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-<h1>How to OBS Studio Download for Windows 7 64 Bit and Use It for Streaming and Recording</h1>
-<p>OBS Studio is a free and open source software that allows you to stream and record your video and audio content. OBS Studio stands for Open Broadcaster Software Studio, and it is one of the most popular tools for live streaming and video recording. OBS Studio supports various platforms, such as Windows, Mac OS, and Linux. OBS Studio also supports various streaming services, such as Twitch, YouTube, Facebook, and more.</p>
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-<p>If you want to use OBS Studio for your streaming and recording needs, you need to download it from the official website and install it on your computer. In this article, we will show you how to do that step by step for Windows 7 64 bit.</p>
-<h2>How to OBS Studio Download for Windows 7 64 Bit</h2>
-<p>Follow these steps to download and install OBS Studio for Windows 7 64 bit:</p>
-<ol>
-<li>Go to the official website of OBS Studio. The URL is https://obsproject.com/.</li>
-<li>On the website, you will see a button that says "Download Installer". Click on it to start the download process.</li>
-<li>You will see a file named OBS-Studio-x.x.x-Full-Installer-x64.exe, where x.x.x is the version number. This is the installer file for OBS Studio. Save it to your preferred location on your computer.</li>
-<li>Once the download is complete, double-click on the installer file to launch it. Follow the instructions on the screen to complete the installation process. You can choose the default settings or customize them according to your preferences.</li>
-<li>After the installation is finished, you will have OBS Studio installed on your computer. You can verify this by opening the Start menu and looking for OBS Studio in the list of programs.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed OBS Studio for Windows 7 64 bit.</p>
-<p></p>
-<h2>How to Use OBS Studio for Streaming and Recording</h2>
-<p>Now that you have OBS Studio installed on your computer, you can start using it for your streaming and recording purposes. Here are some basic steps to get you started:</p>
-<ol>
-<li>Launch OBS Studio by clicking on its icon in the Start menu or on your desktop.</li>
-<li>You will see the main window of OBS Studio with four sections: Scenes, Sources, Mixer, and Controls. Scenes are collections of sources that you want to show on your stream or recording. Sources are the elements that you want to capture, such as your webcam, microphone, game window, browser window, etc. Mixer is where you can adjust the audio levels of your sources. Controls are where you can start and stop your stream or recording, as well as access other settings and options.</li>
-<li>To add a scene, click on the "+" button in the Scenes section. Give your scene a name and click OK.</li>
-<li>To add a source, click on the "+" button in the Sources section. Choose the type of source that you want to add from the list of options. For example, if you want to capture your webcam, choose Video Capture Device. Give your source a name and click OK.</li>
-<li>You will see a window with various settings for your source. Adjust them according to your needs and click OK.</li>
-<li>You can resize and reposition your source by dragging its edges or corners in the preview window. You can also right-click on your source and choose Transform to access more options for cropping, rotating, flipping, etc.</li>
-<li>You can add more scenes and sources as needed by repeating steps 3 to 6.</li>
-<li>To start streaming, click on the Settings button in the Controls section. Go to the Stream tab and choose the service that you want to stream to from the drop-down menu. Enter your stream key or log in with your account credentials. Click Apply and OK.</li>
-<li>To start recording, click on the Settings button in the Controls section. Go to the Output tab and choose the mode that you want to use: Simple or Advanced. Adjust the settings for video quality, audio quality, file format, etc. Click Apply and OK.</li>
-<li>When you are ready to go live or record, click on the Start Streaming or Start Recording button in the Controls section.</li>
-<li>When you are done with your stream or recording, click on the Stop Streaming or Stop Recording button in</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Activation Code Airdroid Premium Crack.md DELETED Viewed

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-<h1>Activation Code Airdroid Premium Crack: What Is It and How to Use It?</h1>
-<p>If you are an Android user who wants to access and manage your device from your computer, you may have heard of <a href="(^6^)">Airdroid</a>, a popular tool that lets you do just that. But what if you want to enjoy more features and benefits without paying for the premium subscription? You may have also heard of <strong>Airdroid Premium Crack</strong>, a modified version of Airdroid that claims to offer you all the premium features for free. But is it safe and legal to use? And are there any alternatives to it? In this article, we will answer these questions and more.</p>
- <h2>What Is Airdroid and What Are Its Features and Benefits?</h2>
-<p><a href="(^6^)">Airdroid</a> is a cross-platform tool that allows you to access and manage your Android devices wirelessly over the web. You can use it to transfer files, control mobile devices remotely, receive and reply to messages, mirror screen, and more. It works on Windows, Mac, Linux, Chrome, Firefox, Safari, Edge, Opera, and other browsers.</p>
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- <h3>Airdroid offers multiple features to enhance productivity and convenience</h3>
-<p>Some of the main features of Airdroid are:</p>
-<ul>
-<li><strong>File Transfer & Management:</strong> You can transfer files across devices, control mobile devices remotely, receive and reply to messages on computer. You can also enjoy high-speed file transfer within the local area network, and switch seamlessly between Wi-Fi, 4G, or 5G network.</li>
-<li><strong>Screen Mirroring:</strong> You can mirror Android phone screens to your computer and view your content on a bigger display. Screen mirroring doesn't require the phones and the computer to be on the same network. It is useful for various scenarios, such as remote meetings, online teaching, live streaming, gaming, etc.</li>
-<li><strong>Remote Control:</strong> You can remotely control your Android devices from your computer without rooting them. You can use your mouse and keyboard to operate your phone or tablet, access any app or file, take screenshots, record screen, etc.</li>
-<li><strong>Remote Monitoring:</strong> You can use your old Android phones as security cameras by using the remote camera feature. You can view the live feed from your phone's camera on your computer, and even switch between the front and rear cameras. You can also record videos or take photos remotely.</li>
-<li><strong>Notification Mirror:</strong> You can receive and reply to notifications from your Android devices on your computer. You can also mute or block unwanted notifications, and customize the notification settings.</li>
-<li><strong>Find Phone:</strong> You can locate your lost or stolen Android devices using Airdroid. You can also lock, erase, or ring your devices remotely.</li>
-<li><strong>Backup & Sync:</strong> You can backup and sync your contacts, photos, videos, music, and other files between your Android devices and your computer. You can also restore your data from the backup easily.</li>
-<li><strong>App Management:</strong> You can install, uninstall, or update apps on your Android devices from your computer. You can also view the app details, permissions, storage usage, etc.</li>
-<li><strong>Clipboard Sharing:</strong> You can share the clipboard content between your Android devices and your computer. You can copy text or images on one device and paste them on another device.</li>
-</ul>
-<p>Airdroid also supports multiple languages, dark mode, QR code login, SMS backup, call logs, etc.</p>
- <h3>Airdroid has some drawbacks and limitations that may affect user experience</h3>
-<p>Despite its many features and benefits, Airdroid is not perfect. Some of the drawbacks and limitations of Airdroid are:</p>
-<ul>
-<li><strong>Limited Free Version:</strong> The free version of Airdroid has some restrictions on the file transfer size, remote control duration, screen mirroring quality, backup storage space, etc. It also shows ads on the app and the web interface.</li>
-<li><strong>Potential Security Risks:</strong> Airdroid requires you to grant various permissions to access and manage your Android devices. This may pose some security risks if your account is hacked or compromised. In 2016, Airdroid was found to have a <a href="">critical vulnerability</a> that could allow attackers to intercept user data or execute malicious code.</li>
-<li><strong>Possible Compatibility Issues:</strong> Airdroid may not work well with some Android devices or versions due to different manufacturers' customizations or system updates. Some features may require rooting or enabling developer options on your devices.</li>
-<li><strong>Dependence on Internet Connection:</strong> Airdroid relies on a stable and fast internet connection to function properly. If your network is slow or unstable, you may experience lagging, buffering, disconnecting, or other issues.</li>
-</ul>
- <h2>What Is Airdroid Premium and How to Get It?</h2>
-<p>Airdroid Premium is a paid subscription that unlocks more features and benefits for Airdroid users. With Airdroid Premium, you can enjoy:</p>
-<ul>
-<li><strong>No Ads:</strong> You can remove all the ads from the app and the web interface.</li>
-<li><strong>No File Transfer Limit:</strong> You can transfer files of any size between your devices without any restriction.</li>
-<li><strong>No Remote Control Limit:</strong> You can remotely control your devices for as long as you want without any interruption.</li>
-<li><strong>No Screen Mirroring Limit:</strong> You can mirror your screen in HD quality without any degradation.</li>
-<li><strong>No Backup Storage Limit:</strong> You can backup and sync unlimited data between your devices without any limitation.</li>
-<li><strong>No Notification Limit:</strong> You can receive and reply to unlimited notifications from your devices without any limitation.</li>
-<li><strong>Premium Customer Support:</strong> You can get priority customer support from the Airdroid team via email or phone.</li>
-</ul>
- <h3>Airdroid Premium costs $1.99 per month or $19.99 per year</h3>
-<p>The price of Airdroid Premium is $1.99 per month or $19.99 per year. You can also get a 7-day free trial before you decide to purchase it. You can pay with PayPal, credit card, debit card, Google Play balance, etc.</p>
-<p></p>
- <h3>Airdroid Premium can be purchased from the official website or the app</h3>
-<p>To buy Airdroid Premium, you can either visit the <a href="">official website</a> or open the app on your device. Then you need to sign in with your Airdroid account or create one if you don't have one. Next, you need to choose the plan that suits you best and follow the instructions to complete the payment process. Once you have purchased Airdroid Premium, you can activate it on up to six devices using the same account.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Download The Last Train - Bullet Train Download] [Torrent]l Everything You Need to Know About the Movie and the Torrent.md DELETED Viewed

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-<p>Its extensive torrent index makes it one of the <strong>best movie torrent sites</strong> out there. You can download movies of all genres from The Pirate Bay without worrying about downloading suspicious files.</p>
-<p>There is a list of backup trackers given on the torrents page listing. Add them to get every last bit of available speed. GloTorrents also has an active forum where you can request torrents, subtitles, and more.</p>
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-<p>It is especially helpful in preventing hackers from stealing your data while connected to an unsecure public Wi-Fi network. A VPN for torrenting allows you the anonymity to download as much as you want.</p>
-<p>Technically, it is safe to torrent. It is based on a P2P (peer-to-peer) network where all participants share bits of a file. As more people download a file or some portion of it, they can become an active participant.</p>
-<p>It depends on where you are downloading the file more than anything else. Public torrents are swarming with trojans that infect your system with malware such as a cryptominer. To prevent this from happening, always be mindful of what you download. Copyrighted material such as games are usually a honeypot for hackers.</p>
-<p>Privacy experts recommend the use of a Torrent VPN to make your torrent activities anonymous. With a VPN for torrenting, you can download torrent files securely in countries dominated by DMCAs and copyright laws.</p>
-<p>Kickasstorrents.to is probably the oldest and still functioning Kickass clones that users can access right now. You can access it using a VPN for all your torrenting needs. it offers complete Kickass torrents database with a whole connection of movies, series, documentaries, and much more for users to download. The site also has its Kickass community where it provides regular updates of the latest torrents available for download.</p>
-<p>Tor-cr.org is yet another great Kickass clone. It has turned up to be a very useful clone website as it offers the complete list of Kickass Torrents. The website is easily accessible from all regions unless your ISP has imposed regional-restrictions on these versions of Kickass. However, using a VPN will give you full access to Tor-cr.org and download torrents from a wide range of content categories.</p>
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-<p>The popular animetorrents indexing website got shut down recently, causing concerns for all torrent fans who relied on the website to download anime content. But it is now back with a new interface and the same directory of torrents. You can download your favorite anime movie and series without any problems.</p>
-<p>ArenaBG is a Bulgarian-torrents indexing website. It has been a target of a lot of investigations for violating copyright laws, but it is still up and running. Initially it was only available to access in Bulgaria, however, since 2011, users from around the world can access it easily. ArenaBG offers a huge selection of torrents for download and you can access it easily from anywhere. But remember, to avoid any trouble, you can use a Kickass VPN to stay anonymous and private.</p>
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-<p>Since 1985, SERTC has provided hands-on, realistic training in surface transportation hazmat response. With new facilities and expanding curriculum, the SERTC trainee community is growing to keep local, state, tribal and territorial communities even safer.</p>
-<p>As he was older and stronger than any of the other members who took upracing, and as he always rode the lightest and best wheel that money couldprocure, he had, without much hard work, easily maintained a lead in theracing field, and had come to consider himself as invincible. He regardedhimself as such a sure winner of this last[Pg 6] race for the Railroad Cup,that he had not taken the trouble to go into training for it. He would noteven give up his cigarette smoking, a habit that he had acquired becausehe considered it fashionable and manly. Now he was beaten, disgracefully,and that by a boy nearly two years younger than himself. It was too much,and he determined to find some excuse for his defeat, that should at thesame time remove the disgrace from him, and place it upon other shoulders.</p>
-<p>With this Rod plunged down the steep bank to the railroad track, anddisappeared in the darkness. He went in the direction of the next stationto Euston, about five miles away, as he did not wish to be recognized whenhe made the attempt to secure a ride on some train to New York. It was tobe an attempt only; for he had not a cent of money in his pockets, and hadno idea of how he should obtain the coveted ride. In addition to beingpenniless, he was hungry, and his hunger was increased tenfold by theknowledge that he had no means of satisfying it. Still he was a boy withunlimited confidence in himself. He always had fallen on his feet; and,though this was the worse fix in which he had ever found himself, he hadfaith that he would come out[Pg 32]of it all right somehow. His heart wasalready so much lighter since he had learned from Dan that some of hisfriends, and especially Eltje Vanderveer, still believed in him, that hissituation did not seem half so desperate as it had an hour before.</p>
-<p>Rod was already enough of a railroad man to know that, as he was goingeast, he must walk on the west bound track. By so doing he would be ableto see trains bound west, while they were still at some distance from him,and would be in no danger from those bound east and overtaking him.</p>
-<p>When he was about half a mile from the little station, toward which he waswalking, he heard the long-drawn, far-away whistle of a locomotive. Was itahead of him or behind? On account of the bewildering echoes he could nottell. To settle the question he kneeled down, and placed his ear againstone of rails of the west bound track. It was cold and silent. Then hetried the east bound track in the same way. This rail seemed to tinglewith life, and a faint, humming sound came from it. It was a perfectrailroad telephone, and it informed the listener as plainly as words couldhave told him, that a train was approaching from the west.</p>
-<p>[Pg 33]He stopped to note its approach. In a few minutes the rails of the eastbound track began to quiver with light from the powerful reflector infront of its locomotive. Then they stretched away toward the oncomingtrain in gleaming bands of indefinite length, while the dazzling lightseemed to cut a bright pathway between walls of solid blackness for theuse of the advancing monster. As the bewildering glare passed him, Rod sawthat the train was a long, heavy-laden freight, and that some of its carscontained cattle. He stood motionless as it rushed past him, shaking thesolid earth with its ponderous weight, and he drew a decided breath ofrelief at the sight of the blinking red eyes on the rear platform of itscaboose. How he wished he was in that caboose, riding comfortably towardNew York, instead of plodding wearily along on foot, with nothing butuncertainties ahead of him.</p>
-<p>As Rod stood gazing at the receding train he noticed a human figure stepfrom the lighted interior of the caboose, through the open doorway, to theplatform, apparently kick at something, and almost instantly return intothe car. At the same time the boy fancied he heard a sharp cry of pain;but was not sure. As he resumed his tiresome walk, gazing longingly afterthe vanishing train lights, he saw another light, a white one that movedtoward him with a swinging motion, close to the ground. While he waswondering what it was, he almost stumbled over a small animal that stoodmotionless on the track, directly in front of him. It was a dog. Now Roddearly loved dogs, and seemed instinctively to know that this one was insome sort of trouble. As he stopped to pat it, the creature uttered alittle whine, as though [Pg 35]askinghis sympathy and help. At the same time it licked his hand.</p>
-<p>The latter told the boy that the young tramp, as they called him, wasbilled through to New York, to look after some cattle that were on thetrain; but that he was a worthless, ugly fellow, who had not paid theslightest attention to them, and whose only object in accepting the jobwas evidently to obtain a free ride in the caboose. Smiler, whom he hadbeen delighted to find on the train when it was turned over to him, hadtaken a great dislike to the[Pg 45] fellowfrom the first. He had growled andshown his teeth whenever the tramp moved about the car, and several timesthe latter had threatened to teach him better manners. When he andBrakeman Joe went to the forward end of the train, to make ready forside-tracking it, they left the dog sitting on the rear platform of thecaboose, and the tramp apparently asleep, as Rod had found him, on one ofthe lockers. He must have taken advantage of their absence to deal the dogthe cruel kick that cut his ear, and landed him, stunned and bruised, onthe track where he had been discovered.</p> aaccfb2cb3<br />
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spaces/1line/AutoGPT/autogpt/processing/text.py DELETED Viewed

@@ -1,132 +0,0 @@
-"""Text processing functions"""
-from typing import Dict, Generator, Optional
-from selenium.webdriver.remote.webdriver import WebDriver
-from autogpt.config import Config
-from autogpt.llm_utils import create_chat_completion
-from autogpt.memory import get_memory
-CFG = Config()
-MEMORY = get_memory(CFG)
-def split_text(text: str, max_length: int = 8192) -> Generator[str, None, None]:
-    """Split text into chunks of a maximum length
-    Args:
-        text (str): The text to split
-        max_length (int, optional): The maximum length of each chunk. Defaults to 8192.
-    Yields:
-        str: The next chunk of text
-    Raises:
-        ValueError: If the text is longer than the maximum length
-    """
-    paragraphs = text.split("\n")
-    current_length = 0
-    current_chunk = []
-    for paragraph in paragraphs:
-        if current_length + len(paragraph) + 1 <= max_length:
-            current_chunk.append(paragraph)
-            current_length += len(paragraph) + 1
-        else:
-            yield "\n".join(current_chunk)
-            current_chunk = [paragraph]
-            current_length = len(paragraph) + 1
-    if current_chunk:
-        yield "\n".join(current_chunk)
-def summarize_text(
-    url: str, text: str, question: str, driver: Optional[WebDriver] = None
-) -> str:
-    """Summarize text using the OpenAI API
-    Args:
-        url (str): The url of the text
-        text (str): The text to summarize
-        question (str): The question to ask the model
-        driver (WebDriver): The webdriver to use to scroll the page
-    Returns:
-        str: The summary of the text
-    """
-    if not text:
-        return "Error: No text to summarize"
-    text_length = len(text)
-    print(f"Text length: {text_length} characters")
-    summaries = []
-    chunks = list(split_text(text))
-    scroll_ratio = 1 / len(chunks)
-    for i, chunk in enumerate(chunks):
-        if driver:
-            scroll_to_percentage(driver, scroll_ratio * i)
-        print(f"Adding chunk {i + 1} / {len(chunks)} to memory")
-        memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
-        MEMORY.add(memory_to_add)
-        print(f"Summarizing chunk {i + 1} / {len(chunks)}")
-        messages = [create_message(chunk, question)]
-        summary = create_chat_completion(
-            model=CFG.fast_llm_model,
-            messages=messages,
-        )
-        summaries.append(summary)
-        print(f"Added chunk {i + 1} summary to memory")
-        memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}"
-        MEMORY.add(memory_to_add)
-    print(f"Summarized {len(chunks)} chunks.")
-    combined_summary = "\n".join(summaries)
-    messages = [create_message(combined_summary, question)]
-    return create_chat_completion(
-        model=CFG.fast_llm_model,
-        messages=messages,
-    )
-def scroll_to_percentage(driver: WebDriver, ratio: float) -> None:
-    """Scroll to a percentage of the page
-    Args:
-        driver (WebDriver): The webdriver to use
-        ratio (float): The percentage to scroll to
-    Raises:
-        ValueError: If the ratio is not between 0 and 1
-    """
-    if ratio < 0 or ratio > 1:
-        raise ValueError("Percentage should be between 0 and 1")
-    driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")
-def create_message(chunk: str, question: str) -> Dict[str, str]:
-    """Create a message for the chat completion
-    Args:
-        chunk (str): The chunk of text to summarize
-        question (str): The question to answer
-    Returns:
-        Dict[str, str]: The message to send to the chat completion
-    """
-    return {
-        "role": "user",
-        "content": f'"""{chunk}""" Using the above text, answer the following'
-        f' question: "{question}" -- if the question cannot be answered using the text,'
-        " summarize the text.",
-    }

spaces/1pelhydcardo/ChatGPT-prompt-generator/Build-a-lot 7 - Fairy Tales - Full PreCracked - Foxy Games Torrent !EXCLUSIVE!.md DELETED Viewed

