Airoboros](https://huggingface.co/datasets/jondurbin/airoboros-2.1).                         |
-| 🥈 **Falcon-40B**      | [Here](https://huggingface.co/tiiuae/falcon-40b)        | *pretrained model*        | 40B parameters trained on 1,000 billion tokens.                    |
-| Falcon-40B-Instruct | [Here](https://huggingface.co/tiiuae/falcon-40b-instruct)        | *instruction/chat model*  | Falcon-40B finetuned on the [Baize](https://github.com/project-baize/baize-chatbot) dataset.                         |
-| 🥉 **Falcon-7B**       | [Here](https://huggingface.co/tiiuae/falcon-7b)                  | *pretrained model*        | 6.7B parameters trained on 1,500 billion tokens.                   |
-| Falcon-7B-Instruct  | [Here](https://huggingface.co/tiiuae/falcon-7b-instruct)         | *instruction/chat model*  | Falcon-7B finetuned on the [Baize](https://github.com/project-baize/baize-chatbot), [GPT4All](https://github.com/nomic-ai/gpt4all), and [GPTeacher](https://github.com/teknium1/GPTeacher) datasets. |
-| 📀 **RefinedWeb**      | [Here](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | *pretraining web dataset* | ~600 billion "high-quality" tokens.                                |
-| Falcon-RW-1B        | [Here](https://huggingface.co/tiiuae/falcon-rw-1b)               | *pretrained model*        | 1.3B parameters trained on 350 billion tokens.                     |
-| Falcon-RW-7B        | [Here](https://huggingface.co/tiiuae/falcon-rw-7b)               | *pretrained model*        | 7.5B parameters trained on 350 billion tokens.                     |
-
-
-# About us
-
-The [Technology Innovation Institute](https://www.tii.ae) (TII) is a leading global research center dedicated to pushing the frontiers of knowledge. Our teams of scientists, researchers and engineers work in an open, flexible and agile environment to deliver discovery science and transformative technologies. Our work means we will not only prepare for the future; we will create it. Working together, we are committed to inspiring innovation for a better tomorrow.
-
-We are part of Abu Dhabi Government’s Advanced Technology Research Council, which oversees technology research in the emirate. As a disruptor in science, we are setting new standards and serve as a catalyst for change.
-
-Faced with a future of limitless possibilities and supported by strategically funded investments, we are encouraging a culture of discovery. Our work reinforces Abu Dhabi and the UAE’s status as an R&D hub and a global leader in breakthrough technologies.
\ No newline at end of file
diff --git "a/spaces/tioseFevbu/cartoon-converter/720p Rudhramadevi /TOP\\ Download.md" "b/spaces/tioseFevbu/cartoon-converter/720p Rudhramadevi /TOP\\ Download.md"
deleted file mode 100644
index 01c82f504ea034cf01b6658650110a1ae777c9b5..0000000000000000000000000000000000000000
--- "a/spaces/tioseFevbu/cartoon-converter/720p Rudhramadevi /TOP\\ Download.md"	
+++ /dev/null
@@ -1,39 +0,0 @@
-## 720p Rudhramadevi Download
-
-
-
-**Download ☆ [https://vercupalo.blogspot.com/?d=2tvYp6](https://vercupalo.blogspot.com/?d=2tvYp6)**
-
-
-
-# Rudhramadevi: A Historical Epic Movie You Can Download in HD Quality
-
- 
-
-Rudhramadevi is a 2015 Indian movie based on the life of Rudramadevi, a brave warrior princess who became one of the prominent rulers of the Kakatiya dynasty in the Deccan Plateau. The movie features Anushka Shetty as Rudramadevi, Allu Arjun as Gona Ganna Reddy, and Rana Daggubati as Chalukya Veerabhadra. The movie was written, produced, and directed by Gunasekhar.
-
- 
-
-If you are interested in watching this historical epic movie, you can download it in high-definition quality from various online sources. Here are some of the options you can choose from:
-
- 
-
-- Bollyflix: This website offers Rudhramadevi in dual audio (Hindi-Telugu) with subtitles. You can download the movie in 480p, 720p, or 1080p WEB-DL quality. The file sizes are 550MB, 1.6GB, and 3.2GB respectively. You can use Google Drive to download the movie from this website[^1^].
-
-- YouTube: You can watch Rudhramadevi 2D Hindi full HD movie on YouTube for free. The movie has a runtime of 2 hours and 36 minutes. You can also enjoy the official theatrical trailer of the movie on YouTube[^2^].
-
-- Trajectoire: This website claims to offer Rudhramadevi full HD movie for free download. However, the website does not provide any details about the quality, language, or file size of the movie. It also asks you to click on a link that may not be safe or reliable[^3^].
-
-
-
-Before you download any movie from online sources, make sure you check the legality and safety of the website. Also, be aware of the potential risks of downloading pirated or copyrighted content.
-
-
-
-Rudramadevi is a movie that showcases the history and culture of the Kakatiya dynasty, which ruled over parts of present-day Andhra Pradesh and Telangana from the 10th to the 14th century. The movie depicts the challenges and achievements of Rudramadevi, who was officially designated as a son through the ancient Putrika ceremony and given the name Rudradeva by her father Ganapatideva, the emperor of the Kakatiya dynasty. Rudramadevi had to face many enemies and conspiracies from within and outside her kingdom, but she managed to overcome them with her courage, intelligence, and leadership. She also had the support of loyal allies like Gona Ganna Reddy, a rebellious chieftain who later became her friend and protector, and Chalukya Veerabhadra, a prince from the neighboring Chalukya dynasty who married her.
-
- 
-
-The movie is praised for its grand visuals, costumes, sets, and action sequences. It also features a star-studded cast of actors from different film industries, such as Prakash Raj, Krishnam Raju, Suman, Nithya Menen, Catherine Tresa, Hamsa Nandini, Baba Sehgal, and Ajay. The movie was released in 2D and 3D formats in Telugu, Tamil, Malayalam, and Hindi languages. It was also dubbed in Kannada and Oriya languages. The movie received mixed reviews from critics and audiences, but it was a commercial success at the box office. It also won several awards and nominations, including two National Film Awards for Best Feature Film on National Integration and Best Costume Design.
-
- 1b8d091108
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Commandos Beyond The Call Of Duty Download] [Patch].md b/spaces/tioseFevbu/cartoon-converter/scripts/Commandos Beyond The Call Of Duty Download] [Patch].md
deleted file mode 100644
index a99cdd72ae86a945bce5e32453e2d7fbe3d78fb5..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Commandos Beyond The Call Of Duty Download] [Patch].md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-<h1>How to Download and Install Commandos: Beyond The Call Of Duty Patch</h1>
-<p>Commandos: Beyond The Call Of Duty is a classic strategy game released in 1999 by Pyro Studios and Eidos Interactive. It is a standalone expansion pack for Commandos: Behind Enemy Lines, and it features eight new missions set in World War II. The game challenges you to control a team of elite commandos and complete various objectives behind enemy lines.</p>
-<p>If you want to enjoy this game on modern systems, you may need to download and install a patch that fixes some compatibility issues and bugs. In this article, we will show you how to do that step by step.</p>
-<h2>Commandos: Beyond The Call Of Duty Download] [Patch]</h2><br /><p><b><b>Download File</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://urlcod.com/2uHvke">https://urlcod.com/2uHvke</a></b></p><br /><br />
-<h2>Step 1: Download the Patch</h2>
-<p>The patch you need is called Commandos: Beyond The Call Of Duty Ultimate Fix, and it was created by a modder named LeFix. It fixes the game's resolution, sound, mouse cursor, and other problems. You can download it from this link[^1^]. The file size is 58.6 MB.</p>
-<h2>Step 2: Install the Patch</h2>
-<p>Once you have downloaded the patch, you need to run the installer and choose the directory where your game is installed. The default location is C:\Program Files (x86)\Eidos Interactive\Commandos - Beyond the Call of Duty. Follow the instructions on the screen and wait for the installation to finish.</p>
-<h2>Step 3: Enjoy the Game</h2>
-<p>Now you can launch the game from your desktop shortcut or start menu. You should be able to play the game in full screen mode with improved graphics and sound. You can also adjust the game settings from the options menu.</p>
-<h3>Bonus: Download Additional Mods</h3>
-<p>If you want to enhance your gaming experience even more, you can also download some additional mods for Commandos: Beyond The Call Of Duty. These mods add new missions, features, and challenges to the game. Here are some of the most popular ones:</p>
-<ul>
-<li><strong>Commandos: BCD - Mission Pack</strong>: This mod adds 6 new missions to the game, sending you to various fictitious locations in Germany, 1943. You can download it from this link[^2^]. The file size is 94.4 MB.</li>
-<li><strong>Commandos Sniper Elite MOD</strong>: This mod transforms the game into a sniper simulator, where you have to eliminate targets from long distances. You can download it from this link[^3^]. The file size is 41 MB.</li>
-<li><strong>Commandos 1: Men of Duty</strong>: This mod remakes the original Commandos: Behind Enemy Lines missions in Commandos: Beyond The Call Of Duty engine. You can download it from this link[^4^]. The file size is 40 MB.</li>
-</ul>
-<p>To install these mods, you need to run their installers and choose your game directory as well. Then you can select which mod you want to play from the main menu.</p>
-<h4>Conclusion</h4>
-<p>Commandos: Beyond The Call Of Duty is a timeless strategy game that still offers a lot of fun and challenge today. With these patches and mods, you can enjoy it even more on your modern PC. We hope this article was helpful and informative. If you have any questions or comments, feel free to leave them below.</p>
-<p></p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Farm Mania Full Version Free Download For Android.md b/spaces/tioseFevbu/cartoon-converter/scripts/Farm Mania Full Version Free Download For Android.md
deleted file mode 100644
index faa3ccdefc5fdecb9babc2c281192d26ec616036..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Farm Mania Full Version Free Download For Android.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-
-<h1>Farm Mania: A Fun and Challenging Farming Game for Android</h1>
-<p>If you love farming games, you will enjoy Farm Mania, a time management game from QUMARON that lets you experience the joys and challenges of running your own farm. You can download the full version of Farm Mania for free on your Android device and play offline anytime, anywhere.</p>
-<h2>Farm Mania Full Version Free Download For Android</h2><br /><p><b><b>Download File</b> &#10040; <a href="https://urlcod.com/2uHx3G">https://urlcod.com/2uHx3G</a></b></p><br /><br />
-<p>In Farm Mania, you will help Anna and her grandfather turn their rundown farm into a profitable business. You will plant crops, raise animals, sell products, and upgrade your equipment. You will also face various obstacles such as pests, droughts, and thieves. You will need to use your skills and strategy to complete 60 levels with different goals and difficulties.</p>
-<p>Farm Mania has colorful graphics, catchy music, and addictive gameplay. You will love the cute characters and animals, the diverse farm settings, and the fun mini-games. You will also learn some interesting facts about farming and agriculture along the way.</p>
-<p>Farm Mania is a game that will keep you entertained for hours. Whether you are a casual gamer or a hardcore fan of farming games, you will find something to enjoy in Farm Mania. Download it now from Google Play and start your farming adventure!</p><p>Farm Mania is not the only farming game available for Android. There are many other games that offer different features and experiences for farming enthusiasts. Here are some of the best farming games on Android that you can try:</p>
-<p></p>
-<ul>
-<li><strong>Stardew Valley</strong>: This is one of the most popular and acclaimed farming games ever. You inherit a farm from your grandfather and move to a rural town where you can grow crops, raise animals, mine, fish, craft, and make friends with the locals. You can also explore a vast open world with secrets and mysteries. Stardew Valley is a game that will keep you hooked for hundreds of hours with its charming pixel art, relaxing soundtrack, and engaging gameplay.</li>
-<li><strong>Hay Day</strong>: This is a classic farming game that lets you plant crops, feed your animals, and help neighbors with the items they need. You can grow and produce for others, start a roadside shop, fish, build your town, and even visit other people to see how their farm is doing. Hay Day is a game that is easy to play and fun to master.</li>
-<li><strong>Farming Simulator 20</strong>: This is a game for those who want a realistic and sophisticated farming simulation. You can drive over 100 vehicles from real brands, cultivate various crops, breed livestock, and manage your farm business. You can also play with your friends online and share your progress. Farming Simulator 20 is a game that will challenge you with its depth and complexity.</li>
-</ul>
-<p>These are just some of the best farming games on Android that you can download and enjoy. Whether you want a casual or hardcore farming experience, there is a game for you. So what are you waiting for? Grab your phone and start your farming adventure today!</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Hublaa Facebook Status Likes.md b/spaces/tioseFevbu/cartoon-converter/scripts/Hublaa Facebook Status Likes.md
deleted file mode 100644
index b0b63ab479fb8445b866512ca3766a1d8bf330a9..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Hublaa Facebook Status Likes.md	
+++ /dev/null
@@ -1,39 +0,0 @@
-
-<h1>How to Get More Likes on Your Facebook Posts with Hublaa</h1>
-<p>Do you want to boost your Facebook engagement and reach more people with your posts? If so, you might be interested in using a Facebook auto liker service like Hublaa. Hublaa is a website that can help you get more likes, comments, followers and reactions on your Facebook posts, photos, videos and pages. In this article, we will explain what Hublaa is, how it works, and how you can use it safely and effectively.</p>
-<h2>What is Hublaa?</h2>
-<p>Hublaa is a free Facebook auto liker website that has been operating since 2011. It claims to be secure, spam-free and trusted by millions of users worldwide. Hublaa uses the access token method to access your Facebook account and deliver likes to your posts. An access token is a code that allows an app or website to use your Facebook account for a limited time and with certain permissions. You can revoke the access token at any time from your Facebook settings.</p>
-<h2>hublaa facebook status likes</h2><br /><p><b><b>DOWNLOAD</b> &#8250;&#8250;&#8250; <a href="https://urlcod.com/2uHvZj">https://urlcod.com/2uHvZj</a></b></p><br /><br />
-<h2>How does Hublaa work?</h2>
-<p>Hublaa works by exchanging likes among its users. When you use Hublaa, you agree to like other users' posts in return for getting likes on your own posts. Hublaa has a large network of users who are willing to like each other's posts automatically. You can choose how many likes you want to get per post, from 10 to 350. You can also choose the country or region where you want the likes to come from, by activating the geo-targeting feature. Hublaa also offers other features such as auto comments, auto reactions, auto followers and auto page liker.</p>
-<h2>How to use Hublaa?</h2>
-<p>To use Hublaa, you need to follow these steps:</p>
-<ol>
-<li>Go to <a href="https://fb.vivoliker.com/hublaa">https://fb.vivoliker.com/hublaa</a> [^1^] and click on "Get Access Token".</li>
-<li>Login with your Facebook account and allow the permissions that Hublaa requests.</li>
-<li>Copy the access token from the URL bar and paste it in the box on Hublaa's website.</li>
-<li>Select the service you want to use, such as auto liker, auto commenter or auto follower.</li>
-<li>Select the post you want to get likes on, or enter the URL or ID of the post.</li>
-<li>Select the number of likes you want to get, from 10 to 350.</li>
-<li>Select the country or region where you want the likes to come from, if you want to use geo-targeting.</li>
-<li>Click on "Submit" and wait for a few minutes for Hublaa to deliver the likes.</li>
-</ol>
-<h2>Is Hublaa safe?</h2>
-<p>Hublaa claims to be safe and not to spam your Facebook account or post anything without your consent. However, using any Facebook auto liker service comes with some risks and drawbacks. Some of these are:</p>
-<ul>
-<li>Your Facebook account may be temporarily or permanently blocked by Facebook for violating its terms of service or community standards.</li>
-<li>Your Facebook account may be hacked or compromised by malicious third parties who may use your access token for nefarious purposes.</li>
-<li>Your Facebook account may be flooded with unwanted notifications from other users' posts that you have liked automatically.</li>
-<li>Your Facebook account may lose its credibility and authenticity by having fake or low-quality likes from unknown users.</li>
-<li>Your Facebook account may be exposed to spam, scams or malware from other websites or apps that Hublaa may redirect you to.</li>
-</ul>
-<p>To avoid these risks and drawbacks, you should use Hublaa with caution and moderation. You should also follow these tips:</p>
-<ul>
-<li>Use a secondary or fake Facebook account instead of your main or personal account.</li>
-<li>Change your Facebook password regularly and enable two-factor authentication.</li>
-<li>Revoke the access token from your Facebook settings after using Hublaa.</li>
-<li>Limit the number of likes you get per post and per day.</li>
-<li>Avoid using other auto liker services at the same time as Hub</p>
-<p></p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Kitchendraw 6.0 Katalog Indir.md b/spaces/tioseFevbu/cartoon-converter/scripts/Kitchendraw 6.0 Katalog Indir.md
deleted file mode 100644
index dfa6ae696f9416c27bdbaf6c1d12c5ce16b88a6a..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Kitchendraw 6.0 Katalog Indir.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-`<h1>KitchenDraw 6.0: A Powerful Software for Kitchen and Bathroom Design</h1>`
-
-`<p>If you are looking for a software that can help you design your dream kitchen or bathroom, you might want to check out KitchenDraw 6.0. KitchenDraw is a software that allows you to create 2D plans and 3D perspectives of your projects in a fast and easy way. You can customize your design with a wide range of furniture, appliances, accessories, materials, colors, and lighting options. You can also generate realistic images, floor plans, elevations, estimates, cutting lists, and more.</p>`
-
-`<p>KitchenDraw 6.0 is available for download from the official website <a href="http://www.kitchendraw.com/download.asp">here</a>. You can try it for free for 20 hours of use, and then pay only 3 Euros per hour of use. You don't need any initial investment or annual maintenance fee. You can also get free email support, online training, and access to various catalogues from different manufacturers.</p>
-<h2>kitchendraw 6.0 katalog indir</h2><br /><p><b><b>Download File</b> &#10026;&#10026;&#10026; <a href="https://urlcod.com/2uHxb9">https://urlcod.com/2uHxb9</a></b></p><br /><br />`
-
-`<p>KitchenDraw 6.0 is compatible with Windows Vista, Seven, and Windows 8. It can also run on a Mac if Windows is installed on it. You will need a modern and powerful graphics card to enjoy the best quality and fluidity of the 3D view modes. A multi-core processor will also speed up the photorealistic rendering process. The recommended RAM size is 8 GB and the screen size is 20 inches or more.</p>`
-
-`<p>KitchenDraw 6.0 is a great tool for professionals and amateurs alike who want to design their own kitchen or bathroom. It is easy to use and produces top quality documents. Download it today and start creating your own projects!</p>`
-
-`<p>But what makes KitchenDraw 6.0 stand out from other software? Well, KitchenDraw 6.0 has some amazing features that will make your design process easier and more enjoyable. Here are some of them:</p>`
-
-`<ul>`
-`<li>Improved "Final Photorealistic" renderer: This feature allows you to create stunning images that look like real photos of your projects. You can also choose to make them look like handmade drawings for a more artistic effect.</li>`
-`<li>New dialog box for scene lighting: This feature helps you adjust the natural and artificial lighting of your scene to create the perfect mood and atmosphere. You can see the changes in real time as you modify the settings.</li>`
-`<li>Heliodon: This feature lets you simulate the real sunshine of your scene by specifying the orientation, location, date, and time of your project. You can see how the sun rays affect the shadows and colors of your scene.</li>`
-`<li>New real-time OpenGL renderer: This feature improves the quality and fluidity of the real-time view modes. You can walk through your scene in 3D and see it from different angles and perspectives.</li>`
-`<li>Import of 3D objects made with SketchUp software: This feature allows you to import 3D models created with SketchUp, a free and easy-to-use 3D modeling software. You can also change the size, color, and attributes of the imported objects.</li>`
-`<li>Access to the "3D warehouse" object database: This feature gives you access to a huge online library of 3D objects that you can download and use in your projects. You can find anything from famous monuments to everyday objects.</li>`
-`</ul>`
-
-`<p>As you can see, KitchenDraw 6.0 is a powerful and versatile software that can help you design any kind of kitchen or bathroom project. Whether you are a professional or an amateur, you will find KitchenDraw 6.0 easy to use and fun to work with. Don't wait any longer and download KitchenDraw 6.0 today!</p>` 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/text.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/text.py
deleted file mode 100644
index 12037d0cf4f597a51b1fb80a9c1aa983f0910154..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/text.py
+++ /dev/null
@@ -1,1286 +0,0 @@
-import re
-from functools import partial, reduce
-from math import gcd
-from operator import itemgetter
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Dict,
-    Iterable,
-    List,
-    NamedTuple,
-    Optional,
-    Tuple,
-    Union,
-)
-
-from ._loop import loop_last
-from ._pick import pick_bool
-from ._wrap import divide_line
-from .align import AlignMethod
-from .cells import cell_len, set_cell_size
-from .containers import Lines
-from .control import strip_control_codes
-from .emoji import EmojiVariant
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import Style, StyleType
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .console import Console, ConsoleOptions, JustifyMethod, OverflowMethod
-
-DEFAULT_JUSTIFY: "JustifyMethod" = "default"
-DEFAULT_OVERFLOW: "OverflowMethod" = "fold"
-
-
-_re_whitespace = re.compile(r"\s+$")
-
-TextType = Union[str, "Text"]
-
-GetStyleCallable = Callable[[str], Optional[StyleType]]
-
-
-class Span(NamedTuple):
-    """A marked up region in some text."""
-
-    start: int
-    """Span start index."""
-    end: int
-    """Span end index."""
-    style: Union[str, Style]
-    """Style associated with the span."""
-
-    def __repr__(self) -> str:
-        return (
-            f"Span({self.start}, {self.end}, {self.style!r})"
-            if (isinstance(self.style, Style) and self.style._meta)
-            else f"Span({self.start}, {self.end}, {repr(self.style)})"
-        )
-
-    def __bool__(self) -> bool:
-        return self.end > self.start
-
-    def split(self, offset: int) -> Tuple["Span", Optional["Span"]]:
-        """Split a span in to 2 from a given offset."""
-
-        if offset < self.start:
-            return self, None
-        if offset >= self.end:
-            return self, None
-
-        start, end, style = self
-        span1 = Span(start, min(end, offset), style)
-        span2 = Span(span1.end, end, style)
-        return span1, span2
-
-    def move(self, offset: int) -> "Span":
-        """Move start and end by a given offset.
-
-        Args:
-            offset (int): Number of characters to add to start and end.
-
-        Returns:
-            TextSpan: A new TextSpan with adjusted position.
-        """
-        start, end, style = self
-        return Span(start + offset, end + offset, style)
-
-    def right_crop(self, offset: int) -> "Span":
-        """Crop the span at the given offset.
-
-        Args:
-            offset (int): A value between start and end.
-
-        Returns:
-            Span: A new (possibly smaller) span.
-        """
-        start, end, style = self
-        if offset >= end:
-            return self
-        return Span(start, min(offset, end), style)
-
-
-class Text(JupyterMixin):
-    """Text with color / style.
-
-    Args:
-        text (str, optional): Default unstyled text. Defaults to "".
-        style (Union[str, Style], optional): Base style for text. Defaults to "".
-        justify (str, optional): Justify method: "left", "center", "full", "right". Defaults to None.
-        overflow (str, optional): Overflow method: "crop", "fold", "ellipsis". Defaults to None.
-        no_wrap (bool, optional): Disable text wrapping, or None for default. Defaults to None.
-        end (str, optional): Character to end text with. Defaults to "\\\\n".
-        tab_size (int): Number of spaces per tab, or ``None`` to use ``console.tab_size``. Defaults to 8.
-        spans (List[Span], optional). A list of predefined style spans. Defaults to None.
-    """
-
-    __slots__ = [
-        "_text",
-        "style",
-        "justify",
-        "overflow",
-        "no_wrap",
-        "end",
-        "tab_size",
-        "_spans",
-        "_length",
-    ]
-
-    def __init__(
-        self,
-        text: str = "",
-        style: Union[str, Style] = "",
-        *,
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        no_wrap: Optional[bool] = None,
-        end: str = "\n",
-        tab_size: Optional[int] = 8,
-        spans: Optional[List[Span]] = None,
-    ) -> None:
-        sanitized_text = strip_control_codes(text)
-        self._text = [sanitized_text]
-        self.style = style
-        self.justify: Optional["JustifyMethod"] = justify
-        self.overflow: Optional["OverflowMethod"] = overflow
-        self.no_wrap = no_wrap
-        self.end = end
-        self.tab_size = tab_size
-        self._spans: List[Span] = spans or []
-        self._length: int = len(sanitized_text)
-
-    def __len__(self) -> int:
-        return self._length
-
-    def __bool__(self) -> bool:
-        return bool(self._length)
-
-    def __str__(self) -> str:
-        return self.plain
-
-    def __repr__(self) -> str:
-        return f"<text {self.plain!r} {self._spans!r}>"
-
-    def __add__(self, other: Any) -> "Text":
-        if isinstance(other, (str, Text)):
-            result = self.copy()
-            result.append(other)
-            return result
-        return NotImplemented
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, Text):
-            return NotImplemented
-        return self.plain == other.plain and self._spans == other._spans
-
-    def __contains__(self, other: object) -> bool:
-        if isinstance(other, str):
-            return other in self.plain
-        elif isinstance(other, Text):
-            return other.plain in self.plain
-        return False
-
-    def __getitem__(self, slice: Union[int, slice]) -> "Text":
-        def get_text_at(offset: int) -> "Text":
-            _Span = Span
-            text = Text(
-                self.plain[offset],
-                spans=[
-                    _Span(0, 1, style)
-                    for start, end, style in self._spans
-                    if end > offset >= start
-                ],
-                end="",
-            )
-            return text
-
-        if isinstance(slice, int):
-            return get_text_at(slice)
-        else:
-            start, stop, step = slice.indices(len(self.plain))
-            if step == 1:
-                lines = self.divide([start, stop])
-                return lines[1]
-            else:
-                # This would be a bit of work to implement efficiently
-                # For now, its not required
-                raise TypeError("slices with step!=1 are not supported")
-
-    @property
-    def cell_len(self) -> int:
-        """Get the number of cells required to render this text."""
-        return cell_len(self.plain)
-
-    @property
-    def markup(self) -> str:
-        """Get console markup to render this Text.
-
-        Returns:
-            str: A string potentially creating markup tags.
-        """
-        from .markup import escape
-
-        output: List[str] = []
-
-        plain = self.plain
-        markup_spans = [
-            (0, False, self.style),
-            *((span.start, False, span.style) for span in self._spans),
-            *((span.end, True, span.style) for span in self._spans),
-            (len(plain), True, self.style),
-        ]
-        markup_spans.sort(key=itemgetter(0, 1))
-        position = 0
-        append = output.append
-        for offset, closing, style in markup_spans:
-            if offset > position:
-                append(escape(plain[position:offset]))
-                position = offset
-            if style:
-                append(f"[/{style}]" if closing else f"[{style}]")
-        markup = "".join(output)
-        return markup
-
-    @classmethod
-    def from_markup(
-        cls,
-        text: str,
-        *,
-        style: Union[str, Style] = "",
-        emoji: bool = True,
-        emoji_variant: Optional[EmojiVariant] = None,
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        end: str = "\n",
-    ) -> "Text":
-        """Create Text instance from markup.
-
-        Args:
-            text (str): A string containing console markup.
-            emoji (bool, optional): Also render emoji code. Defaults to True.
-            justify (str, optional): Justify method: "left", "center", "full", "right". Defaults to None.
-            overflow (str, optional): Overflow method: "crop", "fold", "ellipsis". Defaults to None.
-            end (str, optional): Character to end text with. Defaults to "\\\\n".
-
-        Returns:
-            Text: A Text instance with markup rendered.
-        """
-        from .markup import render
-
-        rendered_text = render(text, style, emoji=emoji, emoji_variant=emoji_variant)
-        rendered_text.justify = justify
-        rendered_text.overflow = overflow
-        rendered_text.end = end
-        return rendered_text
-
-    @classmethod
-    def from_ansi(
-        cls,
-        text: str,
-        *,
-        style: Union[str, Style] = "",
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        no_wrap: Optional[bool] = None,
-        end: str = "\n",
-        tab_size: Optional[int] = 8,
-    ) -> "Text":
-        """Create a Text object from a string containing ANSI escape codes.
-
-        Args:
-            text (str): A string containing escape codes.
-            style (Union[str, Style], optional): Base style for text. Defaults to "".
-            justify (str, optional): Justify method: "left", "center", "full", "right". Defaults to None.
-            overflow (str, optional): Overflow method: "crop", "fold", "ellipsis". Defaults to None.
-            no_wrap (bool, optional): Disable text wrapping, or None for default. Defaults to None.
-            end (str, optional): Character to end text with. Defaults to "\\\\n".
-            tab_size (int): Number of spaces per tab, or ``None`` to use ``console.tab_size``. Defaults to 8.
