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-<p>Driving Zone Russia Mod APK Unlimited Money has many features that make it an exciting and enjoyable racing game. Here are some of them:</p>
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-<h3>Modern graphics and realistic physics</h3>
-<p>The game has modern beautiful graphics that create a realistic atmosphere of street racing. The cars are detailed and well-designed, with accurate body and interior models. The tracks are also diverse and immersive, with different scenery and weather conditions. The game also has realistic car physics that simulate the behavior of real cars on different surfaces and situations. You can feel the speed, acceleration, braking, steering, and drifting of your car as you race along the busy highway or the challenging track.</p>
-<h3>Qualitatively modeled Russian cars</h3>
-<p>The game features a variety of cars produced in Russia, from classic models to modern ones. Each car has its own character and a real engine sound that matches its performance. You can choose from sedans, hatchbacks, coupes, SUVs, sports cars, or even trucks. Some of the cars available in the game are Lada Priora, Lada Granta, Lada Kalina, Lada Niva, Volga GAZ-24, Volga GAZ-3110, Moskvich-2141 AZLK-2141-02 Svyatogor Turbo Plus (Moskvich-2141), Moskvich-412 IE AZLK-412-028 (Moskvich-412), VAZ-2106 Zhiguli (Lada Riva), VAZ-2107 Zhiguli (Lada Riva), VAZ-2108 Sputnik (L ada Samara), VAZ-2109 Sputnik (Lada Samara), VAZ-2110 Lada (Lada 110), VAZ-2112 Lada (Lada 112), VAZ-2113 Lada (Lada 113), VAZ-2114 Lada (Lada 114), VAZ-2115 Lada (Lada 115), VAZ-2121 Niva (Lada Niva), GAZ-66, GAZ-3302 Gazelle, GAZ-3307, GAZ-3309, GAZ-3310 Valdai, GAZ-2330 Tigr, UAZ-469, UAZ-452, UAZ Patriot, UAZ Hunter, UAZ Cargo, Kamaz-4310, Kamaz-43118, Kamaz-5350 Mustang, Kamaz-6520, Kamaz-6522, ZIL-130, ZIL-131, ZIL-133, ZIL-157, ZIL-4331, MAZ-504, MAZ-5337, MAZ-5432, MAZ-6422, MAZ-7310 Uragan (MAZ 543M), and many more.</p>
-<h3>Four tracks with different weather conditions</h3>
-<p>The game offers four different tracks to race on, each with its own weather conditions and time of day. You can choose from a city track with busy traffic and pedestrians, a suburban track with a quieter atmosphere and a scenic view, a winter track with snow and ice on the road, or a desert track with sand and dust. Each track has its own challenges and surprises that will test your driving skills and reflexes. You can also change the weather conditions and the time of day in real-time to create your own unique racing experience.</p>
-<h3>Change the time of day in real-time</h3>
-<p>One of the most impressive features of the game is the ability to change the time of day in real-time. You can switch from day to night or vice versa with a simple swipe on the screen. This will affect the lighting and the visibility of the track, as well as the behavior of other drivers and pedestrians. You can also adjust the speed of time to make the day or night last longer or shorter. This feature adds more realism and variety to the game and allows you to enjoy different scenarios and atmospheres.</p>
-<h3>First person view / interior camera</h3>
-<p>The game also gives you the option to switch from the third person view to the first person view or the interior camera. This will let you see the road from the driver's perspective and feel more immersed in the game. You can also see the dashboard and the steering wheel of your car, as well as the mirrors and the indicators. The interior camera also shows you the damage and the wear of your car parts, such as the tires, the engine, or the brakes. You can use this feature to enhance your driving experience and challenge yourself more.</p>
- <h2>How to download and install Driving Zone Russia Mod APK Unlimited Money</h2>
-<p>If you are interested in trying Driving Zone Russia Mod APK Unlimited Money, you will need to follow these steps to download and install it on your device:</p>
-<h3>Requirements and compatibility</h3>
-<p>Before you download and install Driving Zone Russia Mod APK Unlimited Money, you will need to make sure that your device meets these requirements:</p>
-<ul>
-<li>Your device must have Android 4.1 or higher operating system.</li>
-<li>Your device must have at least 100 MB of free storage space.</li>
-<li>Your device must have a stable internet connection.</li>
-<li>You must enable unknown sources on your device settings to allow installation of apps from third-party sources.</li>
-</ul>
-<p>Driving Zone Russia Mod APK Unlimited Money is compatible with most Android devices, including smartphones and tablets. However, some devices may not support some features or functions of the game due to hardware limitations or software issues.</p>
-<h3>Steps to download and install</h3>
-<p>Once you have checked that your device meets the requirements and compatibility, you can proceed to download and install Driving Zone Russia Mod APK Unlimited Money by following these steps:</p>
-<ol>
-<li>Click on this link  to download Driving Zone Russia Mod APK Unlimited Money file on your device.</li>
-<li>Locate the downloaded file on your device storage and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Launch the game from your app drawer or home screen and enjoy unlimited money and other mod features.</li>
-</ol>
-<h3>How to use the mod features</h3>
-<p>After you have successfully installed Driving Zone Russia Mod APK Unlimited Money on your device, you can start using the mod features to enhance your gameplay. Here are some of the mod features and how to use them:</p>
-<ul>
-<li>Unlimited money: You can use unlimited money to buy any car you want, upgrade your car's performance, or change the appearance of your car. You can also use it to unlock all the tracks, modes, and settings of the game. To use this feature, simply go to the shop or the garage and select the item you want to buy or upgrade. You will see that the price is zero and you can buy or upgrade it without spending any money.</li>
-<li>No ads: You can enjoy the game without any ads or interruptions. This feature will remove all the ads that pop up on the screen or play before or after the game. To use this feature, simply launch the game and play as usual. You will not see any ads on the screen or hear any ads on the sound.</li>
-<li>Other resources: You can also use other resources such as coins, gems, diamonds, or tokens to access some special features or items in the game. These resources are also unlimited and you can use them as much as you want. To use this feature, simply go to the shop or the menu and select the feature or item you want to use or buy. You will see that you have enough resources to use or buy it without any problem.</li>
-</ul>
- <h2>Tips and tricks for playing Driving Zone Russia Mod APK Unlimited Money</h2>
-<p>Driving Zone Russia Mod APK Unlimited Money is a fun and addictive racing game that will keep you entertained for hours. However, if you want to master the game and become a pro racer, you will need some tips and tricks to help you out. Here are some of them:</p>
-<h3>Choose the right car for each track</h3>
-<p>The game offers a variety of cars to choose from, each with its own strengths and weaknesses. Some cars are faster, some are more agile, some are more durable, and some are more balanced. You will need to choose the right car for each track depending on the weather conditions, the road surface, and the traffic situation. For example, if you are racing on a winter track with snow and ice, you might want to choose a car with good traction and stability, such as an SUV or a truck. If you are racing on a desert track with sand and dust, you might want to choose a car with good speed and acceleration, such as a sports car or a coupe.</p>
-<h3>Adjust the settings to suit your preference</h3>
-<p>The game also allows you to adjust the settings to suit your preference and play style. You can change the difficulty level, the camera angle, the control method, the sound volume, and the graphics quality. You can also enable or disable some features such as traffic rules, damage system, police chase, or online mode. You can experiment with different settings until you find the ones that work best for you.</p>
-<h3>Use the brake and accelerator wisely</h3>
-<p>The game has realistic car physics that require you to use the brake and accelerator wisely. You cannot just press the gas pedal all the time and expect to win the race. You will need to slow down when approaching a turn, a curve, an obstacle, or a traffic jam. You will also need to accelerate when exiting a turn, a curve, an obstacle, or a traffic jam. You will need to balance between speed and control to avoid crashing or losing control of your car.</p>
-<h3>Record and share your gameplay videos</h3>
-<p>The game also has a feature that allows you to record and share your gameplay videos with other players online. You can capture your best moments in the game, such as your fastest lap time, your most epic drift, your most daring overtaking, or your most spectacular crash. You can then share your videos with other players on social media platforms such as Facebook, Twitter, Instagram, YouTube, or TikTok. You can also watch other players' videos and learn from their skills and strategies.</p>
- <h2>Pros and cons of Driving Zone Russia Mod APK Unlimited Money</h2>
-<p>Driving Zone Russia Mod APK Unlimited Money is not a perfect game and it has its pros and cons. Here are some of them:</p>
-<h3>Pros</h3>
-<ul>
-<li>The game has modern graphics and realistic physics that create a realistic atmosphere of street racing.</li>
-<li>The game features a variety of cars produced in Russia, each with its own character and a real engine sound.</li>
-<li>The game offers four different tracks with different weather conditions and time of day.</li>
-<li>The game allows you to change the time of day in real-time.</li>
-<li>The game gives you the option to switch from the third person view to the first person view or the interior camera.</li>
-<li>The game has unlimited money and other resources that allow you to buy any car you want, upgrade your car's performance, or change the appearance of your car.</li>
-<li>The game has no ads or interruptions that can ruin your gameplay.</li>
-<li>The game has a feature that allows you to record and share your gameplay videos with other players online.</li>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li>The game can be too easy or boring for some players who prefer more challenge or variety in their racing games.</li>
-<li>The game can be too hard or frustrating for some players who are not used to the realistic car physics or the traffic rules.</li>
-<li>The game can be too repetitive or monotonous for some players who want more tracks, modes, or features in their racing games.</li>
-<li>The game can have some bugs or glitches that can affect the gameplay or the graphics quality.</li>
-<li>The game can be risky or illegal to download and install from third-party sources that may contain viruses or malware.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Driving Zone Russia Mod APK Unlimited Money is a simulator of street racing on the cars produced in Russia, with online and offline game modes. The game has modern graphics and realistic physics that create a realistic atmosphere of street racing. The game features a variety of cars produced in Russia, each with its own character and a real engine sound. The game offers four different tracks with different weather conditions and time of day. The game allows you to change the time of day in real-time. The game gives you the option to switch from the third person view to the first person view or the interior camera. The game has unlimited money and other resources that allow you to buy any car you want, upgrade your car's performance, or change the appearance of your car. The game has no ads or interruptions that can ruin your gameplay. The game has a feature that allows you to record and share your gameplay videos with other players online.</p>
-<p>However, the game also has some drawbacks that may affect your enjoyment of the game. The game can be too easy or boring for some players who prefer more challenge or variety in their racing games. The game can be too hard or frustrating for some players who are not used to the realistic car physics or the traffic rules. The game can be too repetitive or monotonous for some players who want more tracks, modes, or features in their racing games. The game can have some bugs or glitches that can affect the gameplay or the graphics quality. The game can be risky or illegal to download and install from third-party sources that may contain viruses or malware.</p>
-<p>Therefore, we recommend Driving Zone Russia Mod APK Unlimited Money to anyone who loves racing games, especially those that feature cars from Russia. This game will give you a realistic and immersive experience of street racing on Russian cars, with unlimited money and other mod features. However, we also advise you to be careful when downloading and installing this mod apk from third-party sources, as they may not be safe or legal. We also suggest you to try the original version of Driving Zone Russia first before trying this mod apk, as it may suit your preference better.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Driving Zone Russia Mod APK Unlimited Money:</p>
-<ol>
-<li>Q: Is Driving Zone Russia Mod APK Unlimited Money free?</li>
-<li>A: Yes, Driving Zone Russia Mod APK Unlimited Money is free to download and play. However, you may need to pay for some in-app purchases or subscriptions if you want to access some premium features or items in the game.</li>
-<li>Q: Is Driving Zone Russia Mod APK Unlimited Money safe?</li>
-<li>A: Driving Zone Russia Mod APK Unlimited Money is not officially endorsed by the developers of Driving Zone Russia, so it may not be safe or legal to download and install from third-party sources. You may encounter some viruses or malware that can harm your device or compromise your privacy. You may also face some legal issues if you violate the terms and conditions of Driving Zone Russia.</li>
-<li>Q: How do I update Driving Zone Russia Mod APK Unlimited Money?</li>
-<li>A: Driving Zone Russia Mod APK Unlimited Money may not be compatible with the latest version of Driving Zone Russia, so you may need to update it manually from time to time. You will need to check for updates from the source where you downloaded it from, and follow the instructions on how to download and install the updated version.</li>
-<li>Q: How do I uninstall Driving Zone Russia Mod APK Unlimited Money?</li>
-<li>A: If you want to uninstall Driving Zone Russia Mod APK Unlimited Money from your device, you will need to follow these steps:</li>
-<ul>
-<li>Go to your device settings and select apps or applications.</li>
-<li>Find and tap on Driving Zone Russia Mod APK Unlimited Money and tap on uninstall.</li>
-<li>Confirm your action and wait for the process to finish.</li>
-<li>Alternatively, you can also delete the Driving Zone Russia Mod APK Unlimited Money file from your device storage.</li>
-</ul>
-<li>Q: Can I play Driving Zone Russia Mod APK Unlimited Money offline?</li>
-<li>A: Yes, you can play Driving Zone Russia Mod APK Unlimited Money offline without an internet connection. However, you may not be able to access some online features or modes, such as multiplayer mode, leaderboards, or achievements.</li>
-</ol></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download Plants vs Zombies 2 for PC and Protect Your Garden from the Undead.md

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-
-<h1>How to Download Plants vs Zombies 2 on PC</h1>
-<p>Plants vs Zombies 2 is a popular tower defense game that pits you against hordes of zombies who want to eat your brains. You have to use various plants with different abilities to stop them from reaching your house. The game features hundreds of levels across different worlds and time periods, as well as endless modes, mini-games, daily events, and online competitions.</p>
-<p>While Plants vs Zombies 2 is primarily designed for mobile devices, you might want to play it on your PC for various reasons. Maybe you want to enjoy the game on a bigger screen, or use a keyboard and mouse or a controller for better control. Maybe you want to save battery life on your phone or tablet, or avoid interruptions from calls and notifications. Whatever your reason, there are several ways to download Plants vs Zombies 2 on PC. In this article, we will show you three methods that you can use to play this fun and addictive game on your PC.</p>
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- <h2>Method 1: Use Windows 11 and native Android emulation</h2>
-<p>One of the easiest ways to download Plants vs Zombies 2 on PC is to use Windows 11, the latest version of Microsoft's operating system. Windows 11 comes with a built-in feature that allows you to run Android apps natively on your PC, without the need for any third-party software. Here are the steps to follow:</p>
-<ol>
-<li>Check if your PC meets the minimum requirements for Windows 11 and Android emulation. You will need a 64-bit processor, 4 GB of RAM, 64 GB of storage, a TPM 2.0 chip, and a DirectX 12 compatible graphics card. You can use the <a href="">PC Health Check app</a> to see if your PC is eligible for the upgrade.</li>
-<li>Update your PC to Windows 11 and enable the Windows Subsystem for Android. You can download Windows 11 from the <a href="">Windows Update</a> section in the Settings app. To enable the Windows Subsystem for Android, go to the <a href="">Microsoft Store</a> and install the app. You will also need to install the <a href="">Amazon Appstore</a> app, which will allow you to access Android apps on your PC.</li>
-<li>Install Plants vs Zombies 2 from the Amazon Appstore or the Google Play Store. You can launch the Amazon Appstore app from the Start menu or the Taskbar and search for Plants vs Zombies 2. Alternatively, you can also install the Google Play Store app from the Amazon Appstore and use it to download Plants vs Zombies 2. You will need to sign in with your Google account to access the Google Play Store.</li>
-<li>Launch the game and enjoy playing it on your PC. You can find Plants vs Zombies 2 in the Start menu or the Taskbar, along with other Android apps. You can use your mouse or touchpad to interact with the game, or connect a keyboard or a controller for better control. You can also adjust the window size and orientation of the game according to your preference.</li>
-</ol>
- <h2>Method 2: Use an Android emulator such as Bluestacks or Nox Player</h2>
-<p>Another way to download Plants vs Zombies 2 on PC is to use an Android emulator, which is a software that simulates an Android device on your PC. There are many Android emulators available online, but some of the most popular ones are Bluestacks and Nox Player. Here are the steps to follow:</p>
-<ol>
-<li>Download and install an Android emulator of your choice from their official websites. You can visit <a href="">Bluestacks.com</a> or <a href="">Bignox.com</a> to download Bluestacks or Nox Player respectively . Follow the instructions on the screen to install and set up the emulator on your PC.</li>
-<li>Sign in with your Google account and access the Google Play Store. Once you launch the emulator, you will need to sign in with your Google account to access the Google Play Store and other Google services. You can use an existing account or create a new one.</li>
-<li>Search for Plants vs Zombies 2 and install it on your emulator. You can use the search bar in the Google Play Store to find Plants vs Zombies 2 and click on the Install button to download it on your emulator.</li>
-<li>Launch the game and customize the controls according to your preference. You can find Plants vs Zombies 2 on the home screen or in the app drawer of your emulator. You can use your mouse or touchpad to interact with the game, or connect a keyboard or a controller for better control. You can also customize the controls by using the settings menu of your emulator.</li>
-</ol>
- <h2>Method 3: Use Parsec to stream the game from your PC to your Android device</h2>
-<p>A third way to download Plants vs Zombies 2 on PC is to use Parsec, which is a software that allows you to stream games from your PC to your Android device. This way, you can play Plants vs Zombies 2 on your PC without installing it, as long as you have a good internet connection and a compatible device. Here are the steps to follow:</p>
-<ol>
-<li>Download and install Parsec on both your PC and your Android device. You can visit <a href="">Parsecgaming.com</a> to download Parsec for free. Follow the instructions on the screen to install and set up Parsec on your PC and your Android device.</li>
-<li>Create a Parsec account and sign in on both devices. You will need to create a Parsec account and sign in on both your PC and your Android device to use the streaming service. You can use an existing account or create a new one.</li>
-<li>Launch Plants vs Zombies 2 on your PC and start hosting a game session. You can launch Plants vs Zombies 2 on your PC from the Start menu or the Taskbar, or from any other source that you have installed it from. Once the game is running, open Parsec on your PC and click on the Host tab. You will see a code that you can use to invite other devices to join your game session.</li>
-<li>Connect to your PC from your Android device using Parsec and start playing the game. Open Parsec on your Android device and click on the Friends tab. You will see a list of devices that you can connect to, including your PC. Enter the code that you got from your PC and click on the Connect button. You will see the game screen on your Android device and you can start playing the game.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Plants vs Zombies 2 is a fun and addictive game that you can enjoy on your PC using any of the methods mentioned above. Whether you use Windows 11, an Android emulator, or Parsec, you can experience the game on a bigger screen, with better control and performance. Here are some tips and tricks for playing the game on PC:</p>
-<ul>
-<li>Use a keyboard and mouse or a controller for better control and performance. You can use the default controls or customize them according to your preference. You can also use hotkeys to quickly access certain functions such as Plant Food, Power Ups, or Pause.</li>
-<li>Adjust the graphics settings and resolution to suit your PC's capabilities. You can change the graphics quality, resolution, frame rate, and full screen mode of the game from the settings menu. You can also use the zoom function to get a closer or wider view of the lawn.</li>
-<li>Join the online community and compete with other players in Arena mode. You can access Arena mode from the main menu of the game and participate in weekly tournaments that test your skills and strategy. You can earn rewards such as coins, gems, mints, gauntlets, and trophies by ranking high on the leaderboards.</li>
-</ul>
- <h3>FAQs</h3>
-<p>Here are some common questions that readers might have about downloading Plants vs Zombies 2 on PC:</p>
-<dl>
-<dt>Q1: Is Plants vs Zombies 2 free to play?</dt>
-<dd>A1: Yes, Plants vs Zombies 2 is free to play, but it contains ads and in-app purchases that can enhance your gaming experience.</dd>
-<dt>Q2: Can I play Plants vs Zombies 2 offline?</dt>
-<dd>A2: Yes, you can play Plants vs Zombies 2 offline, but you will need an internet connection to access some features such as daily events, leaderboards, and cloud save.</dd>
-<dt>Q3: How many worlds are there in Plants vs Zombies 2?</dt>
-<dd>A3: There are currently 11 worlds in Plants vs Zombies 2, each with its own theme, plants, zombies, and challenges. You can unlock them by collecting stars or paying with gems.</dd>
-<dt>Q4: What is Plant Food and how do I use it?</dt>
-<dd>A4: Plant Food is a special item that can boost your plants' abilities for a short time. You can get Plant Food by killing glowing zombies, planting Power Lily, or buying it with coins. To use Plant Food, just drag it onto any plant on the lawn.</dd>
-<dt>Q5: What is the best strategy for playing Plants vs Zombies 2?</dt>
-<dd>A5: There is no definitive answer to this question, as different strategies work for different levels, modes, and preferences. However, some general tips are to plan ahead, use a variety of plants, upgrade your plants regularly, and experiment with different combinations.</dd>
-</dl>
- <p>I hope you found this article helpful and informative. If you have any questions or feedback, please leave a comment below. Happy gaming!</p> 401be4b1e0<br />
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spaces/2ndelement/voicevox/voicevox_engine/kana_parser.py

@@ -1,146 +0,0 @@
-from typing import List, Optional
-
-from .model import AccentPhrase, Mora, ParseKanaError, ParseKanaErrorCode
-from .mora_list import openjtalk_text2mora
-
-LOOP_LIMIT = 300
-UNVOICE_SYMBOL = "_"
-ACCENT_SYMBOL = "'"
-NOPAUSE_DELIMITER = "/"
-PAUSE_DELIMITER = "、"
-WIDE_INTERROGATION_MARK = "？"
-
-text2mora_with_unvoice = {}
-for text, (consonant, vowel) in openjtalk_text2mora.items():
-    text2mora_with_unvoice[text] = Mora(
-        text=text,
-        consonant=consonant if len(consonant) > 0 else None,
-        consonant_length=0 if len(consonant) > 0 else None,
-        vowel=vowel,
-        vowel_length=0,
-        pitch=0,
-        is_interrogative=False,
-    )
-    if vowel in ["a", "i", "u", "e", "o"]:
-        text2mora_with_unvoice[UNVOICE_SYMBOL + text] = Mora(
-            text=text,
-            consonant=consonant if len(consonant) > 0 else None,
-            consonant_length=0 if len(consonant) > 0 else None,
-            vowel=vowel.upper(),
-            vowel_length=0,
-            pitch=0,
-            is_interrogative=False,
-        )
-
-
-def _text_to_accent_phrase(phrase: str) -> AccentPhrase:
-    """
-    longest matchにより読み仮名からAccentPhraseを生成
-    入力長Nに対し計算量O(N^2)
-    """
-    accent_index: Optional[int] = None
-    moras: List[Mora] = []
-
-    base_index = 0  # パース開始位置。ここから右の文字列をstackに詰めていく。
-    stack = ""  # 保留中の文字列
-    matched_text: Optional[str] = None  # 保留中の文字列内で最後にマッチした仮名
-
-    outer_loop = 0
-    while base_index < len(phrase):
-        outer_loop += 1
-        if phrase[base_index] == ACCENT_SYMBOL:
-            if len(moras) == 0:
-                raise ParseKanaError(ParseKanaErrorCode.ACCENT_TOP, text=phrase)
-            if accent_index is not None:
-                raise ParseKanaError(ParseKanaErrorCode.ACCENT_TWICE, text=phrase)
-            accent_index = len(moras)
-            base_index += 1
-            continue
-        for watch_index in range(base_index, len(phrase)):
-            if phrase[watch_index] == ACCENT_SYMBOL:
-                break
-            # 普通の文字の場合
-            stack += phrase[watch_index]
-            if stack in text2mora_with_unvoice:
-                matched_text = stack
-        # push mora
-        if matched_text is None:
-            raise ParseKanaError(ParseKanaErrorCode.UNKNOWN_TEXT, text=stack)
-        else:
-            moras.append(text2mora_with_unvoice[matched_text].copy(deep=True))
-            base_index += len(matched_text)
-            stack = ""
-            matched_text = None
-        if outer_loop > LOOP_LIMIT:
-            raise ParseKanaError(ParseKanaErrorCode.INFINITE_LOOP)
-    if accent_index is None:
-        raise ParseKanaError(ParseKanaErrorCode.ACCENT_NOTFOUND, text=phrase)
-    else:
-        return AccentPhrase(moras=moras, accent=accent_index, pause_mora=None)
-
-
-def parse_kana(text: str) -> List[AccentPhrase]:
-    """
-    AquesTalkライクな読み仮名をパースして音長・音高未指定のaccent phraseに変換
-    """
-
-    parsed_results: List[AccentPhrase] = []
-    phrase_base = 0
-    if len(text) == 0:
-        raise ParseKanaError(ParseKanaErrorCode.EMPTY_PHRASE, position=1)
-
-    for i in range(len(text) + 1):
-        if i == len(text) or text[i] in [PAUSE_DELIMITER, NOPAUSE_DELIMITER]:
-            phrase = text[phrase_base:i]
-            if len(phrase) == 0:
-                raise ParseKanaError(
-                    ParseKanaErrorCode.EMPTY_PHRASE,
-                    position=str(len(parsed_results) + 1),
-                )
-            phrase_base = i + 1
-
-            is_interrogative = WIDE_INTERROGATION_MARK in phrase
-            if is_interrogative:
-                if WIDE_INTERROGATION_MARK in phrase[:-1]:
-                    raise ParseKanaError(
-                        ParseKanaErrorCode.INTERROGATION_MARK_NOT_AT_END, text=phrase
-                    )
-                phrase = phrase.replace(WIDE_INTERROGATION_MARK, "")
-
-            accent_phrase: AccentPhrase = _text_to_accent_phrase(phrase)
-            if i < len(text) and text[i] == PAUSE_DELIMITER:
-                accent_phrase.pause_mora = Mora(
-                    text="、",
-                    consonant=None,
-                    consonant_length=None,
-                    vowel="pau",
-                    vowel_length=0,
-                    pitch=0,
-                )
-            accent_phrase.is_interrogative = is_interrogative
-
-            parsed_results.append(accent_phrase)
-
-    return parsed_results
-
-
-def create_kana(accent_phrases: List[AccentPhrase]) -> str:
-    text = ""
-    for i, phrase in enumerate(accent_phrases):
-        for j, mora in enumerate(phrase.moras):
-            if mora.vowel in ["A", "I", "U", "E", "O"]:
-                text += UNVOICE_SYMBOL
-
-            text += mora.text
-            if j + 1 == phrase.accent:
-                text += ACCENT_SYMBOL
-
-        if phrase.is_interrogative:
-            text += WIDE_INTERROGATION_MARK
-
-        if i < len(accent_phrases) - 1:
-            if phrase.pause_mora is None:
-                text += NOPAUSE_DELIMITER
-            else:
-                text += PAUSE_DELIMITER
-    return text

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/GenerSpeech/model/prosody_util.py

