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spaces/1acneusushi/gradio-2dmoleculeeditor/Uniop Exor Designer 6 __EXCLUSIVE__.md

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-## Uniop exor designer 6
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- ![Uniop Exor Designer 6 __EXCLUSIVE__](https://3.bp.blogspot.com/-mAjkad6j2hA/To6ew_qrvFI/AAAAAAAAAoo/JyUU98g7cuo/s1600/Designer_v6.01.PNG)
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-# How to Use Uniop Exor Designer 6 to Create Stunning HMI Applications
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-Uniop Exor Designer 6 is a software tool that allows you to design and develop graphical user interfaces for Uniop HMI panels. With Designer 6, you can create dynamic and interactive applications that connect to your plant-floor devices using a wide range of communication protocols. Whether you need a simple display or a complex dashboard, Designer 6 can help you achieve your goals.
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-In this article, we will show you how to use Designer 6 to create a simple HMI application that monitors and controls a temperature sensor and a heater. We will also show you how to test and download your application to a Uniop HMI panel.
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-## Step 1: Create a New Project
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-To start a new project in Designer 6, open the software and click on File > New Project. You will see a dialog box where you can enter the project name, description, and location. You can also choose the target HMI panel model and resolution from the drop-down menus. For this example, we will use an eTOP05 panel with a resolution of 320x240 pixels. Click OK to create the project.
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-## Step 2: Add Pages and Objects
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-A project in Designer 6 consists of one or more pages that contain graphical objects. You can add pages by clicking on the Page menu and selecting New Page. You can rename the pages by double-clicking on their names in the Project Explorer window. You can also set the background color and image of each page by right-clicking on them and choosing Properties.
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-To add objects to a page, you can use the Toolbox window that contains various categories of objects, such as buttons, lamps, gauges, graphs, etc. You can drag and drop the objects from the Toolbox to the page and resize and position them as you like. You can also change their properties by right-clicking on them and choosing Properties.
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- 
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-For this example, we will create two pages: one for monitoring the temperature sensor and one for controlling the heater. On the first page, we will add a text object that displays the current temperature value, a gauge object that shows the temperature level, and a button object that switches to the second page. On the second page, we will add a text object that displays the heater status (on or off), a lamp object that indicates the heater state (red or green), and a button object that toggles the heater state. We will also add another button object that switches back to the first page.
-
- 
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-## Step 3: Define Tags and Variables
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-To communicate with your plant-floor devices, you need to define tags and variables in Designer 6. Tags are symbolic names that represent data points in your devices, such as inputs, outputs, registers, etc. Variables are internal memory locations that store data values in your HMI panel.
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-You can define tags and variables by clicking on the Tag menu and selecting Tag Editor. You will see a table where you can enter the tag name, type, address, format, description, etc. You can also import tags from external files or export tags to external files.
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-For this example, we will define two tags: one for reading the temperature value from the sensor (Temp) and one for writing the heater state to the heater (Heat). We will also define two variables: one for storing the current page number (Page) and one for storing the heater status (Status).
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- 
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-## Step 4: Assign Actions and Expressions
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-To make your HMI application interactive and dynamic, you need to assign actions and expressions to your objects in Designer 6. Actions are commands that execute when an event occurs on an object, such as pressing a button or changing a value. Expressions are formulas that calculate or manipulate data values based on tags or variables.
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-You can assign actions and expressions by right-clicking on an object and choosing Actions or Expressions. You will see a dialog box where you can select an event type (such as On Press or On Change) and enter an action or expression code using a simple scripting language.
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-For this example, we will assign actions and expressions to our objects as follows:
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-- The text object on the first page will have an expression that displays the value of Temp tag formatted as "Temp: #.# ° dfd1c89656
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocom 2.12.2 Keygen Download and Install the Latest Version of Autocom Software.md

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-<h1>Autocom 2.12.2 Keygen: What You Need to Know</h1>
-<p>If you are looking for a reliable and versatile diagnostic tool for your car or truck, you might have heard of <strong>Autocom</strong>. Autocom is a multi-brand car and truck diagnostic software that allows you to perform various tests, repairs, and adjustments on your vehicle. It supports a wide range of models, systems, and functions, making it a useful tool for both professionals and hobbyists.</p>
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-<p>In this article, we will show you how to download, install, and use <strong>Autocom 2.12.2 keygen</strong>, which is one of the latest versions of the software. We will also give you some tips and tricks for using Autocom effectively and efficiently.</p>
-<h2>How to Download and Install Autocom 2.12.2 Keygen</h2>
-<p>The first step is to download the keygen from a reliable source. You can find the download link in this forum thread, where you can also read more about the features and updates of Autocom 2.12.2 keygen.</p>
-<p>The file is compressed in a ZIP archive, so you will need a program like WinRAR or 7-Zip to extract it. The file also requires a password, which you can get by sending a private message to the thread author after thanking him and adding reputation.</p>
-<p>Once you have the password, you can extract the keygen folder to your desktop or any other location of your choice. Inside the folder, you will find two files: <code>Autocom 2020_23.exe</code> and <code>date_patch.exe</code>. The first one is the keygen itself, while the second one is a patch that automatically changes your system date to match the activation date.</p>
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-<p>The keygen will generate an activation file for you based on your device serial number and software version. You can enter these values manually or click on <code>Browse</code> to select them from your installation folder.</p>
-<p>After entering or selecting your serial number and software version, click on <code>Generate FileActivation.xml</code>. The keygen will create an activation file named <code>FileActivation.xml</code> in the same folder as the keygen.</p>
-<p>The next step is to copy this file to your installation folder, which is usually located at <code>C:\Program Files (x86)\Autocom\Delphi Cars 2020_23\bin</code>. You can use Windows Explorer or any other file manager to do this.</p>
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-<p>Now that you have installed the keygen and copied the activation file, you are ready to use Autocom 2.12.2 keygen.</p>
-<p>The first thing you need to do is connect your device (such as CDP+ or Delphi DS150E) to your computer via USB or Bluetooth. Make sure your device is turned on and recognized by your computer.</p>
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-<img src="https://i.imgur.com/4Yc6f9u.png" alt="date_patch.exe window">
-<p>You don't need to do anything else with this window, just leave it open until you finish using Autocom.</p>
-<p>The final step is to run Autocom from your desktop shortcut or start menu. You will see a window like this:</p>
-<img src="https://i.imgur.com/6lL7J4M.png" alt="Autocom window">
-<p>You can now select your vehicle brand, model, year, system, function, etc., and perform various diagnostic tasks with Autocom.</p>
-<h2>Tips and Tricks for Using Autocom 2.12.2 Keygen</h2>
-<p>To make the most out of Autocom 2.12.2 keygen, here are some tips and tricks that you should keep in mind:</p>
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-<li>If you want to customize your settings and preferences with Autocom, such as language, units, sound, appearance, etc., go to <code>Settings</code> > <code>User Preferences</code>.</li>
-<li>If you want to access additional features and functions with Autocom, such as data logging, oscilloscope, flight recorder, service reset, etc., go to <code>Diagnostics</code> > <code>Advanced Features</code>.</li>
-<li>If you want to update your firmware and software with Autocom, go to <code>Diagnostics</code> > <code>Firmware Update</code>. You will need an internet connection for this.</li>
-<li>If you want to learn more about how to use Autocom effectively and efficiently, go to <code>Diagnostics</code> > <code>User Manual</code>, where you can find detailed instructions and tutorials for various tasks.</li>
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-<h2>Conclusion</h2>
-<p>In conclusion, Autocom 2.12.2 keygen is a powerful and versatile diagnostic tool that can help you diagnose and repair your car or truck easily and quickly.</p>
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-<p>If you have any questions or feedback about this article or Autocom 2.12.2 keygen in general, feel free to leave a comment below or contact us via email.</p>
-<p>We hope you enjoyed this article and found it useful!</p>
- Here are some FAQs after the conclusion: <h3>Frequently Asked Questions (FAQs)</h3>
- <ol>
- <li><strong>What are the system requirements for running Autocom 2.12.2 keygen?</strong></li>
- <p>You need a Windows PC (Windows 10 or Windows 8) with at least 4 GB RAM (depending on the OS), 16 GB free space on the hard drive, screen resolution of 1440 x 900 or higher, connection to the internet, Bluetooth (SPP), USB port, Adobe Acrobat Reader 8.0 or higher, and .NET framework 3.5 and 2.0 turned on. You also need a compatible device (such as CDP+ or Delphi DS150E) that supports Autocom software.</p>
- <li><strong>Is Autocom 2.12.2 keygen safe and legal?</strong></li>
- <p>We cannot guarantee that Autocom 2. I'll try to continue the article. Here is the rest of the article: <p>12.2 keygen, it is a program that generates a unique activation file for you, so you can use Autocom without any limitations or restrictions.</p>
-<p>However, you should be aware that using a keygen is not a legal or safe way to activate Autocom. A keygen is a form of software piracy that violates the terms and conditions of Autocom. It may also contain viruses, malware, or spyware that can harm your computer or device.</p>
-<p>Therefore, we do not recommend or endorse using Autocom 2.12.2 keygen or any other keygen for Autocom. The best and safest way to use Autocom is to purchase a license from an authorized dealer or distributor. This way, you can enjoy the full features and functions of Autocom without any risk or trouble.</p>
-<li><strong>What are the alternatives to Autocom 2.12.2 keygen?</strong></li>
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-<li><strong>Autocom Version 23.2020 (Full + Kegen) + Firmware 1 & 2 Platine</strong>: This is a newer version of Autocom software that comes with a keygen and a firmware update for your device. It supports more vehicles, systems, and functions than Autocom 2.12.2 keygen. However, it also requires a password to download and extract the file, and it may not work on older devices or clones.</li>
-<li><strong>Autocom + Delphi 2021.11 software + Keygen</strong>: This is another newer version of Autocom software that comes with a keygen and a software update for your device. It supports both cars and trucks, and it has more features and functions than Autocom 2.12.2 keygen. However, it also requires a higher system requirement to run, and it may not work on older devices or clones.</li>
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-<p>Here are some of the features that you can enjoy when you download and install CSR Racing Classics Mod APK on your device:</p>
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-<p>Do you want to chat with friends and meet new people on a unique social network? If so, you might be interested in 2go, a popular app that allows you to hang out online in chat rooms and participate in various hangouts. 2go is available for free on the Google Play Store, but you might want to download its APK file for some reasons. For example, you might want to install an older version of the app, or you might not have access to the Play Store on your device. In this article, we will show you how to download 2go APK and install it on your Android device.</p>
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-<p>An APK file is an Android Package Kit file that contains all the files and code needed to run an app on an Android device. It is similar to an executable file (.exe) on Windows or a DMG file (.dmg) on Mac. When you download an app from the Play Store, you are actually installing an APK file on your device, but you can't access the file directly. However, there are ways to get the APK file of any app from the Play Store or from your device.</p>
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-<h3>Method 1: Using a Web Tool</h3>
-<p>One way to download APK files from the Play Store is to use a web tool that can generate download links for any app. There are several websites that offer this service, such as <a href="(^1^)">APK Downloader</a> or <a href="(^2^)">Evozi's APK Downloader</a>. Here are the steps to follow:</p>
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-<li>Open the Google Play Store on your Android device or computer and search for the app you want to download. In this case, search for "2go Chat - Chat Rooms & Dating".</li>
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-<p>In this article, we have shown you how to download 2go APK and install it on your Android device. Downloading APK files can be useful if you want to access older versions of apps, or if you don't have access to the Play Store on your device. However, you should be careful when downloading APK files from unknown sources, as they might contain malware or viruses that can harm your device. Always download APK files from trusted websites or apps, and scan them with an antivirus app before installing them. We hope this article has been helpful and informative for you. If you have any questions or feedback, please let us know in the comments below.</p>
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spaces/1phancelerku/anime-remove-background/Download Bitcoin Loophole App and Start Trading Cryptocurrencies Today.md

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-<p>If you want to download the Bitcoin Loophole app for more convenience and security, you can do so by following these steps:</p>
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-<li><b>Open trades:</b> This is where you can see your current trades and their status, as well as modify or cancel them if needed.</li>
-<li><b>Settings:</b> This is where you can customize your trading settings, such as the amount per trade, the number of trades per day, the risk level, and the assets to trade.</li>
-<li><b>Demo account:</b> This is where you can practice trading with virtual money before investing real money.</li>
-<li><b>Withdrawal request:</b> This is where you can request a withdrawal of your profits from the app.</li>
-<li><b>Contact us:</b> This is where you can contact the customer support team of Bitcoin Loophole if you have any questions or issues.</li>
-</ul>
-<p>You can also use the app's tutorials and guides to learn more about how to use it effectively.</p> <h2>How to Withdraw Your Profits from Bitcoin Loophole?</h2>
-<p>One of the best features of Bitcoin Loophole is that it allows you to withdraw your profits anytime you want. You don't have to wait for a long time or pay any fees to cash out your earnings. You can withdraw your money in the same way you deposited it, using various payment methods, such as credit cards, debit cards, e-wallets, or bank transfers.</p>
-<p>To withdraw your profits from Bitcoin Loophole, you need to follow these steps:</p>
-<ol>
-<li><b>Log in to your account and go to the withdrawal request section.</b></li>
-<li><b>Fill out the withdrawal request form with your personal and banking details.</b></li>
-<li><b>Submit the withdrawal request and wait for the confirmation email.</b></li>
-<li><b>Receive your money within 24 hours or less.</b></li>
-</ol>
-<p>Note: The minimum withdrawal amount is $100. You may also need to provide some verification documents, such as your ID or proof of address, to comply with the anti-money laundering and KYC policies of the platform.</p>
-<h2>What are the Risks of Using Bitcoin Loophole?</h2>
-<p>While Bitcoin Loophole is a reliable and trustworthy platform, it is not without risks. Trading crypto involves a high level of risk and uncertainty, which means that you can lose money as well as make money. You should always be aware of the potential pitfalls and challenges of trading crypto, such as:</p>
-<ul>
-<li><b>Market volatility:</b> The crypto markets are highly volatile and unpredictable, which means that they can change rapidly and affect your trades. Sometimes, you may experience high profits, while other times, you may face losses. You should always be prepared for both scenarios and follow the market trends closely.</li>
-<li><b>Technical issues:</b> The app relies on technology and software to operate, which means that it can encounter glitches, bugs, or errors that can affect its performance and accuracy. You should always check the app's status and updates regularly and report any issues to the customer support team.</li>
-<li><b>Human error:</b> The app is not perfect and can make mistakes or miss opportunities. You should always monitor your trades and profits and adjust your settings accordingly. You should also use the app's risk management features, such as stop-loss and take-profit orders, to protect your capital and minimize your losses.</li>
-</ul>
-<p>You should never invest more than you can afford to lose, as trading involves risks. You should also do your own research and test the app before investing real money. You should also consult a financial advisor or a professional trader if you have any doubts or questions.</p>
-<h2>How to Avoid Scams and Fake Apps?</h2>
-<p>Unfortunately, there are many scams and fake apps that claim to be Bitcoin Loophole or offer similar services. These scams are designed to lure unsuspecting users into giving away their personal and financial information or paying for a bogus product or service. You should always be careful and vigilant when dealing with online platforms and apps, especially those related to crypto trading.</p>
-<p>To avoid scams and fake apps, you should follow these tips:</p>
-<ul>
-<li><b>Only use the official website and app of Bitcoin Loophole:</b> The official website of Bitcoin Loophole is https://bitcoinloophole.com/. You should never use any other website or app that claims to be Bitcoin Loophole or offers similar services. You should also check the URL of the website or app carefully and make sure it is secure (https) and has no spelling errors or suspicious characters.</li>
-<li><b>Do not trust unsolicited emails or messages:</b> Some scammers may send you emails or messages claiming to be from Bitcoin Loophole or offering you a special deal or bonus. You should never open these emails or messages or click on any links or attachments they contain. They may contain malware or phishing attempts that can compromise your device or account.</li>
-<li><b>Do not share your personal or financial information with anyone:</b> Some scammers may ask you for your personal or financial information, such as your name, email, phone number, password, credit card number, bank account number, etc. You should never share this information with anyone online or offline. Bitcoin Loophole will never ask you for this information unless it is necessary for verification purposes.</li>
-<li><b>Do not pay any fees or charges upfront:</b> Some scammers may ask you to pay a registration fee, a commission fee, a withdrawal fee, a tax fee, or any other fee or charge before you can use their service or access your profits. You should never pay any fees or charges upfront to anyone online or offline. Bitcoin Loophole does not charge any fees or charges from its users. You can keep all your profits and withdraw them without any hassle.</li>
-<li><b>Do your own research and due diligence:</b> Some scammers may use fake testimonials, reviews, or endorsements to promote their service or app. You should not trust these sources blindly and do your own research and due diligence before using any platform or app. You should also check the reputation and credibility of the platform or app from independent and reliable sources, such as online forums, blogs, social media, etc.</li>
-</ul>
-<p>If you encounter any scams or fake apps, you should report them to the authorities and warn others about them. You should also contact the customer support team of Bitcoin Loophole if you have any doubts or issues.</p>
-<h2>Tips and Tricks for Successful Trading with Bitcoin Loophole</h2>
-<p>Trading crypto with Bitcoin Loophole can be a rewarding and enjoyable experience if you follow some tips and tricks, such as:</p>
-<ul>
-<li><b>Start small and grow gradually:</b> You don't need to invest a lot of money to start trading with Bitcoin Loophole. You can start with as little as $250 and increase your investment as you gain more confidence and experience. You should also reinvest some of your profits to grow your capital and earnings.</li>
-<li><b>Set realistic goals and expectations:</b> You should not expect to become a millionaire overnight with Bitcoin Loophole. Trading crypto involves risks and uncertainties, which means that you can lose money as well as make money. You should set realistic goals and expectations based on your budget, skill level, and market conditions.</li>
-<li><b>Learn from your mistakes and successes:</b> You should always analyze your trades and results and learn from your mistakes and successes. You should also use the app's demo account to practice trading with virtual money before investing real money. You should also use the app's tutorials and guides to learn more about how to use it effectively.</li>
-<li><b>Follow the market trends and news:</b> You should always follow the market trends and news closely and adjust your trading settings accordingly. You should also use the app's trading signals and indicators to help you make smart decisions in the crypto markets.</li>
-<li><b>Diversify your portfolio and risk:</b> You should not put all your eggs in one basket when trading crypto with Bitcoin Loophole. You should diversify your portfolio and risk by trading different digital assets and fiat currencies. You should also use the app's risk management features, such as stop-loss and take-profit orders, to protect your capital and minimize your losses.</li>
-</ul>
-<p>By following these tips and tricks, you can improve your chances of success and enjoy trading crypto with Bitcoin Loophole.</p>
-<h2>Frequently Asked Questions about Bitcoin Loophole</h2>
-<p>Here are some of the frequently asked questions and answers about Bitcoin Loophole:</p>
-<table>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>Is Bitcoin Loophole a scam or a legit platform?</td><td>Bitcoin Loophole is a legit platform that uses artificial intelligence to place trades on your behalf. It is not a scam or a fake app.</td></tr>
-<tr><td>How much does it cost to use Bitcoin Loophole?</td><td>Bitcoin Loophole is free to use. It doesn't charge any registration fees, commissions, or hidden charges from its users.</td></tr>
-<tr><td>How much money can I make with Bitcoin Loophole?</td><td>The amount of money you can make with Bitcoin Loophole depends on several factors, such as your initial investment, your trading settings, and the market conditions. The app claims that some of its users have made thousands of dollars per day with its platform. However, these results are not typical or guaranteed.</td></tr>
-<tr><td>How do I withdraw my profits from Bitcoin Loophole?</td><td>You can withdraw your profits from Bitcoin Loophole anytime you want. You just need to log in to your account and go to the withdrawal request section. You can withdraw your money in the same way you deposited it, using various payment methods, such as credit cards, debit cards, e-wallets, or bank transfers. The minimum withdrawal amount is $100.</td></tr>
-<tr><td>How can I contact the customer support team of Bitcoin Loophole?</td><td>You can contact the customer support team of Bitcoin Loophole by email, phone, or live chat. They are available 24/7 to assist you with any questions or issues.</td></tr>
-</table>
-<h2>Conclusion</h2>
-<p>Bitcoin Loophole is a trading platform that uses artificial intelligence to place trades on your behalf. It claims to have an 85% win rate with its trades, which means that it can generate consistent profits for its users. It also doesn't charge any fees or commissions from its users. You can start trading with as little as $250 and withdraw your earnings anytime you want.</p>
-<p>Bitcoin Loophole supports 14 digital assets, including Bitcoin, Ethereum, Litecoin, Ripple, and more. You can trade them against various fiat currencies, such as USD, EUR, GBP, and others. You can also choose between automated or manual trading modes, depending on your preference and skill level.</p>
-<p>Bitcoin Loophole is compatible with different devices, such as desktops, laptops, tablets, and smartphones. You can access it from any browser or download the app for more convenience and security. The app is user-friendly and has a simple interface that anyone can navigate.</p>
-<p>In this article, we have shown you how to download the Bitcoin Loophole app and start trading crypto like a pro. We have also explained how the app works, what are its benefits, how to avoid scams, and some tips and tricks for successful trading.</p>
-<p>If you are ready to join the crypto revolution and make money from trading Bitcoin and other cryptocurrencies, you should download the Bitcoin Loophole app today and sign up for free. You don't need any prior experience or knowledge to use the app. You just need to follow the simple steps and let the app do the work for you.</p>
-<p>Don't miss this opportunity to join the Bitcoin Loophole community and start making profits from the comfort of your home. Download the Bitcoin Loophole app now and start trading crypto like a pro!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/232labs/VToonify/vtoonify/model/raft/core/utils/frame_utils.py