-        """
-        from .ansi import AnsiDecoder
-
-        joiner = Text(
-            "\n",
-            justify=justify,
-            overflow=overflow,
-            no_wrap=no_wrap,
-            end=end,
-            tab_size=tab_size,
-            style=style,
-        )
-        decoder = AnsiDecoder()
-        result = joiner.join(line for line in decoder.decode(text))
-        return result
-
-    @classmethod
-    def styled(
-        cls,
-        text: str,
-        style: StyleType = "",
-        *,
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-    ) -> "Text":
-        """Construct a Text instance with a pre-applied styled. A style applied in this way won't be used
-        to pad the text when it is justified.
-
-        Args:
-            text (str): A string containing console markup.
-            style (Union[str, Style]): Style to apply to the text. Defaults to "".
-            justify (str, optional): Justify method: "left", "center", "full", "right". Defaults to None.
-            overflow (str, optional): Overflow method: "crop", "fold", "ellipsis". Defaults to None.
-
-        Returns:
-            Text: A text instance with a style applied to the entire string.
-        """
-        styled_text = cls(text, justify=justify, overflow=overflow)
-        styled_text.stylize(style)
-        return styled_text
-
-    @classmethod
-    def assemble(
-        cls,
-        *parts: Union[str, "Text", Tuple[str, StyleType]],
-        style: Union[str, Style] = "",
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        no_wrap: Optional[bool] = None,
-        end: str = "\n",
-        tab_size: int = 8,
-        meta: Optional[Dict[str, Any]] = None,
-    ) -> "Text":
-        """Construct a text instance by combining a sequence of strings with optional styles.
-        The positional arguments should be either strings, or a tuple of string + style.
-
-        Args:
-            style (Union[str, Style], optional): Base style for text. Defaults to "".
-            justify (str, optional): Justify method: "left", "center", "full", "right". Defaults to None.
-            overflow (str, optional): Overflow method: "crop", "fold", "ellipsis". Defaults to None.
-            end (str, optional): Character to end text with. Defaults to "\\\\n".
-            tab_size (int): Number of spaces per tab, or ``None`` to use ``console.tab_size``. Defaults to 8.
-            meta (Dict[str, Any], optional). Meta data to apply to text, or None for no meta data. Default to None
-
-        Returns:
-            Text: A new text instance.
-        """
-        text = cls(
-            style=style,
-            justify=justify,
-            overflow=overflow,
-            no_wrap=no_wrap,
-            end=end,
-            tab_size=tab_size,
-        )
-        append = text.append
-        _Text = Text
-        for part in parts:
-            if isinstance(part, (_Text, str)):
-                append(part)
-            else:
-                append(*part)
-        if meta:
-            text.apply_meta(meta)
-        return text
-
-    @property
-    def plain(self) -> str:
-        """Get the text as a single string."""
-        if len(self._text) != 1:
-            self._text[:] = ["".join(self._text)]
-        return self._text[0]
-
-    @plain.setter
-    def plain(self, new_text: str) -> None:
-        """Set the text to a new value."""
-        if new_text != self.plain:
-            sanitized_text = strip_control_codes(new_text)
-            self._text[:] = [sanitized_text]
-            old_length = self._length
-            self._length = len(sanitized_text)
-            if old_length > self._length:
-                self._trim_spans()
-
-    @property
-    def spans(self) -> List[Span]:
-        """Get a reference to the internal list of spans."""
-        return self._spans
-
-    @spans.setter
-    def spans(self, spans: List[Span]) -> None:
-        """Set spans."""
-        self._spans = spans[:]
-
-    def blank_copy(self, plain: str = "") -> "Text":
-        """Return a new Text instance with copied meta data (but not the string or spans)."""
-        copy_self = Text(
-            plain,
-            style=self.style,
-            justify=self.justify,
-            overflow=self.overflow,
-            no_wrap=self.no_wrap,
-            end=self.end,
-            tab_size=self.tab_size,
-        )
-        return copy_self
-
-    def copy(self) -> "Text":
-        """Return a copy of this instance."""
-        copy_self = Text(
-            self.plain,
-            style=self.style,
-            justify=self.justify,
-            overflow=self.overflow,
-            no_wrap=self.no_wrap,
-            end=self.end,
-            tab_size=self.tab_size,
-        )
-        copy_self._spans[:] = self._spans
-        return copy_self
-
-    def stylize(
-        self,
-        style: Union[str, Style],
-        start: int = 0,
-        end: Optional[int] = None,
-    ) -> None:
-        """Apply a style to the text, or a portion of the text.
-
-        Args:
-            style (Union[str, Style]): Style instance or style definition to apply.
-            start (int): Start offset (negative indexing is supported). Defaults to 0.
-            end (Optional[int], optional): End offset (negative indexing is supported), or None for end of text. Defaults to None.
-
-        """
-        if style:
-            length = len(self)
-            if start < 0:
-                start = length + start
-            if end is None:
-                end = length
-            if end < 0:
-                end = length + end
-            if start >= length or end <= start:
-                # Span not in text or not valid
-                return
-            self._spans.append(Span(start, min(length, end), style))
-
-    def apply_meta(
-        self, meta: Dict[str, Any], start: int = 0, end: Optional[int] = None
-    ) -> None:
-        """Apply meta data to the text, or a portion of the text.
-
-        Args:
-            meta (Dict[str, Any]): A dict of meta information.
-            start (int): Start offset (negative indexing is supported). Defaults to 0.
-            end (Optional[int], optional): End offset (negative indexing is supported), or None for end of text. Defaults to None.
-
-        """
-        style = Style.from_meta(meta)
-        self.stylize(style, start=start, end=end)
-
-    def on(self, meta: Optional[Dict[str, Any]] = None, **handlers: Any) -> "Text":
-        """Apply event handlers (used by Textual project).
-
-        Example:
-            >>> from rich.text import Text
-            >>> text = Text("hello world")
-            >>> text.on(click="view.toggle('world')")
-
-        Args:
-            meta (Dict[str, Any]): Mapping of meta information.
-            **handlers: Keyword args are prefixed with "@" to defined handlers.
-
-        Returns:
-            Text: Self is returned to method may be chained.
-        """
-        meta = {} if meta is None else meta
-        meta.update({f"@{key}": value for key, value in handlers.items()})
-        self.stylize(Style.from_meta(meta))
-        return self
-
-    def remove_suffix(self, suffix: str) -> None:
-        """Remove a suffix if it exists.
-
-        Args:
-            suffix (str): Suffix to remove.
-        """
-        if self.plain.endswith(suffix):
-            self.right_crop(len(suffix))
-
-    def get_style_at_offset(self, console: "Console", offset: int) -> Style:
-        """Get the style of a character at give offset.
-
-        Args:
-            console (~Console): Console where text will be rendered.
-            offset (int): Offset in to text (negative indexing supported)
-
-        Returns:
-            Style: A Style instance.
-        """
-        # TODO: This is a little inefficient, it is only used by full justify
-        if offset < 0:
-            offset = len(self) + offset
-        get_style = console.get_style
-        style = get_style(self.style).copy()
-        for start, end, span_style in self._spans:
-            if end > offset >= start:
-                style += get_style(span_style, default="")
-        return style
-
-    def highlight_regex(
-        self,
-        re_highlight: str,
-        style: Optional[Union[GetStyleCallable, StyleType]] = None,
-        *,
-        style_prefix: str = "",
-    ) -> int:
-        """Highlight text with a regular expression, where group names are
-        translated to styles.
-
-        Args:
-            re_highlight (str): A regular expression.
-            style (Union[GetStyleCallable, StyleType]): Optional style to apply to whole match, or a callable
-                which accepts the matched text and returns a style. Defaults to None.
-            style_prefix (str, optional): Optional prefix to add to style group names.
-
-        Returns:
-            int: Number of regex matches
-        """
-        count = 0
-        append_span = self._spans.append
-        _Span = Span
-        plain = self.plain
-        for match in re.finditer(re_highlight, plain):
-            get_span = match.span
-            if style:
-                start, end = get_span()
-                match_style = style(plain[start:end]) if callable(style) else style
-                if match_style is not None and end > start:
-                    append_span(_Span(start, end, match_style))
-
-            count += 1
-            for name in match.groupdict().keys():
-                start, end = get_span(name)
-                if start != -1 and end > start:
-                    append_span(_Span(start, end, f"{style_prefix}{name}"))
-        return count
-
-    def highlight_words(
-        self,
-        words: Iterable[str],
-        style: Union[str, Style],
-        *,
-        case_sensitive: bool = True,
-    ) -> int:
-        """Highlight words with a style.
-
-        Args:
-            words (Iterable[str]): Worlds to highlight.
-            style (Union[str, Style]): Style to apply.
-            case_sensitive (bool, optional): Enable case sensitive matchings. Defaults to True.
-
-        Returns:
-            int: Number of words highlighted.
-        """
-        re_words = "|".join(re.escape(word) for word in words)
-        add_span = self._spans.append
-        count = 0
-        _Span = Span
-        for match in re.finditer(
-            re_words, self.plain, flags=0 if case_sensitive else re.IGNORECASE
-        ):
-            start, end = match.span(0)
-            add_span(_Span(start, end, style))
-            count += 1
-        return count
-
-    def rstrip(self) -> None:
-        """Strip whitespace from end of text."""
-        self.plain = self.plain.rstrip()
-
-    def rstrip_end(self, size: int) -> None:
-        """Remove whitespace beyond a certain width at the end of the text.
-
-        Args:
-            size (int): The desired size of the text.
-        """
-        text_length = len(self)
-        if text_length > size:
-            excess = text_length - size
-            whitespace_match = _re_whitespace.search(self.plain)
-            if whitespace_match is not None:
-                whitespace_count = len(whitespace_match.group(0))
-                self.right_crop(min(whitespace_count, excess))
-
-    def set_length(self, new_length: int) -> None:
-        """Set new length of the text, clipping or padding is required."""
-        length = len(self)
-        if length != new_length:
-            if length < new_length:
-                self.pad_right(new_length - length)
-            else:
-                self.right_crop(length - new_length)
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> Iterable[Segment]:
-        tab_size: int = console.tab_size or self.tab_size or 8
-        justify = self.justify or options.justify or DEFAULT_JUSTIFY
-
-        overflow = self.overflow or options.overflow or DEFAULT_OVERFLOW
-
-        lines = self.wrap(
-            console,
-            options.max_width,
-            justify=justify,
-            overflow=overflow,
-            tab_size=tab_size or 8,
-            no_wrap=pick_bool(self.no_wrap, options.no_wrap, False),
-        )
-        all_lines = Text("\n").join(lines)
-        yield from all_lines.render(console, end=self.end)
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> Measurement:
-        text = self.plain
-        lines = text.splitlines()
-        max_text_width = max(cell_len(line) for line in lines) if lines else 0
-        words = text.split()
-        min_text_width = (
-            max(cell_len(word) for word in words) if words else max_text_width
-        )
-        return Measurement(min_text_width, max_text_width)
-
-    def render(self, console: "Console", end: str = "") -> Iterable["Segment"]:
-        """Render the text as Segments.
-
-        Args:
-            console (Console): Console instance.
-            end (Optional[str], optional): Optional end character.
-
-        Returns:
-            Iterable[Segment]: Result of render that may be written to the console.
-        """
-        _Segment = Segment
-        text = self.plain
-        if not self._spans:
-            yield Segment(text)
-            if end:
-                yield _Segment(end)
-            return
-        get_style = partial(console.get_style, default=Style.null())
-
-        enumerated_spans = list(enumerate(self._spans, 1))
-        style_map = {index: get_style(span.style) for index, span in enumerated_spans}
-        style_map[0] = get_style(self.style)
-
-        spans = [
-            (0, False, 0),
-            *((span.start, False, index) for index, span in enumerated_spans),
-            *((span.end, True, index) for index, span in enumerated_spans),
-            (len(text), True, 0),
-        ]
-        spans.sort(key=itemgetter(0, 1))
-
-        stack: List[int] = []
-        stack_append = stack.append
-        stack_pop = stack.remove
-
-        style_cache: Dict[Tuple[Style, ...], Style] = {}
-        style_cache_get = style_cache.get
-        combine = Style.combine
-
-        def get_current_style() -> Style:
-            """Construct current style from stack."""
-            styles = tuple(style_map[_style_id] for _style_id in sorted(stack))
-            cached_style = style_cache_get(styles)
-            if cached_style is not None:
-                return cached_style
-            current_style = combine(styles)
-            style_cache[styles] = current_style
-            return current_style
-
-        for (offset, leaving, style_id), (next_offset, _, _) in zip(spans, spans[1:]):
-            if leaving:
-                stack_pop(style_id)
-            else:
-                stack_append(style_id)
-            if next_offset > offset:
-                yield _Segment(text[offset:next_offset], get_current_style())
-        if end:
-            yield _Segment(end)
-
-    def join(self, lines: Iterable["Text"]) -> "Text":
-        """Join text together with this instance as the separator.
-
-        Args:
-            lines (Iterable[Text]): An iterable of Text instances to join.
-
-        Returns:
-            Text: A new text instance containing join text.
-        """
-
-        new_text = self.blank_copy()
-
-        def iter_text() -> Iterable["Text"]:
-            if self.plain:
-                for last, line in loop_last(lines):
-                    yield line
-                    if not last:
-                        yield self
-            else:
-                yield from lines
-
-        extend_text = new_text._text.extend
-        append_span = new_text._spans.append
-        extend_spans = new_text._spans.extend
-        offset = 0
-        _Span = Span
-
-        for text in iter_text():
-            extend_text(text._text)
-            if text.style:
-                append_span(_Span(offset, offset + len(text), text.style))
-            extend_spans(
-                _Span(offset + start, offset + end, style)
-                for start, end, style in text._spans
-            )
-            offset += len(text)
-        new_text._length = offset
-        return new_text
-
-    def expand_tabs(self, tab_size: Optional[int] = None) -> None:
-        """Converts tabs to spaces.
-
-        Args:
-            tab_size (int, optional): Size of tabs. Defaults to 8.
-
-        """
-        if "\t" not in self.plain:
-            return
-        pos = 0
-        if tab_size is None:
-            tab_size = self.tab_size
-        assert tab_size is not None
-        result = self.blank_copy()
-        append = result.append
-
-        _style = self.style
-        for line in self.split("\n", include_separator=True):
-            parts = line.split("\t", include_separator=True)
-            for part in parts:
-                if part.plain.endswith("\t"):
-                    part._text = [part.plain[:-1] + " "]
-                    append(part)
-                    pos += len(part)
-                    spaces = tab_size - ((pos - 1) % tab_size) - 1
-                    if spaces:
-                        append(" " * spaces, _style)
-                        pos += spaces
-                else:
-                    append(part)
-        self._text = [result.plain]
-        self._length = len(self.plain)
-        self._spans[:] = result._spans
-
-    def truncate(
-        self,
-        max_width: int,
-        *,
-        overflow: Optional["OverflowMethod"] = None,
-        pad: bool = False,
-    ) -> None:
-        """Truncate text if it is longer that a given width.
-
-        Args:
-            max_width (int): Maximum number of characters in text.
-            overflow (str, optional): Overflow method: "crop", "fold", or "ellipsis". Defaults to None, to use self.overflow.
-            pad (bool, optional): Pad with spaces if the length is less than max_width. Defaults to False.
-        """
-        _overflow = overflow or self.overflow or DEFAULT_OVERFLOW
-        if _overflow != "ignore":
-            length = cell_len(self.plain)
-            if length > max_width:
-                if _overflow == "ellipsis":
-                    self.plain = set_cell_size(self.plain, max_width - 1) + "…"
-                else:
-                    self.plain = set_cell_size(self.plain, max_width)
-            if pad and length < max_width:
-                spaces = max_width - length
-                self._text = [f"{self.plain}{' ' * spaces}"]
-                self._length = len(self.plain)
-
-    def _trim_spans(self) -> None:
-        """Remove or modify any spans that are over the end of the text."""
-        max_offset = len(self.plain)
-        _Span = Span
-        self._spans[:] = [
-            (
-                span
-                if span.end < max_offset
-                else _Span(span.start, min(max_offset, span.end), span.style)
-            )
-            for span in self._spans
-            if span.start < max_offset
-        ]
-
-    def pad(self, count: int, character: str = " ") -> None:
-        """Pad left and right with a given number of characters.
-
-        Args:
-            count (int): Width of padding.
-        """
-        assert len(character) == 1, "Character must be a string of length 1"
-        if count:
-            pad_characters = character * count
-            self.plain = f"{pad_characters}{self.plain}{pad_characters}"
-            _Span = Span
-            self._spans[:] = [
-                _Span(start + count, end + count, style)
-                for start, end, style in self._spans
-            ]
-
-    def pad_left(self, count: int, character: str = " ") -> None:
-        """Pad the left with a given character.
-
-        Args:
-            count (int): Number of characters to pad.
-            character (str, optional): Character to pad with. Defaults to " ".
-        """
-        assert len(character) == 1, "Character must be a string of length 1"
-        if count:
-            self.plain = f"{character * count}{self.plain}"
-            _Span = Span
-            self._spans[:] = [
-                _Span(start + count, end + count, style)
-                for start, end, style in self._spans
-            ]
-
-    def pad_right(self, count: int, character: str = " ") -> None:
-        """Pad the right with a given character.
-
-        Args:
-            count (int): Number of characters to pad.
-            character (str, optional): Character to pad with. Defaults to " ".
-        """
-        assert len(character) == 1, "Character must be a string of length 1"
-        if count:
-            self.plain = f"{self.plain}{character * count}"
-
-    def align(self, align: AlignMethod, width: int, character: str = " ") -> None:
-        """Align text to a given width.
-
-        Args:
-            align (AlignMethod): One of "left", "center", or "right".
-            width (int): Desired width.
-            character (str, optional): Character to pad with. Defaults to " ".
-        """
-        self.truncate(width)
-        excess_space = width - cell_len(self.plain)
-        if excess_space:
-            if align == "left":
-                self.pad_right(excess_space, character)
-            elif align == "center":
-                left = excess_space // 2
-                self.pad_left(left, character)
-                self.pad_right(excess_space - left, character)
-            else:
-                self.pad_left(excess_space, character)
-
-    def append(
-        self, text: Union["Text", str], style: Optional[Union[str, "Style"]] = None
-    ) -> "Text":
-        """Add text with an optional style.
-
-        Args:
-            text (Union[Text, str]): A str or Text to append.
-            style (str, optional): A style name. Defaults to None.
-
-        Returns:
-            Text: Returns self for chaining.
-        """
-
-        if not isinstance(text, (str, Text)):
-            raise TypeError("Only str or Text can be appended to Text")
-
-        if len(text):
-            if isinstance(text, str):
-                sanitized_text = strip_control_codes(text)
-                self._text.append(sanitized_text)
-                offset = len(self)
-                text_length = len(sanitized_text)
-                if style is not None:
-                    self._spans.append(Span(offset, offset + text_length, style))
-                self._length += text_length
-            elif isinstance(text, Text):
-                _Span = Span
-                if style is not None:
-                    raise ValueError(
-                        "style must not be set when appending Text instance"
-                    )
-                text_length = self._length
-                if text.style is not None:
-                    self._spans.append(
-                        _Span(text_length, text_length + len(text), text.style)
-                    )
-                self._text.append(text.plain)
-                self._spans.extend(
-                    _Span(start + text_length, end + text_length, style)
-                    for start, end, style in text._spans
-                )
-                self._length += len(text)
-        return self
-
-    def append_text(self, text: "Text") -> "Text":
-        """Append another Text instance. This method is more performant that Text.append, but
-        only works for Text.
-
-        Returns:
-            Text: Returns self for chaining.
-        """
-        _Span = Span
-        text_length = self._length
-        if text.style is not None:
-            self._spans.append(_Span(text_length, text_length + len(text), text.style))
-        self._text.append(text.plain)
-        self._spans.extend(
-            _Span(start + text_length, end + text_length, style)
-            for start, end, style in text._spans
-        )
-        self._length += len(text)
-        return self
-
-    def append_tokens(
-        self, tokens: Iterable[Tuple[str, Optional[StyleType]]]
-    ) -> "Text":
-        """Append iterable of str and style. Style may be a Style instance or a str style definition.
-
-        Args:
-            pairs (Iterable[Tuple[str, Optional[StyleType]]]): An iterable of tuples containing str content and style.
-
-        Returns:
-            Text: Returns self for chaining.
-        """
-        append_text = self._text.append
-        append_span = self._spans.append
-        _Span = Span
-        offset = len(self)
-        for content, style in tokens:
-            append_text(content)
-            if style is not None:
-                append_span(_Span(offset, offset + len(content), style))
-            offset += len(content)
-        self._length = offset
-        return self
-
-    def copy_styles(self, text: "Text") -> None:
-        """Copy styles from another Text instance.
-
-        Args:
-            text (Text): A Text instance to copy styles from, must be the same length.
-        """
-        self._spans.extend(text._spans)
-
-    def split(
-        self,
-        separator: str = "\n",
-        *,
-        include_separator: bool = False,
-        allow_blank: bool = False,
-    ) -> Lines:
-        """Split rich text in to lines, preserving styles.
-
-        Args:
-            separator (str, optional): String to split on. Defaults to "\\\\n".
-            include_separator (bool, optional): Include the separator in the lines. Defaults to False.
-            allow_blank (bool, optional): Return a blank line if the text ends with a separator. Defaults to False.
-
-        Returns:
-            List[RichText]: A list of rich text, one per line of the original.
-        """
-        assert separator, "separator must not be empty"
-
-        text = self.plain
-        if separator not in text:
-            return Lines([self.copy()])
-
-        if include_separator:
-            lines = self.divide(
-                match.end() for match in re.finditer(re.escape(separator), text)
-            )
-        else:
-
-            def flatten_spans() -> Iterable[int]:
-                for match in re.finditer(re.escape(separator), text):
-                    start, end = match.span()
-                    yield start
-                    yield end
-
-            lines = Lines(
-                line for line in self.divide(flatten_spans()) if line.plain != separator
-            )
-
-        if not allow_blank and text.endswith(separator):
-            lines.pop()
-
-        return lines
-
-    def divide(self, offsets: Iterable[int]) -> Lines:
-        """Divide text in to a number of lines at given offsets.
-
-        Args:
-            offsets (Iterable[int]): Offsets used to divide text.
-
-        Returns:
-            Lines: New RichText instances between offsets.
-        """
-        _offsets = list(offsets)
-
-        if not _offsets:
-            return Lines([self.copy()])
-
-        text = self.plain
-        text_length = len(text)
-        divide_offsets = [0, *_offsets, text_length]
-        line_ranges = list(zip(divide_offsets, divide_offsets[1:]))
-
-        style = self.style
-        justify = self.justify
-        overflow = self.overflow
-        _Text = Text
-        new_lines = Lines(
-            _Text(
-                text[start:end],
-                style=style,
-                justify=justify,
-                overflow=overflow,
-            )
-            for start, end in line_ranges
-        )
-        if not self._spans:
-            return new_lines
-
-        _line_appends = [line._spans.append for line in new_lines._lines]
-        line_count = len(line_ranges)
-        _Span = Span
-
-        for span_start, span_end, style in self._spans:
-
-            lower_bound = 0
-            upper_bound = line_count
-            start_line_no = (lower_bound + upper_bound) // 2
-
-            while True:
-                line_start, line_end = line_ranges[start_line_no]
-                if span_start < line_start:
-                    upper_bound = start_line_no - 1
-                elif span_start > line_end:
-                    lower_bound = start_line_no + 1
-                else:
-                    break
-                start_line_no = (lower_bound + upper_bound) // 2
-
-            if span_end < line_end:
-                end_line_no = start_line_no
-            else:
-                end_line_no = lower_bound = start_line_no
-                upper_bound = line_count
-
-                while True:
-                    line_start, line_end = line_ranges[end_line_no]
-                    if span_end < line_start:
-                        upper_bound = end_line_no - 1
-                    elif span_end > line_end:
-                        lower_bound = end_line_no + 1
-                    else:
-                        break
-                    end_line_no = (lower_bound + upper_bound) // 2
-
-            for line_no in range(start_line_no, end_line_no + 1):
-                line_start, line_end = line_ranges[line_no]
-                new_start = max(0, span_start - line_start)
-                new_end = min(span_end - line_start, line_end - line_start)
-                if new_end > new_start:
-                    _line_appends[line_no](_Span(new_start, new_end, style))
-
-        return new_lines
-
-    def right_crop(self, amount: int = 1) -> None:
-        """Remove a number of characters from the end of the text."""
-        max_offset = len(self.plain) - amount
-        _Span = Span
-        self._spans[:] = [
-            (
-                span
-                if span.end < max_offset
-                else _Span(span.start, min(max_offset, span.end), span.style)
-            )
-            for span in self._spans
-            if span.start < max_offset
-        ]
-        self._text = [self.plain[:-amount]]
-        self._length -= amount
-
-    def wrap(
-        self,
-        console: "Console",
-        width: int,
-        *,
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        tab_size: int = 8,
-        no_wrap: Optional[bool] = None,
-    ) -> Lines:
-        """Word wrap the text.
-
-        Args:
-            console (Console): Console instance.
-            width (int): Number of characters per line.
-            emoji (bool, optional): Also render emoji code. Defaults to True.
-            justify (str, optional): Justify method: "default", "left", "center", "full", "right". Defaults to "default".
-            overflow (str, optional): Overflow method: "crop", "fold", or "ellipsis". Defaults to None.
-            tab_size (int, optional): Default tab size. Defaults to 8.
-            no_wrap (bool, optional): Disable wrapping, Defaults to False.
-
-        Returns:
-            Lines: Number of lines.
-        """
-        wrap_justify = justify or self.justify or DEFAULT_JUSTIFY
-        wrap_overflow = overflow or self.overflow or DEFAULT_OVERFLOW
-
-        no_wrap = pick_bool(no_wrap, self.no_wrap, False) or overflow == "ignore"
-
-        lines = Lines()
-        for line in self.split(allow_blank=True):
-            if "\t" in line:
-                line.expand_tabs(tab_size)
-            if no_wrap:
-                new_lines = Lines([line])
-            else:
-                offsets = divide_line(str(line), width, fold=wrap_overflow == "fold")
-                new_lines = line.divide(offsets)
-            for line in new_lines:
-                line.rstrip_end(width)
-            if wrap_justify:
-                new_lines.justify(
-                    console, width, justify=wrap_justify, overflow=wrap_overflow
-                )
-            for line in new_lines:
-                line.truncate(width, overflow=wrap_overflow)
-            lines.extend(new_lines)
-        return lines
-
-    def fit(self, width: int) -> Lines:
-        """Fit the text in to given width by chopping in to lines.
-
-        Args:
-            width (int): Maximum characters in a line.
-
-        Returns:
-            Lines: List of lines.
-        """
-        lines: Lines = Lines()
-        append = lines.append
-        for line in self.split():
-            line.set_length(width)
-            append(line)
-        return lines
-
-    def detect_indentation(self) -> int:
-        """Auto-detect indentation of code.
-
-        Returns:
-            int: Number of spaces used to indent code.
-        """
-
-        _indentations = {
-            len(match.group(1))
-            for match in re.finditer(r"^( *)(.*)$", self.plain, flags=re.MULTILINE)
-        }
-
-        try:
-            indentation = (
-                reduce(gcd, [indent for indent in _indentations if not indent % 2]) or 1
-            )
-        except TypeError:
-            indentation = 1
-
-        return indentation
-
-    def with_indent_guides(
-        self,
-        indent_size: Optional[int] = None,
-        *,
-        character: str = "│",
-        style: StyleType = "dim green",
-    ) -> "Text":
-        """Adds indent guide lines to text.
-
-        Args:
-            indent_size (Optional[int]): Size of indentation, or None to auto detect. Defaults to None.
-            character (str, optional): Character to use for indentation. Defaults to "│".
-            style (Union[Style, str], optional): Style of indent guides.
-
-        Returns:
-            Text: New text with indentation guides.