@@ -1,385 +0,0 @@
-from torch import nn
-import copy
-import torch
-from utils.hparams import hparams
-from modules.GenerSpeech.model.wavenet import WN
-import math
-
-from modules.fastspeech.tts_modules import LayerNorm
-import torch.nn.functional as F
-from utils.tts_utils import group_hidden_by_segs, sequence_mask
-
-from scipy.cluster.vq import kmeans2
-from torch.nn import functional as F
-
-
-class VQEmbeddingEMA(nn.Module):
-    def __init__(self, n_embeddings, embedding_dim, commitment_cost=0.25, decay=0.999, epsilon=1e-5,
-                 print_vq_prob=False):
-        super(VQEmbeddingEMA, self).__init__()
-        self.commitment_cost = commitment_cost
-        self.n_embeddings = n_embeddings
-        self.decay = decay
-        self.epsilon = epsilon
-        self.print_vq_prob = print_vq_prob
-        self.register_buffer('data_initialized', torch.zeros(1))
-        init_bound = 1 / 512
-        embedding = torch.Tensor(n_embeddings, embedding_dim)
-        embedding.uniform_(-init_bound, init_bound)
-        self.register_buffer("embedding", embedding)
-        self.register_buffer("ema_count", torch.zeros(n_embeddings))
-        self.register_buffer("ema_weight", self.embedding.clone())
-
-    def encode(self, x):
-        B, T, _ = x.shape
-        M, D = self.embedding.size()
-        x_flat = x.detach().reshape(-1, D)
-
-        distances = torch.addmm(torch.sum(self.embedding ** 2, dim=1) +
-                                torch.sum(x_flat ** 2, dim=1, keepdim=True),
-                                x_flat, self.embedding.t(),
-                                alpha=-2.0, beta=1.0)  # [B*T_mel, N_vq]
-        indices = torch.argmin(distances.float(), dim=-1)  # [B*T_mel]
-        quantized = F.embedding(indices, self.embedding)
-        quantized = quantized.view_as(x)
-        return x_flat, quantized, indices
-
-    def forward(self, x):
-        """
-
-        :param x: [B, T, D]
-        :return: [B, T, D]
-        """
-        B, T, _ = x.shape
-        M, D = self.embedding.size()
-        if self.training and self.data_initialized.item() == 0:
-            print('| running kmeans in VQVAE')  # data driven initialization for the embeddings
-            x_flat = x.detach().reshape(-1, D)
-            rp = torch.randperm(x_flat.size(0))
-            kd = kmeans2(x_flat[rp].data.cpu().numpy(), self.n_embeddings, minit='points')
-            self.embedding.copy_(torch.from_numpy(kd[0]))
-            x_flat, quantized, indices = self.encode(x)
-            encodings = F.one_hot(indices, M).float()
-            self.ema_weight.copy_(torch.matmul(encodings.t(), x_flat))
-            self.ema_count.copy_(torch.sum(encodings, dim=0))
-
-        x_flat, quantized, indices = self.encode(x)
-        encodings = F.one_hot(indices, M).float()
-        indices = indices.reshape(B, T)
-
-        if self.training and self.data_initialized.item() != 0:
-            self.ema_count = self.decay * self.ema_count + (1 - self.decay) * torch.sum(encodings, dim=0)
-
-            n = torch.sum(self.ema_count)
-            self.ema_count = (self.ema_count + self.epsilon) / (n + M * self.epsilon) * n
-
-            dw = torch.matmul(encodings.t(), x_flat)
-            self.ema_weight = self.decay * self.ema_weight + (1 - self.decay) * dw
-
-            self.embedding = self.ema_weight / self.ema_count.unsqueeze(-1)
-        self.data_initialized.fill_(1)
-
-        e_latent_loss = F.mse_loss(x, quantized.detach(), reduction='none')
-        nonpadding = (x.abs().sum(-1) > 0).float()
-        e_latent_loss = (e_latent_loss.mean(-1) * nonpadding).sum() / nonpadding.sum()
-        loss = self.commitment_cost * e_latent_loss
-
-        quantized = x + (quantized - x).detach()
-
-        avg_probs = torch.mean(encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
-        if self.print_vq_prob:
-            print("| VQ code avg_probs: ", avg_probs)
-        return quantized, loss, indices, perplexity
-
-class CrossAttenLayer(nn.Module):
-    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1):
-        super(CrossAttenLayer, self).__init__()
-        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout1 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(d_model)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-        self.dropout2 = nn.Dropout(dropout)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.activation = nn.ReLU()
-
-    def forward(self, src, local_emotion, emotion_key_padding_mask=None, forcing=False):
-        # src: (Tph, B, 256) local_emotion: (Temo, B, 256) emotion_key_padding_mask: (B, Temo)
-        if forcing:
-            maxlength = src.shape[0]
-            k = local_emotion.shape[0] / src.shape[0]
-            lengths1 = torch.ceil(torch.tensor([i for i in range(maxlength)]).to(src.device) * k) + 1
-            lengths2 = torch.floor(torch.tensor([i for i in range(maxlength)]).to(src.device) * k) - 1
-            mask1 = sequence_mask(lengths1, local_emotion.shape[0])
-            mask2 = sequence_mask(lengths2, local_emotion.shape[0])
-            mask = mask1.float() - mask2.float()
-            attn_emo = mask.repeat(src.shape[1], 1, 1) # (B, Tph, Temo)
-            src2 = torch.matmul(local_emotion.permute(1, 2, 0), attn_emo.float().transpose(1, 2)).permute(2, 0, 1)
-        else:
-            src2, attn_emo = self.multihead_attn(src, local_emotion, local_emotion, key_padding_mask=emotion_key_padding_mask)
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.activation(self.linear1(src)))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        return src, attn_emo
-
-
-class ProsodyAligner(nn.Module):
-    def __init__(self, num_layers, guided_sigma=0.3, guided_layers=None, norm=None):
-        super(ProsodyAligner, self).__init__()
-        self.layers = nn.ModuleList([CrossAttenLayer(d_model=hparams['hidden_size'], nhead=2) for _ in range(num_layers)])
-        self.num_layers = num_layers
-        self.norm = norm
-        self.guided_sigma = guided_sigma
-        self.guided_layers = guided_layers if guided_layers is not None else num_layers
-
-    def forward(self, src, local_emotion, src_key_padding_mask=None, emotion_key_padding_mask=None, forcing=False):
-        output = src
-        guided_loss = 0
-        attn_emo_list = []
-        for i, mod in enumerate(self.layers):
-            # output: (Tph, B, 256), global_emotion: (1, B, 256), local_emotion: (Temo, B, 256) mask: None, src_key_padding_mask: (B, Tph),
-            # emotion_key_padding_mask: (B, Temo)
-            output, attn_emo = mod(output, local_emotion, emotion_key_padding_mask=emotion_key_padding_mask, forcing=forcing)
-            attn_emo_list.append(attn_emo.unsqueeze(1))
-            # attn_emo: (B, Tph, Temo) attn: (B, Tph, Tph)
-            if i < self.guided_layers and src_key_padding_mask is not None:
-                s_length = (~src_key_padding_mask).float().sum(-1) # B
-                emo_length = (~emotion_key_padding_mask).float().sum(-1)
-                attn_w_emo = _make_guided_attention_mask(src_key_padding_mask.size(-1), s_length, emotion_key_padding_mask.size(-1), emo_length, self.guided_sigma)
-
-                g_loss_emo = attn_emo * attn_w_emo  # N, L, S
-                non_padding_mask = (~src_key_padding_mask).unsqueeze(-1) & (~emotion_key_padding_mask).unsqueeze(1)
-                guided_loss = g_loss_emo[non_padding_mask].mean() + guided_loss
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output, guided_loss, attn_emo_list
-
-def _make_guided_attention_mask(ilen, rilen, olen, rolen, sigma):
-    grid_x, grid_y = torch.meshgrid(torch.arange(ilen, device=rilen.device), torch.arange(olen, device=rolen.device))
-    grid_x = grid_x.unsqueeze(0).expand(rilen.size(0), -1, -1)
-    grid_y = grid_y.unsqueeze(0).expand(rolen.size(0), -1, -1)
-    rilen = rilen.unsqueeze(1).unsqueeze(1)
-    rolen = rolen.unsqueeze(1).unsqueeze(1)
-    return 1.0 - torch.exp(
-        -((grid_y.float() / rolen - grid_x.float() / rilen) ** 2) / (2 * (sigma ** 2))
-    )
-
-class LocalStyleAdaptor(nn.Module):
-    def __init__(self, hidden_size, num_vq_codes=64, padding_idx=0):
-        super(LocalStyleAdaptor, self).__init__()
-        self.encoder = ConvBlocks(80, hidden_size, [1] * 5, 5, dropout=hparams['vae_dropout'])
-        self.n_embed = num_vq_codes
-        self.vqvae = VQEmbeddingEMA(self.n_embed, hidden_size, commitment_cost=hparams['lambda_commit'])
-        self.wavenet = WN(hidden_channels=80, gin_channels=80, kernel_size=3, dilation_rate=1, n_layers=4)
-        self.padding_idx = padding_idx
-        self.hidden_size = hidden_size
-
-    def forward(self, ref_mels, mel2ph=None, no_vq=False):
-        """
-
-        :param ref_mels: [B, T, 80]
-        :return: [B, 1, H]
-        """
-        padding_mask = ref_mels[:, :, 0].eq(self.padding_idx).data
-        ref_mels = self.wavenet(ref_mels.transpose(1, 2), x_mask=(~padding_mask).unsqueeze(1).repeat([1, 80, 1])).transpose(1, 2)
-        if mel2ph is not None:
-            ref_ph, _ = group_hidden_by_segs(ref_mels, mel2ph, torch.max(mel2ph))
-        else:
-            ref_ph = ref_mels
-        prosody = self.encoder(ref_ph)
-        if no_vq:
-            return prosody
-        z, vq_loss, vq_tokens, ppl = self.vqvae(prosody)
-        vq_loss = vq_loss.mean()
-        return z, vq_loss, ppl
-
-
-
-
-class LambdaLayer(nn.Module):
-    def __init__(self, lambd):
-        super(LambdaLayer, self).__init__()
-        self.lambd = lambd
-
-    def forward(self, x):
-        return self.lambd(x)
-
-
-class Conv1d(nn.Conv1d):
-    """A wrapper around nn.Conv1d, that works on (batch, time, channels)"""
-
-    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, dilation=1, groups=1, bias=True, padding=0):
-        super(Conv1d, self).__init__(in_channels=in_channels, out_channels=out_channels,
-                                     kernel_size=kernel_size, stride=stride, dilation=dilation,
-                                     groups=groups, bias=bias, padding=padding)
-
-    def forward(self, x):
-        return super().forward(x.transpose(2, 1)).transpose(2, 1)
-
-
-def init_weights_func(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv1d") != -1:
-        torch.nn.init.xavier_uniform_(m.weight)
-
-
-class ResidualBlock(nn.Module):
-    """Implements conv->PReLU->norm n-times"""
-
-    def __init__(self, channels, kernel_size, dilation, n=2, norm_type='bn', dropout=0.0,
-                 c_multiple=2, ln_eps=1e-12):
-        super(ResidualBlock, self).__init__()
-
-        if norm_type == 'bn':
-            norm_builder = lambda: nn.BatchNorm1d(channels)
-        elif norm_type == 'in':
-            norm_builder = lambda: nn.InstanceNorm1d(channels, affine=True)
-        elif norm_type == 'gn':
-            norm_builder = lambda: nn.GroupNorm(8, channels)
-        elif norm_type == 'ln':
-            norm_builder = lambda: LayerNorm(channels, dim=1, eps=ln_eps)
-        else:
-            norm_builder = lambda: nn.Identity()
-
-        self.blocks = [
-            nn.Sequential(
-                norm_builder(),
-                nn.Conv1d(channels, c_multiple * channels, kernel_size, dilation=dilation,
-                          padding=(dilation * (kernel_size - 1)) // 2),
-                LambdaLayer(lambda x: x * kernel_size ** -0.5),
-                nn.GELU(),
-                nn.Conv1d(c_multiple * channels, channels, 1, dilation=dilation),
-            )
-            for i in range(n)
-        ]
-
-        self.blocks = nn.ModuleList(self.blocks)
-        self.dropout = dropout
-
-    def forward(self, x):
-        nonpadding = (x.abs().sum(1) > 0).float()[:, None, :]
-        for b in self.blocks:
-            x_ = b(x)
-            if self.dropout > 0 and self.training:
-                x_ = F.dropout(x_, self.dropout, training=self.training)
-            x = x + x_
-            x = x * nonpadding
-        return x
-
-
-class Pad(nn.ZeroPad2d):
-    def __init__(self, kernel_size, dilation):
-        pad_total = dilation * (kernel_size - 1)
-        begin = pad_total // 2
-        end = pad_total - begin
-
-        super(Pad, self).__init__((begin, end, begin, end))
-
-
-class ZeroTemporalPad(nn.ZeroPad2d):
-    """Pad sequences to equal lentgh in the temporal dimension"""
-
-    def __init__(self, kernel_size, dilation, causal=False):
-        total_pad = (dilation * (kernel_size - 1))
-
-        if causal:
-            super(ZeroTemporalPad, self).__init__((total_pad, 0))
-        else:
-            begin = total_pad // 2
-            end = total_pad - begin
-            super(ZeroTemporalPad, self).__init__((begin, end))
-
-
-class ConvBlocks(nn.Module):
-    """Decodes the expanded phoneme encoding into spectrograms"""
-
-    def __init__(self, channels, out_dims, dilations, kernel_size,
-                 norm_type='ln', layers_in_block=2, c_multiple=2,
-                 dropout=0.0, ln_eps=1e-5, init_weights=True):
-        super(ConvBlocks, self).__init__()
-        self.res_blocks = nn.Sequential(
-            *[ResidualBlock(channels, kernel_size, d,
-                            n=layers_in_block, norm_type=norm_type, c_multiple=c_multiple,
-                            dropout=dropout, ln_eps=ln_eps)
-              for d in dilations],
-        )
-        if norm_type == 'bn':
-            norm = nn.BatchNorm1d(channels)
-        elif norm_type == 'in':
-            norm = nn.InstanceNorm1d(channels, affine=True)
-        elif norm_type == 'gn':
-            norm = nn.GroupNorm(8, channels)
-        elif norm_type == 'ln':
-            norm = LayerNorm(channels, dim=1, eps=ln_eps)
-        self.last_norm = norm
-        self.post_net1 = nn.Conv1d(channels, out_dims, kernel_size=3, padding=1)
-        if init_weights:
-            self.apply(init_weights_func)
-
-    def forward(self, x):
-        """
-
-        :param x: [B, T, H]
-        :return:  [B, T, H]
-        """
-        x = x.transpose(1, 2)
-        nonpadding = (x.abs().sum(1) > 0).float()[:, None, :]
-        x = self.res_blocks(x) * nonpadding
-        x = self.last_norm(x) * nonpadding
-        x = self.post_net1(x) * nonpadding
-        return x.transpose(1, 2)
-
-
-class TextConvEncoder(ConvBlocks):
-    def __init__(self, embed_tokens, channels, out_dims, dilations, kernel_size,
-                 norm_type='ln', layers_in_block=2, c_multiple=2,
-                 dropout=0.0, ln_eps=1e-5, init_weights=True):
-        super().__init__(channels, out_dims, dilations, kernel_size,
-                         norm_type, layers_in_block, c_multiple,
-                         dropout, ln_eps, init_weights)
-        self.embed_tokens = embed_tokens
-        self.embed_scale = math.sqrt(channels)
-
-    def forward(self, txt_tokens):
-        """
-
-        :param txt_tokens: [B, T]
-        :return: {
-            'encoder_out': [B x T x C]
-        }
-        """
-        x = self.embed_scale * self.embed_tokens(txt_tokens)
-        return super().forward(x)
-
-
-class ConditionalConvBlocks(ConvBlocks):
-    def __init__(self, channels, g_channels, out_dims, dilations, kernel_size,
-                 norm_type='ln', layers_in_block=2, c_multiple=2,
-                 dropout=0.0, ln_eps=1e-5, init_weights=True, is_BTC=True):
-        super().__init__(channels, out_dims, dilations, kernel_size,
-                         norm_type, layers_in_block, c_multiple,
-                         dropout, ln_eps, init_weights)
-        self.g_prenet = nn.Conv1d(g_channels, channels, 3, padding=1)
-        self.is_BTC = is_BTC
-        if init_weights:
-            self.g_prenet.apply(init_weights_func)
-
-    def forward(self, x, g, x_mask):
-        if self.is_BTC:
-            x = x.transpose(1, 2)
-            g = g.transpose(1, 2)
-            x_mask = x_mask.transpose(1, 2)
-        x = x + self.g_prenet(g)
-        x = x * x_mask
-
-        if not self.is_BTC:
-            x = x.transpose(1, 2)
-        x = super(ConditionalConvBlocks, self).forward(x)  # input needs to be BTC
-        if not self.is_BTC:
-            x = x.transpose(1, 2)
-        return x

spaces/AIGC-Audio/Make_An_Audio_inpaint/README.md

@@ -1,12 +0,0 @@
----
-title: Make An Audio Inpaint
-emoji: 🔥
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ASJMO/freegpt/g4f/Provider/Providers/Bard.py

@@ -1,74 +0,0 @@
-import os, requests, json, browser_cookie3, re, random
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://bard.google.com'
-model = ['Palm2']
-supports_stream = False
-needs_auth = True
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    psid = {cookie.name: cookie.value for cookie in browser_cookie3.chrome(
-        domain_name='.google.com')}['__Secure-1PSID']
-    
-    formatted = '\n'.join([
-        '%s: %s' % (message['role'], message['content']) for message in messages
-    ])
-    prompt = f'{formatted}\nAssistant:'
-
-    proxy = kwargs.get('proxy', False)
-    if proxy == False:
-        print('warning!, you did not give a proxy, a lot of countries are banned from Google Bard, so it may not work')
-    
-    snlm0e = None
-    conversation_id = None
-    response_id = None
-    choice_id = None
-
-    client = requests.Session()
-    client.proxies = {
-        'http': f'http://{proxy}',
-        'https': f'http://{proxy}'} if proxy else None
-
-    client.headers = {
-        'authority': 'bard.google.com',
-        'content-type': 'application/x-www-form-urlencoded;charset=UTF-8',
-        'origin': 'https://bard.google.com',
-        'referer': 'https://bard.google.com/',
-        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
-        'x-same-domain': '1',
-        'cookie': f'__Secure-1PSID={psid}'
-    }
-
-    snlm0e = re.search(r'SNlM0e\":\"(.*?)\"',
-                    client.get('https://bard.google.com/').text).group(1) if not snlm0e else snlm0e
-
-    params = {
-        'bl': 'boq_assistant-bard-web-server_20230326.21_p0',
-        '_reqid': random.randint(1111, 9999),
-        'rt': 'c'
-    }
-
-    data = {
-        'at': snlm0e,
-        'f.req': json.dumps([None, json.dumps([[prompt], None, [conversation_id, response_id, choice_id]])])}
-
-    intents = '.'.join([
-        'assistant',
-        'lamda',
-        'BardFrontendService'
-    ])
-
-    response = client.post(f'https://bard.google.com/_/BardChatUi/data/{intents}/StreamGenerate',
-                        data=data, params=params)
-
-    chat_data = json.loads(response.content.splitlines()[3])[0][2]
-    if chat_data:
-        json_chat_data = json.loads(chat_data)
-
-        yield json_chat_data[0][0]
-        
-    else:
-        yield 'error'
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AbeShinzo0708/AI_Kishida_Fumio_speaker/pre-fix/librosa/__init__.py

@@ -1,10 +0,0 @@
-# https://github.com/librosa/librosa/issues/1682
-
-import lazy_loader as lazy
-from .version import version as __version__
-
-_filename = __file__
-if _filename.endswith('.pyc'):
-    _filename = _filename[:-1]
-
-__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, _filename)

spaces/Abhilashvj/planogram-compliance/classify/predict.py

@@ -1,345 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
-
-Usage - sources:
-    $ python classify/predict.py --weights yolov5s-cls.pt --source 0                               # webcam
-                                                                   img.jpg                         # image
-                                                                   vid.mp4                         # video
-                                                                   screen                          # screenshot
-                                                                   path/                           # directory
-                                                                   list.txt                        # list of images
-                                                                   list.streams                    # list of streams
-                                                                   'path/*.jpg'                    # glob
-                                                                   'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-
-Usage - formats:
-    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
-                                           yolov5s-cls.torchscript        # TorchScript
-                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                           yolov5s-cls_openvino_model     # OpenVINO
-                                           yolov5s-cls.engine             # TensorRT
-                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
-                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
-                                           yolov5s-cls.pb                 # TensorFlow GraphDef
-                                           yolov5s-cls.tflite             # TensorFlow Lite
-                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
-                                           yolov5s-cls_paddle_model       # PaddlePaddle
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-import torch.nn.functional as F
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import DetectMultiBackend
-from utils.augmentations import classify_transforms
-from utils.dataloaders import (
-    IMG_FORMATS,
-    VID_FORMATS,
-    LoadImages,
-    LoadScreenshots,
-    LoadStreams,
-)
-from utils.general import (
-    LOGGER,
-    Profile,
-    check_file,
-    check_img_size,
-    check_imshow,
-    check_requirements,
-    colorstr,
-    cv2,
-    increment_path,
-    print_args,
-    strip_optimizer,
-)
-from utils.plots import Annotator
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-@smart_inference_mode()
-def run(
-    weights=ROOT / "yolov5s-cls.pt",  # model.pt path(s)
-    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
-    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
-    imgsz=(224, 224),  # inference size (height, width)
-    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    view_img=False,  # show results
-    save_txt=False,  # save results to *.txt
-    nosave=False,  # do not save images/videos
-    augment=False,  # augmented inference
-    visualize=False,  # visualize features
-    update=False,  # update all models
-    project=ROOT / "runs/predict-cls",  # save results to project/name
-    name="exp",  # save results to project/name
-    exist_ok=False,  # existing project/name ok, do not increment
-    half=False,  # use FP16 half-precision inference
-    dnn=False,  # use OpenCV DNN for ONNX inference
-    vid_stride=1,  # video frame-rate stride
-):
-    source = str(source)
-    save_img = not nosave and not source.endswith(
-        ".txt"
-    )  # save inference images
-    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(
-        ("rtsp://", "rtmp://", "http://", "https://")
-    )
-    webcam = (
-        source.isnumeric()
-        or source.endswith(".streams")
-        or (is_url and not is_file)
-    )
-    screenshot = source.lower().startswith("screen")
-    if is_url and is_file:
-        source = check_file(source)  # download
-
-    # Directories
-    save_dir = increment_path(
-        Path(project) / name, exist_ok=exist_ok
-    )  # increment run
-    (save_dir / "labels" if save_txt else save_dir).mkdir(
-        parents=True, exist_ok=True
-    )  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(
-        weights, device=device, dnn=dnn, data=data, fp16=half
-    )
-    stride, names, pt = model.stride, model.names, model.pt
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Dataloader
-    bs = 1  # batch_size
-    if webcam:
-        view_img = check_imshow(warn=True)
-        dataset = LoadStreams(
-            source,
-            img_size=imgsz,
-            transforms=classify_transforms(imgsz[0]),
-            vid_stride=vid_stride,
-        )
-        bs = len(dataset)
-    elif screenshot:
-        dataset = LoadScreenshots(
-            source, img_size=imgsz, stride=stride, auto=pt
-        )
-    else:
-        dataset = LoadImages(
-            source,
-            img_size=imgsz,
-            transforms=classify_transforms(imgsz[0]),
-            vid_stride=vid_stride,
-        )
-    vid_path, vid_writer = [None] * bs, [None] * bs
-
-    # Run inference
-    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
-    for path, im, im0s, vid_cap, s in dataset:
-        with dt[0]:
-            im = torch.Tensor(im).to(model.device)
-            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
-            if len(im.shape) == 3:
-                im = im[None]  # expand for batch dim
-
-        # Inference
-        with dt[1]:
-            results = model(im)
-
-        # Post-process
-        with dt[2]:
-            pred = F.softmax(results, dim=1)  # probabilities
-
-        # Process predictions
-        for i, prob in enumerate(pred):  # per image
-            seen += 1
-            if webcam:  # batch_size >= 1
-                p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f"{i}: "
-            else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / "labels" / p.stem) + (
-                "" if dataset.mode == "image" else f"_{frame}"
-            )  # im.txt
-
-            s += "%gx%g " % im.shape[2:]  # print string
-            annotator = Annotator(im0, example=str(names), pil=True)
-
-            # Print results
-            top5i = prob.argsort(0, descending=True)[
-                :5
-            ].tolist()  # top 5 indices
-            s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
-
-            # Write results
-            text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i)
-            if save_img or view_img:  # Add bbox to image
-                annotator.text((32, 32), text, txt_color=(255, 255, 255))
-            if save_txt:  # Write to file
-                with open(f"{txt_path}.txt", "a") as f:
-                    f.write(text + "\n")
-
-            # Stream results
-            im0 = annotator.result()
-            if view_img:
-                if platform.system() == "Linux" and p not in windows:
-                    windows.append(p)
-                    cv2.namedWindow(
-                        str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO
-                    )  # allow window resize (Linux)
-                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == "image":
-                    cv2.imwrite(save_path, im0)
-                else:  # 'video' or 'stream'
-                    if vid_path[i] != save_path:  # new video
-                        vid_path[i] = save_path
-                        if isinstance(vid_writer[i], cv2.VideoWriter):
-                            vid_writer[
-                                i
-                            ].release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                        save_path = str(
-                            Path(save_path).with_suffix(".mp4")
-                        )  # force *.mp4 suffix on results videos
-                        vid_writer[i] = cv2.VideoWriter(
-                            save_path,
-                            cv2.VideoWriter_fourcc(*"mp4v"),
-                            fps,
-                            (w, h),
-                        )
-                    vid_writer[i].write(im0)
-
-        # Print time (inference-only)
-        LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
-
-    # Print results
-    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
-    LOGGER.info(
-        f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}"
-        % t
-    )
-    if save_txt or save_img:
-        s = (
-            f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"
-            if save_txt
-            else ""
-        )
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    if update:
-        strip_optimizer(
-            weights[0]
-        )  # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--weights",
-        nargs="+",
-        type=str,
-        default=ROOT / "yolov5s-cls.pt",
-        help="model path(s)",
-    )
-    parser.add_argument(
-        "--source",
-        type=str,
-        default=ROOT / "data/images",
-        help="file/dir/URL/glob/screen/0(webcam)",
-    )
-    parser.add_argument(
-        "--data",
-        type=str,
-        default=ROOT / "data/coco128.yaml",
-        help="(optional) dataset.yaml path",
-    )
-    parser.add_argument(
-        "--imgsz",
-        "--img",
-        "--img-size",
-        nargs="+",
-        type=int,
-        default=[224],
-        help="inference size h,w",
-    )
-    parser.add_argument(
-        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument("--view-img", action="store_true", help="show results")
-    parser.add_argument(
-        "--save-txt", action="store_true", help="save results to *.txt"
-    )
-    parser.add_argument(
-        "--nosave", action="store_true", help="do not save images/videos"
-    )
-    parser.add_argument(
-        "--augment", action="store_true", help="augmented inference"
-    )
-    parser.add_argument(
-        "--visualize", action="store_true", help="visualize features"
-    )
-    parser.add_argument(
-        "--update", action="store_true", help="update all models"
-    )
-    parser.add_argument(
-        "--project",
-        default=ROOT / "runs/predict-cls",
-        help="save results to project/name",
-    )
-    parser.add_argument(
-        "--name", default="exp", help="save results to project/name"
-    )
-    parser.add_argument(
-        "--exist-ok",
-        action="store_true",
-        help="existing project/name ok, do not increment",
-    )
-    parser.add_argument(
-        "--half", action="store_true", help="use FP16 half-precision inference"
-    )
-    parser.add_argument(
-        "--dnn", action="store_true", help="use OpenCV DNN for ONNX inference"
-    )
-    parser.add_argument(
-        "--vid-stride", type=int, default=1, help="video frame-rate stride"
-    )
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    check_requirements(exclude=("tensorboard", "thop"))
-    run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Vitalentum.py