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-## Build-a-lot 7 - Fairy Tales - Full PreCracked - Foxy Games Torrent
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-# How to Download and Play Build-a-lot 7 - Fairy Tales - Full PreCracked - Foxy Games Torrent
-If you are looking for a fun and relaxing game that combines city-building and fairy tale elements, then you should try Build-a-lot 7 - Fairy Tales. This is the seventh installment of the popular Build-a-lot series, and it offers you a chance to create your own magical kingdom with castles, cottages, fountains, and more. You can also explore different fairy tale worlds, meet famous characters, and complete challenging quests.
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-Build-a-lot 7 - Fairy Tales is a game that will appeal to both casual and hardcore gamers. You can choose from four different modes: Campaign, Casual, Expert, and Sandbox. Each mode has its own objectives and challenges, and you can adjust the difficulty level according to your preference. You can also unlock achievements and trophies as you progress through the game.
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-Build-a-lot 7 - Fairy Tales is a game that will keep you entertained for hours. You can download and play it for free by using the Build-a-lot 7 - Fairy Tales - Full PreCracked - Foxy Games Torrent. Just follow the instructions above and start building your dream kingdom today!
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Beach Buggy Racing 2 How to Unlock and Upgrade Over 40 Powerups.md DELETED Viewed

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-<h1>Beach Buggy Racing 2: A Fun and Exciting Kart Racing Game</h1>
-<p>Do you love kart racing games? Do you want to experience a thrilling adventure on a mysterious island? Do you want to compete against other players from around the world? If you answered yes to any of these questions, then you should try Beach Buggy Racing 2, a fun and exciting kart racing game that you can download from Microsoft Store. In this article, we will tell you everything you need to know about this game, including what it is, how to download it, what are its features, how to play it, and why you should play it.</p>
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- <h2>What is Beach Buggy Racing 2?</h2>
-<p>Beach Buggy Racing 2 is a sequel to the popular Beach Buggy Racing, a game that introduced over 100 million international mobile players to console-style kart racing with a playful off-road twist. Beach Buggy Racing 2 is a fully 3D off-road kart racing game with amazing physics, detailed cars and characters, and spectacular weapons, powered by Vector Engine and NVIDIA's PhysX. It's like a console game in the palm of your hand!</p>
-<p>Beach Buggy Racing 2 is a game that you can play solo or with friends in split screen or online modes. You can join the Beach Buggy Racing League and compete against drivers and cars from around the world. You can race through Egyptian pyramids, dragon-infested castles, pirate ship wrecks, and experimental alien bio-labs. You can collect and upgrade an arsenal of fun and wacky powerups. You can recruit new drivers, assemble a garage full of cars, and race your way to the top of the league.</p>
- <h2>How to download Beach Buggy Racing 2 from Microsoft Store?</h2>
-<p>If you want to download Beach Buggy Racing 2 on your Windows 10 device, you can follow these simple steps:</p>
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-<ol>
-<li>Open Microsoft Store app on your device.</li>
-<li>Search for Beach Buggy Racing 2 in the search bar.</li>
-<li>Select the game from the search results.</li>
-<li>Click on Get or Install button.</li>
-<li>Wait for the download and installation process to complete.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
-<p>The system requirements for Beach Buggy Racing 2 are:</p>
-<ul>
-<li>OS: Windows 10 version 18362.0 or higher</li>
-<li>Architecture: x64</li>
-<li>DirectX: Version 11</li>
-<li>Memory: 4 GB</li>
-<li>Processor: Intel Core i5-6500 or equivalent</li>
-<li>Graphics: NVIDIA GeForce GTX750 Ti or equivalent</li>
-</ul>
-<p>The price of Beach Buggy Racing 2 is $19.99. However, you can also buy the Hot Wheels Edition bundle for $26.98, which includes the game and two DLC packs: Hot Wheels Booster Pack and Oddball Car <p>One of the benefits of downloading the game from Microsoft Store is that you can enjoy the Hot Wheels Booster Pack DLC, an exciting new content expansion that adds seven legendary Hot Wheels cars and four new tracks, complete with twisting orange track pieces, to the Beach Buggy Racing League. You can also get the Oddball Car Pack DLC, which adds four wacky and weird cars to your garage: the Rocket Car, the Shark Car, the Alien Car, and the Monster Truck. These DLC packs are sold separately or as a bundle with the game for a discounted price.</p>
- <h2>What are the features of Beach Buggy Racing 2?</h2>
-<p>Beach Buggy Racing 2 is not just a simple racing game. It has many features that make it a fun and exciting kart racing game. Here are some of them:</p>
-<h3>The different game modes and challenges</h3>
-<p>You can choose from different game modes and challenges to test your skills and have fun. You can play the Adventure mode, where you can explore the island and unlock new tracks, cars, drivers, and powerups. You can also play the Quick Race mode, where you can race on any track you want with any car you want. You can also play the Championship mode, where you can compete in a series of races and earn trophies. You can also play the Daily Challenges mode, where you can complete different tasks and earn rewards. You can also play the Special Events mode, where you can join limited-time events and win exclusive prizes.</p>
-<h3>The variety of cars, drivers, and powerups</h3>
-<p>You can collect and upgrade over 40 cars, each with their own unique stats and abilities. You can also recruit over 20 drivers, each with their own special power. You can also collect and upgrade over 40 powerups, each with their own effects and strategies. You can mix and match different cars, drivers, and powerups to create your own style and strategy.</p>
-<h3>The customization options and the achievements</h3>
-<p>You can customize your cars with different paints, decals, wheels, spoilers, and more. You can also customize your drivers with different outfits, hats, glasses, and more. You can also customize your powerup deck with different combinations of powerups. You can also unlock over 100 achievements and show off your skills and progress.</p>
- <h2>How to play Beach Buggy Racing 2?</h2>
-<p>Beach Buggy Racing 2 is easy to play but hard to master. Here are some tips and tricks to help you play better:</p>
-<h3>The controls and the tips for racing</h3>
-<p>You can choose from different control options: tilt, touch, or gamepad. You can also adjust the sensitivity and the steering assist. The basic controls are: accelerate, brake, steer, drift, use powerup, use driver ability. The tips for racing are: use drift to take sharp turns and fill up your boost meter; use boost to speed up and overtake your opponents; use powerups wisely and strategically; use driver ability at the right time and situation; avoid obstacles and traps; collect coins and gems; look for shortcuts and secrets.</p>
-<h3>The powerup deck and the special abilities</h3>
-<p>You can create your own powerup deck with up to eight powerups. You can choose from offensive, defensive, or utility powerups. You can also upgrade your powerups to make them more effective. Some examples of powerups are: firework (shoots a rocket that explodes on impact); oil slick (drops a slippery puddle that spins out other racers); shield (protects you from attacks for a short time); nitro (gives you a burst of speed); magnet (attracts coins and gems); lightning (zaps nearby racers); tornado (creates a swirling wind that blows away other racers); ice cream (freezes other racers in place). You can also use your driver ability once per race. Each driver has a unique ability that can give you an edge over your opponents. Some examples of driver abilities are: beach ball barrage (launches beach balls everywhere); fire breath (breathes fire in front of you); teleport (teleports you to a random position); coin storm (makes coins rain from the sky); banana split (splits into three copies of yourself).</p>
-<h3>The online competitions and tournaments</h3>
-<p>You can join the Beach Buggy Racing League and compete against other players from around the world in online races. You can earn trophies and rank up in different leagues. You can also join online tournaments and win exclusive rewards. You can also create or join a team and chat with other players.</p>
- <h2>Why should you play Beach Buggy Racing 2?</h2>
-<p>Beach Buggy Racing 2 is a game that you should play if you love kart racing games. Here are some reasons why you should play Beach Buggy Racing 2:</p>
-<h3>The fun and addictive gameplay</h3>
-<p>Beach Buggy Racing 2 is a game that will keep you hooked for hours. You will never get bored of racing on different tracks, using different powerups, and unlocking new cars, drivers, and upgrades. You will also enjoy the challenge of competing against other players and improving your skills and rank. You will also have fun exploring the island and discovering its secrets and surprises.</p>
-<h3>The stunning graphics and sound effects</h3>
-<p>Beach Buggy Racing 2 is a game that will impress you with its graphics and sound effects. You will admire the detailed and colorful 3D graphics that bring the island to life. You will also appreciate the realistic physics and animations that make the racing experience more immersive. You will also enjoy the catchy and upbeat music and sound effects that match the mood and theme of the game.</p>
-<h3>The replay value and the updates</h3>
-<p>Beach Buggy Racing 2 is a game that will keep you coming back for more. You will always find something new and exciting to do in the game. You will also benefit from the regular updates that add new content and features to the game. You will also be able to play the game offline or online, depending on your preference and availability.</p>
- <h2>Conclusion</h2>
-<p>Beach Buggy Racing 2 is a fun and exciting kart racing game that you can download from Microsoft Store. It is a sequel to the popular Beach Buggy Racing, a game that introduced over 100 million international mobile players to console-style kart racing with a playful off-road twist. Beach Buggy Racing 2 is a fully 3D off-road kart racing game with amazing physics, detailed cars and characters, and spectacular weapons, powered by Vector Engine and NVIDIA's PhysX. It's like a console game in the palm of your hand!</p>
-<p>Beach Buggy Racing 2 is a game that you can play solo or with friends in split screen or online modes. You can join the Beach Buggy Racing League and compete against drivers and cars from around the world. You can race through Egyptian pyramids, dragon-infested castles, pirate ship wrecks, and experimental alien bio-labs. You can collect and upgrade an arsenal of fun and wacky powerups. You can recruit new drivers, assemble a garage full of cars, and race your way to the top of the league.</p>
-<p>Beach Buggy Racing 2 is a game that has many features that make it a fun and exciting kart racing game. You can choose from different game modes and challenges to test your skills and have fun. You can collect and upgrade over 40 cars, each with their own unique stats and abilities. You can also recruit over 20 drivers, each with their own special power. You can also collect and upgrade over 40 powerups, each with their own effects and strategies. You can mix and match different cars, drivers, and powerups to create your own style and strategy.</p>
-<p>Beach Buggy Racing 2 is a game that is easy to play but hard to master. You can choose from different control options: tilt, touch, or gamepad. You can also adjust the sensitivity and the steering assist. The basic controls are: accelerate, brake, steer, drift, use powerup, use driver ability. The tips for racing are: use drift to take sharp turns and fill up your boost meter; use boost to speed up and overtake your opponents; use powerups wisely and strategically; use driver ability at the right time and situation; avoid obstacles and traps; collect coins and gems; look for shortcuts and secrets.</p>
-<p>Beach Buggy Racing 2 is a game that you should play if you love kart racing games. You will enjoy the fun and addictive gameplay, the stunning graphics and sound effects, and the replay value and the updates. You will also have fun playing with your friends or other players online. You will also be able to customize your cars, drivers, and powerups to suit your preferences and style.</p>
-<p>If you are ready to join the Beach Buggy Racing League and have a blast on the island, download Beach Buggy Racing 2 from Microsoft Store today and start your engine!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Beach Buggy Racing 2:</p>
-<ol>
-<li>How can I get more coins and gems in the game?</li>
-<p>You can get more coins and gems by racing on different tracks, completing daily challenges, participating in special events, watching ads, or buying them with real money.</p>
-<li>How can I unlock more cars and drivers in the game?</li>
-<p>You can unlock more cars and drivers by progressing through the adventure mode, winning championships, opening chests, or buying them with coins or gems.</p>
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-<p>You can upgrade your cars and powerups by using upgrade cards that you can get from chests, daily challenges, special events, or buying them with coins or gems.</p>
-<li>How can I join a team or create my own team in the game?</li>
-<p>You can join a team or create your own team by tapping on the team icon on the main menu. You can search for an existing team or create a new one with a name, a logo, and a description. You can also invite other players to join your team or accept invitations from other teams. You can chat with your team members, share tips and strategies, and compete in team tournaments.</p>
-<li>How can I contact the developers of the game or report a bug or a problem?</li>
-<p>You can contact the developers of the game or report a bug or a problem by tapping on the settings icon on the main menu. You can then tap on the help icon and choose from different options: FAQ, support, feedback, privacy policy, terms of service, credits. You can also visit their website at https://www.vectorunit.com/ or follow them on social media at https://www.facebook.com/VectorUnit/ or https://twitter.com/VectorUnit/.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Indonesia How to Download and Install Jai Guru Jinn Livery.md DELETED Viewed

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-<h1>Bus Simulator Indonesia: How to Download and Install Jai Guru Livery</h1>
-<p>Do you love driving buses in realistic and authentic environments? Do you want to customize your bus with cool and fun designs? If yes, then you should try Bus Simulator Indonesia, a popular game that lets you experience what it likes being a bus driver in Indonesia. And if you are looking for a unique and stylish livery for your bus, then you should check out the Jai Guru livery, a beautiful and eye-catching design that will make your bus stand out from the crowd. In this article, we will tell you more about Bus Simulator Indonesia, Jai Guru livery, and how to download and install it in your game.</p>
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- <h2>What is Bus Simulator Indonesia?</h2>
-<p>Bus Simulator Indonesia (aka BUSSID) is a game developed by Maleo, an Indonesian game studio. It was released in 2017 and has been updated regularly with new features and improvements. The game is available for Android and iOS devices, as well as PC via emulator. The game has over 100 million downloads on Google Play Store and has received positive reviews from players and critics.</p>
- <h3>Game features</h3>
-<p>Some of the top features of Bus Simulator Indonesia are:</p>
-<ul>
-<li><b>Design your own livery</b>: You can create your own livery for your bus using the template provided by the developer or using your own 3D model. You can also download and use livery from other players or creators.</li>
-<li><b>Very easy and intuitive control</b>: You can choose between tilt, steering wheel, or buttons to control your bus. You can also adjust the sensitivity and camera angle according to your preference.</li>
-<li><b>Authentic Indonesian cities and places</b>: You can drive your bus in various cities and places in Indonesia, such as Jakarta, Surabaya, Bali, Sumatra, Java, etc. You can also see landmarks, buildings, traffic signs, and other details that make the game more realistic.</li>
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-<li><b>High-quality and detailed 3D graphics</b>: The game has stunning graphics that show the beauty of Indonesia. You can see the shadows, reflections, weather effects, day and night cycle, etc.</li>
-<li><b>No obstructive ads while driving</b>: The game does not show ads while you are driving your bus. You can enjoy the game without any interruption or distraction.</li>
-<li><b>Leaderboard and online data saving</b>: You can compete with other players on the leaderboard based on your score and achievements. You can also save your data online so you don't lose your progress.</li>
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-</ul>
- <h3>Livery customization</h3>
-<p>One of the most fun features of Bus Simulator Indonesia is the livery customization. You can design your own livery for your bus using the template provided by the developer or using your own 3D model. You can also download and use livery from other players or creators. Livery is a term that refers to the paint scheme or design of a vehicle, especially a bus or a plane. Livery can be used to express your personality, style, or preference. You can also use livery to promote your brand, business, or cause. Livery can make your bus more attractive, unique, and recognizable.</p>
- <h2>What is Jai Guru Livery?</h2>
-<p>Jai Guru livery is a livery created by Jai Guru, a popular and talented livery maker in the BUSSID community. Jai Guru has made many liveries for different types of buses, such as Srikandi SHD, Jetbus 3+, Legacy SR2 XHD Prime, etc. Jai Guru livery is known for its high-quality, colorful, and artistic design. Jai Guru livery is also inspired by Indian culture and religion, as well as other themes and motifs.</p>
- <h3>Design and style</h3>
-<p>Jai Guru livery has a distinctive design and style that makes it stand out from other liveries. Some of the features of Jai Guru livery are:</p>
-<ul>
-<li><b>Bright and vibrant colors</b>: Jai Guru livery uses a combination of bright and vibrant colors, such as red, yellow, green, blue, purple, etc. The colors create a contrast and harmony that make the livery more eye-catching and appealing.</li>
-<li><b>Indian symbols and images</b>: Jai Guru livery incorporates various symbols and images from Indian culture and religion, such as the Om sign, the lotus flower, the elephant, the peacock, etc. The symbols and images represent different meanings and values, such as peace, wisdom, prosperity, beauty, etc.</li>
-<li><b>Floral and geometric patterns</b>: Jai Guru livery also uses floral and geometric patterns to decorate the bus. The patterns add more detail and texture to the livery. The patterns are also influenced by Indian art and architecture.</li>
-<li><b>Texts and slogans</b>: Jai Guru livery also includes texts and slogans on the bus. The texts and slogans are usually in Hindi or English. They can be the name of the bus company, the destination of the bus, or a message to the passengers or other drivers.</li>
-</ul>
- <h3>Download link and credit</h3>
-<p>If you want to download and use Jai Guru livery in your game, you can find the download link on Jai Guru's YouTube channel or Facebook page. You can also find other liveries made by Jai Guru on these platforms. Please note that you need to have the compatible bus model in your game before you can use the livery. You can also download the bus model from Jai Guru's channel or page.</p>
- <p>When you download and use Jai Guru livery, please give credit to Jai Guru as the original creator of the livery. Do not claim the livery as your own or modify it without permission from Jai Guru. Do not upload or share the livery on other platforms without giving proper credit to Jai Guru. Respect the work and effort of Jai Guru and support him by subscribing to his channel or liking his page.</p>
- <h2>How to Install Jai Guru Livery in Bus Simulator Indonesia?</h2>
-<p>Installing Jai Guru livery in Bus Simulator Indonesia is easy and simple. Just follow these steps:</p>
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- <h3>Step 1: Download the livery file</h3>
-<p>The first step is to download the livery file from Jai Guru's channel or page. The file will be in .bussid format, which is a special format for BUSSID liveries. The file size will vary depending on the type of bus and the complexity of the design.</p>
- <h3>Step 2: Move the livery file to the BUSSID folder</h3>
-<p>The next step is to move the livery file to the BUSSID folder on your device. You can use any file manager app to do this. The BUSSID folder is usually located in Internal Storage > Android > data > com.maleo.bussimulatorid > files > BUSSID.</p>
- <h3>Step 3: Open the game and select the garage menu</h3>
-<p>The third step is to open Bus Simulator Indonesia on your device and select the garage menu from the main menu. The garage menu is where you can choose and customize your bus.</p>
- <h3>Step 4: Select the livery file menu and click BUSSID file manager</h3>
-<p>The fourth step is to select the livery file menu from the garage menu. The l ivery file menu is where you can see the list of livery files that you have downloaded or created. From the livery file menu, click on the BUSSID file manager button. The BUSSID file manager is where you can access the BUSSID folder and see the livery files that you have moved there.</p>
- <h3>Step 5: Choose the livery you want to use and click open</h3>
-<p>The final step is to choose the Jai Guru livery that you want to use for your bus and click on the open button. The game will load the livery and apply it to your bus. You can see the preview of your bus with the Jai Guru livery on the screen. You can also change the color, accessories, or other features of your bus if you want. When you are satisfied with your bus, click on the save button and exit the garage menu.</p>
- <h2>Conclusion</h2>
-<p>Bus Simulator Indonesia is a fun and realistic game that lets you drive buses in Indonesia. You can also customize your bus with different liveries, such as the Jai Guru livery, a beautiful and eye-catching design inspired by Indian culture and religion. To download and install Jai Guru livery in your game, you just need to follow five simple steps: download the livery file, move it to the BUSSID folder, open the game and select the garage menu, select the livery file menu and click BUSSID file manager, and choose the livery you want to use and click open. Enjoy your bus with Jai Guru livery and have a safe and happy journey!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Bus Simulator Indonesia and Jai Guru livery:</p>
-<ul>
-<li><b>Q: How can I get more buses in Bus Simulator Indonesia?</b></li>
-<li><b>A: You can get more buses in Bus Simulator Indonesia by buying them with coins or diamonds. You can earn coins or diamonds by playing the game, completing missions, watching ads, or buying them with real money.</li>
-<li><b>Q: How can I create my own livery in Bus Simulator Indonesia?</b></li>
-<li><b>A: You can create your own livery in Bus Simulator Indonesia by using the template provided by the developer or using your own 3D model. You can find the template and instructions on how to use it on Maleo's website or YouTube channel.</li>
-<li><b>Q: How can I share my livery with other players in Bus Simulator Indonesia?</b></li>
-<li><b>A: You can share your livery with other players in Bus Simulator Indonesia by uploading it to Maleo's website or any other platform that supports .bussid files. You can also join online multiplayer convoys and show off your livery to other players.</li>
-<li><b>Q: How can I contact Jai Guru or request a custom livery from him?</b></li>
-<li><b>A: You can contact Jai Guru or request a custom livery from him by sending him a message on his YouTube channel or Facebook page. He will reply to you as soon as possible.</li>
-<li><b>Q: How can I support Jai Guru and his work?</b></li>
-<li><b>A: You can support Jai Guru and his work by subscribing to his YouTube channel, liking his Facebook page, giving him feedback, sharing his liveries with others, and donating to him if you want.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/onnx_ijbc.py DELETED Viewed