-        """
-
-        _indent_size = self.detect_indentation() if indent_size is None else indent_size
-
-        text = self.copy()
-        text.expand_tabs()
-        indent_line = f"{character}{' ' * (_indent_size - 1)}"
-
-        re_indent = re.compile(r"^( *)(.*)$")
-        new_lines: List[Text] = []
-        add_line = new_lines.append
-        blank_lines = 0
-        for line in text.split(allow_blank=True):
-            match = re_indent.match(line.plain)
-            if not match or not match.group(2):
-                blank_lines += 1
-                continue
-            indent = match.group(1)
-            full_indents, remaining_space = divmod(len(indent), _indent_size)
-            new_indent = f"{indent_line * full_indents}{' ' * remaining_space}"
-            line.plain = new_indent + line.plain[len(new_indent) :]
-            line.stylize(style, 0, len(new_indent))
-            if blank_lines:
-                new_lines.extend([Text(new_indent, style=style)] * blank_lines)
-                blank_lines = 0
-            add_line(line)
-        if blank_lines:
-            new_lines.extend([Text("", style=style)] * blank_lines)
-
-        new_text = text.blank_copy("\n").join(new_lines)
-        return new_text
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from pip._vendor.rich.console import Console
-
-    text = Text(
-        """\nLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n"""
-    )
-    text.highlight_words(["Lorem"], "bold")
-    text.highlight_words(["ipsum"], "italic")
-
-    console = Console()
-
-    console.rule("justify='left'")
-    console.print(text, style="red")
-    console.print()
-
-    console.rule("justify='center'")
-    console.print(text, style="green", justify="center")
-    console.print()
-
-    console.rule("justify='right'")
-    console.print(text, style="blue", justify="right")
-    console.print()
-
-    console.rule("justify='full'")
-    console.print(text, style="magenta", justify="full")
-    console.print()
diff --git a/spaces/tomg-group-umd/pez-dispenser/open_clip/openai.py b/spaces/tomg-group-umd/pez-dispenser/open_clip/openai.py
deleted file mode 100644
index cc4e13e876d6a7a3463b457e62c517cb063b1356..0000000000000000000000000000000000000000
--- a/spaces/tomg-group-umd/pez-dispenser/open_clip/openai.py
+++ /dev/null
@@ -1,144 +0,0 @@
-""" OpenAI pretrained model functions
-
-Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-
-import os
-import warnings
-from typing import List, Optional, Union
-
-import torch
-
-from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype
-from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url
-
-__all__ = ["list_openai_models", "load_openai_model"]
-
-
-def list_openai_models() -> List[str]:
-    """Returns the names of available CLIP models"""
-    return list_pretrained_models_by_tag('openai')
-
-
-def load_openai_model(
-        name: str,
-        precision: Optional[str] = None,
-        device: Optional[Union[str, torch.device]] = None,
-        jit: bool = True,
-        cache_dir: Optional[str] = None,
-):
-    """Load a CLIP model
-
-    Parameters
-    ----------
-    name : str
-        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
-    precision: str
-        Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'.
-    device : Union[str, torch.device]
-        The device to put the loaded model
-    jit : bool
-        Whether to load the optimized JIT model (default) or more hackable non-JIT model.
-    cache_dir : Optional[str]
-        The directory to cache the downloaded model weights
-
-    Returns
-    -------
-    model : torch.nn.Module
-        The CLIP model
-    preprocess : Callable[[PIL.Image], torch.Tensor]
-        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
-    """
-    if device is None:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if precision is None:
-        precision = 'fp32' if device == 'cpu' else 'fp16'
-
-    if get_pretrained_url(name, 'openai'):
-        model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir)
-    elif os.path.isfile(name):
-        model_path = name
-    else:
-        raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}")
-
-    try:
-        # loading JIT archive
-        model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
-        state_dict = None
-    except RuntimeError:
-        # loading saved state dict
-        if jit:
-            warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
-            jit = False
-        state_dict = torch.load(model_path, map_location="cpu")
-
-    if not jit:
-        # Build a non-jit model from the OpenAI jitted model state dict
-        cast_dtype = get_cast_dtype(precision)
-        try:
-            model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype)
-        except KeyError:
-            sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
-            model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype)
-
-        # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use
-        model = model.to(device)
-        if precision.startswith('amp') or precision == 'fp32':
-            model.float()
-        elif precision == 'bf16':
-            convert_weights_to_lp(model, dtype=torch.bfloat16)
-
-        return model
-
-    # patch the device names
-    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
-    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
-
-    def patch_device(module):
-        try:
-            graphs = [module.graph] if hasattr(module, "graph") else []
-        except RuntimeError:
-            graphs = []
-
-        if hasattr(module, "forward1"):
-            graphs.append(module.forward1.graph)
-
-        for graph in graphs:
-            for node in graph.findAllNodes("prim::Constant"):
-                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
-                    node.copyAttributes(device_node)
-
-    model.apply(patch_device)
-    patch_device(model.encode_image)
-    patch_device(model.encode_text)
-
-    # patch dtype to float32 (typically for CPU)
-    if precision == 'fp32':
-        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
-        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
-        float_node = float_input.node()
-
-        def patch_float(module):
-            try:
-                graphs = [module.graph] if hasattr(module, "graph") else []
-            except RuntimeError:
-                graphs = []
-
-            if hasattr(module, "forward1"):
-                graphs.append(module.forward1.graph)
-
-            for graph in graphs:
-                for node in graph.findAllNodes("aten::to"):
-                    inputs = list(node.inputs())
-                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
-                        if inputs[i].node()["value"] == 5:
-                            inputs[i].node().copyAttributes(float_node)
-
-        model.apply(patch_float)
-        patch_float(model.encode_image)
-        patch_float(model.encode_text)
-        model.float()
-
-    # ensure image_size attr available at consistent location for both jit and non-jit
-    model.visual.image_size = model.input_resolution.item()
-    return model
diff --git a/spaces/tomofi/MMOCR/docs/en/make.bat b/spaces/tomofi/MMOCR/docs/en/make.bat
deleted file mode 100644
index 8a3a0e25b49a52ade52c4f69ddeb0bc3d12527ff..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/docs/en/make.bat
+++ /dev/null
@@ -1,36 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_dense_heads/test_yolof_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_dense_heads/test_yolof_head.py
deleted file mode 100644
index ef21b66cf3f81934f27e6d8f27997ebf4b950c48..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_dense_heads/test_yolof_head.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import mmcv
-import torch
-
-from mmdet.models.dense_heads import YOLOFHead
-
-
-def test_yolof_head_loss():
-    """Tests yolof head loss when truth is empty and non-empty."""
-    s = 256
-    img_metas = [{
-        'img_shape': (s, s, 3),
-        'scale_factor': 1,
-        'pad_shape': (s, s, 3)
-    }]
-    train_cfg = mmcv.Config(
-        dict(
-            assigner=dict(
-                type='UniformAssigner',
-                pos_ignore_thr=0.15,
-                neg_ignore_thr=0.7),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False))
-    self = YOLOFHead(
-        num_classes=4,
-        in_channels=1,
-        reg_decoded_bbox=True,
-        train_cfg=train_cfg,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[1, 2, 4, 8, 16],
-            strides=[32]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1., 1., 1., 1.],
-            add_ctr_clamp=True,
-            ctr_clamp=32),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='GIoULoss', loss_weight=1.0))
-    feat = [torch.rand(1, 1, s // 32, s // 32)]
-    cls_scores, bbox_preds = self.forward(feat)
-
-    # Test that empty ground truth encourages the network to predict background
-    gt_bboxes = [torch.empty((0, 4))]
-    gt_labels = [torch.LongTensor([])]
-    gt_bboxes_ignore = None
-    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
-                                img_metas, gt_bboxes_ignore)
-    # When there is no truth, the cls loss should be nonzero but there should
-    # be no box loss.
-    empty_cls_loss = empty_gt_losses['loss_cls']
-    empty_box_loss = empty_gt_losses['loss_bbox']
-    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
-    assert empty_box_loss.item() == 0, (
-        'there should be no box loss when there are no true boxes')
-
-    # When truth is non-empty then both cls and box loss should be nonzero for
-    # random inputs
-    gt_bboxes = [
-        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
-    ]
-    gt_labels = [torch.LongTensor([2])]
-    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
-                              img_metas, gt_bboxes_ignore)
-    onegt_cls_loss = one_gt_losses['loss_cls']
-    onegt_box_loss = one_gt_losses['loss_bbox']
-    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
-    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/spaces/tomzhang1019/ChatGPT/readme/README_ja.md b/spaces/tomzhang1019/ChatGPT/readme/README_ja.md
deleted file mode 100644
index fc56eec0b81c22ff0a49e3960aa52ffd7d6dc5cb..0000000000000000000000000000000000000000
--- a/spaces/tomzhang1019/ChatGPT/readme/README_ja.md
+++ /dev/null
@@ -1,126 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | <a title="English" href="README_en.md">English</a> | 日本語
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>ChatGPT/ChatGLM/LLaMAなどのLLMのための軽量でユーザーフレンドリーなWeb-UI</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        ストリーム出力／会話回数無制限／履歴保存／プリセットプロンプト／ファイルへの質問チャット<br>
-        ウェブ検索／LaTeXレンダリング／表レンダリング／コードハイライト<br>
-        オートダークモード／アダプティブ・ウェブ・インターフェイス／WeChatライク・テーマ<br />
-        マルチパラメーターチューニング／マルチAPI-Key対応／マルチユーザー対応<br>
-        GPT-4対応／LLMのローカルデプロイ可能。
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>動画チュートリアル</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 イントロダクション</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 イントロダクション & チュートリアル</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>オンライントライアル</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>ワンクリックデプロイ</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## 使う上でのTips
-
-- ChatGPTをより適切に制御するために、システムプロンプトを使用できます。
-- プロンプトテンプレートを使用するには、プロンプトテンプレートコレクションを選択し、ドロップダウンメニューから特定のプロンプトを選択。回答が不十分な場合は、`🔄再生成`ボタンを使って再試行します。
-- 入力ボックスで改行するには、<kbd>Shift</kbd> + <kbd>Enter</kbd>キーを押してください。
-- 入力履歴を素早く切り替えるには、入力ボックスで <kbd>↑</kbd>と<kbd>↓</kbd>キーを押す。
-- プログラムをサーバにデプロイするには、プログラムの最終行を `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`に変更します。
-- 共有リンクを取得するには、プログラムの最後の行を `demo.launch(share=True)` に変更してください。なお、公開リンクでアクセスするためには、プログラムが実行されている必要があることに注意してください。
-- Hugging Face Spacesで使用する場合： より速く、より安全に利用するために、**Duplicate Space**を使用し、自分のスペースでプログラムを実行することをお勧めします。
-
-## インストール
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-次に `config_example.json`をコピーして `config.json`にリネームし、そのファイルにAPI-Keyなどの設定を記入する。
-
-```shell
-python ChuanhuChatbot.py
-```
-
-ブラウザのウィンドウが開き、ChatGPTとチャットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるため、依存関係を再度インストールしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-3. Gradioを更新
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/trttung1610/musicgen/docs/TRAINING.md b/spaces/trttung1610/musicgen/docs/TRAINING.md
deleted file mode 100644
index 148de295f2ddfed2e4e893576bf31e1485038b8e..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/docs/TRAINING.md
+++ /dev/null
@@ -1,312 +0,0 @@
-# AudioCraft training pipelines
-
-AudioCraft training pipelines are built on top of PyTorch as our core deep learning library
-and [Flashy](https://github.com/facebookresearch/flashy) as our training pipeline design library,
-and [Dora](https://github.com/facebookresearch/dora) as our experiment manager.
-AudioCraft training pipelines are designed to be research and experiment-friendly.
-
-
-## Environment setup
-
-For the base installation, follow the instructions from the [README.md](../README.md).
-Below are some additional instructions for setting up environment to train new models.
-
-### Team and cluster configuration
-
-In order to support multiple teams and clusters, AudioCraft uses an environment configuration.
-The team configuration allows to specify cluster-specific configurations (e.g. SLURM configuration),
-or convenient mapping of paths between the supported environments.
-
-Each team can have a yaml file under the [configuration folder](../config). To select a team set the
-`AUDIOCRAFT_TEAM` environment variable to a valid team name (e.g. `labs` or `default`):
-```shell
-conda env config vars set AUDIOCRAFT_TEAM=default
-```
-
-Alternatively, you can add it to your `.bashrc`:
-```shell
-export AUDIOCRAFT_TEAM=default
-```
-
-If not defined, the environment will default to the `default` team.
-
-The cluster is automatically detected, but it is also possible to override it by setting
-the `AUDIOCRAFT_CLUSTER` environment variable.
-
-Based on this team and cluster, the environment is then configured with:
-* The dora experiment outputs directory.
-* The available slurm partitions: categorized by global and team.
-* A shared reference directory: In order to facilitate sharing research models while remaining
-agnostic to the used compute cluster, we created the `//reference` symbol that can be used in
-YAML config to point to a defined reference folder containing shared checkpoints
-(e.g. baselines, models for evaluation...).
-
-**Important:** The default output dir for trained models and checkpoints is under `/tmp/`. This is suitable
-only for quick testing. If you are doing anything serious you MUST edit the file `default.yaml` and
-properly set the `dora_dir` entries.
-
-#### Overriding environment configurations
-
-You can set the following environmet variables to bypass the team's environment configuration:
-* `AUDIOCRAFT_CONFIG`: absolute path to a team config yaml file.
-* `AUDIOCRAFT_DORA_DIR`: absolute path to a custom dora directory.
-* `AUDIOCRAFT_REFERENCE_DIR`: absolute path to the shared reference directory.
-
-## Training pipelines
-
-Each task supported in AudioCraft has its own training pipeline and dedicated solver.
-Learn more about solvers and key designs around AudioCraft training pipeline below.
-Please refer to the documentation of each task and model for specific information on a given task.
-
-
-### Solvers
-
-The core training component in AudioCraft is the solver. A solver holds the definition
-of how to solve a given task: It implements the training pipeline logic, combining the datasets,
-model, optimization criterion and components and the full training loop. We refer the reader
-to [Flashy](https://github.com/facebookresearch/flashy) for core principles around solvers.
-
-AudioCraft proposes an initial solver, the `StandardSolver` that is used as the base implementation
-for downstream solvers. This standard solver provides a nice base management of logging,
-checkpoints loading/saving, xp restoration, etc. on top of the base Flashy implementation.
-In AudioCraft, we made the assumption that all tasks are following the same set of stages:
-train, valid, evaluate and generation, each relying on a dedicated dataset.
-
-Each solver is responsible for defining the task to solve and the associated stages
-of the training loop in order to leave the full ownership of the training pipeline
-to the researchers. This includes loading the datasets, building the model and
-optimisation components, registering them and defining the execution of each stage.
-To create a new solver for a given task, one should extend the StandardSolver
-and define each stage of the training loop. One can further customise its own solver
-starting from scratch instead of inheriting from the standard solver.
-
-```python
-from . import base
-from .. import optim
-
-
-class MyNewSolver(base.StandardSolver):
-
-    def __init__(self, cfg: omegaconf.DictConfig):
-        super().__init__(cfg)
-        # one can add custom attributes to the solver
-        self.criterion = torch.nn.L1Loss()
-
-    def best_metric(self):
-        # here optionally specify which metric to use to keep track of best state
-        return 'loss'
-
-    def build_model(self):
-        # here you can instantiate your models and optimization related objects
-        # this method will be called by the StandardSolver init method
-        self.model = ...
-        # the self.cfg attribute contains the raw configuration
-        self.optimizer = optim.build_optimizer(self.model.parameters(), self.cfg.optim)
-        # don't forget to register the states you'd like to include in your checkpoints!
-        self.register_stateful('model', 'optimizer')
-        # keep the model best state based on the best value achieved at validation for the given best_metric
-        self.register_best('model')
-        # if you want to add EMA around the model
-        self.register_ema('model')
-
-    def build_dataloaders(self):
-        # here you can instantiate your dataloaders
-        # this method will be called by the StandardSolver init method
-        self.dataloaders = ...
-
-    ...
-
-    # For both train and valid stages, the StandardSolver relies on
-    # a share common_train_valid implementation that is in charge of
-    # accessing the appropriate loader, iterate over the data up to
-    # the specified number of updates_per_epoch, run the ``run_step``
-    # function that you need to implement to specify the behavior
-    # and finally update the EMA and collect the metrics properly.
-    @abstractmethod
-    def run_step(self, idx: int, batch: tp.Any, metrics: dict):
-        """Perform one training or valid step on a given batch.
-        """
-        ... # provide your implementation of the solver over a batch
-
-    def train(self):
-        """Train stage.
-        """
-        return self.common_train_valid('train')
-
-    def valid(self):
-        """Valid stage.
-        """
-        return self.common_train_valid('valid')
-
-    @abstractmethod
-    def evaluate(self):
-        """Evaluate stage.
-        """
-        ... # provide your implementation here!
-
-    @abstractmethod
-    def generate(self):
-        """Generate stage.
-        """
-        ... # provide your implementation here!
-```
-
-### About Epochs
-
-AudioCraft Solvers uses the concept of Epoch. One epoch doesn't necessarily mean one pass over the entire
-dataset, but instead represent the smallest amount of computation that we want to work with before checkpointing.
-Typically, we find that having an Epoch time around 30min is ideal both in terms of safety (checkpointing often enough)
-and getting updates often enough. One Epoch is at least a `train` stage that lasts for `optim.updates_per_epoch` (2000 by default),
-and a `valid` stage. You can control how long the valid stage takes with `dataset.valid.num_samples`.
-Other stages (`evaluate`, `generate`) will only happen every X epochs, as given by `evaluate.every` and `generate.every`).
-
-
-### Models
-
-In AudioCraft, a model is a container object that wraps one or more torch modules together
-with potential processing logic to use in a solver. For example, a model would wrap an encoder module,
-a quantisation bottleneck module, a decoder and some tensor processing logic. Each of the previous components
-can be considered as a small « model unit » on its own but the container model is a practical component
-to manipulate and train a set of modules together.
-
-### Datasets
-
-See the [dedicated documentation on datasets](./DATASETS.md).
-
-### Metrics
-
-See the [dedicated documentation on metrics](./METRICS.md).
-
-### Conditioners
-
-AudioCraft language models can be conditioned in various ways and the codebase offers a modular implementation
-of different conditioners that can be potentially combined together.
-Learn more in the [dedicated documentation on conditioning](./CONDITIONING.md).
-
-### Configuration
-
-AudioCraft's configuration is defined in yaml files and the framework relies on
-[hydra](https://hydra.cc/docs/intro/) and [omegaconf](https://omegaconf.readthedocs.io/) to parse
-and manipulate the configuration through Dora.
-
-##### :warning: Important considerations around configurations
-
-Our configuration management relies on Hydra and the concept of group configs to structure
-and compose configurations. Updating the root default configuration files will then have
-an impact on all solvers and tasks.
-**One should never change the default configuration files. Instead they should use Hydra config groups in order to store custom configuration.**
-Once this configuration is created and used for running experiments, you should not edit it anymore.
-
-Note that as we are using Dora as our experiment manager, all our experiment tracking is based on
-signatures computed from delta between configurations.
-**One must therefore ensure backward compatibilty of the configuration at all time.**
-See [Dora's README](https://github.com/facebookresearch/dora) and the
-[section below introduction Dora](#running-experiments-with-dora).
-
-##### Configuration structure
-
-The configuration is organized in config groups:
-* `conditioner`: default values for conditioning modules.
-* `dset`: contains all data source related information (paths to manifest files
-and metadata for a given dataset).
-* `model`: contains configuration for each model defined in AudioCraft and configurations
-for different variants of models.
-* `solver`: contains the default configuration for each solver as well as configuration
-for each solver task, combining all the above components.
-* `teams`: contains the cluster configuration per teams. See environment setup for more details.
-
-The `config.yaml` file is the main configuration that composes the above groups
-and contains default configuration for AudioCraft.
-
-##### Solver's core configuration structure
-
-The core configuration structure shared across solver is available in `solvers/default.yaml`.
-
-##### Other configuration modules
-
-AudioCraft configuration contains the different setups we used for our research and publications.
-
-## Running experiments with Dora
-
-### Launching jobs
-
-Try launching jobs for different tasks locally with dora run:
-
-```shell
-# run compression task with lightweight encodec
-dora run solver=compression/debug
-```
-
-Most of the time, the jobs are launched through dora grids, for example:
-
-```shell
-# run compression task through debug grid
-dora grid compression.debug
-```
-
-Learn more about running experiments with Dora below.
-
-### A small introduction to Dora
-
-[Dora](https://github.com/facebookresearch/dora) is the experiment manager tool used in AudioCraft.
-Check out the README to learn how Dora works. Here is a quick summary of what to know:
-* An XP is a unique set of hyper-parameters with a given signature. The signature is a hash
-of those hyper-parameters. We always refer to an XP with its signature, e.g. 9357e12e. We will see
-after that one can retrieve the hyper-params and re-rerun it in a single command.
-* In fact, the hash is defined as a delta between the base config and the one obtained
-with the config overrides you passed from the command line. This means you must never change
-the `conf/**.yaml` files directly., except for editing things like paths. Changing the default values
-in the config files means the XP signature won't reflect that change, and wrong checkpoints might be reused.
-I know, this is annoying, but the reason is that otherwise, any change to the config file would mean
-that all XPs ran so far would see their signature change.
-
-#### Dora commands
-
-```shell
-dora info -f 81de367c  # this will show the hyper-parameter used by a specific XP.
-                       # Be careful some overrides might present twice, and the right most one
-                       # will give you the right value for it.
-
-dora run -d -f 81de367c   # run an XP with the hyper-parameters from XP 81de367c.
-                          # `-d` is for distributed, it will use all available GPUs.
-
-dora run -d -f 81de367c dataset.batch_size=32  # start from the config of XP 81de367c but change some hyper-params.
-                                               # This will give you a new XP with a new signature (e.g. 3fe9c332).
-
-dora info -f SIG -t    # will tail the log (if the XP has scheduled).
-# if you need to access the logs of the process for rank > 0, in particular because a crash didn't happen in the main
-# process, then use `dora info -f SIG` to get the main log name (finished into something like `/5037674_0_0_log.out`)
-# and worker K can accessed as `/5037674_0_{K}_log.out`.
-# This is only for scheduled jobs, for local distributed runs with `-d`, then you should go into the XP folder,
-# and look for `worker_{K}.log` logs.
-```
-
-An XP runs from a specific folder based on its signature, under the
-`<cluster_specific_path>/<user>/experiments/audiocraft/outputs/` folder.
-You can safely interrupt a training and resume it, it will reuse any existing checkpoint,
-as it will reuse the same folder. If you made some change to the code and need to ignore
-a previous checkpoint you can use `dora run --clear [RUN ARGS]`.
-
-If you have a Slurm cluster, you can also use the dora grid command, e.g.
-
-```shell
-# run a dummy grid located at `audiocraft/grids/my_grid_folder/my_grid_name.py`
-dora grid my_grid_folder.my_grid_name
-# Run the following will simply display the grid and also initialized the Dora experiments database.
-# You can then simply refer to a config using its signature (e.g. as `dora run -f SIG`).
-dora grid my_grid_folder.my_grid_name --dry_run --init
-```
-
-Please refer to the [Dora documentation](https://github.com/facebookresearch/dora) for more information.
-
-
-#### Clearing up past experiments
-
-```shell
-# This will cancel all the XPs and delete their folder and checkpoints.
-# It will then reschedule them starting from scratch.
-dora grid my_grid_folder.my_grid_name --clear
-# The following will delete the folder and checkpoint for a single XP,
-# and then run it afresh.
-dora run [-f BASE_SIG] [ARGS] --clear
-```
diff --git a/spaces/uSerNameDDHL/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/uSerNameDDHL/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/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/ucalyptus/PTI/models/e4e/encoders/__init__.py b/spaces/ucalyptus/PTI/models/e4e/encoders/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Autocad 2009 Blogspot Download Software Comparison with Other Autocad Versions and Alternatives.md b/spaces/usbethFlerru/sovits-modelsV2/example/Autocad 2009 Blogspot Download Software Comparison with Other Autocad Versions and Alternatives.md
deleted file mode 100644
index 912051a57fd7efce32943b9ccf98bc02d6efc001..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Autocad 2009 Blogspot Download Software Comparison with Other Autocad Versions and Alternatives.md	
+++ /dev/null
@@ -1,14 +0,0 @@
-
-<p>The "Install Now" method I avoid. If you have to uninstall and reinstall the Autodesk software you have to connect back up to the subscription center and do it again, you don't have any files stored locally that you can use again, you can try but you'll receive a "Communication Failure" error. To me this is 1 huge drawback. Also, you aren't always able to login to subscription center and download it again or the particular version you need may no longer be available under your login.</p>
-<h2>autocad 2009 blogspot download software</h2><br /><p><b><b>Download</b> &bull;&bull;&bull; <a href="https://urlcod.com/2uyV5K">https://urlcod.com/2uyV5K</a></b></p><br /><br />
-<p>You can download AutoCAD 2009 for 32 (x86) systems as well as 64 (x64) bit systems, in English. It is compatible with all versions of Windows, it even works with Windows 10. Do not hesitate to download the program, the best of all is that you will have to skip the link shorteners, we leave you the direct links Per Mega and MediaFire.</p>
-<p>Autodesk AutoCAD 2009 32 Bit 64 makes the navigation and working streamline more convenient and easy. It has automated modules such as Automated floor plans, elevations, and sections. Users can auto-generate annotations, scedules, layers, list, tables and much more. Users can model, draw piping, circuit models, with included assets libraries. The application also supports availability for Web and Mobile apps for viewing, editing, creating, and much more. The model documentation takes less time and provides better details and control over the process. You can also download Mesh2Surface Premium Plugin for Rhino.</p>
-<p>Autodesk AutoCAD 2009 32 Bit 64 bit helps users to deliver results in effective and optimized ways. With this detailed application, users can create 2D and 3D models, drawings, and structures. It lets users create models with complex structures and surfaces. It includes all the required tools and function which may request by the user. It offers full control and access over different settings and options. You can also download CDCS Variation Analyst 7.6.0.1 for NX / CATIA / Creo / MultiCAD Free Download.</p>
-<p></p>
-<p>Autodesk AutoCAD 2009 32 Bit 64 makes the navigation and working streamline more convenient and easy. It has automated modules such as Automated floor plans, elevations, and sections. Users can auto-generate annotations, scedules, layers, list, tables and much more. Users can model, draw piping, circuit models, with included assets libraries. The application also supports availability for Web and Mobile apps for viewing, editing, creating, and much more. The model documentation takes less time and provides better details and control over the process. You can also download Download Mesh2Surface Premium Plugin for Rhino.</p>
-<p>When you download the software, you must download the correct version of <strong>MDT</strong>. The <strong>MDT2009-Disc1.zip</strong> and <strong>MDT2009-Disc2.zip</strong> downloads are designed to look for a type of 'Inventor" installed on your computer. The files are named the same but are actually different. For example, if you just download the first <strong>MDT2009-Disk1.zip</strong>, you will not know if it works with <strong>Autodesk Product Design Suite/Inventor 2012</strong> or with <strong>A</strong><strong>utoCAD Inventor Tooling Suite 2011/2010. </strong></p>
-<p><b>AutoCAD 2009 Free Download Full Version</b> layout and interface is user friendly and al the categories you can see in this software easy and under stand how you can do own task in this software. we can easy enable the grid and we can give the shape easy also.layout you can help to create the objects and parameters .we can save the file in other formats also like PDF and FBX.we can do east engineering drawing from this software also . AutoCAD Download For PC work in all windows <b>windows 7 , Windows 8</b> and <b>Windows 10</b> .</p>
-<p>AutoCAD is a leader in creating 2D as well as 3D engineering designs. Developed by Autodesk, a top company in design and drafting, AutoCAD is released every year with enhanced features. In this review, we will look into AutoCAD 2009. You can also download AutoCAD 2008.</p>
-<p>AutoCAD 2009 has got various new features which has made AutoCAD more productive, reliable as well as flexible. It lets you design and visualize your ideas easily and efficiently. The user interface has been redesigned for accelerating the tasks. The interface is ribbon-based which has increased the drafting productivity as the number of the steps has been decreased. In this application, you can move applications quickly and easily. It has got The ViewCube tool which can be used for rotating and orienting any solid or surface model in AutoCAD. The model will quickly be brought to that predefined orientation by selecting a face, edge or a corner. It has also got a new SteeringWheels tool which will let you access the pan, zoom and center command quickly. You can also download AutoCAD 2006.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/checks.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/checks.md
deleted file mode 100644
index 14bc9bd36b80123f86077a4aa159ef37f72ddec6..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/checks.md
+++ /dev/null
@@ -1,89 +0,0 @@
----
-description: 'Check functions for YOLO utils: image size, version, font, requirements, filename suffix, YAML file, YOLO, and Git version.'