@@ -1,69 +0,0 @@
-from __future__ import annotations
-
-import json
-from aiohttp import ClientSession
-
-from .base_provider import AsyncGeneratorProvider
-from ..typing import AsyncResult, Messages
-
-class Vitalentum(AsyncGeneratorProvider):
-    url                   = "https://app.vitalentum.io"
-    working               = True
-    supports_gpt_35_turbo = True
-
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncResult:
-        headers = {
-            "User-Agent"         : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-            "Accept"             : "text/event-stream",
-            "Accept-language"    : "de,en-US;q=0.7,en;q=0.3",
-            "Origin"             : cls.url,
-            "Referer"            : cls.url + "/",
-            "Sec-Fetch-Dest"     : "empty",
-            "Sec-Fetch-Mode"     : "cors",
-            "Sec-Fetch-Site"     : "same-origin",
-        }
-        conversation = json.dumps({"history": [{
-            "speaker": "human" if message["role"] == "user" else "bot",
-            "text": message["content"],
-        } for message in messages]})
-        data = {
-            "conversation": conversation,
-            "temperature": 0.7,
-            **kwargs
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            async with session.post(cls.url + "/api/converse-edge", json=data, proxy=proxy) as response:
-                response.raise_for_status()
-                async for line in response.content:
-                    line = line.decode()
-                    if line.startswith("data: "):
-                        if line.startswith("data: [DONE]"):
-                            break
-                        line = json.loads(line[6:-1])
-                        content = line["choices"][0]["delta"].get("content")
-                        if content:
-                            yield content
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("proxy", "str"),
-            ("temperature", "float"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customshapes/CustomShapes.d.ts

@@ -1,2 +0,0 @@
-import CustomShapes from '../../../plugins/customshapes';
-export default CustomShapes;

spaces/AjulorC/question_answering_bot_deployed_with_Gradio/README.md

@@ -1,12 +0,0 @@
----
-title: Question_answering_bot_deployed_with_Gradio
-emoji: 🦀
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 2.8.11
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/Akmyradov/TurkmenTTSweSTT/vits/train_ms.py

@@ -1,294 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-
-import commons
-import utils
-from data_utils import (
-  TextAudioSpeakerLoader,
-  TextAudioSpeakerCollate,
-  DistributedBucketSampler
-)
-from models import (
-  SynthesizerTrn,
-  MultiPeriodDiscriminator,
-)
-from losses import (
-  generator_loss,
-  discriminator_loss,
-  feature_loss,
-  kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-
-torch.backends.cudnn.benchmark = True
-global_step = 0
-
-
-def main():
-  """Assume Single Node Multi GPUs Training Only"""
-  assert torch.cuda.is_available(), "CPU training is not allowed."
-
-  n_gpus = torch.cuda.device_count()
-  os.environ['MASTER_ADDR'] = 'localhost'
-  os.environ['MASTER_PORT'] = '80000'
-
-  hps = utils.get_hparams()
-  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-  global global_step
-  if rank == 0:
-    logger = utils.get_logger(hps.model_dir)
-    logger.info(hps)
-    utils.check_git_hash(hps.model_dir)
-    writer = SummaryWriter(log_dir=hps.model_dir)
-    writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-  dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
-  torch.manual_seed(hps.train.seed)
-  torch.cuda.set_device(rank)
-
-  train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
-  train_sampler = DistributedBucketSampler(
-      train_dataset,
-      hps.train.batch_size,
-      [32,300,400,500,600,700,800,900,1000],
-      num_replicas=n_gpus,
-      rank=rank,
-      shuffle=True)
-  collate_fn = TextAudioSpeakerCollate()
-  train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
-      collate_fn=collate_fn, batch_sampler=train_sampler)
-  if rank == 0:
-    eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
-    eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
-        batch_size=hps.train.batch_size, pin_memory=True,
-        drop_last=False, collate_fn=collate_fn)
-
-  net_g = SynthesizerTrn(
-      len(symbols),
-      hps.data.filter_length // 2 + 1,
-      hps.train.segment_size // hps.data.hop_length,
-      n_speakers=hps.data.n_speakers,
-      **hps.model).cuda(rank)
-  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-  optim_g = torch.optim.AdamW(
-      net_g.parameters(), 
-      hps.train.learning_rate, 
-      betas=hps.train.betas, 
-      eps=hps.train.eps)
-  optim_d = torch.optim.AdamW(
-      net_d.parameters(),
-      hps.train.learning_rate, 
-      betas=hps.train.betas, 
-      eps=hps.train.eps)
-  net_g = DDP(net_g, device_ids=[rank])
-  net_d = DDP(net_d, device_ids=[rank])
-
-  try:
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
-    global_step = (epoch_str - 1) * len(train_loader)
-  except:
-    epoch_str = 1
-    global_step = 0
-
-  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-
-  scaler = GradScaler(enabled=hps.train.fp16_run)
-
-  for epoch in range(epoch_str, hps.train.epochs + 1):
-    if rank==0:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-    else:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
-    scheduler_g.step()
-    scheduler_d.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-  net_g, net_d = nets
-  optim_g, optim_d = optims
-  scheduler_g, scheduler_d = schedulers
-  train_loader, eval_loader = loaders
-  if writers is not None:
-    writer, writer_eval = writers
-
-  train_loader.batch_sampler.set_epoch(epoch)
-  global global_step
-
-  net_g.train()
-  net_d.train()
-  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(train_loader):
-    x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-    spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-    y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-    speakers = speakers.cuda(rank, non_blocking=True)
-
-    with autocast(enabled=hps.train.fp16_run):
-      y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
-      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)
-
-      mel = spec_to_mel_torch(
-          spec, 
-          hps.data.filter_length, 
-          hps.data.n_mel_channels, 
-          hps.data.sampling_rate,
-          hps.data.mel_fmin, 
-          hps.data.mel_fmax)
-      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-      y_hat_mel = mel_spectrogram_torch(
-          y_hat.squeeze(1), 
-          hps.data.filter_length, 
-          hps.data.n_mel_channels, 
-          hps.data.sampling_rate, 
-          hps.data.hop_length, 
-          hps.data.win_length, 
-          hps.data.mel_fmin, 
-          hps.data.mel_fmax
-      )
-
-      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice 
-
-      # Discriminator
-      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-      with autocast(enabled=False):
-        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-        loss_disc_all = loss_disc
-    optim_d.zero_grad()
-    scaler.scale(loss_disc_all).backward()
-    scaler.unscale_(optim_d)
-    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-    scaler.step(optim_d)
-
-    with autocast(enabled=hps.train.fp16_run):
-      # Generator
-      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-      with autocast(enabled=False):
-        loss_dur = torch.sum(l_length.float())
-        loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-        loss_fm = feature_loss(fmap_r, fmap_g)
-        loss_gen, losses_gen = generator_loss(y_d_hat_g)
-        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-    optim_g.zero_grad()
-    scaler.scale(loss_gen_all).backward()
-    scaler.unscale_(optim_g)
-    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-    scaler.step(optim_g)
-    scaler.update()
-
-    if rank==0:
-      if global_step % hps.train.log_interval == 0:
-        lr = optim_g.param_groups[0]['lr']
-        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-        logger.info('Train Epoch: {} [{:.0f}%]'.format(
-          epoch,
-          100. * batch_idx / len(train_loader)))
-        logger.info([x.item() for x in losses] + [global_step, lr])
-        
-        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-
-        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-        image_dict = { 
-            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), 
-            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-            "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
-        }
-        utils.summarize(
-          writer=writer,
-          global_step=global_step, 
-          images=image_dict,
-          scalars=scalar_dict)
-
-      if global_step % hps.train.eval_interval == 0:
-        evaluate(hps, net_g, eval_loader, writer_eval)
-        utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-        utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-    global_step += 1
-  
-  if rank == 0:
-    logger.info('====> Epoch: {}'.format(epoch))
-
- 
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    with torch.no_grad():
-      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(eval_loader):
-        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
-        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
-        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
-        speakers = speakers.cuda(0)
-
-        # remove else
-        x = x[:1]
-        x_lengths = x_lengths[:1]
-        spec = spec[:1]
-        spec_lengths = spec_lengths[:1]
-        y = y[:1]
-        y_lengths = y_lengths[:1]
-        speakers = speakers[:1]
-        break
-      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, max_len=1000)
-      y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
-
-      mel = spec_to_mel_torch(
-        spec, 
-        hps.data.filter_length, 
-        hps.data.n_mel_channels, 
-        hps.data.sampling_rate,
-        hps.data.mel_fmin, 
-        hps.data.mel_fmax)
-      y_hat_mel = mel_spectrogram_torch(
-        y_hat.squeeze(1).float(),
-        hps.data.filter_length,
-        hps.data.n_mel_channels,
-        hps.data.sampling_rate,
-        hps.data.hop_length,
-        hps.data.win_length,
-        hps.data.mel_fmin,
-        hps.data.mel_fmax
-      )
-    image_dict = {
-      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-    }
-    audio_dict = {
-      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
-    }
-    if global_step == 0:
-      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-      audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
-
-    utils.summarize(
-      writer=writer_eval,
-      global_step=global_step, 
-      images=image_dict,
-      audios=audio_dict,
-      audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-                           
-if __name__ == "__main__":
-  main()

spaces/Alpaca233/SadTalker/src/test_audio2coeff.py

@@ -1,123 +0,0 @@
-import os 
-import torch
-import numpy as np
-from scipy.io import savemat, loadmat
-from yacs.config import CfgNode as CN
-from scipy.signal import savgol_filter
-
-import safetensors
-import safetensors.torch 
-
-from src.audio2pose_models.audio2pose import Audio2Pose
-from src.audio2exp_models.networks import SimpleWrapperV2 
-from src.audio2exp_models.audio2exp import Audio2Exp
-from src.utils.safetensor_helper import load_x_from_safetensor  
-
-def load_cpk(checkpoint_path, model=None, optimizer=None, device="cpu"):
-    checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
-    if model is not None:
-        model.load_state_dict(checkpoint['model'])
-    if optimizer is not None:
-        optimizer.load_state_dict(checkpoint['optimizer'])
-
-    return checkpoint['epoch']
-
-class Audio2Coeff():
-
-    def __init__(self, sadtalker_path, device):
-        #load config
-        fcfg_pose = open(sadtalker_path['audio2pose_yaml_path'])
-        cfg_pose = CN.load_cfg(fcfg_pose)
-        cfg_pose.freeze()
-        fcfg_exp = open(sadtalker_path['audio2exp_yaml_path'])
-        cfg_exp = CN.load_cfg(fcfg_exp)
-        cfg_exp.freeze()
-
-        # load audio2pose_model
-        self.audio2pose_model = Audio2Pose(cfg_pose, None, device=device)
-        self.audio2pose_model = self.audio2pose_model.to(device)
-        self.audio2pose_model.eval()
-        for param in self.audio2pose_model.parameters():
-            param.requires_grad = False 
-        
-        try:
-            if sadtalker_path['use_safetensor']:
-                checkpoints = safetensors.torch.load_file(sadtalker_path['checkpoint'])
-                self.audio2pose_model.load_state_dict(load_x_from_safetensor(checkpoints, 'audio2pose'))
-            else:
-                load_cpk(sadtalker_path['audio2pose_checkpoint'], model=self.audio2pose_model, device=device)
-        except:
-            raise Exception("Failed in loading audio2pose_checkpoint")
-
-        # load audio2exp_model
-        netG = SimpleWrapperV2()
-        netG = netG.to(device)
-        for param in netG.parameters():
-            netG.requires_grad = False
-        netG.eval()
-        try:
-            if sadtalker_path['use_safetensor']:
-                checkpoints = safetensors.torch.load_file(sadtalker_path['checkpoint'])
-                netG.load_state_dict(load_x_from_safetensor(checkpoints, 'audio2exp'))
-            else:
-                load_cpk(sadtalker_path['audio2exp_checkpoint'], model=netG, device=device)
-        except:
-            raise Exception("Failed in loading audio2exp_checkpoint")
-        self.audio2exp_model = Audio2Exp(netG, cfg_exp, device=device, prepare_training_loss=False)
-        self.audio2exp_model = self.audio2exp_model.to(device)
-        for param in self.audio2exp_model.parameters():
-            param.requires_grad = False
-        self.audio2exp_model.eval()
- 
-        self.device = device
-
-    def generate(self, batch, coeff_save_dir, pose_style, ref_pose_coeff_path=None):
-
-        with torch.no_grad():
-            #test
-            results_dict_exp= self.audio2exp_model.test(batch)
-            exp_pred = results_dict_exp['exp_coeff_pred']                         #bs T 64
-
-            #for class_id in  range(1):
-            #class_id = 0#(i+10)%45
-            #class_id = random.randint(0,46)                                   #46 styles can be selected 
-            batch['class'] = torch.LongTensor([pose_style]).to(self.device)
-            results_dict_pose = self.audio2pose_model.test(batch) 
-            pose_pred = results_dict_pose['pose_pred']                        #bs T 6
-
-            pose_len = pose_pred.shape[1]
-            if pose_len<13: 
-                pose_len = int((pose_len-1)/2)*2+1
-                pose_pred = torch.Tensor(savgol_filter(np.array(pose_pred.cpu()), pose_len, 2, axis=1)).to(self.device)
-            else:
-                pose_pred = torch.Tensor(savgol_filter(np.array(pose_pred.cpu()), 13, 2, axis=1)).to(self.device) 
-            
-            coeffs_pred = torch.cat((exp_pred, pose_pred), dim=-1)            #bs T 70
-
-            coeffs_pred_numpy = coeffs_pred[0].clone().detach().cpu().numpy() 
-
-            if ref_pose_coeff_path is not None: 
-                 coeffs_pred_numpy = self.using_refpose(coeffs_pred_numpy, ref_pose_coeff_path)
-        
-            savemat(os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name'])),  
-                    {'coeff_3dmm': coeffs_pred_numpy})
-
-            return os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name']))
-    
-    def using_refpose(self, coeffs_pred_numpy, ref_pose_coeff_path):
-        num_frames = coeffs_pred_numpy.shape[0]
-        refpose_coeff_dict = loadmat(ref_pose_coeff_path)
-        refpose_coeff = refpose_coeff_dict['coeff_3dmm'][:,64:70]
-        refpose_num_frames = refpose_coeff.shape[0]
-        if refpose_num_frames<num_frames:
-            div = num_frames//refpose_num_frames
-            re = num_frames%refpose_num_frames
-            refpose_coeff_list = [refpose_coeff for i in range(div)]
-            refpose_coeff_list.append(refpose_coeff[:re, :])
-            refpose_coeff = np.concatenate(refpose_coeff_list, axis=0)
-
-        #### relative head pose
-        coeffs_pred_numpy[:, 64:70] = coeffs_pred_numpy[:, 64:70] + ( refpose_coeff[:num_frames, :] - refpose_coeff[0:1, :] )
-        return coeffs_pred_numpy
-
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_flax_objects.py

@@ -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"])

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py

@@ -1,314 +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 gc
-import random
-import unittest
-
-import numpy as np
-import torch
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
-
-from diffusers import (
-    AutoencoderKL,
-    DPMSolverMultistepScheduler,
-    PNDMScheduler,
-    StableDiffusionImageVariationPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
-
-
-enable_full_determinism()
-
-
-class StableDiffusionImageVariationPipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
-    pipeline_class = StableDiffusionImageVariationPipeline
-    params = IMAGE_VARIATION_PARAMS
-    batch_params = IMAGE_VARIATION_BATCH_PARAMS
-    image_params = frozenset([])
-    # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
-    image_latents_params = frozenset([])
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        scheduler = PNDMScheduler(skip_prk_steps=True)
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        image_encoder_config = CLIPVisionConfig(
-            hidden_size=32,
-            projection_dim=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            image_size=32,
-            patch_size=4,
-        )
-        image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
-        feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
-
-        components = {
-            "unet": unet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "image_encoder": image_encoder,
-            "feature_extractor": feature_extractor,
-            "safety_checker": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))
-        image = image.cpu().permute(0, 2, 3, 1)[0]
-        image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32))
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-        inputs = {
-            "image": image,
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-        }
-        return inputs
-
-    def test_stable_diffusion_img_variation_default_case(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionImageVariationPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        image = sd_pipe(**inputs).images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
-
-    def test_stable_diffusion_img_variation_multiple_images(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        sd_pipe = StableDiffusionImageVariationPipeline(**components)
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        inputs["image"] = 2 * [inputs["image"]]
-        output = sd_pipe(**inputs)
-
-        image = output.images
-
-        image_slice = image[-1, -3:, -3:, -1]
-
-        assert image.shape == (2, 64, 64, 3)
-        expected_slice = np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
-
-    def test_inference_batch_single_identical(self):
-        super().test_inference_batch_single_identical(expected_max_diff=3e-3)
-
-
-@slow
-@require_torch_gpu
-class StableDiffusionImageVariationPipelineSlowTests(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
-        generator = torch.Generator(device=generator_device).manual_seed(seed)
-        init_image = load_image(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
-            "/stable_diffusion_imgvar/input_image_vermeer.png"
-        )
-        latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
-        latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
-        inputs = {
-            "image": init_image,
-            "latents": latents,
-            "generator": generator,
-            "num_inference_steps": 3,
-            "guidance_scale": 7.5,
-            "output_type": "numpy",
-        }
-        return inputs
-
-    def test_stable_diffusion_img_variation_pipeline_default(self):
-        sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
-            "lambdalabs/sd-image-variations-diffusers", safety_checker=None
-        )
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_inputs(torch_device)
-        image = sd_pipe(**inputs).images
-        image_slice = image[0, -3:, -3:, -1].flatten()
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379])
-        assert np.abs(image_slice - expected_slice).max() < 6e-3
-
-    def test_stable_diffusion_img_variation_intermediate_state(self):
-        number_of_steps = 0
-
-        def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
-            callback_fn.has_been_called = True
-            nonlocal number_of_steps
-            number_of_steps += 1
-            if step == 1:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array(
-                    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]
-                )
-
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
-            elif step == 2:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273])
-
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
-
-        callback_fn.has_been_called = False
-
-        pipe = StableDiffusionImageVariationPipeline.from_pretrained(
-            "fusing/sd-image-variations-diffusers",
-            safety_checker=None,
-            torch_dtype=torch.float16,
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        inputs = self.get_inputs(torch_device, dtype=torch.float16)
-        pipe(**inputs, callback=callback_fn, callback_steps=1)
-        assert callback_fn.has_been_called
-        assert number_of_steps == inputs["num_inference_steps"]
-
-    def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        model_id = "fusing/sd-image-variations-diffusers"
-        pipe = StableDiffusionImageVariationPipeline.from_pretrained(
-            model_id, safety_checker=None, torch_dtype=torch.float16
-        )
-        pipe = pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing(1)
-        pipe.enable_sequential_cpu_offload()
-
-        inputs = self.get_inputs(torch_device, dtype=torch.float16)
-        _ = pipe(**inputs)
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        # make sure that less than 2.6 GB is allocated
-        assert mem_bytes < 2.6 * 10**9
-
-
-@nightly
-@require_torch_gpu
-class StableDiffusionImageVariationPipelineNightlyTests(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
-        generator = torch.Generator(device=generator_device).manual_seed(seed)
-        init_image = load_image(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
-            "/stable_diffusion_imgvar/input_image_vermeer.png"
-        )
-        latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
-        latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
-        inputs = {
-            "image": init_image,
-            "latents": latents,
-            "generator": generator,
-            "num_inference_steps": 50,
-            "guidance_scale": 7.5,
-            "output_type": "numpy",
-        }
-        return inputs
-
-    def test_img_variation_pndm(self):
-        sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
-        sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_inputs(torch_device)
-        image = sd_pipe(**inputs).images[0]
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
-            "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"
-        )
-        max_diff = np.abs(expected_image - image).max()
-        assert max_diff < 1e-3
-
-    def test_img_variation_dpm(self):
-        sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
-        sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
-        sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_inputs(torch_device)
-        inputs["num_inference_steps"] = 25
-        image = sd_pipe(**inputs).images[0]
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
-            "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"
-        )
-        max_diff = np.abs(expected_image - image).max()
-        assert max_diff < 1e-3

spaces/Andy1621/uniformer_image_detection/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py

@@ -1,45 +0,0 @@
-_base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
-# model settings
-model = dict(
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            aggregation='sum',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2)),
-        mask_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2))))

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/apcnet_r50-d8.py

@@ -1,44 +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='APCHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        pool_scales=(1, 2, 3, 6),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        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'))

spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './gcnet_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/stable_diffusion_pipeline.py