@@ -1,137 +0,0 @@
-import numpy as np
-from PIL import Image
-from os.path import *
-import re
-
-import cv2
-cv2.setNumThreads(0)
-cv2.ocl.setUseOpenCL(False)
-
-TAG_CHAR = np.array([202021.25], np.float32)
-
-def readFlow(fn):
-    """ Read .flo file in Middlebury format"""
-    # Code adapted from:
-    # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
-
-    # WARNING: this will work on little-endian architectures (eg Intel x86) only!
-    # print 'fn = %s'%(fn)
-    with open(fn, 'rb') as f:
-        magic = np.fromfile(f, np.float32, count=1)
-        if 202021.25 != magic:
-            print('Magic number incorrect. Invalid .flo file')
-            return None
-        else:
-            w = np.fromfile(f, np.int32, count=1)
-            h = np.fromfile(f, np.int32, count=1)
-            # print 'Reading %d x %d flo file\n' % (w, h)
-            data = np.fromfile(f, np.float32, count=2*int(w)*int(h))
-            # Reshape data into 3D array (columns, rows, bands)
-            # The reshape here is for visualization, the original code is (w,h,2)
-            return np.resize(data, (int(h), int(w), 2))
-
-def readPFM(file):
-    file = open(file, 'rb')
-
-    color = None
-    width = None
-    height = None
-    scale = None
-    endian = None
-
-    header = file.readline().rstrip()
-    if header == b'PF':
-        color = True
-    elif header == b'Pf':
-        color = False
-    else:
-        raise Exception('Not a PFM file.')
-
-    dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline())
-    if dim_match:
-        width, height = map(int, dim_match.groups())
-    else:
-        raise Exception('Malformed PFM header.')
-
-    scale = float(file.readline().rstrip())
-    if scale < 0: # little-endian
-        endian = '<'
-        scale = -scale
-    else:
-        endian = '>' # big-endian
-
-    data = np.fromfile(file, endian + 'f')
-    shape = (height, width, 3) if color else (height, width)
-
-    data = np.reshape(data, shape)
-    data = np.flipud(data)
-    return data
-
-def writeFlow(filename,uv,v=None):
-    """ Write optical flow to file.
-    
-    If v is None, uv is assumed to contain both u and v channels,
-    stacked in depth.
-    Original code by Deqing Sun, adapted from Daniel Scharstein.
-    """
-    nBands = 2
-
-    if v is None:
-        assert(uv.ndim == 3)
-        assert(uv.shape[2] == 2)
-        u = uv[:,:,0]
-        v = uv[:,:,1]
-    else:
-        u = uv
-
-    assert(u.shape == v.shape)
-    height,width = u.shape
-    f = open(filename,'wb')
-    # write the header
-    f.write(TAG_CHAR)
-    np.array(width).astype(np.int32).tofile(f)
-    np.array(height).astype(np.int32).tofile(f)
-    # arrange into matrix form
-    tmp = np.zeros((height, width*nBands))
-    tmp[:,np.arange(width)*2] = u
-    tmp[:,np.arange(width)*2 + 1] = v
-    tmp.astype(np.float32).tofile(f)
-    f.close()
-
-
-def readFlowKITTI(filename):
-    flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH|cv2.IMREAD_COLOR)
-    flow = flow[:,:,::-1].astype(np.float32)
-    flow, valid = flow[:, :, :2], flow[:, :, 2]
-    flow = (flow - 2**15) / 64.0
-    return flow, valid
-
-def readDispKITTI(filename):
-    disp = cv2.imread(filename, cv2.IMREAD_ANYDEPTH) / 256.0
-    valid = disp > 0.0
-    flow = np.stack([-disp, np.zeros_like(disp)], -1)
-    return flow, valid
-
-
-def writeFlowKITTI(filename, uv):
-    uv = 64.0 * uv + 2**15
-    valid = np.ones([uv.shape[0], uv.shape[1], 1])
-    uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16)
-    cv2.imwrite(filename, uv[..., ::-1])
-    
-
-def read_gen(file_name, pil=False):
-    ext = splitext(file_name)[-1]
-    if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg':
-        return Image.open(file_name)
-    elif ext == '.bin' or ext == '.raw':
-        return np.load(file_name)
-    elif ext == '.flo':
-        return readFlow(file_name).astype(np.float32)
-    elif ext == '.pfm':
-        flow = readPFM(file_name).astype(np.float32)
-        if len(flow.shape) == 2:
-            return flow
-        else:
-            return flow[:, :, :-1]
-    return []

spaces/839871171w/newbingAI/README.md

@@ -1,12 +0,0 @@
----
-title: Newbing
-emoji: 😅
-colorFrom: green
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AFOL/GigaGan/README.md

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

spaces/AI4PD/hexviz/hexviz/models.py

@@ -1,64 +0,0 @@
-from enum import Enum
-
-import streamlit as st
-import torch
-from tape import ProteinBertModel, TAPETokenizer
-from tokenizers import Tokenizer
-from transformers import (
-    AutoTokenizer,
-    BertModel,
-    BertTokenizer,
-    GPT2LMHeadModel,
-    GPT2TokenizerFast,
-    T5EncoderModel,
-    T5Tokenizer,
-)
-
-
-class ModelType(str, Enum):
-    TAPE_BERT = "TapeBert"
-    ZymCTRL = "ZymCTRL"
-    PROT_BERT = "ProtBert"
-    PROT_T5 = "ProtT5"
-
-
-class Model:
-    def __init__(self, name, layers, heads):
-        self.name: ModelType = name
-        self.layers: int = layers
-        self.heads: int = heads
-
-
-@st.cache
-def get_tape_bert() -> tuple[TAPETokenizer, ProteinBertModel]:
-    tokenizer = TAPETokenizer()
-    model = ProteinBertModel.from_pretrained("bert-base", output_attentions=True)
-    return tokenizer, model
-
-
-# Streamlit is not able to hash the tokenizer for ZymCTRL
-# With streamlit 1.19 cache_object should work without this
-@st.cache(hash_funcs={Tokenizer: lambda _: None})
-def get_zymctrl() -> tuple[GPT2TokenizerFast, GPT2LMHeadModel]:
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    tokenizer = AutoTokenizer.from_pretrained("nferruz/ZymCTRL")
-    model = GPT2LMHeadModel.from_pretrained("nferruz/ZymCTRL").to(device)
-    return tokenizer, model
-
-
-@st.cache(hash_funcs={BertTokenizer: lambda _: None})
-def get_prot_bert() -> tuple[BertTokenizer, BertModel]:
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False)
-    model = BertModel.from_pretrained("Rostlab/prot_bert").to(device)
-    return tokenizer, model
-
-
-@st.cache
-def get_prot_t5():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    tokenizer = T5Tokenizer.from_pretrained(
-        "Rostlab/prot_t5_xl_half_uniref50-enc", do_lower_case=False
-    )
-    model = T5EncoderModel.from_pretrained("Rostlab/prot_t5_xl_half_uniref50-enc").to(device)
-    return tokenizer, model

spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/config.py

@@ -1,94 +0,0 @@
-import numpy as np
-import csv
-
-sample_rate = 32000
-clip_samples = sample_rate * 10     # Audio clips are 10-second
-
-# Load label
-with open('./audio_detection/audio_infer/metadata/class_labels_indices.csv', 'r') as f:
-    reader = csv.reader(f, delimiter=',')
-    lines = list(reader)
-
-labels = []
-ids = []    # Each label has a unique id such as "/m/068hy"
-for i1 in range(1, len(lines)):
-    id = lines[i1][1]
-    label = lines[i1][2]
-    ids.append(id)
-    labels.append(label)
-
-classes_num = len(labels)
-
-lb_to_ix = {label : i for i, label in enumerate(labels)}
-ix_to_lb = {i : label for i, label in enumerate(labels)}
-
-id_to_ix = {id : i for i, id in enumerate(ids)}
-ix_to_id = {i : id for i, id in enumerate(ids)}
-
-full_samples_per_class = np.array([
-        937432,  16344,   7822,  10271,   2043,  14420,    733,   1511,
-         1258,    424,   1751,    704,    369,    590,   1063,   1375,
-         5026,    743,    853,   1648,    714,   1497,   1251,   2139,
-         1093,    133,    224,  39469,   6423,    407,   1559,   4546,
-         6826,   7464,   2468,    549,   4063,    334,    587,    238,
-         1766,    691,    114,   2153,    236,    209,    421,    740,
-          269,    959,    137,   4192,    485,   1515,    655,    274,
-           69,    157,   1128,    807,   1022,    346,     98,    680,
-          890,    352,   4169,   2061,   1753,   9883,   1339,    708,
-        37857,  18504,  12864,   2475,   2182,    757,   3624,    677,
-         1683,   3583,    444,   1780,   2364,    409,   4060,   3097,
-         3143,    502,    723,    600,    230,    852,   1498,   1865,
-         1879,   2429,   5498,   5430,   2139,   1761,   1051,    831,
-         2401,   2258,   1672,   1711,    987,    646,    794,  25061,
-         5792,   4256,     96,   8126,   2740,    752,    513,    554,
-          106,    254,   1592,    556,    331,    615,   2841,    737,
-          265,   1349,    358,   1731,   1115,    295,   1070,    972,
-          174, 937780, 112337,  42509,  49200,  11415,   6092,  13851,
-         2665,   1678,  13344,   2329,   1415,   2244,   1099,   5024,
-         9872,  10948,   4409,   2732,   1211,   1289,   4807,   5136,
-         1867,  16134,  14519,   3086,  19261,   6499,   4273,   2790,
-         8820,   1228,   1575,   4420,   3685,   2019,    664,    324,
-          513,    411,    436,   2997,   5162,   3806,   1389,    899,
-         8088,   7004,   1105,   3633,   2621,   9753,   1082,  26854,
-         3415,   4991,   2129,   5546,   4489,   2850,   1977,   1908,
-         1719,   1106,   1049,    152,    136,    802,    488,    592,
-         2081,   2712,   1665,   1128,    250,    544,    789,   2715,
-         8063,   7056,   2267,   8034,   6092,   3815,   1833,   3277,
-         8813,   2111,   4662,   2678,   2954,   5227,   1472,   2591,
-         3714,   1974,   1795,   4680,   3751,   6585,   2109,  36617,
-         6083,  16264,  17351,   3449,   5034,   3931,   2599,   4134,
-         3892,   2334,   2211,   4516,   2766,   2862,   3422,   1788,
-         2544,   2403,   2892,   4042,   3460,   1516,   1972,   1563,
-         1579,   2776,   1647,   4535,   3921,   1261,   6074,   2922,
-         3068,   1948,   4407,    712,   1294,   1019,   1572,   3764,
-         5218,    975,   1539,   6376,   1606,   6091,   1138,   1169,
-         7925,   3136,   1108,   2677,   2680,   1383,   3144,   2653,
-         1986,   1800,   1308,   1344, 122231,  12977,   2552,   2678,
-         7824,    768,   8587,  39503,   3474,    661,    430,    193,
-         1405,   1442,   3588,   6280,  10515,    785,    710,    305,
-          206,   4990,   5329,   3398,   1771,   3022,   6907,   1523,
-         8588,  12203,    666,   2113,   7916,    434,   1636,   5185,
-         1062,    664,    952,   3490,   2811,   2749,   2848,  15555,
-          363,    117,   1494,   1647,   5886,   4021,    633,   1013,
-         5951,  11343,   2324,    243,    372,    943,    734,    242,
-         3161,    122,    127,    201,   1654,    768,    134,   1467,
-          642,   1148,   2156,   1368,   1176,    302,   1909,     61,
-          223,   1812,    287,    422,    311,    228,    748,    230,
-         1876,    539,   1814,    737,    689,   1140,    591,    943,
-          353,    289,    198,    490,   7938,   1841,    850,    457,
-        814,    146,    551,    728,   1627,    620,    648,   1621,
-         2731,    535,     88,   1736,    736,    328,    293,   3170,
-          344,    384,   7640,    433,    215,    715,    626,    128,
-         3059,   1833,   2069,   3732,   1640,   1508,    836,    567,
-         2837,   1151,   2068,    695,   1494,   3173,    364,     88,
-          188,    740,    677,    273,   1533,    821,   1091,    293,
-          647,    318,   1202,    328,    532,   2847,    526,    721,
-          370,    258,    956,   1269,   1641,    339,   1322,   4485,
-          286,   1874,    277,    757,   1393,   1330,    380,    146,
-          377,    394,    318,    339,   1477,   1886,    101,   1435,
-          284,   1425,    686,    621,    221,    117,     87,   1340,
-          201,   1243,   1222,    651,   1899,    421,    712,   1016,
-         1279,    124,    351,    258,   7043,    368,    666,    162,
-         7664,    137,  70159,  26179,   6321,  32236,  33320,    771,
-         1169,    269,   1103,    444,    364,   2710,    121,    751,
-         1609,    855,   1141,   2287,   1940,   3943,    289])

spaces/AbandonedMuse/UnlimitedMusicGen/web-ui.bat

@@ -1 +0,0 @@
-py -m app

spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/executor/tool_using.py

@@ -1,315 +0,0 @@
-import json
-import ast
-import openai
-from string import Template
-from colorama import Fore
-from aiohttp import ClientSession
-from copy import deepcopy
-from typing import TYPE_CHECKING, Any, List, Tuple
-
-from agentverse.agents import ExecutorAgent
-from agentverse.message import Message, ExecutorMessage, SolverMessage
-from agentverse.logging import logger
-
-from . import BaseExecutor, executor_registry
-import asyncio
-
-
-url = "http://127.0.0.1:8080"
-# url = "http://8.217.97.110:8080"
-
-SUMMARIZE_PROMPT = """Here is the text gathered from a webpage, and a question you need to answer from the webpage. 
--- Webpage -- 
-${webpage}
--- Question --
-${question}
-
-Now summarize the webpage to answer the question. If the question cannot be answer from the webpage, return the summarization of the webpage."""
-
-
-@executor_registry.register("tool-using")
-class ToolUsingExecutor(BaseExecutor):
-    num_agents: int = 3
-    max_tool_call_times: int = 10
-    tools: List[dict] = []
-    tool_names: List[str] = []
-    tool_config: str = None
-    cookies: dict = {}
-    tool_retrieval: bool = False
-    real_execution_agents: dict = {}
-    agent_names: List[str] = []
-    # tool_description: str
-
-    def __init__(self, *args, **kwargs):
-        assert kwargs.get("tool_config", None) is not None
-        with open(kwargs.get("tool_config"), "r") as f:
-            tools_dict = json.load(f)
-        tools = tools_dict["tools_json"]
-        tool_names = [t["name"] for t in tools]
-
-        # For each tool, we manually add a "thought" argument to achieve
-        # chain-of-thought in OpenAI's function call.
-        for t in tools:
-            properties = t["parameters"]["properties"]
-            thought = {
-                "thought": {
-                    "type": "string",
-                    "description": "Your internal reasoning and thoughts on the task, and how you plan to solve it based on the current attempts.",
-                }
-            }
-            thought.update(properties)
-            t["parameters"]["properties"] = thought
-            t["parameters"]["required"].insert(0, "thought")
-        super().__init__(
-            tools=tools,
-            tool_names=tool_names,
-            # tool_description=tool_description,
-            *args,
-            **kwargs,
-        )
-
-    async def astep(
-        self,
-        agent: ExecutorAgent,
-        task_description: str,
-        plans: List[SolverMessage],
-        *args,
-        **kwargs,
-    ):
-        plan_this_turn = {}
-        agent_name_this_turn = []
-        for i in range(len(plans)):
-            name = plans[i].content.split("-")[0].strip()
-            if name not in self.real_execution_agents:
-                self.real_execution_agents[name] = deepcopy(agent)
-                self.real_execution_agents[name].name = name
-                self.agent_names.append(name)
-            plan_this_turn[name] = plans[i].content.split("-")[1].strip()
-            agent_name_this_turn.append(name)
-        # agents = [deepcopy(agent) for _ in range(len(plans))]
-
-        if self.tool_retrieval:
-            # We retrieve 5 related tools for each agent
-            tools_and_cookies = await asyncio.gather(
-                *[
-                    self.retrieve_tools(plan_this_turn[name], self.tools)
-                    for name in agent_name_this_turn
-                ]
-            )
-            tools = {
-                name: t[0] for name, t in zip(agent_name_this_turn, tools_and_cookies)
-            }
-            cookies = {
-                name: t[1] for name, t in zip(agent_name_this_turn, tools_and_cookies)
-            }
-            self.update_cookies(cookies)
-        else:
-            # We just use the tools that are provided in the config file
-            tools = {name: self.tools for name in agent_name_this_turn}
-
-        # Record the indices of agents that have finished their tasks
-        # so that they will not be called again
-        finished_agent_names = set()
-        # result = ["" for _ in range(len(plan_this_turn))]
-        result = {name: "" for name in agent_name_this_turn}
-        for current_turn in range(self.max_tool_call_times):
-            if len(finished_agent_names) == len(agent_name_this_turn):
-                # All agents have finished their tasks. Break the loop.
-                break
-
-            # Filter out agents that have finished and gather tool actions for the rest
-            tool_calls = []
-            active_agents_names = [
-                name
-                for name in agent_name_this_turn
-                if name not in finished_agent_names
-            ]
-            for name in active_agents_names:
-                if current_turn == self.max_tool_call_times - 1:
-                    tool = [t for t in tools[name] if t["name"] == "submit_task"]
-                else:
-                    tool = tools[name]
-                tool_calls.append(
-                    self.real_execution_agents[name].astep(
-                        task_description,
-                        plan_this_turn[name],
-                        tool,
-                        current_turn=current_turn + 1,
-                    )
-                )
-            # Use asyncio.gather to run astep concurrently
-            tool_call_decisions = await asyncio.gather(*tool_calls)
-            for name, tool_call_result in zip(active_agents_names, tool_call_decisions):
-                self.real_execution_agents[name].add_message_to_memory(
-                    [tool_call_result]
-                )
-
-            # Actually call the tool and get the observation
-            tool_responses = await asyncio.gather(
-                *[
-                    ToolUsingExecutor.call_tool(
-                        tool.tool_name,
-                        tool.tool_input,
-                        self.cookies.get(name, None),
-                    )
-                    for name, tool in zip(active_agents_names, tool_call_decisions)
-                ]
-            )
-            # Update each agent's memory and check if they have finished
-            cookies = {}
-            for name, response in zip(active_agents_names, tool_responses):
-                observation = response["observation"]
-                is_finish = response["is_finish"]
-                cookies[name] = response["cookies"]
-                self.real_execution_agents[name].add_message_to_memory([observation])
-                logger.info(
-                    f"\nTool: {observation.tool_name}\nTool Input: {observation.tool_input}\nObservation: {observation.content}",
-                    name,
-                    Fore.YELLOW,
-                )
-                if is_finish:
-                    finished_agent_names.add(name)
-                    result[name] = observation.content
-            self.update_cookies(cookies)
-
-        message_result = []
-        for name, conclusion in result.items():
-            if conclusion != "":
-                message_result.append(
-                    ExecutorMessage(
-                        content=f"[{name}]: My execution result:\n{conclusion}",
-                        sender=name,
-                    )
-                )
-        return message_result
-
-    def update_cookies(self, cookies: dict):
-        for name, cookie in cookies.items():
-            self.cookies[name] = cookie
-
-    @classmethod
-    async def retrieve_tools(
-        cls, plan: SolverMessage, curr_tools: List = [], cookies=None
-    ):
-        async with ClientSession(cookies=cookies) as session:
-            if cookies is None:
-                async with session.post(f"{url}/get_cookie", timeout=30) as response:
-                    cookies = response.cookies
-                    session.cookie_jar.update_cookies(cookies)
-                    await response.text()
-                    # Sometimes the toolserver's docker container is not ready yet
-                    # So we need to wait for a while
-                    await asyncio.sleep(10)
-            async with session.post(
-                f"{url}/retrieving_tools", json={"question": plan.content, "top_k": 5}
-            ) as response:
-                retrieved_tools = await response.json()
-                retrieved_tools = ast.literal_eval(retrieved_tools)
-        tools = deepcopy(curr_tools)
-        existed_tool_names = set([t["name"] for t in tools])
-        # Add the retrieved tools into the final tools
-        for tool in retrieved_tools["tools_json"]:
-            if tool["name"] not in existed_tool_names:
-                existed_tool_names.add(tool["name"])
-                tools.append(tool)
-        return tools, cookies
-
-    @classmethod
-    async def call_tool(cls, command: str, arguments: dict, cookies=None):
-        async def _summarize_webpage(webpage, question):
-            summarize_prompt = Template(SUMMARIZE_PROMPT).safe_substitute(
-                webpage=webpage, question=question
-            )
-            for _ in range(3):
-                try:
-                    response = await openai.ChatCompletion.acreate(
-                        messages=[{"role": "user", "content": summarize_prompt}],
-                        model="gpt-3.5-turbo-16k",
-                    )
-                except:
-                    continue
-            return response["choices"][0]["message"]["content"]
-
-        if command == "submit_task":
-            return {
-                "observation": ExecutorMessage(
-                    content=f"Task Status: {arguments['status']}\nConclusion: {arguments['conclusion']}",
-                    sender="function",
-                    tool_name=command,
-                    tool_input=arguments,
-                ),
-                "is_finish": True,
-                "cookies": cookies,
-            }
-        if command == "":
-            return {
-                "observation": ExecutorMessage(
-                    content=f"The function calling format is incorrect.",
-                    sender="function",
-                    tool_name=command,
-                    tool_input=arguments,
-                ),
-                "is_finish": False,
-                "cookies": cookies,
-            }
-
-        for i in range(3):
-            try:
-                async with ClientSession(cookies=cookies) as session:
-                    if cookies is None:
-                        async with session.post(
-                            f"{url}/get_cookie", timeout=30
-                        ) as response:
-                            cookies = response.cookies
-                            session.cookie_jar.update_cookies(cookies)
-                            await response.text()
-                            # Sometimes the toolserver's docker container is not ready yet
-                            # So we need to wait for a while
-                            await asyncio.sleep(10)
-
-                    payload_arguments = deepcopy(arguments)
-                    if "thought" in payload_arguments:
-                        del payload_arguments["thought"]
-                    payload = {
-                        "tool_name": command,
-                        "arguments": payload_arguments,
-                    }
-                    # async with ClientSession() as session:
-                    async with session.post(
-                        f"{url}/execute_tool",
-                        json=payload,
-                        headers={
-                            "toolbench_key": "p5ZASSLBO0EknAQLE5ecNZ7kq5i1YfY9eoWUXNxL3TM6lXwdXs"
-                        },
-                        timeout=30,
-                    ) as response:
-                        content = await response.text()
-                        if command == "WebEnv_browse_website":
-                            content = await _summarize_webpage(
-                                content, arguments["question"]
-                            )
-
-                        message = ExecutorMessage(
-                            content=content,
-                            sender="function",
-                            tool_name=command,
-                            tool_input=arguments,
-                        )
-                    # async with session.post(
-                    #     f"{url}/release_session", timeout=30
-                    # ) as response:
-                    #     await response.text()
-                break
-            except Exception as e:
-                message = ExecutorMessage(
-                    content="Failed to call the tool. Exception: " + str(e),
-                    sender="function",
-                    tool_name=command,
-                    tool_input=arguments,
-                )
-                continue
-        return {"observation": message, "is_finish": False, "cookies": cookies}
-
-    def broadcast_messages(self, agents, messages) -> None:
-        for agent in agents:
-            agent.add_message_to_memory(messages)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Factory.d.ts