@@ -1,267 +0,0 @@
-import argparse
-import os
-import pickle
-import timeit
-import cv2
-import mxnet as mx
-import numpy as np
-import pandas as pd
-import prettytable
-import skimage.transform
-from sklearn.metrics import roc_curve
-from sklearn.preprocessing import normalize
-from onnx_helper import ArcFaceORT
-SRC = np.array(
-    [
-        [30.2946, 51.6963],
-        [65.5318, 51.5014],
-        [48.0252, 71.7366],
-        [33.5493, 92.3655],
-        [62.7299, 92.2041]]
-    , dtype=np.float32)
-SRC[:, 0] += 8.0
-class AlignedDataSet(mx.gluon.data.Dataset):
-    def __init__(self, root, lines, align=True):
-        self.lines = lines
-        self.root = root
-        self.align = align
-    def __len__(self):
-        return len(self.lines)
-    def __getitem__(self, idx):
-        each_line = self.lines[idx]
-        name_lmk_score = each_line.strip().split(' ')
-        name = os.path.join(self.root, name_lmk_score[0])
-        img = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB)
-        landmark5 = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32).reshape((5, 2))
-        st = skimage.transform.SimilarityTransform()
-        st.estimate(landmark5, SRC)
-        img = cv2.warpAffine(img, st.params[0:2, :], (112, 112), borderValue=0.0)
-        img_1 = np.expand_dims(img, 0)
-        img_2 = np.expand_dims(np.fliplr(img), 0)
-        output = np.concatenate((img_1, img_2), axis=0).astype(np.float32)
-        output = np.transpose(output, (0, 3, 1, 2))
-        output = mx.nd.array(output)
-        return output
-def extract(model_root, dataset):
-    model = ArcFaceORT(model_path=model_root)
-    model.check()
-    feat_mat = np.zeros(shape=(len(dataset), 2 * model.feat_dim))
-    def batchify_fn(data):
-        return mx.nd.concat(*data, dim=0)
-    data_loader = mx.gluon.data.DataLoader(
-        dataset, 128, last_batch='keep', num_workers=4,
-        thread_pool=True, prefetch=16, batchify_fn=batchify_fn)
-    num_iter = 0
-    for batch in data_loader:
-        batch = batch.asnumpy()
-        batch = (batch - model.input_mean) / model.input_std
-        feat = model.session.run(model.output_names, {model.input_name: batch})[0]
-        feat = np.reshape(feat, (-1, model.feat_dim * 2))
-        feat_mat[128 * num_iter: 128 * num_iter + feat.shape[0], :] = feat
-        num_iter += 1
-        if num_iter % 50 == 0:
-            print(num_iter)
-    return feat_mat
-def read_template_media_list(path):
-    ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
-    return templates, medias
-def read_template_pair_list(path):
-    pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
-    return t1, t2, label
-def read_image_feature(path):
-    with open(path, 'rb') as fid:
-        img_feats = pickle.load(fid)
-    return img_feats
-def image2template_feature(img_feats=None,
-                           templates=None,
-                           medias=None):
-    unique_templates = np.unique(templates)
-    template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
-    for count_template, uqt in enumerate(unique_templates):
-        (ind_t,) = np.where(templates == uqt)
-        face_norm_feats = img_feats[ind_t]
-        face_medias = medias[ind_t]
-        unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)
-        media_norm_feats = []
-        for u, ct in zip(unique_medias, unique_media_counts):
-            (ind_m,) = np.where(face_medias == u)
-            if ct == 1:
-                media_norm_feats += [face_norm_feats[ind_m]]
-            else:  # image features from the same video will be aggregated into one feature
-                media_norm_feats += [np.mean(face_norm_feats[ind_m], axis=0, keepdims=True), ]
-        media_norm_feats = np.array(media_norm_feats)
-        template_feats[count_template] = np.sum(media_norm_feats, axis=0)
-        if count_template % 2000 == 0:
-            print('Finish Calculating {} template features.'.format(
-                count_template))
-    template_norm_feats = normalize(template_feats)
-    return template_norm_feats, unique_templates
-def verification(template_norm_feats=None,
-                 unique_templates=None,
-                 p1=None,
-                 p2=None):
-    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
-    for count_template, uqt in enumerate(unique_templates):
-        template2id[uqt] = count_template
-    score = np.zeros((len(p1),))
-    total_pairs = np.array(range(len(p1)))
-    batchsize = 100000
-    sublists = [total_pairs[i: i + batchsize] for i in range(0, len(p1), batchsize)]
-    total_sublists = len(sublists)
-    for c, s in enumerate(sublists):
-        feat1 = template_norm_feats[template2id[p1[s]]]
-        feat2 = template_norm_feats[template2id[p2[s]]]
-        similarity_score = np.sum(feat1 * feat2, -1)
-        score[s] = similarity_score.flatten()
-        if c % 10 == 0:
-            print('Finish {}/{} pairs.'.format(c, total_sublists))
-    return score
-def verification2(template_norm_feats=None,
-                  unique_templates=None,
-                  p1=None,
-                  p2=None):
-    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
-    for count_template, uqt in enumerate(unique_templates):
-        template2id[uqt] = count_template
-    score = np.zeros((len(p1),))  # save cosine distance between pairs
-    total_pairs = np.array(range(len(p1)))
-    batchsize = 100000  # small batchsize instead of all pairs in one batch due to the memory limiation
-    sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]
-    total_sublists = len(sublists)
-    for c, s in enumerate(sublists):
-        feat1 = template_norm_feats[template2id[p1[s]]]
-        feat2 = template_norm_feats[template2id[p2[s]]]
-        similarity_score = np.sum(feat1 * feat2, -1)
-        score[s] = similarity_score.flatten()
-        if c % 10 == 0:
-            print('Finish {}/{} pairs.'.format(c, total_sublists))
-    return score
-def main(args):
-    use_norm_score = True  # if Ture, TestMode(N1)
-    use_detector_score = True  # if Ture, TestMode(D1)
-    use_flip_test = True  # if Ture, TestMode(F1)
-    assert args.target == 'IJBC' or args.target == 'IJBB'
-    start = timeit.default_timer()
-    templates, medias = read_template_media_list(
-        os.path.join('%s/meta' % args.image_path, '%s_face_tid_mid.txt' % args.target.lower()))
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-    start = timeit.default_timer()
-    p1, p2, label = read_template_pair_list(
-        os.path.join('%s/meta' % args.image_path,
-                     '%s_template_pair_label.txt' % args.target.lower()))
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-    start = timeit.default_timer()
-    img_path = '%s/loose_crop' % args.image_path
-    img_list_path = '%s/meta/%s_name_5pts_score.txt' % (args.image_path, args.target.lower())
-    img_list = open(img_list_path)
-    files = img_list.readlines()
-    dataset = AlignedDataSet(root=img_path, lines=files, align=True)
-    img_feats = extract(args.model_root, dataset)
-    faceness_scores = []
-    for each_line in files:
-        name_lmk_score = each_line.split()
-        faceness_scores.append(name_lmk_score[-1])
-    faceness_scores = np.array(faceness_scores).astype(np.float32)
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-    print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))
-    start = timeit.default_timer()
-    if use_flip_test:
-        img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + img_feats[:, img_feats.shape[1] // 2:]
-    else:
-        img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2]
-    if use_norm_score:
-        img_input_feats = img_input_feats
-    else:
-        img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True))
-    if use_detector_score:
-        print(img_input_feats.shape, faceness_scores.shape)
-        img_input_feats = img_input_feats * faceness_scores[:, np.newaxis]
-    else:
-        img_input_feats = img_input_feats
-    template_norm_feats, unique_templates = image2template_feature(
-        img_input_feats, templates, medias)
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-    start = timeit.default_timer()
-    score = verification(template_norm_feats, unique_templates, p1, p2)
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-    save_path = os.path.join(args.result_dir, "{}_result".format(args.target))
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-    score_save_file = os.path.join(save_path, "{}.npy".format(args.model_root))
-    np.save(score_save_file, score)
-    files = [score_save_file]
-    methods = []
-    scores = []
-    for file in files:
-        methods.append(os.path.basename(file))
-        scores.append(np.load(file))
-    methods = np.array(methods)
-    scores = dict(zip(methods, scores))
-    x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
-    tpr_fpr_table = prettytable.PrettyTable(['Methods'] + [str(x) for x in x_labels])
-    for method in methods:
-        fpr, tpr, _ = roc_curve(label, scores[method])
-        fpr = np.flipud(fpr)
-        tpr = np.flipud(tpr)
-        tpr_fpr_row = []
-        tpr_fpr_row.append("%s-%s" % (method, args.target))
-        for fpr_iter in np.arange(len(x_labels)):
-            _, min_index = min(
-                list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
-            tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
-        tpr_fpr_table.add_row(tpr_fpr_row)
-    print(tpr_fpr_table)
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='do ijb test')
-    # general
-    parser.add_argument('--model-root', default='', help='path to load model.')
-    parser.add_argument('--image-path', default='', type=str, help='')
-    parser.add_argument('--result-dir', default='.', type=str, help='')
-    parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
-    main(parser.parse_args())

spaces/8star/DeepDanbooru_string/app.py DELETED Viewed

@@ -1,185 +0,0 @@
-#!/usr/bin/env python
-from __future__ import annotations
-import argparse
-import functools
-import os
-import html
-import pathlib
-import tarfile
-import deepdanbooru as dd
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import tensorflow as tf
-import piexif
-import piexif.helper
-TITLE = 'DeepDanbooru String'
-TOKEN = os.environ['TOKEN']
-MODEL_REPO = 'CikeyQI/DeepDanbooru_string'
-MODEL_FILENAME = 'model-resnet_custom_v3.h5'
-LABEL_FILENAME = 'tags.txt'
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--score-slider-step', type=float, default=0.05)
-    parser.add_argument('--score-threshold', type=float, default=0.5)
-    parser.add_argument('--theme', type=str, default='dark-grass')
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    return parser.parse_args()
-def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
-    if not image_dir.exists():
-        dataset_repo = 'hysts/sample-images-TADNE'
-        path = huggingface_hub.hf_hub_download(dataset_repo,
-                                               'images.tar.gz',
-                                               repo_type='dataset',
-                                               use_auth_token=TOKEN)
-        with tarfile.open(path) as f:
-            f.extractall()
-    return sorted(image_dir.glob('*'))
-def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           MODEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    model = tf.keras.models.load_model(path)
-    return model
-def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           LABEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    with open(path) as f:
-        labels = [line.strip() for line in f.readlines()]
-    return labels
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-def predict(image: PIL.Image.Image, score_threshold: float,
-            model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
-    rawimage = image
-    _, height, width, _ = model.input_shape
-    image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
-    image = image.numpy()
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.
-    probs = model.predict(image[None, ...])[0]
-    probs = probs.astype(float)
-    res = dict()
-    for prob, label in zip(probs.tolist(), labels):
-        if prob < score_threshold:
-            continue
-        res[label] = prob
-    b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True))
-    a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)')
-    c = ', '.join(list(b.keys()))
-    items = rawimage.info
-    geninfo = ''
-    if "exif" in rawimage.info:
-        exif = piexif.load(rawimage.info["exif"])
-        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
-        try:
-            exif_comment = piexif.helper.UserComment.load(exif_comment)
-        except ValueError:
-            exif_comment = exif_comment.decode('utf8', errors="ignore")
-        items['exif comment'] = exif_comment
-        geninfo = exif_comment
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
-                      'loop', 'background', 'timestamp', 'duration']:
-            items.pop(field, None)
-    geninfo = items.get('parameters', geninfo)
-    info = f"""
-<p><h4>PNG Info</h4></p>
-"""
-    for key, text in items.items():
-        info += f"""
-<div>
-<p><b>{plaintext_to_html(str(key))}</b></p>
-<p>{plaintext_to_html(str(text))}</p>
-</div>
-""".strip()+"\n"
-    if len(info) == 0:
-        message = "Nothing found in the image."
-        info = f"<div><p>{message}<p></div>"
-    return (a,c,res,info)
-def main():
-    args = parse_args()
-    model = load_model()
-    labels = load_labels()
-    func = functools.partial(predict, model=model, labels=labels)
-    func = functools.update_wrapper(func, predict)
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='pil', label='Input'),
-            gr.inputs.Slider(0,
-                             1,
-                             step=args.score_slider_step,
-                             default=args.score_threshold,
-                             label='Score Threshold'),
-        ],
-        [
-            gr.outputs.Textbox(label='Output (string)'),
-            gr.outputs.Textbox(label='Output (raw string)'),
-            gr.outputs.Label(label='Output (label)'),
-            gr.outputs.HTML()
-        ],
-        examples=[
-        ['miku.jpg',0.5],
-        ['miku2.jpg',0.5]
-        ],
-        title=TITLE,
-        description='''
-Demo for [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) with "ready to copy" prompt and a prompt analyzer.
-Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-        ''',
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-if __name__ == '__main__':
-    main()

spaces/AI4PD/hexviz/README.md DELETED Viewed

@@ -1,36 +0,0 @@
----
-title: Hexviz
-emoji: 👁️🧬
-colorFrom: green
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-python_version: 3.10.5
-app_file: ./hexviz/🧬Attention_Visualization.py
-pinned: true
-tags:
-  - protein language models
-  - attention analysis
-  - protein structure
-  - biology
----
-# hexviz
-Visualize attention pattern on 3D protein structures
-## Install and run
-```shell
-poetry install
-poetry run streamlit run hexviz/streamlit/Attention_On_Structure.py
-```
-## Export dependecies from poetry
-Spaces [require](https://huggingface.co/docs/hub/spaces-dependencies#adding-your-own-dependencies) dependencies in a `requirements.txt` file. Export depencies from poetry's `pyproject.toml` file with:
-```shell
-poetry export -f requirements.txt --output requirements.txt --without-hashes
-```
-## Acknowledgements
-This project builds on the attention visualization introduced and developed in
-https://github.com/salesforce/provis#provis-attention-visualizer

spaces/AIConsultant/MusicGen/audiocraft/modules/streaming.py DELETED Viewed

@@ -1,131 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Streaming module API that should be implemented by all Streaming components,
-"""
-from contextlib import contextmanager
-import typing as tp
-from torch import nn
-import torch
-State = tp.Dict[str, torch.Tensor]
-class StreamingModule(nn.Module):
-    """Common API for streaming components.
-    Each streaming component has a streaming state, which is just a dict[str, Tensor].
-    By convention, the first dim of each tensor must be the batch size.
-    Don't use dots in the key names, as this would clash with submodules
-    (like in state_dict).
-    If `self._is_streaming` is True, the component should use and remember
-    the proper state inside `self._streaming_state`.
-    To set a streaming component in streaming state, use
-        with module.streaming():
-            ...
-    This will automatically reset the streaming state when exiting the context manager.
-    This also automatically propagates to all streaming children module.
-    Some module might also implement the `StreamingModule.flush` method, although
-    this one is trickier, as all parents module must be StreamingModule and implement
-    it as well for it to work properly. See `StreamingSequential` after.
-    """
-    def __init__(self) -> None:
-        super().__init__()
-        self._streaming_state: State = {}
-        self._is_streaming = False
-    def _apply_named_streaming(self, fn: tp.Any):
-        for name, module in self.named_modules():
-            if isinstance(module, StreamingModule):
-                fn(name, module)
-    def _set_streaming(self, streaming: bool):
-        def _set_streaming(name, module):
-            module._is_streaming = streaming
-        self._apply_named_streaming(_set_streaming)
-    @contextmanager
-    def streaming(self):
-        """Context manager to enter streaming mode. Reset streaming state on exit."""
-        self._set_streaming(True)
-        try:
-            yield
-        finally:
-            self._set_streaming(False)
-            self.reset_streaming()
-    def reset_streaming(self):
-        """Reset the streaming state."""
-        def _reset(name: str, module: StreamingModule):
-            module._streaming_state.clear()
-        self._apply_named_streaming(_reset)
-    def get_streaming_state(self) -> State:
-        """Return the streaming state, including that of sub-modules."""
-        state: State = {}
-        def _add(name: str, module: StreamingModule):
-            if name:
-                name += "."
-            for key, value in module._streaming_state.items():
-                state[name + key] = value
-        self._apply_named_streaming(_add)
-        return state
-    def set_streaming_state(self, state: State):
-        """Set the streaming state, including that of sub-modules."""
-        state = dict(state)
-        def _set(name: str, module: StreamingModule):
-            if name:
-                name += "."
-            module._streaming_state.clear()
-            for key, value in list(state.items()):
-                # complexity is not ideal here, but probably fine.
-                if key.startswith(name):
-                    local_key = key[len(name):]
-                    if '.' not in local_key:
-                        module._streaming_state[local_key] = value
-                        del state[key]
-        self._apply_named_streaming(_set)
-        assert len(state) == 0, list(state.keys())
-    def flush(self, x: tp.Optional[torch.Tensor] = None):
-        """Flush any remaining outputs that were waiting for completion.
-        Typically, for convolutions, this will add the final padding
-        and process the last buffer.
-        This should take an optional argument `x`, which will be provided
-        if a module before this one in the streaming pipeline has already
-        spitted out a flushed out buffer.
-        """
-        if x is None:
-            return None
-        else:
-            return self(x)
-class StreamingSequential(StreamingModule, nn.Sequential):
-    """A streaming compatible alternative of `nn.Sequential`.
-    """
-    def flush(self, x: tp.Optional[torch.Tensor] = None):
-        for module in self:
-            if isinstance(module, StreamingModule):
-                x = module.flush(x)
-            elif x is not None:
-                x = module(x)
-        return x

spaces/AIZeroToHero/Video-Automatic-Speech-Recognition/README.md DELETED Viewed

@@ -1,13 +0,0 @@
----
-title: Video Automatic Speech Recognition
-emoji: 💻
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIatUIUC/CodeLATS/executors/py_executor.py DELETED Viewed