@@ -1,848 +0,0 @@
-import inspect
-import json
-import math
-import time
-from pathlib import Path
-from typing import Callable, List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from diffusers.configuration_utils import FrozenDict
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from diffusers.utils import deprecate, logging
-from packaging import version
-from torch import nn
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from .upsampling import RealESRGANModel
-from .utils import get_timesteps_arr, make_video_pyav, slerp
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-class StableDiffusionWalkPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for generating videos by interpolating  Stable Diffusion's latent space.
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        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`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. 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`.
-        """
-        if slice_size == "auto":
-            if isinstance(self.unet.config.attention_head_dim, int):
-                # half the attention head size is usually a good trade-off between
-                # speed and memory
-                slice_size = self.unet.config.attention_head_dim // 2
-            else:
-                # if `attention_head_dim` is a list, take the smallest head size
-                slice_size = min(self.unet.config.attention_head_dim)
-
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Optional[Union[str, List[str]]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-        text_embeddings: Optional[torch.FloatTensor] = None,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`, *optional*, defaults to `None`):
-                The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
-                Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
-                `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
-                the supplied `prompt`.
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if text_embeddings is None:
-            if isinstance(prompt, str):
-                batch_size = 1
-            elif isinstance(prompt, list):
-                batch_size = len(prompt)
-            else:
-                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-            # get prompt text embeddings
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-
-            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-                removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-                print(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
-        else:
-            batch_size = text_embeddings.shape[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
-        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""]
-            elif text_embeddings is None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = self.tokenizer.model_max_length
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
-            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        # get the initial random noise unless the user supplied it
-
-        # Unlike in other pipelines, latents need to be generated in the target device
-        # for 1-to-1 results reproducibility with the CompVis implementation.
-        # However this currently doesn't work in `mps`.
-        latents_shape = (
-            batch_size * num_images_per_prompt,
-            self.unet.in_channels,
-            height // 8,
-            width // 8,
-        )
-        latents_dtype = text_embeddings.dtype
-        if latents is None:
-            if self.device.type == "mps":
-                # randn does not exist on mps
-                latents = torch.randn(
-                    latents_shape,
-                    generator=generator,
-                    device="cpu",
-                    dtype=latents_dtype,
-                ).to(self.device)
-            else:
-                latents = torch.randn(
-                    latents_shape,
-                    generator=generator,
-                    device=self.device,
-                    dtype=latents_dtype,
-                )
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-            latents = latents.to(self.device)
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimized to move all timesteps to correct device beforehand
-        timesteps_tensor = self.scheduler.timesteps.to(self.device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image,
-                clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype),
-            )
-        else:
-            has_nsfw_concept = None
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
-
-    def generate_inputs(self, prompt_a, prompt_b, seed_a, seed_b, noise_shape, T, batch_size):
-        embeds_a = self.embed_text(prompt_a)
-        embeds_b = self.embed_text(prompt_b)
-        latents_dtype = embeds_a.dtype
-        latents_a = self.init_noise(seed_a, noise_shape, latents_dtype)
-        latents_b = self.init_noise(seed_b, noise_shape, latents_dtype)
-
-        batch_idx = 0
-        embeds_batch, noise_batch = None, None
-        for i, t in enumerate(T):
-            embeds = torch.lerp(embeds_a, embeds_b, t)
-            noise = slerp(float(t), latents_a, latents_b)
-
-            embeds_batch = embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
-            noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise])
-            batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
-            if not batch_is_ready:
-                continue
-            yield batch_idx, embeds_batch, noise_batch
-            batch_idx += 1
-            del embeds_batch, noise_batch
-            torch.cuda.empty_cache()
-            embeds_batch, noise_batch = None, None
-
-    def make_clip_frames(
-        self,
-        prompt_a: str,
-        prompt_b: str,
-        seed_a: int,
-        seed_b: int,
-        num_interpolation_steps: int = 5,
-        save_path: Union[str, Path] = "outputs/",
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        eta: float = 0.0,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        upsample: bool = False,
-        batch_size: int = 1,
-        image_file_ext: str = ".png",
-        T: np.ndarray = None,
-        skip: int = 0,
-        negative_prompt: str = None,
-        step: Optional[Tuple[int, int]] = None,
-    ):
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        save_path = Path(save_path)
-        save_path.mkdir(parents=True, exist_ok=True)
-
-        T = T if T is not None else np.linspace(0.0, 1.0, num_interpolation_steps)
-        if T.shape[0] != num_interpolation_steps:
-            raise ValueError(f"Unexpected T shape, got {T.shape}, expected dim 0 to be {num_interpolation_steps}")
-
-        if upsample:
-            if getattr(self, "upsampler", None) is None:
-                self.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
-            self.upsampler.to(self.device)
-
-        batch_generator = self.generate_inputs(
-            prompt_a,
-            prompt_b,
-            seed_a,
-            seed_b,
-            (1, self.unet.in_channels, height // 8, width // 8),
-            T[skip:],
-            batch_size,
-        )
-        num_batches = math.ceil(num_interpolation_steps / batch_size)
-
-        log_prefix = "" if step is None else f"[{step[0]}/{step[1]}] "
-
-        frame_index = skip
-        for batch_idx, embeds_batch, noise_batch in batch_generator:
-            if batch_size == 1:
-                msg = f"Generating frame {frame_index}"
-            else:
-                msg = f"Generating frames {frame_index}-{frame_index+embeds_batch.shape[0]-1}"
-            logger.info(f"{log_prefix}[{batch_idx}/{num_batches}] {msg}")
-            outputs = self(
-                latents=noise_batch,
-                text_embeddings=embeds_batch,
-                height=height,
-                width=width,
-                guidance_scale=guidance_scale,
-                eta=eta,
-                num_inference_steps=num_inference_steps,
-                output_type="pil" if not upsample else "numpy",
-                negative_prompt=negative_prompt,
-            )["images"]
-
-            for image in outputs:
-                frame_filepath = save_path / (f"frame%06d{image_file_ext}" % frame_index)
-                image = image if not upsample else self.upsampler(image)
-                image.save(frame_filepath)
-                frame_index += 1
-
-    def walk(
-        self,
-        prompts: Optional[List[str]] = None,
-        seeds: Optional[List[int]] = None,
-        num_interpolation_steps: Optional[Union[int, List[int]]] = 5,  # int or list of int
-        output_dir: Optional[str] = "./dreams",
-        name: Optional[str] = None,
-        image_file_ext: Optional[str] = ".png",
-        fps: Optional[int] = 30,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        eta: Optional[float] = 0.0,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        upsample: Optional[bool] = False,
-        batch_size: Optional[int] = 1,
-        resume: Optional[bool] = False,
-        audio_filepath: str = None,
-        audio_start_sec: Optional[Union[int, float]] = None,
-        margin: Optional[float] = 1.0,
-        smooth: Optional[float] = 0.0,
-        negative_prompt: Optional[str] = None,
-        make_video: Optional[bool] = True,
-    ):
-        """Generate a video from a sequence of prompts and seeds. Optionally, add audio to the
-        video to interpolate to the intensity of the audio.
-        Args:
-            prompts (Optional[List[str]], optional):
-                list of text prompts. Defaults to None.
-            seeds (Optional[List[int]], optional):
-                list of random seeds corresponding to prompts. Defaults to None.
-            num_interpolation_steps (Union[int, List[int]], *optional*):
-                How many interpolation steps between each prompt. Defaults to None.
-            output_dir (Optional[str], optional):
-                Where to save the video. Defaults to './dreams'.
-            name (Optional[str], optional):
-                Name of the subdirectory of output_dir. Defaults to None.
-            image_file_ext (Optional[str], *optional*, defaults to '.png'):
-                The extension to use when writing video frames.
-            fps (Optional[int], *optional*, defaults to 30):
-                The frames per second in the resulting output videos.
-            num_inference_steps (Optional[int], *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (Optional[float], *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            eta (Optional[float], *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            height (Optional[int], *optional*, defaults to None):
-                height of the images to generate.
-            width (Optional[int], *optional*, defaults to None):
-                width of the images to generate.
-            upsample (Optional[bool], *optional*, defaults to False):
-                When True, upsamples images with realesrgan.
-            batch_size (Optional[int], *optional*, defaults to 1):
-                Number of images to generate at once.
-            resume (Optional[bool], *optional*, defaults to False):
-                When True, resumes from the last frame in the output directory based
-                on available prompt config. Requires you to provide the `name` argument.
-            audio_filepath (str, *optional*, defaults to None):
-                Optional path to an audio file to influence the interpolation rate.
-            audio_start_sec (Optional[Union[int, float]], *optional*, defaults to 0):
-                Global start time of the provided audio_filepath.
-            margin (Optional[float], *optional*, defaults to 1.0):
-                Margin from librosa hpss to use for audio interpolation.
-            smooth (Optional[float], *optional*, defaults to 0.0):
-                Smoothness of the audio interpolation. 1.0 means linear interpolation.
-            negative_prompt (Optional[str], *optional*, defaults to None):
-                Optional negative prompt to use. Same across all prompts.
-            make_video (Optional[bool], *optional*, defaults to True):
-                When True, makes a video from the generated frames. If False, only
-                generates the frames.
-        This function will create sub directories for each prompt and seed pair.
-        For example, if you provide the following prompts and seeds:
-        ```
-        prompts = ['a dog', 'a cat', 'a bird']
-        seeds = [1, 2, 3]
-        num_interpolation_steps = 5
-        output_dir = 'output_dir'
-        name = 'name'
-        fps = 5
-        ```
-        Then the following directories will be created:
-        ```
-        output_dir
-        ├── name
-        │   ├── name_000000
-        │   │   ├── frame000000.png
-        │   │   ├── ...
-        │   │   ├── frame000004.png
-        │   │   ├── name_000000.mp4
-        │   ├── name_000001
-        │   │   ├── frame000000.png
-        │   │   ├── ...
-        │   │   ├── frame000004.png
-        │   │   ├── name_000001.mp4
-        │   ├── ...
-        │   ├── name.mp4
-        |   |── prompt_config.json
-        ```
-        Returns:
-            str: The resulting video filepath. This video includes all sub directories' video clips.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        output_path = Path(output_dir)
-
-        name = name or time.strftime("%Y%m%d-%H%M%S")
-        save_path_root = output_path / name
-        save_path_root.mkdir(parents=True, exist_ok=True)
-
-        # Where the final video of all the clips combined will be saved
-        output_filepath = save_path_root / f"{name}.mp4"
-
-        # If using same number of interpolation steps between, we turn into list
-        if not resume and isinstance(num_interpolation_steps, int):
-            num_interpolation_steps = [num_interpolation_steps] * (len(prompts) - 1)
-
-        if not resume:
-            audio_start_sec = audio_start_sec or 0
-
-        # Save/reload prompt config
-        prompt_config_path = save_path_root / "prompt_config.json"
-        if not resume:
-            prompt_config_path.write_text(
-                json.dumps(
-                    dict(
-                        prompts=prompts,
-                        seeds=seeds,
-                        num_interpolation_steps=num_interpolation_steps,
-                        fps=fps,
-                        num_inference_steps=num_inference_steps,
-                        guidance_scale=guidance_scale,
-                        eta=eta,
-                        upsample=upsample,
-                        height=height,
-                        width=width,
-                        audio_filepath=audio_filepath,
-                        audio_start_sec=audio_start_sec,
-                        negative_prompt=negative_prompt,
-                    ),
-                    indent=2,
-                    sort_keys=False,
-                )
-            )
-        else:
-            data = json.load(open(prompt_config_path))
-            prompts = data["prompts"]
-            seeds = data["seeds"]
-            num_interpolation_steps = data["num_interpolation_steps"]
-            fps = data["fps"]
-            num_inference_steps = data["num_inference_steps"]
-            guidance_scale = data["guidance_scale"]
-            eta = data["eta"]
-            upsample = data["upsample"]
-            height = data["height"]
-            width = data["width"]
-            audio_filepath = data["audio_filepath"]
-            audio_start_sec = data["audio_start_sec"]
-            negative_prompt = data.get("negative_prompt", None)
-
-        for i, (prompt_a, prompt_b, seed_a, seed_b, num_step) in enumerate(
-            zip(prompts, prompts[1:], seeds, seeds[1:], num_interpolation_steps)
-        ):
-            # {name}_000000 / {name}_000001 / ...
-            save_path = save_path_root / f"{name}_{i:06d}"
-
-            # Where the individual clips will be saved
-            step_output_filepath = save_path / f"{name}_{i:06d}.mp4"
-
-            # Determine if we need to resume from a previous run
-            skip = 0
-            if resume:
-                if step_output_filepath.exists():
-                    print(f"Skipping {save_path} because frames already exist")
-                    continue
-
-                existing_frames = sorted(save_path.glob(f"*{image_file_ext}"))
-                if existing_frames:
-                    skip = int(existing_frames[-1].stem[-6:]) + 1
-                    if skip + 1 >= num_step:
-                        print(f"Skipping {save_path} because frames already exist")
-                        continue
-                    print(f"Resuming {save_path.name} from frame {skip}")
-
-            audio_offset = audio_start_sec + sum(num_interpolation_steps[:i]) / fps
-            audio_duration = num_step / fps
-
-            self.make_clip_frames(
-                prompt_a,
-                prompt_b,
-                seed_a,
-                seed_b,
-                num_interpolation_steps=num_step,
-                save_path=save_path,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale,
-                eta=eta,
-                height=height,
-                width=width,
-                upsample=upsample,
-                batch_size=batch_size,
-                T=get_timesteps_arr(
-                    audio_filepath,
-                    offset=audio_offset,
-                    duration=audio_duration,
-                    fps=fps,
-                    margin=margin,
-                    smooth=smooth,
-                )
-                if audio_filepath
-                else None,
-                skip=skip,
-                negative_prompt=negative_prompt,
-                step=(i, len(prompts) - 1),
-            )
-            if make_video:
-                make_video_pyav(
-                    save_path,
-                    audio_filepath=audio_filepath,
-                    fps=fps,
-                    output_filepath=step_output_filepath,
-                    glob_pattern=f"*{image_file_ext}",
-                    audio_offset=audio_offset,
-                    audio_duration=audio_duration,
-                    sr=44100,
-                )
-        if make_video:
-            return make_video_pyav(
-                save_path_root,
-                audio_filepath=audio_filepath,
-                fps=fps,
-                audio_offset=audio_start_sec,
-                audio_duration=sum(num_interpolation_steps) / fps,
-                output_filepath=output_filepath,
-                glob_pattern=f"**/*{image_file_ext}",
-                sr=44100,
-            )
-
-    def embed_text(self, text, negative_prompt=None):
-        """Helper to embed some text"""
-        text_input = self.tokenizer(
-            text,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        with torch.no_grad():
-            embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
-        return embed
-
-    def init_noise(self, seed, noise_shape, dtype):
-        """Helper to initialize noise"""
-        # randn does not exist on mps, so we create noise on CPU here and move it to the device after initialization
-        if self.device.type == "mps":
-            noise = torch.randn(
-                noise_shape,
-                device="cpu",
-                generator=torch.Generator(device="cpu").manual_seed(seed),
-            ).to(self.device)
-        else:
-            noise = torch.randn(
-                noise_shape,
-                device=self.device,
-                generator=torch.Generator(device=self.device).manual_seed(seed),
-                dtype=dtype,
-            )
-        return noise
-
-    @classmethod
-    def from_pretrained(cls, *args, tiled=False, **kwargs):
-        """Same as diffusers `from_pretrained` but with tiled option, which makes images tilable"""
-        if tiled:
-
-            def patch_conv(**patch):
-                cls = nn.Conv2d
-                init = cls.__init__
-
-                def __init__(self, *args, **kwargs):
-                    return init(self, *args, **kwargs, **patch)
-
-                cls.__init__ = __init__
-
-            patch_conv(padding_mode="circular")
-
-        pipeline = super().from_pretrained(*args, **kwargs)
-        pipeline.tiled = tiled
-        return pipeline

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h

@@ -1,33 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-#include <torch/extension.h>
-
-namespace groundingdino {
-
-at::Tensor ms_deform_attn_cuda_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step);
-
-std::vector<at::Tensor> ms_deform_attn_cuda_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step);
-
-} // namespace groundingdino

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/metadata/_json.py

@@ -1,84 +0,0 @@
-# Extracted from https://github.com/pfmoore/pkg_metadata
-
-from email.header import Header, decode_header, make_header
-from email.message import Message
-from typing import Any, Dict, List, Union
-
-METADATA_FIELDS = [
-    # Name, Multiple-Use
-    ("Metadata-Version", False),
-    ("Name", False),
-    ("Version", False),
-    ("Dynamic", True),
-    ("Platform", True),
-    ("Supported-Platform", True),
-    ("Summary", False),
-    ("Description", False),
-    ("Description-Content-Type", False),
-    ("Keywords", False),
-    ("Home-page", False),
-    ("Download-URL", False),
-    ("Author", False),
-    ("Author-email", False),
-    ("Maintainer", False),
-    ("Maintainer-email", False),
-    ("License", False),
-    ("Classifier", True),
-    ("Requires-Dist", True),
-    ("Requires-Python", False),
-    ("Requires-External", True),
-    ("Project-URL", True),
-    ("Provides-Extra", True),
-    ("Provides-Dist", True),
-    ("Obsoletes-Dist", True),
-]
-
-
-def json_name(field: str) -> str:
-    return field.lower().replace("-", "_")
-
-
-def msg_to_json(msg: Message) -> Dict[str, Any]:
-    """Convert a Message object into a JSON-compatible dictionary."""
-
-    def sanitise_header(h: Union[Header, str]) -> str:
-        if isinstance(h, Header):
-            chunks = []
-            for bytes, encoding in decode_header(h):
-                if encoding == "unknown-8bit":
-                    try:
-                        # See if UTF-8 works
-                        bytes.decode("utf-8")
-                        encoding = "utf-8"
-                    except UnicodeDecodeError:
-                        # If not, latin1 at least won't fail
-                        encoding = "latin1"
-                chunks.append((bytes, encoding))
-            return str(make_header(chunks))
-        return str(h)
-
-    result = {}
-    for field, multi in METADATA_FIELDS:
-        if field not in msg:
-            continue
-        key = json_name(field)
-        if multi:
-            value: Union[str, List[str]] = [
-                sanitise_header(v) for v in msg.get_all(field)
-            ]
-        else:
-            value = sanitise_header(msg.get(field))
-            if key == "keywords":
-                # Accept both comma-separated and space-separated
-                # forms, for better compatibility with old data.
-                if "," in value:
-                    value = [v.strip() for v in value.split(",")]
-                else:
-                    value = value.split()
-        result[key] = value
-
-    payload = msg.get_payload()
-    if payload:
-        result["description"] = payload
-
-    return result

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

@@ -1,17 +0,0 @@
-from typing import Optional
-
-
-def pick_bool(*values: Optional[bool]) -> bool:
-    """Pick the first non-none bool or return the last value.
-
-    Args:
-        *values (bool): Any number of boolean or None values.
-
-    Returns:
-        bool: First non-none boolean.
-    """
-    assert values, "1 or more values required"
-    for value in values:
-        if value is not None:
-            return value
-    return bool(value)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/py36compat.py

@@ -1,134 +0,0 @@
-import os
-from glob import glob
-from distutils.util import convert_path
-from distutils.command import sdist
-
-
-class sdist_add_defaults:
-    """
-    Mix-in providing forward-compatibility for functionality as found in
-    distutils on Python 3.7.
-
-    Do not edit the code in this class except to update functionality
-    as implemented in distutils. Instead, override in the subclass.
-    """
-
-    def add_defaults(self):
-        """Add all the default files to self.filelist:
-          - README or README.txt
-          - setup.py
-          - test/test*.py
-          - all pure Python modules mentioned in setup script
-          - all files pointed by package_data (build_py)
-          - all files defined in data_files.
-          - all files defined as scripts.
-          - all C sources listed as part of extensions or C libraries
-            in the setup script (doesn't catch C headers!)
-        Warns if (README or README.txt) or setup.py are missing; everything
-        else is optional.
-        """
-        self._add_defaults_standards()
-        self._add_defaults_optional()
-        self._add_defaults_python()
-        self._add_defaults_data_files()
-        self._add_defaults_ext()
-        self._add_defaults_c_libs()
-        self._add_defaults_scripts()
-
-    @staticmethod
-    def _cs_path_exists(fspath):
-        """
-        Case-sensitive path existence check
-
-        >>> sdist_add_defaults._cs_path_exists(__file__)
-        True
-        >>> sdist_add_defaults._cs_path_exists(__file__.upper())
-        False
-        """
-        if not os.path.exists(fspath):
-            return False
-        # make absolute so we always have a directory
-        abspath = os.path.abspath(fspath)
-        directory, filename = os.path.split(abspath)
-        return filename in os.listdir(directory)
-
-    def _add_defaults_standards(self):
-        standards = [self.READMES, self.distribution.script_name]
-        for fn in standards:
-            if isinstance(fn, tuple):
-                alts = fn
-                got_it = False
-                for fn in alts:
-                    if self._cs_path_exists(fn):
-                        got_it = True
-                        self.filelist.append(fn)
-                        break
-
-                if not got_it:
-                    self.warn("standard file not found: should have one of " +
-                              ', '.join(alts))
-            else:
-                if self._cs_path_exists(fn):
-                    self.filelist.append(fn)
-                else:
-                    self.warn("standard file '%s' not found" % fn)
-
-    def _add_defaults_optional(self):
-        optional = ['test/test*.py', 'setup.cfg']
-        for pattern in optional:
-            files = filter(os.path.isfile, glob(pattern))
-            self.filelist.extend(files)
-
-    def _add_defaults_python(self):
-        # build_py is used to get:
-        #  - python modules
-        #  - files defined in package_data
-        build_py = self.get_finalized_command('build_py')
-
-        # getting python files
-        if self.distribution.has_pure_modules():
-            self.filelist.extend(build_py.get_source_files())
-
-        # getting package_data files
-        # (computed in build_py.data_files by build_py.finalize_options)
-        for pkg, src_dir, build_dir, filenames in build_py.data_files:
-            for filename in filenames:
-                self.filelist.append(os.path.join(src_dir, filename))
-
-    def _add_defaults_data_files(self):
-        # getting distribution.data_files
-        if self.distribution.has_data_files():
-            for item in self.distribution.data_files:
-                if isinstance(item, str):
-                    # plain file
-                    item = convert_path(item)
-                    if os.path.isfile(item):
-                        self.filelist.append(item)
-                else:
-                    # a (dirname, filenames) tuple
-                    dirname, filenames = item
-                    for f in filenames:
-                        f = convert_path(f)
-                        if os.path.isfile(f):
-                            self.filelist.append(f)
-
-    def _add_defaults_ext(self):
-        if self.distribution.has_ext_modules():
-            build_ext = self.get_finalized_command('build_ext')
-            self.filelist.extend(build_ext.get_source_files())
-
-    def _add_defaults_c_libs(self):
-        if self.distribution.has_c_libraries():
-            build_clib = self.get_finalized_command('build_clib')
-            self.filelist.extend(build_clib.get_source_files())
-
-    def _add_defaults_scripts(self):
-        if self.distribution.has_scripts():
-            build_scripts = self.get_finalized_command('build_scripts')
-            self.filelist.extend(build_scripts.get_source_files())
-
-
-if hasattr(sdist.sdist, '_add_defaults_standards'):
-    # disable the functionality already available upstream
-    class sdist_add_defaults:  # noqa
-        pass

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py

@@ -1,29 +0,0 @@
-from .mask_rcnn_R_50_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-from detectron2.config import LazyCall as L
-from detectron2.modeling.backbone import RegNet
-from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
-
-# Config source:
-# https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py  # noqa
-model.backbone.bottom_up = L(RegNet)(
-    stem_class=SimpleStem,
-    stem_width=32,
-    block_class=ResBottleneckBlock,
-    depth=23,
-    w_a=38.65,
-    w_0=96,
-    w_m=2.43,
-    group_width=40,
-    norm="SyncBN",
-    out_features=["s1", "s2", "s3", "s4"],
-)
-model.pixel_std = [57.375, 57.120, 58.395]
-
-# RegNets benefit from enabling cudnn benchmark mode
-train.cudnn_benchmark = True

spaces/BREWDAcademy/Brewd-Diffusion/style.css

@@ -1,77 +0,0 @@
-/* Main background and font styles to fit the steampunk/parchment theme */
-body {
-    background-color: #f5f5dc; /* Parchment color */
-    font-family: 'IM Fell English SC', serif; /* Steampunk-inspired font */
-    color: #3a2e22; /* Dark brown text color */
-}
-
-/* Style for header element with class 'pretty' */
-.pretty h1 {
-    text-align: center;
-    font-family: 'IM Fell English SC', serif; /* Steampunk font */
-    color: #806c50; /* Muted brown color */
-}
-
-/* Style for button element with ID 'duplicate-button' */
-#duplicate-button {
-    margin: auto;
-    color: #efe0c9; /* Light parchment color */
-    background-color: #806c50; /* Leather-like brown */
-    border-radius: 4px; /* Less roundness for a more vintage look */
-    cursor: pointer;
-    padding: 10px 20px;
-    border: none;
-    font-size: 1em;
-}
-
-/* Style for the Gradio interface elements to match the steampunk theme */
-.gradio_container {
-    background-color: #f2e5bc; /* Light beige for input areas */
-    border: 1px solid #9e7053; /* Darker border to stand out on parchment */
-}
-
-/* Style for gallery/result container */
-.gr-gallery {
-    background-color: #fff; /* Clean white for results to stand out */
-    border: 2px solid #9e7053; /* A darker border for contrast */
-}
-
-/* Style for input text and text areas */
-input[type='text'], textarea {
-    background-color: #f2e5bc; /* Light beige, like old paper */
-    color: #3a2e22; /* Dark brown text color */
-    border: 1px solid #9e7053; /* Leather-like border */
-}
-
-/* Style for sliders */
-input[type='range'] {
-    background: #806c50; /* A leather-like slider background */
-}
-
-/* Style for radio buttons and checkboxes */
-input[type='radio'], input[type='checkbox'] {
-    accent-color: #806c50; /* Leather-like accent color */
-}
-
-/* Adjust the style for buttons in the interface */
-button {
-    background-color: #806c50; /* Leather-like brown */
-    color: #efe0c9; /* Parchment color text */
-    border: none; /* Remove default border */
-}
-
-/* Style adjustments for the accordion */
-.gr-accordion {
-    background-color: #f2e5bc; /* Light beige */
-    color: #3a2e22; /* Dark brown text color */
-}
-
-/* Ensure links match the theme as well */
-a {
-    color: #3a2e22; /* Dark brown, similar to the text */
-}
-
-/* Style for the progress bar */
-.gr-progress-bar {
-    background-color: #c0a080; /* A muted brown progress bar */
-}

spaces/Benson/text-generation/Examples/Coche De Carreras Juego De Configuracin Para Pc Windows 7.md