-keywords: YOLO, Ultralytics, Utils, Checks, image sizing, version updates, font compatibility, Python requirements, file suffixes, YAML syntax, image showing, AMP
----
-
-## is_ascii
----
-### ::: ultralytics.yolo.utils.checks.is_ascii
-<br><br>
-
-## check_imgsz
----
-### ::: ultralytics.yolo.utils.checks.check_imgsz
-<br><br>
-
-## check_version
----
-### ::: ultralytics.yolo.utils.checks.check_version
-<br><br>
-
-## check_latest_pypi_version
----
-### ::: ultralytics.yolo.utils.checks.check_latest_pypi_version
-<br><br>
-
-## check_pip_update_available
----
-### ::: ultralytics.yolo.utils.checks.check_pip_update_available
-<br><br>
-
-## check_font
----
-### ::: ultralytics.yolo.utils.checks.check_font
-<br><br>
-
-## check_python
----
-### ::: ultralytics.yolo.utils.checks.check_python
-<br><br>
-
-## check_requirements
----
-### ::: ultralytics.yolo.utils.checks.check_requirements
-<br><br>
-
-## check_suffix
----
-### ::: ultralytics.yolo.utils.checks.check_suffix
-<br><br>
-
-## check_yolov5u_filename
----
-### ::: ultralytics.yolo.utils.checks.check_yolov5u_filename
-<br><br>
-
-## check_file
----
-### ::: ultralytics.yolo.utils.checks.check_file
-<br><br>
-
-## check_yaml
----
-### ::: ultralytics.yolo.utils.checks.check_yaml
-<br><br>
-
-## check_imshow
----
-### ::: ultralytics.yolo.utils.checks.check_imshow
-<br><br>
-
-## check_yolo
----
-### ::: ultralytics.yolo.utils.checks.check_yolo
-<br><br>
-
-## check_amp
----
-### ::: ultralytics.yolo.utils.checks.check_amp
-<br><br>
-
-## git_describe
----
-### ::: ultralytics.yolo.utils.checks.git_describe
-<br><br>
-
-## print_args
----
-### ::: ultralytics.yolo.utils.checks.print_args
-<br><br>
diff --git a/spaces/victor/test-autotrain/Dockerfile b/spaces/victor/test-autotrain/Dockerfile
deleted file mode 100644
index 94ee76a4f45af463ab7f945633c9258172f9cc80..0000000000000000000000000000000000000000
--- a/spaces/victor/test-autotrain/Dockerfile
+++ /dev/null
@@ -1,2 +0,0 @@
-FROM huggingface/autotrain-advanced:latest
-CMD autotrain app --port 7860
diff --git a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/box_ops.py b/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/box_ops.py
deleted file mode 100644
index 781068d294e576954edb4bd07b6e0f30e4e1bcd9..0000000000000000000000000000000000000000
--- a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/box_ops.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities for bounding box manipulation and GIoU.
-"""
-import torch
-from torchvision.ops.boxes import box_area
-
-
-def box_cxcywh_to_xyxy(x):
-    x_c, y_c, w, h = x.unbind(-1)
-    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
-    return torch.stack(b, dim=-1)
-
-
-def box_xyxy_to_cxcywh(x):
-    x0, y0, x1, y1 = x.unbind(-1)
-    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
-    return torch.stack(b, dim=-1)
-
-
-# modified from torchvision to also return the union
-def box_iou(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    # import ipdb; ipdb.set_trace()
-    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
-
-    union = area1[:, None] + area2 - inter
-
-    iou = inter / (union + 1e-6)
-    return iou, union
-
-
-def generalized_box_iou(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/
-
-    The boxes should be in [x0, y0, x1, y1] format
-
-    Returns a [N, M] pairwise matrix, where N = len(boxes1)
-    and M = len(boxes2)
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
-    # except:
-    #     import ipdb; ipdb.set_trace()
-    iou, union = box_iou(boxes1, boxes2)
-
-    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
-    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    area = wh[:, :, 0] * wh[:, :, 1]
-
-    return iou - (area - union) / (area + 1e-6)
-
-
-# modified from torchvision to also return the union
-def box_iou_pairwise(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    lt = torch.max(boxes1[:, :2], boxes2[:, :2])  # [N,2]
-    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])  # [N,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,2]
-    inter = wh[:, 0] * wh[:, 1]  # [N]
-
-    union = area1 + area2 - inter
-
-    iou = inter / union
-    return iou, union
-
-
-def generalized_box_iou_pairwise(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/
-
-    Input:
-        - boxes1, boxes2: N,4
-    Output:
-        - giou: N, 4
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
-    assert boxes1.shape == boxes2.shape
-    iou, union = box_iou_pairwise(boxes1, boxes2)  # N, 4
-
-    lt = torch.min(boxes1[:, :2], boxes2[:, :2])
-    rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
-
-    wh = (rb - lt).clamp(min=0)  # [N,2]
-    area = wh[:, 0] * wh[:, 1]
-
-    return iou - (area - union) / area
-
-
-def masks_to_boxes(masks):
-    """Compute the bounding boxes around the provided masks
-
-    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
-
-    Returns a [N, 4] tensors, with the boxes in xyxy format
-    """
-    if masks.numel() == 0:
-        return torch.zeros((0, 4), device=masks.device)
-
-    h, w = masks.shape[-2:]
-
-    y = torch.arange(0, h, dtype=torch.float)
-    x = torch.arange(0, w, dtype=torch.float)
-    y, x = torch.meshgrid(y, x)
-
-    x_mask = masks * x.unsqueeze(0)
-    x_max = x_mask.flatten(1).max(-1)[0]
-    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
-    y_mask = masks * y.unsqueeze(0)
-    y_max = y_mask.flatten(1).max(-1)[0]
-    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
-    return torch.stack([x_min, y_min, x_max, y_max], 1)
-
-
-if __name__ == "__main__":
-    x = torch.rand(5, 4)
-    y = torch.rand(3, 4)
-    iou, union = box_iou(x, y)
-    import ipdb
-
-    ipdb.set_trace()
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/losses/dice_loss.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/losses/dice_loss.py
deleted file mode 100644
index 27a77b962d7d8b3079c7d6cd9db52280c6fb4970..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/losses/dice_loss.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
-segmentron/solver/loss.py (Apache-2.0 License)"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weighted_loss
-
-
-@weighted_loss
-def dice_loss(pred,
-              target,
-              valid_mask,
-              smooth=1,
-              exponent=2,
-              class_weight=None,
-              ignore_index=255):
-    assert pred.shape[0] == target.shape[0]
-    total_loss = 0
-    num_classes = pred.shape[1]
-    for i in range(num_classes):
-        if i != ignore_index:
-            dice_loss = binary_dice_loss(
-                pred[:, i],
-                target[..., i],
-                valid_mask=valid_mask,
-                smooth=smooth,
-                exponent=exponent)
-            if class_weight is not None:
-                dice_loss *= class_weight[i]
-            total_loss += dice_loss
-    return total_loss / num_classes
-
-
-@weighted_loss
-def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwards):
-    assert pred.shape[0] == target.shape[0]
-    pred = pred.reshape(pred.shape[0], -1)
-    target = target.reshape(target.shape[0], -1)
-    valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
-
-    num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
-    den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth
-
-    return 1 - num / den
-
-
-@LOSSES.register_module()
-class DiceLoss(nn.Module):
-    """DiceLoss.
-
-    This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
-    Volumetric Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.
-
-    Args:
-        loss_type (str, optional): Binary or multi-class loss.
-            Default: 'multi_class'. Options are "binary" and "multi_class".
-        smooth (float): A float number to smooth loss, and avoid NaN error.
-            Default: 1
-        exponent (float): An float number to calculate denominator
-            value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        class_weight (list[float] | str, optional): Weight of each class. If in
-            str format, read them from a file. Defaults to None.
-        loss_weight (float, optional): Weight of the loss. Default to 1.0.
-        ignore_index (int | None): The label index to be ignored. Default: 255.
-    """
-
-    def __init__(self,
-                 smooth=1,
-                 exponent=2,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0,
-                 ignore_index=255,
-                 **kwards):
-        super(DiceLoss, self).__init__()
-        self.smooth = smooth
-        self.exponent = exponent
-        self.reduction = reduction
-        self.class_weight = get_class_weight(class_weight)
-        self.loss_weight = loss_weight
-        self.ignore_index = ignore_index
-
-    def forward(self,
-                pred,
-                target,
-                avg_factor=None,
-                reduction_override=None,
-                **kwards):
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = pred.new_tensor(self.class_weight)
-        else:
-            class_weight = None
-
-        pred = F.softmax(pred, dim=1)
-        num_classes = pred.shape[1]
-        one_hot_target = F.one_hot(
-            torch.clamp(target.long(), 0, num_classes - 1),
-            num_classes=num_classes)
-        valid_mask = (target != self.ignore_index).long()
-
-        loss = self.loss_weight * dice_loss(
-            pred,
-            one_hot_target,
-            valid_mask=valid_mask,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            smooth=self.smooth,
-            exponent=self.exponent,
-            class_weight=class_weight,
-            ignore_index=self.ignore_index)
-        return loss
diff --git a/spaces/wangzhang/ChatSDB/README.md b/spaces/wangzhang/ChatSDB/README.md
deleted file mode 100644
index 51eb3864988d11f84442ae3a2ad4ca740466fc31..0000000000000000000000000000000000000000
--- a/spaces/wangzhang/ChatSDB/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChatSDB
-emoji: 🐢
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/weishao2019/ChuanhuChatGPT/ChuanhuChatbot.py b/spaces/weishao2019/ChuanhuChatGPT/ChuanhuChatbot.py
deleted file mode 100644
index 086dc6a1e3da91f4078e163ffac03ab54ed0a7d0..0000000000000000000000000000000000000000
--- a/spaces/weishao2019/ChuanhuChatGPT/ChuanhuChatbot.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import gradio as gr
-# import openai
-import os
-import sys
-import argparse
-from utils import *
-from presets import *
-
-
-my_api_key = ""    # 在这里输入你的 API 密钥
-
-#if we are running in Docker
-if os.environ.get('dockerrun') == 'yes':
-    dockerflag = True
-else:
-    dockerflag = False
-
-authflag = False
-
-if dockerflag:
-    my_api_key = os.environ.get('my_api_key')
-    if my_api_key == "empty":
-        print("Please give a api key!")
-        sys.exit(1)
-    #auth
-    username = os.environ.get('USERNAME')
-    password = os.environ.get('PASSWORD')
-    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
-        authflag = True
-else:
-    if not my_api_key and os.path.exists("api_key.txt") and os.path.getsize("api_key.txt"):
-        with open("api_key.txt", "r") as f:
-            my_api_key = f.read().strip()
-    if os.path.exists("auth.json"):
-        with open("auth.json", "r") as f:
-            auth = json.load(f)
-            username = auth["username"]
-            password = auth["password"]
-            if username != "" and password != "":
-                authflag = True
-
-gr.Chatbot.postprocess = postprocess
-
-with gr.Blocks(css=customCSS) as demo:
-    gr.HTML(title)
-    with gr.Row():
-        keyTxt = gr.Textbox(show_label=False, placeholder=f"在这里输入你的OpenAI API-key...",
-                            value=my_api_key, type="password", visible=not HIDE_MY_KEY).style(container=True)
-        use_streaming_checkbox = gr.Checkbox(label="实时传输回答", value=True, visible=enable_streaming_option)
-    chatbot = gr.Chatbot()  # .style(color_map=("#1D51EE", "#585A5B"))
-    history = gr.State([])
-    token_count = gr.State([])
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    TRUECOMSTANT = gr.State(True)
-    FALSECONSTANT = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    with gr.Row():
-        with gr.Column(scale=12):
-            user_input = gr.Textbox(show_label=False, placeholder="在这里输入").style(
-                container=False)
-        with gr.Column(min_width=50, scale=1):
-            submitBtn = gr.Button("🚀", variant="primary")
-    with gr.Row():
-        emptyBtn = gr.Button("🧹 新的对话")
-        retryBtn = gr.Button("🔄 重新生成")
-        delLastBtn = gr.Button("🗑️ 删除最近一条对话")
-        reduceTokenBtn = gr.Button("♻️ 总结对话")
-    status_display = gr.Markdown("status: ready")
-    systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...",
-                                 label="System prompt", value=initial_prompt).style(container=True)
-    with gr.Accordion(label="加载Prompt模板", open=False):
-        with gr.Column():
-            with gr.Row():
-                with gr.Column(scale=6):
-                    templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件", choices=get_template_names(plain=True), multiselect=False, value=get_template_names(plain=True)[0])
-                with gr.Column(scale=1):
-                    templateRefreshBtn = gr.Button("🔄 刷新")
-                    templaeFileReadBtn = gr.Button("📂 读入模板")
-            with gr.Row():
-                with gr.Column(scale=6):
-                    templateSelectDropdown = gr.Dropdown(label="从Prompt模板中加载", choices=load_template(get_template_names(plain=True)[0], mode=1), multiselect=False, value=load_template(get_template_names(plain=True)[0], mode=1)[0])
-                with gr.Column(scale=1):
-                    templateApplyBtn = gr.Button("⬇️ 应用")
-    with gr.Accordion(label="保存/加载对话历史记录", open=False):
-        with gr.Column():
-            with gr.Row():
-                with gr.Column(scale=6):
-                    saveFileName = gr.Textbox(
-                        show_label=True, placeholder=f"在这里输入保存的文件名...", label="设置保存文件名", value="对话历史记录").style(container=True)
-                with gr.Column(scale=1):
-                    saveHistoryBtn = gr.Button("💾 保存对话")
-            with gr.Row():
-                with gr.Column(scale=6):
-                    historyFileSelectDropdown = gr.Dropdown(label="从列表中加载对话", choices=get_history_names(plain=True), multiselect=False, value=get_history_names(plain=True)[0])
-                with gr.Column(scale=1):
-                    historyRefreshBtn = gr.Button("🔄 刷新")
-                    historyReadBtn = gr.Button("📂 读入对话")
-    #inputs, top_p, temperature, top_k, repetition_penalty
-    with gr.Accordion("参数", 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",)
-        #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
-        #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
-    gr.Markdown(description)
-
-
-    user_input.submit(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-    user_input.submit(reset_textbox, [], [user_input])
-
-    submitBtn.click(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-    submitBtn.click(reset_textbox, [], [user_input])
-
-    emptyBtn.click(reset_state, outputs=[chatbot, history, token_count, status_display], show_progress=True)
-
-    retryBtn.click(retry, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-
-    delLastBtn.click(delete_last_conversation, [chatbot, history, token_count, use_streaming_checkbox], [
-                     chatbot, history, token_count, status_display], show_progress=True)
-
-    reduceTokenBtn.click(reduce_token_size, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-
-    saveHistoryBtn.click(save_chat_history, [
-                  saveFileName, systemPromptTxt, history, chatbot], None, show_progress=True)
-
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-
-    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-
-    historyReadBtn.click(load_chat_history, [historyFileSelectDropdown, systemPromptTxt, history, chatbot],  [saveFileName, systemPromptTxt, history, chatbot], show_progress=True)
-
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-
-    templaeFileReadBtn.click(load_template, [templateFileSelectDropdown],  [promptTemplates, templateSelectDropdown], show_progress=True)
-
-    templateApplyBtn.click(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt],  [systemPromptTxt], show_progress=True)
-
-print("川虎的温馨提示：访问 http://localhost:7860 查看界面")
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "川虎ChatGPT 🚀"
-
-if __name__ == "__main__":
-    #if running in Docker
-    if dockerflag:
-        if authflag:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password))
-        else:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
-    #if not running in Docker
-    else:
-        if authflag:
-            demo.queue().launch(share=False, auth=(username, password))
-        else:
-            demo.queue().launch(share=False) # 改为 share=True 可以创建公开分享链接
-        #demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-        #demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-        #demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/xfh/min-stable-diffusion-web/clip_tokenizer/__init__.py b/spaces/xfh/min-stable-diffusion-web/clip_tokenizer/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/xiang2811/ChatGPT/modules/__init__.py b/spaces/xiang2811/ChatGPT/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/xiaohuolong/ChuanhuChatGPT/custom.css b/spaces/xiaohuolong/ChuanhuChatGPT/custom.css
deleted file mode 100644
index 97a1c2e681f4cc09e2237a92b37ab6cadd545a71..0000000000000000000000000000000000000000
--- a/spaces/xiaohuolong/ChuanhuChatGPT/custom.css
+++ /dev/null
@@ -1,184 +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;
-}
-
-ol, ul {
-    list-style-position: inside;
-    padding-left: 0;
-}
-
-ol li, ul:not(.options) li {
-    padding-left: 1.5em;
-    text-indent: -1.5em;
-}
-
-/* 亮色 */
-@media (prefers-color-scheme: light) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-light) !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #FFFFFF !important;
-    }
-    [data-testid = "user"] {
-        background-color: #95EC69 !important;
-    }
-}
-/* 暗色 */
-@media (prefers-color-scheme: dark) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-dark) !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #2C2C2C !important;
-    }
-    [data-testid = "user"] {
-        background-color: #26B561 !important;
-    }
-    body {
-        background-color: var(--neutral-950) !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;
-}
-[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: 1rem 1.2rem 1rem;
-    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 */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.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/xiaoshi/test/app.py b/spaces/xiaoshi/test/app.py
deleted file mode 100644
index 6c6812fd16ad047f1b9eb8a1f3d8dd265df35dd2..0000000000000000000000000000000000000000
--- a/spaces/xiaoshi/test/app.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import os
-import torch
-
-import gradio as gr
-
-def greet(name):
-    return torch.cuda.device_count(), os.system("nvidia-smi")
-
-iface = gr.Interface(fn=greet, inputs="text", outputs=["text", "text"])
-iface.launch()
\ No newline at end of file
diff --git a/spaces/xiaozhong/Real-CUGAN/upcunet_v3.py b/spaces/xiaozhong/Real-CUGAN/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/xiaozhong/Real-CUGAN/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)
diff --git a/spaces/xl2533/FinDoc/build_index/parser/base.py b/spaces/xl2533/FinDoc/build_index/parser/base.py
deleted file mode 100644
index f120b49773a8e4011f644e12f04e3b47929cdab4..0000000000000000000000000000000000000000
--- a/spaces/xl2533/FinDoc/build_index/parser/base.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# -*-coding:utf-8 -*-
-"""
-    解析不同格式的文档
-"""
-import re
-import abc
-from typing import Union, List
-
-
-class BaseParser(abc.ABC):
-
-    def __init__(self, config=None):
-        self.config = config
-
-
-    @staticmethod
-    def full2half(text):
-        s = ''
-        for c in text:
-            num = ord(c)
-            if num == 0x3000:
-                num = 0x20
-            elif 0xFF01 <= num <= 0xFF5E:
-                num = num - 0xFEE0
-            s += chr(num)
-        return s
-
-    @staticmethod
-    def remove_dup_space(text):
-        text = re.sub(r'\s{1,}', '', text, flags=re.MULTILINE|re.DOTALL)  # Extra Spacee Remove
-        return text
-
-    @staticmethod
-    def remove_empty_line(text):
-        text = re.sub(r'\n', '', text, flags=re.MULTILINE|re.DOTALL)  # Extra Spacee Remove
-        return text
-
-    def parse_file(self, file):
-        raise NotImplementedError
diff --git a/spaces/yanli01/gpt01/README.md b/spaces/yanli01/gpt01/README.md
deleted file mode 100644
index 1ad7518a1cf54f078e4f0eb925cf401ec8322af8..0000000000000000000000000000000000000000
--- a/spaces/yanli01/gpt01/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.25.0
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: yanli01/wrwjGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/yaoshining/text-generation-webui/modules/loaders.py b/spaces/yaoshining/text-generation-webui/modules/loaders.py
deleted file mode 100644
index 44e893fbffb1644f801ba5d90a99f7180ca8ff68..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/modules/loaders.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import functools
-
-import gradio as gr
-
-from modules import shared
-
-loaders_and_params = {
-    'AutoGPTQ': [
-        'triton',
-        'no_inject_fused_attention',
-        'no_inject_fused_mlp',
-        'no_use_cuda_fp16',
-        'wbits',
-        'groupsize',
-        'desc_act',
-        'gpu_memory',
-        'cpu_memory',
-        'cpu',
-        'disk',
-        'auto_devices',
-        'trust_remote_code',
-        'autogptq_info',
-    ],
-    'GPTQ-for-LLaMa': [
-        'wbits',
-        'groupsize',
-        'model_type',
-        'pre_layer',
-        'gptq_for_llama_info',
-    ],
-    'llama.cpp': [
-        'n_ctx',
-        'n_gpu_layers',
-        'n_batch',
-        'threads',
-        'no_mmap',
-        'mlock',
-        'llama_cpp_seed',
-    ],
-    'Transformers': [
-        'cpu_memory',
-        'gpu_memory',
-        'trust_remote_code',
-        'load_in_8bit',
-        'bf16',
-        'cpu',
-        'disk',
-        'auto_devices',
-        'load_in_4bit',
-        'use_double_quant',
-        'quant_type',
-        'compute_dtype',
-        'trust_remote_code',
-        'transformers_info'
-    ],
-    'ExLlama' : [
-        'gpu_split',
-        'max_seq_len',
-        'compress_pos_emb',
-        'exllama_info',
-    ],
-    'ExLlama_HF' : [
-        'gpu_split',
-        'max_seq_len',
-        'compress_pos_emb',
-        'exllama_HF_info',
-    ]
-}
-
-
-def get_gpu_memory_keys():
-    return [k for k in shared.gradio if k.startswith('gpu_memory')]
-
-
-@functools.cache
-def get_all_params():
-    all_params = set()
-    for k in loaders_and_params:
-        for el in loaders_and_params[k]:
-            all_params.add(el)
-
-    if 'gpu_memory' in all_params:
-        all_params.remove('gpu_memory')
-        for k in get_gpu_memory_keys():
-            all_params.add(k)
-
-    return sorted(all_params)
-
-
-def make_loader_params_visible(loader):
-    params = []
-    all_params = get_all_params()
-    if loader in loaders_and_params:
-        params = loaders_and_params[loader]
-
-        if 'gpu_memory' in params:
-            params.remove('gpu_memory')
-            params += get_gpu_memory_keys()
-
-    return [gr.update(visible=True) if k in params else gr.update(visible=False) for k in all_params]
diff --git a/spaces/yeqingmei123/face-test/e4e/utils/__init__.py b/spaces/yeqingmei123/face-test/e4e/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/yfzhoucs/TinyLanguageRobots/initializer.py b/spaces/yfzhoucs/TinyLanguageRobots/initializer.py
deleted file mode 100644
index a0a22f9cf3bca8dea72fac2a22e661c18977f983..0000000000000000000000000000000000000000
--- a/spaces/yfzhoucs/TinyLanguageRobots/initializer.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import random
-import copy
-import numpy as np
-
-
-class Initializer:
-    def __init__(self, config, obj_num_low=3, obj_num_high=5):
-        self.config = config
-        self.obj_num_low = obj_num_low
-        self.obj_num_high = obj_num_high
-        self.available_actions = [
-            'push_forward', 
-            'push_backward', 
-            'push_left', 
-            'push_right', 
-            'rotate_clock', 
-            'rotate_counterclock']
-
-        self.verb_template = {
-            'push_forward': [
-                'push',
-                'drag',
-                'move',
-                'get',
-            ], 
-            'push_backward': [
-                'push',
-                'drag',
-                'move',
-                'get',
-            ], 
-            'push_left': [
-                'push',
-                'drag',
-                'move',
-                'get',
-            ],  
-            'push_right': [
-                'push',
-                'drag',
-                'move',
-                'get',
-            ],  
-            'rotate_clock': [
-                'rotate',
-                'revolve',
-                'turn',
-                'spin',
-            ],  
-            'rotate_counterclock': [
-                'rotate',
-                'revolve',
-                'turn',
-                'spin',
-            ], 
-        }
-        self.adv_template = {
-            'push_forward': [
-                'forward',
-                'to the front',
-                'ahead'
-            ], 
-            'push_backward': [
-                'backward',
-                'back',
-                'to the back',
-            ], 
-            'push_left': [
-                'to the left',
-                'left',
-                'to the left hand side',
-            ],  
-            'push_right': [
-                'to the right',
-                'right',
-                'to the right hand side',
-            ],  
-            'rotate_clock': [
-                'clockwise',
-                'clock wise',
-                'right'
-            ],  
-            'rotate_counterclock': [
-                'counterclockwise',
-                'anticlockwise',
-                'anti clock wise',
-                'counter clock wise',
-                'left',
-            ], 
-        }
-        self.np_template = {
-            'orange': [
-                'orange',
-                'citrus',
-                'sweet orange',
-                'lime'
-            ],
-            'apple': [
-                'apple',
-                'red apple',
-                'red delicious apple',
-                'gala'
-            ],
-            'tomato':[
-                'tomato'
-            ],
-            'strawberry':[
-                'strawberry'
-            ],
-            'watermelon':[
-                'watermelon'
-            ],
-            'banana':[
-                'banana'
-            ],
-            'milk_bottle':[
-                'bottle',
-                'glass bottle',
-                'milk bottle'
-            ],
-            'clock':[
-                'clock',
-                'timer',
-                'watch'
-            ],
-            'camera':[
-                'camera',
-                'DSLR',
-                'nikon',
-                'canon'
-            ]
-        }
-        return
-    
-    def _random_place_(self, positions):
-        
-        def l2(x1, y1, x2, y2):
-            return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** (1/2)
-
-        def not_too_close(positions, x, y):
-            for pos in positions:
-                if l2(x, y, pos['x'], pos['y']) <= 100:
-                    return False
-            return True
-
-        done = False
-
-        while not done:
-            # https://programming.guide/random-point-within-circle.html
-            a = random.random() * np.pi
-            r = 450 * np.sqrt(random.random())
-            x = r * np.cos(a) + self.config['desk_width'] * self.config['scale']
-            y = r * np.sin(a) + 100
-
-            if not_too_close(positions, x, y) and r > 250:
-                done = True
-        
-        return {
-            'x': x,
-            'y': y,
-            'z': 0
-        }
-
-    
-    def get_config_and_task(self):
-        available_objs = []
-        for obj in self.config['objects']:
-            if 'position' in self.config['objects'][obj]:
-                available_objs.append(obj)
-
-        num_obj = random.randint(self.obj_num_low, self.obj_num_high)
-        sampled_objs = random.sample(available_objs, num_obj)
-
-        config = {}
-        for key in self.config:
-            if not key == 'objects':
-                config[key] = copy.deepcopy(self.config[key])
-        
-        config['objects'] = {}
-        positions = []
-        for obj in self.config['objects']:
-            if 'position' not in self.config['objects'][obj]:
-                config['objects'][obj] = copy.deepcopy(self.config['objects'][obj])
-            elif obj in sampled_objs:
-                obj_dict = copy.deepcopy(self.config['objects'][obj])
-                obj_dict['position'] = self._random_place_(positions)
-                positions.append(obj_dict['position'])
-                config['objects'][obj] = obj_dict
-
-        task = {
-            'action': random.sample(self.available_actions, 1)[0],
-            'target': random.sample(sampled_objs, 1)[0],
-        }
-
-        config['init_joints'] = np.random.uniform(-np.pi / 2, np.pi / 2, size=(4,))
-        config['init_joints'][-1] *= 0.2
-
-        self.new_config = config
-        self.task = task
-        print(config)
-        print(task)
-        return config, task
-    
-    def get_verb(self, verb):
-        return random.sample(self.verb_template[verb], 1)[0]
-    
-    def get_adv(self, verb):
-        return random.sample(self.adv_template[verb], 1)[0]
-
-    def get_np(self, noun):
-        return random.sample(self.np_template[noun], 1)[0]
-
-    def get_sentence(self):
-        v = self.get_verb(self.task['action'])
-        adv = self.get_adv(self.task['action'])
-        np = self.get_np(self.task['target'])
-        sentence = v + ' ' + np + ' ' + adv
-        return sentence
\ No newline at end of file
diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/PIPNet/run_test.sh b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/PIPNet/run_test.sh
deleted file mode 100644
index c81b0a03155df79d38b149a86956ca82a3f339d4..0000000000000000000000000000000000000000
--- a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/PIPNet/run_test.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-# supervised learning
-
-# 300W, resnet18
-#python lib/test.py experiments/data_300W/pip_32_16_60_r18_l2_l1_10_1_nb10.py test.txt images_test
-# 300W, resnet101
-#python lib/test.py experiments/data_300W/pip_32_16_60_r101_l2_l1_10_1_nb10.py test.txt images_test
-
-# COFW, resnet18
-#python lib/test.py experiments/COFW/pip_32_16_60_r18_l2_l1_10_1_nb10.py test.txt images_test
-# COFW, resnet101
-#python lib/test.py experiments/COFW/pip_32_16_60_r101_l2_l1_10_1_nb10.py test.txt images_test
-
-# WFLW, resnet18
-#python lib/test.py experiments/WFLW/pip_32_16_60_r18_l2_l1_10_1_nb10.py test.txt images_test
-# WFLW, resnet101
-#python lib/test.py experiments/WFLW/pip_32_16_60_r101_l2_l1_10_1_nb10.py test.txt images_test
-
-# AFLW, resnet18
-#python lib/test.py experiments/AFLW/pip_32_16_60_r18_l2_l1_10_1_nb10.py test.txt images_test
-# AFLW, resnet101
-#python lib/test.py experiments/AFLW/pip_32_16_60_r101_l2_l1_10_1_nb10.py test.txt images_test
-
-######################################################################################
-# GSSL
-
-# 300W + COFW_68 (unlabeled) + WFLW_68 (unlabeled), resnet18, with curriculum
-#python lib/test.py experiments/data_300W_COFW_WFLW/pip_32_16_60_r18_l2_l1_10_1_nb10_wcc.py test_300W.txt images_test_300W
-#python lib/test.py experiments/data_300W_COFW_WFLW/pip_32_16_60_r18_l2_l1_10_1_nb10_wcc.py test_COFW.txt images_test_COFW
-#python lib/test.py experiments/data_300W_COFW_WFLW/pip_32_16_60_r18_l2_l1_10_1_nb10_wcc.py test_WFLW.txt images_test_WFLW
-
-# 300W + CelebA (unlabeled), resnet18, with curriculum
-#python lib/test.py experiments/data_300W_CELEBA/pip_32_16_60_r18_l2_l1_10_1_nb10_wcc.py test_300W.txt images_test_300W
-#python lib/test.py experiments/data_300W_CELEBA/pip_32_16_60_r18_l2_l1_10_1_nb10_wcc.py test_COFW.txt images_test_COFW
-#python lib/test.py experiments/data_300W_CELEBA/pip_32_16_60_r18_l2_l1_10_1_nb10_wcc.py test_WFLW.txt images_test_WFLW
diff --git a/spaces/yhavinga/rosetta/README.md b/spaces/yhavinga/rosetta/README.md
deleted file mode 100644
index ae332d04043b3b7c44c20cc34d2e9fc5904c8d28..0000000000000000000000000000000000000000
--- a/spaces/yhavinga/rosetta/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: Rosetta - translate between Dutch and English
-emoji: ‍🧑💻📑
-colorFrom: gray
-colorTo: granola
-sdk: streamlit
-app_file: app.py
-pinned: false
-sdk_version: 1.25.0
-license: postgresql
----
-
-# 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/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/data/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/data/__init__.py
deleted file mode 100644
index 1a8ef35ff439e48caf92dba731f7c551f6dcf285..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/data/__init__.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .data_collator import (
-    DataCollatorForLanguageModeling,
-    DataCollatorForPermutationLanguageModeling,
-    DataCollatorForSeq2Seq,
-    DataCollatorForSOP,
-    DataCollatorForTokenClassification,
-    DataCollatorForWholeWordMask,
-    DataCollatorWithPadding,
-    DefaultDataCollator,
-    default_data_collator,
-)
-from .metrics import glue_compute_metrics, xnli_compute_metrics
-from .processors import (
-    DataProcessor,
-    InputExample,
-    InputFeatures,
-    SingleSentenceClassificationProcessor,
-    SquadExample,
-    SquadFeatures,
-    SquadV1Processor,
-    SquadV2Processor,
-    glue_convert_examples_to_features,
-    glue_output_modes,
-    glue_processors,
-    glue_tasks_num_labels,
-    squad_convert_examples_to_features,
-    xnli_output_modes,
-    xnli_processors,
-    xnli_tasks_num_labels,
-)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bert_generation/modeling_bert_generation.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bert_generation/modeling_bert_generation.py
deleted file mode 100644
index f245ac155e75ca768fcd9d7e3bb4b8884d449f76..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bert_generation/modeling_bert_generation.py
+++ /dev/null
@@ -1,1008 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Google AI Language Team Authors and The HuggingFace Inc. team.