@@ -1,88 +0,0 @@
-import ast
-import signal
-import astunparse
-from .executor_utils import function_with_timeout
-from typing import List
-from .executor_types import ExecuteResult, Executor
-class PyExecutor(Executor):
-    def execute(self, func: str, tests: List[str], timeout: int = 5) -> ExecuteResult:
-        # Combine function code and assert statement
-        imports = 'from typing import *'
-        func_test_list = [f'{imports}\n{func}\n{test}' for test in tests]
-        # Run the tests and collect the results
-        success_tests = []
-        failed_tests = []
-        is_passing = True
-        num_tests = len(func_test_list)
-        for i in range(num_tests):
-            try:
-                function_with_timeout(exec, (func_test_list[i], globals()), timeout)
-                success_tests += [tests[i]]
-            except Exception:
-                output = get_output(func, tests[i], timeout=timeout)
-                failed_tests += [f"{tests[i]} # output: {output}"]
-                is_passing = False
-        state = []
-        for test in tests:
-            if test in success_tests:
-                state += [True]
-            else:
-                state += [False]
-        state = tuple(state)
-        feedback = "Tested passed:"
-        for test in success_tests:
-            feedback += f"\n{test}"
-        feedback += "\n\nTests failed:"
-        for test in failed_tests:
-            feedback += f"\n{test}"
-        return ExecuteResult(is_passing, feedback, state)
-    def evaluate(self, name: str, func: str, test: str, timeout: int = 5) -> bool:
-        """
-        Evaluates the implementation on Human-Eval Python.
-        probably should be written in a dataset-agnostic way but not now
-        """
-        code = f"""{func}
-{test}
-check({name})
-    """
-        try:
-            function_with_timeout(exec, (code, globals()), timeout)
-            return True
-        except Exception:
-            return False
-def get_call_str(assert_statement: str) -> str:
-    ast_parsed = ast.parse(assert_statement)
-    try:
-        call_str = ast_parsed.body[0].test.left # type: ignore
-    except:
-        call_str = ast_parsed.body[0].test # type: ignore
-    return astunparse.unparse(call_str).strip()
-def get_output(func: str, assert_statement: str, timeout: int = 5) -> str:
-    try:
-        exec(f"from typing import *\n{func}", globals())
-        func_call = get_call_str(assert_statement)
-        output = function_with_timeout(eval, (func_call, globals()), timeout)
-        return output
-    except TimeoutError:
-        return "TIMEOUT"
-    except Exception as e:
-        return str(e)

spaces/Adapter/CoAdapter/ldm/modules/extra_condition/api.py DELETED Viewed

@@ -1,269 +0,0 @@
-from enum import Enum, unique
-import cv2
-import torch
-from basicsr.utils import img2tensor
-from ldm.util import resize_numpy_image
-from PIL import Image
-from torch import autocast
-@unique
-class ExtraCondition(Enum):
-    sketch = 0
-    keypose = 1
-    seg = 2
-    depth = 3
-    canny = 4
-    style = 5
-    color = 6
-    openpose = 7
-def get_cond_model(opt, cond_type: ExtraCondition):
-    if cond_type == ExtraCondition.sketch:
-        from ldm.modules.extra_condition.model_edge import pidinet
-        model = pidinet()
-        ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict']
-        model.load_state_dict({k.replace('module.', ''): v for k, v in ckp.items()}, strict=True)
-        model.to(opt.device)
-        return model
-    elif cond_type == ExtraCondition.seg:
-        raise NotImplementedError
-    elif cond_type == ExtraCondition.keypose:
-        import mmcv
-        from mmdet.apis import init_detector
-        from mmpose.apis import init_pose_model
-        det_config = 'configs/mm/faster_rcnn_r50_fpn_coco.py'
-        det_checkpoint = 'models/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
-        pose_config = 'configs/mm/hrnet_w48_coco_256x192.py'
-        pose_checkpoint = 'models/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
-        det_config_mmcv = mmcv.Config.fromfile(det_config)
-        det_model = init_detector(det_config_mmcv, det_checkpoint, device=opt.device)
-        pose_config_mmcv = mmcv.Config.fromfile(pose_config)
-        pose_model = init_pose_model(pose_config_mmcv, pose_checkpoint, device=opt.device)
-        return {'pose_model': pose_model, 'det_model': det_model}
-    elif cond_type == ExtraCondition.depth:
-        from ldm.modules.extra_condition.midas.api import MiDaSInference
-        model = MiDaSInference(model_type='dpt_hybrid').to(opt.device)
-        return model
-    elif cond_type == ExtraCondition.canny:
-        return None
-    elif cond_type == ExtraCondition.style:
-        from transformers import CLIPProcessor, CLIPVisionModel
-        version = 'openai/clip-vit-large-patch14'
-        processor = CLIPProcessor.from_pretrained(version)
-        clip_vision_model = CLIPVisionModel.from_pretrained(version).to(opt.device)
-        return {'processor': processor, 'clip_vision_model': clip_vision_model}
-    elif cond_type == ExtraCondition.color:
-        return None
-    elif cond_type == ExtraCondition.openpose:
-        from ldm.modules.extra_condition.openpose.api import OpenposeInference
-        model = OpenposeInference().to(opt.device)
-        return model
-    else:
-        raise NotImplementedError
-def get_cond_sketch(opt, cond_image, cond_inp_type, cond_model=None):
-    if isinstance(cond_image, str):
-        edge = cv2.imread(cond_image)
-    else:
-        # for gradio input, pay attention, it's rgb numpy
-        edge = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
-    edge = resize_numpy_image(edge, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
-    opt.H, opt.W = edge.shape[:2]
-    if cond_inp_type == 'sketch':
-        edge = img2tensor(edge)[0].unsqueeze(0).unsqueeze(0) / 255.
-        edge = edge.to(opt.device)
-    elif cond_inp_type == 'image':
-        edge = img2tensor(edge).unsqueeze(0) / 255.
-        edge = cond_model(edge.to(opt.device))[-1]
-    else:
-        raise NotImplementedError
-    # edge = 1-edge # for white background
-    edge = edge > 0.5
-    edge = edge.float()
-    return edge
-def get_cond_seg(opt, cond_image, cond_inp_type='image', cond_model=None):
-    if isinstance(cond_image, str):
-        seg = cv2.imread(cond_image)
-    else:
-        seg = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
-    seg = resize_numpy_image(seg, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
-    opt.H, opt.W = seg.shape[:2]
-    if cond_inp_type == 'seg':
-        seg = img2tensor(seg).unsqueeze(0) / 255.
-        seg = seg.to(opt.device)
-    else:
-        raise NotImplementedError
-    return seg
-def get_cond_keypose(opt, cond_image, cond_inp_type='image', cond_model=None):
-    if isinstance(cond_image, str):
-        pose = cv2.imread(cond_image)
-    else:
-        pose = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
-    pose = resize_numpy_image(pose, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
-    opt.H, opt.W = pose.shape[:2]
-    if cond_inp_type == 'keypose':
-        pose = img2tensor(pose).unsqueeze(0) / 255.
-        pose = pose.to(opt.device)
-    elif cond_inp_type == 'image':
-        from ldm.modules.extra_condition.utils import imshow_keypoints
-        from mmdet.apis import inference_detector
-        from mmpose.apis import (inference_top_down_pose_model, process_mmdet_results)
-        # mmpose seems not compatible with autocast fp16
-        with autocast("cuda", dtype=torch.float32):
-            mmdet_results = inference_detector(cond_model['det_model'], pose)
-            # keep the person class bounding boxes.
-            person_results = process_mmdet_results(mmdet_results, 1)
-            # optional
-            return_heatmap = False
-            dataset = cond_model['pose_model'].cfg.data['test']['type']
-            # e.g. use ('backbone', ) to return backbone feature
-            output_layer_names = None
-            pose_results, returned_outputs = inference_top_down_pose_model(
-                cond_model['pose_model'],
-                pose,
-                person_results,
-                bbox_thr=0.2,
-                format='xyxy',
-                dataset=dataset,
-                dataset_info=None,
-                return_heatmap=return_heatmap,
-                outputs=output_layer_names)
-        # show the results
-        pose = imshow_keypoints(pose, pose_results, radius=2, thickness=2)
-        pose = img2tensor(pose).unsqueeze(0) / 255.
-        pose = pose.to(opt.device)
-    else:
-        raise NotImplementedError
-    return pose
-def get_cond_depth(opt, cond_image, cond_inp_type='image', cond_model=None):
-    if isinstance(cond_image, str):
-        depth = cv2.imread(cond_image)
-    else:
-        depth = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
-    depth = resize_numpy_image(depth, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
-    opt.H, opt.W = depth.shape[:2]
-    if cond_inp_type == 'depth':
-        depth = img2tensor(depth).unsqueeze(0) / 255.
-        depth = depth.to(opt.device)
-    elif cond_inp_type == 'image':
-        depth = img2tensor(depth).unsqueeze(0) / 127.5 - 1.0
-        depth = cond_model(depth.to(opt.device)).repeat(1, 3, 1, 1)
-        depth -= torch.min(depth)
-        depth /= torch.max(depth)
-    else:
-        raise NotImplementedError
-    return depth
-def get_cond_canny(opt, cond_image, cond_inp_type='image', cond_model=None):
-    if isinstance(cond_image, str):
-        canny = cv2.imread(cond_image)
-    else:
-        canny = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
-    canny = resize_numpy_image(canny, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
-    opt.H, opt.W = canny.shape[:2]
-    if cond_inp_type == 'canny':
-        canny = img2tensor(canny)[0:1].unsqueeze(0) / 255.
-        canny = canny.to(opt.device)
-    elif cond_inp_type == 'image':
-        canny = cv2.Canny(canny, 100, 200)[..., None]
-        canny = img2tensor(canny).unsqueeze(0) / 255.
-        canny = canny.to(opt.device)
-    else:
-        raise NotImplementedError
-    return canny
-def get_cond_style(opt, cond_image, cond_inp_type='image', cond_model=None):
-    assert cond_inp_type == 'image'
-    if isinstance(cond_image, str):
-        style = Image.open(cond_image)
-    else:
-        # numpy image to PIL image
-        style = Image.fromarray(cond_image)
-    style_for_clip = cond_model['processor'](images=style, return_tensors="pt")['pixel_values']
-    style_feat = cond_model['clip_vision_model'](style_for_clip.to(opt.device))['last_hidden_state']
-    return style_feat
-def get_cond_color(opt, cond_image, cond_inp_type='image', cond_model=None):
-    if isinstance(cond_image, str):
-        color = cv2.imread(cond_image)
-    else:
-        color = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
-    color = resize_numpy_image(color, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
-    opt.H, opt.W = color.shape[:2]
-    if cond_inp_type == 'image':
-        color = cv2.resize(color, (opt.W//64, opt.H//64), interpolation=cv2.INTER_CUBIC)
-        color = cv2.resize(color, (opt.W, opt.H), interpolation=cv2.INTER_NEAREST)
-    color = img2tensor(color).unsqueeze(0) / 255.
-    color = color.to(opt.device)
-    return color
-def get_cond_openpose(opt, cond_image, cond_inp_type='image', cond_model=None):
-    if isinstance(cond_image, str):
-        openpose_keypose = cv2.imread(cond_image)
-    else:
-        openpose_keypose = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
-    openpose_keypose = resize_numpy_image(
-        openpose_keypose, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
-    opt.H, opt.W = openpose_keypose.shape[:2]
-    if cond_inp_type == 'openpose':
-        openpose_keypose = img2tensor(openpose_keypose).unsqueeze(0) / 255.
-        openpose_keypose = openpose_keypose.to(opt.device)
-    elif cond_inp_type == 'image':
-        with autocast('cuda', dtype=torch.float32):
-            openpose_keypose = cond_model(openpose_keypose)
-        openpose_keypose = img2tensor(openpose_keypose).unsqueeze(0) / 255.
-        openpose_keypose = openpose_keypose.to(opt.device)
-    else:
-        raise NotImplementedError
-    return openpose_keypose
-def get_adapter_feature(inputs, adapters):
-    ret_feat_map = None
-    ret_feat_seq = None
-    if not isinstance(inputs, list):
-        inputs = [inputs]
-        adapters = [adapters]
-    for input, adapter in zip(inputs, adapters):
-        cur_feature = adapter['model'](input)
-        if isinstance(cur_feature, list):
-            if ret_feat_map is None:
-                ret_feat_map = list(map(lambda x: x * adapter['cond_weight'], cur_feature))
-            else:
-                ret_feat_map = list(map(lambda x, y: x + y * adapter['cond_weight'], ret_feat_map, cur_feature))
-        else:
-            if ret_feat_seq is None:
-                ret_feat_seq = cur_feature * adapter['cond_weight']
-            else:
-                ret_feat_seq = torch.cat([ret_feat_seq, cur_feature * adapter['cond_weight']], dim=1)
-    return ret_feat_map, ret_feat_seq

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/Fill.js DELETED Viewed

@@ -1,36 +0,0 @@
-/*
-1. Fill empty grids
-*/
-var Fill = function (map) {
-    var upperBoard = false;
-    if (typeof (map) === 'boolean') {
-        upperBoard = map;
-        map = undefined;
-    }
-    var symbol;
-    var board = this.board,
-        symbols = this.candidateSymbols;
-    var height = this.board.height;
-    if (upperBoard) {
-        height /= 2;
-    }
-    for (var tileY = 0; tileY < height; tileY++) {
-        for (var tileX = 0, width = this.board.width; tileX < width; tileX++) {
-            if (board.contains(tileX, tileY, this.chessTileZ)) { // not empty
-                continue;
-            }
-            if (map !== undefined) {
-                symbol = map[tileX][tileY];
-                if (symbol !== '?') {
-                    symbols = symbol;
-                }
-            }
-            this.createChess(tileX, tileY, symbols);
-        }
-    }
-}
-export default Fill;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/click/Click.js DELETED Viewed

	@@ -1,2 +0,0 @@
1	- import Click from '../../../plugins/button.js'
2	- export default Click;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdownlist/DropDownList.d.ts DELETED Viewed

@@ -1,130 +0,0 @@
-import Label from '../label/Label';
-export default DropDownList;
-declare namespace DropDownList {
-    type CreateButtonCallbackType = (
-        this: DropDownList,
-        scene: Phaser.Scene,
-        option: any,
-        index: number,
-        options: any[]
-    ) => Phaser.GameObjects.GameObject;
-    type CreateBackgroundCallbackType = (
-        this: DropDownList,
-        scene: Phaser.Scene,
-    ) => Phaser.GameObjects.GameObject;
-    type OnButtonClickCallbackType = (
-        this: DropDownList,
-        button: Phaser.GameObjects.GameObject,
-        index: number,
-        pointer: Phaser.Input.Pointer,
-        event: Phaser.Types.Input.EventData
-    ) => void;
-    type OnButtonOverCallbackType = (
-        this: DropDownList,
-        button: Phaser.GameObjects.GameObject,
-        index: number,
-        pointer: Phaser.Input.Pointer,
-        event: Phaser.Types.Input.EventData
-    ) => void;
-    type OnButtonOutCallbackType = (
-        this: DropDownList,
-        button: Phaser.GameObjects.GameObject,
-        index: number,
-        pointer: Phaser.Input.Pointer,
-        event: Phaser.Types.Input.EventData
-    ) => void;
-    type AlignParentType = 'text' | 'icon';
-    type ExpandDirectionType = 0 | 1 | 'down' | 'up';
-    type SetValueCallbackType = (
-        dropDownList: DropDownList,
-        value?: any,
-        previousValue?: any,
-    ) => void;
-    type ListSpaceType = {
-        left?: number, right?: number, top?: number, bottom?: number, item?: number
-    };
-    type WrapListSpaceType = {
-        left?: number, right?: number, top?: number, bottom?: number, item?: number, line?: number
-    }
-    interface IConfig extends Label.IConfig {
-        options?: any[],
-        list?: {
-            createBackgroundCallback?: CreateBackgroundCallbackType;
-            createButtonCallback?: CreateButtonCallbackType;
-            onButtonClick?: OnButtonClickCallbackType;
-            onButtonOver?: OnButtonOverCallbackType;
-            onButtonOut?: OnButtonOutCallbackType;
-            easeIn?: number;
-            easeOut?: number;
-            wrap?: boolean;
-            width?: number;
-            height?: number;
-            alignParent?: AlignParentType;
-            alignSide?: string;
-            expandDirection?: ExpandDirectionType;
-            bounds?: Phaser.Geom.Rectangle;
-            space?: ListSpaceType | WrapListSpaceType;
-            draggable?: boolean;
-        },
-        setValueCallback?: SetValueCallbackType;
-        setValueCallbackScope?: object;
-        value?: any;
-    }
-}
-declare class DropDownList extends Label {
-    constructor(
-        scene: Phaser.Scene,
-        config?: DropDownList.IConfig
-    );
-    setOptions(options: any[]): this;
-    openListPanel(): this;
-    closeListPanel(): this;
-    toggleListPanel(): this;
-    setValue(value?: any): this;
-    value: any;
-    setCreateButtonCallback(callback?: DropDownList.CreateBackgroundCallbackType): this;
-    setCreateBackgroundCallback(callback?: DropDownList.CreateBackgroundCallbackType): this;
-    setButtonClickCallback(callback?: DropDownList.OnButtonClickCallbackType): this;
-    setButtonOverCallback(callback?: DropDownList.OnButtonOverCallbackType): this;
-    setButtonOutCallback(callback?: DropDownList.OnButtonOutCallbackType): this;
-    setListEaseInDuration(duration?: number): this;
-    setListEaseOutDuration(duration?: number): this;
-    setWrapEnable(enable?: boolean): this;
-    setListWidth(width?: number): this;
-    setListHeight(height?: number): this;
-    setListSize(width?: number, height?: number): this;
-    setListAlignmentMode(mode?: DropDownList.AlignParentType): this;
-    setListAlignmentSide(side?: string): this;
-    setListBounds(bounds: Phaser.Geom.Rectangle): this;
-    setListSpace(space?: DropDownList.ListSpaceType | DropDownList.WrapListSpaceType): this;
-    setListDraggable(enable?: boolean): this;
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/roundrectangle/RoundRectangle.d.ts DELETED Viewed