@@ -1,85 +0,0 @@
-
-<h1>Configuración del juego de carreras de coches Descargar para PC Windows 7</h1>
-<p>Si eres un fan de la velocidad, la adrenalina y la emoción, es posible que te interese jugar juegos de carreras de coches. Los juegos de carreras de autos son videojuegos que simulan conducir un vehículo en una pista, una carretera o un terreno todoterreno. Pueden ser realistas, árcade o futuristas, dependiendo del estilo y el tema del juego. Los juegos de carreras de coches son populares entre los jugadores de todas las edades y preferencias, ya que ofrecen una variedad de desafíos, modos, vehículos y entornos para elegir. </p>
-<p>Una de las ventajas de jugar juegos de carreras de coches es que se puede disfrutar de ellos en diferentes plataformas, incluyendo PC Windows 7. PC Windows 7 es un sistema operativo confiable y compatible que puede ejecutar muchos juegos de carreras de coches sin problemas y de manera eficiente. Jugar juegos de carreras de coches en PC Windows 7 también le da más control sobre la configuración, los gráficos y el rendimiento del juego. También puede utilizar diferentes dispositivos de entrada, como un teclado, un ratón, un joystick o un volante, para mejorar su experiencia de juego. </p>
-<h2>coche de carreras juego de configuración para pc windows 7</h2><br /><p><b><b>Download Zip</b> &#10003; <a href="https://bltlly.com/2v6Mln">https://bltlly.com/2v6Mln</a></b></p><br /><br />
-<p>Pero ¿cómo encontrar y jugar juegos de carreras de coches en PC Windows 7? Hay muchas fuentes donde se puede descargar juegos de carreras de coches para PC Windows 7, tales como sitios web oficiales, tiendas en línea, o plataformas de terceros. Sin embargo, debes tener cuidado con la calidad, seguridad y legalidad de los archivos de juego que descargas. También debe seguir las instrucciones para instalar y ejecutar el juego en su PC Windows 7.</p>
-<p>En este artículo, le presentaremos algunos de los mejores juegos de carreras de coches para PC Windows 7 que puede descargar y jugar. También le proporcionaremos información sobre sus características, pros y contras, y cómo descargarlos e instalarlos en su PC Windows 7. ¡Comencemos! </p>
- <h2>BeamNG.drive</h2>
-<h3>¿Qué es BeamNG.drive y cuáles son sus características? </h3>
-
-<p>BeamNG.drive no es solo un juego de carreras de coches, sino también un juego de caja de arena que le permite experimentar con diferentes situaciones y resultados. Puede estrellar sus vehículos contra paredes, árboles, edificios u otros vehículos, y ver cómo se deforman y se rompen. También puedes probar tus habilidades de conducción en varios desafíos, como pruebas de tiempo, cursos de acrobacias, persecuciones policiales o aventuras fuera de la carretera <p>. También puedes jugar con tus amigos online o offline en modo multijugador. </p>
-<h3>Cómo descargar e instalar BeamNG.drive en PC Windows 7?</h3>
-<p>Para descargar e instalar BeamNG.drive en PC Windows 7, debe seguir estos pasos:</p>
-<ol>
-<li>Vaya al sitio web oficial de BeamNG.drive y haga clic en el botón "Comprar ahora". Serás redirigido a la tienda de Steam, donde puedes comprar el juego por $24.99. </li>
-<li>Después de comprar el juego, necesita descargar e instalar Steam en su PC Windows 7. Steam es una plataforma de distribución digital que le permite administrar sus juegos y acceder a las funciones en línea. </li>
-<li>Una vez que haya instalado Steam, inicie sesión con su cuenta. Luego, vaya a su biblioteca y busque BeamNG.drive. Haga clic en el botón "Instalar" y espere a que el juego se descargue e instale en su PC Windows 7.</li>
-<li>Después de que la instalación se haya completado, puede iniciar el juego desde Steam o desde el acceso directo de su escritorio. Disfrute! </li>
-</ol>
-<h3>Pros y contras de BeamNG.drive</h3>
-<p>BeamNG.drive es un divertido y realista juego de simulación de vehículos que ofrece muchas posibilidades y libertad. Sin embargo, también tiene algunos inconvenientes que debe tener en cuenta. Estos son algunos de los pros y contras de BeamNG.drive:</p>
-<tabla>
-<tr><th>Pros</th><th>Contras</th></tr>
-<tr><td>- Increíbles gráficos y física que hacen que los vehículos y entornos se vean y se sientan reales. </td><td>- Altos requisitos del sistema que pueden no funcionar bien en PC más viejos o más débiles.</td></tr>
-
-<tr><td>- Una comunidad creativa y activa que crea y comparte nuevos contenidos y comentarios. </td><td>- Falta de una historia clara u objetivos que puedan hacer que el juego sea aburrido o repetitivo para algunos jugadores. </td></tr>
-<tr><td>- Un modo multijugador que te permite jugar con tus amigos online o offline. </td><td>- Algunos errores y fallos que pueden afectar el juego o el rendimiento del juego. </td></tr>
-</tabla>
- <h2>Necesidad de velocidad</h2>
-<h3>¿Cuál es la necesidad de velocidad y cuáles son sus características? </h3>
-<p>Need for Speed es una de las franquicias de juegos de carreras de coches más populares y exitosas del mundo. Ha existido desde 1994 y ha lanzado más de 20 títulos en diferentes plataformas. Los juegos de Need for Speed son conocidos por su ritmo rápido, estilo árcade y juego de carreras callejeras. También cuentan con una variedad de coches, pistas, modos, opciones de personalización e historias. </p>
-<p></p>
-<p>Uno de los mejores juegos de Need for Speed para PC Windows 7 es Need for Speed: Most Wanted (2012). Este juego es un reinicio de la original Need for Speed: Most Wanted (2005) y se desarrolla en una ciudad de mundo abierto llamada Fairhaven. Juegas como un corredor callejero que tiene que competir con otros corredores, evadir a la policía y desafiar a la lista de los más buscados. Puedes conducir cualquier coche que veas en la ciudad, desde coches deportivos exóticos hasta coches deportivos y todoterrenos. También puede actualizar sus coches con piezas de rendimiento, trabajos de pintura, vinilos, matrículas y más. También puedes participar en diferentes eventos, como carreras, persecuciones, carreras de velocidad, emboscadas o hitos. </p>
-<h3> ¿Cómo descargar e instalar Necesidad de velocidad en PC Windows 7?</h3>
-<p>Para descargar e instalar Need for Speed: Most Wanted (2012) en PC Windows 7, debe seguir estos pasos:</p>
-<ol>
-<li>Ir al sitio web oficial de Need for Speed: Most Wanted (2012) y haga clic en el botón "Comprar ahora". Serás redirigido a la tienda de Origin, donde puedes comprar el juego por $19.99. </li>
-
-<li>Una vez que tenga Origin instalado, ejecútelo e inicie sesión con su cuenta. Luego, vaya a su biblioteca y encuentre Need for Speed: Most Wanted (2012). Haga clic en el botón "Descargar" y espere a que el juego se descargue e instale en su PC Windows 7.</li>
-<li>Después de que la instalación se haya completado, puede iniciar el juego desde Origin o desde el acceso directo de su escritorio. Disfrute! </li>
-</ol>
-<h3>Pros y contras de la necesidad de velocidad</h3>
-<p>Need for Speed: Most Wanted (2012) es un emocionante y adictivo juego de carreras de coches que ofrece mucha acción y diversión. Sin embargo, también tiene algunos inconvenientes que debes tener en cuenta. Estos son algunos de los pros y contras de Need for Speed: Most Wanted (2012):</p>
-<tabla>
-<tr><th>Pros</th><th>Contras</th></tr>
-<tr><td>- Impresionantes gráficos y efectos de sonido que hacen que la ciudad y los coches se ven y suenan increíble. </td><td>- Altos requisitos del sistema que pueden no funcionar bien en PC más viejos o más débiles.</td></tr>
-<tr><td>- Una gran y diversa ciudad de mundo abierto que puedes explorar y descubrir. </td><td>- Una historia repetitiva y superficial que puede no atraer a algunos jugadores. </td></tr>
-<tr><td>- Una gran selección de coches, opciones de personalización y eventos para elegir. </td><td>- La falta de una opción de transmisión manual que puede decepcionar a algunos corredores hardcore. </td></tr>
-<tr><td>- Un modo multijugador que te permite jugar con tus amigos online o offline. </td><td>- Algunos errores y fallos que pueden afectar el juego o el rendimiento del juego. </td></tr>
-</tabla>
- <h2>Carreras de la ciudad</h2>
-<h3>¿Qué es City Racing y cuáles son sus características? </h3>
-<p>City Racing es un juego de carreras de coches gratis que te permite conducir por una gran ciudad y competir con otros corredores. Puede elegir entre diferentes coches, desde sedanes hasta autos deportivos, y personalizarlos con diferentes colores, ruedas, spoilers y más. También puede actualizar sus coches con mejores motores, frenos, neumáticos y suspensión. También puede reparar sus coches cuando se dañan o se ensucian. </p>
-
-<h3> ¿Cómo descargar e instalar City Racing en PC Windows 7?</h3>
-<p>Para descargar e instalar City Racing en PC Windows 7, debe seguir estos pasos:</p>
-<ol>
-<li>Ir a la página web oficial de City Racing y haga clic en el "Descargar" botón. Serás redirigido a una plataforma de terceros llamada GameTop, donde puedes descargar el juego gratis. </li>
-<li>Después de descargar el archivo del juego, haga doble clic en él y siga el asistente de instalación. Es posible que necesite aceptar algunos términos y condiciones y elegir una carpeta de destino para el juego. </li>
-<li> Después de la instalación se ha completado, puede iniciar el juego desde el acceso directo del escritorio o desde el menú de inicio. Disfrute! </li>
-</ol>
-<h3>Pros y contras de City Racing</h3>
-<p>City Racing es un juego de carreras de coches divertido y gratuito que ofrece mucha variedad y emoción. Sin embargo, también tiene algunos inconvenientes que debes tener en cuenta. Estos son algunos de los pros y contras de City Racing:</p>
-<tabla>
-<tr><th>Pros</th><th>Contras</th></tr>
-<tr><td>- Gratis para descargar y jugar sin limitaciones o anuncios. </td><td>- Gráficos y efectos de sonido de baja calidad que pueden no parecer o sonar atractivos. </td></tr>
-<tr><td>- Una gran y diversa ciudad de mundo abierto que puedes explorar y disfrutar. </td><td>- La falta de un mapa o un sistema GPS que puede dificultar la navegación o encontrar el camino. </td></tr>
-<tr><td>- Una amplia gama de coches, opciones de personalización y carreras para elegir. </td><td>- Un sistema de física poco realista y fácil que puede hacer la conducción demasiado simple o aburrido. </td></tr>
-<tr><td>- Un modo multijugador que te permite jugar con tus amigos online o offline. </td><td>- Algunos malware o virus que pueden venir con el archivo del juego o la plataforma de terceros. </td></tr>
-</tabla>
- <h2>Conclusión</h2>
-
-<p>En este artículo, le hemos presentado algunos de los mejores juegos de carreras de coches para PC Windows 7 que puede descargar y jugar. También le hemos proporcionado alguna información sobre sus características, pros y contras, y cómo descargar e instalar confirmar que desea eliminar el juego de su PC.</li>
-<li>Siga las instrucciones y avisos que aparecen en la pantalla para completar el proceso de actualización o desinstalación. </li>
-</ol>
-<h3> ¿Dónde encontrar más juegos de carreras de coches para PC Windows 7?</h3>
-<p>Si está buscando más juegos de carreras de coches para PC Windows 7, puede consultar algunos de estos sitios web que ofrecen una variedad de juegos de forma gratuita o por una tarifa:</p>
-<ul>
-<li>[GameTop]: Un sitio web que ofrece juegos de carreras de coches gratuitos y legales para PC Windows 7, como City Racing, Moto Racing y Super Bikes.</li>
-<li>[Steam]: Un sitio web que ofrece una gran colección de juegos de carreras de coches para PC Windows 7, como BeamNG.drive, Assetto Corsa y Dirt Rally.</li>
-<li>[Origin]: Un sitio web que ofrece algunos de los mejores juegos de carreras de coches para PC Windows 7, como Need for Speed, Burnout Paradise y Shift 2 Unleashed.</li>
-<li>[GOG]: Un sitio web que ofrece juegos de carreras de coches clásicos y libres de DRM para PC Windows 7, como FlatOut, Carmageddon y Test Drive.</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Entre Nosotros Gamejolt.md

@@ -1,58 +0,0 @@
-<br />
-<h1>Cómo descargar entre nosotros desde GameJolt</h1>
-<p>Si está buscando un juego divertido y atractivo para jugar con sus amigos en línea, es posible que haya oído hablar de Among Us. Este es un juego multijugador donde tienes que trabajar junto con otros jugadores para arreglar una nave espacial mientras tratas de averiguar quién de ustedes es un impostor. En este artículo, explicaremos qué es Among Us, qué es GameJolt y cómo descargar e instalar Among Us desde GameJolt. También te daremos algunos consejos y trucos para jugar el juego y responder a algunas preguntas frecuentes. </p>
-<h2>descargar entre nosotros gamejolt</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash; <a href="https://bltlly.com/2v6KXj">https://bltlly.com/2v6KXj</a></b></p><br /><br />
- <h2>¿Qué hay entre nosotros? </h2>
-<p>Among Us es un juego que fue lanzado en 2018 por Innersloth, un estudio de juegos estadounidense. El juego fue inspirado por el juego de fiesta Mafia y la película de terror de ciencia ficción The Thing. El juego permite el juego multiplataforma, lo que significa que puedes jugarlo en diferentes dispositivos como Android, iOS, Windows, Nintendo Switch, PlayStation o Xbox.</p>
- <h3>Un juego de deducción social en el espacio</h3>
-<p>El juego se lleva a cabo en el espacio con temas de configuración donde los jugadores son coloridos, astronautas de dibujos animados sin brazos. Cada jugador toma uno de dos roles: la mayoría son compañeros de equipo, pero un pequeño número son impostores. Los compañeros de equipo trabajan para completar las tareas asignadas en el juego mientras identifican y expulsan a los presuntos Impostores (que parecen idénticos a los Compañeros de Equipo) por medio de deducción social, mientras que los Impostores tienen el objetivo de matar a los Compañeros de Equipo o sabotear un sistema crítico en el mapa. </p>
-<p>El juego puede ser jugado por cuatro a quince jugadores, con hasta tres impostores por ronda. Hay cuatro mapas jugables disponibles: una nave espacial llamada "The Skeld", un edificio de oficinas llamado "MIRA HQ", una base planetaria llamada "Polus" o "The Airship", una configuración de la serie de Innersloth Henry Stickmin. </p>
- <h3>¿Por qué es tan popular? </h3>
-
-<p>El juego es simple y al estilo de fiesta, con los jugadores asumiendo el papel de una persona del espacio de dibujos animados a bordo de una nave que necesita algunas reparaciones bastante urgentes. Es un juego sobre el trabajo en equipo, donde trabajan juntos para averiguar quién puede y no se puede confiar dentro del grupo de jugadores. Esto puede ser un beneficio, ya que ayuda a perfeccionar la lógica y las habilidades sociales de los niños, especialmente durante un momento en que los niños pueden estar atrapados en casa y necesitan socialización. </p>
- <h2>¿Qué es GameJolt? </h2>
-<p>GameJolt es una plataforma para juegos indie, donde los desarrolladores pueden compartir sus creaciones con jugadores de todo el mundo. GameJolt alberga miles de juegos de varios géneros, como acción, aventura, terror, rompecabezas, simulación, estrategia y más. Puedes encontrar juegos gratuitos o de pago, completos o en desarrollo, para un jugador o multijugador. </p>
- <h3>Una plataforma para juegos indie</h3>
-<p>GameJolt fue fundada en 2004 por David DeCarmine como una forma de mostrar sus propios juegos. Desde entonces, se ha convertido en un sitio impulsado por la comunidad que apoya a los desarrolladores independientes y los jugadores por igual. GameJolt permite a los desarrolladores subir sus juegos, establecer sus propios precios, obtener ingresos de anuncios o donaciones , e interactuar con sus fans. GameJolt también cuenta con un sistema de clasificación, un sistema de trofeos, un sistema de chat, un foro y un blog para cada juego. </p>
-<p></p>
- <h3>Cómo crear una cuenta y navegar por los juegos</h3>
-<p>Para usar GameJolt, primero necesitas crear una cuenta. Puedes registrarte con tu dirección de correo electrónico o tus cuentas de redes sociales, como Facebook, Twitter o Google. Una vez que tenga una cuenta, puede personalizar su perfil, seguir sus juegos y desarrolladores favoritos, unirse a grupos y ganar trofeos y puntos XP. </p>
-
- <h2>Cómo descargar e instalar Entre nosotros desde GameJolt</h2>
-<p>Ahora que sabes lo que entre nosotros y GameJolt son, vamos a ver cómo se puede descargar e instalar entre nosotros desde GameJolt. El proceso es simple y sencillo, pero debe asegurarse de que tiene un dispositivo compatible y suficiente espacio de almacenamiento. Estos son los pasos a seguir:</p>
- <h3>Paso 1: Encuentra el juego en GameJolt</h3>
-<p>El primer paso es encontrar el juego en GameJolt. Puedes usar este enlace para ir directamente a la página del juego: <a href="">https://gamejolt.com/games/among-us/516139</a>. Alternativamente, puede buscar "Entre nosotros" en el sitio web de GameJolt y buscar el juego con el logotipo oficial y el nombre del desarrollador "Innersloth". </p>
-<p>En la página del juego, verá una breve descripción del juego, algunas capturas de pantalla y videos, la calificación y comentarios, y las opciones de descarga. También verás un botón que dice "Seguir" si quieres ser notificado de cualquier actualización o noticia sobre el juego. </p>
- <h3>Paso 2: Elija la versión y descargue el archivo</h3>
-<p>El siguiente paso es elegir la versión del juego que desea descargar. Hay dos versiones disponibles en GameJolt: una para Windows y otra para Android. La versión para Windows es de 57 MB y la versión para Android es de 70 MB. Asegúrese de que tiene suficiente espacio de almacenamiento en su dispositivo antes de descargar. </p>
-<p>Para descargar el archivo, haga clic en el botón verde que dice "Descargar" junto a la versión que desee. Será redirigido a otra página donde verá un temporizador de cuenta atrás. Espere unos segundos hasta que el temporizador llegue a cero y luego haga clic en el botón azul que dice "Descargar ahora". El archivo comenzará a descargarse automáticamente. </p>
- <h3>Paso 3: Extraer el archivo y ejecutar el juego</h3>
-
-<p>Una vez que haya extraído el archivo, verá una carpeta llamada "Entre nosotros". Ábrala y busque el archivo ejecutable que tiene el logotipo del juego. Haga doble clic en él para ejecutar el juego. Verá una ventana que le pide que elija su idioma. Seleccione su idioma preferido y haga clic en "Aceptar". El juego se iniciará y verás el menú principal. </p>
- <h2>Consejos y trucos para jugar entre nosotros</h2>
-<p>Felicidades! Usted ha descargado e instalado con éxito entre nosotros de GameJolt. Ahora usted está listo para jugar el juego con sus amigos u otros jugadores en línea. Pero antes de entrar en un juego, aquí hay algunos consejos y trucos que te ayudarán a disfrutar más del juego:</p>
- <h3>Cómo personalizar el carácter y la configuración</h3>
-<p>Antes de unirse o alojar un juego, puede personalizar su personaje y la configuración en el menú principal. Para personalizar tu personaje, haz clic en "Personalizar" en la esquina inferior derecha de la pantalla. Puede cambiar su nombre, color, sombrero, mascota, piel o atuendo haciendo clic en ellos. También puede usar flechas para desplazarse a través de diferentes opciones. </p>
-<p>Para personalizar la configuración, haga clic en "Configuración" en la esquina inferior izquierda de la pantalla. Puede ajustar varias opciones como efectos de sonido, volumen de música, calidad de gráficos, resolución, modo de pantalla completa, idioma o tipo de chat haciendo clic en ellos. También puede usar deslizadores o botones para cambiar los valores. Para restablecer los valores predeterminados, haga clic en "Restablecer a Predeterminado" en la parte inferior de la pantalla. </p>
- <h3>Cómo unirse o organizar un juego en línea o localmente</h3>
-<p>Para unirse o alojar un juego en línea o localmente, haga clic en "Online" o "Local" en el menú principal. Si eliges "Online", puedes unirte a un juego público, crear un juego privado o introducir un código para unirte a un juego privado. Si eliges "Local", puedes alojar un juego o unirte a un juego alojado en la misma red Wi-Fi que tú. </p>
-
- <h3>Cómo jugar como un compañero de equipo o un impostor</h3>
-<p>Una vez que comience el juego, serás asignado como Compañero de Equipo o Impostor. Su papel se mostrará en la pantalla junto con sus compañeros de equipo si usted es un impostor. También verás una lista de tareas que necesitas completar si eres un Crewmate.</p>
-<p>Si usted es un compañero de equipo, su objetivo es completar sus tareas y averiguar quiénes son los impostores. Puede moverse por el mapa utilizando el joystick en el lado izquierdo de la pantalla e interactuar con los objetos utilizando el botón en el lado derecho de la pantalla. También puede usar respiraderos, cámaras, tabla de administración, signos vitales o registro de puertas para recopilar información. Puedes reportar un cadáver o llamar a una reunión de emergencia si encuentras algo sospechoso. A continuación, puede votar por quien cree que es un impostor o saltar la votación si no está seguro. </p>
-<p>Si eres un impostor, tu objetivo es matar a todos los compañeros de equipo o sabotear un sistema crítico. Puedes moverte por el mapa e interactuar con objetos como un compañero de equipo, pero también tienes algunas habilidades especiales. Puedes usar los respiraderos para viajar rápida y discretamente, matar a los Compañeros de Tripulación cuando están solos o en grupos, y sabotear los sistemas para causar caos o distraer a los Compañeros de Equipo. También puede falsificar tareas, mentir y acusar a otros para evitar sospechas. Puede votar por quién desea eliminar o omitir el voto si desea mezclarse. </p>
- <h2>Conclusión y preguntas frecuentes</h2>
-<p>En conclusión, Entre nosotros es un juego divertido y atractivo que puedes jugar con tus amigos en línea o localmente. Puedes descargarlo e instalarlo desde GameJolt siguiendo los pasos que hemos explicado en este artículo. También puedes personalizar tu personaje y tu configuración, unirte o organizar un juego y jugar como Crewmate o Impostor. Esperamos que este artículo te haya ayudado a aprender más sobre Among Us y GameJolt y que disfrutes jugando el juego. </p>
-<p>Aquí hay algunas preguntas frecuentes que puede tener sobre Among Us y GameJolt:</p>
- <h4>Q: ¿Cuánto cuesta Among Us en GameJolt? </h4>
-
- <h4>Q: ¿Puedo jugar entre nosotros con personas que tienen diferentes dispositivos? </h4>
-<p>A: Sí, puedes jugar entre nosotros con personas que tienen diferentes dispositivos, siempre y cuando tengan la misma versión del juego. El juego admite el juego multiplataforma entre dispositivos Android, iOS, Windows, Nintendo Switch, PlayStation y Xbox. </p>
- <h4>Q: ¿Cómo puedo actualizar entre nosotros en GameJolt? </h4>
-<p>A: Para actualizar entre nosotros en GameJolt, es necesario descargar e instalar la última versión del juego de GameJolt. Puedes comprobar si hay una nueva versión disponible visitando la página del juego en GameJolt y buscando actualizaciones o noticias de los desarrolladores. </p>
- <h4>Q: ¿Cómo puedo reportar errores o problemas con Among Us? </h4>
-<p>A: Si encuentra algún error o problema con Among Us, puede informar a los desarrolladores utilizando su dirección de correo electrónico oficial: [email protected]. También puede utilizar su servidor de discordia: https://discord.gg/innersloth.</p>
- <h4>Q: ¿Cómo puedo encontrar más juegos como Entre nosotros en GameJolt? </h4>
-<p>A: Si te gusta Among Us y quieres encontrar más juegos como Among Us en GameJolt, puedes usar las etiquetas o los géneros para filtrar los juegos. Por ejemplo, puedes buscar juegos que tengan etiquetas como "multijugador", "deducción social", "misterio de asesinato", "espacio" o "horror". También puedes buscar juegos que pertenezcan a géneros como "acción", "aventura", "rompecabezas" o "simulación". También puede navegar por los juegos destacados, populares o de tendencia en GameJolt para descubrir juegos nuevos y emocionantes. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/build_ext.py

@@ -1,383 +0,0 @@
-import os
-import sys
-import itertools
-from importlib.machinery import EXTENSION_SUFFIXES
-from importlib.util import cache_from_source as _compiled_file_name
-from typing import Dict, Iterator, List, Tuple
-
-from distutils.command.build_ext import build_ext as _du_build_ext
-from distutils.ccompiler import new_compiler
-from distutils.sysconfig import customize_compiler, get_config_var
-from distutils import log
-
-from setuptools.errors import BaseError
-from setuptools.extension import Extension, Library
-
-try:
-    # Attempt to use Cython for building extensions, if available
-    from Cython.Distutils.build_ext import build_ext as _build_ext
-    # Additionally, assert that the compiler module will load
-    # also. Ref #1229.
-    __import__('Cython.Compiler.Main')
-except ImportError:
-    _build_ext = _du_build_ext
-
-# make sure _config_vars is initialized
-get_config_var("LDSHARED")
-from distutils.sysconfig import _config_vars as _CONFIG_VARS  # noqa
-
-
-def _customize_compiler_for_shlib(compiler):
-    if sys.platform == "darwin":
-        # building .dylib requires additional compiler flags on OSX; here we
-        # temporarily substitute the pyconfig.h variables so that distutils'
-        # 'customize_compiler' uses them before we build the shared libraries.
-        tmp = _CONFIG_VARS.copy()
-        try:
-            # XXX Help!  I don't have any idea whether these are right...
-            _CONFIG_VARS['LDSHARED'] = (
-                "gcc -Wl,-x -dynamiclib -undefined dynamic_lookup")
-            _CONFIG_VARS['CCSHARED'] = " -dynamiclib"
-            _CONFIG_VARS['SO'] = ".dylib"
-            customize_compiler(compiler)
-        finally:
-            _CONFIG_VARS.clear()
-            _CONFIG_VARS.update(tmp)
-    else:
-        customize_compiler(compiler)
-
-
-have_rtld = False
-use_stubs = False
-libtype = 'shared'
-
-if sys.platform == "darwin":
-    use_stubs = True
-elif os.name != 'nt':
-    try:
-        import dl
-        use_stubs = have_rtld = hasattr(dl, 'RTLD_NOW')
-    except ImportError:
-        pass
-
-
-def if_dl(s):
-    return s if have_rtld else ''
-
-
-def get_abi3_suffix():
-    """Return the file extension for an abi3-compliant Extension()"""
-    for suffix in EXTENSION_SUFFIXES:
-        if '.abi3' in suffix:  # Unix
-            return suffix
-        elif suffix == '.pyd':  # Windows
-            return suffix
-
-
-class build_ext(_build_ext):
-    editable_mode: bool = False
-    inplace: bool = False
-
-    def run(self):
-        """Build extensions in build directory, then copy if --inplace"""
-        old_inplace, self.inplace = self.inplace, 0
-        _build_ext.run(self)
-        self.inplace = old_inplace
-        if old_inplace:
-            self.copy_extensions_to_source()
-
-    def _get_inplace_equivalent(self, build_py, ext: Extension) -> Tuple[str, str]:
-        fullname = self.get_ext_fullname(ext.name)
-        filename = self.get_ext_filename(fullname)
-        modpath = fullname.split('.')
-        package = '.'.join(modpath[:-1])
-        package_dir = build_py.get_package_dir(package)
-        inplace_file = os.path.join(package_dir, os.path.basename(filename))
-        regular_file = os.path.join(self.build_lib, filename)
-        return (inplace_file, regular_file)
-
-    def copy_extensions_to_source(self):
-        build_py = self.get_finalized_command('build_py')
-        for ext in self.extensions:
-            inplace_file, regular_file = self._get_inplace_equivalent(build_py, ext)
-
-            # Always copy, even if source is older than destination, to ensure
-            # that the right extensions for the current Python/platform are
-            # used.
-            if os.path.exists(regular_file) or not ext.optional:
-                self.copy_file(regular_file, inplace_file, level=self.verbose)
-
-            if ext._needs_stub:
-                inplace_stub = self._get_equivalent_stub(ext, inplace_file)
-                self._write_stub_file(inplace_stub, ext, compile=True)
-                # Always compile stub and remove the original (leave the cache behind)
-                # (this behaviour was observed in previous iterations of the code)
-
-    def _get_equivalent_stub(self, ext: Extension, output_file: str) -> str:
-        dir_ = os.path.dirname(output_file)
-        _, _, name = ext.name.rpartition(".")
-        return f"{os.path.join(dir_, name)}.py"
-
-    def _get_output_mapping(self) -> Iterator[Tuple[str, str]]:
-        if not self.inplace:
-            return
-
-        build_py = self.get_finalized_command('build_py')
-        opt = self.get_finalized_command('install_lib').optimize or ""
-
-        for ext in self.extensions:
-            inplace_file, regular_file = self._get_inplace_equivalent(build_py, ext)
-            yield (regular_file, inplace_file)
-
-            if ext._needs_stub:
-                # This version of `build_ext` always builds artifacts in another dir,
-                # when "inplace=True" is given it just copies them back.
-                # This is done in the `copy_extensions_to_source` function, which
-                # always compile stub files via `_compile_and_remove_stub`.
-                # At the end of the process, a `.pyc` stub file is created without the
-                # corresponding `.py`.
-
-                inplace_stub = self._get_equivalent_stub(ext, inplace_file)
-                regular_stub = self._get_equivalent_stub(ext, regular_file)
-                inplace_cache = _compiled_file_name(inplace_stub, optimization=opt)
-                output_cache = _compiled_file_name(regular_stub, optimization=opt)
-                yield (output_cache, inplace_cache)
-
-    def get_ext_filename(self, fullname):
-        so_ext = os.getenv('SETUPTOOLS_EXT_SUFFIX')
-        if so_ext:
-            filename = os.path.join(*fullname.split('.')) + so_ext
-        else:
-            filename = _build_ext.get_ext_filename(self, fullname)
-            so_ext = get_config_var('EXT_SUFFIX')
-
-        if fullname in self.ext_map:
-            ext = self.ext_map[fullname]
-            use_abi3 = getattr(ext, 'py_limited_api') and get_abi3_suffix()
-            if use_abi3:
-                filename = filename[:-len(so_ext)]
-                so_ext = get_abi3_suffix()
-                filename = filename + so_ext
-            if isinstance(ext, Library):
-                fn, ext = os.path.splitext(filename)
-                return self.shlib_compiler.library_filename(fn, libtype)
-            elif use_stubs and ext._links_to_dynamic:
-                d, fn = os.path.split(filename)
-                return os.path.join(d, 'dl-' + fn)
-        return filename
-
-    def initialize_options(self):
-        _build_ext.initialize_options(self)
-        self.shlib_compiler = None
-        self.shlibs = []
-        self.ext_map = {}
-        self.editable_mode = False
-
-    def finalize_options(self):
-        _build_ext.finalize_options(self)
-        self.extensions = self.extensions or []
-        self.check_extensions_list(self.extensions)
-        self.shlibs = [ext for ext in self.extensions
-                       if isinstance(ext, Library)]
-        if self.shlibs:
-            self.setup_shlib_compiler()
-        for ext in self.extensions:
-            ext._full_name = self.get_ext_fullname(ext.name)
-        for ext in self.extensions:
-            fullname = ext._full_name
-            self.ext_map[fullname] = ext
-
-            # distutils 3.1 will also ask for module names
-            # XXX what to do with conflicts?
-            self.ext_map[fullname.split('.')[-1]] = ext
-
-            ltd = self.shlibs and self.links_to_dynamic(ext) or False
-            ns = ltd and use_stubs and not isinstance(ext, Library)
-            ext._links_to_dynamic = ltd
-            ext._needs_stub = ns
-            filename = ext._file_name = self.get_ext_filename(fullname)
-            libdir = os.path.dirname(os.path.join(self.build_lib, filename))
-            if ltd and libdir not in ext.library_dirs:
-                ext.library_dirs.append(libdir)
-            if ltd and use_stubs and os.curdir not in ext.runtime_library_dirs:
-                ext.runtime_library_dirs.append(os.curdir)
-
-        if self.editable_mode:
-            self.inplace = True
-
-    def setup_shlib_compiler(self):
-        compiler = self.shlib_compiler = new_compiler(
-            compiler=self.compiler, dry_run=self.dry_run, force=self.force
-        )
-        _customize_compiler_for_shlib(compiler)
-
-        if self.include_dirs is not None:
-            compiler.set_include_dirs(self.include_dirs)
-        if self.define is not None:
-            # 'define' option is a list of (name,value) tuples
-            for (name, value) in self.define:
-                compiler.define_macro(name, value)
-        if self.undef is not None:
-            for macro in self.undef:
-                compiler.undefine_macro(macro)
-        if self.libraries is not None:
-            compiler.set_libraries(self.libraries)
-        if self.library_dirs is not None:
-            compiler.set_library_dirs(self.library_dirs)
-        if self.rpath is not None:
-            compiler.set_runtime_library_dirs(self.rpath)
-        if self.link_objects is not None:
-            compiler.set_link_objects(self.link_objects)
-
-        # hack so distutils' build_extension() builds a library instead
-        compiler.link_shared_object = link_shared_object.__get__(compiler)
-
-    def get_export_symbols(self, ext):
-        if isinstance(ext, Library):
-            return ext.export_symbols
-        return _build_ext.get_export_symbols(self, ext)
-
-    def build_extension(self, ext):
-        ext._convert_pyx_sources_to_lang()
-        _compiler = self.compiler
-        try:
-            if isinstance(ext, Library):
-                self.compiler = self.shlib_compiler
-            _build_ext.build_extension(self, ext)
-            if ext._needs_stub:
-                build_lib = self.get_finalized_command('build_py').build_lib
-                self.write_stub(build_lib, ext)
-        finally:
-            self.compiler = _compiler
-
-    def links_to_dynamic(self, ext):
-        """Return true if 'ext' links to a dynamic lib in the same package"""
-        # XXX this should check to ensure the lib is actually being built
-        # XXX as dynamic, and not just using a locally-found version or a
-        # XXX static-compiled version
-        libnames = dict.fromkeys([lib._full_name for lib in self.shlibs])
-        pkg = '.'.join(ext._full_name.split('.')[:-1] + [''])
-        return any(pkg + libname in libnames for libname in ext.libraries)
-
-    def get_outputs(self) -> List[str]:
-        if self.inplace:
-            return list(self.get_output_mapping().keys())
-        return sorted(_build_ext.get_outputs(self) + self.__get_stubs_outputs())
-
-    def get_output_mapping(self) -> Dict[str, str]:
-        """See :class:`setuptools.commands.build.SubCommand`"""
-        mapping = self._get_output_mapping()
-        return dict(sorted(mapping, key=lambda x: x[0]))
-
-    def __get_stubs_outputs(self):
-        # assemble the base name for each extension that needs a stub
-        ns_ext_bases = (
-            os.path.join(self.build_lib, *ext._full_name.split('.'))
-            for ext in self.extensions
-            if ext._needs_stub
-        )
-        # pair each base with the extension
-        pairs = itertools.product(ns_ext_bases, self.__get_output_extensions())
-        return list(base + fnext for base, fnext in pairs)
-
-    def __get_output_extensions(self):
-        yield '.py'
-        yield '.pyc'
-        if self.get_finalized_command('build_py').optimize:
-            yield '.pyo'
-
-    def write_stub(self, output_dir, ext, compile=False):
-        stub_file = os.path.join(output_dir, *ext._full_name.split('.')) + '.py'
-        self._write_stub_file(stub_file, ext, compile)
-
-    def _write_stub_file(self, stub_file: str, ext: Extension, compile=False):
-        log.info("writing stub loader for %s to %s", ext._full_name, stub_file)
-        if compile and os.path.exists(stub_file):
-            raise BaseError(stub_file + " already exists! Please delete.")
-        if not self.dry_run:
-            f = open(stub_file, 'w')
-            f.write(
-                '\n'.join([
-                    "def __bootstrap__():",
-                    "   global __bootstrap__, __file__, __loader__",
-                    "   import sys, os, pkg_resources, importlib.util" +
-                    if_dl(", dl"),
-                    "   __file__ = pkg_resources.resource_filename"
-                    "(__name__,%r)"
-                    % os.path.basename(ext._file_name),
-                    "   del __bootstrap__",
-                    "   if '__loader__' in globals():",
-                    "       del __loader__",
-                    if_dl("   old_flags = sys.getdlopenflags()"),
-                    "   old_dir = os.getcwd()",
-                    "   try:",
-                    "     os.chdir(os.path.dirname(__file__))",
-                    if_dl("     sys.setdlopenflags(dl.RTLD_NOW)"),
-                    "     spec = importlib.util.spec_from_file_location(",
-                    "                __name__, __file__)",
-                    "     mod = importlib.util.module_from_spec(spec)",
-                    "     spec.loader.exec_module(mod)",
-                    "   finally:",
-                    if_dl("     sys.setdlopenflags(old_flags)"),
-                    "     os.chdir(old_dir)",
-                    "__bootstrap__()",
-                    ""  # terminal \n
-                ])
-            )
-            f.close()
-        if compile:
-            self._compile_and_remove_stub(stub_file)
-
-    def _compile_and_remove_stub(self, stub_file: str):
-        from distutils.util import byte_compile
-
-        byte_compile([stub_file], optimize=0,
-                     force=True, dry_run=self.dry_run)
-        optimize = self.get_finalized_command('install_lib').optimize
-        if optimize > 0:
-            byte_compile([stub_file], optimize=optimize,
-                         force=True, dry_run=self.dry_run)
-        if os.path.exists(stub_file) and not self.dry_run:
-            os.unlink(stub_file)
-
-
-if use_stubs or os.name == 'nt':
-    # Build shared libraries
-    #
-    def link_shared_object(
-            self, objects, output_libname, output_dir=None, libraries=None,
-            library_dirs=None, runtime_library_dirs=None, export_symbols=None,
-            debug=0, extra_preargs=None, extra_postargs=None, build_temp=None,
-            target_lang=None):
-        self.link(
-            self.SHARED_LIBRARY, objects, output_libname,
-            output_dir, libraries, library_dirs, runtime_library_dirs,
-            export_symbols, debug, extra_preargs, extra_postargs,
-            build_temp, target_lang
-        )
-else:
-    # Build static libraries everywhere else
-    libtype = 'static'
-
-    def link_shared_object(
-            self, objects, output_libname, output_dir=None, libraries=None,
-            library_dirs=None, runtime_library_dirs=None, export_symbols=None,
-            debug=0, extra_preargs=None, extra_postargs=None, build_temp=None,
-            target_lang=None):
-        # XXX we need to either disallow these attrs on Library instances,
-        # or warn/abort here if set, or something...
-        # libraries=None, library_dirs=None, runtime_library_dirs=None,
-        # export_symbols=None, extra_preargs=None, extra_postargs=None,
-        # build_temp=None
-
-        assert output_dir is None  # distutils build_ext doesn't pass this
-        output_dir, filename = os.path.split(output_libname)
-        basename, ext = os.path.splitext(filename)
-        if self.library_filename("x").startswith('lib'):
-            # strip 'lib' prefix; this is kludgy if some platform uses
-            # a different prefix
-            basename = basename[3:]
-
-        self.create_static_lib(
-            objects, basename, output_dir, debug, target_lang
-        )