@@ -1,5 +0,0 @@
-import Chart from './Chart';
-
-export default function ChartFactory(
-    config?: Chart.IConfig
-): Chart;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/Factory.d.ts

@@ -1,5 +0,0 @@
-import GridTable from './GridTable';
-
-export default function (
-    config?: GridTable.IConfig
-): GridTable;

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/training/instructpix2pix.md

@@ -1,215 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# InstructPix2Pix 
-
-[InstructPix2Pix](https://arxiv.org/abs/2211.09800) is a method to fine-tune text-conditioned diffusion models such that they can follow an edit instruction for an input image. Models fine-tuned using this method take the following as inputs:
-
-<p align="center">
-    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png" alt="instructpix2pix-inputs" width=600/>
-</p>
-
-The output is an "edited" image that reflects the edit instruction applied on the input image:
-
-<p align="center">
-    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/output-gs%407-igs%401-steps%4050.png" alt="instructpix2pix-output" width=600/>
-</p>
-
-The `train_instruct_pix2pix.py` script (you can find the it [here](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py)) shows how to implement the training procedure and adapt it for Stable Diffusion.
-
-***Disclaimer: Even though `train_instruct_pix2pix.py` implements the InstructPix2Pix
-training procedure while being faithful to the [original implementation](https://github.com/timothybrooks/instruct-pix2pix) we have only tested it on a [small-scale dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples). This can impact the end results. For better results, we recommend longer training runs with a larger dataset. [Here](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) you can find a large dataset for InstructPix2Pix training.***
-
-## Running locally with PyTorch
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then cd in the example folder
-```bash
-cd examples/instruct_pix2pix
-```
-
-Now run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell e.g. a notebook
-
-```python
-from accelerate.utils import write_basic_config
-
-write_basic_config()
-```
-
-### Toy example
-
-As mentioned before, we'll use a [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) for training. The dataset 
-is a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) used in the InstructPix2Pix paper. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
-
-Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument. You'll also need to specify the dataset name in `DATASET_ID`:
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export DATASET_ID="fusing/instructpix2pix-1000-samples"
-```
-
-Now, we can launch training. The script saves all the components (`feature_extractor`, `scheduler`, `text_encoder`, `unet`, etc) in a subfolder in your repository.
-
-```bash
-accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \
-    --pretrained_model_name_or_path=$MODEL_NAME \
-    --dataset_name=$DATASET_ID \
-    --enable_xformers_memory_efficient_attention \
-    --resolution=256 --random_flip \
-    --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
-    --max_train_steps=15000 \
-    --checkpointing_steps=5000 --checkpoints_total_limit=1 \
-    --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \
-    --conditioning_dropout_prob=0.05 \
-    --mixed_precision=fp16 \
-    --seed=42 \
-    --push_to_hub
-```
-
-Additionally, we support performing validation inference to monitor training progress
-with Weights and Biases. You can enable this feature with `report_to="wandb"`:
-
-```bash
-accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \
-    --pretrained_model_name_or_path=$MODEL_NAME \
-    --dataset_name=$DATASET_ID \
-    --enable_xformers_memory_efficient_attention \
-    --resolution=256 --random_flip \
-    --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
-    --max_train_steps=15000 \
-    --checkpointing_steps=5000 --checkpoints_total_limit=1 \
-    --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \
-    --conditioning_dropout_prob=0.05 \
-    --mixed_precision=fp16 \
-    --val_image_url="https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" \
-    --validation_prompt="make the mountains snowy" \
-    --seed=42 \
-    --report_to=wandb \
-    --push_to_hub
- ```
-
- We recommend this type of validation as it can be useful for model debugging. Note that you need `wandb` installed to use this. You can install `wandb` by running `pip install wandb`. 
-
- [Here](https://wandb.ai/sayakpaul/instruct-pix2pix/runs/ctr3kovq), you can find an example training run that includes some validation samples and the training hyperparameters.
-
- ***Note: In the original paper, the authors observed that even when the model is trained with an image resolution of 256x256, it generalizes well to bigger resolutions such as 512x512. This is likely because of the larger dataset they used during training.***
-
- ## Training with multiple GPUs
-
-`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
-for running distributed training with `accelerate`. Here is an example command:
-
-```bash 
-accelerate launch --mixed_precision="fp16" --multi_gpu train_instruct_pix2pix.py \
- --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \
- --dataset_name=sayakpaul/instructpix2pix-1000-samples \
- --use_ema \
- --enable_xformers_memory_efficient_attention \
- --resolution=512 --random_flip \
- --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
- --max_train_steps=15000 \
- --checkpointing_steps=5000 --checkpoints_total_limit=1 \
- --learning_rate=5e-05 --lr_warmup_steps=0 \
- --conditioning_dropout_prob=0.05 \
- --mixed_precision=fp16 \
- --seed=42 \
- --push_to_hub
-```
-
- ## Inference
-
- Once training is complete, we can perform inference:
-
- ```python
-import PIL
-import requests
-import torch
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-model_id = "your_model_id"  # <- replace this
-pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-generator = torch.Generator("cuda").manual_seed(0)
-
-url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png"
-
-
-def download_image(url):
-    image = PIL.Image.open(requests.get(url, stream=True).raw)
-    image = PIL.ImageOps.exif_transpose(image)
-    image = image.convert("RGB")
-    return image
-
-
-image = download_image(url)
-prompt = "wipe out the lake"
-num_inference_steps = 20
-image_guidance_scale = 1.5
-guidance_scale = 10
-
-edited_image = pipe(
-    prompt,
-    image=image,
-    num_inference_steps=num_inference_steps,
-    image_guidance_scale=image_guidance_scale,
-    guidance_scale=guidance_scale,
-    generator=generator,
-).images[0]
-edited_image.save("edited_image.png")
-```
-
-An example model repo obtained using this training script can be found
-here - [sayakpaul/instruct-pix2pix](https://huggingface.co/sayakpaul/instruct-pix2pix).
-
-We encourage you to play with the following three parameters to control
-speed and quality during performance:
-
-* `num_inference_steps`
-* `image_guidance_scale`
-* `guidance_scale`
-
-Particularly, `image_guidance_scale` and `guidance_scale` can have a profound impact
-on the generated ("edited") image (see [here](https://twitter.com/RisingSayak/status/1628392199196151808?s=20) for an example).
-
-If you're looking for some interesting ways to use the InstructPix2Pix training methodology, we welcome you to check out this blog post: [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd). 
-
-## Stable Diffusion XL
-
-We support fine-tuning of the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with DreamBooth and LoRA via the `train_dreambooth_lora_sdxl.py` script. Please refer to the docs [here](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/README_sdxl.md). 

spaces/Andy1621/uniformer_image_detection/configs/cityscapes/README.md

@@ -1,33 +0,0 @@
-# Cityscapes Dataset
-
-[DATASET]
-
-```
-@inproceedings{Cordts2016Cityscapes,
-   title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
-   author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
-   booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-   year={2016}
-}
-```
-
-## Common settings
-
-- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate.
-- All models were trained on `cityscapes_train`, and tested on `cityscapes_val`.
-- 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870)
-- COCO pre-trained weights are used to initialize.
-- A conversion [script](../../tools/dataset_converters/cityscapes.py) is provided to convert Cityscapes into COCO format. Please refer to [install.md](../../docs/1_exist_data_model.md#prepare-datasets) for details.
-- `CityscapesDataset` implemented three evaluation methods. `bbox` and `segm` are standard COCO bbox/mask AP. `cityscapes` is the cityscapes dataset official evaluation, which may be slightly higher than COCO.
-
-### Faster R-CNN
-
-|    Backbone     |  Style  | Lr schd | Scale    | Mem (GB) | Inf time (fps) | box AP | Config | Download   |
-| :-------------: | :-----: | :-----: | :---:    | :------: | :------------: | :----: | :------: | :--------: |
-|    R-50-FPN     | pytorch |   1x    | 800-1024 |   5.2    |       -        |  40.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502-829424c0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502_114915.log.json) |
-
-### Mask R-CNN
-
-|    Backbone     |  Style  | Lr schd | Scale    | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-| :-------------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :-----: | :------: | :------: |
-|    R-50-FPN     | pytorch |   1x    | 800-1024 |   5.3    |       -        |  40.9  |  36.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733-d2858245.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733.log.json) |

spaces/Andy1621/uniformer_image_detection/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py

@@ -1,74 +0,0 @@
-_base_ = [
-    '../_base_/models/mask_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-norm_cfg = dict(type='BN', requires_grad=True)
-model = dict(
-    backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        norm_cfg=norm_cfg,
-        num_outs=5),
-    roi_head=dict(
-        bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg)))
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(
-        type='Resize',
-        img_scale=(640, 640),
-        ratio_range=(0.8, 1.2),
-        keep_ratio=True),
-    dict(type='RandomCrop', crop_size=(640, 640)),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=(640, 640)),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(640, 640),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=64),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=4,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# learning policy
-optimizer = dict(
-    type='SGD',
-    lr=0.08,
-    momentum=0.9,
-    weight_decay=0.0001,
-    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=0.1,
-    step=[30, 40])
-# runtime settings
-runner = dict(max_epochs=50)
-evaluation = dict(interval=2)

spaces/Andy1621/uniformer_image_detection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py

@@ -1,4 +0,0 @@
-_base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(stem_channels=128, depth=101))

spaces/Andy1621/uniformer_image_detection/tools/test.py

@@ -1,220 +0,0 @@
-import argparse
-import os
-import warnings
-
-import mmcv
-import torch
-from mmcv import Config, DictAction
-from mmcv.cnn import fuse_conv_bn
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
-                         wrap_fp16_model)
-
-from mmdet.apis import multi_gpu_test, single_gpu_test
-from mmdet.datasets import (build_dataloader, build_dataset,
-                            replace_ImageToTensor)
-from mmdet.models import build_detector
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='MMDet test (and eval) a model')
-    parser.add_argument('config', help='test config file path')
-    parser.add_argument('checkpoint', help='checkpoint file')
-    parser.add_argument('--out', help='output result file in pickle format')
-    parser.add_argument(
-        '--fuse-conv-bn',
-        action='store_true',
-        help='Whether to fuse conv and bn, this will slightly increase'
-        'the inference speed')
-    parser.add_argument(
-        '--format-only',
-        action='store_true',
-        help='Format the output results without perform evaluation. It is'
-        'useful when you want to format the result to a specific format and '
-        'submit it to the test server')
-    parser.add_argument(
-        '--eval',
-        type=str,
-        nargs='+',
-        help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
-        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
-    parser.add_argument('--show', action='store_true', help='show results')
-    parser.add_argument(
-        '--show-dir', help='directory where painted images will be saved')
-    parser.add_argument(
-        '--show-score-thr',
-        type=float,
-        default=0.3,
-        help='score threshold (default: 0.3)')
-    parser.add_argument(
-        '--gpu-collect',
-        action='store_true',
-        help='whether to use gpu to collect results.')
-    parser.add_argument(
-        '--tmpdir',
-        help='tmp directory used for collecting results from multiple '
-        'workers, available when gpu-collect is not specified')
-    parser.add_argument(
-        '--cfg-options',
-        nargs='+',
-        action=DictAction,
-        help='override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file. If the value to '
-        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
-        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
-        'Note that the quotation marks are necessary and that no white space '
-        'is allowed.')
-    parser.add_argument(
-        '--options',
-        nargs='+',
-        action=DictAction,
-        help='custom options for evaluation, the key-value pair in xxx=yyy '
-        'format will be kwargs for dataset.evaluate() function (deprecate), '
-        'change to --eval-options instead.')
-    parser.add_argument(
-        '--eval-options',
-        nargs='+',
-        action=DictAction,
-        help='custom options for evaluation, the key-value pair in xxx=yyy '
-        'format will be kwargs for dataset.evaluate() function')
-    parser.add_argument(
-        '--launcher',
-        choices=['none', 'pytorch', 'slurm', 'mpi'],
-        default='none',
-        help='job launcher')
-    parser.add_argument('--local_rank', type=int, default=0)
-    args = parser.parse_args()
-    if 'LOCAL_RANK' not in os.environ:
-        os.environ['LOCAL_RANK'] = str(args.local_rank)
-
-    if args.options and args.eval_options:
-        raise ValueError(
-            '--options and --eval-options cannot be both '
-            'specified, --options is deprecated in favor of --eval-options')
-    if args.options:
-        warnings.warn('--options is deprecated in favor of --eval-options')
-        args.eval_options = args.options
-    return args
-
-
-def main():
-    args = parse_args()
-
-    assert args.out or args.eval or args.format_only or args.show \
-        or args.show_dir, \
-        ('Please specify at least one operation (save/eval/format/show the '
-         'results / save the results) with the argument "--out", "--eval"'
-         ', "--format-only", "--show" or "--show-dir"')
-
-    if args.eval and args.format_only:
-        raise ValueError('--eval and --format_only cannot be both specified')
-
-    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
-        raise ValueError('The output file must be a pkl file.')
-
-    cfg = Config.fromfile(args.config)
-    if args.cfg_options is not None:
-        cfg.merge_from_dict(args.cfg_options)
-    # import modules from string list.
-    if cfg.get('custom_imports', None):
-        from mmcv.utils import import_modules_from_strings
-        import_modules_from_strings(**cfg['custom_imports'])
-    # set cudnn_benchmark
-    if cfg.get('cudnn_benchmark', False):
-        torch.backends.cudnn.benchmark = True
-    cfg.model.pretrained = None
-    if cfg.model.get('neck'):
-        if isinstance(cfg.model.neck, list):
-            for neck_cfg in cfg.model.neck:
-                if neck_cfg.get('rfp_backbone'):
-                    if neck_cfg.rfp_backbone.get('pretrained'):
-                        neck_cfg.rfp_backbone.pretrained = None
-        elif cfg.model.neck.get('rfp_backbone'):
-            if cfg.model.neck.rfp_backbone.get('pretrained'):
-                cfg.model.neck.rfp_backbone.pretrained = None
-
-    # in case the test dataset is concatenated
-    samples_per_gpu = 1
-    if isinstance(cfg.data.test, dict):
-        cfg.data.test.test_mode = True
-        samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
-        if samples_per_gpu > 1:
-            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
-            cfg.data.test.pipeline = replace_ImageToTensor(
-                cfg.data.test.pipeline)
-    elif isinstance(cfg.data.test, list):
-        for ds_cfg in cfg.data.test:
-            ds_cfg.test_mode = True
-        samples_per_gpu = max(
-            [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
-        if samples_per_gpu > 1:
-            for ds_cfg in cfg.data.test:
-                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
-
-    # init distributed env first, since logger depends on the dist info.
-    if args.launcher == 'none':
-        distributed = False
-    else:
-        distributed = True
-        init_dist(args.launcher, **cfg.dist_params)
-
-    # build the dataloader
-    dataset = build_dataset(cfg.data.test)
-    data_loader = build_dataloader(
-        dataset,
-        samples_per_gpu=samples_per_gpu,
-        workers_per_gpu=cfg.data.workers_per_gpu,
-        dist=distributed,
-        shuffle=False)
-
-    # build the model and load checkpoint
-    cfg.model.train_cfg = None
-    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        wrap_fp16_model(model)
-    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
-    if args.fuse_conv_bn:
-        model = fuse_conv_bn(model)
-    # old versions did not save class info in checkpoints, this walkaround is
-    # for backward compatibility
-    if 'CLASSES' in checkpoint.get('meta', {}):
-        model.CLASSES = checkpoint['meta']['CLASSES']
-    else:
-        model.CLASSES = dataset.CLASSES
-
-    if not distributed:
-        model = MMDataParallel(model, device_ids=[0])
-        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
-                                  args.show_score_thr)
-    else:
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False)
-        outputs = multi_gpu_test(model, data_loader, args.tmpdir,
-                                 args.gpu_collect)
-
-    rank, _ = get_dist_info()
-    if rank == 0:
-        if args.out:
-            print(f'\nwriting results to {args.out}')
-            mmcv.dump(outputs, args.out)
-        kwargs = {} if args.eval_options is None else args.eval_options
-        if args.format_only:
-            dataset.format_results(outputs, **kwargs)
-        if args.eval:
-            eval_kwargs = cfg.get('evaluation', {}).copy()
-            # hard-code way to remove EvalHook args
-            for key in [
-                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
-                    'rule'
-            ]:
-                eval_kwargs.pop(key, None)
-            eval_kwargs.update(dict(metric=args.eval, **kwargs))
-            print(dataset.evaluate(outputs, **eval_kwargs))
-
-
-if __name__ == '__main__':
-    main()

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_hr18.py

@@ -1,52 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://msra/hrnetv2_w18',
-    backbone=dict(
-        type='HRNet',
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        extra=dict(
-            stage1=dict(
-                num_modules=1,
-                num_branches=1,
-                block='BOTTLENECK',
-                num_blocks=(4, ),
-                num_channels=(64, )),
-            stage2=dict(
-                num_modules=1,
-                num_branches=2,
-                block='BASIC',
-                num_blocks=(4, 4),
-                num_channels=(18, 36)),
-            stage3=dict(
-                num_modules=4,
-                num_branches=3,
-                block='BASIC',
-                num_blocks=(4, 4, 4),
-                num_channels=(18, 36, 72)),
-            stage4=dict(
-                num_modules=3,
-                num_branches=4,
-                block='BASIC',
-                num_blocks=(4, 4, 4, 4),
-                num_channels=(18, 36, 72, 144)))),
-    decode_head=dict(
-        type='FCNHead',
-        in_channels=[18, 36, 72, 144],
-        in_index=(0, 1, 2, 3),
-        channels=sum([18, 36, 72, 144]),
-        input_transform='resize_concat',
-        kernel_size=1,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=-1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/data_preprocessor.py