	@@ -1,2 +0,0 @@
1	- import RoundRectangle from "../../../plugins/roundrectangle";
2	- export default RoundRectangle;

spaces/Ajit025/Text_to_Image_conversion/app.py DELETED Viewed

@@ -1,15 +0,0 @@
-from text_to_image import TextToImageTool
-import gradio as gr
-tool = TextToImageTool()
-def fn(*args, **kwargs):
-    return tool(*args, **kwargs)
-gr.Interface(
-    fn=fn,
-    inputs=tool.inputs,
-    outputs=tool.outputs,
-    title="Text_to_Image",
-    article=tool.description,
-).queue(concurrency_count=5).launch()

spaces/Aki004/herta-so-vits/flask_api.py DELETED Viewed

@@ -1,62 +0,0 @@
-import io
-import logging
-import soundfile
-import torch
-import torchaudio
-from flask import Flask, request, send_file
-from flask_cors import CORS
-from inference.infer_tool import Svc, RealTimeVC
-app = Flask(__name__)
-CORS(app)
-logging.getLogger('numba').setLevel(logging.WARNING)
-@app.route("/voiceChangeModel", methods=["POST"])
-def voice_change_model():
-    request_form = request.form
-    wave_file = request.files.get("sample", None)
-    # pitch changing information
-    f_pitch_change = float(request_form.get("fPitchChange", 0))
-    # DAW required sampling rate
-    daw_sample = int(float(request_form.get("sampleRate", 0)))
-    speaker_id = int(float(request_form.get("sSpeakId", 0)))
-    # get wav from http and convert
-    input_wav_path = io.BytesIO(wave_file.read())
-    # inference
-    if raw_infer:
-        # out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
-        out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0,
-                                            auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
-        tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample)
-    else:
-        out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0,
-                                auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
-        tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample)
-    # return
-    out_wav_path = io.BytesIO()
-    soundfile.write(out_wav_path, tar_audio.cpu().numpy(), daw_sample, format="wav")
-    out_wav_path.seek(0)
-    return send_file(out_wav_path, download_name="temp.wav", as_attachment=True)
-if __name__ == '__main__':
-    # True means splice directly. There may be explosive sounds at the splice.
-    # False means use cross fade. There may be slight overlapping sounds at the splice.
-    # Using 0.3-0.5s in VST plugin can reduce latency.
-    # You can adjust the maximum slicing time of VST plugin to 1 second and set it to ture here to get a stable sound quality and a relatively large delay。
-    # Choose an acceptable method on your own.
-    raw_infer = True
-    # each model and config are corresponding
-    model_name = "logs/32k/G_174000-Copy1.pth"
-    config_name = "configs/config.json"
-    cluster_model_path = "logs/44k/kmeans_10000.pt"
-    svc_model = Svc(model_name, config_name, cluster_model_path=cluster_model_path)
-    svc = RealTimeVC()
-    # corresponding to the vst plugin here
-    app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)

spaces/AlexWang/lama/bin/paper_runfiles/env.sh DELETED Viewed

@@ -1,8 +0,0 @@
-DIRNAME="$(dirname $0)"
-DIRNAME="$(realpath ""$DIRNAME"")"
-BINDIR="$DIRNAME/.."
-SRCDIR="$BINDIR/.."
-CONFIGDIR="$SRCDIR/configs"
-export PYTHONPATH="$SRCDIR:$PYTHONPATH"

spaces/Alfasign/nomic-ai-gpt4all-13b-snoozy/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Chat
-emoji: 📈
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/ops/__init__.py DELETED Viewed

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-# empty

spaces/Amrrs/DragGan-Inversion/dnnlib/util.py DELETED Viewed

@@ -1,504 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-"""Miscellaneous utility classes and functions."""
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import tempfile
-import urllib
-import urllib.request
-import uuid
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-# Util classes
-# ------------------------------------------------------------------------------------------
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-        sys.stdout = self
-        sys.stderr = self
-    def __enter__(self) -> "Logger":
-        return self
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-    def write(self, text: Union[str, bytes]) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if isinstance(text, bytes):
-            text = text.decode()
-        if len(text) == 0:  # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-        if self.file is not None:
-            self.file.write(text)
-        self.stdout.write(text)
-        if self.should_flush:
-            self.flush()
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-        self.stdout.flush()
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-        if self.file is not None:
-            self.file.close()
-            self.file = None
-# Cache directories
-# ------------------------------------------------------------------------------------------
-_dnnlib_cache_dir = None
-def set_cache_dir(path: str) -> None:
-    global _dnnlib_cache_dir
-    _dnnlib_cache_dir = path
-def make_cache_dir_path(*paths: str) -> str:
-    if _dnnlib_cache_dir is not None:
-        return os.path.join(_dnnlib_cache_dir, *paths)
-    if 'DNNLIB_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
-# Small util functions
-# ------------------------------------------------------------------------------------------
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-def format_time_brief(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m".format(s // (60 * 60), (s // 60) % 60)
-    else:
-        return "{0}d {1:02}h".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24)
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-    for v in t:
-        result *= v
-    return result
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-    assert type_str in _str_to_ctype.keys()
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-    return my_dtype, my_ctype
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:]))
-                  for i in range(len(parts), 0, -1)]
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(
-                module_name)  # may raise ImportError
-            # may raise AttributeError
-            get_obj_from_module(module, local_obj_name)
-            return module, local_obj_name
-        except:
-            pass
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name)  # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(
-                module_name)  # may raise ImportError
-            # may raise AttributeError
-            get_obj_from_module(module, local_obj_name)
-        except ImportError:
-            pass
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    func_obj = get_obj_by_name(func_name)
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
-    """Finds the python class with the given name and constructs it with the given arguments."""
-    return call_func_by_name(*args, func_name=class_name, **kwargs)
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    module = obj.__module__
-    if module == '__main__':
-        module = os.path.splitext(os.path.basename(
-            sys.modules[module].__file__))[0]
-    return module + "." + obj.__name__
-# File system helpers
-# ------------------------------------------------------------------------------------------
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-    if ignores is None:
-        ignores = []
-    result = []
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p)
-                              for p in relative_paths]
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-    return result
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-        shutil.copyfile(file[0], file[1])
-# URL helpers
-# ------------------------------------------------------------------------------------------
-def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    if allow_file_urls and obj.startswith('file://'):
-        return True
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert num_attempts >= 1
-    assert not (return_filename and (not cache))
-    # Doesn't look like an URL scheme so interpret it as a local filename.
-    if not re.match('^[a-z]+://', url):
-        return url if return_filename else open(url, "rb")
-    # Handle file URLs.  This code handles unusual file:// patterns that
-    # arise on Windows:
-    #
-    # file:///c:/foo.txt
-    #
-    # which would translate to a local '/c:/foo.txt' filename that's
-    # invalid.  Drop the forward slash for such pathnames.
-    #
-    # If you touch this code path, you should test it on both Linux and
-    # Windows.
-    #
-    # Some internet resources suggest using urllib.request.url2pathname() but
-    # but that converts forward slashes to backslashes and this causes
-    # its own set of problems.
-    if url.startswith('file://'):
-        filename = urllib.parse.urlparse(url).path
-        if re.match(r'^/[a-zA-Z]:', filename):
-            filename = filename[1:]
-        return filename if return_filename else open(filename, "rb")
-    assert is_url(url)
-    # Lookup from cache.
-    if cache_dir is None:
-        cache_dir = make_cache_dir_path('downloads')
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            filename = cache_files[0]
-            return filename if return_filename else open(filename, "rb")
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split(
-                                '"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError(
-                                "Google Drive download quota exceeded -- please try again later")
-                    match = re.search(
-                        r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except KeyboardInterrupt:
-                raise
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-    # Save to cache.
-    if cache:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(
-            cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file)  # atomic
-        if return_filename:
-            return cache_file
-    # Return data as file object.
-    assert not return_filename
-    return io.BytesIO(url_data)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/.github/ISSUE_TEMPLATE/feedback.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-name: "💬 Feedback about API Design"
-about: Give feedback about the current API design
-title: ''
-labels: ''
-assignees: ''
----
-**What API design would you like to have changed or added to the library? Why?**
-**What use case would this enable or better enable? Can you give us a code example?**

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/generate_logits.py DELETED Viewed

@@ -1,127 +0,0 @@
-import random
-import torch
-from huggingface_hub import HfApi
-from diffusers import UNet2DModel
-api = HfApi()
-results = {}
-# fmt: off
-results["google_ddpm_cifar10_32"] = torch.tensor([
-    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
-    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
-    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
-])
-results["google_ddpm_ema_bedroom_256"] = torch.tensor([
-    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
-    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
-    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
-])
-results["CompVis_ldm_celebahq_256"] = torch.tensor([
-    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
-    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
-])
-results["google_ncsnpp_ffhq_1024"] = torch.tensor([
-    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
-    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
-])
-results["google_ncsnpp_bedroom_256"] = torch.tensor([
-    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
-    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
-])
-results["google_ncsnpp_celebahq_256"] = torch.tensor([
-    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
-    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
-])
-results["google_ncsnpp_church_256"] = torch.tensor([
-    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
-    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
-])
-results["google_ncsnpp_ffhq_256"] = torch.tensor([
-    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
-    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
-])
-results["google_ddpm_cat_256"] = torch.tensor([
-    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
-    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
-    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
-results["google_ddpm_celebahq_256"] = torch.tensor([
-    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
-    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
-    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
-])
-results["google_ddpm_ema_celebahq_256"] = torch.tensor([
-    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
-    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
-    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
-])
-results["google_ddpm_church_256"] = torch.tensor([
-    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
-    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
-    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
-])
-results["google_ddpm_bedroom_256"] = torch.tensor([
-    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
-    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
-    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
-])
-results["google_ddpm_ema_church_256"] = torch.tensor([
-    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
-    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
-    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
-])
-results["google_ddpm_ema_cat_256"] = torch.tensor([
-    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
-    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
-    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
-])
-# fmt: on
-models = api.list_models(filter="diffusers")
-for mod in models:
-    if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
-        local_checkpoint = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
-        print(f"Started running {mod.modelId}!!!")
-        if mod.modelId.startswith("CompVis"):
-            model = UNet2DModel.from_pretrained(local_checkpoint, subfolder="unet")
-        else:
-            model = UNet2DModel.from_pretrained(local_checkpoint)
-        torch.manual_seed(0)
-        random.seed(0)
-        noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
-        time_step = torch.tensor([10] * noise.shape[0])
-        with torch.no_grad():
-            logits = model(noise, time_step).sample
-        assert torch.allclose(
-            logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
-        )
-        print(f"{mod.modelId} has passed successfully!!!")

spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py DELETED Viewed

@@ -1,13 +0,0 @@
-_base_ = './ga_faster_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py DELETED Viewed

@@ -1,18 +0,0 @@
-_base_ = './htc_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'))
-data = dict(samples_per_gpu=1, workers_per_gpu=1)
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)

spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py DELETED Viewed

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py DELETED Viewed

@@ -1,11 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet18',
-    backbone=dict(type='ResNet', depth=18),
-    decode_head=dict(
-        c1_in_channels=64,
-        c1_channels=12,
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))

spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py DELETED Viewed

@@ -1,39 +0,0 @@
-_base_ = './ocrnet_hr18_512x512_20k_voc12aug.py'
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w48',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(48, 96)),
-            stage3=dict(num_channels=(48, 96, 192)),
-            stage4=dict(num_channels=(48, 96, 192, 384)))),
-    decode_head=[
-        dict(
-            type='FCNHead',
-            in_channels=[48, 96, 192, 384],
-            channels=sum([48, 96, 192, 384]),
-            input_transform='resize_concat',
-            in_index=(0, 1, 2, 3),
-            kernel_size=1,
-            num_convs=1,
-            norm_cfg=norm_cfg,
-            concat_input=False,
-            dropout_ratio=-1,
-            num_classes=21,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-        dict(
-            type='OCRHead',
-            in_channels=[48, 96, 192, 384],
-            channels=512,
-            ocr_channels=256,
-            input_transform='resize_concat',
-            in_index=(0, 1, 2, 3),
-            norm_cfg=norm_cfg,
-            dropout_ratio=-1,
-            num_classes=21,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
-    ])

spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/generators.py DELETED Viewed

@@ -1,151 +0,0 @@
-from fastai.vision import *
-from fastai.vision.learner import cnn_config
-from .unet import DynamicUnetWide, DynamicUnetDeep
-from .loss import FeatureLoss
-from .dataset import *
-# Weights are implicitly read from ./models/ folder
-def gen_inference_wide(
-    root_folder: Path, weights_name: str, nf_factor: int = 2, arch=models.resnet101) -> Learner:
-    data = get_dummy_databunch()
-    learn = gen_learner_wide(
-        data=data, gen_loss=F.l1_loss, nf_factor=nf_factor, arch=arch
-    )
-    learn.path = root_folder
-    learn.load(weights_name)
-    learn.model.eval()
-    return learn
-def gen_learner_wide(
-    data: ImageDataBunch, gen_loss, arch=models.resnet101, nf_factor: int = 2
-) -> Learner:
-    return unet_learner_wide(
-        data,
-        arch=arch,
-        wd=1e-3,
-        blur=True,
-        norm_type=NormType.Spectral,
-        self_attention=True,
-        y_range=(-3.0, 3.0),
-        loss_func=gen_loss,
-        nf_factor=nf_factor,
-    )
-# The code below is meant to be merged into fastaiv1 ideally
-def unet_learner_wide(
-    data: DataBunch,
-    arch: Callable,
-    pretrained: bool = True,
-    blur_final: bool = True,
-    norm_type: Optional[NormType] = NormType,
-    split_on: Optional[SplitFuncOrIdxList] = None,
-    blur: bool = False,
-    self_attention: bool = False,
-    y_range: Optional[Tuple[float, float]] = None,
-    last_cross: bool = True,
-    bottle: bool = False,
-    nf_factor: int = 1,
-    **kwargs: Any
-) -> Learner:
-    "Build Unet learner from `data` and `arch`."
-    meta = cnn_config(arch)
-    body = create_body(arch, pretrained)
-    model = to_device(
-        DynamicUnetWide(
-            body,
-            n_classes=data.c,
-            blur=blur,
-            blur_final=blur_final,
-            self_attention=self_attention,
-            y_range=y_range,
-            norm_type=norm_type,
-            last_cross=last_cross,
-            bottle=bottle,
-            nf_factor=nf_factor,
-        ),
-        data.device,
-    )
-    learn = Learner(data, model, **kwargs)
-    learn.split(ifnone(split_on, meta['split']))
-    if pretrained:
-        learn.freeze()
-    apply_init(model[2], nn.init.kaiming_normal_)
-    return learn
-# ----------------------------------------------------------------------
-# Weights are implicitly read from ./models/ folder
-def gen_inference_deep(
-    root_folder: Path, weights_name: str, arch=models.resnet34, nf_factor: float = 1.5) -> Learner:
-    data = get_dummy_databunch()
-    learn = gen_learner_deep(
-        data=data, gen_loss=F.l1_loss, arch=arch, nf_factor=nf_factor
-    )
-    learn.path = root_folder
-    learn.load(weights_name)
-    learn.model.eval()
-    return learn
-def gen_learner_deep(
-    data: ImageDataBunch, gen_loss, arch=models.resnet34, nf_factor: float = 1.5
-) -> Learner:
-    return unet_learner_deep(
-        data,
-        arch,
-        wd=1e-3,
-        blur=True,
-        norm_type=NormType.Spectral,
-        self_attention=True,
-        y_range=(-3.0, 3.0),
-        loss_func=gen_loss,
-        nf_factor=nf_factor,
-    )
-# The code below is meant to be merged into fastaiv1 ideally
-def unet_learner_deep(
-    data: DataBunch,
-    arch: Callable,
-    pretrained: bool = True,
-    blur_final: bool = True,
-    norm_type: Optional[NormType] = NormType,
-    split_on: Optional[SplitFuncOrIdxList] = None,
-    blur: bool = False,
-    self_attention: bool = False,
-    y_range: Optional[Tuple[float, float]] = None,
-    last_cross: bool = True,
-    bottle: bool = False,
-    nf_factor: float = 1.5,
-    **kwargs: Any
-) -> Learner:
-    "Build Unet learner from `data` and `arch`."
-    meta = cnn_config(arch)
-    body = create_body(arch, pretrained)
-    model = to_device(
-        DynamicUnetDeep(
-            body,
-            n_classes=data.c,
-            blur=blur,
-            blur_final=blur_final,
-            self_attention=self_attention,
-            y_range=y_range,
-            norm_type=norm_type,
-            last_cross=last_cross,
-            bottle=bottle,
-            nf_factor=nf_factor,
-        ),
-        data.device,
-    )
-    learn = Learner(data, model, **kwargs)
-    learn.split(ifnone(split_on, meta['split']))
-    if pretrained:
-        learn.freeze()
-    apply_init(model[2], nn.init.kaiming_normal_)
-    return learn
-# -----------------------------

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

@@ -1,111 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-from tempfile import NamedTemporaryFile
-import mmap
-class CallbackFileWrapper(object):
-    """
-    Small wrapper around a fp object which will tee everything read into a
-    buffer, and when that file is closed it will execute a callback with the
-    contents of that buffer.
-    All attributes are proxied to the underlying file object.
-    This class uses members with a double underscore (__) leading prefix so as
-    not to accidentally shadow an attribute.
-    The data is stored in a temporary file until it is all available.  As long
-    as the temporary files directory is disk-based (sometimes it's a
-    memory-backed-``tmpfs`` on Linux), data will be unloaded to disk if memory
-    pressure is high.  For small files the disk usually won't be used at all,
-    it'll all be in the filesystem memory cache, so there should be no
-    performance impact.
-    """
-    def __init__(self, fp, callback):
-        self.__buf = NamedTemporaryFile("rb+", delete=True)
-        self.__fp = fp
-        self.__callback = callback
-    def __getattr__(self, name):
-        # The vaguaries of garbage collection means that self.__fp is
-        # not always set.  By using __getattribute__ and the private
-        # name[0] allows looking up the attribute value and raising an
-        # AttributeError when it doesn't exist. This stop thigns from
-        # infinitely recursing calls to getattr in the case where
-        # self.__fp hasn't been set.
-        #
-        # [0] https://docs.python.org/2/reference/expressions.html#atom-identifiers
-        fp = self.__getattribute__("_CallbackFileWrapper__fp")
-        return getattr(fp, name)
-    def __is_fp_closed(self):
-        try:
-            return self.__fp.fp is None
-        except AttributeError:
-            pass
-        try:
-            return self.__fp.closed
-        except AttributeError:
-            pass
-        # We just don't cache it then.
-        # TODO: Add some logging here...
-        return False
-    def _close(self):
-        if self.__callback:
-            if self.__buf.tell() == 0:
-                # Empty file:
-                result = b""
-            else:
-                # Return the data without actually loading it into memory,
-                # relying on Python's buffer API and mmap(). mmap() just gives
-                # a view directly into the filesystem's memory cache, so it
-                # doesn't result in duplicate memory use.
-                self.__buf.seek(0, 0)
-                result = memoryview(
-                    mmap.mmap(self.__buf.fileno(), 0, access=mmap.ACCESS_READ)
-                )
-            self.__callback(result)
-        # We assign this to None here, because otherwise we can get into
-        # really tricky problems where the CPython interpreter dead locks
-        # because the callback is holding a reference to something which
-        # has a __del__ method. Setting this to None breaks the cycle
-        # and allows the garbage collector to do it's thing normally.
-        self.__callback = None
-        # Closing the temporary file releases memory and frees disk space.
-        # Important when caching big files.
-        self.__buf.close()
-    def read(self, amt=None):
-        data = self.__fp.read(amt)
-        if data:
-            # We may be dealing with b'', a sign that things are over:
-            # it's passed e.g. after we've already closed self.__buf.
-            self.__buf.write(data)
-        if self.__is_fp_closed():
-            self._close()
-        return data
-    def _safe_read(self, amt):
-        data = self.__fp._safe_read(amt)
-        if amt == 2 and data == b"\r\n":
-            # urllib executes this read to toss the CRLF at the end
-            # of the chunk.
-            return data
-        self.__buf.write(data)
-        if self.__is_fp_closed():
-            self._close()
-        return data