-#
-# 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 BERT model specific for generation."""
-
-import math
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
-from ...utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_bert_generation import BertGenerationConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "google/bert_for_seq_generation_L-24_bbc_encoder"
-_CONFIG_FOR_DOC = "BertGenerationConfig"
-
-
-# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->BertGeneration
-class BertGenerationSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->BertGeneration
-class BertGenerationSelfAttention(nn.Module):
-    def __init__(self, config, position_embedding_type=None):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
-                f"heads ({config.num_attention_heads})"
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = position_embedding_type or getattr(
-            config, "position_embedding_type", "absolute"
-        )
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-
-        self.is_decoder = config.is_decoder
-
-    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        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,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention and past_key_value is not None:
-            # reuse k,v, cross_attentions
-            key_layer = past_key_value[0]
-            value_layer = past_key_value[1]
-            attention_mask = encoder_attention_mask
-        elif is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        use_cache = past_key_value is not None
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_layer, value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
-            if use_cache:
-                position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-                    -1, 1
-                )
-            else:
-                position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertGenerationModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-
-        # 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, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        if self.is_decoder:
-            outputs = outputs + (past_key_value,)
-        return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->BertGeneration
-class BertGenerationAttention(nn.Module):
-    def __init__(self, config, position_embedding_type=None):
-        super().__init__()
-        self.self = BertGenerationSelfAttention(config, position_embedding_type=position_embedding_type)
-        self.output = BertGenerationSelfOutput(config)
-        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,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->BertGeneration
-class BertGenerationIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->BertGeneration
-class BertGenerationOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->BertGeneration
-class BertGenerationLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertGenerationAttention(config)
-        self.is_decoder = config.is_decoder
-        self.add_cross_attention = config.add_cross_attention
-        if self.add_cross_attention:
-            if not self.is_decoder:
-                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
-            self.crossattention = BertGenerationAttention(config, position_embedding_type="absolute")
-        self.intermediate = BertGenerationIntermediate(config)
-        self.output = BertGenerationOutput(config)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-
-        # if decoder, the last output is tuple of self-attn cache
-        if self.is_decoder:
-            outputs = self_attention_outputs[1:-1]
-            present_key_value = self_attention_outputs[-1]
-        else:
-            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
-
-        cross_attn_present_key_value = None
-        if self.is_decoder and encoder_hidden_states is not None:
-            if not hasattr(self, "crossattention"):
-                raise ValueError(
-                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
-                    " by setting `config.add_cross_attention=True`"
-                )
-
-            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
-            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
-            cross_attention_outputs = self.crossattention(
-                attention_output,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                cross_attn_past_key_value,
-                output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
-
-            # add cross-attn cache to positions 3,4 of present_key_value tuple
-            cross_attn_present_key_value = cross_attention_outputs[-1]
-            present_key_value = present_key_value + cross_attn_present_key_value
-
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-        outputs = (layer_output,) + outputs
-
-        # if decoder, return the attn key/values as the last output
-        if self.is_decoder:
-            outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->BertGeneration
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([BertGenerationLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = False,
-        output_hidden_states: Optional[bool] = False,
-        return_dict: Optional[bool] = True,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        next_decoder_cache = () if use_cache else None
-        for i, layer_module in enumerate(self.layer):
-            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
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-                if self.config.add_cross_attention:
-                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-def load_tf_weights_in_bert_generation(
-    model, tf_hub_path, model_class, is_encoder_named_decoder=False, is_encoder=False
-):
-    try:
-        import numpy as np
-        import tensorflow.compat.v1 as tf
-        import tensorflow_hub as hub
-        import tensorflow_text  # noqa: F401
-
-        tf.disable_eager_execution()
-    except ImportError:
-        logger.error(
-            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
-            "https://www.tensorflow.org/install/ for installation instructions."
-        )
-        raise
-    tf_model = hub.Module(tf_hub_path)
-    init = tf.global_variables_initializer()
-    with tf.Session() as sess:
-        init.run()
-        all_variables = tf_model.variable_map
-        keep_track_variables = all_variables.copy()
-        for key in list(all_variables.keys()):
-            if "global" in key:
-                logger.info(f"Skipping {key}...")
-                continue
-            if not is_encoder:
-                model_pointer = getattr(model, model_class)
-            else:
-                model_pointer = model
-            is_embedding = False
-            logger.info(f"Trying to match {key}...")
-            # remove start_string = "module/bert/"
-            sub_layers = key.split("/")[2:]
-            if is_encoder_named_decoder and sub_layers[0] == "encoder":
-                logger.info(f"Skipping encoder layer {key} for decoder")
-                continue
-            if is_encoder and sub_layers[0] == "decoder":
-                logger.info(f"Skipping decoder layer {key} for encoder")
-                continue
-            for i, sub_layer in enumerate(sub_layers):
-                if sub_layer == "embeddings":
-                    is_embedding = True
-                elif sub_layer == "LayerNorm":
-                    is_embedding = False
-                if "layer" in sub_layer:
-                    model_pointer = model_pointer.layer[int(sub_layer.split("_")[-1])]
-                elif sub_layer in ["kernel", "gamma"]:
-                    model_pointer = model_pointer.weight
-                elif sub_layer == "beta":
-                    model_pointer = model_pointer.bias
-                elif sub_layer == "encdec":
-                    model_pointer = model_pointer.crossattention.self
-                elif sub_layer == "encdec_output":
-                    model_pointer = model_pointer.crossattention.output
-                elif is_encoder_named_decoder and sub_layer == "decoder":
-                    model_pointer = model_pointer.encoder
-                else:
-                    if sub_layer == "attention" and "encdec" in sub_layers[i + 1]:
-                        continue
-                    try:
-                        model_pointer = getattr(model_pointer, sub_layer)
-                    except AttributeError:
-                        logger.info(f"Skipping to initialize {key} at {sub_layer}...")
-                        raise AttributeError
-
-            array = np.asarray(sess.run(all_variables[key]))
-            if not is_embedding:
-                logger.info(f"Transposing numpy weight of shape {array.shape} for {key}")
-                array = np.transpose(array)
-            else:
-                model_pointer = model_pointer.weight
-
-            if model_pointer.shape != array.shape:
-                raise ValueError(f"Pointer shape {model_pointer.shape} and array shape {array.shape} mismatched")
-            logger.info(f"Initialize PyTorch weight {key}")
-
-            model_pointer.data = torch.from_numpy(array.astype(np.float32))
-            keep_track_variables.pop(key, None)
-
-        logger.info(f"Weights not copied to PyTorch model: {', '.join(keep_track_variables.keys())}")
-        return model
-
-
-class BertGenerationEmbeddings(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer(
-            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
-        )
-
-    def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        position_embeddings = self.position_embeddings(position_ids)
-
-        embeddings = inputs_embeds + position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertGenerationPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertGenerationConfig
-    base_model_prefix = "bert"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            # 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 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)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, BertEncoder):
-            module.gradient_checkpointing = value
-
-
-BERT_GENERATION_START_DOCSTRING = r"""
-
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`BertGenerationConfig`]): 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.
-"""
-
-BERT_GENERATION_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
-            [`PreTrainedTokenizer.encode`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        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)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        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**.
-
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        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 bare BertGeneration model transformer outputting raw hidden-states without any specific head on top.",
-    BERT_GENERATION_START_DOCSTRING,
-)
-class BertGenerationEncoder(BertGenerationPreTrainedModel):
-    """
-
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in [Attention is
-    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-
-    This model should be used when leveraging Bert or Roberta checkpoints for the [`EncoderDecoderModel`] class as
-    described in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461)
-    by Sascha Rothe, Shashi Narayan, and Aliaksei Severyn.
-
-    To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
-    to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
-    `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
-    """
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertGenerationEmbeddings(config)
-        self.encoder = BertEncoder(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = 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(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPastAndCrossAttentions,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
-        r"""
-        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: `1` for
-            tokens that are NOT MASKED, `0` for MASKED tokens.
-        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        """
-        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.config.is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
-            input_shape = input_ids.size()
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        batch_size, seq_length = input_shape
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask = None
-        if not use_cache:
-            extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # 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_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-
-        if not return_dict:
-            return (sequence_output,) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-class BertGenerationOnlyLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        logits = self.decoder(hidden_states)
-        return logits
-
-    def _tie_weights(self):
-        # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
-        self.bias = self.decoder.bias
-
-
-@add_start_docstrings(
-    """BertGeneration Model with a `language modeling` head on top for CLM fine-tuning.""",
-    BERT_GENERATION_START_DOCSTRING,
-)
-class BertGenerationDecoder(BertGenerationPreTrainedModel):
-    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        if not config.is_decoder:
-            logger.warning("If you want to use `BertGenerationDecoder` as a standalone, add `is_decoder=True.`")
-
-        self.bert = BertGenerationEncoder(config)
-        self.lm_head = BertGenerationOnlyLMHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.lm_head.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
-        r"""
-        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the left-to-right language modeling loss (next word prediction). 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]`
-        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, BertGenerationDecoder, BertGenerationConfig
-        >>> import torch
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
-        >>> config = BertGenerationConfig.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
-        >>> config.is_decoder = True
-        >>> model = BertGenerationDecoder.from_pretrained(
-        ...     "google/bert_for_seq_generation_L-24_bbc_encoder", config=config
-        ... )
-
-        >>> inputs = tokenizer("Hello, my dog is cute", return_token_type_ids=False, return_tensors="pt")
-        >>> outputs = model(**inputs)
-
-        >>> prediction_logits = outputs.logits
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if labels is not None:
-            use_cache = False
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-        prediction_scores = self.lm_head(sequence_output)
-
-        lm_loss = None
-        if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss()
-            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[1:]
-            return ((lm_loss,) + output) if lm_loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=lm_loss,
-            logits=prediction_scores,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
-        input_shape = input_ids.shape
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_shape)
-
-        # cut decoder_input_ids if past is used
-        if past_key_values is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
-
-    def _reorder_cache(self, past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
-            )
-        return reordered_past
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/detr/image_processing_detr.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/detr/image_processing_detr.py
deleted file mode 100644
index 816fad102b5ec98e2948c1d2e32610068b7609fd..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/detr/image_processing_detr.py
+++ /dev/null
@@ -1,1862 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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.
-"""Image processor class for DETR."""
-
-import io
-import pathlib
-from collections import defaultdict
-from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
-
-import numpy as np
-
-from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
-from ...image_transforms import (
-    PaddingMode,
-    center_to_corners_format,
-    corners_to_center_format,
-    id_to_rgb,
-    pad,
-    rescale,
-    resize,
-    rgb_to_id,
-    to_channel_dimension_format,
-)
-from ...image_utils import (
-    IMAGENET_DEFAULT_MEAN,
-    IMAGENET_DEFAULT_STD,
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    get_image_size,
-    infer_channel_dimension_format,
-    is_scaled_image,
-    make_list_of_images,
-    to_numpy_array,
-    valid_coco_detection_annotations,
-    valid_coco_panoptic_annotations,
-    valid_images,
-)
-from ...utils import (
-    ExplicitEnum,
-    TensorType,
-    is_flax_available,
-    is_jax_tensor,
-    is_scipy_available,
-    is_tf_available,
-    is_tf_tensor,
-    is_torch_available,
-    is_torch_tensor,
-    is_vision_available,
-    logging,
-)
-
-
-if is_torch_available():
-    import torch
-    from torch import nn
-
-
-if is_vision_available():
-    import PIL
-
-
-if is_scipy_available():
-    import scipy.special
-    import scipy.stats
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-AnnotationType = Dict[str, Union[int, str, List[Dict]]]
-
-
-class AnnotionFormat(ExplicitEnum):
-    COCO_DETECTION = "coco_detection"
-    COCO_PANOPTIC = "coco_panoptic"
-
-
-SUPPORTED_ANNOTATION_FORMATS = (AnnotionFormat.COCO_DETECTION, AnnotionFormat.COCO_PANOPTIC)
-
-
-def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]:
-    """
-    Computes the output image size given the input image size and the desired output size.
-
-    Args:
-        image_size (`Tuple[int, int]`):
-            The input image size.
-        size (`int`):
-            The desired output size.
-        max_size (`int`, *optional*):
-            The maximum allowed output size.
-    """
-    height, width = image_size
-    if max_size is not None:
-        min_original_size = float(min((height, width)))
-        max_original_size = float(max((height, width)))
-        if max_original_size / min_original_size * size > max_size:
-            size = int(round(max_size * min_original_size / max_original_size))
-
-    if (height <= width and height == size) or (width <= height and width == size):
-        return height, width
-
-    if width < height:
-        ow = size
-        oh = int(size * height / width)
-    else:
-        oh = size
-        ow = int(size * width / height)
-    return (oh, ow)
-
-
-def get_resize_output_image_size(
-    input_image: np.ndarray,
-    size: Union[int, Tuple[int, int], List[int]],
-    max_size: Optional[int] = None,
-    input_data_format: Optional[Union[str, ChannelDimension]] = None,
-) -> Tuple[int, int]:
-    """
-    Computes the output image size given the input image size and the desired output size. If the desired output size
-    is a tuple or list, the output image size is returned as is. If the desired output size is an integer, the output
-    image size is computed by keeping the aspect ratio of the input image size.
-
-    Args:
-        image_size (`Tuple[int, int]`):
-            The input image size.
-        size (`int`):
-            The desired output size.
-        max_size (`int`, *optional*):
-            The maximum allowed output size.
-        input_data_format (`ChannelDimension` or `str`, *optional*):
-            The channel dimension format of the input image. If not provided, it will be inferred from the input image.
-    """
-    image_size = get_image_size(input_image, input_data_format)
-    if isinstance(size, (list, tuple)):
-        return size
-
-    return get_size_with_aspect_ratio(image_size, size, max_size)
-
-
-def get_numpy_to_framework_fn(arr) -> Callable:
-    """
-    Returns a function that converts a numpy array to the framework of the input array.
-
-    Args:
-        arr (`np.ndarray`): The array to convert.
-    """
-    if isinstance(arr, np.ndarray):
-        return np.array
-    if is_tf_available() and is_tf_tensor(arr):
-        import tensorflow as tf
-
-        return tf.convert_to_tensor
-    if is_torch_available() and is_torch_tensor(arr):
-        import torch
-
-        return torch.tensor
-    if is_flax_available() and is_jax_tensor(arr):
-        import jax.numpy as jnp
-
-        return jnp.array
-    raise ValueError(f"Cannot convert arrays of type {type(arr)}")
-
-
-def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
-    """
-    Squeezes an array, but only if the axis specified has dim 1.
-    """
-    if axis is None:
-        return arr.squeeze()
-
-    try:
-        return arr.squeeze(axis=axis)
-    except ValueError:
-        return arr
-
-
-def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
-    image_height, image_width = image_size
-    norm_annotation = {}
-    for key, value in annotation.items():
-        if key == "boxes":
-            boxes = value
-            boxes = corners_to_center_format(boxes)
-            boxes /= np.asarray([image_width, image_height, image_width, image_height], dtype=np.float32)
-            norm_annotation[key] = boxes
-        else:
-            norm_annotation[key] = value
-    return norm_annotation
-
-
-# Copied from transformers.models.vilt.image_processing_vilt.max_across_indices
-def max_across_indices(values: Iterable[Any]) -> List[Any]:
-    """
-    Return the maximum value across all indices of an iterable of values.
-    """
-    return [max(values_i) for values_i in zip(*values)]
-
-
-# Copied from transformers.models.vilt.image_processing_vilt.get_max_height_width
-def get_max_height_width(
-    images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
-) -> List[int]:
-    """
-    Get the maximum height and width across all images in a batch.
-    """
-    if input_data_format is None:
-        input_data_format = infer_channel_dimension_format(images[0])
-
-    if input_data_format == ChannelDimension.FIRST:
-        _, max_height, max_width = max_across_indices([img.shape for img in images])
-    elif input_data_format == ChannelDimension.LAST:
-        max_height, max_width, _ = max_across_indices([img.shape for img in images])
-    else:
-        raise ValueError(f"Invalid channel dimension format: {input_data_format}")
-    return (max_height, max_width)
-
-
-# Copied from transformers.models.vilt.image_processing_vilt.make_pixel_mask
-def make_pixel_mask(
-    image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
-) -> np.ndarray:
-    """
-    Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
-
-    Args:
-        image (`np.ndarray`):
-            Image to make the pixel mask for.
-        output_size (`Tuple[int, int]`):
-            Output size of the mask.
-    """
-    input_height, input_width = get_image_size(image, channel_dim=input_data_format)
-    mask = np.zeros(output_size, dtype=np.int64)
-    mask[:input_height, :input_width] = 1
-    return mask
-
-
-# inspired by https://github.com/facebookresearch/detr/blob/master/datasets/coco.py#L33
-def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray:
-    """
-    Convert a COCO polygon annotation to a mask.
-
-    Args:
-        segmentations (`List[List[float]]`):
-            List of polygons, each polygon represented by a list of x-y coordinates.
-        height (`int`):
-            Height of the mask.
-        width (`int`):
-            Width of the mask.
-    """
-    try:
-        from pycocotools import mask as coco_mask
-    except ImportError:
-        raise ImportError("Pycocotools is not installed in your environment.")
-
-    masks = []
-    for polygons in segmentations:
-        rles = coco_mask.frPyObjects(polygons, height, width)
-        mask = coco_mask.decode(rles)
-        if len(mask.shape) < 3:
-            mask = mask[..., None]
-        mask = np.asarray(mask, dtype=np.uint8)
-        mask = np.any(mask, axis=2)
-        masks.append(mask)
-    if masks:
-        masks = np.stack(masks, axis=0)
-    else:
-        masks = np.zeros((0, height, width), dtype=np.uint8)
-
-    return masks
-
-
-# inspired by https://github.com/facebookresearch/detr/blob/master/datasets/coco.py#L50
-def prepare_coco_detection_annotation(
-    image,
-    target,
-    return_segmentation_masks: bool = False,
-    input_data_format: Optional[Union[ChannelDimension, str]] = None,
-):
-    """
-    Convert the target in COCO format into the format expected by DETR.
-    """
-    image_height, image_width = get_image_size(image, channel_dim=input_data_format)
-
-    image_id = target["image_id"]
-    image_id = np.asarray([image_id], dtype=np.int64)
-
-    # Get all COCO annotations for the given image.
-    annotations = target["annotations"]
-    annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0]
-
-    classes = [obj["category_id"] for obj in annotations]
-    classes = np.asarray(classes, dtype=np.int64)
-
-    # for conversion to coco api
-    area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32)
-    iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in annotations], dtype=np.int64)
-
-    boxes = [obj["bbox"] for obj in annotations]
-    # guard against no boxes via resizing
-    boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
-    boxes[:, 2:] += boxes[:, :2]
-    boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width)
-    boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height)
-
-    keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
-
-    new_target = {}
-    new_target["image_id"] = image_id
-    new_target["class_labels"] = classes[keep]
-    new_target["boxes"] = boxes[keep]
-    new_target["area"] = area[keep]
-    new_target["iscrowd"] = iscrowd[keep]
-    new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64)
-
-    if annotations and "keypoints" in annotations[0]:
-        keypoints = [obj["keypoints"] for obj in annotations]
-        keypoints = np.asarray(keypoints, dtype=np.float32)
-        num_keypoints = keypoints.shape[0]
-        keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
-        new_target["keypoints"] = keypoints[keep]
-
-    if return_segmentation_masks:
-        segmentation_masks = [obj["segmentation"] for obj in annotations]
-        masks = convert_coco_poly_to_mask(segmentation_masks, image_height, image_width)
-        new_target["masks"] = masks[keep]
-
-    return new_target
-
-
-def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
-    """
-    Compute the bounding boxes around the provided panoptic segmentation masks.
-
-    Args:
-        masks: masks in format `[number_masks, height, width]` where N is the number of masks
-
-    Returns:
-        boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
-    """
-    if masks.size == 0:
-        return np.zeros((0, 4))
-
-    h, w = masks.shape[-2:]
-    y = np.arange(0, h, dtype=np.float32)
-    x = np.arange(0, w, dtype=np.float32)
-    # see https://github.com/pytorch/pytorch/issues/50276
-    y, x = np.meshgrid(y, x, indexing="ij")
-
-    x_mask = masks * np.expand_dims(x, axis=0)
-    x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
-    x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
-    x_min = x.filled(fill_value=1e8)
-    x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
-
-    y_mask = masks * np.expand_dims(y, axis=0)
-    y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
-    y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
-    y_min = y.filled(fill_value=1e8)
-    y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
-
-    return np.stack([x_min, y_min, x_max, y_max], 1)
-
-
-def prepare_coco_panoptic_annotation(
-    image: np.ndarray,
-    target: Dict,
-    masks_path: Union[str, pathlib.Path],
-    return_masks: bool = True,
-    input_data_format: Union[ChannelDimension, str] = None,
-) -> Dict:
-    """
-    Prepare a coco panoptic annotation for DETR.
-    """
-    image_height, image_width = get_image_size(image, channel_dim=input_data_format)
-    annotation_path = pathlib.Path(masks_path) / target["file_name"]
-
-    new_target = {}
-    new_target["image_id"] = np.asarray([target["image_id"] if "image_id" in target else target["id"]], dtype=np.int64)
-    new_target["size"] = np.asarray([image_height, image_width], dtype=np.int64)
-    new_target["orig_size"] = np.asarray([image_height, image_width], dtype=np.int64)
-
-    if "segments_info" in target:
-        masks = np.asarray(PIL.Image.open(annotation_path), dtype=np.uint32)
-        masks = rgb_to_id(masks)
-
-        ids = np.array([segment_info["id"] for segment_info in target["segments_info"]])
-        masks = masks == ids[:, None, None]
-        masks = masks.astype(np.uint8)
-        if return_masks:
-            new_target["masks"] = masks
-        new_target["boxes"] = masks_to_boxes(masks)
-        new_target["class_labels"] = np.array(
-            [segment_info["category_id"] for segment_info in target["segments_info"]], dtype=np.int64
-        )
-        new_target["iscrowd"] = np.asarray(
-            [segment_info["iscrowd"] for segment_info in target["segments_info"]], dtype=np.int64
-        )
-        new_target["area"] = np.asarray(
-            [segment_info["area"] for segment_info in target["segments_info"]], dtype=np.float32
-        )
-
-    return new_target
-
-
-def get_segmentation_image(
-    masks: np.ndarray, input_size: Tuple, target_size: Tuple, stuff_equiv_classes, deduplicate=False
-):
-    h, w = input_size
-    final_h, final_w = target_size
-
-    m_id = scipy.special.softmax(masks.transpose(0, 1), -1)
-
-    if m_id.shape[-1] == 0:
-        # We didn't detect any mask :(
-        m_id = np.zeros((h, w), dtype=np.int64)
-    else:
-        m_id = m_id.argmax(-1).reshape(h, w)
-
-    if deduplicate:
-        # Merge the masks corresponding to the same stuff class
-        for equiv in stuff_equiv_classes.values():
-            for eq_id in equiv:
-                m_id[m_id == eq_id] = equiv[0]
-
-    seg_img = id_to_rgb(m_id)
-    seg_img = resize(seg_img, (final_w, final_h), resample=PILImageResampling.NEAREST)
-    return seg_img
-
-
-def get_mask_area(seg_img: np.ndarray, target_size: Tuple[int, int], n_classes: int) -> np.ndarray:
-    final_h, final_w = target_size
-    np_seg_img = seg_img.astype(np.uint8)
-    np_seg_img = np_seg_img.reshape(final_h, final_w, 3)
-    m_id = rgb_to_id(np_seg_img)
-    area = [(m_id == i).sum() for i in range(n_classes)]
-    return area
-
-
-def score_labels_from_class_probabilities(logits: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    probs = scipy.special.softmax(logits, axis=-1)
-    labels = probs.argmax(-1, keepdims=True)
-    scores = np.take_along_axis(probs, labels, axis=-1)
-    scores, labels = scores.squeeze(-1), labels.squeeze(-1)
-    return scores, labels
-
-
-def post_process_panoptic_sample(
-    out_logits: np.ndarray,
-    masks: np.ndarray,
-    boxes: np.ndarray,
-    processed_size: Tuple[int, int],
-    target_size: Tuple[int, int],
-    is_thing_map: Dict,
-    threshold=0.85,
-) -> Dict:
-    """
-    Converts the output of [`DetrForSegmentation`] into panoptic segmentation predictions for a single sample.
-
-    Args:
-        out_logits (`torch.Tensor`):
-            The logits for this sample.
-        masks (`torch.Tensor`):
-            The predicted segmentation masks for this sample.
-        boxes (`torch.Tensor`):
-            The prediced bounding boxes for this sample. The boxes are in the normalized format `(center_x, center_y,
-            width, height)` and values between `[0, 1]`, relative to the size the image (disregarding padding).
-        processed_size (`Tuple[int, int]`):
-            The processed size of the image `(height, width)`, as returned by the preprocessing step i.e. the size
-            after data augmentation but before batching.
-        target_size (`Tuple[int, int]`):
-            The target size of the image, `(height, width)` corresponding to the requested final size of the
-            prediction.
-        is_thing_map (`Dict`):
-            A dictionary mapping class indices to a boolean value indicating whether the class is a thing or not.
-        threshold (`float`, *optional*, defaults to 0.85):
-            The threshold used to binarize the segmentation masks.
-    """
-    # we filter empty queries and detection below threshold
-    scores, labels = score_labels_from_class_probabilities(out_logits)
-    keep = (labels != out_logits.shape[-1] - 1) & (scores > threshold)
-
-    cur_scores = scores[keep]
-    cur_classes = labels[keep]
-    cur_boxes = center_to_corners_format(boxes[keep])
-
-    if len(cur_boxes) != len(cur_classes):
-        raise ValueError("Not as many boxes as there are classes")
-
-    cur_masks = masks[keep]
-    cur_masks = resize(cur_masks[:, None], processed_size, resample=PILImageResampling.BILINEAR)
-    cur_masks = safe_squeeze(cur_masks, 1)
-    b, h, w = cur_masks.shape
-
-    # It may be that we have several predicted masks for the same stuff class.