spaces/BigSalmon/AbstractTwst/README.md

@@ -1,12 +0,0 @@
----
-title: AbstractTwst
-emoji: 🐨
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Bingsu/color_textual_inversion/app.py

@@ -1,128 +0,0 @@
-from __future__ import annotations
-
-import shlex
-import subprocess
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from textwrap import dedent
-
-import numpy as np
-import streamlit as st
-import torch
-from PIL import Image
-from transformers import CLIPTokenizer
-
-
-def hex_to_rgb(s: str) -> tuple[int, int, int]:
-    value = s.lstrip("#")
-    return (int(value[:2], 16), int(value[2:4], 16), int(value[4:6], 16))
-
-
-st.header("Color Textual Inversion")
-with st.expander(label="info"):
-    with open("info.txt", "r", encoding="utf-8") as f:
-        st.markdown(f.read())
-
-duplicate_button = """<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/Bingsu/color_textual_inversion?duplicate=true"><img style="margin: 0" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>"""
-st.markdown(duplicate_button, unsafe_allow_html=True)
-
-col1, col2 = st.columns([15, 85])
-color = col1.color_picker("Pick a color", "#00f900")
-col2.text_input("", color, disabled=True)
-
-emb_name = st.text_input("Embedding name", color.lstrip("#").upper())
-init_token = st.text_input("Initializer token", "init token name")
-rgb = hex_to_rgb(color)
-
-img_array = np.zeros((128, 128, 3), dtype=np.uint8)
-for i in range(3):
-    img_array[..., i] = rgb[i]
-
-dataset_temp = TemporaryDirectory(prefix="dataset_", dir=".")
-dataset_path = Path(dataset_temp.name)
-output_temp = TemporaryDirectory(prefix="output_", dir=".")
-output_path = Path(output_temp.name)
-
-img_path = dataset_path / f"{emb_name}.png"
-Image.fromarray(img_array).save(img_path)
-
-with st.sidebar:
-    model_name = st.text_input("Model name", "Linaqruf/anything-v3.0")
-    steps = st.slider("Steps", 1, 100, value=1, step=1)
-    learning_rate = st.text_input("Learning rate", "0.001")
-    learning_rate = float(learning_rate)
-
-tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder="tokenizer")
-
-# case 1: init_token is not a single token
-token = tokenizer.tokenize(init_token)
-if len(token) > 1:
-    st.warning("Initializer token must be a single token")
-    st.stop()
-
-# case 2: init_token already exists in the tokenizer
-num_added_tokens = tokenizer.add_tokens(emb_name)
-if num_added_tokens == 0:
-    st.warning(f"The tokenizer already contains the token {emb_name}")
-    st.stop()
-
-cmd = """
-accelerate launch textual_inversion.py \
-  --pretrained_model_name_or_path={model_name} \
-  --train_data_dir={dataset_path} \
-  --learnable_property="style" \
-  --placeholder_token="{emb_name}" \
-  --initializer_token="{init}" \
-  --resolution=128 \
-  --train_batch_size=1 \
-  --repeats=1 \
-  --gradient_accumulation_steps=1 \
-  --max_train_steps={steps} \
-  --learning_rate={lr} \
-  --output_dir={output_path} \
-  --only_save_embeds
-""".strip()
-
-cmd = dedent(cmd).format(
-    model_name=model_name,
-    dataset_path=dataset_path.as_posix(),
-    emb_name=emb_name,
-    init=init_token,
-    steps=steps,
-    lr=learning_rate,
-    output_path=output_path.as_posix(),
-)
-cmd = shlex.split(cmd)
-
-result_path = output_path / "learned_embeds.bin"
-captured = ""
-
-start_button = st.button("Start")
-download_button = st.empty()
-
-if start_button:
-    with st.spinner("Training..."):
-        placeholder = st.empty()
-        p = subprocess.Popen(
-            cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding="utf-8"
-        )
-
-        while line := p.stderr.readline():
-            captured += line
-            placeholder.code(captured, language="bash")
-
-if not result_path.exists():
-    st.stop()
-
-# fix unknown file volume bug
-trained_emb = torch.load(result_path, map_location="cpu")
-for k, v in trained_emb.items():
-    trained_emb[k] = torch.from_numpy(v.numpy())
-torch.save(trained_emb, result_path)
-
-file = result_path.read_bytes()
-download_button.download_button(f"Download {emb_name}.pt", file, f"{emb_name}.pt")
-st.download_button(f"Download {emb_name}.pt ", file, f"{emb_name}.pt")
-
-dataset_temp.cleanup()
-output_temp.cleanup()

spaces/BridgeEight/internlm-20B-chat-w4-turbomind/app.py

@@ -1,128 +0,0 @@
-import os,random
-os.system('sh install_lmdeploy.sh')
-import gradio as gr
-from lmdeploy.serve.gradio.app import *
-os.system('sh download.sh')
-
-InterFace.async_engine = AsyncEngine(model_path='turbomind-internlm-chat-20b-w4',
-                                        instance_num=2,
-                                        tp=1)
-
-
-async def reset_local_demo(instruction_txtbox: gr.Textbox,
-                           state_chatbot: gr.State, request: gr.Request):
-    """reset the session.
-
-    Args:
-        instruction_txtbox (str): user's prompt
-        state_chatbot (Sequence): the chatting history
-        request (gr.Request): the request from a user
-    """
-    state_chatbot = []
-
-    return (
-        state_chatbot,
-        state_chatbot,
-        gr.Textbox.update(value=''),
-    )
-
-
-async def cancel_local_demo(state_chatbot: gr.State, cancel_btn: gr.Button,
-                            reset_btn: gr.Button, request: gr.Request):
-    """stop the session.
-
-    Args:
-        instruction_txtbox (str): user's prompt
-        state_chatbot (Sequence): the chatting history
-        request (gr.Request): the request from a user
-    """
-    return (state_chatbot, disable_btn, disable_btn)
-
-async def chat_stream_demo(
-    instruction: str,
-    state_chatbot: Sequence,
-    cancel_btn: gr.Button,
-    reset_btn: gr.Button,
-    request: gr.Request,
-):
-    """Chat with AI assistant.
-
-    Args:
-        instruction (str): user's prompt
-        state_chatbot (Sequence): the chatting history
-        request (gr.Request): the request from a user
-    """
-    session_id = random.randint(0,100000)
-    bot_summarized_response = ''
-    state_chatbot = state_chatbot + [(instruction, None)]
-    messages = []
-    for item in state_chatbot:
-        messages.append(dict(role='user', content=item[0]))
-        if item[1] is not None:
-            messages.append(dict(role='assistant', content=item[1]))
-
-    yield (state_chatbot, state_chatbot, disable_btn, disable_btn,
-           f'{bot_summarized_response}'.strip())
-
-    async for outputs in InterFace.async_engine.generate(
-            messages,
-            session_id,
-            stream_response=True,
-            sequence_start=True,
-            sequence_end=True):
-        response = outputs.response
-        if outputs.finish_reason == 'length':
-            gr.Warning('WARNING: exceed session max length.'
-                       ' Please restart the session by reset button.')
-        if outputs.generate_token_len < 0:
-            gr.Warning('WARNING: running on the old session.'
-                       ' Please restart the session by reset button.')
-        if state_chatbot[-1][-1] is None:
-            state_chatbot[-1] = (state_chatbot[-1][0], response)
-        else:
-            state_chatbot[-1] = (state_chatbot[-1][0],
-                                 state_chatbot[-1][1] + response
-                                 )  # piece by piece
-        yield (state_chatbot, state_chatbot, disable_btn, disable_btn,
-               f'{bot_summarized_response}'.strip())
-
-    yield (state_chatbot, state_chatbot, disable_btn, disable_btn,
-           f'{bot_summarized_response}'.strip())
-
-
-with gr.Blocks(css=CSS, theme=THEME) as demo:
-    state_chatbot = gr.State([])
-
-    with gr.Column(elem_id='container'):
-        gr.Markdown('## LMDeploy Playground')
-
-        chatbot = gr.Chatbot(
-            elem_id='chatbot',
-            label=InterFace.async_engine.tm_model.model_name)
-        instruction_txtbox = gr.Textbox(
-            placeholder='Please input the instruction',
-            label='Instruction')
-        with gr.Row():
-            cancel_btn = gr.Button(value='Cancel', interactive=False, visible=False)
-            reset_btn = gr.Button(value='Reset', interactive=False, visible=False)
-
-    send_event = instruction_txtbox.submit(
-        chat_stream_demo,
-        [instruction_txtbox, state_chatbot, cancel_btn, reset_btn],
-        [state_chatbot, chatbot, cancel_btn, reset_btn])
-    instruction_txtbox.submit(
-        lambda: gr.Textbox.update(value=''),
-        [],
-        [instruction_txtbox],
-    )
-    cancel_btn.click(cancel_local_demo,
-                        [state_chatbot, cancel_btn, reset_btn],
-                        [state_chatbot, cancel_btn, reset_btn],
-                        cancels=[send_event])
-
-    reset_btn.click(reset_local_demo, [instruction_txtbox, state_chatbot],
-                    [state_chatbot, chatbot, instruction_txtbox],
-                    cancels=[send_event])
-
-# print(f'server is gonna mount on: http://{server_name}:{server_port}')
-    demo.queue(concurrency_count=4, max_size=100).launch()

spaces/CVPR/LIVE/thrust/thrust/host_vector.h

@@ -1,514 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file host_vector.h
- *  \brief A dynamically-sizable array of elements which reside in the "host" memory space
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/memory_wrapper.h>
-#include <thrust/detail/vector_base.h>
-#include <vector>
-#include <utility>
-
-namespace thrust
-{
-
-// forward declaration of device_vector
-template<typename T, typename Alloc> class device_vector;
-
-/*! \addtogroup container_classes Container Classes
- *  \addtogroup host_containers Host Containers
- *  \ingroup container_classes
- *  \{
- */
-
-/*! A \p host_vector is a container that supports random access to elements,
- *  constant time removal of elements at the end, and linear time insertion
- *  and removal of elements at the beginning or in the middle. The number of
- *  elements in a \p host_vector may vary dynamically; memory management is
- *  automatic. The memory associated with a \p host_vector resides in the memory
- *  space of the host associated with a parallel device.
- *
- *  \see http://www.sgi.com/tech/stl/Vector.html
- *  \see device_vector
- */
-template<typename T, typename Alloc = std::allocator<T> >
-  class host_vector
-    : public detail::vector_base<T,Alloc>
-{
-  private:
-    typedef detail::vector_base<T,Alloc> Parent;
-
-  public:
-    /*! \cond
-     */
-    typedef typename Parent::size_type  size_type;
-    typedef typename Parent::value_type value_type;
-    /*! \endcond
-     */
-
-    /*! This constructor creates an empty \p host_vector.
-     */
-    __host__
-    host_vector(void)
-      :Parent() {}
-
-    /*! This constructor creates an empty \p host_vector.
-     *  \param alloc The allocator to use by this host_vector.
-     */
-    __host__
-    host_vector(const Alloc &alloc)
-      :Parent(alloc) {}
-
-    /*! The destructor erases the elements.
-     */
-    //  Define an empty destructor to explicitly specify
-    //  its execution space qualifier, as a workaround for nvcc warning
-    __host__
-    ~host_vector(void) {}
-
-    /*! This constructor creates a \p host_vector with the given
-     *  size.
-     *  \param n The number of elements to initially create.
-     */
-    __host__
-    explicit host_vector(size_type n)
-      :Parent(n) {}
-
-    /*! This constructor creates a \p host_vector with the given
-     *  size.
-     *  \param n The number of elements to initially create.
-     *  \param alloc The allocator to use by this host_vector.
-     */
-    __host__
-    explicit host_vector(size_type n, const Alloc &alloc)
-      :Parent(n,alloc) {}
-
-    /*! This constructor creates a \p host_vector with copies
-     *  of an exemplar element.
-     *  \param n The number of elements to initially create.
-     *  \param value An element to copy.
-     */
-    __host__
-    explicit host_vector(size_type n, const value_type &value)
-      :Parent(n,value) {}
-
-    /*! This constructor creates a \p host_vector with copies
-     *  of an exemplar element.
-     *  \param n The number of elements to initially create.
-     *  \param value An element to copy.
-     *  \param alloc The allocator to use by this host_vector.
-     */
-    __host__
-    explicit host_vector(size_type n, const value_type &value, const Alloc &alloc)
-      :Parent(n,value,alloc) {}
-
-    /*! Copy constructor copies from an exemplar \p host_vector.
-     *  \param v The \p host_vector to copy.
-     */
-    __host__
-    host_vector(const host_vector &v)
-      :Parent(v) {}
-
-    /*! Copy constructor copies from an exemplar \p host_vector.
-     *  \param v The \p host_vector to copy.
-     *  \param alloc The allocator to use by this host_vector.
-     */
-    __host__
-    host_vector(const host_vector &v, const Alloc &alloc)
-      :Parent(v,alloc) {}
-
-  #if THRUST_CPP_DIALECT >= 2011
-    /*! Move constructor moves from another host_vector.
-     *  \param v The host_vector to move.
-     */
-     __host__
-    host_vector(host_vector &&v)
-      :Parent(std::move(v)) {}
-
-    /*! Move constructor moves from another host_vector.
-     *  \param v The host_vector to move.
-     *  \param alloc The allocator to use by this host_vector.
-     */
-     __host__
-    host_vector(host_vector &&v, const Alloc &alloc)
-      :Parent(std::move(v),alloc) {}
-  #endif
-
-  /*! Assign operator copies from an exemplar \p host_vector.
-   *  \param v The \p host_vector to copy.
-   */
-  __host__
-  host_vector &operator=(const host_vector &v)
-  { Parent::operator=(v); return *this; }
-
-  #if THRUST_CPP_DIALECT >= 2011
-    /*! Move assign operator moves from another host_vector.
-     *  \param v The host_vector to move.
-     */
-     __host__
-     host_vector &operator=(host_vector &&v)
-     { Parent::operator=(std::move(v)); return *this; }
-  #endif
-
-    /*! Copy constructor copies from an exemplar \p host_vector with different type.
-     *  \param v The \p host_vector to copy.
-     */
-    template<typename OtherT, typename OtherAlloc>
-    __host__
-    host_vector(const host_vector<OtherT,OtherAlloc> &v)
-      :Parent(v) {}
-
-    /*! Assign operator copies from an exemplar \p host_vector with different type.
-     *  \param v The \p host_vector to copy.
-     */
-    template<typename OtherT, typename OtherAlloc>
-    __host__
-    host_vector &operator=(const host_vector<OtherT,OtherAlloc> &v)
-    { Parent::operator=(v); return *this; }
-
-    /*! Copy constructor copies from an exemplar <tt>std::vector</tt>.
-     *  \param v The <tt>std::vector</tt> to copy.
-     */
-    template<typename OtherT, typename OtherAlloc>
-    __host__
-    host_vector(const std::vector<OtherT,OtherAlloc> &v)
-      :Parent(v) {}
-
-    /*! Assign operator copies from an exemplar <tt>std::vector</tt>.
-     *  \param v The <tt>std::vector</tt> to copy.
-     */
-    template<typename OtherT, typename OtherAlloc>
-    __host__
-    host_vector &operator=(const std::vector<OtherT,OtherAlloc> &v)
-    { Parent::operator=(v); return *this;}
-
-    /*! Copy constructor copies from an exemplar \p device_vector with possibly different type.
-     *  \param v The \p device_vector to copy.
-     */
-    template<typename OtherT, typename OtherAlloc>
-    __host__
-    host_vector(const device_vector<OtherT,OtherAlloc> &v);
-
-    /*! Assign operator copies from an exemplar \p device_vector.
-     *  \param v The \p device_vector to copy.
-     */
-    template<typename OtherT, typename OtherAlloc>
-    __host__
-    host_vector &operator=(const device_vector<OtherT,OtherAlloc> &v)
-    { Parent::operator=(v); return *this; }
-
-    /*! This constructor builds a \p host_vector from a range.
-     *  \param first The beginning of the range.
-     *  \param last The end of the range.
-     */
-    template<typename InputIterator>
-    __host__
-    host_vector(InputIterator first, InputIterator last)
-      :Parent(first, last) {}
-
-    /*! This constructor builds a \p host_vector from a range.
-     *  \param first The beginning of the range.
-     *  \param last The end of the range.
-     *  \param alloc The allocator to use by this host_vector.
-     */
-    template<typename InputIterator>
-    __host__
-    host_vector(InputIterator first, InputIterator last, const Alloc &alloc)
-      :Parent(first, last, alloc) {}
-
-// declare these members for the purpose of Doxygenating them
-// they actually exist in a derived-from class
-#if 0
-    /*! \brief Resizes this vector to the specified number of elements.
-     *  \param new_size Number of elements this vector should contain.
-     *  \param x Data with which new elements should be populated.
-     *  \throw std::length_error If n exceeds max_size().
-     *
-     *  This method will resize this vector to the specified number of
-     *  elements.  If the number is smaller than this vector's current
-     *  size this vector is truncated, otherwise this vector is
-     *  extended and new elements are populated with given data.
-     */
-    void resize(size_type new_size, const value_type &x = value_type());
-
-    /*! Returns the number of elements in this vector.
-     */
-    size_type size(void) const;
-
-    /*! Returns the size() of the largest possible vector.
-     *  \return The largest possible return value of size().
-     */
-    size_type max_size(void) const;
-
-    /*! \brief If n is less than or equal to capacity(), this call has no effect.
-     *         Otherwise, this method is a request for allocation of additional memory. If
-     *         the request is successful, then capacity() is greater than or equal to
-     *         n; otherwise, capacity() is unchanged. In either case, size() is unchanged.
-     *  \throw std::length_error If n exceeds max_size().
-     */
-    void reserve(size_type n);
-
-    /*! Returns the number of elements which have been reserved in this
-     *  vector.
-     */
-    size_type capacity(void) const;
-
-    /*! This method shrinks the capacity of this vector to exactly
-     *  fit its elements.
-     */
-    void shrink_to_fit(void);
-
-    /*! \brief Subscript access to the data contained in this vector_dev.
-     *  \param n The index of the element for which data should be accessed.
-     *  \return Read/write reference to data.
-     *
-     *  This operator allows for easy, array-style, data access.
-     *  Note that data access with this operator is unchecked and
-     *  out_of_range lookups are not defined.
-     */
-    reference operator[](size_type n);
-
-    /*! \brief Subscript read access to the data contained in this vector_dev.
-     *  \param n The index of the element for which data should be accessed.
-     *  \return Read reference to data.
-     *
-     *  This operator allows for easy, array-style, data access.
-     *  Note that data access with this operator is unchecked and
-     *  out_of_range lookups are not defined.
-     */
-    const_reference operator[](size_type n) const;
-
-    /*! This method returns an iterator pointing to the beginning of
-     *  this vector.
-     *  \return mStart
-     */
-    iterator begin(void);
-
-    /*! This method returns a const_iterator pointing to the beginning
-     *  of this vector.
-     *  \return mStart
-     */
-    const_iterator begin(void) const;
-
-    /*! This method returns a const_iterator pointing to the beginning
-     *  of this vector.
-     *  \return mStart
-     */
-    const_iterator cbegin(void) const;
-
-    /*! This method returns a reverse_iterator pointing to the beginning of
-     *  this vector's reversed sequence.
-     *  \return A reverse_iterator pointing to the beginning of this
-     *          vector's reversed sequence.
-     */
-    reverse_iterator rbegin(void);
-
-    /*! This method returns a const_reverse_iterator pointing to the beginning of
-     *  this vector's reversed sequence.
-     *  \return A const_reverse_iterator pointing to the beginning of this
-     *          vector's reversed sequence.
-     */
-    const_reverse_iterator rbegin(void) const;
-
-    /*! This method returns a const_reverse_iterator pointing to the beginning of
-     *  this vector's reversed sequence.
-     *  \return A const_reverse_iterator pointing to the beginning of this
-     *          vector's reversed sequence.
-     */
-    const_reverse_iterator crbegin(void) const;
-
-    /*! This method returns an iterator pointing to one element past the
-     *  last of this vector.
-     *  \return begin() + size().
-     */
-    iterator end(void);
-
-    /*! This method returns a const_iterator pointing to one element past the
-     *  last of this vector.
-     *  \return begin() + size().
-     */
-    const_iterator end(void) const;
-
-    /*! This method returns a const_iterator pointing to one element past the
-     *  last of this vector.
-     *  \return begin() + size().
-     */
-    const_iterator cend(void) const;
-
-    /*! This method returns a reverse_iterator pointing to one element past the
-     *  last of this vector's reversed sequence.
-     *  \return rbegin() + size().
-     */
-    reverse_iterator rend(void);
-
-    /*! This method returns a const_reverse_iterator pointing to one element past the
-     *  last of this vector's reversed sequence.
-     *  \return rbegin() + size().
-     */
-    const_reverse_iterator rend(void) const;
-
-    /*! This method returns a const_reverse_iterator pointing to one element past the
-     *  last of this vector's reversed sequence.
-     *  \return rbegin() + size().
-     */
-    const_reverse_iterator crend(void) const;
-
-    /*! This method returns a const_reference referring to the first element of this
-     *  vector.
-     *  \return The first element of this vector.
-     */
-    const_reference front(void) const;
-
-    /*! This method returns a reference pointing to the first element of this
-     *  vector.
-     *  \return The first element of this vector.
-     */
-    reference front(void);
-
-    /*! This method returns a const reference pointing to the last element of
-     *  this vector.
-     *  \return The last element of this vector.
-     */
-    const_reference back(void) const;
-
-    /*! This method returns a reference referring to the last element of
-     *  this vector_dev.
-     *  \return The last element of this vector.
-     */
-    reference back(void);
-
-    /*! This method returns a pointer to this vector's first element.
-     *  \return A pointer to the first element of this vector.
-     */
-    pointer data(void);
-
-    /*! This method returns a const_pointer to this vector's first element.
-     *  \return a const_pointer to the first element of this vector.
-     */
-    const_pointer data(void) const;
-
-    /*! This method resizes this vector to 0.
-     */
-    void clear(void);
-
-    /*! This method returns true iff size() == 0.
-     *  \return true if size() == 0; false, otherwise.
-     */
-    bool empty(void) const;
-
-    /*! This method appends the given element to the end of this vector.
-     *  \param x The element to append.
-     */
-    void push_back(const value_type &x);
-
-    /*! This method erases the last element of this vector, invalidating
-     *  all iterators and references to it.
-     */
-    void pop_back(void);
-
-    /*! This method swaps the contents of this host_vector with another vector.
-     *  \param v The vector with which to swap.
-     */
-    void swap(host_vector &v);
-
-    /*! This method removes the element at position pos.
-     *  \param pos The position of the element of interest.
-     *  \return An iterator pointing to the new location of the element that followed the element
-     *          at position pos.
-     */
-    iterator erase(iterator pos);
-
-    /*! This method removes the range of elements [first,last) from this vector.
-     *  \param first The beginning of the range of elements to remove.
-     *  \param last The end of the range of elements to remove.
-     *  \return An iterator pointing to the new location of the element that followed the last
-     *          element in the sequence [first,last).
-     */
-    iterator erase(iterator first, iterator last);
-
-    /*! This method inserts a single copy of a given exemplar value at the
-     *  specified position in this vector.
-     *  \param position The insertion position.
-     *  \param x The exemplar element to copy & insert.
-     *  \return An iterator pointing to the newly inserted element.
-     */
-    iterator insert(iterator position, const T &x); 
-
-    /*! This method inserts a copy of an exemplar value to a range at the
-     *  specified position in this vector.
-     *  \param position The insertion position
-     *  \param n The number of insertions to perform.
-     *  \param x The value to replicate and insert.
-     */
-    void insert(iterator position, size_type n, const T &x);
-
-    /*! This method inserts a copy of an input range at the specified position
-     *  in this vector.
-     *  \param position The insertion position.
-     *  \param first The beginning of the range to copy.
-     *  \param last  The end of the range to copy.
-     *
-     *  \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 a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>.
-     */
-    template<typename InputIterator>
-    void insert(iterator position, InputIterator first, InputIterator last);
-
-    /*! This version of \p assign replicates a given exemplar
-     *  \p n times into this vector.
-     *  \param n The number of times to copy \p x.
-     *  \param x The exemplar element to replicate.
-     */
-    void assign(size_type n, const T &x);
-
-    /*! This version of \p assign makes this vector a copy of a given input range.
-     *  \param first The beginning of the range to copy.
-     *  \param last  The end of the range to copy.
-     *
-     *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator">Input Iterator</a>.
-     */
-    template<typename InputIterator>
-    void assign(InputIterator first, InputIterator last);
-
-    /*! This method returns a copy of this vector's allocator.
-     *  \return A copy of the alloctor used by this vector.
-     */
-    allocator_type get_allocator(void) const;
-#endif // end doxygen-only members
-}; // end host_vector
-
-/*! Exchanges the values of two vectors.
- *  \p x The first \p host_vector of interest.
- *  \p y The second \p host_vector of interest.
- */
-template<typename T, typename Alloc>
-  void swap(host_vector<T,Alloc> &a, host_vector<T,Alloc> &b)
-{
-  a.swap(b);
-} // end swap()
-
-/*! \}
- */
-
-} // end thrust
-
-#include <thrust/detail/host_vector.inl>
-