@@ -1,199 +0,0 @@
-"""
-This module contains utils for preprocessing the text before converting it to embeddings.
-
-- TextPreprocessorBuilder preprocesses individual strings.
-    * lowering cases
-    * converting numbers to words or characters
-    * merging and stripping spaces
-    * removing punctuation
-    * removing stop words
-    * lemmatizing
-    * removing specific parts of speech (adverbs and interjections)
-- TextSummarizer extracts the most important sentences from a long string using text-ranking.
-"""
-import pytextrank
-import string
-import spacy
-import math
-import nltk
-import re
-
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-from num2words import num2words
-
-
-class TextPreprocessorBuilder:
-     # Define class variables as None initially
-    _stop_words = set(stopwords.words('english'))
-    _lemmatizer = WordNetLemmatizer()
-    
-    # Some of the functions are expensive. We cache the results.
-    _lemmatizer_cache = {}
-    _pos_remove_cache = {}
-
-
-    def __init__(self, text: str):
-        self.text = text
-
-
-    def to_lower(self):
-        # Match both words and non-word characters
-        tokens = re.findall(r'\b\w+\b|\W+', self.text)
-        for i, token in enumerate(tokens):
-            # Check if token is a word
-            if re.match(r'^\w+$', token):
-                # Check if token is not an abbreviation or constant
-                if not re.match(r'^[A-Z]+$', token) and not re.match(r'^[A-Z_]+$', token):
-                    tokens[i] = token.lower()
-        self.text = "".join(tokens)
-        return self
-
-
-    def num_to_word(self, min_len: int = 1):
-        # Match both words and non-word characters
-        tokens = re.findall(r'\b\w+\b|\W+', self.text)
-        for i, token in enumerate(tokens):
-            # Check if token is a number of length `min_len` or more
-            if token.isdigit() and len(token) >= min_len:
-                # This is done to pay better attention to numbers (e.g. ticket numbers, thread numbers, post numbers)
-                # 740700 will become "seven hundred and forty thousand seven hundred".
-                tokens[i] = num2words(int(token)).replace(",","") # Remove commas from num2words.
-        self.text = "".join(tokens)
-        return self
-
-
-    def num_to_char_long(self, min_len: int = 1):
-        # Match both words and non-word characters
-        tokens = re.findall(r'\b\w+\b|\W+', self.text)
-        for i, token in enumerate(tokens):
-            # Check if token is a number of length `min_len` or more
-            if token.isdigit() and len(token) >= min_len:
-                # This is done to pay better attention to numbers (e.g. ticket numbers, thread numbers, post numbers)
-                # 740700 will become HHHHHHEEEEEAAAAHHHAAA
-                convert_token = lambda token: ''.join((chr(int(digit) + 65) * (i + 1)) for i, digit in enumerate(token[::-1]))[::-1]
-                tokens[i] = convert_token(tokens[i])
-        self.text = "".join(tokens)
-        return self
-    
-    def num_to_char(self, min_len: int = 1):
-        # Match both words and non-word characters
-        tokens = re.findall(r'\b\w+\b|\W+', self.text)
-        for i, token in enumerate(tokens):
-            # Check if token is a number of length `min_len` or more
-            if token.isdigit() and len(token) >= min_len:
-                # This is done to pay better attention to numbers (e.g. ticket numbers, thread numbers, post numbers)
-                # 740700 will become HEAHAA
-                tokens[i] = ''.join(chr(int(digit) + 65) for digit in token)
-        self.text = "".join(tokens)
-        return self
-    
-    def merge_spaces(self):
-        self.text = re.sub(' +', ' ', self.text)
-        return self
-    
-    def strip(self):
-        self.text = self.text.strip()
-        return self
-        
-    def remove_punctuation(self):
-        self.text = self.text.translate(str.maketrans('', '', string.punctuation))
-        return self
-
-    def remove_stopwords(self):
-        self.text = "".join([word for word in re.findall(r'\b\w+\b|\W+', self.text) if word not in TextPreprocessorBuilder._stop_words])
-        return self
-    
-    def remove_specific_pos(self):
-        """
-        In the English language, adverbs and interjections rarely provide meaningul information.
-        Removing them improves the embedding precision. Don't tell JK Rowling, though.
-        """
-        processed_text = TextPreprocessorBuilder._pos_remove_cache.get(self.text)
-        if processed_text:
-            self.text = processed_text
-            return self
-
-        # Match both words and non-word characters
-        tokens = re.findall(r'\b\w+\b|\W+', self.text)
-
-        # Exclude adverbs and interjections
-        excluded_tags = ['RB', 'RBR', 'RBS', 'UH']
-
-        for i, token in enumerate(tokens):
-            # Check if token is a word
-            if re.match(r'^\w+$', token):
-                # Part-of-speech tag the word
-                pos = nltk.pos_tag([token])[0][1]
-                # If the word's POS tag is in the excluded list, remove the word
-                if pos in excluded_tags:
-                    tokens[i] = ''
-
-        new_text = "".join(tokens)
-        TextPreprocessorBuilder._pos_remove_cache[self.text] = new_text
-        self.text = new_text
-
-        return self
-
-    def lemmatize(self):
-        processed_text = TextPreprocessorBuilder._lemmatizer_cache.get(self.text)
-        if processed_text:
-            self.text = processed_text
-            return self
-        
-        new_text = "".join([TextPreprocessorBuilder._lemmatizer.lemmatize(word) for word in re.findall(r'\b\w+\b|\W+', self.text)])
-        TextPreprocessorBuilder._lemmatizer_cache[self.text] = new_text
-        self.text = new_text
-
-        return self
-
-    def build(self):
-        return self.text
-
-class TextSummarizer:
-    _nlp_pipeline = None
-    _cache = {}
-
-    @staticmethod
-    def _load_nlp_pipeline():
-        # Lazy-load it.
-        if TextSummarizer._nlp_pipeline is None:
-            TextSummarizer._nlp_pipeline = spacy.load('en_core_web_sm')
-            TextSummarizer._nlp_pipeline.add_pipe("textrank", last=True)
-        return TextSummarizer._nlp_pipeline
-
-    @staticmethod
-    def process_long_text(text: str, min_num_sent: int) -> list[str]:
-        """
-        This function applies a text summarization process on a given text string, extracting 
-        the most important sentences based on the principle that 20% of the content is responsible
-        for 80% of the meaning (the Pareto Principle).
-
-        Returns:
-        list: A list of the most important sentences
-        """
-
-        # Attempt to get the result from cache
-        cache_key = (text, min_num_sent)
-        cached_result = TextSummarizer._cache.get(cache_key, None)
-        if cached_result is not None:
-            return cached_result
-
-        nlp_pipeline = TextSummarizer._load_nlp_pipeline()
-        doc = nlp_pipeline(text)
-
-        num_sent = len(list(doc.sents))
-        result = []
-
-        if num_sent >= min_num_sent:
-
-            limit_phrases = math.ceil(len(doc._.phrases) * 0.20)  # 20% of the phrases, rounded up
-            limit_sentences = math.ceil(num_sent * 0.20)  # 20% of the sentences, rounded up
-            result = [str(sent) for sent in doc._.textrank.summary(limit_phrases=limit_phrases, limit_sentences=limit_sentences)]
-
-        else:
-            result = [text]
-        
-        # Store the result in cache before returning it
-        TextSummarizer._cache[cache_key] = result
-        return result

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

@@ -1,353 +0,0 @@
-"""This is invoked in a subprocess to call the build backend hooks.
-
-It expects:
-- Command line args: hook_name, control_dir
-- Environment variables:
-      PEP517_BUILD_BACKEND=entry.point:spec
-      PEP517_BACKEND_PATH=paths (separated with os.pathsep)
-- control_dir/input.json:
-  - {"kwargs": {...}}
-
-Results:
-- control_dir/output.json
-  - {"return_val": ...}
-"""
-import json
-import os
-import os.path
-import re
-import shutil
-import sys
-import traceback
-from glob import glob
-from importlib import import_module
-from os.path import join as pjoin
-
-# This file is run as a script, and `import wrappers` is not zip-safe, so we
-# include write_json() and read_json() from wrappers.py.
-
-
-def write_json(obj, path, **kwargs):
-    with open(path, 'w', encoding='utf-8') as f:
-        json.dump(obj, f, **kwargs)
-
-
-def read_json(path):
-    with open(path, encoding='utf-8') as f:
-        return json.load(f)
-
-
-class BackendUnavailable(Exception):
-    """Raised if we cannot import the backend"""
-    def __init__(self, traceback):
-        self.traceback = traceback
-
-
-class BackendInvalid(Exception):
-    """Raised if the backend is invalid"""
-    def __init__(self, message):
-        self.message = message
-
-
-class HookMissing(Exception):
-    """Raised if a hook is missing and we are not executing the fallback"""
-    def __init__(self, hook_name=None):
-        super().__init__(hook_name)
-        self.hook_name = hook_name
-
-
-def contained_in(filename, directory):
-    """Test if a file is located within the given directory."""
-    filename = os.path.normcase(os.path.abspath(filename))
-    directory = os.path.normcase(os.path.abspath(directory))
-    return os.path.commonprefix([filename, directory]) == directory
-
-
-def _build_backend():
-    """Find and load the build backend"""
-    # Add in-tree backend directories to the front of sys.path.
-    backend_path = os.environ.get('PEP517_BACKEND_PATH')
-    if backend_path:
-        extra_pathitems = backend_path.split(os.pathsep)
-        sys.path[:0] = extra_pathitems
-
-    ep = os.environ['PEP517_BUILD_BACKEND']
-    mod_path, _, obj_path = ep.partition(':')
-    try:
-        obj = import_module(mod_path)
-    except ImportError:
-        raise BackendUnavailable(traceback.format_exc())
-
-    if backend_path:
-        if not any(
-            contained_in(obj.__file__, path)
-            for path in extra_pathitems
-        ):
-            raise BackendInvalid("Backend was not loaded from backend-path")
-
-    if obj_path:
-        for path_part in obj_path.split('.'):
-            obj = getattr(obj, path_part)
-    return obj
-
-
-def _supported_features():
-    """Return the list of options features supported by the backend.
-
-    Returns a list of strings.
-    The only possible value is 'build_editable'.
-    """
-    backend = _build_backend()
-    features = []
-    if hasattr(backend, "build_editable"):
-        features.append("build_editable")
-    return features
-
-
-def get_requires_for_build_wheel(config_settings):
-    """Invoke the optional get_requires_for_build_wheel hook
-
-    Returns [] if the hook is not defined.
-    """
-    backend = _build_backend()
-    try:
-        hook = backend.get_requires_for_build_wheel
-    except AttributeError:
-        return []
-    else:
-        return hook(config_settings)
-
-
-def get_requires_for_build_editable(config_settings):
-    """Invoke the optional get_requires_for_build_editable hook
-
-    Returns [] if the hook is not defined.
-    """
-    backend = _build_backend()
-    try:
-        hook = backend.get_requires_for_build_editable
-    except AttributeError:
-        return []
-    else:
-        return hook(config_settings)
-
-
-def prepare_metadata_for_build_wheel(
-        metadata_directory, config_settings, _allow_fallback):
-    """Invoke optional prepare_metadata_for_build_wheel
-
-    Implements a fallback by building a wheel if the hook isn't defined,
-    unless _allow_fallback is False in which case HookMissing is raised.
-    """
-    backend = _build_backend()
-    try:
-        hook = backend.prepare_metadata_for_build_wheel
-    except AttributeError:
-        if not _allow_fallback:
-            raise HookMissing()
-    else:
-        return hook(metadata_directory, config_settings)
-    # fallback to build_wheel outside the try block to avoid exception chaining
-    # which can be confusing to users and is not relevant
-    whl_basename = backend.build_wheel(metadata_directory, config_settings)
-    return _get_wheel_metadata_from_wheel(whl_basename, metadata_directory,
-                                          config_settings)
-
-
-def prepare_metadata_for_build_editable(
-        metadata_directory, config_settings, _allow_fallback):
-    """Invoke optional prepare_metadata_for_build_editable
-
-    Implements a fallback by building an editable wheel if the hook isn't
-    defined, unless _allow_fallback is False in which case HookMissing is
-    raised.
-    """
-    backend = _build_backend()
-    try:
-        hook = backend.prepare_metadata_for_build_editable
-    except AttributeError:
-        if not _allow_fallback:
-            raise HookMissing()
-        try:
-            build_hook = backend.build_editable
-        except AttributeError:
-            raise HookMissing(hook_name='build_editable')
-        else:
-            whl_basename = build_hook(metadata_directory, config_settings)
-            return _get_wheel_metadata_from_wheel(whl_basename,
-                                                  metadata_directory,
-                                                  config_settings)
-    else:
-        return hook(metadata_directory, config_settings)
-
-
-WHEEL_BUILT_MARKER = 'PEP517_ALREADY_BUILT_WHEEL'
-
-
-def _dist_info_files(whl_zip):
-    """Identify the .dist-info folder inside a wheel ZipFile."""
-    res = []
-    for path in whl_zip.namelist():
-        m = re.match(r'[^/\\]+-[^/\\]+\.dist-info/', path)
-        if m:
-            res.append(path)
-    if res:
-        return res
-    raise Exception("No .dist-info folder found in wheel")
-
-
-def _get_wheel_metadata_from_wheel(
-        whl_basename, metadata_directory, config_settings):
-    """Extract the metadata from a wheel.
-
-    Fallback for when the build backend does not
-    define the 'get_wheel_metadata' hook.
-    """
-    from zipfile import ZipFile
-    with open(os.path.join(metadata_directory, WHEEL_BUILT_MARKER), 'wb'):
-        pass  # Touch marker file
-
-    whl_file = os.path.join(metadata_directory, whl_basename)
-    with ZipFile(whl_file) as zipf:
-        dist_info = _dist_info_files(zipf)
-        zipf.extractall(path=metadata_directory, members=dist_info)
-    return dist_info[0].split('/')[0]
-
-
-def _find_already_built_wheel(metadata_directory):
-    """Check for a wheel already built during the get_wheel_metadata hook.
-    """
-    if not metadata_directory:
-        return None
-    metadata_parent = os.path.dirname(metadata_directory)
-    if not os.path.isfile(pjoin(metadata_parent, WHEEL_BUILT_MARKER)):
-        return None
-
-    whl_files = glob(os.path.join(metadata_parent, '*.whl'))
-    if not whl_files:
-        print('Found wheel built marker, but no .whl files')
-        return None
-    if len(whl_files) > 1:
-        print('Found multiple .whl files; unspecified behaviour. '
-              'Will call build_wheel.')
-        return None
-
-    # Exactly one .whl file
-    return whl_files[0]
-
-
-def build_wheel(wheel_directory, config_settings, metadata_directory=None):
-    """Invoke the mandatory build_wheel hook.
-
-    If a wheel was already built in the
-    prepare_metadata_for_build_wheel fallback, this
-    will copy it rather than rebuilding the wheel.
-    """
-    prebuilt_whl = _find_already_built_wheel(metadata_directory)
-    if prebuilt_whl:
-        shutil.copy2(prebuilt_whl, wheel_directory)
-        return os.path.basename(prebuilt_whl)
-
-    return _build_backend().build_wheel(wheel_directory, config_settings,
-                                        metadata_directory)
-
-
-def build_editable(wheel_directory, config_settings, metadata_directory=None):
-    """Invoke the optional build_editable hook.
-
-    If a wheel was already built in the
-    prepare_metadata_for_build_editable fallback, this
-    will copy it rather than rebuilding the wheel.
-    """
-    backend = _build_backend()
-    try:
-        hook = backend.build_editable
-    except AttributeError:
-        raise HookMissing()
-    else:
-        prebuilt_whl = _find_already_built_wheel(metadata_directory)
-        if prebuilt_whl:
-            shutil.copy2(prebuilt_whl, wheel_directory)
-            return os.path.basename(prebuilt_whl)
-
-        return hook(wheel_directory, config_settings, metadata_directory)
-
-
-def get_requires_for_build_sdist(config_settings):
-    """Invoke the optional get_requires_for_build_wheel hook
-
-    Returns [] if the hook is not defined.
-    """
-    backend = _build_backend()
-    try:
-        hook = backend.get_requires_for_build_sdist
-    except AttributeError:
-        return []
-    else:
-        return hook(config_settings)
-
-
-class _DummyException(Exception):
-    """Nothing should ever raise this exception"""
-
-
-class GotUnsupportedOperation(Exception):
-    """For internal use when backend raises UnsupportedOperation"""
-    def __init__(self, traceback):
-        self.traceback = traceback
-
-
-def build_sdist(sdist_directory, config_settings):
-    """Invoke the mandatory build_sdist hook."""
-    backend = _build_backend()
-    try:
-        return backend.build_sdist(sdist_directory, config_settings)
-    except getattr(backend, 'UnsupportedOperation', _DummyException):
-        raise GotUnsupportedOperation(traceback.format_exc())
-
-
-HOOK_NAMES = {
-    'get_requires_for_build_wheel',
-    'prepare_metadata_for_build_wheel',
-    'build_wheel',
-    'get_requires_for_build_editable',
-    'prepare_metadata_for_build_editable',
-    'build_editable',
-    'get_requires_for_build_sdist',
-    'build_sdist',
-    '_supported_features',
-}
-
-
-def main():
-    if len(sys.argv) < 3:
-        sys.exit("Needs args: hook_name, control_dir")
-    hook_name = sys.argv[1]
-    control_dir = sys.argv[2]
-    if hook_name not in HOOK_NAMES:
-        sys.exit("Unknown hook: %s" % hook_name)
-    hook = globals()[hook_name]
-
-    hook_input = read_json(pjoin(control_dir, 'input.json'))
-
-    json_out = {'unsupported': False, 'return_val': None}
-    try:
-        json_out['return_val'] = hook(**hook_input['kwargs'])
-    except BackendUnavailable as e:
-        json_out['no_backend'] = True
-        json_out['traceback'] = e.traceback
-    except BackendInvalid as e:
-        json_out['backend_invalid'] = True
-        json_out['backend_error'] = e.message
-    except GotUnsupportedOperation as e:
-        json_out['unsupported'] = True
-        json_out['traceback'] = e.traceback
-    except HookMissing as e:
-        json_out['hook_missing'] = True
-        json_out['missing_hook_name'] = e.hook_name or hook_name
-
-    write_json(json_out, pjoin(control_dir, 'output.json'), indent=2)
-
-
-if __name__ == '__main__':
-    main()

spaces/Aveygo/AstroSleuth/training.md

@@ -1,23 +0,0 @@
-# Training details
-
-Astrosleuth_v1 is a 6b RealESR-GAN model, trained on 15 thousand images of various deep space objects, taken from multiple sources including:
- - [AstroBin](https://welcome.astrobin.com/)
- - [NASA](https://www.nasa.gov/multimedia/imagegallery/index.html)
- - [r/Astrophotography](https://www.reddit.com/r/astrophotography/)
- - [r/Astronomy](https://www.reddit.com/r/astronomy/)
-
-Astrosleuth_v2 is a continuation of v1, where I add the following improvements:
- - [Projected discriminator](https://github.com/autonomousvision/projected-gan)
- - Greator emphasis on visual quality rather than accuracy
- - Custom VGG model for perceptual loss
- - Less JPG degredations and more motion / guassian blur
- - Trained with a much lower learning rate
- - Pruned the dataset with CLIP for more high quality ground truths
-
-As of writing, v2 is not publicly available, but will be added to the hugging face repository in the very near future.
-
-## Future plans
-
-Astrosleuth_v3 will be v2 with more emphasis on accuracy (no discriminator) to complete with BlurXTerminator as I believe that the community will appreciate such changes.
-
-I am also considering a true "zero-knowledge" model, as described by [Deep Image Prior](https://arxiv.org/abs/1711.10925), but will leave alone for now to focus on current work.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_export.py