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/resources.py DELETED Viewed

@@ -1,358 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2017 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from __future__ import unicode_literals
-import bisect
-import io
-import logging
-import os
-import pkgutil
-import sys
-import types
-import zipimport
-from . import DistlibException
-from .util import cached_property, get_cache_base, Cache
-logger = logging.getLogger(__name__)
-cache = None    # created when needed
-class ResourceCache(Cache):
-    def __init__(self, base=None):
-        if base is None:
-            # Use native string to avoid issues on 2.x: see Python #20140.
-            base = os.path.join(get_cache_base(), str('resource-cache'))
-        super(ResourceCache, self).__init__(base)
-    def is_stale(self, resource, path):
-        """
-        Is the cache stale for the given resource?
-        :param resource: The :class:`Resource` being cached.
-        :param path: The path of the resource in the cache.
-        :return: True if the cache is stale.
-        """
-        # Cache invalidation is a hard problem :-)
-        return True
-    def get(self, resource):
-        """
-        Get a resource into the cache,
-        :param resource: A :class:`Resource` instance.
-        :return: The pathname of the resource in the cache.
-        """
-        prefix, path = resource.finder.get_cache_info(resource)
-        if prefix is None:
-            result = path
-        else:
-            result = os.path.join(self.base, self.prefix_to_dir(prefix), path)
-            dirname = os.path.dirname(result)
-            if not os.path.isdir(dirname):
-                os.makedirs(dirname)
-            if not os.path.exists(result):
-                stale = True
-            else:
-                stale = self.is_stale(resource, path)
-            if stale:
-                # write the bytes of the resource to the cache location
-                with open(result, 'wb') as f:
-                    f.write(resource.bytes)
-        return result
-class ResourceBase(object):
-    def __init__(self, finder, name):
-        self.finder = finder
-        self.name = name
-class Resource(ResourceBase):
-    """
-    A class representing an in-package resource, such as a data file. This is
-    not normally instantiated by user code, but rather by a
-    :class:`ResourceFinder` which manages the resource.
-    """
-    is_container = False        # Backwards compatibility
-    def as_stream(self):
-        """
-        Get the resource as a stream.
-        This is not a property to make it obvious that it returns a new stream
-        each time.
-        """
-        return self.finder.get_stream(self)
-    @cached_property
-    def file_path(self):
-        global cache
-        if cache is None:
-            cache = ResourceCache()
-        return cache.get(self)
-    @cached_property
-    def bytes(self):
-        return self.finder.get_bytes(self)
-    @cached_property
-    def size(self):
-        return self.finder.get_size(self)
-class ResourceContainer(ResourceBase):
-    is_container = True     # Backwards compatibility
-    @cached_property
-    def resources(self):
-        return self.finder.get_resources(self)
-class ResourceFinder(object):
-    """
-    Resource finder for file system resources.
-    """
-    if sys.platform.startswith('java'):
-        skipped_extensions = ('.pyc', '.pyo', '.class')
-    else:
-        skipped_extensions = ('.pyc', '.pyo')
-    def __init__(self, module):
-        self.module = module
-        self.loader = getattr(module, '__loader__', None)
-        self.base = os.path.dirname(getattr(module, '__file__', ''))
-    def _adjust_path(self, path):
-        return os.path.realpath(path)
-    def _make_path(self, resource_name):
-        # Issue #50: need to preserve type of path on Python 2.x
-        # like os.path._get_sep
-        if isinstance(resource_name, bytes):    # should only happen on 2.x
-            sep = b'/'
-        else:
-            sep = '/'
-        parts = resource_name.split(sep)
-        parts.insert(0, self.base)
-        result = os.path.join(*parts)
-        return self._adjust_path(result)
-    def _find(self, path):
-        return os.path.exists(path)
-    def get_cache_info(self, resource):
-        return None, resource.path
-    def find(self, resource_name):
-        path = self._make_path(resource_name)
-        if not self._find(path):
-            result = None
-        else:
-            if self._is_directory(path):
-                result = ResourceContainer(self, resource_name)
-            else:
-                result = Resource(self, resource_name)
-            result.path = path
-        return result
-    def get_stream(self, resource):
-        return open(resource.path, 'rb')
-    def get_bytes(self, resource):
-        with open(resource.path, 'rb') as f:
-            return f.read()
-    def get_size(self, resource):
-        return os.path.getsize(resource.path)
-    def get_resources(self, resource):
-        def allowed(f):
-            return (f != '__pycache__' and not
-                    f.endswith(self.skipped_extensions))
-        return set([f for f in os.listdir(resource.path) if allowed(f)])
-    def is_container(self, resource):
-        return self._is_directory(resource.path)
-    _is_directory = staticmethod(os.path.isdir)
-    def iterator(self, resource_name):
-        resource = self.find(resource_name)
-        if resource is not None:
-            todo = [resource]
-            while todo:
-                resource = todo.pop(0)
-                yield resource
-                if resource.is_container:
-                    rname = resource.name
-                    for name in resource.resources:
-                        if not rname:
-                            new_name = name
-                        else:
-                            new_name = '/'.join([rname, name])
-                        child = self.find(new_name)
-                        if child.is_container:
-                            todo.append(child)
-                        else:
-                            yield child
-class ZipResourceFinder(ResourceFinder):
-    """
-    Resource finder for resources in .zip files.
-    """
-    def __init__(self, module):
-        super(ZipResourceFinder, self).__init__(module)
-        archive = self.loader.archive
-        self.prefix_len = 1 + len(archive)
-        # PyPy doesn't have a _files attr on zipimporter, and you can't set one
-        if hasattr(self.loader, '_files'):
-            self._files = self.loader._files
-        else:
-            self._files = zipimport._zip_directory_cache[archive]
-        self.index = sorted(self._files)
-    def _adjust_path(self, path):
-        return path
-    def _find(self, path):
-        path = path[self.prefix_len:]
-        if path in self._files:
-            result = True
-        else:
-            if path and path[-1] != os.sep:
-                path = path + os.sep
-            i = bisect.bisect(self.index, path)
-            try:
-                result = self.index[i].startswith(path)
-            except IndexError:
-                result = False
-        if not result:
-            logger.debug('_find failed: %r %r', path, self.loader.prefix)
-        else:
-            logger.debug('_find worked: %r %r', path, self.loader.prefix)
-        return result
-    def get_cache_info(self, resource):
-        prefix = self.loader.archive
-        path = resource.path[1 + len(prefix):]
-        return prefix, path
-    def get_bytes(self, resource):
-        return self.loader.get_data(resource.path)
-    def get_stream(self, resource):
-        return io.BytesIO(self.get_bytes(resource))
-    def get_size(self, resource):
-        path = resource.path[self.prefix_len:]
-        return self._files[path][3]
-    def get_resources(self, resource):
-        path = resource.path[self.prefix_len:]
-        if path and path[-1] != os.sep:
-            path += os.sep
-        plen = len(path)
-        result = set()
-        i = bisect.bisect(self.index, path)
-        while i < len(self.index):
-            if not self.index[i].startswith(path):
-                break
-            s = self.index[i][plen:]
-            result.add(s.split(os.sep, 1)[0])   # only immediate children
-            i += 1
-        return result
-    def _is_directory(self, path):
-        path = path[self.prefix_len:]
-        if path and path[-1] != os.sep:
-            path += os.sep
-        i = bisect.bisect(self.index, path)
-        try:
-            result = self.index[i].startswith(path)
-        except IndexError:
-            result = False
-        return result
-_finder_registry = {
-    type(None): ResourceFinder,
-    zipimport.zipimporter: ZipResourceFinder
-}
-try:
-    # In Python 3.6, _frozen_importlib -> _frozen_importlib_external
-    try:
-        import _frozen_importlib_external as _fi
-    except ImportError:
-        import _frozen_importlib as _fi
-    _finder_registry[_fi.SourceFileLoader] = ResourceFinder
-    _finder_registry[_fi.FileFinder] = ResourceFinder
-    # See issue #146
-    _finder_registry[_fi.SourcelessFileLoader] = ResourceFinder
-    del _fi
-except (ImportError, AttributeError):
-    pass
-def register_finder(loader, finder_maker):
-    _finder_registry[type(loader)] = finder_maker
-_finder_cache = {}
-def finder(package):
-    """
-    Return a resource finder for a package.
-    :param package: The name of the package.
-    :return: A :class:`ResourceFinder` instance for the package.
-    """
-    if package in _finder_cache:
-        result = _finder_cache[package]
-    else:
-        if package not in sys.modules:
-            __import__(package)
-        module = sys.modules[package]
-        path = getattr(module, '__path__', None)
-        if path is None:
-            raise DistlibException('You cannot get a finder for a module, '
-                                   'only for a package')
-        loader = getattr(module, '__loader__', None)
-        finder_maker = _finder_registry.get(type(loader))
-        if finder_maker is None:
-            raise DistlibException('Unable to locate finder for %r' % package)
-        result = finder_maker(module)
-        _finder_cache[package] = result
-    return result
-_dummy_module = types.ModuleType(str('__dummy__'))
-def finder_for_path(path):
-    """
-    Return a resource finder for a path, which should represent a container.
-    :param path: The path.
-    :return: A :class:`ResourceFinder` instance for the path.
-    """
-    result = None
-    # calls any path hooks, gets importer into cache
-    pkgutil.get_importer(path)
-    loader = sys.path_importer_cache.get(path)
-    finder = _finder_registry.get(type(loader))
-    if finder:
-        module = _dummy_module
-        module.__file__ = os.path.join(path, '')
-        module.__loader__ = loader
-        result = finder(module)
-    return result

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/extension.py DELETED Viewed

@@ -1,248 +0,0 @@
-"""distutils.extension
-Provides the Extension class, used to describe C/C++ extension
-modules in setup scripts."""
-import os
-import warnings
-# This class is really only used by the "build_ext" command, so it might
-# make sense to put it in distutils.command.build_ext.  However, that
-# module is already big enough, and I want to make this class a bit more
-# complex to simplify some common cases ("foo" module in "foo.c") and do
-# better error-checking ("foo.c" actually exists).
-#
-# Also, putting this in build_ext.py means every setup script would have to
-# import that large-ish module (indirectly, through distutils.core) in
-# order to do anything.
-class Extension:
-    """Just a collection of attributes that describes an extension
-    module and everything needed to build it (hopefully in a portable
-    way, but there are hooks that let you be as unportable as you need).
-    Instance attributes:
-      name : string
-        the full name of the extension, including any packages -- ie.
-        *not* a filename or pathname, but Python dotted name
-      sources : [string]
-        list of source filenames, relative to the distribution root
-        (where the setup script lives), in Unix form (slash-separated)
-        for portability.  Source files may be C, C++, SWIG (.i),
-        platform-specific resource files, or whatever else is recognized
-        by the "build_ext" command as source for a Python extension.
-      include_dirs : [string]
-        list of directories to search for C/C++ header files (in Unix
-        form for portability)
-      define_macros : [(name : string, value : string|None)]
-        list of macros to define; each macro is defined using a 2-tuple,
-        where 'value' is either the string to define it to or None to
-        define it without a particular value (equivalent of "#define
-        FOO" in source or -DFOO on Unix C compiler command line)
-      undef_macros : [string]
-        list of macros to undefine explicitly
-      library_dirs : [string]
-        list of directories to search for C/C++ libraries at link time
-      libraries : [string]
-        list of library names (not filenames or paths) to link against
-      runtime_library_dirs : [string]
-        list of directories to search for C/C++ libraries at run time
-        (for shared extensions, this is when the extension is loaded)
-      extra_objects : [string]
-        list of extra files to link with (eg. object files not implied
-        by 'sources', static library that must be explicitly specified,
-        binary resource files, etc.)
-      extra_compile_args : [string]
-        any extra platform- and compiler-specific information to use
-        when compiling the source files in 'sources'.  For platforms and
-        compilers where "command line" makes sense, this is typically a
-        list of command-line arguments, but for other platforms it could
-        be anything.
-      extra_link_args : [string]
-        any extra platform- and compiler-specific information to use
-        when linking object files together to create the extension (or
-        to create a new static Python interpreter).  Similar
-        interpretation as for 'extra_compile_args'.
-      export_symbols : [string]
-        list of symbols to be exported from a shared extension.  Not
-        used on all platforms, and not generally necessary for Python
-        extensions, which typically export exactly one symbol: "init" +
-        extension_name.
-      swig_opts : [string]
-        any extra options to pass to SWIG if a source file has the .i
-        extension.
-      depends : [string]
-        list of files that the extension depends on
-      language : string
-        extension language (i.e. "c", "c++", "objc"). Will be detected
-        from the source extensions if not provided.
-      optional : boolean
-        specifies that a build failure in the extension should not abort the
-        build process, but simply not install the failing extension.
-    """
-    # When adding arguments to this constructor, be sure to update
-    # setup_keywords in core.py.
-    def __init__(
-        self,
-        name,
-        sources,
-        include_dirs=None,
-        define_macros=None,
-        undef_macros=None,
-        library_dirs=None,
-        libraries=None,
-        runtime_library_dirs=None,
-        extra_objects=None,
-        extra_compile_args=None,
-        extra_link_args=None,
-        export_symbols=None,
-        swig_opts=None,
-        depends=None,
-        language=None,
-        optional=None,
-        **kw  # To catch unknown keywords
-    ):
-        if not isinstance(name, str):
-            raise AssertionError("'name' must be a string")
-        if not (isinstance(sources, list) and all(isinstance(v, str) for v in sources)):
-            raise AssertionError("'sources' must be a list of strings")
-        self.name = name
-        self.sources = sources
-        self.include_dirs = include_dirs or []
-        self.define_macros = define_macros or []
-        self.undef_macros = undef_macros or []
-        self.library_dirs = library_dirs or []
-        self.libraries = libraries or []
-        self.runtime_library_dirs = runtime_library_dirs or []
-        self.extra_objects = extra_objects or []
-        self.extra_compile_args = extra_compile_args or []
-        self.extra_link_args = extra_link_args or []
-        self.export_symbols = export_symbols or []
-        self.swig_opts = swig_opts or []
-        self.depends = depends or []
-        self.language = language
-        self.optional = optional
-        # If there are unknown keyword options, warn about them
-        if len(kw) > 0:
-            options = [repr(option) for option in kw]
-            options = ', '.join(sorted(options))
-            msg = "Unknown Extension options: %s" % options
-            warnings.warn(msg)
-    def __repr__(self):
-        return '<{}.{}({!r}) at {:#x}>'.format(
-            self.__class__.__module__,
-            self.__class__.__qualname__,
-            self.name,
-            id(self),
-        )
-def read_setup_file(filename):  # noqa: C901
-    """Reads a Setup file and returns Extension instances."""
-    from distutils.sysconfig import parse_makefile, expand_makefile_vars, _variable_rx
-    from distutils.text_file import TextFile
-    from distutils.util import split_quoted
-    # First pass over the file to gather "VAR = VALUE" assignments.
-    vars = parse_makefile(filename)
-    # Second pass to gobble up the real content: lines of the form
-    #   <module> ... [<sourcefile> ...] [<cpparg> ...] [<library> ...]
-    file = TextFile(
-        filename,
-        strip_comments=1,
-        skip_blanks=1,
-        join_lines=1,
-        lstrip_ws=1,
-        rstrip_ws=1,
-    )
-    try:
-        extensions = []
-        while True:
-            line = file.readline()
-            if line is None:  # eof
-                break
-            if _variable_rx.match(line):  # VAR=VALUE, handled in first pass
-                continue
-            if line[0] == line[-1] == "*":
-                file.warn("'%s' lines not handled yet" % line)
-                continue
-            line = expand_makefile_vars(line, vars)
-            words = split_quoted(line)
-            # NB. this parses a slightly different syntax than the old
-            # makesetup script: here, there must be exactly one extension per
-            # line, and it must be the first word of the line.  I have no idea
-            # why the old syntax supported multiple extensions per line, as
-            # they all wind up being the same.
-            module = words[0]
-            ext = Extension(module, [])
-            append_next_word = None
-            for word in words[1:]:
-                if append_next_word is not None:
-                    append_next_word.append(word)
-                    append_next_word = None
-                    continue
-                suffix = os.path.splitext(word)[1]
-                switch = word[0:2]
-                value = word[2:]
-                if suffix in (".c", ".cc", ".cpp", ".cxx", ".c++", ".m", ".mm"):
-                    # hmm, should we do something about C vs. C++ sources?
-                    # or leave it up to the CCompiler implementation to
-                    # worry about?
-                    ext.sources.append(word)
-                elif switch == "-I":
-                    ext.include_dirs.append(value)
-                elif switch == "-D":
-                    equals = value.find("=")
-                    if equals == -1:  # bare "-DFOO" -- no value
-                        ext.define_macros.append((value, None))
-                    else:  # "-DFOO=blah"
-                        ext.define_macros.append((value[0:equals], value[equals + 2 :]))
-                elif switch == "-U":
-                    ext.undef_macros.append(value)
-                elif switch == "-C":  # only here 'cause makesetup has it!
-                    ext.extra_compile_args.append(word)
-                elif switch == "-l":
-                    ext.libraries.append(value)
-                elif switch == "-L":
-                    ext.library_dirs.append(value)
-                elif switch == "-R":
-                    ext.runtime_library_dirs.append(value)
-                elif word == "-rpath":
-                    append_next_word = ext.runtime_library_dirs
-                elif word == "-Xlinker":
-                    append_next_word = ext.extra_link_args
-                elif word == "-Xcompiler":
-                    append_next_word = ext.extra_compile_args
-                elif switch == "-u":
-                    ext.extra_link_args.append(word)
-                    if not value:
-                        append_next_word = ext.extra_link_args
-                elif suffix in (".a", ".so", ".sl", ".o", ".dylib"):
-                    # NB. a really faithful emulation of makesetup would
-                    # append a .o file to extra_objects only if it
-                    # had a slash in it; otherwise, it would s/.o/.c/
-                    # and append it to sources.  Hmmmm.
-                    ext.extra_objects.append(word)
-                else:
-                    file.warn("unrecognized argument '%s'" % word)
-            extensions.append(ext)
-    finally:
-        file.close()
-    return extensions