spaces/CVPR/ml-talking-face/app.py

@@ -1,202 +0,0 @@
-# https://huggingface.co/deepkyu/ml-talking-face
-import os
-import subprocess
-
-REST_IP = os.environ['REST_IP']
-SERVICE_PORT = int(os.environ['SERVICE_PORT'])
-TRANSLATION_APIKEY_URL = os.environ['TRANSLATION_APIKEY_URL']
-GOOGLE_APPLICATION_CREDENTIALS = os.environ['GOOGLE_APPLICATION_CREDENTIALS']
-subprocess.call(f"wget --no-check-certificate -O {GOOGLE_APPLICATION_CREDENTIALS} {TRANSLATION_APIKEY_URL}", shell=True)
-
-TOXICITY_THRESHOLD = float(os.getenv('TOXICITY_THRESHOLD', 0.7))
-
-import gradio as gr
-from toxicity_estimator import PerspectiveAPI
-from translator import Translator
-from client_rest import RestAPIApplication
-from pathlib import Path
-import argparse
-import threading
-import yaml
-
-TITLE = Path("docs/title.txt").read_text()
-DESCRIPTION = Path("docs/description.md").read_text()
-    
-    
-class GradioApplication:
-    def __init__(self, rest_ip, rest_port, max_seed):
-        self.lang_list = {
-            'ko': 'ko_KR',
-            'en': 'en_US',
-            'ja': 'ja_JP',
-            'zh': 'zh_CN',
-            'zh-CN': 'zh_CN'
-        }
-        self.background_list = [None,
-                                "background_image/cvpr.png",
-                                "background_image/black.png",
-                                "background_image/river.mp4",
-                                "background_image/sky.mp4"]
-        
-        self.perspective_api = PerspectiveAPI()
-        self.translator = Translator()
-        self.rest_application = RestAPIApplication(rest_ip, rest_port)
-        self.output_dir = Path("output_file")
-
-        inputs = prepare_input()
-        outputs = prepare_output()
-
-        self.iface = gr.Interface(fn=self.infer,
-                                  title=TITLE,
-                                  description=DESCRIPTION,
-                                  inputs=inputs,
-                                  outputs=outputs,
-                                  allow_flagging='never',
-                                  article=Path("docs/article.md").read_text())
-
-        self.max_seed = max_seed
-        self._file_seed = 0
-        self.lock = threading.Lock()
-        
-    
-    def _get_file_seed(self):
-        return f"{self._file_seed % self.max_seed:02d}"
-
-    def _reset_file_seed(self):
-        self._file_seed = 0
-
-    def _counter_file_seed(self):
-        with self.lock:
-            self._file_seed += 1
-
-    def get_lang_code(self, lang):
-        return self.lang_list[lang]
-
-    def get_background_data(self, background_index):
-        # get background filename and its extension
-        data_path = self.background_list[background_index]
-
-        if data_path is not None:
-            with open(data_path, 'rb') as rf:
-                background_data = rf.read()
-            is_video_background = str(data_path).endswith(".mp4")
-        else:
-            background_data = None
-            is_video_background = False
-
-        return background_data, is_video_background
-    
-    @staticmethod
-    def return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=""):
-        return {'Toxicity': toxicity_prob}, f"Language: {lang_dest}\nText: {target_text}\n-\nDetails: {detail}", str(video_filename)   
-
-    def infer(self, text, lang, duration_rate, action, background_index):
-        self._counter_file_seed()
-        print(f"File Seed: {self._file_seed}")
-        toxicity_prob = 0.0
-        target_text = ""
-        lang_dest = ""
-        video_filename = "vacant.mp4"
-        
-        # Toxicity estimation
-        try:
-            toxicity_prob = self.perspective_api.get_score(text)
-        except Exception as e:  # when Perspective API doesn't work
-            pass
-        
-        if toxicity_prob > TOXICITY_THRESHOLD:
-            detail = "Sorry, it seems that the input text is too toxic."
-            return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {detail}")
-        
-        # Google Translate API
-        try:
-            target_text, lang_dest = self.translator.get_translation(text, lang)
-        except Exception as e:
-            target_text = ""
-            lang_dest = ""
-            detail = f"Error from language translation: ({e})"
-            return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {detail}")
-        
-        try:
-            self.translator.length_check(lang_dest, target_text)  # assertion check
-        except AssertionError as e:
-            return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {str(e)}")
-            
-        lang_rpc_code = self.get_lang_code(lang_dest)
-
-        # Video Inference
-        background_data, is_video_background = self.get_background_data(background_index)
-        
-        video_data = self.rest_application.get_video(target_text, lang_rpc_code, duration_rate, action.lower(),
-                                                     background_data, is_video_background)
-        print(f"Video data size: {len(video_data)}")
-
-        video_filename = self.output_dir / f"{self._file_seed:02d}.mkv"
-        with open(video_filename, "wb") as video_file:
-            video_file.write(video_data)
-        
-        return self.return_format(toxicity_prob, target_text, lang_dest, video_filename)     
-
-    def run(self, server_port=7860, share=False):
-        try:
-            self.iface.launch(height=900,
-                              share=share, server_port=server_port,
-                              enable_queue=True)
-        
-        except KeyboardInterrupt:
-            gr.close_all()
-
-
-def prepare_input():
-    text_input = gr.Textbox(lines=2,
-                            placeholder="Type your text with English, Chinese, Korean, and Japanese.",
-                            value="Hello, this is demonstration for talking face generation "
-                            "with multilingual text-to-speech.",
-                            label="Text")
-    lang_input = gr.Radio(['Korean', 'English', 'Japanese', 'Chinese'],
-                          type='value',
-                          value=None,
-                          label="Language")
-    duration_rate_input = gr.Slider(minimum=0.8,
-                                    maximum=1.2,
-                                    step=0.01,
-                                    value=1.0,
-                                    label="Duration (The bigger the value, the slower the speech)")
-    action_input = gr.Radio(['Default', 'Hand', 'BothHand', 'HandDown', 'Sorry'],
-                            type='value',
-                            value='Default',
-                            label="Select an action ...")
-    background_input = gr.Radio(['None', 'CVPR', 'Black', 'River', 'Sky'],
-                                type='index',
-                                value='None',
-                                label="Select a background image/video ...")
-
-    return [text_input, lang_input, duration_rate_input,
-            action_input, background_input]
-
-
-def prepare_output():
-    toxicity_output = gr.Label(num_top_classes=1, label="Toxicity (from Perspective API)")
-    translation_result_otuput = gr.Textbox(type="str", label="Translation Result")
-    video_output = gr.Video(format='mp4')
-    return [toxicity_output, translation_result_otuput, video_output]
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='GRADIO DEMO for talking face generation submitted to CVPR2022')
-    parser.add_argument('-p', '--port', dest='gradio_port', type=int, default=7860, help="Port for gradio")
-    parser.add_argument('--rest_ip', type=str, default=REST_IP, help="IP for REST API")
-    parser.add_argument('--rest_port', type=int, default=SERVICE_PORT, help="Port for REST API")
-    parser.add_argument('--max_seed', type=int, default=20, help="Max seed for saving video")
-    parser.add_argument('--share', action='store_true', help='get publicly sharable link')
-    args = parser.parse_args()
-    return args    
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    
-    gradio_application = GradioApplication(args.rest_ip, args.rest_port, args.max_seed)
-    gradio_application.run(server_port=args.gradio_port, share=args.share)
-    

spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/resources/admin/index.css

@@ -1,75 +0,0 @@
-body {
-  transform: scale(1);
-  width: 660px;
-}
-.container {
-  background: url("./imgs/bg.png") #000144 left top repeat-y;
-  background-size: 700px auto;
-  width: 660px;
-}
-.head-box {
-  margin: 0 0 80px 0;
-}
-.cfg-box {
-  border-radius: 15px;
-  margin-top: 20px;
-  margin-bottom: 20px;
-  padding: 5px 15px;
-  overflow: hidden;
-  background: #f5f5f5;
-  box-shadow: 0 5px 10px 0 rgba(0, 0, 0, 0.15);
-  position: relative;
-  background: rgba(35, 38, 57, 0.8);
-}
-.cfg-group {
-  color: #ceb78b;
-  font-size: 18px;
-  font-weight: bold;
-  padding: 10px 20px;
-}
-.cfg-li {
-  border-radius: 18px;
-  min-height: 36px;
-  position: relative;
-  overflow: hidden;
-  margin-bottom: 10px;
-  background: rgba(203, 196, 190, 0);
-}
-.cfg-line {
-  color: #4e5769;
-  line-height: 36px;
-  padding-left: 20px;
-  font-weight: bold;
-  border-radius: 16px;
-  box-shadow: 0 0 2px rgba(0, 0, 0, 0.5);
-  background: url("./imgs/cfg-right.jpg") right top #cbc4be no-repeat;
-  background-size: auto 36px;
-}
-.cfg-hint {
-  font-size: 12px;
-  font-weight: normal;
-  margin-top: 3px;
-  margin-bottom: -3px;
-}
-.cfg-status {
-  position: absolute;
-  top: 0;
-  right: 0;
-  height: 36px;
-  width: 160px;
-  text-align: center;
-  line-height: 36px;
-  font-size: 16px;
-  color: #495366;
-  font-weight: bold;
-  border-radius: 0 16px 16px 0;
-}
-.cfg-status.status-off {
-  color: #a95151;
-}
-.cfg-desc {
-  font-size: 12px;
-  color: #cbc4be;
-  margin: 5px 0 5px 20px;
-}
-/*# sourceMappingURL=index.css.map */

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

@@ -1,65 +0,0 @@
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-from meme_generator.utils import run_sync, translate
-
-
-@run_sync
-def _dianzhongdian(img: BuildImage, text: str, trans: str):
-    img = img.convert("L").resize_width(500)
-    text_img1 = BuildImage.new("RGBA", (500, 60))
-    text_img2 = BuildImage.new("RGBA", (500, 35))
-
-    try:
-        text_img1.draw_text(
-            (20, 0, text_img1.width - 20, text_img1.height),
-            text,
-            max_fontsize=50,
-            min_fontsize=25,
-            fill="white",
-        )
-    except ValueError:
-        raise TextOverLength(text)
-
-    try:
-        text_img2.draw_text(
-            (20, 0, text_img2.width - 20, text_img2.height),
-            trans,
-            max_fontsize=25,
-            min_fontsize=10,
-            fill="white",
-        )
-    except ValueError:
-        raise TextOverLength(text)
-
-    frame = BuildImage.new("RGBA", (500, img.height + 100), "black")
-    frame.paste(img, alpha=True)
-    frame.paste(text_img1, (0, img.height), alpha=True)
-    frame.paste(text_img2, (0, img.height + 60), alpha=True)
-    return frame.save_jpg()
-
-
-async def dianzhongdian(images: List[BuildImage], texts: List[str], args):
-    if len(texts) == 1:
-        text = texts[0]
-        trans = await translate(text, lang_to="jp")
-    else:
-        text = texts[0]
-        trans = texts[1]
-
-    return await _dianzhongdian(images[0], text, trans)
-
-
-add_meme(
-    "dianzhongdian",
-    dianzhongdian,
-    min_images=1,
-    max_images=1,
-    min_texts=1,
-    max_texts=2,
-    default_texts=["救命啊"],
-    keywords=["入典", "典中典", "黑白草图"],
-)

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

@@ -1,58 +0,0 @@
-from typing import List
-
-from pil_utils import BuildImage
-from pydantic import Field
-
-from meme_generator import MemeArgsModel, MemeArgsParser, MemeArgsType, add_meme
-from meme_generator.exception import TextOverLength
-from meme_generator.utils import make_jpg_or_gif
-
-help = "图片“压扁”比例"
-
-parser = MemeArgsParser()
-parser.add_argument("-r", "--ratio", type=int, default=2, help=help)
-
-
-class Model(MemeArgsModel):
-    ratio: int = Field(2, description=help)
-
-
-def look_flat(images: List[BuildImage], texts: List[str], args: Model):
-    text = texts[0] if texts else "可恶...被人看扁了"
-    ratio = args.ratio
-
-    img_w = 500
-    text_h = 80
-    text_frame = BuildImage.new("RGBA", (img_w, text_h), "white")
-    try:
-        text_frame.draw_text(
-            (10, 0, img_w - 10, text_h),
-            text,
-            max_fontsize=55,
-            min_fontsize=30,
-            weight="bold",
-        )
-    except ValueError:
-        raise TextOverLength(text)
-
-    def make(img: BuildImage) -> BuildImage:
-        img = img.convert("RGBA").resize_width(img_w)
-        img = img.resize((img_w, img.height // ratio))
-        img_h = img.height
-        frame = BuildImage.new("RGBA", (img_w, img_h + text_h), "white")
-        return frame.paste(img, alpha=True).paste(text_frame, (0, img_h), alpha=True)
-
-    return make_jpg_or_gif(images[0], make)
-
-
-add_meme(
-    "look_flat",
-    look_flat,
-    min_images=1,
-    max_images=1,
-    min_texts=0,
-    max_texts=1,
-    default_texts=["可恶...被人看扁了"],
-    args_type=MemeArgsType(parser, Model),
-    keywords=["看扁"],
-)

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/registry.py

@@ -1,12 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-
-from maskrcnn_benchmark.utils.registry import Registry
-
-BACKBONES = Registry()
-RPN_HEADS = Registry()
-ROI_BOX_FEATURE_EXTRACTORS = Registry()
-ROI_BOX_PREDICTOR = Registry()
-ROI_KEYPOINT_FEATURE_EXTRACTORS = Registry()
-ROI_KEYPOINT_PREDICTOR = Registry()
-ROI_MASK_FEATURE_EXTRACTORS = Registry()
-ROI_MASK_PREDICTOR = Registry()

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

@@ -1,448 +0,0 @@
-from fontTools.voltLib.error import VoltLibError
-from typing import NamedTuple
-
-
-class Pos(NamedTuple):
-    adv: int
-    dx: int
-    dy: int
-    adv_adjust_by: dict
-    dx_adjust_by: dict
-    dy_adjust_by: dict
-
-    def __str__(self):
-        res = " POS"
-        for attr in ("adv", "dx", "dy"):
-            value = getattr(self, attr)
-            if value is not None:
-                res += f" {attr.upper()} {value}"
-                adjust_by = getattr(self, f"{attr}_adjust_by", {})
-                for size, adjustment in adjust_by.items():
-                    res += f" ADJUST_BY {adjustment} AT {size}"
-        res += " END_POS"
-        return res
-
-
-class Element(object):
-    def __init__(self, location=None):
-        self.location = location
-
-    def build(self, builder):
-        pass
-
-    def __str__(self):
-        raise NotImplementedError
-
-
-class Statement(Element):
-    pass
-
-
-class Expression(Element):
-    pass
-
-
-class VoltFile(Statement):
-    def __init__(self):
-        Statement.__init__(self, location=None)
-        self.statements = []
-
-    def build(self, builder):
-        for s in self.statements:
-            s.build(builder)
-
-    def __str__(self):
-        return "\n" + "\n".join(str(s) for s in self.statements) + " END\n"
-
-
-class GlyphDefinition(Statement):
-    def __init__(self, name, gid, gunicode, gtype, components, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.id = gid
-        self.unicode = gunicode
-        self.type = gtype
-        self.components = components
-
-    def __str__(self):
-        res = f'DEF_GLYPH "{self.name}" ID {self.id}'
-        if self.unicode is not None:
-            if len(self.unicode) > 1:
-                unicodes = ",".join(f"U+{u:04X}" for u in self.unicode)
-                res += f' UNICODEVALUES "{unicodes}"'
-            else:
-                res += f" UNICODE {self.unicode[0]}"
-        if self.type is not None:
-            res += f" TYPE {self.type}"
-        if self.components is not None:
-            res += f" COMPONENTS {self.components}"
-        res += " END_GLYPH"
-        return res
-
-
-class GroupDefinition(Statement):
-    def __init__(self, name, enum, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.enum = enum
-        self.glyphs_ = None
-
-    def glyphSet(self, groups=None):
-        if groups is not None and self.name in groups:
-            raise VoltLibError(
-                'Group "%s" contains itself.' % (self.name), self.location
-            )
-        if self.glyphs_ is None:
-            if groups is None:
-                groups = set({self.name})
-            else:
-                groups.add(self.name)
-            self.glyphs_ = self.enum.glyphSet(groups)
-        return self.glyphs_
-
-    def __str__(self):
-        enum = self.enum and str(self.enum) or ""
-        return f'DEF_GROUP "{self.name}"\n{enum}\nEND_GROUP'
-
-
-class GlyphName(Expression):
-    """A single glyph name, such as cedilla."""
-
-    def __init__(self, glyph, location=None):
-        Expression.__init__(self, location)
-        self.glyph = glyph
-
-    def glyphSet(self):
-        return (self.glyph,)
-
-    def __str__(self):
-        return f' GLYPH "{self.glyph}"'
-
-
-class Enum(Expression):
-    """An enum"""
-
-    def __init__(self, enum, location=None):
-        Expression.__init__(self, location)
-        self.enum = enum
-
-    def __iter__(self):
-        for e in self.glyphSet():
-            yield e
-
-    def glyphSet(self, groups=None):
-        glyphs = []
-        for element in self.enum:
-            if isinstance(element, (GroupName, Enum)):
-                glyphs.extend(element.glyphSet(groups))
-            else:
-                glyphs.extend(element.glyphSet())
-        return tuple(glyphs)
-
-    def __str__(self):
-        enum = "".join(str(e) for e in self.enum)
-        return f" ENUM{enum} END_ENUM"
-
-
-class GroupName(Expression):
-    """A glyph group"""
-
-    def __init__(self, group, parser, location=None):
-        Expression.__init__(self, location)
-        self.group = group
-        self.parser_ = parser
-
-    def glyphSet(self, groups=None):
-        group = self.parser_.resolve_group(self.group)
-        if group is not None:
-            self.glyphs_ = group.glyphSet(groups)
-            return self.glyphs_
-        else:
-            raise VoltLibError(
-                'Group "%s" is used but undefined.' % (self.group), self.location
-            )
-
-    def __str__(self):
-        return f' GROUP "{self.group}"'
-
-
-class Range(Expression):
-    """A glyph range"""
-
-    def __init__(self, start, end, parser, location=None):
-        Expression.__init__(self, location)
-        self.start = start
-        self.end = end
-        self.parser = parser
-
-    def glyphSet(self):
-        return tuple(self.parser.glyph_range(self.start, self.end))
-
-    def __str__(self):
-        return f' RANGE "{self.start}" TO "{self.end}"'
-
-
-class ScriptDefinition(Statement):
-    def __init__(self, name, tag, langs, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.tag = tag
-        self.langs = langs
-
-    def __str__(self):
-        res = "DEF_SCRIPT"
-        if self.name is not None:
-            res += f' NAME "{self.name}"'
-        res += f' TAG "{self.tag}"\n\n'
-        for lang in self.langs:
-            res += f"{lang}"
-        res += "END_SCRIPT"
-        return res
-
-
-class LangSysDefinition(Statement):
-    def __init__(self, name, tag, features, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.tag = tag
-        self.features = features
-
-    def __str__(self):
-        res = "DEF_LANGSYS"
-        if self.name is not None:
-            res += f' NAME "{self.name}"'
-        res += f' TAG "{self.tag}"\n\n'
-        for feature in self.features:
-            res += f"{feature}"
-        res += "END_LANGSYS\n"
-        return res
-
-
-class FeatureDefinition(Statement):
-    def __init__(self, name, tag, lookups, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.tag = tag
-        self.lookups = lookups
-
-    def __str__(self):
-        res = f'DEF_FEATURE NAME "{self.name}" TAG "{self.tag}"\n'
-        res += " " + " ".join(f'LOOKUP "{l}"' for l in self.lookups) + "\n"
-        res += "END_FEATURE\n"
-        return res
-
-
-class LookupDefinition(Statement):
-    def __init__(
-        self,
-        name,
-        process_base,
-        process_marks,
-        mark_glyph_set,
-        direction,
-        reversal,
-        comments,
-        context,
-        sub,
-        pos,
-        location=None,
-    ):
-        Statement.__init__(self, location)
-        self.name = name
-        self.process_base = process_base
-        self.process_marks = process_marks
-        self.mark_glyph_set = mark_glyph_set
-        self.direction = direction
-        self.reversal = reversal
-        self.comments = comments
-        self.context = context
-        self.sub = sub
-        self.pos = pos
-
-    def __str__(self):
-        res = f'DEF_LOOKUP "{self.name}"'
-        res += f' {self.process_base and "PROCESS_BASE" or "SKIP_BASE"}'
-        if self.process_marks:
-            res += " PROCESS_MARKS "
-            if self.mark_glyph_set:
-                res += f'MARK_GLYPH_SET "{self.mark_glyph_set}"'
-            elif isinstance(self.process_marks, str):
-                res += f'"{self.process_marks}"'
-            else:
-                res += "ALL"
-        else:
-            res += " SKIP_MARKS"
-        if self.direction is not None:
-            res += f" DIRECTION {self.direction}"
-        if self.reversal:
-            res += " REVERSAL"
-        if self.comments is not None:
-            comments = self.comments.replace("\n", r"\n")
-            res += f'\nCOMMENTS "{comments}"'
-        if self.context:
-            res += "\n" + "\n".join(str(c) for c in self.context)
-        else:
-            res += "\nIN_CONTEXT\nEND_CONTEXT"
-        if self.sub:
-            res += f"\n{self.sub}"
-        if self.pos:
-            res += f"\n{self.pos}"
-        return res
-
-
-class SubstitutionDefinition(Statement):
-    def __init__(self, mapping, location=None):
-        Statement.__init__(self, location)
-        self.mapping = mapping
-
-    def __str__(self):
-        res = "AS_SUBSTITUTION\n"
-        for src, dst in self.mapping.items():
-            src = "".join(str(s) for s in src)
-            dst = "".join(str(d) for d in dst)
-            res += f"SUB{src}\nWITH{dst}\nEND_SUB\n"
-        res += "END_SUBSTITUTION"
-        return res
-
-
-class SubstitutionSingleDefinition(SubstitutionDefinition):
-    pass
-
-
-class SubstitutionMultipleDefinition(SubstitutionDefinition):
-    pass
-
-
-class SubstitutionLigatureDefinition(SubstitutionDefinition):
-    pass
-
-
-class SubstitutionReverseChainingSingleDefinition(SubstitutionDefinition):
-    pass
-
-
-class PositionAttachDefinition(Statement):
-    def __init__(self, coverage, coverage_to, location=None):
-        Statement.__init__(self, location)
-        self.coverage = coverage
-        self.coverage_to = coverage_to
-
-    def __str__(self):
-        coverage = "".join(str(c) for c in self.coverage)
-        res = f"AS_POSITION\nATTACH{coverage}\nTO"
-        for coverage, anchor in self.coverage_to:
-            coverage = "".join(str(c) for c in coverage)
-            res += f'{coverage} AT ANCHOR "{anchor}"'
-        res += "\nEND_ATTACH\nEND_POSITION"
-        return res
-
-
-class PositionAttachCursiveDefinition(Statement):
-    def __init__(self, coverages_exit, coverages_enter, location=None):
-        Statement.__init__(self, location)
-        self.coverages_exit = coverages_exit
-        self.coverages_enter = coverages_enter
-
-    def __str__(self):
-        res = "AS_POSITION\nATTACH_CURSIVE"
-        for coverage in self.coverages_exit:
-            coverage = "".join(str(c) for c in coverage)
-            res += f"\nEXIT {coverage}"
-        for coverage in self.coverages_enter:
-            coverage = "".join(str(c) for c in coverage)
-            res += f"\nENTER {coverage}"
-        res += "\nEND_ATTACH\nEND_POSITION"
-        return res
-
-
-class PositionAdjustPairDefinition(Statement):
-    def __init__(self, coverages_1, coverages_2, adjust_pair, location=None):
-        Statement.__init__(self, location)
-        self.coverages_1 = coverages_1
-        self.coverages_2 = coverages_2
-        self.adjust_pair = adjust_pair
-
-    def __str__(self):
-        res = "AS_POSITION\nADJUST_PAIR\n"
-        for coverage in self.coverages_1:
-            coverage = " ".join(str(c) for c in coverage)
-            res += f" FIRST {coverage}"
-        res += "\n"
-        for coverage in self.coverages_2:
-            coverage = " ".join(str(c) for c in coverage)
-            res += f" SECOND {coverage}"
-        res += "\n"
-        for (id_1, id_2), (pos_1, pos_2) in self.adjust_pair.items():
-            res += f" {id_1} {id_2} BY{pos_1}{pos_2}\n"
-        res += "\nEND_ADJUST\nEND_POSITION"
-        return res
-
-
-class PositionAdjustSingleDefinition(Statement):
-    def __init__(self, adjust_single, location=None):
-        Statement.__init__(self, location)
-        self.adjust_single = adjust_single
-
-    def __str__(self):
-        res = "AS_POSITION\nADJUST_SINGLE"
-        for coverage, pos in self.adjust_single:
-            coverage = "".join(str(c) for c in coverage)
-            res += f"{coverage} BY{pos}"
-        res += "\nEND_ADJUST\nEND_POSITION"
-        return res
-
-
-class ContextDefinition(Statement):
-    def __init__(self, ex_or_in, left=None, right=None, location=None):
-        Statement.__init__(self, location)
-        self.ex_or_in = ex_or_in
-        self.left = left if left is not None else []
-        self.right = right if right is not None else []
-
-    def __str__(self):
-        res = self.ex_or_in + "\n"
-        for coverage in self.left:
-            coverage = "".join(str(c) for c in coverage)
-            res += f" LEFT{coverage}\n"
-        for coverage in self.right:
-            coverage = "".join(str(c) for c in coverage)
-            res += f" RIGHT{coverage}\n"
-        res += "END_CONTEXT"
-        return res
-
-
-class AnchorDefinition(Statement):
-    def __init__(self, name, gid, glyph_name, component, locked, pos, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.gid = gid
-        self.glyph_name = glyph_name
-        self.component = component
-        self.locked = locked
-        self.pos = pos
-
-    def __str__(self):
-        locked = self.locked and " LOCKED" or ""
-        return (
-            f'DEF_ANCHOR "{self.name}"'
-            f" ON {self.gid}"
-            f" GLYPH {self.glyph_name}"
-            f" COMPONENT {self.component}"
-            f"{locked}"
-            f" AT {self.pos} END_ANCHOR"
-        )
-
-
-class SettingDefinition(Statement):
-    def __init__(self, name, value, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.value = value
-
-    def __str__(self):
-        if self.value is True:
-            return f"{self.name}"
-        if isinstance(self.value, (tuple, list)):
-            value = " ".join(str(v) for v in self.value)
-            return f"{self.name} {value}"
-        return f"{self.name} {self.value}"