@@ -1,207 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import copy
-import io
-import logging
-import numpy as np
-from typing import List
-import onnx
-import torch
-from caffe2.proto import caffe2_pb2
-from caffe2.python import core
-from caffe2.python.onnx.backend import Caffe2Backend
-from tabulate import tabulate
-from termcolor import colored
-from torch.onnx import OperatorExportTypes
-
-from .shared import (
-    ScopedWS,
-    construct_init_net_from_params,
-    fuse_alias_placeholder,
-    fuse_copy_between_cpu_and_gpu,
-    get_params_from_init_net,
-    group_norm_replace_aten_with_caffe2,
-    infer_device_type,
-    remove_dead_end_ops,
-    remove_reshape_for_fc,
-    save_graph,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def export_onnx_model(model, inputs):
-    """
-    Trace and export a model to onnx format.
-
-    Args:
-        model (nn.Module):
-        inputs (tuple[args]): the model will be called by `model(*inputs)`
-
-    Returns:
-        an onnx model
-    """
-    assert isinstance(model, torch.nn.Module)
-
-    # make sure all modules are in eval mode, onnx may change the training state
-    # of the module if the states are not consistent
-    def _check_eval(module):
-        assert not module.training
-
-    model.apply(_check_eval)
-
-    # Export the model to ONNX
-    with torch.no_grad():
-        with io.BytesIO() as f:
-            torch.onnx.export(
-                model,
-                inputs,
-                f,
-                operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,
-                # verbose=True,  # NOTE: uncomment this for debugging
-                # export_params=True,
-            )
-            onnx_model = onnx.load_from_string(f.getvalue())
-
-    # Apply ONNX's Optimization
-    all_passes = onnx.optimizer.get_available_passes()
-    passes = ["fuse_bn_into_conv"]
-    assert all(p in all_passes for p in passes)
-    onnx_model = onnx.optimizer.optimize(onnx_model, passes)
-    return onnx_model
-
-
-def _op_stats(net_def):
-    type_count = {}
-    for t in [op.type for op in net_def.op]:
-        type_count[t] = type_count.get(t, 0) + 1
-    type_count_list = sorted(type_count.items(), key=lambda kv: kv[0])  # alphabet
-    type_count_list = sorted(type_count_list, key=lambda kv: -kv[1])  # count
-    return "\n".join("{:>4}x {}".format(count, name) for name, count in type_count_list)
-
-
-def _assign_device_option(
-    predict_net: caffe2_pb2.NetDef, init_net: caffe2_pb2.NetDef, tensor_inputs: List[torch.Tensor]
-):
-    """
-    ONNX exported network doesn't have concept of device, assign necessary
-    device option for each op in order to make it runable on GPU runtime.
-    """
-
-    def _get_device_type(torch_tensor):
-        assert torch_tensor.device.type in ["cpu", "cuda"]
-        assert torch_tensor.device.index == 0
-        return torch_tensor.device.type
-
-    def _assign_op_device_option(net_proto, net_ssa, blob_device_types):
-        for op, ssa_i in zip(net_proto.op, net_ssa):
-            if op.type in ["CopyCPUToGPU", "CopyGPUToCPU"]:
-                op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0))
-            else:
-                devices = [blob_device_types[b] for b in ssa_i[0] + ssa_i[1]]
-                assert all(d == devices[0] for d in devices)
-                if devices[0] == "cuda":
-                    op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0))
-
-    # update ops in predict_net
-    predict_net_input_device_types = {
-        (name, 0): _get_device_type(tensor)
-        for name, tensor in zip(predict_net.external_input, tensor_inputs)
-    }
-    predict_net_device_types = infer_device_type(
-        predict_net, known_status=predict_net_input_device_types, device_name_style="pytorch"
-    )
-    predict_net_ssa, _ = core.get_ssa(predict_net)
-    _assign_op_device_option(predict_net, predict_net_ssa, predict_net_device_types)
-
-    # update ops in init_net
-    init_net_ssa, versions = core.get_ssa(init_net)
-    init_net_output_device_types = {
-        (name, versions[name]): predict_net_device_types[(name, 0)]
-        for name in init_net.external_output
-    }
-    init_net_device_types = infer_device_type(
-        init_net, known_status=init_net_output_device_types, device_name_style="pytorch"
-    )
-    _assign_op_device_option(init_net, init_net_ssa, init_net_device_types)
-
-
-def export_caffe2_detection_model(model: torch.nn.Module, tensor_inputs: List[torch.Tensor]):
-    """
-    Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX.
-
-    Arg:
-        model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py
-        tensor_inputs: a list of tensors that caffe2 model takes as input.
-    """
-    model = copy.deepcopy(model)
-    assert isinstance(model, torch.nn.Module)
-    assert hasattr(model, "encode_additional_info")
-
-    # Export via ONNX
-    logger.info(
-        "Exporting a {} model via ONNX ...".format(type(model).__name__)
-        + " Some warnings from ONNX are expected and are usually not to worry about."
-    )
-    onnx_model = export_onnx_model(model, (tensor_inputs,))
-    # Convert ONNX model to Caffe2 protobuf
-    init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model)
-    ops_table = [[op.type, op.input, op.output] for op in predict_net.op]
-    table = tabulate(ops_table, headers=["type", "input", "output"], tablefmt="pipe")
-    logger.info(
-        "ONNX export Done. Exported predict_net (before optimizations):\n" + colored(table, "cyan")
-    )
-
-    # Apply protobuf optimization
-    fuse_alias_placeholder(predict_net, init_net)
-    if any(t.device.type != "cpu" for t in tensor_inputs):
-        fuse_copy_between_cpu_and_gpu(predict_net)
-        remove_dead_end_ops(init_net)
-        _assign_device_option(predict_net, init_net, tensor_inputs)
-    params, device_options = get_params_from_init_net(init_net)
-    predict_net, params = remove_reshape_for_fc(predict_net, params)
-    init_net = construct_init_net_from_params(params, device_options)
-    group_norm_replace_aten_with_caffe2(predict_net)
-
-    # Record necessary information for running the pb model in Detectron2 system.
-    model.encode_additional_info(predict_net, init_net)
-
-    logger.info("Operators used in predict_net: \n{}".format(_op_stats(predict_net)))
-    logger.info("Operators used in init_net: \n{}".format(_op_stats(init_net)))
-
-    return predict_net, init_net
-
-
-def run_and_save_graph(predict_net, init_net, tensor_inputs, graph_save_path):
-    """
-    Run the caffe2 model on given inputs, recording the shape and draw the graph.
-
-    predict_net/init_net: caffe2 model.
-    tensor_inputs: a list of tensors that caffe2 model takes as input.
-    graph_save_path: path for saving graph of exported model.
-    """
-
-    logger.info("Saving graph of ONNX exported model to {} ...".format(graph_save_path))
-    save_graph(predict_net, graph_save_path, op_only=False)
-
-    # Run the exported Caffe2 net
-    logger.info("Running ONNX exported model ...")
-    with ScopedWS("__ws_tmp__", True) as ws:
-        ws.RunNetOnce(init_net)
-        initialized_blobs = set(ws.Blobs())
-        uninitialized = [inp for inp in predict_net.external_input if inp not in initialized_blobs]
-        for name, blob in zip(uninitialized, tensor_inputs):
-            ws.FeedBlob(name, blob)
-
-        try:
-            ws.RunNetOnce(predict_net)
-        except RuntimeError as e:
-            logger.warning("Encountered RuntimeError: \n{}".format(str(e)))
-
-        ws_blobs = {b: ws.FetchBlob(b) for b in ws.Blobs()}
-        blob_sizes = {b: ws_blobs[b].shape for b in ws_blobs if isinstance(ws_blobs[b], np.ndarray)}
-
-        logger.info("Saving graph with blob shapes to {} ...".format(graph_save_path))
-        save_graph(predict_net, graph_save_path, op_only=False, blob_sizes=blob_sizes)
-
-        return ws_blobs

spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/__init__.py

@@ -1,15 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-def maximum_path(neg_cent, mask):
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-  path = zeros(neg_cent.shape, dtype=int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-  maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-  return from_numpy(path).to(device=device, dtype=dtype)

spaces/Benson/text-generation/Examples/Casos Criminales Misterios Del Pasado Mod Apk ltima Versin.md

@@ -1,39 +0,0 @@
-<br />
-<h1>Caso penal: Misterios del pasado Mod APK - Una guía para los jugadores</h1>
- <p>Si eres un fan de los juegos de detectives, es posible que hayas oído hablar de Criminal Case: Mysteries of the Past, un popular juego desarrollado por Pretty Simple. En este juego, se puede viajar en el tiempo al siglo 19 y resolver cientos de casos de crimen en diferentes lugares. También puedes unir fuerzas con otros jugadores y competir por la mejor puntuación. Pero, ¿qué pasa si quieres disfrutar del juego sin limitaciones ni restricciones? Ahí es donde Caso Criminal: Misterios del Pasado Mod APK viene muy bien. En este artículo, te contaremos todo lo que necesitas saber sobre esta versión modificada del juego, cómo descargarlo e instalarlo, y cómo jugarlo como un profesional. </p>
- <h2>¿Qué es el Caso Criminal: Misterios del Pasado? </h2>
- <p>Criminal Case: Mysteries of the Past es un juego de aventura de objetos ocultos que te permite convertirte en un detective en la era victoriana. Puedes explorar varias escenas, encontrar pistas, interrogar sospechosos y analizar pruebas para atrapar a los asesinos. También puedes personalizar a tu personaje, coleccionar atuendos y desbloquear logros. El juego tiene más de 60 casos para resolver, cada uno con su propia historia y personajes. También puedes jugar con tus amigos y comparar tus puntuaciones en la clasificación. </p>
-<h2>casos criminales misterios del pasado mod apk última versión</h2><br /><p><b><b>Download</b> &#9999; <a href="https://bltlly.com/2v6KQQ">https://bltlly.com/2v6KQQ</a></b></p><br /><br />
- <h2>¿Qué es el caso penal: Misterios del pasado Mod APK? </h2>
- <p>Caso Penal: Misterios del Pasado Mod APK es una versión modificada del juego original que le da algunos beneficios adicionales. Por ejemplo, puedes obtener estrellas, energía, monedas y pistas ilimitadas en esta versión. Esto significa que puedes jugar todo el tiempo que quieras, sin esperar a que la energía se llene o gastar dinero real en compras dentro de la aplicación. También puede acceder a todos los casos y trajes sin restricciones. Con este mod, podrás disfrutar del juego al máximo y resolver todos los misterios con facilidad. </p>
- <h2>Cómo descargar e instalar Caso Penal: Misterios del Pasado Mod APK? </h2>
-
- <ol>
-<li>Ir a <a href="( 1 )">HappyMod.com</a> y buscar Caso Criminal: Misterios del Pasado Mod APK.</li>
-<li>Seleccione la última versión (2.39) y haga clic en Descargar.</li>
-<li>Espere a que el archivo se descargue en su dispositivo. </li>
-<li>Ir a su gestor de archivos y localizar el archivo descargado. </li>
-<li>Toque en él y permitir la instalación de fuentes desconocidas si se le solicita. </li>
-<li>Espere a que se complete la instalación. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <p>Nota: Es posible que tenga que desinstalar la versión original del juego antes de instalar el modded uno. </p>
- <h2>¿Cómo se juega caso penal: Misterios del pasado Mod APK? </h2>
- <p>Jugar Criminal Case: Mysteries of the Past Mod APK es muy similar a jugar el juego original. Solo tienes que seguir estos consejos y trucos:</p>
- <ul>
-<li>Seleccione un caso del mapa y comience a investigar. </li>
-<li>Toque en los objetos de la escena para recogerlos como pistas. </li>
-<li>Usa sugerencias si te quedas atascado o quieres acelerar tu progreso. </li>
-<li>Analiza tus pistas en el laboratorio o con tu pareja. </li>
-<li>Interroga a sospechosos y testigos para obtener más información. </li>
-<li>Usa estrellas para desbloquear nuevas escenas o acciones. </li>
-<li>Acusar al asesino cuando se tiene suficiente evidencia : Misterios del pasado Mod APK? </h3>
- <p>No, no es necesario raíz de su dispositivo para utilizar Caso Penal: Misterios del Pasado Mod APK. El mod funciona bien en ambos dispositivos arraigados y no arraigados. Sin embargo, algunas características pueden requerir acceso root, como eliminar anuncios o cambiar permisos. Puede comprobar los detalles del mod en HappyMod.com antes de descargarlo. </p>
- <h3>¿Puedo jugar Caso Penal: Misterios del pasado Mod APK en línea? </h3>
-
- <h3>¿Puedo actualizar Caso Penal: Misterios del pasado Mod APK? </h3>
- <p>Sí, puede actualizar Caso Penal: Misterios del Pasado Mod APK cada vez que una nueva versión está disponible. Puedes buscar actualizaciones en HappyMod.com o activar las notificaciones en tu dispositivo. También puede desinstalar la versión anterior e instalar la nueva siguiendo los mismos pasos que hemos proporcionado en este artículo. </p>
- <h3>¿Puedo usar Caso Penal: Misterios del pasado Mod APK en PC? </h3>
- <p>Sí, puede utilizar Caso Penal: Misterios del pasado Mod APK en el PC utilizando un emulador de Android. Un emulador es un software que le permite ejecutar aplicaciones Android en su computadora. Algunos emuladores populares son BlueStacks, NoxPlayer y LDPlayer. Puedes descargar cualquiera de estos emuladores desde sus sitios web oficiales e instalarlos en tu PC. A continuación, puede descargar e instalar Criminal Case: Mysteries of the Past Mod APK de HappyMod.com y reproducirlo en su PC.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/__init__.py

@@ -1,18 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""CacheControl import Interface.
-
-Make it easy to import from cachecontrol without long namespaces.
-"""
-__author__ = "Eric Larson"
-__email__ = "[email protected]"
-__version__ = "0.12.11"
-
-from .wrapper import CacheControl
-from .adapter import CacheControlAdapter
-from .controller import CacheController
-
-import logging
-logging.getLogger(__name__).addHandler(logging.NullHandler())