-    # In the following, we track the list of masks ids for each stuff class (they are merged later on)
-    cur_masks = cur_masks.reshape(b, -1)
-    stuff_equiv_classes = defaultdict(list)
-    for k, label in enumerate(cur_classes):
-        if not is_thing_map[label]:
-            stuff_equiv_classes[label].append(k)
-
-    seg_img = get_segmentation_image(cur_masks, processed_size, target_size, stuff_equiv_classes, deduplicate=True)
-    area = get_mask_area(cur_masks, processed_size, n_classes=len(cur_scores))
-
-    # We filter out any mask that is too small
-    if cur_classes.size() > 0:
-        # We know filter empty masks as long as we find some
-        filtered_small = np.array([a <= 4 for a in area], dtype=bool)
-        while filtered_small.any():
-            cur_masks = cur_masks[~filtered_small]
-            cur_scores = cur_scores[~filtered_small]
-            cur_classes = cur_classes[~filtered_small]
-            seg_img = get_segmentation_image(cur_masks, (h, w), target_size, stuff_equiv_classes, deduplicate=True)
-            area = get_mask_area(seg_img, target_size, n_classes=len(cur_scores))
-            filtered_small = np.array([a <= 4 for a in area], dtype=bool)
-    else:
-        cur_classes = np.ones((1, 1), dtype=np.int64)
-
-    segments_info = [
-        {"id": i, "isthing": is_thing_map[cat], "category_id": int(cat), "area": a}
-        for i, (cat, a) in enumerate(zip(cur_classes, area))
-    ]
-    del cur_classes
-
-    with io.BytesIO() as out:
-        PIL.Image.fromarray(seg_img).save(out, format="PNG")
-        predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
-
-    return predictions
-
-
-def resize_annotation(
-    annotation: Dict[str, Any],
-    orig_size: Tuple[int, int],
-    target_size: Tuple[int, int],
-    threshold: float = 0.5,
-    resample: PILImageResampling = PILImageResampling.NEAREST,
-):
-    """
-    Resizes an annotation to a target size.
-
-    Args:
-        annotation (`Dict[str, Any]`):
-            The annotation dictionary.
-        orig_size (`Tuple[int, int]`):
-            The original size of the input image.
-        target_size (`Tuple[int, int]`):
-            The target size of the image, as returned by the preprocessing `resize` step.
-        threshold (`float`, *optional*, defaults to 0.5):
-            The threshold used to binarize the segmentation masks.
-        resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
-            The resampling filter to use when resizing the masks.
-    """
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size))
-    ratio_height, ratio_width = ratios
-
-    new_annotation = {}
-    new_annotation["size"] = target_size
-
-    for key, value in annotation.items():
-        if key == "boxes":
-            boxes = value
-            scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
-            new_annotation["boxes"] = scaled_boxes
-        elif key == "area":
-            area = value
-            scaled_area = area * (ratio_width * ratio_height)
-            new_annotation["area"] = scaled_area
-        elif key == "masks":
-            masks = value[:, None]
-            masks = np.array([resize(mask, target_size, resample=resample) for mask in masks])
-            masks = masks.astype(np.float32)
-            masks = masks[:, 0] > threshold
-            new_annotation["masks"] = masks
-        elif key == "size":
-            new_annotation["size"] = target_size
-        else:
-            new_annotation[key] = value
-
-    return new_annotation
-
-
-# TODO - (Amy) make compatible with other frameworks
-def binary_mask_to_rle(mask):
-    """
-    Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
-
-    Args:
-        mask (`torch.Tensor` or `numpy.array`):
-            A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
-            segment_id or class_id.
-    Returns:
-        `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
-        format.
-    """
-    if is_torch_tensor(mask):
-        mask = mask.numpy()
-
-    pixels = mask.flatten()
-    pixels = np.concatenate([[0], pixels, [0]])
-    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
-    runs[1::2] -= runs[::2]
-    return list(runs)
-
-
-# TODO - (Amy) make compatible with other frameworks
-def convert_segmentation_to_rle(segmentation):
-    """
-    Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
-
-    Args:
-        segmentation (`torch.Tensor` or `numpy.array`):
-            A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
-    Returns:
-        `List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
-    """
-    segment_ids = torch.unique(segmentation)
-
-    run_length_encodings = []
-    for idx in segment_ids:
-        mask = torch.where(segmentation == idx, 1, 0)
-        rle = binary_mask_to_rle(mask)
-        run_length_encodings.append(rle)
-
-    return run_length_encodings
-
-
-def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
-    """
-    Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
-    `labels`.
-
-    Args:
-        masks (`torch.Tensor`):
-            A tensor of shape `(num_queries, height, width)`.
-        scores (`torch.Tensor`):
-            A tensor of shape `(num_queries)`.
-        labels (`torch.Tensor`):
-            A tensor of shape `(num_queries)`.
-        object_mask_threshold (`float`):
-            A number between 0 and 1 used to binarize the masks.
-    Raises:
-        `ValueError`: Raised when the first dimension doesn't match in all input tensors.
-    Returns:
-        `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
-        < `object_mask_threshold`.
-    """
-    if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
-        raise ValueError("mask, scores and labels must have the same shape!")
-
-    to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
-
-    return masks[to_keep], scores[to_keep], labels[to_keep]
-
-
-def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
-    # Get the mask associated with the k class
-    mask_k = mask_labels == k
-    mask_k_area = mask_k.sum()
-
-    # Compute the area of all the stuff in query k
-    original_area = (mask_probs[k] >= mask_threshold).sum()
-    mask_exists = mask_k_area > 0 and original_area > 0
-
-    # Eliminate disconnected tiny segments
-    if mask_exists:
-        area_ratio = mask_k_area / original_area
-        if not area_ratio.item() > overlap_mask_area_threshold:
-            mask_exists = False
-
-    return mask_exists, mask_k
-
-
-def compute_segments(
-    mask_probs,
-    pred_scores,
-    pred_labels,
-    mask_threshold: float = 0.5,
-    overlap_mask_area_threshold: float = 0.8,
-    label_ids_to_fuse: Optional[Set[int]] = None,
-    target_size: Tuple[int, int] = None,
-):
-    height = mask_probs.shape[1] if target_size is None else target_size[0]
-    width = mask_probs.shape[2] if target_size is None else target_size[1]
-
-    segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
-    segments: List[Dict] = []
-
-    if target_size is not None:
-        mask_probs = nn.functional.interpolate(
-            mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
-        )[0]
-
-    current_segment_id = 0
-
-    # Weigh each mask by its prediction score
-    mask_probs *= pred_scores.view(-1, 1, 1)
-    mask_labels = mask_probs.argmax(0)  # [height, width]
-
-    # Keep track of instances of each class
-    stuff_memory_list: Dict[str, int] = {}
-    for k in range(pred_labels.shape[0]):
-        pred_class = pred_labels[k].item()
-        should_fuse = pred_class in label_ids_to_fuse
-
-        # Check if mask exists and large enough to be a segment
-        mask_exists, mask_k = check_segment_validity(
-            mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
-        )
-
-        if mask_exists:
-            if pred_class in stuff_memory_list:
-                current_segment_id = stuff_memory_list[pred_class]
-            else:
-                current_segment_id += 1
-
-            # Add current object segment to final segmentation map
-            segmentation[mask_k] = current_segment_id
-            segment_score = round(pred_scores[k].item(), 6)
-            segments.append(
-                {
-                    "id": current_segment_id,
-                    "label_id": pred_class,
-                    "was_fused": should_fuse,
-                    "score": segment_score,
-                }
-            )
-            if should_fuse:
-                stuff_memory_list[pred_class] = current_segment_id
-
-    return segmentation, segments
-
-
-class DetrImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a Detr image processor.
-
-    Args:
-        format (`str`, *optional*, defaults to `"coco_detection"`):
-            Data format of the annotations. One of "coco_detection" or "coco_panoptic".
-        do_resize (`bool`, *optional*, defaults to `True`):
-            Controls whether to resize the image's `(height, width)` dimensions to the specified `size`. Can be
-            overridden by the `do_resize` parameter in the `preprocess` method.
-        size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
-            Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter
-            in the `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
-            Resampling filter to use if resizing the image.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
-            `do_rescale` parameter in the `preprocess` method.
-        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
-            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
-            `preprocess` method.
-        do_normalize:
-            Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
-            `preprocess` method.
-        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
-            Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
-            channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
-            Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
-            for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
-        do_pad (`bool`, *optional*, defaults to `True`):
-            Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
-            overridden by the `do_pad` parameter in the `preprocess` method.
-    """
-
-    model_input_names = ["pixel_values", "pixel_mask"]
-
-    def __init__(
-        self,
-        format: Union[str, AnnotionFormat] = AnnotionFormat.COCO_DETECTION,
-        do_resize: bool = True,
-        size: Dict[str, int] = None,
-        resample: PILImageResampling = PILImageResampling.BILINEAR,
-        do_rescale: bool = True,
-        rescale_factor: Union[int, float] = 1 / 255,
-        do_normalize: bool = True,
-        image_mean: Union[float, List[float]] = None,
-        image_std: Union[float, List[float]] = None,
-        do_pad: bool = True,
-        **kwargs,
-    ) -> None:
-        if "pad_and_return_pixel_mask" in kwargs:
-            do_pad = kwargs.pop("pad_and_return_pixel_mask")
-
-        if "max_size" in kwargs:
-            logger.warning_once(
-                "The `max_size` parameter is deprecated and will be removed in v4.26. "
-                "Please specify in `size['longest_edge'] instead`.",
-            )
-            max_size = kwargs.pop("max_size")
-        else:
-            max_size = None if size is None else 1333
-
-        size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333}
-        size = get_size_dict(size, max_size=max_size, default_to_square=False)
-
-        super().__init__(**kwargs)
-        self.format = format
-        self.do_resize = do_resize
-        self.size = size
-        self.resample = resample
-        self.do_rescale = do_rescale
-        self.rescale_factor = rescale_factor
-        self.do_normalize = do_normalize
-        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
-        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
-        self.do_pad = do_pad
-
-    @classmethod
-    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
-        """
-        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
-        created using from_dict and kwargs e.g. `DetrImageProcessor.from_pretrained(checkpoint, size=600,
-        max_size=800)`
-        """
-        image_processor_dict = image_processor_dict.copy()
-        if "max_size" in kwargs:
-            image_processor_dict["max_size"] = kwargs.pop("max_size")
-        if "pad_and_return_pixel_mask" in kwargs:
-            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
-        return super().from_dict(image_processor_dict, **kwargs)
-
-    def prepare_annotation(
-        self,
-        image: np.ndarray,
-        target: Dict,
-        format: Optional[AnnotionFormat] = None,
-        return_segmentation_masks: bool = None,
-        masks_path: Optional[Union[str, pathlib.Path]] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> Dict:
-        """
-        Prepare an annotation for feeding into DETR model.
-        """
-        format = format if format is not None else self.format
-
-        if format == AnnotionFormat.COCO_DETECTION:
-            return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks
-            target = prepare_coco_detection_annotation(
-                image, target, return_segmentation_masks, input_data_format=input_data_format
-            )
-        elif format == AnnotionFormat.COCO_PANOPTIC:
-            return_segmentation_masks = True if return_segmentation_masks is None else return_segmentation_masks
-            target = prepare_coco_panoptic_annotation(
-                image,
-                target,
-                masks_path=masks_path,
-                return_masks=return_segmentation_masks,
-                input_data_format=input_data_format,
-            )
-        else:
-            raise ValueError(f"Format {format} is not supported.")
-        return target
-
-    def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
-        logger.warning_once(
-            "The `prepare` method is deprecated and will be removed in a v4.33. "
-            "Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
-            "does not return the image anymore.",
-        )
-        target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
-        return image, target
-
-    def convert_coco_poly_to_mask(self, *args, **kwargs):
-        logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
-        return convert_coco_poly_to_mask(*args, **kwargs)
-
-    def prepare_coco_detection(self, *args, **kwargs):
-        logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
-        return prepare_coco_detection_annotation(*args, **kwargs)
-
-    def prepare_coco_panoptic(self, *args, **kwargs):
-        logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
-        return prepare_coco_panoptic_annotation(*args, **kwargs)
-
-    def resize(
-        self,
-        image: np.ndarray,
-        size: Dict[str, int],
-        resample: PILImageResampling = PILImageResampling.BILINEAR,
-        data_format: Optional[ChannelDimension] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ) -> np.ndarray:
-        """
-        Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
-        int, smaller edge of the image will be matched to this number.
-
-        Args:
-            image (`np.ndarray`):
-                Image to resize.
-            size (`Dict[str, int]`):
-                Dictionary containing the size to resize to. Can contain the keys `shortest_edge` and `longest_edge` or
-                `height` and `width`.
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
-                Resampling filter to use if resizing the image.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format for the output image. If unset, the channel dimension format of the input
-                image is used.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format of the input image. If not provided, it will be inferred.
-        """
-        if "max_size" in kwargs:
-            logger.warning_once(
-                "The `max_size` parameter is deprecated and will be removed in v4.26. "
-                "Please specify in `size['longest_edge'] instead`.",
-            )
-            max_size = kwargs.pop("max_size")
-        else:
-            max_size = None
-        size = get_size_dict(size, max_size=max_size, default_to_square=False)
-        if "shortest_edge" in size and "longest_edge" in size:
-            size = get_resize_output_image_size(
-                image, size["shortest_edge"], size["longest_edge"], input_data_format=input_data_format
-            )
-        elif "height" in size and "width" in size:
-            size = (size["height"], size["width"])
-        else:
-            raise ValueError(
-                "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
-                f" {size.keys()}."
-            )
-        image = resize(
-            image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
-        )
-        return image
-
-    def resize_annotation(
-        self,
-        annotation,
-        orig_size,
-        size,
-        resample: PILImageResampling = PILImageResampling.NEAREST,
-    ) -> Dict:
-        """
-        Resize the annotation to match the resized image. If size is an int, smaller edge of the mask will be matched
-        to this number.
-        """
-        return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample)
-
-    # TODO (Amy) - update to use `rescale_factor` instead of `scale`
-    def rescale(
-        self,
-        image: np.ndarray,
-        rescale_factor: float,
-        data_format: Optional[Union[str, ChannelDimension]] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> np.ndarray:
-        """
-        Rescale the image by the given factor. image = image * rescale_factor.
-
-        Args:
-            image (`np.ndarray`):
-                Image to rescale.
-            rescale_factor (`float`):
-                The value to use for rescaling.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format for the output image. If unset, the channel dimension format of the input
-                image is used. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-            input_data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format for the input image. If unset, is inferred from the input image. Can be
-                one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-        """
-        return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
-
-    def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
-        """
-        Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
-        `[center_x, center_y, width, height]` format.
-        """
-        return normalize_annotation(annotation, image_size=image_size)
-
-    def _pad_image(
-        self,
-        image: np.ndarray,
-        output_size: Tuple[int, int],
-        constant_values: Union[float, Iterable[float]] = 0,
-        data_format: Optional[ChannelDimension] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> np.ndarray:
-        """
-        Pad an image with zeros to the given size.
-        """
-        input_height, input_width = get_image_size(image, channel_dim=input_data_format)
-        output_height, output_width = output_size
-
-        pad_bottom = output_height - input_height
-        pad_right = output_width - input_width
-        padding = ((0, pad_bottom), (0, pad_right))
-        padded_image = pad(
-            image,
-            padding,
-            mode=PaddingMode.CONSTANT,
-            constant_values=constant_values,
-            data_format=data_format,
-            input_data_format=input_data_format,
-        )
-        return padded_image
-
-    def pad(
-        self,
-        images: List[np.ndarray],
-        constant_values: Union[float, Iterable[float]] = 0,
-        return_pixel_mask: bool = True,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Optional[ChannelDimension] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> BatchFeature:
-        """
-        Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
-        in the batch and optionally returns their corresponding pixel mask.
-
-        Args:
-            image (`np.ndarray`):
-                Image to pad.
-            constant_values (`float` or `Iterable[float]`, *optional*):
-                The value to use for the padding if `mode` is `"constant"`.
-            return_pixel_mask (`bool`, *optional*, defaults to `True`):
-                Whether to return a pixel mask.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format of the input image. If not provided, it will be inferred.
-        """
-        pad_size = get_max_height_width(images, input_data_format=input_data_format)
-
-        padded_images = [
-            self._pad_image(
-                image,
-                pad_size,
-                constant_values=constant_values,
-                data_format=data_format,
-                input_data_format=input_data_format,
-            )
-            for image in images
-        ]
-        data = {"pixel_values": padded_images}
-
-        if return_pixel_mask:
-            masks = [
-                make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format)
-                for image in images
-            ]
-            data["pixel_mask"] = masks
-
-        return BatchFeature(data=data, tensor_type=return_tensors)
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
-        return_segmentation_masks: bool = None,
-        masks_path: Optional[Union[str, pathlib.Path]] = None,
-        do_resize: Optional[bool] = None,
-        size: Optional[Dict[str, int]] = None,
-        resample=None,  # PILImageResampling
-        do_rescale: Optional[bool] = None,
-        rescale_factor: Optional[Union[int, float]] = None,
-        do_normalize: Optional[bool] = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        do_pad: Optional[bool] = None,
-        format: Optional[Union[str, AnnotionFormat]] = None,
-        return_tensors: Optional[Union[TensorType, str]] = None,
-        data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ) -> BatchFeature:
-        """
-        Preprocess an image or a batch of images so that it can be used by the model.
-
-        Args:
-            images (`ImageInput`):
-                Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
-                from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
-            annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
-                List of annotations associated with the image or batch of images. If annotation is for object
-                detection, the annotations should be a dictionary with the following keys:
-                - "image_id" (`int`): The image id.
-                - "annotations" (`List[Dict]`): List of annotations for an image. Each annotation should be a
-                  dictionary. An image can have no annotations, in which case the list should be empty.
-                If annotation is for segmentation, the annotations should be a dictionary with the following keys:
-                - "image_id" (`int`): The image id.
-                - "segments_info" (`List[Dict]`): List of segments for an image. Each segment should be a dictionary.
-                  An image can have no segments, in which case the list should be empty.
-                - "file_name" (`str`): The file name of the image.
-            return_segmentation_masks (`bool`, *optional*, defaults to self.return_segmentation_masks):
-                Whether to return segmentation masks.
-            masks_path (`str` or `pathlib.Path`, *optional*):
-                Path to the directory containing the segmentation masks.
-            do_resize (`bool`, *optional*, defaults to self.do_resize):
-                Whether to resize the image.
-            size (`Dict[str, int]`, *optional*, defaults to self.size):
-                Size of the image after resizing.
-            resample (`PILImageResampling`, *optional*, defaults to self.resample):
-                Resampling filter to use when resizing the image.
-            do_rescale (`bool`, *optional*, defaults to self.do_rescale):
-                Whether to rescale the image.
-            rescale_factor (`float`, *optional*, defaults to self.rescale_factor):
-                Rescale factor to use when rescaling the image.
-            do_normalize (`bool`, *optional*, defaults to self.do_normalize):
-                Whether to normalize the image.
-            image_mean (`float` or `List[float]`, *optional*, defaults to self.image_mean):
-                Mean to use when normalizing the image.
-            image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
-                Standard deviation to use when normalizing the image.
-            do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image.
-            format (`str` or `AnnotionFormat`, *optional*, defaults to self.format):
-                Format of the annotations.
-            return_tensors (`str` or `TensorType`, *optional*, defaults to self.return_tensors):
-                Type of tensors to return. If `None`, will return the list of images.
-            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
-                The channel dimension format for the output image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - Unset: Use the channel dimension format of the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format for the input image. If unset, the channel dimension format is inferred
-                from the input image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
-        """
-        if "pad_and_return_pixel_mask" in kwargs:
-            logger.warning_once(
-                "The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
-                "use `do_pad` instead."
-            )
-            do_pad = kwargs.pop("pad_and_return_pixel_mask")
-
-        max_size = None
-        if "max_size" in kwargs:
-            logger.warning_once(
-                "The `max_size` argument is deprecated and will be removed in a future version, use"
-                " `size['longest_edge']` instead."
-            )
-            size = kwargs.pop("max_size")
-
-        do_resize = self.do_resize if do_resize is None else do_resize
-        size = self.size if size is None else size
-        size = get_size_dict(size=size, max_size=max_size, default_to_square=False)
-        resample = self.resample if resample is None else resample
-        do_rescale = self.do_rescale if do_rescale is None else do_rescale
-        rescale_factor = self.rescale_factor if rescale_factor is None else rescale_factor
-        do_normalize = self.do_normalize if do_normalize is None else do_normalize
-        image_mean = self.image_mean if image_mean is None else image_mean
-        image_std = self.image_std if image_std is None else image_std
-        do_pad = self.do_pad if do_pad is None else do_pad
-        format = self.format if format is None else format
-
-        if do_resize is not None and size is None:
-            raise ValueError("Size and max_size must be specified if do_resize is True.")
-
-        if do_rescale is not None and rescale_factor is None:
-            raise ValueError("Rescale factor must be specified if do_rescale is True.")
-
-        if do_normalize is not None and (image_mean is None or image_std is None):
-            raise ValueError("Image mean and std must be specified if do_normalize is True.")
-
-        images = make_list_of_images(images)
-        if annotations is not None and isinstance(annotations, dict):
-            annotations = [annotations]
-
-        if annotations is not None and len(images) != len(annotations):
-            raise ValueError(
-                f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match."
-            )
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        format = AnnotionFormat(format)
-        if annotations is not None:
-            if format == AnnotionFormat.COCO_DETECTION and not valid_coco_detection_annotations(annotations):
-                raise ValueError(
-                    "Invalid COCO detection annotations. Annotations must a dict (single image) of list of dicts"
-                    "(batch of images) with the following keys: `image_id` and `annotations`, with the latter "
-                    "being a list of annotations in the COCO format."
-                )
-            elif format == AnnotionFormat.COCO_PANOPTIC and not valid_coco_panoptic_annotations(annotations):
-                raise ValueError(
-                    "Invalid COCO panoptic annotations. Annotations must a dict (single image) of list of dicts "
-                    "(batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with "
-                    "the latter being a list of annotations in the COCO format."
-                )
-            elif format not in SUPPORTED_ANNOTATION_FORMATS:
-                raise ValueError(
-                    f"Unsupported annotation format: {format} must be one of {SUPPORTED_ANNOTATION_FORMATS}"
-                )
-
-        if (
-            masks_path is not None
-            and format == AnnotionFormat.COCO_PANOPTIC
-            and not isinstance(masks_path, (pathlib.Path, str))
-        ):
-            raise ValueError(
-                "The path to the directory containing the mask PNG files should be provided as a"
-                f" `pathlib.Path` or string object, but is {type(masks_path)} instead."
-            )
-
-        # All transformations expect numpy arrays
-        images = [to_numpy_array(image) for image in images]
-
-        if is_scaled_image(images[0]) and do_rescale:
-            logger.warning_once(
-                "It looks like you are trying to rescale already rescaled images. If the input"
-                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
-            )
-
-        if input_data_format is None:
-            # We assume that all images have the same channel dimension format.
-            input_data_format = infer_channel_dimension_format(images[0])
-
-        # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
-        if annotations is not None:
-            prepared_images = []
-            prepared_annotations = []
-            for image, target in zip(images, annotations):
-                target = self.prepare_annotation(
-                    image,
-                    target,
-                    format,
-                    return_segmentation_masks=return_segmentation_masks,
-                    masks_path=masks_path,
-                    input_data_format=input_data_format,
-                )
-                prepared_images.append(image)
-                prepared_annotations.append(target)
-            images = prepared_images
-            annotations = prepared_annotations
-            del prepared_images, prepared_annotations
-
-        # transformations
-        if do_resize:
-            if annotations is not None:
-                resized_images, resized_annotations = [], []
-                for image, target in zip(images, annotations):
-                    orig_size = get_image_size(image, input_data_format)
-                    resized_image = self.resize(
-                        image, size=size, max_size=max_size, resample=resample, input_data_format=input_data_format
-                    )
-                    resized_annotation = self.resize_annotation(
-                        target, orig_size, get_image_size(resized_image, input_data_format)
-                    )
-                    resized_images.append(resized_image)
-                    resized_annotations.append(resized_annotation)
-                images = resized_images
-                annotations = resized_annotations
-                del resized_images, resized_annotations
-            else:
-                images = [
-                    self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
-                    for image in images
-                ]
-
-        if do_rescale:
-            images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
-
-        if do_normalize:
-            images = [
-                self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
-            ]
-            if annotations is not None:
-                annotations = [
-                    self.normalize_annotation(annotation, get_image_size(image, input_data_format))
-                    for annotation, image in zip(annotations, images)
-                ]
-
-        if do_pad:
-            # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...}
-            data = self.pad(
-                images, return_pixel_mask=True, data_format=data_format, input_data_format=input_data_format
-            )
-        else:
-            images = [
-                to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
-                for image in images
-            ]
-            data = {"pixel_values": images}
-
-        encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
-        if annotations is not None:
-            encoded_inputs["labels"] = [
-                BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations
-            ]
-
-        return encoded_inputs
-
-    # POSTPROCESSING METHODS - TODO: add support for other frameworks
-    # inspired by https://github.com/facebookresearch/detr/blob/master/models/detr.py#L258
-    def post_process(self, outputs, target_sizes):
-        """
-        Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
-        bottom_right_x, bottom_right_y) format. Only supports PyTorch.
-
-        Args:
-            outputs ([`DetrObjectDetectionOutput`]):
-                Raw outputs of the model.
-            target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
-                Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
-                original image size (before any data augmentation). For visualization, this should be the image size
-                after data augment, but before padding.
-        Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
-            in the batch as predicted by the model.
-        """
-        logger.warning_once(
-            "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
-        )
-
-        out_logits, out_bbox = outputs.logits, outputs.pred_boxes
-
-        if len(out_logits) != len(target_sizes):
-            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
-        if target_sizes.shape[1] != 2:
-            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
-
-        prob = nn.functional.softmax(out_logits, -1)
-        scores, labels = prob[..., :-1].max(-1)
-
-        # convert to [x0, y0, x1, y1] format
-        boxes = center_to_corners_format(out_bbox)
-        # and from relative [0, 1] to absolute [0, height] coordinates
-        img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
-        boxes = boxes * scale_fct[:, None, :]
-
-        results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
-        return results
-
-    def post_process_segmentation(self, outputs, target_sizes, threshold=0.9, mask_threshold=0.5):
-        """
-        Converts the output of [`DetrForSegmentation`] into image segmentation predictions. Only supports PyTorch.
-
-        Args:
-            outputs ([`DetrSegmentationOutput`]):
-                Raw outputs of the model.
-            target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
-                Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction.
-            threshold (`float`, *optional*, defaults to 0.9):
-                Threshold to use to filter out queries.
-            mask_threshold (`float`, *optional*, defaults to 0.5):
-                Threshold to use when turning the predicted masks into binary values.
-        Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, and masks for an image
-            in the batch as predicted by the model.
-        """
-        logger.warning_once(
-            "`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_semantic_segmentation`.",
-        )
-        out_logits, raw_masks = outputs.logits, outputs.pred_masks
-        empty_label = out_logits.shape[-1] - 1
-        preds = []
-
-        def to_tuple(tup):
-            if isinstance(tup, tuple):
-                return tup
-            return tuple(tup.cpu().tolist())
-
-        for cur_logits, cur_masks, size in zip(out_logits, raw_masks, target_sizes):
-            # we filter empty queries and detection below threshold
-            cur_scores, cur_labels = cur_logits.softmax(-1).max(-1)
-            keep = cur_labels.ne(empty_label) & (cur_scores > threshold)
-            cur_scores = cur_scores[keep]
-            cur_labels = cur_labels[keep]
-            cur_masks = cur_masks[keep]
-            cur_masks = nn.functional.interpolate(cur_masks[:, None], to_tuple(size), mode="bilinear").squeeze(1)
-            cur_masks = (cur_masks.sigmoid() > mask_threshold) * 1
-
-            predictions = {"scores": cur_scores, "labels": cur_labels, "masks": cur_masks}
-            preds.append(predictions)
-        return preds
-
-    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L218
-    def post_process_instance(self, results, outputs, orig_target_sizes, max_target_sizes, threshold=0.5):
-        """
-        Converts the output of [`DetrForSegmentation`] into actual instance segmentation predictions. Only supports
-        PyTorch.
-
-        Args:
-            results (`List[Dict]`):
-                Results list obtained by [`~DetrImageProcessor.post_process`], to which "masks" results will be added.