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docker/README.md DELETED Viewed

@@ -1,45 +0,0 @@
-## Use the container (with docker ≥ 19.03)
-```
-cd docker/
-# Build:
-docker build --build-arg USER_ID=$UID -t detectron2:v0 .
-# Launch (require GPUs):
-docker run --gpus all -it \
-  --shm-size=8gb --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
-  --name=detectron2 detectron2:v0
-# Grant docker access to host X server to show images
-xhost +local:`docker inspect --format='{{ .Config.Hostname }}' detectron2`
-```
-## Use the container (with docker-compose ≥ 1.28.0)
-Install docker-compose and nvidia-docker-toolkit, then run:
-```
-cd docker && USER_ID=$UID docker-compose run detectron2
-```
-## Use the deployment container (to test C++ examples)
-After building the base detectron2 container as above, do:
-```
-# Build:
-docker build -t detectron2-deploy:v0 -f deploy.Dockerfile .
-# Launch:
-docker run --gpus all -it detectron2-deploy:v0
-```
-#### Using a persistent cache directory
-You can prevent models from being re-downloaded on every run,
-by storing them in a cache directory.
-To do this, add `--volume=$HOME/.torch/fvcore_cache:/tmp:rw` in the run command.
-## Install new dependencies
-Add the following to `Dockerfile` to make persistent changes.
-```
-RUN sudo apt-get update && sudo apt-get install -y vim
-```
-Or run them in the container to make temporary changes.

spaces/Axolotlily/DalleMini/app.py DELETED Viewed

@@ -1,3 +0,0 @@
-import gradio as gr
-gr.Interface.load("huggingface/osanseviero/dalle-mini-fork").launch()

spaces/Bart92/RVC_HF/lib/infer_pack/onnx_inference.py DELETED Viewed

@@ -1,145 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-    def __call__(self, wav):
-        return self.forward(wav)
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import (
-            HarvestF0Predictor,
-        )
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]

spaces/Benjov/Demo-IR/README.md DELETED Viewed

@@ -1,13 +0,0 @@
----
-title: Demo IR
-emoji: 📚
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: openrail
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/Anime Life Simulator.md DELETED Viewed

@@ -1,110 +0,0 @@
-<h1>¿Qué es un simulador de vida de anime? </h1>
-<p>Simulador de vida de anime es un género de videojuegos que te permite crear y controlar un personaje en un mundo virtual inspirado en el anime. Anime es un término para la animación japonesa que es conocido por su estilo distintivo, gráficos coloridos, y diversos temas. Los fans del anime a menudo disfrutan sumergirse en las historias y personajes de sus programas o películas favoritas. Los juegos de simulador de vida de anime ofrecen una manera de experimentar una vida diferente o alternativa en un entorno de anime. </p>
-<h2>anime life simulator</h2><br /><p><b><b>DOWNLOAD</b> &#127379; <a href="https://bltlly.com/2v6IyI">https://bltlly.com/2v6IyI</a></b></p><br /><br />
-<p>Los juegos de simulador de vida de anime pueden variar en su alcance y enfoque, pero por lo general comparten algunas características comunes. A menudo tienen herramientas de creación de personajes que te permiten personalizar tu apariencia, personalidad, habilidades y preferencias. También tienen mecanismos de simulación que te permiten interactuar con otros personajes, explorar el entorno, realizar tareas y tomar decisiones. Algunos juegos también pueden tener elementos de otros géneros, como juegos de rol, estrategia o acción. </p>
-<p>Los juegos de simulador de vida de anime pueden atraer a diferentes tipos de jugadores por diferentes razones. Algunos pueden disfrutar de la libertad y la creatividad de crear su propio personaje e historia. Algunos pueden gustar el desafío y la variedad de la gestión de diferentes aspectos de su vida virtual. Algunos pueden buscar la diversión y la emoción de experimentar nuevas situaciones y aventuras. Algunos simplemente quieren relajarse y escapar de la realidad por un tiempo. </p>
- <h2>¿Cómo jugar un simulador de vida de anime? </h2>
-<p>No hay una respuesta definitiva a cómo jugar un simulador de vida de anime, ya que cada juego puede tener sus propias reglas y objetivos. Sin embargo, hay algunos pasos generales que pueden ayudarte a empezar con cualquier juego de este género. </p>
-<ol>
-<li>Elige un juego que se adapte a tus preferencias e intereses. Hay muchos juegos de simulación de anime disponibles en varias plataformas, como PC, móvil o consola. Puedes buscar reseñas, valoraciones, capturas de pantalla, vídeos o demos en línea para encontrar un juego que te guste. </li>
-<li>Comienza tu simulación y explora el mundo del juego. Normalmente puedes moverte usando el teclado, el ratón o los controles de la pantalla táctil. También puede interactuar con objetos o personajes haciendo clic o tocando en ellos. También puedes acceder a menús o inventarios para comprobar tu estado, artículos, misiones, etc.</li>
-<li>Sigue la historia del juego o crea la tuya. Algunos juegos pueden tener una trama <p>lineal o ramificada que te guía a través de los principales eventos y opciones. Algunos juegos pueden tener un estilo más abierto o sandbox que te permite crear tu propia historia y objetivos. Normalmente puedes avanzar la historia completando misiones, tareas u objetivos, o tomando decisiones que afecten el resultado. </li>
-<li>Disfruta de la simulación y diviértete. Por lo general, puede hacer varias actividades en el mundo del juego, como hablar con otros personajes, hacer amigos o enemigos, citas o casarse, trabajar o estudiar, ir de compras o hacer manualidades, luchar o explorar, etc. También puede experimentar diferentes emociones, como felicidad, tristeza, ira, miedo, etc. También puede desbloquear nuevo contenido, como elementos, ubicaciones, caracteres, etc.</li>
-</ol>
- <h3>Tipos de juegos de simulador de vida de anime</h3>
-<p>Los juegos de simulador de vida de anime se pueden clasificar en diferentes tipos o subgéneros según su tema, configuración o enfoque. Aquí están algunos de los tipos más comunes y populares de juegos de simulador de vida de anime:</p>
- <h4>Sim de citas</h4>
-<p>Un simulador de citas es un tipo de juego de simulador de vida de anime que se centra en el romance y las relaciones. En este tipo de juego, generalmente puedes elegir entre una variedad de intereses amorosos potenciales, cada uno con su propia personalidad, apariencia y trasfondo. También puedes interactuar con ellos de diferentes maneras, como hablar, coquetear, dar regalos, salir con alguien, etc. Tu objetivo generalmente es ganar su afecto y lograr un final feliz con ellos. </p>
-<p>Algunos ejemplos de juegos de simulación de citas son:</p>
-<p></p>
-<ul>
-<li><a href="">Dream Daddy: A Dad Dating Simulator</a>: Un juego que cuenta con un padre soltero que se muda a una nueva ciudad y se reúne con otros padres solteros que también son potenciales intereses amorosos. </li>
-<li><a href="">Hatoful Boyfriend</a>: Un juego que parodia el género haciendo que el jugador salga con palomas en un mundo post-apocalíptico. </li>
-</ul>
- <h4>Sim de la escuela</h4>
-<p>Un simulador de escuela es un tipo de juego de simulador de vida de anime que simula la vida diaria de un estudiante en una escuela de anime. En este tipo de juego, generalmente puedes crear tu propio personaje e inscribirte en una escuela de tu elección. También puedes asistir a clases, unirte a clubes, hacer amigos, estudiar para los exámenes, participar en eventos, etc. Tu objetivo generalmente es equilibrar tu vida académica y social y lograr tus sueños. </p>
-<p>Algunos ejemplos de juegos de simulación escolar son:</p>
-<ul>
-<li><a href="">Persona 5</a>: juego que combina elementos de simulación escolar con elementos de rol y acción. El jugador controla un grupo de estudiantes que utilizan sus habilidades sobrenaturales para luchar contra las fuerzas del mal en una dimensión alternativa. </li>
-<li><a href="">Academia: School Simulator</a>: Un juego que permite al jugador diseñar y gestionar su propia escuela. El jugador puede contratar personal, construir instalaciones, establecer políticas, abordar problemas, etc.</li>
-<li><a href="">High School Story</a>: Un juego que permite al jugador crear su propio personaje y construir su propia escuela secundaria. El jugador puede personalizar su escuela, reclutar estudiantes, organizar fiestas, ir a citas, etc.</li>
-</ul>
- <h4>Sim de fantasía</h4>
-<p>Un simulador de fantasía es un tipo de juego de simulador de vida de anime que incorpora elementos de magia, aventura y combate. En este tipo de juego, normalmente puedes crear tu propio personaje y entrar en un mundo de fantasía lleno de maravillas y peligros. También puedes aprender hechizos, empuñar armas, luchar contra enemigos, explorar mazmorras, recoger tesoros, etc. Tu objetivo suele ser completar misiones, salvar el mundo o cumplir tu destino. </p>
-<p>Algunos ejemplos de juegos de simulación de fantasía son:</p>
-<ul>
-<li><a href="">Stardew Valley</a>: Un juego que mezcla elementos de simulación agrícolas con elementos de fantasía. El jugador hereda una granja en un pueblo rural y puede cultivar, criar animales, extraer minerales, pescar, hacerse amigo de los aldeanos, etc.</li>
-<li><a href="">Final Fantasy XIV</a>: Un juego que es un juego de rol multijugador masivo en línea ubicado en un mundo de fantasía. El jugador puede elegir entre varias razas, clases y trabajos, y unirse a otros jugadores en misiones, incursiones, mazmorras, etc.</li>
-</ul>
- <h4>Sim de agricultura</h4>
-<p>Un simulador de agricultura es un tipo de juego de simulador de vida de anime que involucra el manejo de una granja e interactuar con animales y aldeanos. En este tipo de juego, normalmente puedes crear tu propio personaje y heredar o comprar una granja. También puede plantar cultivos, cosechar productos, criar ganado, vender bienes, etc. También puede socializar con la comunidad local, hacer amigos, citas, casarse, tener hijos, etc. Su objetivo es generalmente mejorar su granja y su vida. </p>
-<p>Algunos ejemplos de juegos de simulación de agricultura son:</p>
-<ul>
-<li><a href="">Harvest Moon</a>: Una serie de juegos que es uno de los pioneros del género. Los juegos cuentan con varios ajustes y personajes, pero todos comparten la misma jugabilidad básica de la agricultura y la simulación de la vida. </li>
-<li><a href="">Historia de las Estaciones</a>: Una serie de juegos que es un sucesor espiritual de Harvest Moon. Los juegos tienen elementos de juego similares, pero también introducen nuevas características, como personalización, multijugador y personajes cruzados. </li>
-<li><a href="">Rune Factory</a>: Una serie de juegos que es un spin-off de Harvest Moon. Los juegos combinan elementos de simulación de granja con elementos de simulación de fantasía, como magia, combate y mazmorras. </li>
-</ul>
- <h2>Beneficios de jugar un simulador de vida de anime? </h2>
-<p>Jugar un simulador de vida de anime puede tener varios beneficios para diferentes jugadores. Aquí están algunos de los posibles beneficios de jugar este género:</p>
-<ul>
-<li>Relajación: Jugar un simulador de vida anime puede ayudarle a relajarse y relajarse. Puede disfrutar de los gráficos coloridos y la música relajante. También puedes escapar del estrés y la presión de la realidad por un tiempo. </li>
-<li>Habilidades sociales: Jugar un simulador de vida de anime puede mejorar sus habilidades sociales y la confianza. Puede interactuar con varios personajes y aprender a comunicarse, empatizar y negociar. También puedes hacer amigos o encontrar el amor en el mundo del juego. </li>
-</ul>
- <h2>Desafíos de jugar un simulador de vida de anime? </h2>
-<p>Jugar un simulador de vida de anime también puede tener algunos desafíos o dificultades para algunos jugadores. Aquí están algunos de los posibles desafíos de jugar este género:</p>
-<ul>
-<li>Adicción: Jugar un simulador de vida de anime puede ser adictivo y consumir mucho tiempo. Puedes pasar horas o días jugando el juego sin darte cuenta. También puede descuidar sus responsabilidades o relaciones de la vida real. </li>
-<li>Expectativas poco realistas: Jugar un simulador de vida de anime puede crear expectativas o fantasías poco realistas. Puede comparar su vida real con su vida virtual y sentirse insatisfecho o infeliz. También puedes idealizar o idealizar los personajes o situaciones del juego. </li>
-<li>Diferencias culturales: Jugar un simulador de vida de anime puede exponerte a diferencias culturales o malentendidos. Es posible que encuentre términos, referencias o comportamientos que no le resultan familiares o confusos. También puedes ofender o faltar el respeto a los personajes u otros jugadores sin querer. </li>
-</ul>
- <h2>Consejos y trucos para jugar un simulador de vida de anime? </h2>
-<p>Jugar un simulador de vida de anime puede ser más agradable y gratificante si sigues algunos consejos y trucos. Estos son algunos de los consejos y trucos útiles para jugar este género:</p>
-<ul>
-<li>Guardar: Durante la reproducción de un simulador de vida de anime, usted debe guardar su progreso con frecuencia y en diferentes ranuras. De esta manera, puede evitar perder sus datos o el progreso debido a fallos o errores. También puede volver a los puntos o opciones anteriores si desea cambiar algo o probar algo diferente. </li>
-<li>Experimento: Mientras juegas un simulador de vida de anime, debes experimentar con diferentes opciones y resultados. No debes tener miedo de cometer errores o fallar. También deberías probar diferentes personajes, actividades, rutas, etc. para descubrir nuevos contenidos y posibilidades. </li>
-</ul>
- <h2>Ejemplos de juegos populares de simulador de vida de anime</h2>
-<p>Hay muchos juegos de simulador de vida de anime disponibles en varias plataformas y dispositivos. Aquí están algunos de los ejemplos de los juegos populares del simulador de la vida del anime:</p>
- <h4>Anime Play Life: Ilimitado</h4>
-<p>Anime Play Life: Unlimited es un juego <p>que te permite hacer misiones, encontrar un trabajo, comprar casas, pescado, picnic y más en un mundo de anime. También puedes personalizar tu personaje, ropa, mascotas, vehículos, etc. También puedes interactuar con otros jugadores en línea y unirte a clubes, fiestas o eventos. El juego está disponible en PC y dispositivos móviles. </p>
- <h4>Gotas de XOXO</h4>
-<p>XOXO gotitas es un juego que cuenta con una comedia citas sim con múltiples finales y personajes. Juegas como una chica que se une a una escuela para estudiantes problemáticos y conoce a seis chicos que son todos idiotas a su manera. También puede explorar la ciudad, tienda, trabajo, estudio, etc. El juego está disponible en PC y dispositivos móviles. </p>
- <h4>Viva la reina</h4>
-<p>Larga vida a la reina es un juego que te desafía a gobernar un reino como una princesa joven. Tienes que manejar tus estadísticas, habilidades, humor, atuendos, eventos, etc. También tienes que lidiar con la intriga política, la guerra, los intentos de asesinato, etc. El juego tiene muchos caminos ramificados y finales dependiendo de tus elecciones. El juego está disponible en PC y dispositivos móviles. </p>
- <h4>Mon-cuties para todos</h4>
- <h2>Conclusión</h2>
-<p>Simulador de vida anime es un género de videojuegos que te permite crear y controlar un personaje en un mundo virtual inspirado en el anime. Los juegos de simulador de vida de anime pueden tener diferentes tipos, características, beneficios, desafíos, consejos y ejemplos. Jugar un simulador de vida de anime puede ser una experiencia divertida y gratificante para los fanáticos del anime y los jugadores por igual. </p>
-<p>Si estás interesado en jugar un juego de simulador de vida de anime, puedes ver algunos de los juegos mencionados en este artículo o buscar otros juegos en línea. También puede compartir sus pensamientos y opiniones sobre este género en la sección de comentarios a continuación. ¡Gracias por leer y tener un gran día! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas y respuestas frecuentes sobre los juegos de simulador de vida de anime:</p>
-<ol>
-<li>¿Cuál es la diferencia entre un simulador de vida de anime y una novela visual de anime? </li>
-<p>Un simulador de vida de anime es un juego que simula la vida diaria de un personaje en un mundo de anime. Una novela visual anime es un juego que cuenta una historia a través de texto e imágenes en un estilo anime. Los juegos de simulador de vida de anime suelen tener más mecánica de juego e interactividad que las novelas visuales de anime. </p>
-<li>¿Cuáles son algunos de los mejores juegos de simulador de vida de anime para principiantes? </li>
-<p>Algunos de los mejores juegos de simulador de vida de anime para principiantes son:</p>
-<ul>
-<li><a href="">Animal Crossing: New Horizons</a>: Un juego que te permite crear tu propio paraíso en la isla e interactuar con lindos aldeanos de animales. </li>
-<li><a href="">Mi tiempo en Portia</a>: Un juego que te permite construir y ejecutar tu propio taller en un mundo post-apocalíptico. </li>
-<li><a href="">Doki Doki Literature Club</a>: Un juego que parece un lindo simulador de citas pero tiene un toque oscuro. </li>
-</ul>
-<li>¿Cómo puedo jugar un juego de simulador de vida de anime en mi teléfono? </li>
-<li>¿Cómo puedo hacer mi propio juego de simulador de vida de anime? </li>
-<p>Usted puede hacer su propio juego de simulador de vida anime mediante el uso de un motor de juego o una herramienta de software que le permite crear juegos sin codificación. Algunas de las herramientas populares son:</p>
-<ul>
-<li><a href="">Ren'Py</a>: Una herramienta que te permite crear novelas visuales y sims de citas. </li>
-<li><a href="">RPG Maker</a>: Una herramienta que te permite crear juegos de rol y sims de fantasía. </li>
-<li><a href="">Unity</a>: Una herramienta que te permite crear cualquier tipo de juego con gráficos 2D o 3D. </li>
-</ul>
-<li>¿Cómo puedo aprender más sobre los juegos de simulación de vida de anime? </li>
-<p>Puedes aprender más sobre juegos de simulador de vida de anime leyendo artículos en línea, blogs, revistas, libros, etc. sobre este género. También puedes ver videos en línea, transmisiones, podcasts, etc. sobre este género. También puede unirse a comunidades en línea, foros, grupos, etc. donde se puede discutir este género con otros fans y jugadores. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/encoding.py DELETED Viewed

@@ -1,36 +0,0 @@
-import codecs
-import locale
-import re
-import sys
-from typing import List, Tuple
-BOMS: List[Tuple[bytes, str]] = [
-    (codecs.BOM_UTF8, "utf-8"),
-    (codecs.BOM_UTF16, "utf-16"),
-    (codecs.BOM_UTF16_BE, "utf-16-be"),
-    (codecs.BOM_UTF16_LE, "utf-16-le"),
-    (codecs.BOM_UTF32, "utf-32"),
-    (codecs.BOM_UTF32_BE, "utf-32-be"),
-    (codecs.BOM_UTF32_LE, "utf-32-le"),
-]
-ENCODING_RE = re.compile(rb"coding[:=]\s*([-\w.]+)")
-def auto_decode(data: bytes) -> str:
-    """Check a bytes string for a BOM to correctly detect the encoding
-    Fallback to locale.getpreferredencoding(False) like open() on Python3"""
-    for bom, encoding in BOMS:
-        if data.startswith(bom):
-            return data[len(bom) :].decode(encoding)
-    # Lets check the first two lines as in PEP263
-    for line in data.split(b"\n")[:2]:
-        if line[0:1] == b"#" and ENCODING_RE.search(line):
-            result = ENCODING_RE.search(line)
-            assert result is not None
-            encoding = result.groups()[0].decode("ascii")
-            return data.decode(encoding)
-    return data.decode(
-        locale.getpreferredencoding(False) or sys.getdefaultencoding(),
-    )