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/wheel.py

@@ -1,1082 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2020 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from __future__ import unicode_literals
-
-import base64
-import codecs
-import datetime
-from email import message_from_file
-import hashlib
-import json
-import logging
-import os
-import posixpath
-import re
-import shutil
-import sys
-import tempfile
-import zipfile
-
-from . import __version__, DistlibException
-from .compat import sysconfig, ZipFile, fsdecode, text_type, filter
-from .database import InstalledDistribution
-from .metadata import (Metadata, METADATA_FILENAME, WHEEL_METADATA_FILENAME,
-                       LEGACY_METADATA_FILENAME)
-from .util import (FileOperator, convert_path, CSVReader, CSVWriter, Cache,
-                   cached_property, get_cache_base, read_exports, tempdir,
-                   get_platform)
-from .version import NormalizedVersion, UnsupportedVersionError
-
-logger = logging.getLogger(__name__)
-
-cache = None    # created when needed
-
-if hasattr(sys, 'pypy_version_info'):  # pragma: no cover
-    IMP_PREFIX = 'pp'
-elif sys.platform.startswith('java'):  # pragma: no cover
-    IMP_PREFIX = 'jy'
-elif sys.platform == 'cli':  # pragma: no cover
-    IMP_PREFIX = 'ip'
-else:
-    IMP_PREFIX = 'cp'
-
-VER_SUFFIX = sysconfig.get_config_var('py_version_nodot')
-if not VER_SUFFIX:   # pragma: no cover
-    VER_SUFFIX = '%s%s' % sys.version_info[:2]
-PYVER = 'py' + VER_SUFFIX
-IMPVER = IMP_PREFIX + VER_SUFFIX
-
-ARCH = get_platform().replace('-', '_').replace('.', '_')
-
-ABI = sysconfig.get_config_var('SOABI')
-if ABI and ABI.startswith('cpython-'):
-    ABI = ABI.replace('cpython-', 'cp').split('-')[0]
-else:
-    def _derive_abi():
-        parts = ['cp', VER_SUFFIX]
-        if sysconfig.get_config_var('Py_DEBUG'):
-            parts.append('d')
-        if IMP_PREFIX == 'cp':
-            vi = sys.version_info[:2]
-            if vi < (3, 8):
-                wpm = sysconfig.get_config_var('WITH_PYMALLOC')
-                if wpm is None:
-                    wpm = True
-                if wpm:
-                    parts.append('m')
-                if vi < (3, 3):
-                    us = sysconfig.get_config_var('Py_UNICODE_SIZE')
-                    if us == 4 or (us is None and sys.maxunicode == 0x10FFFF):
-                        parts.append('u')
-        return ''.join(parts)
-    ABI = _derive_abi()
-    del _derive_abi
-
-FILENAME_RE = re.compile(r'''
-(?P<nm>[^-]+)
--(?P<vn>\d+[^-]*)
-(-(?P<bn>\d+[^-]*))?
--(?P<py>\w+\d+(\.\w+\d+)*)
--(?P<bi>\w+)
--(?P<ar>\w+(\.\w+)*)
-\.whl$
-''', re.IGNORECASE | re.VERBOSE)
-
-NAME_VERSION_RE = re.compile(r'''
-(?P<nm>[^-]+)
--(?P<vn>\d+[^-]*)
-(-(?P<bn>\d+[^-]*))?$
-''', re.IGNORECASE | re.VERBOSE)
-
-SHEBANG_RE = re.compile(br'\s*#![^\r\n]*')
-SHEBANG_DETAIL_RE = re.compile(br'^(\s*#!("[^"]+"|\S+))\s+(.*)$')
-SHEBANG_PYTHON = b'#!python'
-SHEBANG_PYTHONW = b'#!pythonw'
-
-if os.sep == '/':
-    to_posix = lambda o: o
-else:
-    to_posix = lambda o: o.replace(os.sep, '/')
-
-if sys.version_info[0] < 3:
-    import imp
-else:
-    imp = None
-    import importlib.machinery
-    import importlib.util
-
-def _get_suffixes():
-    if imp:
-        return [s[0] for s in imp.get_suffixes()]
-    else:
-        return importlib.machinery.EXTENSION_SUFFIXES
-
-def _load_dynamic(name, path):
-    # https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly
-    if imp:
-        return imp.load_dynamic(name, path)
-    else:
-        spec = importlib.util.spec_from_file_location(name, path)
-        module = importlib.util.module_from_spec(spec)
-        sys.modules[name] = module
-        spec.loader.exec_module(module)
-        return module
-
-class Mounter(object):
-    def __init__(self):
-        self.impure_wheels = {}
-        self.libs = {}
-
-    def add(self, pathname, extensions):
-        self.impure_wheels[pathname] = extensions
-        self.libs.update(extensions)
-
-    def remove(self, pathname):
-        extensions = self.impure_wheels.pop(pathname)
-        for k, v in extensions:
-            if k in self.libs:
-                del self.libs[k]
-
-    def find_module(self, fullname, path=None):
-        if fullname in self.libs:
-            result = self
-        else:
-            result = None
-        return result
-
-    def load_module(self, fullname):
-        if fullname in sys.modules:
-            result = sys.modules[fullname]
-        else:
-            if fullname not in self.libs:
-                raise ImportError('unable to find extension for %s' % fullname)
-            result = _load_dynamic(fullname, self.libs[fullname])
-            result.__loader__ = self
-            parts = fullname.rsplit('.', 1)
-            if len(parts) > 1:
-                result.__package__ = parts[0]
-        return result
-
-_hook = Mounter()
-
-
-class Wheel(object):
-    """
-    Class to build and install from Wheel files (PEP 427).
-    """
-
-    wheel_version = (1, 1)
-    hash_kind = 'sha256'
-
-    def __init__(self, filename=None, sign=False, verify=False):
-        """
-        Initialise an instance using a (valid) filename.
-        """
-        self.sign = sign
-        self.should_verify = verify
-        self.buildver = ''
-        self.pyver = [PYVER]
-        self.abi = ['none']
-        self.arch = ['any']
-        self.dirname = os.getcwd()
-        if filename is None:
-            self.name = 'dummy'
-            self.version = '0.1'
-            self._filename = self.filename
-        else:
-            m = NAME_VERSION_RE.match(filename)
-            if m:
-                info = m.groupdict('')
-                self.name = info['nm']
-                # Reinstate the local version separator
-                self.version = info['vn'].replace('_', '-')
-                self.buildver = info['bn']
-                self._filename = self.filename
-            else:
-                dirname, filename = os.path.split(filename)
-                m = FILENAME_RE.match(filename)
-                if not m:
-                    raise DistlibException('Invalid name or '
-                                           'filename: %r' % filename)
-                if dirname:
-                    self.dirname = os.path.abspath(dirname)
-                self._filename = filename
-                info = m.groupdict('')
-                self.name = info['nm']
-                self.version = info['vn']
-                self.buildver = info['bn']
-                self.pyver = info['py'].split('.')
-                self.abi = info['bi'].split('.')
-                self.arch = info['ar'].split('.')
-
-    @property
-    def filename(self):
-        """
-        Build and return a filename from the various components.
-        """
-        if self.buildver:
-            buildver = '-' + self.buildver
-        else:
-            buildver = ''
-        pyver = '.'.join(self.pyver)
-        abi = '.'.join(self.abi)
-        arch = '.'.join(self.arch)
-        # replace - with _ as a local version separator
-        version = self.version.replace('-', '_')
-        return '%s-%s%s-%s-%s-%s.whl' % (self.name, version, buildver,
-                                         pyver, abi, arch)
-
-    @property
-    def exists(self):
-        path = os.path.join(self.dirname, self.filename)
-        return os.path.isfile(path)
-
-    @property
-    def tags(self):
-        for pyver in self.pyver:
-            for abi in self.abi:
-                for arch in self.arch:
-                    yield pyver, abi, arch
-
-    @cached_property
-    def metadata(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        wrapper = codecs.getreader('utf-8')
-        with ZipFile(pathname, 'r') as zf:
-            wheel_metadata = self.get_wheel_metadata(zf)
-            wv = wheel_metadata['Wheel-Version'].split('.', 1)
-            file_version = tuple([int(i) for i in wv])
-            # if file_version < (1, 1):
-                # fns = [WHEEL_METADATA_FILENAME, METADATA_FILENAME,
-                       # LEGACY_METADATA_FILENAME]
-            # else:
-                # fns = [WHEEL_METADATA_FILENAME, METADATA_FILENAME]
-            fns = [WHEEL_METADATA_FILENAME, LEGACY_METADATA_FILENAME]
-            result = None
-            for fn in fns:
-                try:
-                    metadata_filename = posixpath.join(info_dir, fn)
-                    with zf.open(metadata_filename) as bf:
-                        wf = wrapper(bf)
-                        result = Metadata(fileobj=wf)
-                        if result:
-                            break
-                except KeyError:
-                    pass
-            if not result:
-                raise ValueError('Invalid wheel, because metadata is '
-                                 'missing: looked in %s' % ', '.join(fns))
-        return result
-
-    def get_wheel_metadata(self, zf):
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        metadata_filename = posixpath.join(info_dir, 'WHEEL')
-        with zf.open(metadata_filename) as bf:
-            wf = codecs.getreader('utf-8')(bf)
-            message = message_from_file(wf)
-        return dict(message)
-
-    @cached_property
-    def info(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        with ZipFile(pathname, 'r') as zf:
-            result = self.get_wheel_metadata(zf)
-        return result
-
-    def process_shebang(self, data):
-        m = SHEBANG_RE.match(data)
-        if m:
-            end = m.end()
-            shebang, data_after_shebang = data[:end], data[end:]
-            # Preserve any arguments after the interpreter
-            if b'pythonw' in shebang.lower():
-                shebang_python = SHEBANG_PYTHONW
-            else:
-                shebang_python = SHEBANG_PYTHON
-            m = SHEBANG_DETAIL_RE.match(shebang)
-            if m:
-                args = b' ' + m.groups()[-1]
-            else:
-                args = b''
-            shebang = shebang_python + args
-            data = shebang + data_after_shebang
-        else:
-            cr = data.find(b'\r')
-            lf = data.find(b'\n')
-            if cr < 0 or cr > lf:
-                term = b'\n'
-            else:
-                if data[cr:cr + 2] == b'\r\n':
-                    term = b'\r\n'
-                else:
-                    term = b'\r'
-            data = SHEBANG_PYTHON + term + data
-        return data
-
-    def get_hash(self, data, hash_kind=None):
-        if hash_kind is None:
-            hash_kind = self.hash_kind
-        try:
-            hasher = getattr(hashlib, hash_kind)
-        except AttributeError:
-            raise DistlibException('Unsupported hash algorithm: %r' % hash_kind)
-        result = hasher(data).digest()
-        result = base64.urlsafe_b64encode(result).rstrip(b'=').decode('ascii')
-        return hash_kind, result
-
-    def write_record(self, records, record_path, archive_record_path):
-        records = list(records) # make a copy, as mutated
-        records.append((archive_record_path, '', ''))
-        with CSVWriter(record_path) as writer:
-            for row in records:
-                writer.writerow(row)
-
-    def write_records(self, info, libdir, archive_paths):
-        records = []
-        distinfo, info_dir = info
-        hasher = getattr(hashlib, self.hash_kind)
-        for ap, p in archive_paths:
-            with open(p, 'rb') as f:
-                data = f.read()
-            digest = '%s=%s' % self.get_hash(data)
-            size = os.path.getsize(p)
-            records.append((ap, digest, size))
-
-        p = os.path.join(distinfo, 'RECORD')
-        ap = to_posix(os.path.join(info_dir, 'RECORD'))
-        self.write_record(records, p, ap)
-        archive_paths.append((ap, p))
-
-    def build_zip(self, pathname, archive_paths):
-        with ZipFile(pathname, 'w', zipfile.ZIP_DEFLATED) as zf:
-            for ap, p in archive_paths:
-                logger.debug('Wrote %s to %s in wheel', p, ap)
-                zf.write(p, ap)
-
-    def build(self, paths, tags=None, wheel_version=None):
-        """
-        Build a wheel from files in specified paths, and use any specified tags
-        when determining the name of the wheel.
-        """
-        if tags is None:
-            tags = {}
-
-        libkey = list(filter(lambda o: o in paths, ('purelib', 'platlib')))[0]
-        if libkey == 'platlib':
-            is_pure = 'false'
-            default_pyver = [IMPVER]
-            default_abi = [ABI]
-            default_arch = [ARCH]
-        else:
-            is_pure = 'true'
-            default_pyver = [PYVER]
-            default_abi = ['none']
-            default_arch = ['any']
-
-        self.pyver = tags.get('pyver', default_pyver)
-        self.abi = tags.get('abi', default_abi)
-        self.arch = tags.get('arch', default_arch)
-
-        libdir = paths[libkey]
-
-        name_ver = '%s-%s' % (self.name, self.version)
-        data_dir = '%s.data' % name_ver
-        info_dir = '%s.dist-info' % name_ver
-
-        archive_paths = []
-
-        # First, stuff which is not in site-packages
-        for key in ('data', 'headers', 'scripts'):
-            if key not in paths:
-                continue
-            path = paths[key]
-            if os.path.isdir(path):
-                for root, dirs, files in os.walk(path):
-                    for fn in files:
-                        p = fsdecode(os.path.join(root, fn))
-                        rp = os.path.relpath(p, path)
-                        ap = to_posix(os.path.join(data_dir, key, rp))
-                        archive_paths.append((ap, p))
-                        if key == 'scripts' and not p.endswith('.exe'):
-                            with open(p, 'rb') as f:
-                                data = f.read()
-                            data = self.process_shebang(data)
-                            with open(p, 'wb') as f:
-                                f.write(data)
-
-        # Now, stuff which is in site-packages, other than the
-        # distinfo stuff.
-        path = libdir
-        distinfo = None
-        for root, dirs, files in os.walk(path):
-            if root == path:
-                # At the top level only, save distinfo for later
-                # and skip it for now
-                for i, dn in enumerate(dirs):
-                    dn = fsdecode(dn)
-                    if dn.endswith('.dist-info'):
-                        distinfo = os.path.join(root, dn)
-                        del dirs[i]
-                        break
-                assert distinfo, '.dist-info directory expected, not found'
-
-            for fn in files:
-                # comment out next suite to leave .pyc files in
-                if fsdecode(fn).endswith(('.pyc', '.pyo')):
-                    continue
-                p = os.path.join(root, fn)
-                rp = to_posix(os.path.relpath(p, path))
-                archive_paths.append((rp, p))
-
-        # Now distinfo. Assumed to be flat, i.e. os.listdir is enough.
-        files = os.listdir(distinfo)
-        for fn in files:
-            if fn not in ('RECORD', 'INSTALLER', 'SHARED', 'WHEEL'):
-                p = fsdecode(os.path.join(distinfo, fn))
-                ap = to_posix(os.path.join(info_dir, fn))
-                archive_paths.append((ap, p))
-
-        wheel_metadata = [
-            'Wheel-Version: %d.%d' % (wheel_version or self.wheel_version),
-            'Generator: distlib %s' % __version__,
-            'Root-Is-Purelib: %s' % is_pure,
-        ]
-        for pyver, abi, arch in self.tags:
-            wheel_metadata.append('Tag: %s-%s-%s' % (pyver, abi, arch))
-        p = os.path.join(distinfo, 'WHEEL')
-        with open(p, 'w') as f:
-            f.write('\n'.join(wheel_metadata))
-        ap = to_posix(os.path.join(info_dir, 'WHEEL'))
-        archive_paths.append((ap, p))
-
-        # sort the entries by archive path. Not needed by any spec, but it
-        # keeps the archive listing and RECORD tidier than they would otherwise
-        # be. Use the number of path segments to keep directory entries together,
-        # and keep the dist-info stuff at the end.
-        def sorter(t):
-            ap = t[0]
-            n = ap.count('/')
-            if '.dist-info' in ap:
-                n += 10000
-            return (n, ap)
-        archive_paths = sorted(archive_paths, key=sorter)
-
-        # Now, at last, RECORD.
-        # Paths in here are archive paths - nothing else makes sense.
-        self.write_records((distinfo, info_dir), libdir, archive_paths)
-        # Now, ready to build the zip file
-        pathname = os.path.join(self.dirname, self.filename)
-        self.build_zip(pathname, archive_paths)
-        return pathname
-
-    def skip_entry(self, arcname):
-        """
-        Determine whether an archive entry should be skipped when verifying
-        or installing.
-        """
-        # The signature file won't be in RECORD,
-        # and we  don't currently don't do anything with it
-        # We also skip directories, as they won't be in RECORD
-        # either. See:
-        #
-        # https://github.com/pypa/wheel/issues/294
-        # https://github.com/pypa/wheel/issues/287
-        # https://github.com/pypa/wheel/pull/289
-        #
-        return arcname.endswith(('/', '/RECORD.jws'))
-
-    def install(self, paths, maker, **kwargs):
-        """
-        Install a wheel to the specified paths. If kwarg ``warner`` is
-        specified, it should be a callable, which will be called with two
-        tuples indicating the wheel version of this software and the wheel
-        version in the file, if there is a discrepancy in the versions.
-        This can be used to issue any warnings to raise any exceptions.
-        If kwarg ``lib_only`` is True, only the purelib/platlib files are
-        installed, and the headers, scripts, data and dist-info metadata are
-        not written. If kwarg ``bytecode_hashed_invalidation`` is True, written
-        bytecode will try to use file-hash based invalidation (PEP-552) on
-        supported interpreter versions (CPython 2.7+).
-
-        The return value is a :class:`InstalledDistribution` instance unless
-        ``options.lib_only`` is True, in which case the return value is ``None``.
-        """
-
-        dry_run = maker.dry_run
-        warner = kwargs.get('warner')
-        lib_only = kwargs.get('lib_only', False)
-        bc_hashed_invalidation = kwargs.get('bytecode_hashed_invalidation', False)
-
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        data_dir = '%s.data' % name_ver
-        info_dir = '%s.dist-info' % name_ver
-
-        metadata_name = posixpath.join(info_dir, LEGACY_METADATA_FILENAME)
-        wheel_metadata_name = posixpath.join(info_dir, 'WHEEL')
-        record_name = posixpath.join(info_dir, 'RECORD')
-
-        wrapper = codecs.getreader('utf-8')
-
-        with ZipFile(pathname, 'r') as zf:
-            with zf.open(wheel_metadata_name) as bwf:
-                wf = wrapper(bwf)
-                message = message_from_file(wf)
-            wv = message['Wheel-Version'].split('.', 1)
-            file_version = tuple([int(i) for i in wv])
-            if (file_version != self.wheel_version) and warner:
-                warner(self.wheel_version, file_version)
-
-            if message['Root-Is-Purelib'] == 'true':
-                libdir = paths['purelib']
-            else:
-                libdir = paths['platlib']
-
-            records = {}
-            with zf.open(record_name) as bf:
-                with CSVReader(stream=bf) as reader:
-                    for row in reader:
-                        p = row[0]
-                        records[p] = row
-
-            data_pfx = posixpath.join(data_dir, '')
-            info_pfx = posixpath.join(info_dir, '')
-            script_pfx = posixpath.join(data_dir, 'scripts', '')
-
-            # make a new instance rather than a copy of maker's,
-            # as we mutate it
-            fileop = FileOperator(dry_run=dry_run)
-            fileop.record = True    # so we can rollback if needed
-
-            bc = not sys.dont_write_bytecode    # Double negatives. Lovely!
-
-            outfiles = []   # for RECORD writing
-
-            # for script copying/shebang processing
-            workdir = tempfile.mkdtemp()
-            # set target dir later
-            # we default add_launchers to False, as the
-            # Python Launcher should be used instead
-            maker.source_dir = workdir
-            maker.target_dir = None
-            try:
-                for zinfo in zf.infolist():
-                    arcname = zinfo.filename
-                    if isinstance(arcname, text_type):
-                        u_arcname = arcname
-                    else:
-                        u_arcname = arcname.decode('utf-8')
-                    if self.skip_entry(u_arcname):
-                        continue
-                    row = records[u_arcname]
-                    if row[2] and str(zinfo.file_size) != row[2]:
-                        raise DistlibException('size mismatch for '
-                                               '%s' % u_arcname)
-                    if row[1]:
-                        kind, value = row[1].split('=', 1)
-                        with zf.open(arcname) as bf:
-                            data = bf.read()
-                        _, digest = self.get_hash(data, kind)
-                        if digest != value:
-                            raise DistlibException('digest mismatch for '
-                                                   '%s' % arcname)
-
-                    if lib_only and u_arcname.startswith((info_pfx, data_pfx)):
-                        logger.debug('lib_only: skipping %s', u_arcname)
-                        continue
-                    is_script = (u_arcname.startswith(script_pfx)
-                                 and not u_arcname.endswith('.exe'))
-
-                    if u_arcname.startswith(data_pfx):
-                        _, where, rp = u_arcname.split('/', 2)
-                        outfile = os.path.join(paths[where], convert_path(rp))
-                    else:
-                        # meant for site-packages.
-                        if u_arcname in (wheel_metadata_name, record_name):
-                            continue
-                        outfile = os.path.join(libdir, convert_path(u_arcname))
-                    if not is_script:
-                        with zf.open(arcname) as bf:
-                            fileop.copy_stream(bf, outfile)
-                        # Issue #147: permission bits aren't preserved. Using
-                        # zf.extract(zinfo, libdir) should have worked, but didn't,
-                        # see https://www.thetopsites.net/article/53834422.shtml
-                        # So ... manually preserve permission bits as given in zinfo
-                        if os.name == 'posix':
-                            # just set the normal permission bits
-                            os.chmod(outfile, (zinfo.external_attr >> 16) & 0x1FF)
-                        outfiles.append(outfile)
-                        # Double check the digest of the written file
-                        if not dry_run and row[1]:
-                            with open(outfile, 'rb') as bf:
-                                data = bf.read()
-                                _, newdigest = self.get_hash(data, kind)
-                                if newdigest != digest:
-                                    raise DistlibException('digest mismatch '
-                                                           'on write for '
-                                                           '%s' % outfile)
-                        if bc and outfile.endswith('.py'):
-                            try:
-                                pyc = fileop.byte_compile(outfile,
-                                                          hashed_invalidation=bc_hashed_invalidation)
-                                outfiles.append(pyc)
-                            except Exception:
-                                # Don't give up if byte-compilation fails,
-                                # but log it and perhaps warn the user
-                                logger.warning('Byte-compilation failed',
-                                               exc_info=True)
-                    else:
-                        fn = os.path.basename(convert_path(arcname))
-                        workname = os.path.join(workdir, fn)
-                        with zf.open(arcname) as bf:
-                            fileop.copy_stream(bf, workname)
-
-                        dn, fn = os.path.split(outfile)
-                        maker.target_dir = dn
-                        filenames = maker.make(fn)
-                        fileop.set_executable_mode(filenames)
-                        outfiles.extend(filenames)
-
-                if lib_only:
-                    logger.debug('lib_only: returning None')
-                    dist = None
-                else:
-                    # Generate scripts
-
-                    # Try to get pydist.json so we can see if there are
-                    # any commands to generate. If this fails (e.g. because
-                    # of a legacy wheel), log a warning but don't give up.
-                    commands = None
-                    file_version = self.info['Wheel-Version']
-                    if file_version == '1.0':
-                        # Use legacy info
-                        ep = posixpath.join(info_dir, 'entry_points.txt')
-                        try:
-                            with zf.open(ep) as bwf:
-                                epdata = read_exports(bwf)
-                            commands = {}
-                            for key in ('console', 'gui'):
-                                k = '%s_scripts' % key
-                                if k in epdata:
-                                    commands['wrap_%s' % key] = d = {}
-                                    for v in epdata[k].values():
-                                        s = '%s:%s' % (v.prefix, v.suffix)
-                                        if v.flags:
-                                            s += ' [%s]' % ','.join(v.flags)
-                                        d[v.name] = s
-                        except Exception:
-                            logger.warning('Unable to read legacy script '
-                                           'metadata, so cannot generate '
-                                           'scripts')
-                    else:
-                        try:
-                            with zf.open(metadata_name) as bwf:
-                                wf = wrapper(bwf)
-                                commands = json.load(wf).get('extensions')
-                                if commands:
-                                    commands = commands.get('python.commands')
-                        except Exception:
-                            logger.warning('Unable to read JSON metadata, so '
-                                           'cannot generate scripts')
-                    if commands:
-                        console_scripts = commands.get('wrap_console', {})
-                        gui_scripts = commands.get('wrap_gui', {})
-                        if console_scripts or gui_scripts:
-                            script_dir = paths.get('scripts', '')
-                            if not os.path.isdir(script_dir):
-                                raise ValueError('Valid script path not '
-                                                 'specified')
-                            maker.target_dir = script_dir
-                            for k, v in console_scripts.items():
-                                script = '%s = %s' % (k, v)
-                                filenames = maker.make(script)
-                                fileop.set_executable_mode(filenames)
-
-                            if gui_scripts:
-                                options = {'gui': True }
-                                for k, v in gui_scripts.items():
-                                    script = '%s = %s' % (k, v)
-                                    filenames = maker.make(script, options)
-                                    fileop.set_executable_mode(filenames)
-
-                    p = os.path.join(libdir, info_dir)
-                    dist = InstalledDistribution(p)
-
-                    # Write SHARED
-                    paths = dict(paths)     # don't change passed in dict
-                    del paths['purelib']
-                    del paths['platlib']
-                    paths['lib'] = libdir
-                    p = dist.write_shared_locations(paths, dry_run)
-                    if p:
-                        outfiles.append(p)
-
-                    # Write RECORD
-                    dist.write_installed_files(outfiles, paths['prefix'],
-                                               dry_run)
-                return dist
-            except Exception:  # pragma: no cover
-                logger.exception('installation failed.')
-                fileop.rollback()
-                raise
-            finally:
-                shutil.rmtree(workdir)
-
-    def _get_dylib_cache(self):
-        global cache
-        if cache is None:
-            # Use native string to avoid issues on 2.x: see Python #20140.
-            base = os.path.join(get_cache_base(), str('dylib-cache'),
-                                '%s.%s' % sys.version_info[:2])
-            cache = Cache(base)
-        return cache
-
-    def _get_extensions(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        arcname = posixpath.join(info_dir, 'EXTENSIONS')
-        wrapper = codecs.getreader('utf-8')
-        result = []
-        with ZipFile(pathname, 'r') as zf:
-            try:
-                with zf.open(arcname) as bf:
-                    wf = wrapper(bf)
-                    extensions = json.load(wf)
-                    cache = self._get_dylib_cache()
-                    prefix = cache.prefix_to_dir(pathname)
-                    cache_base = os.path.join(cache.base, prefix)
-                    if not os.path.isdir(cache_base):
-                        os.makedirs(cache_base)
-                    for name, relpath in extensions.items():
-                        dest = os.path.join(cache_base, convert_path(relpath))
-                        if not os.path.exists(dest):
-                            extract = True
-                        else:
-                            file_time = os.stat(dest).st_mtime
-                            file_time = datetime.datetime.fromtimestamp(file_time)
-                            info = zf.getinfo(relpath)
-                            wheel_time = datetime.datetime(*info.date_time)
-                            extract = wheel_time > file_time
-                        if extract:
-                            zf.extract(relpath, cache_base)
-                        result.append((name, dest))
-            except KeyError:
-                pass
-        return result
-
-    def is_compatible(self):
-        """
-        Determine if a wheel is compatible with the running system.
-        """
-        return is_compatible(self)
-
-    def is_mountable(self):
-        """
-        Determine if a wheel is asserted as mountable by its metadata.
-        """
-        return True # for now - metadata details TBD
-
-    def mount(self, append=False):
-        pathname = os.path.abspath(os.path.join(self.dirname, self.filename))
-        if not self.is_compatible():
-            msg = 'Wheel %s not compatible with this Python.' % pathname
-            raise DistlibException(msg)
-        if not self.is_mountable():
-            msg = 'Wheel %s is marked as not mountable.' % pathname
-            raise DistlibException(msg)
-        if pathname in sys.path:
-            logger.debug('%s already in path', pathname)
-        else:
-            if append:
-                sys.path.append(pathname)
-            else:
-                sys.path.insert(0, pathname)
-            extensions = self._get_extensions()
-            if extensions:
-                if _hook not in sys.meta_path:
-                    sys.meta_path.append(_hook)
-                _hook.add(pathname, extensions)
-
-    def unmount(self):
-        pathname = os.path.abspath(os.path.join(self.dirname, self.filename))
-        if pathname not in sys.path:
-            logger.debug('%s not in path', pathname)
-        else:
-            sys.path.remove(pathname)
-            if pathname in _hook.impure_wheels:
-                _hook.remove(pathname)
-            if not _hook.impure_wheels:
-                if _hook in sys.meta_path:
-                    sys.meta_path.remove(_hook)
-
-    def verify(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        data_dir = '%s.data' % name_ver
-        info_dir = '%s.dist-info' % name_ver
-
-        metadata_name = posixpath.join(info_dir, LEGACY_METADATA_FILENAME)
-        wheel_metadata_name = posixpath.join(info_dir, 'WHEEL')
-        record_name = posixpath.join(info_dir, 'RECORD')
-
-        wrapper = codecs.getreader('utf-8')
-
-        with ZipFile(pathname, 'r') as zf:
-            with zf.open(wheel_metadata_name) as bwf:
-                wf = wrapper(bwf)
-                message = message_from_file(wf)
-            wv = message['Wheel-Version'].split('.', 1)
-            file_version = tuple([int(i) for i in wv])
-            # TODO version verification
-
-            records = {}
-            with zf.open(record_name) as bf:
-                with CSVReader(stream=bf) as reader:
-                    for row in reader:
-                        p = row[0]
-                        records[p] = row
-
-            for zinfo in zf.infolist():
-                arcname = zinfo.filename
-                if isinstance(arcname, text_type):
-                    u_arcname = arcname
-                else:
-                    u_arcname = arcname.decode('utf-8')
-                # See issue #115: some wheels have .. in their entries, but
-                # in the filename ... e.g. __main__..py ! So the check is
-                # updated to look for .. in the directory portions
-                p = u_arcname.split('/')
-                if '..' in p:
-                    raise DistlibException('invalid entry in '
-                                           'wheel: %r' % u_arcname)
-
-                if self.skip_entry(u_arcname):
-                    continue
-                row = records[u_arcname]
-                if row[2] and str(zinfo.file_size) != row[2]:
-                    raise DistlibException('size mismatch for '
-                                           '%s' % u_arcname)
-                if row[1]:
-                    kind, value = row[1].split('=', 1)
-                    with zf.open(arcname) as bf:
-                        data = bf.read()
-                    _, digest = self.get_hash(data, kind)
-                    if digest != value:
-                        raise DistlibException('digest mismatch for '
-                                               '%s' % arcname)
-
-    def update(self, modifier, dest_dir=None, **kwargs):
-        """
-        Update the contents of a wheel in a generic way. The modifier should
-        be a callable which expects a dictionary argument: its keys are
-        archive-entry paths, and its values are absolute filesystem paths
-        where the contents the corresponding archive entries can be found. The
-        modifier is free to change the contents of the files pointed to, add
-        new entries and remove entries, before returning. This method will
-        extract the entire contents of the wheel to a temporary location, call
-        the modifier, and then use the passed (and possibly updated)
-        dictionary to write a new wheel. If ``dest_dir`` is specified, the new
-        wheel is written there -- otherwise, the original wheel is overwritten.
-
-        The modifier should return True if it updated the wheel, else False.
-        This method returns the same value the modifier returns.
-        """
-
-        def get_version(path_map, info_dir):
-            version = path = None
-            key = '%s/%s' % (info_dir, LEGACY_METADATA_FILENAME)
-            if key not in path_map:
-                key = '%s/PKG-INFO' % info_dir
-            if key in path_map:
-                path = path_map[key]
-                version = Metadata(path=path).version
-            return version, path
-
-        def update_version(version, path):
-            updated = None
-            try:
-                v = NormalizedVersion(version)
-                i = version.find('-')
-                if i < 0:
-                    updated = '%s+1' % version
-                else:
-                    parts = [int(s) for s in version[i + 1:].split('.')]
-                    parts[-1] += 1
-                    updated = '%s+%s' % (version[:i],
-                                         '.'.join(str(i) for i in parts))
-            except UnsupportedVersionError:
-                logger.debug('Cannot update non-compliant (PEP-440) '
-                             'version %r', version)
-            if updated:
-                md = Metadata(path=path)
-                md.version = updated
-                legacy = path.endswith(LEGACY_METADATA_FILENAME)
-                md.write(path=path, legacy=legacy)
-                logger.debug('Version updated from %r to %r', version,
-                             updated)
-
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        record_name = posixpath.join(info_dir, 'RECORD')
-        with tempdir() as workdir:
-            with ZipFile(pathname, 'r') as zf:
-                path_map = {}
-                for zinfo in zf.infolist():
-                    arcname = zinfo.filename
-                    if isinstance(arcname, text_type):
-                        u_arcname = arcname
-                    else:
-                        u_arcname = arcname.decode('utf-8')
-                    if u_arcname == record_name:
-                        continue
-                    if '..' in u_arcname:
-                        raise DistlibException('invalid entry in '
-                                               'wheel: %r' % u_arcname)
-                    zf.extract(zinfo, workdir)
-                    path = os.path.join(workdir, convert_path(u_arcname))
-                    path_map[u_arcname] = path
-
-            # Remember the version.
-            original_version, _ = get_version(path_map, info_dir)
-            # Files extracted. Call the modifier.
-            modified = modifier(path_map, **kwargs)
-            if modified:
-                # Something changed - need to build a new wheel.
-                current_version, path = get_version(path_map, info_dir)
-                if current_version and (current_version == original_version):
-                    # Add or update local version to signify changes.
-                    update_version(current_version, path)
-                # Decide where the new wheel goes.
-                if dest_dir is None:
-                    fd, newpath = tempfile.mkstemp(suffix='.whl',
-                                                   prefix='wheel-update-',
-                                                   dir=workdir)
-                    os.close(fd)
-                else:
-                    if not os.path.isdir(dest_dir):
-                        raise DistlibException('Not a directory: %r' % dest_dir)
-                    newpath = os.path.join(dest_dir, self.filename)
-                archive_paths = list(path_map.items())
-                distinfo = os.path.join(workdir, info_dir)
-                info = distinfo, info_dir
-                self.write_records(info, workdir, archive_paths)
-                self.build_zip(newpath, archive_paths)
-                if dest_dir is None:
-                    shutil.copyfile(newpath, pathname)
-        return modified
-
-def _get_glibc_version():
-    import platform
-    ver = platform.libc_ver()
-    result = []
-    if ver[0] == 'glibc':
-        for s in ver[1].split('.'):
-            result.append(int(s) if s.isdigit() else 0)
-        result = tuple(result)
-    return result
-
-def compatible_tags():
-    """
-    Return (pyver, abi, arch) tuples compatible with this Python.
-    """
-    versions = [VER_SUFFIX]
-    major = VER_SUFFIX[0]
-    for minor in range(sys.version_info[1] - 1, - 1, -1):
-        versions.append(''.join([major, str(minor)]))
-
-    abis = []
-    for suffix in _get_suffixes():
-        if suffix.startswith('.abi'):
-            abis.append(suffix.split('.', 2)[1])
-    abis.sort()
-    if ABI != 'none':
-        abis.insert(0, ABI)
-    abis.append('none')
-    result = []
-
-    arches = [ARCH]
-    if sys.platform == 'darwin':
-        m = re.match(r'(\w+)_(\d+)_(\d+)_(\w+)$', ARCH)
-        if m:
-            name, major, minor, arch = m.groups()
-            minor = int(minor)
-            matches = [arch]
-            if arch in ('i386', 'ppc'):
-                matches.append('fat')
-            if arch in ('i386', 'ppc', 'x86_64'):
-                matches.append('fat3')
-            if arch in ('ppc64', 'x86_64'):
-                matches.append('fat64')
-            if arch in ('i386', 'x86_64'):
-                matches.append('intel')
-            if arch in ('i386', 'x86_64', 'intel', 'ppc', 'ppc64'):
-                matches.append('universal')
-            while minor >= 0:
-                for match in matches:
-                    s = '%s_%s_%s_%s' % (name, major, minor, match)
-                    if s != ARCH:   # already there
-                        arches.append(s)
-                minor -= 1
-
-    # Most specific - our Python version, ABI and arch
-    for abi in abis:
-        for arch in arches:
-            result.append((''.join((IMP_PREFIX, versions[0])), abi, arch))
-            # manylinux
-            if abi != 'none' and sys.platform.startswith('linux'):
-                arch = arch.replace('linux_', '')
-                parts = _get_glibc_version()
-                if len(parts) == 2:
-                    if parts >= (2, 5):
-                        result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                       'manylinux1_%s' % arch))
-                    if parts >= (2, 12):
-                        result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                       'manylinux2010_%s' % arch))
-                    if parts >= (2, 17):
-                        result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                       'manylinux2014_%s' % arch))
-                    result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                   'manylinux_%s_%s_%s' % (parts[0], parts[1],
-                                                           arch)))
-
-    # where no ABI / arch dependency, but IMP_PREFIX dependency
-    for i, version in enumerate(versions):
-        result.append((''.join((IMP_PREFIX, version)), 'none', 'any'))
-        if i == 0:
-            result.append((''.join((IMP_PREFIX, version[0])), 'none', 'any'))
-
-    # no IMP_PREFIX, ABI or arch dependency
-    for i, version in enumerate(versions):
-        result.append((''.join(('py', version)), 'none', 'any'))
-        if i == 0:
-            result.append((''.join(('py', version[0])), 'none', 'any'))
-
-    return set(result)
-
-
-COMPATIBLE_TAGS = compatible_tags()
-
-del compatible_tags
-
-
-def is_compatible(wheel, tags=None):
-    if not isinstance(wheel, Wheel):
-        wheel = Wheel(wheel)    # assume it's a filename
-    result = False
-    if tags is None:
-        tags = COMPATIBLE_TAGS
-    for ver, abi, arch in tags:
-        if ver in wheel.pyver and abi in wheel.abi and arch in wheel.arch:
-            result = True
-            break
-    return result