-            outputs ([`DetrSegmentationOutput`]):
-                Raw outputs of the model.
-            orig_target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
-                Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
-                image size (before any data augmentation).
-            max_target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
-                Tensor containing the maximum size (h, w) of each image of the batch. For evaluation, this must be the
-                original image size (before any data augmentation).
-            threshold (`float`, *optional*, defaults to 0.5):
-                Threshold to use when turning the predicted masks into binary values.
-        Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, boxes and masks for an
-            image in the batch as predicted by the model.
-        """
-        logger.warning_once(
-            "`post_process_instance` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_instance_segmentation`.",
-        )
-
-        if len(orig_target_sizes) != len(max_target_sizes):
-            raise ValueError("Make sure to pass in as many orig_target_sizes as max_target_sizes")
-        max_h, max_w = max_target_sizes.max(0)[0].tolist()
-        outputs_masks = outputs.pred_masks.squeeze(2)
-        outputs_masks = nn.functional.interpolate(
-            outputs_masks, size=(max_h, max_w), mode="bilinear", align_corners=False
-        )
-        outputs_masks = (outputs_masks.sigmoid() > threshold).cpu()
-
-        for i, (cur_mask, t, tt) in enumerate(zip(outputs_masks, max_target_sizes, orig_target_sizes)):
-            img_h, img_w = t[0], t[1]
-            results[i]["masks"] = cur_mask[:, :img_h, :img_w].unsqueeze(1)
-            results[i]["masks"] = nn.functional.interpolate(
-                results[i]["masks"].float(), size=tuple(tt.tolist()), mode="nearest"
-            ).byte()
-
-        return results
-
-    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L241
-    def post_process_panoptic(self, outputs, processed_sizes, target_sizes=None, is_thing_map=None, threshold=0.85):
-        """
-        Converts the output of [`DetrForSegmentation`] into actual panoptic predictions. Only supports PyTorch.
-
-        Args:
-            outputs ([`DetrSegmentationOutput`]):
-                Raw outputs of the model.
-            processed_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
-                Torch Tensor (or list) containing the size (h, w) of each image of the batch, i.e. the size after data
-                augmentation but before batching.
-            target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`, *optional*):
-                Torch Tensor (or list) corresponding to the requested final size `(height, width)` of each prediction.
-                If left to None, it will default to the `processed_sizes`.
-            is_thing_map (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
-                Dictionary mapping class indices to either True or False, depending on whether or not they are a thing.
-                If not set, defaults to the `is_thing_map` of COCO panoptic.
-            threshold (`float`, *optional*, defaults to 0.85):
-                Threshold to use to filter out queries.
-        Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing a PNG string and segments_info values for
-            an image in the batch as predicted by the model.
-        """
-        logger.warning_once(
-            "`post_process_panoptic is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_panoptic_segmentation`.",
-        )
-        if target_sizes is None:
-            target_sizes = processed_sizes
-        if len(processed_sizes) != len(target_sizes):
-            raise ValueError("Make sure to pass in as many processed_sizes as target_sizes")
-
-        if is_thing_map is None:
-            # default to is_thing_map of COCO panoptic
-            is_thing_map = {i: i <= 90 for i in range(201)}
-
-        out_logits, raw_masks, raw_boxes = outputs.logits, outputs.pred_masks, outputs.pred_boxes
-        if not len(out_logits) == len(raw_masks) == len(target_sizes):
-            raise ValueError(
-                "Make sure that you pass in as many target sizes as the batch dimension of the logits and masks"
-            )
-        empty_label = out_logits.shape[-1] - 1
-        preds = []
-
-        def to_tuple(tup):
-            if isinstance(tup, tuple):
-                return tup
-            return tuple(tup.cpu().tolist())
-
-        for cur_logits, cur_masks, cur_boxes, size, target_size in zip(
-            out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes
-        ):
-            # we filter empty queries and detection below threshold
-            cur_scores, cur_labels = cur_logits.softmax(-1).max(-1)
-            keep = cur_labels.ne(empty_label) & (cur_scores > threshold)
-            cur_scores = cur_scores[keep]
-            cur_labels = cur_labels[keep]
-            cur_masks = cur_masks[keep]
-            cur_masks = nn.functional.interpolate(cur_masks[:, None], to_tuple(size), mode="bilinear").squeeze(1)
-            cur_boxes = center_to_corners_format(cur_boxes[keep])
-
-            h, w = cur_masks.shape[-2:]
-            if len(cur_boxes) != len(cur_labels):
-                raise ValueError("Not as many boxes as there are classes")
-
-            # It may be that we have several predicted masks for the same stuff class.
-            # In the following, we track the list of masks ids for each stuff class (they are merged later on)
-            cur_masks = cur_masks.flatten(1)
-            stuff_equiv_classes = defaultdict(lambda: [])
-            for k, label in enumerate(cur_labels):
-                if not is_thing_map[label.item()]:
-                    stuff_equiv_classes[label.item()].append(k)
-
-            def get_ids_area(masks, scores, dedup=False):
-                # This helper function creates the final panoptic segmentation image
-                # It also returns the area of the masks that appears on the image
-
-                m_id = masks.transpose(0, 1).softmax(-1)
-
-                if m_id.shape[-1] == 0:
-                    # We didn't detect any mask :(
-                    m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device)
-                else:
-                    m_id = m_id.argmax(-1).view(h, w)
-
-                if dedup:
-                    # Merge the masks corresponding to the same stuff class
-                    for equiv in stuff_equiv_classes.values():
-                        if len(equiv) > 1:
-                            for eq_id in equiv:
-                                m_id.masked_fill_(m_id.eq(eq_id), equiv[0])
-
-                final_h, final_w = to_tuple(target_size)
-
-                seg_img = PIL.Image.fromarray(id_to_rgb(m_id.view(h, w).cpu().numpy()))
-                seg_img = seg_img.resize(size=(final_w, final_h), resample=PILImageResampling.NEAREST)
-
-                np_seg_img = torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes()))
-                np_seg_img = np_seg_img.view(final_h, final_w, 3)
-                np_seg_img = np_seg_img.numpy()
-
-                m_id = torch.from_numpy(rgb_to_id(np_seg_img))
-
-                area = []
-                for i in range(len(scores)):
-                    area.append(m_id.eq(i).sum().item())
-                return area, seg_img
-
-            area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True)
-            if cur_labels.numel() > 0:
-                # We know filter empty masks as long as we find some
-                while True:
-                    filtered_small = torch.as_tensor(
-                        [area[i] <= 4 for i, c in enumerate(cur_labels)], dtype=torch.bool, device=keep.device
-                    )
-                    if filtered_small.any().item():
-                        cur_scores = cur_scores[~filtered_small]
-                        cur_labels = cur_labels[~filtered_small]
-                        cur_masks = cur_masks[~filtered_small]
-                        area, seg_img = get_ids_area(cur_masks, cur_scores)
-                    else:
-                        break
-
-            else:
-                cur_labels = torch.ones(1, dtype=torch.long, device=cur_labels.device)
-
-            segments_info = []
-            for i, a in enumerate(area):
-                cat = cur_labels[i].item()
-                segments_info.append({"id": i, "isthing": is_thing_map[cat], "category_id": cat, "area": a})
-            del cur_labels
-
-            with io.BytesIO() as out:
-                seg_img.save(out, format="PNG")
-                predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
-            preds.append(predictions)
-        return preds
-
-    # inspired by https://github.com/facebookresearch/detr/blob/master/models/detr.py#L258
-    def post_process_object_detection(
-        self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
-    ):
-        """
-        Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
-        bottom_right_x, bottom_right_y) format. Only supports PyTorch.
-
-        Args:
-            outputs ([`DetrObjectDetectionOutput`]):
-                Raw outputs of the model.
-            threshold (`float`, *optional*):
-                Score threshold to keep object detection predictions.
-            target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
-                Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
-                `(height, width)` of each image in the batch. If unset, predictions will not be resized.
-        Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
-            in the batch as predicted by the model.
-        """
-        out_logits, out_bbox = outputs.logits, outputs.pred_boxes
-
-        if target_sizes is not None:
-            if len(out_logits) != len(target_sizes):
-                raise ValueError(
-                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
-                )
-
-        prob = nn.functional.softmax(out_logits, -1)
-        scores, labels = prob[..., :-1].max(-1)
-
-        # Convert to [x0, y0, x1, y1] format
-        boxes = center_to_corners_format(out_bbox)
-
-        # Convert from relative [0, 1] to absolute [0, height] coordinates
-        if target_sizes is not None:
-            if isinstance(target_sizes, List):
-                img_h = torch.Tensor([i[0] for i in target_sizes])
-                img_w = torch.Tensor([i[1] for i in target_sizes])
-            else:
-                img_h, img_w = target_sizes.unbind(1)
-
-            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
-            boxes = boxes * scale_fct[:, None, :]
-
-        results = []
-        for s, l, b in zip(scores, labels, boxes):
-            score = s[s > threshold]
-            label = l[s > threshold]
-            box = b[s > threshold]
-            results.append({"scores": score, "labels": label, "boxes": box})
-
-        return results
-
-    def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
-        """
-        Converts the output of [`DetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch.
-
-        Args:
-            outputs ([`DetrForSegmentation`]):
-                Raw outputs of the model.
-            target_sizes (`List[Tuple[int, int]]`, *optional*):
-                A list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the
-                batch. If unset, predictions will not be resized.
-        Returns:
-            `List[torch.Tensor]`:
-                A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
-                corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
-                `torch.Tensor` correspond to a semantic class id.
-        """
-        class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
-        masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
-
-        # Remove the null class `[..., :-1]`
-        masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
-        masks_probs = masks_queries_logits.sigmoid()  # [batch_size, num_queries, height, width]
-
-        # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
-        segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
-        batch_size = class_queries_logits.shape[0]
-
-        # Resize logits and compute semantic segmentation maps
-        if target_sizes is not None:
-            if batch_size != len(target_sizes):
-                raise ValueError(
-                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
-                )
-
-            semantic_segmentation = []
-            for idx in range(batch_size):
-                resized_logits = nn.functional.interpolate(
-                    segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
-                )
-                semantic_map = resized_logits[0].argmax(dim=0)
-                semantic_segmentation.append(semantic_map)
-        else:
-            semantic_segmentation = segmentation.argmax(dim=1)
-            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
-
-        return semantic_segmentation
-
-    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L218
-    def post_process_instance_segmentation(
-        self,
-        outputs,
-        threshold: float = 0.5,
-        mask_threshold: float = 0.5,
-        overlap_mask_area_threshold: float = 0.8,
-        target_sizes: Optional[List[Tuple[int, int]]] = None,
-        return_coco_annotation: Optional[bool] = False,
-    ) -> List[Dict]:
-        """
-        Converts the output of [`DetrForSegmentation`] into instance segmentation predictions. Only supports PyTorch.
-
-        Args:
-            outputs ([`DetrForSegmentation`]):
-                Raw outputs of the model.
-            threshold (`float`, *optional*, defaults to 0.5):
-                The probability score threshold to keep predicted instance masks.
-            mask_threshold (`float`, *optional*, defaults to 0.5):
-                Threshold to use when turning the predicted masks into binary values.
-            overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
-                The overlap mask area threshold to merge or discard small disconnected parts within each binary
-                instance mask.
-            target_sizes (`List[Tuple]`, *optional*):
-                List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
-                final size (height, width) of each prediction. If unset, predictions will not be resized.
-            return_coco_annotation (`bool`, *optional*):
-                Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE)
-                format.
-        Returns:
-            `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
-            - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or
-              `List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
-              `True`. Set to `None` if no mask if found above `threshold`.
-            - **segments_info** -- A dictionary that contains additional information on each segment.
-                - **id** -- An integer representing the `segment_id`.
-                - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
-                - **score** -- Prediction score of segment with `segment_id`.
-        """
-        class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
-        masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
-
-        batch_size = class_queries_logits.shape[0]
-        num_labels = class_queries_logits.shape[-1] - 1
-
-        mask_probs = masks_queries_logits.sigmoid()  # [batch_size, num_queries, height, width]
-
-        # Predicted label and score of each query (batch_size, num_queries)
-        pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
-
-        # Loop over items in batch size
-        results: List[Dict[str, TensorType]] = []
-
-        for i in range(batch_size):
-            mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
-                mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
-            )
-
-            # No mask found
-            if mask_probs_item.shape[0] <= 0:
-                height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
-                segmentation = torch.zeros((height, width)) - 1
-                results.append({"segmentation": segmentation, "segments_info": []})
-                continue
-
-            # Get segmentation map and segment information of batch item
-            target_size = target_sizes[i] if target_sizes is not None else None
-            segmentation, segments = compute_segments(
-                mask_probs=mask_probs_item,
-                pred_scores=pred_scores_item,
-                pred_labels=pred_labels_item,
-                mask_threshold=mask_threshold,
-                overlap_mask_area_threshold=overlap_mask_area_threshold,
-                label_ids_to_fuse=[],
-                target_size=target_size,
-            )
-
-            # Return segmentation map in run-length encoding (RLE) format
-            if return_coco_annotation:
-                segmentation = convert_segmentation_to_rle(segmentation)
-
-            results.append({"segmentation": segmentation, "segments_info": segments})
-        return results
-
-    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L241
-    def post_process_panoptic_segmentation(
-        self,
-        outputs,
-        threshold: float = 0.5,
-        mask_threshold: float = 0.5,
-        overlap_mask_area_threshold: float = 0.8,
-        label_ids_to_fuse: Optional[Set[int]] = None,
-        target_sizes: Optional[List[Tuple[int, int]]] = None,
-    ) -> List[Dict]:
-        """
-        Converts the output of [`DetrForSegmentation`] into image panoptic segmentation predictions. Only supports
-        PyTorch.
-
-        Args:
-            outputs ([`DetrForSegmentation`]):
-                The outputs from [`DetrForSegmentation`].
-            threshold (`float`, *optional*, defaults to 0.5):
-                The probability score threshold to keep predicted instance masks.
-            mask_threshold (`float`, *optional*, defaults to 0.5):
-                Threshold to use when turning the predicted masks into binary values.
-            overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
-                The overlap mask area threshold to merge or discard small disconnected parts within each binary
-                instance mask.
-            label_ids_to_fuse (`Set[int]`, *optional*):
-                The labels in this state will have all their instances be fused together. For instance we could say
-                there can only be one sky in an image, but several persons, so the label ID for sky would be in that
-                set, but not the one for person.
-            target_sizes (`List[Tuple]`, *optional*):
-                List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
-                final size (height, width) of each prediction in batch. If unset, predictions will not be resized.
-        Returns:
-            `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
-            - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id` or
-              `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized to
-              the corresponding `target_sizes` entry.
-            - **segments_info** -- A dictionary that contains additional information on each segment.
-                - **id** -- an integer representing the `segment_id`.
-                - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
-                - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
-                  Multiple instances of the same class / label were fused and assigned a single `segment_id`.
-                - **score** -- Prediction score of segment with `segment_id`.
-        """
-
-        if label_ids_to_fuse is None:
-            logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
-            label_ids_to_fuse = set()
-
-        class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
-        masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
-
-        batch_size = class_queries_logits.shape[0]
-        num_labels = class_queries_logits.shape[-1] - 1
-
-        mask_probs = masks_queries_logits.sigmoid()  # [batch_size, num_queries, height, width]
-
-        # Predicted label and score of each query (batch_size, num_queries)
-        pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
-
-        # Loop over items in batch size
-        results: List[Dict[str, TensorType]] = []
-
-        for i in range(batch_size):
-            mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
-                mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
-            )
-
-            # No mask found
-            if mask_probs_item.shape[0] <= 0:
-                height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
-                segmentation = torch.zeros((height, width)) - 1
-                results.append({"segmentation": segmentation, "segments_info": []})
-                continue
-
-            # Get segmentation map and segment information of batch item
-            target_size = target_sizes[i] if target_sizes is not None else None
-            segmentation, segments = compute_segments(
-                mask_probs=mask_probs_item,
-                pred_scores=pred_scores_item,
-                pred_labels=pred_labels_item,
-                mask_threshold=mask_threshold,
-                overlap_mask_area_threshold=overlap_mask_area_threshold,
-                label_ids_to_fuse=label_ids_to_fuse,
-                target_size=target_size,
-            )
-
-            results.append({"segmentation": segmentation, "segments_info": segments})
-        return results
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/vencoder/whisper/utils.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/vencoder/whisper/utils.py
deleted file mode 100644
index 5dacc173c40bcd6e999d728862e29a968000b12e..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/vencoder/whisper/utils.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import json
-import os
-import sys
-import zlib
-from typing import Callable, TextIO
-
-system_encoding = sys.getdefaultencoding()
-
-if system_encoding != "utf-8":
-    def make_safe(string):
-        # replaces any character not representable using the system default encoding with an '?',
-        # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729).
-        return string.encode(system_encoding, errors="replace").decode(system_encoding)
-else:
-    def make_safe(string):
-        # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding
-        return string
-
-
-def exact_div(x, y):
-    assert x % y == 0
-    return x // y
-
-
-def str2bool(string):
-    str2val = {"True": True, "False": False}
-    if string in str2val:
-        return str2val[string]
-    else:
-        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
-
-
-def optional_int(string):
-    return None if string == "None" else int(string)
-
-
-def optional_float(string):
-    return None if string == "None" else float(string)
-
-
-def compression_ratio(text) -> float:
-    text_bytes = text.encode("utf-8")
-    return len(text_bytes) / len(zlib.compress(text_bytes))
-
-
-def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
-    assert seconds >= 0, "non-negative timestamp expected"
-    milliseconds = round(seconds * 1000.0)
-
-    hours = milliseconds // 3_600_000
-    milliseconds -= hours * 3_600_000
-
-    minutes = milliseconds // 60_000
-    milliseconds -= minutes * 60_000
-
-    seconds = milliseconds // 1_000
-    milliseconds -= seconds * 1_000
-
-    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
-    return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
-
-
-class ResultWriter:
-    extension: str
-
-    def __init__(self, output_dir: str):
-        self.output_dir = output_dir
-
-    def __call__(self, result: dict, audio_path: str):
-        audio_basename = os.path.basename(audio_path)
-        output_path = os.path.join(self.output_dir, audio_basename + "." + self.extension)
-
-        with open(output_path, "w", encoding="utf-8") as f:
-            self.write_result(result, file=f)
-
-    def write_result(self, result: dict, file: TextIO):
-        raise NotImplementedError
-
-
-class WriteTXT(ResultWriter):
-    extension: str = "txt"
-
-    def write_result(self, result: dict, file: TextIO):
-        for segment in result["segments"]:
-            print(segment['text'].strip(), file=file, flush=True)
-
-
-class WriteVTT(ResultWriter):
-    extension: str = "vtt"
-
-    def write_result(self, result: dict, file: TextIO):
-        print("WEBVTT\n", file=file)
-        for segment in result["segments"]:
-            print(
-                f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-                f"{segment['text'].strip().replace('-->', '->')}\n",
-                file=file,
-                flush=True,
-            )
-
-
-class WriteSRT(ResultWriter):
-    extension: str = "srt"
-
-    def write_result(self, result: dict, file: TextIO):
-        for i, segment in enumerate(result["segments"], start=1):
-            # write srt lines
-            print(
-                f"{i}\n"
-                f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
-                f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
-                f"{segment['text'].strip().replace('-->', '->')}\n",
-                file=file,
-                flush=True,
-            )
-
-
-class WriteTSV(ResultWriter):
-    """
-    Write a transcript to a file in TSV (tab-separated values) format containing lines like:
-    <start time in integer milliseconds>\t<end time in integer milliseconds>\t<transcript text>
-
-    Using integer milliseconds as start and end times means there's no chance of interference from
-    an environment setting a language encoding that causes the decimal in a floating point number
-    to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++.
-    """
-    extension: str = "tsv"
-
-    def write_result(self, result: dict, file: TextIO):
-        print("start", "end", "text", sep="\t", file=file)
-        for segment in result["segments"]:
-            print(round(1000 * segment['start']), file=file, end="\t")
-            print(round(1000 * segment['end']), file=file, end="\t")
-            print(segment['text'].strip().replace("\t", " "), file=file, flush=True)
-
-
-class WriteJSON(ResultWriter):
-    extension: str = "json"
-
-    def write_result(self, result: dict, file: TextIO):
-        json.dump(result, file)
-
-
-def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]:
-    writers = {
-        "txt": WriteTXT,
-        "vtt": WriteVTT,
-        "srt": WriteSRT,
-        "tsv": WriteTSV,
-        "json": WriteJSON,
-    }
-
-    if output_format == "all":
-        all_writers = [writer(output_dir) for writer in writers.values()]
-
-        def write_all(result: dict, file: TextIO):
-            for writer in all_writers:
-                writer(result, file)
-
-        return write_all
-
-    return writers[output_format](output_dir)
-
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/whisper/tokenizer.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/whisper/tokenizer.py
deleted file mode 100644
index a27cb359ee891590d3f793624f9f8ec768a26cc3..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/whisper/tokenizer.py
+++ /dev/null
@@ -1,331 +0,0 @@
-import os
-from dataclasses import dataclass
-from functools import lru_cache
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from transformers import GPT2TokenizerFast
-
-LANGUAGES = {
-    "en": "english",
-    "zh": "chinese",
-    "de": "german",
-    "es": "spanish",
-    "ru": "russian",
-    "ko": "korean",
-    "fr": "french",
-    "ja": "japanese",
-    "pt": "portuguese",
-    "tr": "turkish",
-    "pl": "polish",
-    "ca": "catalan",
-    "nl": "dutch",
-    "ar": "arabic",
-    "sv": "swedish",
-    "it": "italian",
-    "id": "indonesian",
-    "hi": "hindi",
-    "fi": "finnish",
-    "vi": "vietnamese",
-    "he": "hebrew",
-    "uk": "ukrainian",
-    "el": "greek",
-    "ms": "malay",
-    "cs": "czech",
-    "ro": "romanian",
-    "da": "danish",
-    "hu": "hungarian",
-    "ta": "tamil",
-    "no": "norwegian",
-    "th": "thai",
-    "ur": "urdu",
-    "hr": "croatian",
-    "bg": "bulgarian",
-    "lt": "lithuanian",
-    "la": "latin",
-    "mi": "maori",
-    "ml": "malayalam",
-    "cy": "welsh",
-    "sk": "slovak",
-    "te": "telugu",
-    "fa": "persian",
-    "lv": "latvian",
-    "bn": "bengali",
-    "sr": "serbian",
-    "az": "azerbaijani",
-    "sl": "slovenian",
-    "kn": "kannada",
-    "et": "estonian",
-    "mk": "macedonian",
-    "br": "breton",
-    "eu": "basque",
-    "is": "icelandic",
-    "hy": "armenian",
-    "ne": "nepali",
-    "mn": "mongolian",
-    "bs": "bosnian",
-    "kk": "kazakh",
-    "sq": "albanian",
-    "sw": "swahili",
-    "gl": "galician",
-    "mr": "marathi",
-    "pa": "punjabi",
-    "si": "sinhala",
-    "km": "khmer",
-    "sn": "shona",
-    "yo": "yoruba",
-    "so": "somali",
-    "af": "afrikaans",
-    "oc": "occitan",
-    "ka": "georgian",
-    "be": "belarusian",
-    "tg": "tajik",
-    "sd": "sindhi",
-    "gu": "gujarati",
-    "am": "amharic",
-    "yi": "yiddish",
-    "lo": "lao",
-    "uz": "uzbek",
-    "fo": "faroese",
-    "ht": "haitian creole",
-    "ps": "pashto",
-    "tk": "turkmen",
-    "nn": "nynorsk",
-    "mt": "maltese",
-    "sa": "sanskrit",
-    "lb": "luxembourgish",
-    "my": "myanmar",
-    "bo": "tibetan",
-    "tl": "tagalog",
-    "mg": "malagasy",
-    "as": "assamese",
-    "tt": "tatar",
-    "haw": "hawaiian",
-    "ln": "lingala",
-    "ha": "hausa",
-    "ba": "bashkir",
-    "jw": "javanese",
-    "su": "sundanese",
-}
-
-# language code lookup by name, with a few language aliases
-TO_LANGUAGE_CODE = {
-    **{language: code for code, language in LANGUAGES.items()},
-    "burmese": "my",
-    "valencian": "ca",
-    "flemish": "nl",
-    "haitian": "ht",
-    "letzeburgesch": "lb",
-    "pushto": "ps",
-    "panjabi": "pa",
-    "moldavian": "ro",
-    "moldovan": "ro",
-    "sinhalese": "si",
-    "castilian": "es",
-}
-
-
-@dataclass(frozen=True)
-class Tokenizer:
-    """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
-
-    tokenizer: "GPT2TokenizerFast"
-    language: Optional[str]
-    sot_sequence: Tuple[int]
-
-    def encode(self, text, **kwargs):
-        return self.tokenizer.encode(text, **kwargs)
-
-    def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs):
-        return self.tokenizer.decode(token_ids, **kwargs)
-
-    def decode_with_timestamps(self, tokens) -> str:
-        """
-        Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
-        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
-        """
-        outputs = [[]]
-        for token in tokens:
-            if token >= self.timestamp_begin:
-                timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
-                outputs.append(timestamp)
-                outputs.append([])
-            else:
-                outputs[-1].append(token)
-        outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
-        return "".join(outputs)
-
-    @property
-    @lru_cache()
-    def eot(self) -> int:
-        return self.tokenizer.eos_token_id
-
-    @property
-    @lru_cache()
-    def sot(self) -> int:
-        return self._get_single_token_id("<|startoftranscript|>")
-
-    @property
-    @lru_cache()
-    def sot_lm(self) -> int:
-        return self._get_single_token_id("<|startoflm|>")
-
-    @property
-    @lru_cache()
-    def sot_prev(self) -> int:
-        return self._get_single_token_id("<|startofprev|>")
-
-    @property
-    @lru_cache()
-    def no_speech(self) -> int:
-        return self._get_single_token_id("<|nospeech|>")
-
-    @property
-    @lru_cache()
-    def no_timestamps(self) -> int:
-        return self._get_single_token_id("<|notimestamps|>")
-
-    @property
-    @lru_cache()
-    def timestamp_begin(self) -> int:
-        return self.tokenizer.all_special_ids[-1] + 1
-
-    @property
-    @lru_cache()
-    def language_token(self) -> int:
-        """Returns the token id corresponding to the value of the `language` field"""
-        if self.language is None:
-            raise ValueError(f"This tokenizer does not have language token configured")
-
-        additional_tokens = dict(
-            zip(
-                self.tokenizer.additional_special_tokens,
-                self.tokenizer.additional_special_tokens_ids,
-            )
-        )
-        candidate = f"<|{self.language}|>"
-        if candidate in additional_tokens:
-            return additional_tokens[candidate]
-
-        raise KeyError(f"Language {self.language} not found in tokenizer.")
-
-    @property
-    @lru_cache()
-    def all_language_tokens(self) -> Tuple[int]:
-        result = []
-        for token, token_id in zip(
-            self.tokenizer.additional_special_tokens,
-            self.tokenizer.additional_special_tokens_ids,
-        ):
-            if token.strip("<|>") in LANGUAGES:
-                result.append(token_id)
-        return tuple(result)
-
-    @property
-    @lru_cache()
-    def all_language_codes(self) -> Tuple[str]:
-        return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
-
-    @property
-    @lru_cache()
-    def sot_sequence_including_notimestamps(self) -> Tuple[int]:
-        return tuple(list(self.sot_sequence) + [self.no_timestamps])
-
-    @property
-    @lru_cache()
-    def non_speech_tokens(self) -> Tuple[int]:
-        """
-        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
-        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
-
-        - ♪♪♪
-        - ( SPEAKING FOREIGN LANGUAGE )
-        - [DAVID] Hey there,
-
-        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
-        """
-        symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
-        symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
-
-        # symbols that may be a single token or multiple tokens depending on the tokenizer.