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Column-2853eb31.css

@@ -1 +0,0 @@
-div.svelte-vt1mxs{display:flex;position:relative;flex-direction:column}div.svelte-vt1mxs>*,div.svelte-vt1mxs>.form>*{width:var(--size-full)}.gap.svelte-vt1mxs{gap:var(--layout-gap)}.hide.svelte-vt1mxs{display:none}.compact.svelte-vt1mxs>*,.compact.svelte-vt1mxs .box{border-radius:0}.compact.svelte-vt1mxs,.panel.svelte-vt1mxs{border:solid var(--panel-border-width) var(--panel-border-color);border-radius:var(--container-radius);background:var(--panel-background-fill);padding:var(--spacing-lg)}

spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/pages/02_📼_Upload_Video_File.py

@@ -1,230 +0,0 @@
-import whisper
-import streamlit as st
-from streamlit_lottie import st_lottie
-from utils import write_vtt, write_srt
-import ffmpeg
-import requests
-from typing import Iterator
-from io import StringIO
-import numpy as np
-import pathlib
-import os
-
-st.set_page_config(page_title="Auto Subtitled Video Generator", page_icon=":movie_camera:", layout="wide")
-
-# Define a function that we can use to load lottie files from a link.
-@st.cache(allow_output_mutation=True)
-def load_lottieurl(url: str):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-
-APP_DIR = pathlib.Path(__file__).parent.absolute()
-
-LOCAL_DIR = APP_DIR / "local"
-LOCAL_DIR.mkdir(exist_ok=True)
-save_dir = LOCAL_DIR / "output"
-save_dir.mkdir(exist_ok=True)
-
-
-loaded_model = whisper.load_model("base")
-current_size = "None"
-
-
-col1, col2 = st.columns([1, 3])
-with col1:
-    lottie = load_lottieurl("https://assets1.lottiefiles.com/packages/lf20_HjK9Ol.json")
-    st_lottie(lottie)
-
-with col2:
-    st.write("""
-    ## Auto Subtitled Video Generator 
-    ##### Upload a video file and get a video with subtitles.
-    ###### ➠ If you want to transcribe the video in its original language, select the task as "Transcribe"
-    ###### ➠ If you want to translate the subtitles to English, select the task as "Translate" 
-    ###### I recommend starting with the base model and then experimenting with the larger models, the small and medium models often work well. """)
-
-
-@st.cache(allow_output_mutation=True)
-def change_model(current_size, size):
-    if current_size != size:
-        loaded_model = whisper.load_model(size)
-        return loaded_model
-    else:
-        raise Exception("Model size is the same as the current size.")
-
-
-@st.cache(allow_output_mutation=True)
-def inferecence(loaded_model, uploaded_file, task):
-    with open(f"{save_dir}/input.mp4", "wb") as f:
-            f.write(uploaded_file.read())
-    audio = ffmpeg.input(f"{save_dir}/input.mp4")
-    audio = ffmpeg.output(audio, f"{save_dir}/output.wav", acodec="pcm_s16le", ac=1, ar="16k")
-    ffmpeg.run(audio, overwrite_output=True)
-    if task == "Transcribe":
-        options = dict(task="transcribe", best_of=5)
-        results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
-        vtt = getSubs(results["segments"], "vtt", 80)
-        srt = getSubs(results["segments"], "srt", 80)
-        lang = results["language"]
-        return results["text"], vtt, srt, lang
-    elif task == "Translate":
-        options = dict(task="translate", best_of=5)
-        results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
-        vtt = getSubs(results["segments"], "vtt", 80)
-        srt = getSubs(results["segments"], "srt", 80)
-        lang = results["language"]
-        return results["text"], vtt, srt, lang
-    else:
-        raise ValueError("Task not supported")
-
-
-def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
-    segmentStream = StringIO()
-
-    if format == 'vtt':
-        write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    elif format == 'srt':
-        write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    else:
-        raise Exception("Unknown format " + format)
-
-    segmentStream.seek(0)
-    return segmentStream.read()
-
-
-def generate_subtitled_video(video, audio, transcript):
-    video_file = ffmpeg.input(video)
-    audio_file = ffmpeg.input(audio)
-    ffmpeg.concat(video_file.filter("subtitles", transcript), audio_file, v=1, a=1).output("final.mp4").run(quiet=True, overwrite_output=True)
-    video_with_subs = open("final.mp4", "rb")
-    return video_with_subs
-
-
-def main():
-    size = st.selectbox("Select Model Size (The larger the model, the more accurate the transcription will be, but it will take longer)", ["tiny", "base", "small", "medium", "large"], index=1)
-    loaded_model = change_model(current_size, size)
-    st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
-        f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
-    input_file = st.file_uploader("File", type=["mp4", "avi", "mov", "mkv"])
-    # get the name of the input_file
-    if input_file is not None:
-        filename = input_file.name[:-4]
-    else:
-        filename = None
-    task = st.selectbox("Select Task", ["Transcribe", "Translate"], index=0)
-    if task == "Transcribe":
-        if st.button("Transcribe"):
-            results = inferecence(loaded_model, input_file, task)
-            col3, col4 = st.columns(2)
-            col5, col6, col7, col8 = st.columns(4)
-            col9, col10 = st.columns(2)
-            with col3:
-                st.video(input_file)
-                
-            with open("transcript.txt", "w+", encoding='utf8') as f:
-                f.writelines(results[0])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
-                datatxt = f.read()
-                
-            with open("transcript.vtt", "w+",encoding='utf8') as f:
-                f.writelines(results[1])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
-                datavtt = f.read()
-                
-            with open("transcript.srt", "w+",encoding='utf8') as f:
-                f.writelines(results[2])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
-                datasrt = f.read()
-
-            with col5:
-                st.download_button(label="Download Transcript (.txt)",
-                                data=datatxt,
-                                file_name="transcript.txt")
-            with col6:   
-                st.download_button(label="Download Transcript (.vtt)",
-                                    data=datavtt,
-                                    file_name="transcript.vtt")
-            with col7:
-                st.download_button(label="Download Transcript (.srt)",
-                                    data=datasrt,
-                                    file_name="transcript.srt")
-            with col9:
-                st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
-            with col10:
-                st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
-                        
-            with col4:
-                with st.spinner("Generating Subtitled Video"):
-                    video_with_subs = generate_subtitled_video(f"{save_dir}/input.mp4", f"{save_dir}/output.wav", "transcript.srt")
-                st.video(video_with_subs)
-                st.snow()
-            with col8:
-                st.download_button(label="Download Video with Subtitles",
-                                data=video_with_subs,
-                                file_name=f"{filename}_with_subs.mp4")
-    elif task == "Translate":
-        if st.button("Translate to English"):
-            results = inferecence(loaded_model, input_file, task)
-            col3, col4 = st.columns(2)
-            col5, col6, col7, col8 = st.columns(4)
-            col9, col10 = st.columns(2)
-            with col3:
-                st.video(input_file)
-                
-            with open("transcript.txt", "w+", encoding='utf8') as f:
-                f.writelines(results[0])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
-                datatxt = f.read()
-                
-            with open("transcript.vtt", "w+",encoding='utf8') as f:
-                f.writelines(results[1])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
-                datavtt = f.read()
-                
-            with open("transcript.srt", "w+",encoding='utf8') as f:
-                f.writelines(results[2])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
-                datasrt = f.read()
-                
-            with col5:
-                st.download_button(label="Download Transcript (.txt)",
-                                data=datatxt,
-                                file_name="transcript.txt")
-            with col6:   
-                st.download_button(label="Download Transcript (.vtt)",
-                                    data=datavtt,
-                                    file_name="transcript.vtt")
-            with col7:
-                st.download_button(label="Download Transcript (.srt)",
-                                    data=datasrt,
-                                    file_name="transcript.srt")
-            with col9:
-                st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
-            with col10:
-                st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
-                        
-            with col4:
-                with st.spinner("Generating Subtitled Video"):
-                    video_with_subs = generate_subtitled_video(f"{save_dir}/input.mp4", f"{save_dir}/output.wav", "transcript.srt")
-                st.video(video_with_subs)
-                st.snow()
-            with col8:
-                st.download_button(label="Download Video with Subtitles",
-                                data=video_with_subs,
-                                file_name=f"{filename}_with_subs.mp4")
-    else:
-        st.error("Please select a task.")
-
-
-if __name__ == "__main__":
-    main()
-    st.markdown("###### Made with :heart: by [@BatuhanYılmaz](https://twitter.com/batuhan3326) [![this is an image link](https://i.imgur.com/thJhzOO.png)](https://www.buymeacoffee.com/batuhanylmz)")

spaces/DemoLou/moe-tts/modules.py

@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dilated and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x

spaces/Devaholic/fruit-demo/README.md

@@ -1,12 +0,0 @@
----
-title: Fruit Demo
-emoji: 🐨
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.0.18
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/convert_weight.py

@@ -1,283 +0,0 @@
-import argparse
-import os
-import sys
-import pickle
-import math
-
-import torch
-import numpy as np
-from torchvision import utils
-
-from model import Generator, Discriminator
-
-
-def convert_modconv(vars, source_name, target_name, flip=False):
-    weight = vars[source_name + '/weight'].value().eval()
-    mod_weight = vars[source_name + '/mod_weight'].value().eval()
-    mod_bias = vars[source_name + '/mod_bias'].value().eval()
-    noise = vars[source_name + '/noise_strength'].value().eval()
-    bias = vars[source_name + '/bias'].value().eval()
-
-    dic = {
-        'conv.weight': np.expand_dims(weight.transpose((3, 2, 0, 1)), 0),
-        'conv.modulation.weight': mod_weight.transpose((1, 0)),
-        'conv.modulation.bias': mod_bias + 1,
-        'noise.weight': np.array([noise]),
-        'activate.bias': bias,
-    }
-
-    dic_torch = {}
-
-    for k, v in dic.items():
-        dic_torch[target_name + '.' + k] = torch.from_numpy(v)
-
-    if flip:
-        dic_torch[target_name + '.conv.weight'] = torch.flip(
-            dic_torch[target_name + '.conv.weight'], [3, 4]
-        )
-
-    return dic_torch
-
-
-def convert_conv(vars, source_name, target_name, bias=True, start=0):
-    weight = vars[source_name + '/weight'].value().eval()
-
-    dic = {'weight': weight.transpose((3, 2, 0, 1))}
-
-    if bias:
-        dic['bias'] = vars[source_name + '/bias'].value().eval()
-
-    dic_torch = {}
-
-    dic_torch[target_name + f'.{start}.weight'] = torch.from_numpy(dic['weight'])
-
-    if bias:
-        dic_torch[target_name + f'.{start + 1}.bias'] = torch.from_numpy(dic['bias'])
-
-    return dic_torch
-
-
-def convert_torgb(vars, source_name, target_name):
-    weight = vars[source_name + '/weight'].value().eval()
-    mod_weight = vars[source_name + '/mod_weight'].value().eval()
-    mod_bias = vars[source_name + '/mod_bias'].value().eval()
-    bias = vars[source_name + '/bias'].value().eval()
-
-    dic = {
-        'conv.weight': np.expand_dims(weight.transpose((3, 2, 0, 1)), 0),
-        'conv.modulation.weight': mod_weight.transpose((1, 0)),
-        'conv.modulation.bias': mod_bias + 1,
-        'bias': bias.reshape((1, 3, 1, 1)),
-    }
-
-    dic_torch = {}
-
-    for k, v in dic.items():
-        dic_torch[target_name + '.' + k] = torch.from_numpy(v)
-
-    return dic_torch
-
-
-def convert_dense(vars, source_name, target_name):
-    weight = vars[source_name + '/weight'].value().eval()
-    bias = vars[source_name + '/bias'].value().eval()
-
-    dic = {'weight': weight.transpose((1, 0)), 'bias': bias}
-
-    dic_torch = {}
-
-    for k, v in dic.items():
-        dic_torch[target_name + '.' + k] = torch.from_numpy(v)
-
-    return dic_torch
-
-
-def update(state_dict, new):
-    for k, v in new.items():
-        if k not in state_dict:
-            raise KeyError(k + ' is not found')
-
-        if v.shape != state_dict[k].shape:
-            raise ValueError(f'Shape mismatch: {v.shape} vs {state_dict[k].shape}')
-
-        state_dict[k] = v
-
-
-def discriminator_fill_statedict(statedict, vars, size):
-    log_size = int(math.log(size, 2))
-
-    update(statedict, convert_conv(vars, f'{size}x{size}/FromRGB', 'convs.0'))
-
-    conv_i = 1
-
-    for i in range(log_size - 2, 0, -1):
-        reso = 4 * 2 ** i
-        update(
-            statedict,
-            convert_conv(vars, f'{reso}x{reso}/Conv0', f'convs.{conv_i}.conv1'),
-        )
-        update(
-            statedict,
-            convert_conv(
-                vars, f'{reso}x{reso}/Conv1_down', f'convs.{conv_i}.conv2', start=1
-            ),
-        )
-        update(
-            statedict,
-            convert_conv(
-                vars, f'{reso}x{reso}/Skip', f'convs.{conv_i}.skip', start=1, bias=False
-            ),
-        )
-        conv_i += 1
-
-    update(statedict, convert_conv(vars, f'4x4/Conv', 'final_conv'))
-    update(statedict, convert_dense(vars, f'4x4/Dense0', 'final_linear.0'))
-    update(statedict, convert_dense(vars, f'Output', 'final_linear.1'))
-
-    return statedict
-
-
-def fill_statedict(state_dict, vars, size):
-    log_size = int(math.log(size, 2))
-
-    for i in range(8):
-        update(state_dict, convert_dense(vars, f'G_mapping/Dense{i}', f'style.{i + 1}'))
-
-    update(
-        state_dict,
-        {
-            'input.input': torch.from_numpy(
-                vars['G_synthesis/4x4/Const/const'].value().eval()
-            )
-        },
-    )
-
-    update(state_dict, convert_torgb(vars, 'G_synthesis/4x4/ToRGB', 'to_rgb1'))
-
-    for i in range(log_size - 2):
-        reso = 4 * 2 ** (i + 1)
-        update(
-            state_dict,
-            convert_torgb(vars, f'G_synthesis/{reso}x{reso}/ToRGB', f'to_rgbs.{i}'),
-        )
-
-    update(state_dict, convert_modconv(vars, 'G_synthesis/4x4/Conv', 'conv1'))
-
-    conv_i = 0
-
-    for i in range(log_size - 2):
-        reso = 4 * 2 ** (i + 1)
-        update(
-            state_dict,
-            convert_modconv(
-                vars,
-                f'G_synthesis/{reso}x{reso}/Conv0_up',
-                f'convs.{conv_i}',
-                flip=True,
-            ),
-        )
-        update(
-            state_dict,
-            convert_modconv(
-                vars, f'G_synthesis/{reso}x{reso}/Conv1', f'convs.{conv_i + 1}'
-            ),
-        )
-        conv_i += 2
-
-    for i in range(0, (log_size - 2) * 2 + 1):
-        update(
-            state_dict,
-            {
-                f'noises.noise_{i}': torch.from_numpy(
-                    vars[f'G_synthesis/noise{i}'].value().eval()
-                )
-            },
-        )
-
-    return state_dict
-
-
-if __name__ == '__main__':
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    print('Using PyTorch device', device)
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--repo', type=str, required=True)
-    parser.add_argument('--gen', action='store_true')
-    parser.add_argument('--disc', action='store_true')
-    parser.add_argument('--channel_multiplier', type=int, default=2)
-    parser.add_argument('path', metavar='PATH')
-
-    args = parser.parse_args()
-
-    sys.path.append(args.repo)
-
-    import dnnlib
-    from dnnlib import tflib
-
-    tflib.init_tf()
-
-    with open(args.path, 'rb') as f:
-        generator, discriminator, g_ema = pickle.load(f)
-
-    size = g_ema.output_shape[2]
-
-    g = Generator(size, 512, 8, channel_multiplier=args.channel_multiplier)
-    state_dict = g.state_dict()
-    state_dict = fill_statedict(state_dict, g_ema.vars, size)
-
-    g.load_state_dict(state_dict)
-
-    latent_avg = torch.from_numpy(g_ema.vars['dlatent_avg'].value().eval())
-
-    ckpt = {'g_ema': state_dict, 'latent_avg': latent_avg}
-
-    if args.gen:
-        g_train = Generator(size, 512, 8, channel_multiplier=args.channel_multiplier)
-        g_train_state = g_train.state_dict()
-        g_train_state = fill_statedict(g_train_state, generator.vars, size)
-        ckpt['g'] = g_train_state
-
-    if args.disc:
-        disc = Discriminator(size, channel_multiplier=args.channel_multiplier)
-        d_state = disc.state_dict()
-        d_state = discriminator_fill_statedict(d_state, discriminator.vars, size)
-        ckpt['d'] = d_state
-
-    name = os.path.splitext(os.path.basename(args.path))[0]
-    outpath = os.path.join(os.getcwd(), f'{name}.pt')
-    print('Saving', outpath)
-    try:
-        torch.save(ckpt, outpath, _use_new_zipfile_serialization=False)
-    except TypeError:
-        torch.save(ckpt, outpath)
-    
-
-    print('Generating TF-Torch comparison images')
-    batch_size = {256: 8, 512: 4, 1024: 2}
-    n_sample = batch_size.get(size, 4)
-
-    g = g.to(device)
-
-    z = np.random.RandomState(0).randn(n_sample, 512).astype('float32')
-
-    with torch.no_grad():
-        img_pt, _ = g(
-            [torch.from_numpy(z).to(device)],
-            truncation=0.5,
-            truncation_latent=latent_avg.to(device),
-        )
-
-    img_tf = g_ema.run(z, None, randomize_noise=False)
-    img_tf = torch.from_numpy(img_tf).to(device)
-
-    img_diff = ((img_pt + 1) / 2).clamp(0.0, 1.0) - ((img_tf.to(device) + 1) / 2).clamp(
-        0.0, 1.0
-    )
-
-    img_concat = torch.cat((img_tf, img_pt, img_diff), dim=0)
-    utils.save_image(
-        img_concat, name + '.png', nrow=n_sample, normalize=True, range=(-1, 1)
-    )
-    print('Done')
-

spaces/DragGan/DragGan/stylegan_human/docs/Dataset.md

@@ -1,74 +0,0 @@
-# SHHQ Dataset
-<img src="../img/preview_samples1.png" width="96%" height="96%">
-
-## Overview
-SHHQ is a dataset with high-quality full-body human images in a resolution of 1024 × 512.
-Since we need to follow a rigorous legal review in our institute, we can not release all of the data at once.
-
-For now, SHHQ-1.0 with 40K images is released! More data will be released in the later versions.
-
-
-## Data Sources
-Images are collected in two main ways: 
-1) From the Internet. 
-We developed a crawler tool with an official API, mainly downloading images from Flickr, Pixabay and Pexels. So you need to meet all the following licenses when using the dataset: CC0, [Pixabay License](https://pixabay.com/service/license/), and [Pexels Licenses](https://www.pexels.com/license/).
-2) From the data providers. 
-We purchased images from databases of individual photographers, modeling agencies and other suppliers.
-Images were reviewed by our legal team prior to purchase to ensure permission for use in research.
-
-### Note: 
-The composition of SHHQ-1.0: 
-
-1) Images obtained from the above sources.
-2) Processed 9991 DeepFashion [[1]](#1) images (retain only full body images).
-3) 1940 African images from the InFashAI [[2]](#2) dataset to increase data diversity.
-
-## Data License
-We are aware of privacy concerns and seriously treat the license and privacy issues. All released data will be ensured under the license of CC0 and free for research use. Also, persons in the dataset are anonymised without additional private or sensitive metadata.
-
-## Agreement
-The SHHQ is available for non-commercial research purposes only. 
-
-You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit any portion of the images and any portion of the derived data for commercial purposes. 
-
-You agree NOT to further copy, publish or distribute any portion of SHHQ to any third party for any purpose. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
-
-Shanghai AI Lab reserves the right to terminate your access to the SHHQ at any time.
-
-## Dataset Preview
-For those interested in our dataset, we provide a preview version with 100 images randomly sampled from SHHQ-1.0: [SHHQ-1.0_samples](https://drive.google.com/file/d/1tnNFfmFtzRbYL3qEnNXQ_ShaN9YV5tI5/view?usp=sharing).
-
-In SHHQ-1.0, we provide aligned raw images along with machine-calculated segmentation masks. Later we are planning to release manually annotated human-parsing version of these 40,000 images. Please stay tuned. 
- 
-> We also provide script [bg_white.py](../bg_white.py) to whiten the background of the raw image using its segmentation mask.
-
-If you want to access the full SHHQ-1.0, please read the following instructions.
-
-## Model trained using SHHQ-1.0
-
-| Structure | 1024x512 | Metric | Scores |  512x256 | Metric | Scores | 
-| --------- |:----------:| :----------:| :----------:|  :-----: |  :-----: |  :-----: | 
-| StyleGAN1 | to be released | - | - | to be released | - | - |
-| StyleGAN2 | [SHHQ-1.0_sg2_1024.pkl](https://drive.google.com/file/d/1PuvE72xpc69Zq4y58dohuKbG9dFnnjEX/view?usp=sharing) | fid50k_full | 3.56 | [SHHQ-1.0_sg2_512.pkl](https://drive.google.com/file/d/170t2FRWxR8_TG3_y0nVtDBogLPOClnyf/view?usp=sharing) | fid50k_full | 3.68 |
-| StyleGAN3 | to be released | - | - |to be released | - | - |
-
-
-## Download Instructions
-Please download the SHHQ Dataset Release Agreement from [link](./SHHQ_Dataset_Release_Agreement.pdf).
-Read it carefully, complete and sign it appropriately. 
-
-Please send the completed form to Jianglin Fu ([email protected]) and Shikai Li ([email protected]), and cc to Wayne Wu ([email protected]) using institutional email address. The email Subject Title is "SHHQ Dataset Release Agreement". We will verify your request and contact you with the dataset link and password to unzip the image data.
-
-Note:
-
-1. We are currently facing large incoming applications, and we need to carefully verify all the applicants, please be patient, and we will reply to you as soon as possible.
-
-2. The signature in the agreement should be hand-written.
-
-## References
-<a id="1">[1]</a> 
-Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou. DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations. CVPR (2016)
-
-<a id="2">[2]</a> 
-Hacheme, Gilles and Sayouti, Noureini. Neural fashion image captioning: Accounting for data diversity. arXiv preprint arXiv:2106.12154 (2021)
-