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

@@ -1,375 +0,0 @@
-import sys
-from threading import Event, RLock, Thread
-from types import TracebackType
-from typing import IO, Any, Callable, List, Optional, TextIO, Type, cast
-
-from . import get_console
-from .console import Console, ConsoleRenderable, RenderableType, RenderHook
-from .control import Control
-from .file_proxy import FileProxy
-from .jupyter import JupyterMixin
-from .live_render import LiveRender, VerticalOverflowMethod
-from .screen import Screen
-from .text import Text
-
-
-class _RefreshThread(Thread):
-    """A thread that calls refresh() at regular intervals."""
-
-    def __init__(self, live: "Live", refresh_per_second: float) -> None:
-        self.live = live
-        self.refresh_per_second = refresh_per_second
-        self.done = Event()
-        super().__init__(daemon=True)
-
-    def stop(self) -> None:
-        self.done.set()
-
-    def run(self) -> None:
-        while not self.done.wait(1 / self.refresh_per_second):
-            with self.live._lock:
-                if not self.done.is_set():
-                    self.live.refresh()
-
-
-class Live(JupyterMixin, RenderHook):
-    """Renders an auto-updating live display of any given renderable.
-
-    Args:
-        renderable (RenderableType, optional): The renderable to live display. Defaults to displaying nothing.
-        console (Console, optional): Optional Console instance. Default will an internal Console instance writing to stdout.
-        screen (bool, optional): Enable alternate screen mode. Defaults to False.
-        auto_refresh (bool, optional): Enable auto refresh. If disabled, you will need to call `refresh()` or `update()` with refresh flag. Defaults to True
-        refresh_per_second (float, optional): Number of times per second to refresh the live display. Defaults to 4.
-        transient (bool, optional): Clear the renderable on exit (has no effect when screen=True). Defaults to False.
-        redirect_stdout (bool, optional): Enable redirection of stdout, so ``print`` may be used. Defaults to True.
-        redirect_stderr (bool, optional): Enable redirection of stderr. Defaults to True.
-        vertical_overflow (VerticalOverflowMethod, optional): How to handle renderable when it is too tall for the console. Defaults to "ellipsis".
-        get_renderable (Callable[[], RenderableType], optional): Optional callable to get renderable. Defaults to None.
-    """
-
-    def __init__(
-        self,
-        renderable: Optional[RenderableType] = None,
-        *,
-        console: Optional[Console] = None,
-        screen: bool = False,
-        auto_refresh: bool = True,
-        refresh_per_second: float = 4,
-        transient: bool = False,
-        redirect_stdout: bool = True,
-        redirect_stderr: bool = True,
-        vertical_overflow: VerticalOverflowMethod = "ellipsis",
-        get_renderable: Optional[Callable[[], RenderableType]] = None,
-    ) -> None:
-        assert refresh_per_second > 0, "refresh_per_second must be > 0"
-        self._renderable = renderable
-        self.console = console if console is not None else get_console()
-        self._screen = screen
-        self._alt_screen = False
-
-        self._redirect_stdout = redirect_stdout
-        self._redirect_stderr = redirect_stderr
-        self._restore_stdout: Optional[IO[str]] = None
-        self._restore_stderr: Optional[IO[str]] = None
-
-        self._lock = RLock()
-        self.ipy_widget: Optional[Any] = None
-        self.auto_refresh = auto_refresh
-        self._started: bool = False
-        self.transient = True if screen else transient
-
-        self._refresh_thread: Optional[_RefreshThread] = None
-        self.refresh_per_second = refresh_per_second
-
-        self.vertical_overflow = vertical_overflow
-        self._get_renderable = get_renderable
-        self._live_render = LiveRender(
-            self.get_renderable(), vertical_overflow=vertical_overflow
-        )
-
-    @property
-    def is_started(self) -> bool:
-        """Check if live display has been started."""
-        return self._started
-
-    def get_renderable(self) -> RenderableType:
-        renderable = (
-            self._get_renderable()
-            if self._get_renderable is not None
-            else self._renderable
-        )
-        return renderable or ""
-
-    def start(self, refresh: bool = False) -> None:
-        """Start live rendering display.
-
-        Args:
-            refresh (bool, optional): Also refresh. Defaults to False.
-        """
-        with self._lock:
-            if self._started:
-                return
-            self.console.set_live(self)
-            self._started = True
-            if self._screen:
-                self._alt_screen = self.console.set_alt_screen(True)
-            self.console.show_cursor(False)
-            self._enable_redirect_io()
-            self.console.push_render_hook(self)
-            if refresh:
-                try:
-                    self.refresh()
-                except Exception:
-                    # If refresh fails, we want to stop the redirection of sys.stderr,
-                    # so the error stacktrace is properly displayed in the terminal.
-                    # (or, if the code that calls Rich captures the exception and wants to display something,
-                    # let this be displayed in the terminal).
-                    self.stop()
-                    raise
-            if self.auto_refresh:
-                self._refresh_thread = _RefreshThread(self, self.refresh_per_second)
-                self._refresh_thread.start()
-
-    def stop(self) -> None:
-        """Stop live rendering display."""
-        with self._lock:
-            if not self._started:
-                return
-            self.console.clear_live()
-            self._started = False
-
-            if self.auto_refresh and self._refresh_thread is not None:
-                self._refresh_thread.stop()
-                self._refresh_thread = None
-            # allow it to fully render on the last even if overflow
-            self.vertical_overflow = "visible"
-            with self.console:
-                try:
-                    if not self._alt_screen and not self.console.is_jupyter:
-                        self.refresh()
-                finally:
-                    self._disable_redirect_io()
-                    self.console.pop_render_hook()
-                    if not self._alt_screen and self.console.is_terminal:
-                        self.console.line()
-                    self.console.show_cursor(True)
-                    if self._alt_screen:
-                        self.console.set_alt_screen(False)
-
-                    if self.transient and not self._alt_screen:
-                        self.console.control(self._live_render.restore_cursor())
-                    if self.ipy_widget is not None and self.transient:
-                        self.ipy_widget.close()  # pragma: no cover
-
-    def __enter__(self) -> "Live":
-        self.start(refresh=self._renderable is not None)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.stop()
-
-    def _enable_redirect_io(self) -> None:
-        """Enable redirecting of stdout / stderr."""
-        if self.console.is_terminal or self.console.is_jupyter:
-            if self._redirect_stdout and not isinstance(sys.stdout, FileProxy):
-                self._restore_stdout = sys.stdout
-                sys.stdout = cast("TextIO", FileProxy(self.console, sys.stdout))
-            if self._redirect_stderr and not isinstance(sys.stderr, FileProxy):
-                self._restore_stderr = sys.stderr
-                sys.stderr = cast("TextIO", FileProxy(self.console, sys.stderr))
-
-    def _disable_redirect_io(self) -> None:
-        """Disable redirecting of stdout / stderr."""
-        if self._restore_stdout:
-            sys.stdout = cast("TextIO", self._restore_stdout)
-            self._restore_stdout = None
-        if self._restore_stderr:
-            sys.stderr = cast("TextIO", self._restore_stderr)
-            self._restore_stderr = None
-
-    @property
-    def renderable(self) -> RenderableType:
-        """Get the renderable that is being displayed
-
-        Returns:
-            RenderableType: Displayed renderable.
-        """
-        renderable = self.get_renderable()
-        return Screen(renderable) if self._alt_screen else renderable
-
-    def update(self, renderable: RenderableType, *, refresh: bool = False) -> None:
-        """Update the renderable that is being displayed
-
-        Args:
-            renderable (RenderableType): New renderable to use.
-            refresh (bool, optional): Refresh the display. Defaults to False.
-        """
-        if isinstance(renderable, str):
-            renderable = self.console.render_str(renderable)
-        with self._lock:
-            self._renderable = renderable
-            if refresh:
-                self.refresh()
-
-    def refresh(self) -> None:
-        """Update the display of the Live Render."""
-        with self._lock:
-            self._live_render.set_renderable(self.renderable)
-            if self.console.is_jupyter:  # pragma: no cover
-                try:
-                    from IPython.display import display
-                    from ipywidgets import Output
-                except ImportError:
-                    import warnings
-
-                    warnings.warn('install "ipywidgets" for Jupyter support')
-                else:
-                    if self.ipy_widget is None:
-                        self.ipy_widget = Output()
-                        display(self.ipy_widget)
-
-                    with self.ipy_widget:
-                        self.ipy_widget.clear_output(wait=True)
-                        self.console.print(self._live_render.renderable)
-            elif self.console.is_terminal and not self.console.is_dumb_terminal:
-                with self.console:
-                    self.console.print(Control())
-            elif (
-                not self._started and not self.transient
-            ):  # if it is finished allow files or dumb-terminals to see final result
-                with self.console:
-                    self.console.print(Control())
-
-    def process_renderables(
-        self, renderables: List[ConsoleRenderable]
-    ) -> List[ConsoleRenderable]:
-        """Process renderables to restore cursor and display progress."""
-        self._live_render.vertical_overflow = self.vertical_overflow
-        if self.console.is_interactive:
-            # lock needs acquiring as user can modify live_render renderable at any time unlike in Progress.
-            with self._lock:
-                reset = (
-                    Control.home()
-                    if self._alt_screen
-                    else self._live_render.position_cursor()
-                )
-                renderables = [reset, *renderables, self._live_render]
-        elif (
-            not self._started and not self.transient
-        ):  # if it is finished render the final output for files or dumb_terminals
-            renderables = [*renderables, self._live_render]
-
-        return renderables
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import random
-    import time
-    from itertools import cycle
-    from typing import Dict, List, Tuple
-
-    from .align import Align
-    from .console import Console
-    from .live import Live as Live
-    from .panel import Panel
-    from .rule import Rule
-    from .syntax import Syntax
-    from .table import Table
-
-    console = Console()
-
-    syntax = Syntax(
-        '''def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]:
-    """Iterate and generate a tuple with a flag for last value."""
-    iter_values = iter(values)
-    try:
-        previous_value = next(iter_values)
-    except StopIteration:
-        return
-    for value in iter_values:
-        yield False, previous_value
-        previous_value = value
-    yield True, previous_value''',
-        "python",
-        line_numbers=True,
-    )
-
-    table = Table("foo", "bar", "baz")
-    table.add_row("1", "2", "3")
-
-    progress_renderables = [
-        "You can make the terminal shorter and taller to see the live table hide"
-        "Text may be printed while the progress bars are rendering.",
-        Panel("In fact, [i]any[/i] renderable will work"),
-        "Such as [magenta]tables[/]...",
-        table,
-        "Pretty printed structures...",
-        {"type": "example", "text": "Pretty printed"},
-        "Syntax...",
-        syntax,
-        Rule("Give it a try!"),
-    ]
-
-    examples = cycle(progress_renderables)
-
-    exchanges = [
-        "SGD",
-        "MYR",
-        "EUR",
-        "USD",
-        "AUD",
-        "JPY",
-        "CNH",
-        "HKD",
-        "CAD",
-        "INR",
-        "DKK",
-        "GBP",
-        "RUB",
-        "NZD",
-        "MXN",
-        "IDR",
-        "TWD",
-        "THB",
-        "VND",
-    ]
-    with Live(console=console) as live_table:
-        exchange_rate_dict: Dict[Tuple[str, str], float] = {}
-
-        for index in range(100):
-            select_exchange = exchanges[index % len(exchanges)]
-
-            for exchange in exchanges:
-                if exchange == select_exchange:
-                    continue
-                time.sleep(0.4)
-                if random.randint(0, 10) < 1:
-                    console.log(next(examples))
-                exchange_rate_dict[(select_exchange, exchange)] = 200 / (
-                    (random.random() * 320) + 1
-                )
-                if len(exchange_rate_dict) > len(exchanges) - 1:
-                    exchange_rate_dict.pop(list(exchange_rate_dict.keys())[0])
-                table = Table(title="Exchange Rates")
-
-                table.add_column("Source Currency")
-                table.add_column("Destination Currency")
-                table.add_column("Exchange Rate")
-
-                for ((source, dest), exchange_rate) in exchange_rate_dict.items():
-                    table.add_row(
-                        source,
-                        dest,
-                        Text(
-                            f"{exchange_rate:.4f}",
-                            style="red" if exchange_rate < 1.0 else "green",
-                        ),
-                    )
-
-                live_table.update(Align.center(table))