-        # In case they're multiple tokens, suppress the first token, which is safe because:
-        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
-        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
-        miscellaneous = set("♩♪♫♬♭♮♯")
-        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
-
-        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
-        result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
-        for symbol in symbols + list(miscellaneous):
-            for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
-                if len(tokens) == 1 or symbol in miscellaneous:
-                    result.add(tokens[0])
-
-        return tuple(sorted(result))
-
-    def _get_single_token_id(self, text) -> int:
-        tokens = self.tokenizer.encode(text)
-        assert len(tokens) == 1, f"{text} is not encoded as a single token"
-        return tokens[0]
-
-
-@lru_cache(maxsize=None)
-def build_tokenizer(name: str = "gpt2"):
-    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-    path = os.path.join(os.path.dirname(__file__), "assets", name)
-    tokenizer = GPT2TokenizerFast.from_pretrained(path)
-
-    specials = [
-        "<|startoftranscript|>",
-        *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
-        "<|translate|>",
-        "<|transcribe|>",
-        "<|startoflm|>",
-        "<|startofprev|>",
-        "<|nospeech|>",
-        "<|notimestamps|>",
-    ]
-
-    tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
-    return tokenizer
-
-
-@lru_cache(maxsize=None)
-def get_tokenizer(
-    multilingual: bool,
-    *,
-    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
-    language: Optional[str] = None,
-) -> Tokenizer:
-    if language is not None:
-        language = language.lower()
-        if language not in LANGUAGES:
-            if language in TO_LANGUAGE_CODE:
-                language = TO_LANGUAGE_CODE[language]
-            else:
-                raise ValueError(f"Unsupported language: {language}")
-
-    if multilingual:
-        tokenizer_name = "multilingual"
-        task = task or "transcribe"
-        language = language or "en"
-    else:
-        tokenizer_name = "gpt2"
-        task = None
-        language = None
-
-    tokenizer = build_tokenizer(name=tokenizer_name)
-    all_special_ids: List[int] = tokenizer.all_special_ids
-    sot: int = all_special_ids[1]
-    translate: int = all_special_ids[-6]
-    transcribe: int = all_special_ids[-5]
-
-    langs = tuple(LANGUAGES.keys())
-    sot_sequence = [sot]
-    if language is not None:
-        sot_sequence.append(sot + 1 + langs.index(language))
-    if task is not None:
-        sot_sequence.append(transcribe if task == "transcribe" else translate)
-
-    return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
diff --git a/spaces/yoinked/da_nsfw_checker/app.py b/spaces/yoinked/da_nsfw_checker/app.py
deleted file mode 100644
index b41225aff90909ef42b4ffeb14035f660d9c5342..0000000000000000000000000000000000000000
--- a/spaces/yoinked/da_nsfw_checker/app.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os, re, cv2 
-from typing import Mapping, Tuple, Dict
-import gradio as gr
-import numpy as np
-import io
-import pandas as pd
-from PIL import Image
-from huggingface_hub import hf_hub_download
-from onnxruntime import InferenceSession
-
-# noinspection PyUnresolvedReferences
-def make_square(img, target_size):
-    old_size = img.shape[:2]
-    desired_size = max(old_size)
-    desired_size = max(desired_size, target_size)
-
-    delta_w = desired_size - old_size[1]
-    delta_h = desired_size - old_size[0]
-    top, bottom = delta_h // 2, delta_h - (delta_h // 2)
-    left, right = delta_w // 2, delta_w - (delta_w // 2)
-
-    color = [255, 255, 255]
-    return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
-
-
-# noinspection PyUnresolvedReferences
-def smart_resize(img, size):
-    # Assumes the image has already gone through make_square
-    if img.shape[0] > size:
-        img = cv2.resize(img, (size, size), interpolation=cv2.INTER_AREA)
-    elif img.shape[0] < size:
-        img = cv2.resize(img, (size, size), interpolation=cv2.INTER_CUBIC)
-    else:  # just do nothing
-        pass
-
-    return img
-
-
-class WaifuDiffusionInterrogator:
-    def __init__(
-            self,
-            repo='SmilingWolf/wd-v1-4-vit-tagger',
-            model_path='model.onnx',
-            tags_path='selected_tags.csv',
-            mode: str = "auto"
-    ) -> None:
-        self.__repo = repo
-        self.__model_path = model_path
-        self.__tags_path = tags_path
-        self._provider_mode = mode
-
-        self.__initialized = False
-        self._model, self._tags = None, None
-        self.cache = {}
-    def _init(self) -> None:
-        if self.__initialized:
-            return
-
-        model_path = hf_hub_download(self.__repo, filename=self.__model_path)
-        tags_path = hf_hub_download(self.__repo, filename=self.__tags_path)
-
-        self._model = InferenceSession(str(model_path))
-        self._tags = pd.read_csv(tags_path)
-
-        self.__initialized = True
-
-    def _calculation(self, image: Image.Image) -> pd.DataFrame:
-        self._init()
-
-        # code for converting the image and running the model is taken from the link below
-        # thanks, SmilingWolf!
-        # https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags/blob/main/app.py
-
-        # convert an image to fit the model
-        _, height, _, _ = self._model.get_inputs()[0].shape
-
-        # alpha to white
-        image = image.convert('RGBA')
-        new_image = Image.new('RGBA', image.size, 'WHITE')
-        new_image.paste(image, mask=image)
-        image = new_image.convert('RGB')
-        image = np.asarray(image)
-
-        # PIL RGB to OpenCV BGR
-        image = image[:, :, ::-1]
-
-        image = make_square(image, height)
-        image = smart_resize(image, height)
-        image = image.astype(np.float32)
-        image = np.expand_dims(image, 0)
-
-        # evaluate model
-        input_name = self._model.get_inputs()[0].name
-        label_name = self._model.get_outputs()[0].name
-        confidence = self._model.run([label_name], {input_name: image})[0]
-
-        full_tags = self._tags[['name', 'category']].copy()
-        full_tags['confidence'] = confidence[0]
-
-        return full_tags
-    def interrogate(self, image: Image) -> Tuple[Dict[str, float], Dict[str, float]]:
-        
-        imgByteArr = io.BytesIO()
-        image.save(imgByteArr, format="png")
-        imgByteArr = imgByteArr.getvalue()
-        if imgByteArr in self.cache:
-            return self.cache[imgByteArr]
-
-        full_tags = self._calculation(image)
-
-        # first 4 items are for rating (general, sensitive, questionable, explicit)
-        ratings = dict(full_tags[full_tags['category'] == 9][['name', 'confidence']].values)
-
-        # rest are regular tags
-        tags = dict(full_tags[full_tags['category'] != 9][['name', 'confidence']].values)
-        self.cache[imgByteArr] = (ratings, tags)
-        if len(self.cache) > 25:
-            self.cache.popitem(last=False)
-
-        return ratings, tags
-
-
-WAIFU_MODELS: Mapping[str, WaifuDiffusionInterrogator] = {
-    'chen-vit': WaifuDiffusionInterrogator(),
-    'chen-convnext': WaifuDiffusionInterrogator(
-        repo='SmilingWolf/wd-v1-4-convnext-tagger'
-    ),
-}
-RE_SPECIAL = re.compile(r'([\\()])')
-
-
-def image_to_wd14_tags(image: Image.Image, model_name: str, threshold: float,
-                       use_spaces: bool, use_escape: bool, include_ranks=False, score_descend=True) \
-        -> Tuple[Mapping[str, float], str, Mapping[str, float]]:
-    model = WAIFU_MODELS[model_name]
-    ratings, tags = model.interrogate(image)
-
-    filtered_tags = {
-        tag: score for tag, score in tags.items()
-        if score >= threshold
-    }
-
-    text_items = []
-    tags_pairs = filtered_tags.items()
-    if score_descend:
-        tags_pairs = sorted(tags_pairs, key=lambda x: (-x[1], x[0]))
-    for tag, score in tags_pairs:
-        tag_outformat = tag
-        if use_spaces:
-            tag_outformat = tag_outformat.replace('_', ' ')
-            tag_outformat = tag_outformat.replace(' ', '-')
-        else:
-            tag_outformat = tag_outformat.replace('_', '-')
-        if use_escape:
-            tag_outformat = re.sub(RE_SPECIAL, r'\\\1', tag_outformat)
-        if include_ranks:
-            tag_outformat = f"({tag_outformat}:{score:.3f})"
-        text_items.append(tag_outformat)
-    output_text = ' '.join(text_items)
-
-    return ratings, output_text, filtered_tags
-
-
-if __name__ == '__main__':
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                gr_input_image = gr.Image(type='pil', label='Chen Chen')
-                with gr.Row():
-                    gr_model = gr.Radio(list(WAIFU_MODELS.keys()), value='chen-vit', label='Chen')
-                    gr_threshold = gr.Slider(0.0, 1.0, 0.5, label='Chen Chen Chen Chen Chen')
-                with gr.Row():
-                    gr_space = gr.Checkbox(value=True, label='Chen " " Chen Chen "_"')
-                    gr_escape = gr.Checkbox(value=True, label='Chen Text Escape')
-
-                gr_btn_submit = gr.Button(value='橙', variant='primary')
-
-            with gr.Column():
-                gr_ratings = gr.Label(label='橙 橙')
-                with gr.Tabs():
-                    with gr.Tab("Chens"):
-                        gr_tags = gr.Label(label='Chens')
-                    with gr.Tab("Chen Text"):
-                        gr_output_text = gr.TextArea(label='Chen Text')
-
-        gr_btn_submit.click(
-            image_to_wd14_tags,
-            inputs=[gr_input_image, gr_model, gr_threshold, gr_space, gr_escape],
-            outputs=[gr_ratings, gr_output_text, gr_tags],
-            api_name="classify"
-        )
-    demo.queue(os.cpu_count()).launch()
\ No newline at end of file
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/filter.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/filter.js
deleted file mode 100644
index 7ec6fbee88600438c7bd9f5c2e6dc98c2e27fd85..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/filter.js
+++ /dev/null
@@ -1,19 +0,0 @@
-let Declaration = require('../declaration')
-
-class Filter extends Declaration {
-  /**
-   * Check is it Internet Explorer filter
-   */
-  check(decl) {
-    let v = decl.value
-    return (
-      !v.toLowerCase().includes('alpha(') &&
-      !v.includes('DXImageTransform.Microsoft') &&
-      !v.includes('data:image/svg+xml')
-    )
-  }
-}
-
-Filter.names = ['filter']
-
-module.exports = Filter
diff --git a/spaces/zhang-wei-jian/docker/node_modules/inherits/inherits.js b/spaces/zhang-wei-jian/docker/node_modules/inherits/inherits.js
deleted file mode 100644
index f71f2d93294a67ad5d9300aae07973e259f26068..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/inherits/inherits.js
+++ /dev/null
@@ -1,9 +0,0 @@
-try {
-  var util = require('util');
-  /* istanbul ignore next */
-  if (typeof util.inherits !== 'function') throw '';
-  module.exports = util.inherits;
-} catch (e) {
-  /* istanbul ignore next */
-  module.exports = require('./inherits_browser.js');
-}
diff --git a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/classes/range.js b/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/classes/range.js
deleted file mode 100644
index 90876c389f3bec7cb91fa8e68ced8d4fbfa0587d..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/classes/range.js
+++ /dev/null
@@ -1,448 +0,0 @@
-// hoisted class for cyclic dependency
-class Range {
-  constructor (range, options) {
-    if (!options || typeof options !== 'object') {
-      options = {
-        loose: !!options,
-        includePrerelease: false
-      }
-    }
-
-    if (range instanceof Range) {
-      if (
-        range.loose === !!options.loose &&
-        range.includePrerelease === !!options.includePrerelease
-      ) {
-        return range
-      } else {
-        return new Range(range.raw, options)
-      }
-    }
-
-    if (range instanceof Comparator) {
-      // just put it in the set and return
-      this.raw = range.value
-      this.set = [[range]]
-      this.format()
-      return this
-    }
-
-    this.options = options
-    this.loose = !!options.loose
-    this.includePrerelease = !!options.includePrerelease
-
-    // First, split based on boolean or ||
-    this.raw = range
-    this.set = range
-      .split(/\s*\|\|\s*/)
-      // map the range to a 2d array of comparators
-      .map(range => this.parseRange(range.trim()))
-      // throw out any comparator lists that are empty
-      // this generally means that it was not a valid range, which is allowed
-      // in loose mode, but will still throw if the WHOLE range is invalid.
-      .filter(c => c.length)
-
-    if (!this.set.length) {
-      throw new TypeError(`Invalid SemVer Range: ${range}`)
-    }
-
-    this.format()
-  }
-
-  format () {
-    this.range = this.set
-      .map((comps) => {
-        return comps.join(' ').trim()
-      })
-      .join('||')
-      .trim()
-    return this.range
-  }
-
-  toString () {
-    return this.range
-  }
-
-  parseRange (range) {
-    const loose = this.options.loose
-    range = range.trim()
-    // `1.2.3 - 1.2.4` => `>=1.2.3 <=1.2.4`
-    const hr = loose ? re[t.HYPHENRANGELOOSE] : re[t.HYPHENRANGE]
-    range = range.replace(hr, hyphenReplace)
-    debug('hyphen replace', range)
-    // `> 1.2.3 < 1.2.5` => `>1.2.3 <1.2.5`
-    range = range.replace(re[t.COMPARATORTRIM], comparatorTrimReplace)
-    debug('comparator trim', range, re[t.COMPARATORTRIM])
-
-    // `~ 1.2.3` => `~1.2.3`
-    range = range.replace(re[t.TILDETRIM], tildeTrimReplace)
-
-    // `^ 1.2.3` => `^1.2.3`
-    range = range.replace(re[t.CARETTRIM], caretTrimReplace)
-
-    // normalize spaces
-    range = range.split(/\s+/).join(' ')
-
-    // At this point, the range is completely trimmed and
-    // ready to be split into comparators.
-
-    const compRe = loose ? re[t.COMPARATORLOOSE] : re[t.COMPARATOR]
-    return range
-      .split(' ')
-      .map(comp => parseComparator(comp, this.options))
-      .join(' ')
-      .split(/\s+/)
-      // in loose mode, throw out any that are not valid comparators
-      .filter(this.options.loose ? comp => !!comp.match(compRe) : () => true)
-      .map(comp => new Comparator(comp, this.options))
-  }
-
-  intersects (range, options) {
-    if (!(range instanceof Range)) {
-      throw new TypeError('a Range is required')
-    }
-
-    return this.set.some((thisComparators) => {
-      return (
-        isSatisfiable(thisComparators, options) &&
-        range.set.some((rangeComparators) => {
-          return (
-            isSatisfiable(rangeComparators, options) &&
-            thisComparators.every((thisComparator) => {
-              return rangeComparators.every((rangeComparator) => {
-                return thisComparator.intersects(rangeComparator, options)
-              })
-            })
-          )
-        })
-      )
-    })
-  }
-
-  // if ANY of the sets match ALL of its comparators, then pass
-  test (version) {
-    if (!version) {
-      return false
-    }
-
-    if (typeof version === 'string') {
-      try {
-        version = new SemVer(version, this.options)
-      } catch (er) {
-        return false
-      }
-    }
-
-    for (let i = 0; i < this.set.length; i++) {
-      if (testSet(this.set[i], version, this.options)) {
-        return true
-      }
-    }
-    return false
-  }
-}
-module.exports = Range
-
-const Comparator = require('./comparator')
-const debug = require('../internal/debug')
-const SemVer = require('./semver')
-const {
-  re,
-  t,
-  comparatorTrimReplace,
-  tildeTrimReplace,
-  caretTrimReplace
-} = require('../internal/re')
-
-// take a set of comparators and determine whether there
-// exists a version which can satisfy it
-const isSatisfiable = (comparators, options) => {
-  let result = true
-  const remainingComparators = comparators.slice()
-  let testComparator = remainingComparators.pop()
-
-  while (result && remainingComparators.length) {
-    result = remainingComparators.every((otherComparator) => {
-      return testComparator.intersects(otherComparator, options)
-    })
-
-    testComparator = remainingComparators.pop()
-  }
-
-  return result
-}
-
-// comprised of xranges, tildes, stars, and gtlt's at this point.
-// already replaced the hyphen ranges
-// turn into a set of JUST comparators.
-const parseComparator = (comp, options) => {
-  debug('comp', comp, options)
-  comp = replaceCarets(comp, options)
-  debug('caret', comp)
-  comp = replaceTildes(comp, options)
-  debug('tildes', comp)
-  comp = replaceXRanges(comp, options)
-  debug('xrange', comp)
-  comp = replaceStars(comp, options)
-  debug('stars', comp)
-  return comp
-}
-
-const isX = id => !id || id.toLowerCase() === 'x' || id === '*'
-
-// ~, ~> --> * (any, kinda silly)
-// ~2, ~2.x, ~2.x.x, ~>2, ~>2.x ~>2.x.x --> >=2.0.0 <3.0.0
-// ~2.0, ~2.0.x, ~>2.0, ~>2.0.x --> >=2.0.0 <2.1.0
-// ~1.2, ~1.2.x, ~>1.2, ~>1.2.x --> >=1.2.0 <1.3.0
-// ~1.2.3, ~>1.2.3 --> >=1.2.3 <1.3.0
-// ~1.2.0, ~>1.2.0 --> >=1.2.0 <1.3.0
-const replaceTildes = (comp, options) =>
-  comp.trim().split(/\s+/).map((comp) => {
-    return replaceTilde(comp, options)
-  }).join(' ')
-
-const replaceTilde = (comp, options) => {
-  const r = options.loose ? re[t.TILDELOOSE] : re[t.TILDE]
-  return comp.replace(r, (_, M, m, p, pr) => {
-    debug('tilde', comp, _, M, m, p, pr)
-    let ret
-
-    if (isX(M)) {
-      ret = ''
-    } else if (isX(m)) {
-      ret = `>=${M}.0.0 <${+M + 1}.0.0`
-    } else if (isX(p)) {
-      // ~1.2 == >=1.2.0 <1.3.0
-      ret = `>=${M}.${m}.0 <${M}.${+m + 1}.0`
-    } else if (pr) {
-      debug('replaceTilde pr', pr)
-      ret = `>=${M}.${m}.${p}-${pr
-      } <${M}.${+m + 1}.0`
-    } else {
-      // ~1.2.3 == >=1.2.3 <1.3.0
-      ret = `>=${M}.${m}.${p
-      } <${M}.${+m + 1}.0`
-    }
-
-    debug('tilde return', ret)
-    return ret
-  })
-}
-
-// ^ --> * (any, kinda silly)
-// ^2, ^2.x, ^2.x.x --> >=2.0.0 <3.0.0
-// ^2.0, ^2.0.x --> >=2.0.0 <3.0.0
-// ^1.2, ^1.2.x --> >=1.2.0 <2.0.0
-// ^1.2.3 --> >=1.2.3 <2.0.0
-// ^1.2.0 --> >=1.2.0 <2.0.0
-const replaceCarets = (comp, options) =>
-  comp.trim().split(/\s+/).map((comp) => {
-    return replaceCaret(comp, options)
-  }).join(' ')
-
-const replaceCaret = (comp, options) => {
-  debug('caret', comp, options)
-  const r = options.loose ? re[t.CARETLOOSE] : re[t.CARET]
-  return comp.replace(r, (_, M, m, p, pr) => {
-    debug('caret', comp, _, M, m, p, pr)
-    let ret
-
-    if (isX(M)) {
-      ret = ''
-    } else if (isX(m)) {
-      ret = `>=${M}.0.0 <${+M + 1}.0.0`
-    } else if (isX(p)) {
-      if (M === '0') {
-        ret = `>=${M}.${m}.0 <${M}.${+m + 1}.0`
-      } else {
-        ret = `>=${M}.${m}.0 <${+M + 1}.0.0`
-      }
-    } else if (pr) {
-      debug('replaceCaret pr', pr)
-      if (M === '0') {
-        if (m === '0') {
-          ret = `>=${M}.${m}.${p}-${pr
-          } <${M}.${m}.${+p + 1}`
-        } else {
-          ret = `>=${M}.${m}.${p}-${pr
-          } <${M}.${+m + 1}.0`
-        }
-      } else {
-        ret = `>=${M}.${m}.${p}-${pr
-        } <${+M + 1}.0.0`
-      }
-    } else {
-      debug('no pr')
-      if (M === '0') {
-        if (m === '0') {
-          ret = `>=${M}.${m}.${p
-          } <${M}.${m}.${+p + 1}`
-        } else {
-          ret = `>=${M}.${m}.${p
-          } <${M}.${+m + 1}.0`
-        }
-      } else {
-        ret = `>=${M}.${m}.${p
-        } <${+M + 1}.0.0`
-      }
-    }
-
-    debug('caret return', ret)
-    return ret
-  })
-}
-
-const replaceXRanges = (comp, options) => {
-  debug('replaceXRanges', comp, options)
-  return comp.split(/\s+/).map((comp) => {
-    return replaceXRange(comp, options)
-  }).join(' ')
-}
-
-const replaceXRange = (comp, options) => {
-  comp = comp.trim()
-  const r = options.loose ? re[t.XRANGELOOSE] : re[t.XRANGE]
-  return comp.replace(r, (ret, gtlt, M, m, p, pr) => {
-    debug('xRange', comp, ret, gtlt, M, m, p, pr)
-    const xM = isX(M)
-    const xm = xM || isX(m)
-    const xp = xm || isX(p)
-    const anyX = xp
-
-    if (gtlt === '=' && anyX) {
-      gtlt = ''
-    }
-
-    // if we're including prereleases in the match, then we need
-    // to fix this to -0, the lowest possible prerelease value
-    pr = options.includePrerelease ? '-0' : ''
-
-    if (xM) {
-      if (gtlt === '>' || gtlt === '<') {
-        // nothing is allowed
-        ret = '<0.0.0-0'
-      } else {
-        // nothing is forbidden
-        ret = '*'
-      }
-    } else if (gtlt && anyX) {
-      // we know patch is an x, because we have any x at all.
-      // replace X with 0
-      if (xm) {
-        m = 0
-      }
-      p = 0
-
-      if (gtlt === '>') {
-        // >1 => >=2.0.0
-        // >1.2 => >=1.3.0
-        gtlt = '>='
-        if (xm) {
-          M = +M + 1
-          m = 0
-          p = 0
-        } else {
-          m = +m + 1
-          p = 0
-        }
-      } else if (gtlt === '<=') {
-        // <=0.7.x is actually <0.8.0, since any 0.7.x should
-        // pass.  Similarly, <=7.x is actually <8.0.0, etc.
-        gtlt = '<'
-        if (xm) {
-          M = +M + 1
-        } else {
-          m = +m + 1
-        }
-      }
-
-      ret = `${gtlt + M}.${m}.${p}${pr}`
-    } else if (xm) {
-      ret = `>=${M}.0.0${pr} <${+M + 1}.0.0${pr}`
-    } else if (xp) {
-      ret = `>=${M}.${m}.0${pr
-      } <${M}.${+m + 1}.0${pr}`
-    }
-
-    debug('xRange return', ret)
-
-    return ret
-  })
-}
-
-// Because * is AND-ed with everything else in the comparator,
-// and '' means "any version", just remove the *s entirely.
-const replaceStars = (comp, options) => {
-  debug('replaceStars', comp, options)
-  // Looseness is ignored here.  star is always as loose as it gets!
-  return comp.trim().replace(re[t.STAR], '')
-}
-
-// This function is passed to string.replace(re[t.HYPHENRANGE])
-// M, m, patch, prerelease, build
-// 1.2 - 3.4.5 => >=1.2.0 <=3.4.5
-// 1.2.3 - 3.4 => >=1.2.0 <3.5.0 Any 3.4.x will do
-// 1.2 - 3.4 => >=1.2.0 <3.5.0
-const hyphenReplace = ($0,
-  from, fM, fm, fp, fpr, fb,
-  to, tM, tm, tp, tpr, tb) => {
-  if (isX(fM)) {
-    from = ''
-  } else if (isX(fm)) {
-    from = `>=${fM}.0.0`
-  } else if (isX(fp)) {
-    from = `>=${fM}.${fm}.0`
-  } else {
-    from = `>=${from}`
-  }
-
-  if (isX(tM)) {
-    to = ''
-  } else if (isX(tm)) {
-    to = `<${+tM + 1}.0.0`
-  } else if (isX(tp)) {
-    to = `<${tM}.${+tm + 1}.0`
-  } else if (tpr) {
-    to = `<=${tM}.${tm}.${tp}-${tpr}`
-  } else {
-    to = `<=${to}`
-  }
-
-  return (`${from} ${to}`).trim()
-}
-
-const testSet = (set, version, options) => {
-  for (let i = 0; i < set.length; i++) {
-    if (!set[i].test(version)) {
-      return false
-    }
-  }
-
-  if (version.prerelease.length && !options.includePrerelease) {
-    // Find the set of versions that are allowed to have prereleases
-    // For example, ^1.2.3-pr.1 desugars to >=1.2.3-pr.1 <2.0.0
-    // That should allow `1.2.3-pr.2` to pass.
-    // However, `1.2.4-alpha.notready` should NOT be allowed,
-    // even though it's within the range set by the comparators.
-    for (let i = 0; i < set.length; i++) {
-      debug(set[i].semver)
-      if (set[i].semver === Comparator.ANY) {
-        continue
-      }
-
-      if (set[i].semver.prerelease.length > 0) {
-        const allowed = set[i].semver
-        if (allowed.major === version.major &&
-            allowed.minor === version.minor &&
-            allowed.patch === version.patch) {
-          return true
-        }
-      }
-    }
-
-    // Version has a -pre, but it's not one of the ones we like.
-    return false
-  }
-
-  return true
-}
diff --git a/spaces/zhanpj/ChatGPT/run_Windows.bat b/spaces/zhanpj/ChatGPT/run_Windows.bat
deleted file mode 100644
index 4c18f9ccaeea0af972301ffdf48778641221f76d..0000000000000000000000000000000000000000
--- a/spaces/zhanpj/ChatGPT/run_Windows.bat
+++ /dev/null
@@ -1,5 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
diff --git a/spaces/zhiyin123/MyNewBing8/Dockerfile b/spaces/zhiyin123/MyNewBing8/Dockerfile
deleted file mode 100644
index cd517719ec7362b03d055b7f7212529e0fc963cd..0000000000000000000000000000000000000000
--- a/spaces/zhiyin123/MyNewBing8/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoEWYtX5rG6bE3fc4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/zwhe99/MAPS-mt/data/format_base.py b/spaces/zwhe99/MAPS-mt/data/format_base.py
deleted file mode 100644
index 5fef4cba2f69ae4d1d0e41c7dc306cc918c76036..0000000000000000000000000000000000000000
--- a/spaces/zwhe99/MAPS-mt/data/format_base.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import random
-import os
-from langcodes import Language
-import argparse
-
-def parse_args():
-    parser = argparse.ArgumentParser("", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('-w', "--workspace", type=str, default=os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'), help="Workspace dir")
-    parser.add_argument('-tn', "--test-name", type=str, required=True, help="wmt22/wmt21/...")
-    parser.add_argument('-vn', "--valid-name", type=str, help="wmt22/wmt21/...")
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument('-s', "--src", type=str, required=True, help='source lang')
-    parser.add_argument('-t', "--tgt", type=str, required=True, help='target lang')
-    parser.add_argument('-n', "--n-shot", type=int, required=True, help='# shot.')
-    return parser.parse_args()
-
-def main(args):
-    workspace = args.workspace
-    data_dir=os.path.join(workspace, "data")
-    raw_dir=os.path.join(data_dir, "raw")
-    format_dir=os.path.join(data_dir, "format")
-    test_name = args.test_name
-    valid_name = args.valid_name
-    seed = args.seed
-    src = args.src
-    tgt = args.tgt
-    src_full = Language.make(language=src).display_name()
-    tgt_full = Language.make(language=tgt).display_name()
-    shot = args.n_shot
-
-    assert shot == 0 or (shot > 0 and valid_name)
-
-    # seed random
-    random.seed(seed)
-
-    # read files
-    with open(os.path.join(raw_dir, f"{test_name}.{src}-{tgt}.{src}")) as test_src_f:
-        test_src_lines = [l.strip() for l in test_src_f.readlines()]
-
-    if shot == 0:
-        out_file_path = os.path.join(format_dir, f"{test_name}.{src}-{tgt}.{src}.{0}-shot")
-        valid_src_lines = None
-        valid_tgt_lines = None
-    else:
-        with open(os.path.join(raw_dir, f"{valid_name}.{src}-{tgt}.{src}")) as valid_src_f, \
-            open(os.path.join(raw_dir, f"{valid_name}.{src}-{tgt}.{tgt}")) as valid_tgt_f:
-            valid_src_lines = [l.strip() for l in valid_src_f.readlines()]
-            valid_tgt_lines = [l.strip() for l in valid_tgt_f.readlines()]
-        out_file_path = os.path.join(format_dir, f"{test_name}.{src}-{tgt}.{src}.{shot}-shot.{seed}-seed")
-
-    demos = []
-    if shot > 0:
-        demos = random.sample(list(zip(valid_src_lines, valid_tgt_lines)), shot)
-
-    with open(out_file_path, 'w') as out_f:
-        for id, src_line in enumerate(test_src_lines):
-            all_items = demos + [(src_line, None)]
-            prompt_lst = []
-            for it in all_items:
-                it_src, it_tgt = it
-                s = f"Instruction: Translate the following {src_full} text into {tgt_full}.\n" + \
-                f"{src_full}: {it_src}\n" + \
-                (f"{tgt_full}: {it_tgt}" if it_tgt else f"{tgt_full}:")
-                prompt_lst.append(s)
-
-            prompt = "\n\n".join(prompt_lst)
-            out_f.write(
-                f"{id:04}\n"
-                f"{prompt}\n\n\n"
-            )
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)