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/euctwprober.py DELETED Viewed

@@ -1,47 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-from .chardistribution import EUCTWDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import EUCTW_SM_MODEL
-class EUCTWProber(MultiByteCharSetProber):
-    def __init__(self) -> None:
-        super().__init__()
-        self.coding_sm = CodingStateMachine(EUCTW_SM_MODEL)
-        self.distribution_analyzer = EUCTWDistributionAnalysis()
-        self.reset()
-    @property
-    def charset_name(self) -> str:
-        return "EUC-TW"
-    @property
-    def language(self) -> str:
-        return "Taiwan"

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/CODE_OF_CONDUCT.md DELETED Viewed

@@ -1,76 +0,0 @@
-# Code of Conduct
-## Our Pledge
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-## Our Standards
-Examples of behavior that contributes to creating a positive environment
-include:
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-Examples of unacceptable behavior by participants include:
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-## Our Responsibilities
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-## Scope
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-## Enforcement
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <[email protected]>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-## Attribution
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-[homepage]: https://www.contributor-covenant.org
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq

spaces/CVPR/GFPGAN-example/PaperModel.md DELETED Viewed

@@ -1,76 +0,0 @@
-# Installation
-We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. See [here](README.md#installation) for this easier installation.<br>
-If you want want to use the original model in our paper, please follow the instructions below.
-1. Clone repo
-    ```bash
-    git clone https://github.com/xinntao/GFPGAN.git
-    cd GFPGAN
-    ```
-1. Install dependent packages
-    As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them just-in-time(JIT)**.
-    You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details.
-    **Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues)
-    ```bash
-    # Install basicsr - https://github.com/xinntao/BasicSR
-    # We use BasicSR for both training and inference
-    pip install basicsr
-    # Install facexlib - https://github.com/xinntao/facexlib
-    # We use face detection and face restoration helper in the facexlib package
-    pip install facexlib
-    pip install -r requirements.txt
-    python setup.py develop
-    # remember to set BASICSR_JIT=True before your running commands
-    ```
-    **Option 2: Compile extensions during installation** (For those need to train/inference for many times)
-    ```bash
-    # Install basicsr - https://github.com/xinntao/BasicSR
-    # We use BasicSR for both training and inference
-    # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
-    # Add -vvv for detailed log prints
-    BASICSR_EXT=True pip install basicsr -vvv
-    # Install facexlib - https://github.com/xinntao/facexlib
-    # We use face detection and face restoration helper in the facexlib package
-    pip install facexlib
-    pip install -r requirements.txt
-    python setup.py develop
-    ```
-## :zap: Quick Inference
-Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
-```bash
-wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
-```
-- Option 1: Load extensions just-in-time(JIT)
-    ```bash
-    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
-    # for aligned images
-    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
-    ```
-- Option 2: Have successfully compiled extensions during installation
-    ```bash
-    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
-    # for aligned images
-    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
-    ```

spaces/CVPR/WALT/mmcv_custom/runner/epoch_based_runner.py DELETED Viewed

@@ -1,104 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import torch
-from torch.optim import Optimizer
-import mmcv
-from mmcv.runner import RUNNERS, EpochBasedRunner
-from .checkpoint import save_checkpoint
-try:
-    import apex
-except:
-    print('apex is not installed')
-@RUNNERS.register_module()
-class EpochBasedRunnerAmp(EpochBasedRunner):
-    """Epoch-based Runner with AMP support.
-    This runner train models epoch by epoch.
-    """
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl='epoch_{}.pth',
-                        save_optimizer=True,
-                        meta=None,
-                        create_symlink=True):
-        """Save the checkpoint.
-        Args:
-            out_dir (str): The directory that checkpoints are saved.
-            filename_tmpl (str, optional): The checkpoint filename template,
-                which contains a placeholder for the epoch number.
-                Defaults to 'epoch_{}.pth'.
-            save_optimizer (bool, optional): Whether to save the optimizer to
-                the checkpoint. Defaults to True.
-            meta (dict, optional): The meta information to be saved in the
-                checkpoint. Defaults to None.
-            create_symlink (bool, optional): Whether to create a symlink
-                "latest.pth" to point to the latest checkpoint.
-                Defaults to True.
-        """
-        if meta is None:
-            meta = dict(epoch=self.epoch + 1, iter=self.iter)
-        elif isinstance(meta, dict):
-            meta.update(epoch=self.epoch + 1, iter=self.iter)
-        else:
-            raise TypeError(
-                f'meta should be a dict or None, but got {type(meta)}')
-        if self.meta is not None:
-            meta.update(self.meta)
-        filename = filename_tmpl.format(self.epoch + 1)
-        filepath = osp.join(out_dir, filename)
-        optimizer = self.optimizer if save_optimizer else None
-        save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
-        # in some environments, `os.symlink` is not supported, you may need to
-        # set `create_symlink` to False
-        if create_symlink:
-            dst_file = osp.join(out_dir, 'latest.pth')
-            if platform.system() != 'Windows':
-                mmcv.symlink(filename, dst_file)
-            else:
-                shutil.copy(filepath, dst_file)
-    def resume(self,
-               checkpoint,
-               resume_optimizer=True,
-               map_location='default'):
-        if map_location == 'default':
-            if torch.cuda.is_available():
-                device_id = torch.cuda.current_device()
-                checkpoint = self.load_checkpoint(
-                    checkpoint,
-                    map_location=lambda storage, loc: storage.cuda(device_id))
-            else:
-                checkpoint = self.load_checkpoint(checkpoint)
-        else:
-            checkpoint = self.load_checkpoint(
-                checkpoint, map_location=map_location)
-        self._epoch = checkpoint['meta']['epoch']
-        self._iter = checkpoint['meta']['iter']
-        if 'optimizer' in checkpoint and resume_optimizer:
-            if isinstance(self.optimizer, Optimizer):
-                self.optimizer.load_state_dict(checkpoint['optimizer'])
-            elif isinstance(self.optimizer, dict):
-                for k in self.optimizer.keys():
-                    self.optimizer[k].load_state_dict(
-                        checkpoint['optimizer'][k])
-            else:
-                raise TypeError(
-                    'Optimizer should be dict or torch.optim.Optimizer '
-                    f'but got {type(self.optimizer)}')
-        if 'amp' in checkpoint:
-            apex.amp.load_state_dict(checkpoint['amp'])
-            self.logger.info('load amp state dict')
-        self.logger.info('resumed epoch %d, iter %d', self.epoch, self.iter)

spaces/CVPR/WALT/mmdet/core/bbox/coder/base_bbox_coder.py DELETED Viewed

@@ -1,17 +0,0 @@
-from abc import ABCMeta, abstractmethod
-class BaseBBoxCoder(metaclass=ABCMeta):
-    """Base bounding box coder."""
-    def __init__(self, **kwargs):
-        pass
-    @abstractmethod
-    def encode(self, bboxes, gt_bboxes):
-        """Encode deltas between bboxes and ground truth boxes."""
-    @abstractmethod
-    def decode(self, bboxes, bboxes_pred):
-        """Decode the predicted bboxes according to prediction and base
-        boxes."""

spaces/CVPR/WALT/mmdet/core/bbox/transforms.py DELETED Viewed

@@ -1,240 +0,0 @@
-import numpy as np
-import torch
-def bbox_flip(bboxes, img_shape, direction='horizontal'):
-    """Flip bboxes horizontally or vertically.
-    Args:
-        bboxes (Tensor): Shape (..., 4*k)
-        img_shape (tuple): Image shape.
-        direction (str): Flip direction, options are "horizontal", "vertical",
-            "diagonal". Default: "horizontal"
-    Returns:
-        Tensor: Flipped bboxes.
-    """
-    assert bboxes.shape[-1] % 4 == 0
-    assert direction in ['horizontal', 'vertical', 'diagonal']
-    flipped = bboxes.clone()
-    if direction == 'horizontal':
-        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
-        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
-    elif direction == 'vertical':
-        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
-        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
-    else:
-        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
-        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
-        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
-        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
-    return flipped
-def bbox_mapping(bboxes,
-                 img_shape,
-                 scale_factor,
-                 flip,
-                 flip_direction='horizontal'):
-    """Map bboxes from the original image scale to testing scale."""
-    new_bboxes = bboxes * bboxes.new_tensor(scale_factor)
-    if flip:
-        new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction)
-    return new_bboxes
-def bbox_mapping_back(bboxes,
-                      img_shape,
-                      scale_factor,
-                      flip,
-                      flip_direction='horizontal'):
-    """Map bboxes from testing scale to original image scale."""
-    new_bboxes = bbox_flip(bboxes, img_shape,
-                           flip_direction) if flip else bboxes
-    new_bboxes = new_bboxes.view(-1, 4) / new_bboxes.new_tensor(scale_factor)
-    return new_bboxes.view(bboxes.shape)
-def bbox2roi(bbox_list):
-    """Convert a list of bboxes to roi format.
-    Args:
-        bbox_list (list[Tensor]): a list of bboxes corresponding to a batch
-            of images.
-    Returns:
-        Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2]
-    """
-    rois_list = []
-    for img_id, bboxes in enumerate(bbox_list):
-        if bboxes.size(0) > 0:
-            img_inds = bboxes.new_full((bboxes.size(0), 1), img_id)
-            rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1)
-        else:
-            rois = bboxes.new_zeros((0, 5))
-        rois_list.append(rois)
-    rois = torch.cat(rois_list, 0)
-    return rois
-def roi2bbox(rois):
-    """Convert rois to bounding box format.
-    Args:
-        rois (torch.Tensor): RoIs with the shape (n, 5) where the first
-            column indicates batch id of each RoI.
-    Returns:
-        list[torch.Tensor]: Converted boxes of corresponding rois.
-    """
-    bbox_list = []
-    img_ids = torch.unique(rois[:, 0].cpu(), sorted=True)
-    for img_id in img_ids:
-        inds = (rois[:, 0] == img_id.item())
-        bbox = rois[inds, 1:]
-        bbox_list.append(bbox)
-    return bbox_list
-def bbox2result(bboxes, labels, num_classes):
-    """Convert detection results to a list of numpy arrays.
-    Args:
-        bboxes (torch.Tensor | np.ndarray): shape (n, 5)
-        labels (torch.Tensor | np.ndarray): shape (n, )
-        num_classes (int): class number, including background class
-    Returns:
-        list(ndarray): bbox results of each class
-    """
-    if bboxes.shape[0] == 0:
-        return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)]
-    else:
-        if isinstance(bboxes, torch.Tensor):
-            bboxes = bboxes.detach().cpu().numpy()
-            labels = labels.detach().cpu().numpy()
-        return [bboxes[labels == i, :] for i in range(num_classes)]
-def distance2bbox(points, distance, max_shape=None):
-    """Decode distance prediction to bounding box.
-    Args:
-        points (Tensor): Shape (B, N, 2) or (N, 2).
-        distance (Tensor): Distance from the given point to 4
-            boundaries (left, top, right, bottom). Shape (B, N, 4) or (N, 4)
-        max_shape (Sequence[int] or torch.Tensor or Sequence[
-            Sequence[int]],optional): Maximum bounds for boxes, specifies
-            (H, W, C) or (H, W). If priors shape is (B, N, 4), then
-            the max_shape should be a Sequence[Sequence[int]]
-            and the length of max_shape should also be B.
-    Returns:
-        Tensor: Boxes with shape (N, 4) or (B, N, 4)
-    """
-    x1 = points[..., 0] - distance[..., 0]
-    y1 = points[..., 1] - distance[..., 1]
-    x2 = points[..., 0] + distance[..., 2]
-    y2 = points[..., 1] + distance[..., 3]
-    bboxes = torch.stack([x1, y1, x2, y2], -1)
-    if max_shape is not None:
-        if not isinstance(max_shape, torch.Tensor):
-            max_shape = x1.new_tensor(max_shape)
-        max_shape = max_shape[..., :2].type_as(x1)
-        if max_shape.ndim == 2:
-            assert bboxes.ndim == 3
-            assert max_shape.size(0) == bboxes.size(0)
-        min_xy = x1.new_tensor(0)
-        max_xy = torch.cat([max_shape, max_shape],
-                           dim=-1).flip(-1).unsqueeze(-2)
-        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
-        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
-    return bboxes
-def bbox2distance(points, bbox, max_dis=None, eps=0.1):
-    """Decode bounding box based on distances.
-    Args:
-        points (Tensor): Shape (n, 2), [x, y].
-        bbox (Tensor): Shape (n, 4), "xyxy" format
-        max_dis (float): Upper bound of the distance.
-        eps (float): a small value to ensure target < max_dis, instead <=
-    Returns:
-        Tensor: Decoded distances.
-    """
-    left = points[:, 0] - bbox[:, 0]
-    top = points[:, 1] - bbox[:, 1]
-    right = bbox[:, 2] - points[:, 0]
-    bottom = bbox[:, 3] - points[:, 1]
-    if max_dis is not None:
-        left = left.clamp(min=0, max=max_dis - eps)
-        top = top.clamp(min=0, max=max_dis - eps)
-        right = right.clamp(min=0, max=max_dis - eps)
-        bottom = bottom.clamp(min=0, max=max_dis - eps)
-    return torch.stack([left, top, right, bottom], -1)
-def bbox_rescale(bboxes, scale_factor=1.0):
-    """Rescale bounding box w.r.t. scale_factor.
-    Args:
-        bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois
-        scale_factor (float): rescale factor
-    Returns:
-        Tensor: Rescaled bboxes.
-    """
-    if bboxes.size(1) == 5:
-        bboxes_ = bboxes[:, 1:]
-        inds_ = bboxes[:, 0]
-    else:
-        bboxes_ = bboxes
-    cx = (bboxes_[:, 0] + bboxes_[:, 2]) * 0.5
-    cy = (bboxes_[:, 1] + bboxes_[:, 3]) * 0.5
-    w = bboxes_[:, 2] - bboxes_[:, 0]
-    h = bboxes_[:, 3] - bboxes_[:, 1]
-    w = w * scale_factor
-    h = h * scale_factor
-    x1 = cx - 0.5 * w
-    x2 = cx + 0.5 * w
-    y1 = cy - 0.5 * h
-    y2 = cy + 0.5 * h
-    if bboxes.size(1) == 5:
-        rescaled_bboxes = torch.stack([inds_, x1, y1, x2, y2], dim=-1)
-    else:
-        rescaled_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
-    return rescaled_bboxes
-def bbox_cxcywh_to_xyxy(bbox):
-    """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2).
-    Args:
-        bbox (Tensor): Shape (n, 4) for bboxes.
-    Returns:
-        Tensor: Converted bboxes.
-    """
-    cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1)
-    bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)]
-    return torch.cat(bbox_new, dim=-1)
-def bbox_xyxy_to_cxcywh(bbox):
-    """Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h).
-    Args:
-        bbox (Tensor): Shape (n, 4) for bboxes.
-    Returns:
-        Tensor: Converted bboxes.
-    """
-    x1, y1, x2, y2 = bbox.split((1, 1, 1, 1), dim=-1)
-    bbox_new = [(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)]
-    return torch.cat(bbox_new, dim=-1)

spaces/Catmeow/Face2Painting_From_Photo/paintingface.py DELETED Viewed

@@ -1,110 +0,0 @@
-import os
-os.system("pip install dlib")
-import sys
-import face_detection
-from PIL import Image, ImageOps, ImageFile
-import numpy as np
-import cv2 as cv
-import torch
-import gradio as gr
-torch.set_grad_enabled(False)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", device=device).eval()
-model2 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1",  device=device).eval()
-face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device)
-image_format = "png" #@param ["jpeg", "png"]
-def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=2.0, threshold=0):
-    """Return a sharpened version of the image, using an unsharp mask."""
-    blurred = cv.GaussianBlur(image, kernel_size, sigma)
-    sharpened = float(amount + 1) * image - float(amount) * blurred
-    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
-    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
-    sharpened = sharpened.round().astype(np.uint8)
-    if threshold > 0:
-        low_contrast_mask = np.absolute(image - blurred) < threshold
-        np.copyto(sharpened, image, where=low_contrast_mask)
-    return sharpened
-def normPRED(d):
-    ma = np.max(d)
-    mi = np.min(d)
-    dn = (d-mi)/(ma-mi)
-    return dn
-def array_to_np(array_in):
-    array_in = normPRED(array_in)
-    array_in = np.squeeze(255.0*(array_in))
-    array_in = np.transpose(array_in, (1, 2, 0))
-    return array_in
-def array_to_image(array_in):
-    array_in = normPRED(array_in)
-    array_in = np.squeeze(255.0*(array_in))
-    array_in = np.transpose(array_in, (1, 2, 0))
-    im = Image.fromarray(array_in.astype(np.uint8))
-    return im
-def image_as_array(image_in):
-    image_in = np.array(image_in, np.float32)
-    tmpImg = np.zeros((image_in.shape[0],image_in.shape[1],3))
-    image_in = image_in/np.max(image_in)
-    if image_in.shape[2]==1:
-        tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229
-        tmpImg[:,:,1] = (image_in[:,:,0]-0.485)/0.229
-        tmpImg[:,:,2] = (image_in[:,:,0]-0.485)/0.229
-    else:
-        tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229
-        tmpImg[:,:,1] = (image_in[:,:,1]-0.456)/0.224
-        tmpImg[:,:,2] = (image_in[:,:,2]-0.406)/0.225
-    tmpImg = tmpImg.transpose((2, 0, 1))
-    image_out = np.expand_dims(tmpImg, 0)
-    return image_out
-# detect a face
-def find_aligned_face(image_in, size=400):
-    aligned_image, n_faces, quad = face_detection.align(image_in, face_index=0, output_size=size)
-    return aligned_image, n_faces, quad
-# clip the face, return array
-def align_first_face(image_in, size=400):
-    aligned_image, n_faces, quad = find_aligned_face(image_in,size=size)
-    if n_faces == 0:
-        try:
-            image_in = ImageOps.exif_transpose(image_in)
-        except:
-            print("exif problem, not rotating")
-        image_in = image_in.resize((size, size))
-        im_array = image_as_array(image_in)
-    else:
-        im_array = image_as_array(aligned_image)
-    return im_array
-def img_concat_h(im1, im2):
-    dst = Image.new('RGB', (im1.width + im2.width, im1.height))
-    dst.paste(im1, (0, 0))
-    dst.paste(im2, (im1.width, 0))
-    return dst
-def paintface(img: Image.Image,size: int) -> Image.Image:
-    aligned_img = align_first_face(img,size)
-    if aligned_img is None:
-        output=None
-    else:
-        im_in = array_to_image(aligned_img).convert("RGB")
-        im_out1 = face2paint(model, im_in, side_by_side=False)
-        im_out2 = face2paint(model2, im_in, side_by_side=False)
-        output = img_concat_h(im_out1, im_out2)
-    return output
-def generate(img):
-    out = paintface(img, 400)
-    return out