spaces/BigDL/bigdl_nano_demo/data.py

@@ -1,233 +0,0 @@
-# This file is copied from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/data.py
-
-# MIT License
-
-# Copyright (c) 2022 Lorenzo Breschi
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from typing import Callable, Dict
-
-import torch
-
-from torch.utils.data import Dataset
-
-import torchvision.transforms.functional as F
-from torchvision import transforms
-import pytorch_lightning as pl
-
-from collections.abc import Iterable
-
-
-# image reader writer
-from pathlib import Path
-from PIL import Image
-from typing import Tuple
-
-
-def read_image(filepath: Path, mode: str = None) -> Image:
-    with open(filepath, 'rb') as file:
-        image = Image.open(file)
-        return image.convert(mode)
-
-
-image2tensor = transforms.ToTensor()
-tensor2image = transforms.ToPILImage()
-
-
-def write_image(image: Image, filepath: Path):
-    filepath.parent.mkdir(parents=True, exist_ok=True)
-    image.save(str(filepath))
-
-
-def read_image_tensor(filepath: Path, mode: str = 'RGB') -> torch.Tensor:
-    return image2tensor(read_image(filepath, mode))
-
-
-def write_image_tensor(input: torch.Tensor, filepath: Path):
-    write_image(tensor2image(input), filepath)
-
-
-def get_valid_indices(H: int, W: int, patch_size: int, random_overlap: int = 0):
-
-    vih = torch.arange(random_overlap, H-patch_size -
-                       random_overlap+1, patch_size)
-    viw = torch.arange(random_overlap, W-patch_size -
-                       random_overlap+1, patch_size)
-    if random_overlap > 0:
-        rih = torch.randint_like(vih, -random_overlap, random_overlap)
-        riw = torch.randint_like(viw, -random_overlap, random_overlap)
-        vih += rih
-        viw += riw
-    vi = torch.stack(torch.meshgrid(vih, viw)).view(2, -1).t()
-    return vi
-
-
-def cut_patches(input: torch.Tensor, indices: Tuple[Tuple[int, int]], patch_size: int, padding: int = 0):
-    # TODO use slices to get all patches at the same time ?
-
-    patches_l = []
-    for n in range(len(indices)):
-
-        patch = F.crop(input, *(indices[n]-padding),
-                       *(patch_size+padding*2,)*2)
-        patches_l.append(patch)
-    patches = torch.cat(patches_l, dim=0)
-
-    return patches
-
-
-def prepare_data(data_path: Path, read_func: Callable = read_image_tensor) -> Dict:
-    """
-    Takes a data_path of a folder which contains subfolders with input, target, etc.
-    lablelled by the same names.
-    :param data_path: Path of the folder containing data
-    :param read_func: function that reads data and returns a tensor
-    """
-    data_dict = {}
-
-    subdir_names = ["target", "input", "mask"]  # ,"helper"
-
-    # checks only files for which there is an target
-    # TODO check for images
-    name_ls = [file.name for file in (
-        data_path / "target").iterdir() if file.is_file()]
-
-    subdirs = [data_path / sdn for sdn in subdir_names]
-    for sd in subdirs:
-        if sd.is_dir():
-            data_ls = []
-            files = [sd / name for name in name_ls]
-            for file in files:
-                tensor = read_func(file)
-                H, W = tensor.shape[-2:]
-                data_ls.append(tensor)
-            # TODO check that all sizes match
-            data_dict[sd.name] = torch.stack(data_ls, dim=0)
-
-    data_dict['name'] = name_ls
-    data_dict['len'] = len(data_dict['name'])
-    data_dict['H'] = H
-    data_dict['W'] = W
-    return data_dict
-
-
-# TODO an image is loaded whenever a patch is needed, this may be a bottleneck
-class DataDictLoader():
-    def __init__(self, data_dict: Dict,
-                 batch_size: int = 16,
-                 max_length: int = 128,
-                 shuffle: bool = False):
-        """
-        """
-
-        self.batch_size = batch_size
-        self.shuffle = shuffle
-
-        self.batch_size = batch_size
-
-        self.data_dict = data_dict
-        self.dataset_len = data_dict['len']
-        self.len = self.dataset_len if max_length is None else min(
-            self.dataset_len, max_length)
-        # Calculate # batches
-        num_batches, remainder = divmod(self.len, self.batch_size)
-        if remainder > 0:
-            num_batches += 1
-        self.num_batches = num_batches
-
-    def __iter__(self):
-        if self.shuffle:
-            r = torch.randperm(self.dataset_len)
-            self.data_dict = {k: v[r] if isinstance(
-                v, Iterable) else v for k, v in self.data_dict.items()}
-        self.i = 0
-        return self
-
-    def __next__(self):
-        if self.i >= self.len:
-            raise StopIteration
-        batch = {k: v[self.i:self.i+self.batch_size]
-                 if isinstance(v, Iterable) else v for k, v in self.data_dict.items()}
-
-        self.i += self.batch_size
-        return batch
-
-    def __len__(self):
-        return self.num_batches
-
-
-class PatchDataModule(pl.LightningDataModule):
-
-    def __init__(self, data_dict,
-                 patch_size: int = 2**5,
-                 batch_size: int = 2**4,
-                 patch_num: int = 2**6):
-        super().__init__()
-        self.data_dict = data_dict
-        self.H, self.W = data_dict['H'], data_dict['W']
-        self.len = data_dict['len']
-
-        self.batch_size = batch_size
-        self.patch_size = patch_size
-        self.patch_num = patch_num
-
-    def dataloader(self, data_dict,  **kwargs):
-        return DataDictLoader(data_dict, **kwargs)
-
-    def train_dataloader(self):
-        patches = self.cut_patches()
-        return self.dataloader(patches, batch_size=self.batch_size, shuffle=True,
-                               max_length=self.patch_num)
-
-    def val_dataloader(self):
-        return self.dataloader(self.data_dict, batch_size=1)
-
-    def test_dataloader(self):
-        return self.dataloader(self.data_dict)  # TODO batch size
-
-    def cut_patches(self):
-        # TODO cycle once
-        patch_indices = get_valid_indices(
-            self.H, self.W, self.patch_size, self.patch_size//4)
-        dd = {k: cut_patches(
-            v, patch_indices, self.patch_size) for k, v in self.data_dict.items()
-            if isinstance(v, torch.Tensor)
-        }
-        threshold = 0.1
-        mask_p = torch.mean(
-            dd.get('mask', torch.ones_like(dd['input'])), dim=(-1, -2, -3))
-        masked_idx = (mask_p > threshold).nonzero(as_tuple=True)[0]
-        dd = {k: v[masked_idx] for k, v in dd.items()}
-        dd['len'] = len(masked_idx)
-        dd['H'], dd['W'] = (self.patch_size,)*2
-
-        return dd
-
-
-class ImageDataset(Dataset):
-    def __init__(self, file_paths: Iterable, read_func: Callable = read_image_tensor):
-        self.file_paths = file_paths
-
-    def __getitem__(self, idx: int) -> dict:
-        file = self.file_paths[idx]
-        return read_image_tensor(file), file.name
-
-    def __len__(self) -> int:
-        return len(self.file_paths)

spaces/BraydenMoore/MARCI-NFL-Betting/Source/Train/xgboost_ML.py

@@ -1,69 +0,0 @@
-import xgboost as xgb
-import pandas as pd
-import pickle as pkl
-import numpy as np
-from tqdm import tqdm
-from IPython.display import clear_output
-from sklearn.metrics import accuracy_score
-from sklearn.model_selection import train_test_split
-import os
-
-current_directory = os.path.dirname(os.path.abspath(__file__))
-parent_directory = os.path.dirname(current_directory)
-data_directory = os.path.join(parent_directory, 'Data')
-model_directory = os.path.join(parent_directory, 'Models')
-pickle_directory = os.path.join(parent_directory, 'Pickles')
-
-file_path = os.path.join(data_directory, 'gbg_and_odds.csv')
-data = pd.read_csv(file_path).dropna()
-
-margin = data['Home-Team-Win']
-data.drop(columns=['Home-Team-Win','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings', 'Home Odds', 'Away Odds'], inplace=True)
-
-acc_results = []
-
-for x in tqdm(range(100)):
-    X_train, X_test, y_train, y_test = train_test_split(data, margin, test_size=.1)
-
-    train_games = X_train['game_id']
-    test_games = X_test['game_id']
-
-    X_train.drop(columns=['game_id'], inplace=True)
-    X_test.drop(columns=['game_id'], inplace=True)
-
-    train = xgb.DMatrix(X_train.astype(float).values, label=y_train)
-    test = xgb.DMatrix(X_test.astype(float).values, label=y_test)
-
-    param = {
-        'max_depth': 2,
-        'eta': 0.01,
-        'objective': 'multi:softprob',
-        'num_class': 2
-    }
-    epochs = 500
-
-    model = xgb.train(param, train, epochs)
-    predictions = model.predict(test)
-    y = []
-    for z in predictions:
-        y.append(np.argmax(z))
-
-    acc = round(accuracy_score(y_test, y)*100, 1)
-    acc_results.append(acc)
-    clear_output(wait=True)
-    print(f"Best accuracy: {max(acc_results)}%")
-
-    # only save results if they are the best so far
-    if acc == max(acc_results):
-        file_path = os.path.join(pickle_directory, 'train_games_ML_no_odds.pkl')
-        with open(file_path,'wb') as f:
-            pkl.dump(train_games,f)
-
-        file_path = os.path.join(pickle_directory, 'test_games_ML_no_odds.pkl')
-        with open(file_path,'wb') as f:
-            pkl.dump(test_games,f)
-
-        file_path = os.path.join(model_directory, f'xgboost_ML_no_odds_{acc}%.json')
-        model.save_model(file_path)
-
-print('Done')

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/README.md

@@ -1,75 +0,0 @@
-# In Defense of Grid Features for Visual Question Answering
-**Grid Feature Pre-Training Code**
-
-<p align="center">
-  <img src="http://xinleic.xyz/images/grid-vqa.png" width="500" />
-</p>
-
-This is a feature pre-training code release of the [paper](https://arxiv.org/abs/2001.03615):
-```
-@InProceedings{jiang2020defense,
-  title={In Defense of Grid Features for Visual Question Answering},
-  author={Jiang, Huaizu and Misra, Ishan and Rohrbach, Marcus and Learned-Miller, Erik and Chen, Xinlei},
-  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-  year={2020}
-}
-```
-For more sustained maintenance, we release code using [Detectron2](https://github.com/facebookresearch/detectron2) instead of [mask-rcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) which the original code is based on. The current repository should reproduce the results reported in the paper, *e.g.*, reporting **~72.5** single model VQA score for a X-101 backbone paired with [MCAN](https://github.com/MILVLG/mcan-vqa)-large.
-
-## Installation
-Install Detectron 2 following [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). Since Detectron 2 is also being actively updated which can result in breaking behaviors, it is **highly recommended** to install via the following command:
-```bash
-python -m pip install 'git+https://github.com/facebookresearch/detectron2.git@ffff8ac'
-```
-Commits before or after `ffff8ac` might also work, but it could be risky.
-Then clone this repository:
-```bash
-git clone [email protected]:facebookresearch/grid-feats-vqa.git
-cd grid-feats-vqa
-```
-
-## Data
-[Visual Genome](http://visualgenome.org/) `train+val` splits released from the bottom-up-attention [code](https://github.com/peteanderson80/bottom-up-attention) are used for pre-training, and `test` split is used for evaluating detection performance. All of them are prepared in [COCO](http://cocodataset.org/) format but include an additional field for `attribute` prediction. We provide the `.json` files [here](https://dl.fbaipublicfiles.com/grid-feats-vqa/json/visual_genome.tgz) which can be directly loaded by Detectron2. Same as in Detectron2, the expected dataset structure under the `DETECTRON2_DATASETS` (default is `./datasets` relative to your current working directory) folder should be:
-```
-visual_genome/
-  annotations/
-    visual_genome_{train,val,test}.json
-  images/
-    # visual genome images (~108K)
-```
-
-## Training
-Once the dataset is setup, to train a model, run (by default we use 8 GPUs):
-```bash
-python train_net.py --num-gpus 8 --config-file <config.yaml>
-```
-For example, to launch grid-feature pre-training with ResNet-50 backbone on 8 GPUs, one should execute:
-```bash
-python train_net.py --num-gpus 8 --config-file configs/R-50-grid.yaml
-```
-The final model by default should be saved under `./output` of your current working directory once it is done training. We also provide the region-feature pre-training configuration `configs/R-50-updn.yaml` for reference. Note that we use `0.2` attribute loss (`MODEL.ROI_ATTRIBUTE_HEAD.LOSS_WEIGHT = 0.2`), which is better for down-stream tasks like VQA per our analysis.
-
-We also release the configuration (`configs/R-50-updn.yaml`) for training the region features described in **bottom-up-attention** paper, which is a faithful re-implementation of the original [one](https://github.com/peteanderson80/bottom-up-attention) in Detectron2.
-
-## Feature Extraction
-Grid feature extraction can be done by simply running once the model is trained (or you can directly download our pre-trained models, see below):
-```bash
-python extract_grid_feature.py -config-file configs/R-50-grid.yaml --dataset <dataset>
-```
-and the code will load the final model from `cfg.OUTPUT_DIR` (which one can override in command line) and start extracting features for `<dataset>`, we provide three options for the dataset: `coco_2014_train`, `coco_2014_val` and `coco_2015_test`, they correspond to `train`, `val` and `test` splits of the VQA dataset. The extracted features can be conveniently loaded in [Pythia](https://github.com/facebookresearch/pythia).
-
-To extract features on your customized dataset, you may want to dump the image information into [COCO](http://cocodataset.org/) `.json` format, and add the dataset information to use `extract_grid_feature.py`, or you can hack `extract_grid_feature.py` and directly loop over images. 
-
-## Pre-Trained Models and Features
-We release several pre-trained models for grid features: one with R-50 backbone, one with X-101, one with X-152, and one with additional improvements used for the 2020 VQA Challenge (see `X-152-challenge.yaml`). The models can be used directly to extract features. For your convenience, we also release the pre-extracted features for direct download.
-
-| Backbone | AP<sub>50:95</sub> | Download |
-| -------- | ---- | -------- |
-| R-50     | 3.1 | <a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/R-50/R-50.pth">model</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/R-50/metrics.json">metrics</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/R-50/R-50-features.tgz">features</a> |
-| X-101    | 4.3 | <a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-101/X-101.pth">model</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-101/metrics.json">metrics</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-101/X-101-features.tgz">features</a> |
-| X-152    | 4.7 | <a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-152/X-152.pth">model</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-152/metrics.json">metrics</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-152/X-152-features.tgz">features</a> |
-| X-152++  | 3.7 | <a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-152pp/X-152pp.pth">model</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-152pp/metrics.json">metrics</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/grid-feats-vqa/X-152pp/X-152pp-features.tgz">features</a> |
-
-## License
-
-The code is released under the [Apache 2.0 license](LICENSE).

spaces/ChrisCaviar/ControlNet-v1-1/model.py

@@ -1,591 +0,0 @@
-from __future__ import annotations
-
-import gc
-
-import numpy as np
-import PIL.Image
-import torch
-from controlnet_aux.util import HWC3
-from diffusers import (ControlNetModel, DiffusionPipeline,
-                       StableDiffusionControlNetPipeline,
-                       UniPCMultistepScheduler)
-
-from cv_utils import resize_image
-from preprocessor import Preprocessor
-
-CONTROLNET_MODEL_IDS = {
-    'Openpose': 'lllyasviel/control_v11p_sd15_openpose',
-    'Canny': 'lllyasviel/control_v11p_sd15_canny',
-    'MLSD': 'lllyasviel/control_v11p_sd15_mlsd',
-    'scribble': 'lllyasviel/control_v11p_sd15_scribble',
-    'softedge': 'lllyasviel/control_v11p_sd15_softedge',
-    'segmentation': 'lllyasviel/control_v11p_sd15_seg',
-    'depth': 'lllyasviel/control_v11f1p_sd15_depth',
-    'NormalBae': 'lllyasviel/control_v11p_sd15_normalbae',
-    'lineart': 'lllyasviel/control_v11p_sd15_lineart',
-    'lineart_anime': 'lllyasviel/control_v11p_sd15s2_lineart_anime',
-    'shuffle': 'lllyasviel/control_v11e_sd15_shuffle',
-    'ip2p': 'lllyasviel/control_v11e_sd15_ip2p',
-    'inpaint': 'lllyasviel/control_v11e_sd15_inpaint',
-}
-
-
-def download_all_controlnet_weights() -> None:
-    for model_id in CONTROLNET_MODEL_IDS.values():
-        ControlNetModel.from_pretrained(model_id)
-
-
-class Model:
-    def __init__(self,
-                 base_model_id: str = 'runwayml/stable-diffusion-v1-5',
-                 task_name: str = 'Canny'):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.base_model_id = ''
-        self.task_name = ''
-        self.pipe = self.load_pipe(base_model_id, task_name)
-        self.preprocessor = Preprocessor()
-
-    def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
-        if base_model_id == self.base_model_id and task_name == self.task_name and hasattr(
-                self, 'pipe') and self.pipe is not None:
-            return self.pipe
-        model_id = CONTROLNET_MODEL_IDS[task_name]
-        controlnet = ControlNetModel.from_pretrained(model_id,
-                                                     torch_dtype=torch.float16)
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            base_model_id,
-            safety_checker=None,
-            controlnet=controlnet,
-            torch_dtype=torch.float16)
-        pipe.scheduler = UniPCMultistepScheduler.from_config(
-            pipe.scheduler.config)
-        if self.device.type == 'cuda':
-            pipe.enable_xformers_memory_efficient_attention()
-        pipe.to(self.device)
-        torch.cuda.empty_cache()
-        gc.collect()
-        self.base_model_id = base_model_id
-        self.task_name = task_name
-        return pipe
-
-    def set_base_model(self, base_model_id: str) -> str:
-        if not base_model_id or base_model_id == self.base_model_id:
-            return self.base_model_id
-        del self.pipe
-        torch.cuda.empty_cache()
-        gc.collect()
-        try:
-            self.pipe = self.load_pipe(base_model_id, self.task_name)
-        except Exception:
-            self.pipe = self.load_pipe(self.base_model_id, self.task_name)
-        return self.base_model_id
-
-    def load_controlnet_weight(self, task_name: str) -> None:
-        if task_name == self.task_name:
-            return
-        if self.pipe is not None and hasattr(self.pipe, 'controlnet'):
-            del self.pipe.controlnet
-        torch.cuda.empty_cache()
-        gc.collect()
-        model_id = CONTROLNET_MODEL_IDS[task_name]
-        controlnet = ControlNetModel.from_pretrained(model_id,
-                                                     torch_dtype=torch.float16)
-        controlnet.to(self.device)
-        torch.cuda.empty_cache()
-        gc.collect()
-        self.pipe.controlnet = controlnet
-        self.task_name = task_name
-
-    def get_prompt(self, prompt: str, additional_prompt: str) -> str:
-        if not prompt:
-            prompt = additional_prompt
-        else:
-            prompt = f'{prompt}, {additional_prompt}'
-        return prompt
-
-    @torch.autocast('cuda')
-    def run_pipe(
-        self,
-        prompt: str,
-        negative_prompt: str,
-        control_image: PIL.Image.Image,
-        num_images: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        if seed == -1:
-            seed = np.random.randint(0, np.iinfo(np.int64).max)
-        generator = torch.Generator().manual_seed(seed)
-        return self.pipe(prompt=prompt,
-                         negative_prompt=negative_prompt,
-                         guidance_scale=guidance_scale,
-                         num_images_per_prompt=num_images,
-                         num_inference_steps=num_steps,
-                         generator=generator,
-                         image=control_image).images
-
-    @torch.inference_mode()
-    def process_canny(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        low_threshold: int,
-        high_threshold: int,
-    ) -> list[PIL.Image.Image]:
-        self.preprocessor.load('Canny')
-        control_image = self.preprocessor(image=image,
-                                          low_threshold=low_threshold,
-                                          high_threshold=high_threshold,
-                                          detect_resolution=image_resolution)
-
-        self.load_controlnet_weight('Canny')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_mlsd(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        value_threshold: float,
-        distance_threshold: float,
-    ) -> list[PIL.Image.Image]:
-        self.preprocessor.load('MLSD')
-        control_image = self.preprocessor(
-            image=image,
-            image_resolution=image_resolution,
-            detect_resolution=preprocess_resolution,
-            thr_v=value_threshold,
-            thr_d=distance_threshold,
-        )
-        self.load_controlnet_weight('MLSD')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_scribble(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name == 'None':
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        elif preprocessor_name == 'HED':
-            self.preprocessor.load(preprocessor_name)
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-                scribble=False,
-            )
-        elif preprocessor_name == 'PidiNet':
-            self.preprocessor.load(preprocessor_name)
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-                safe=False,
-            )
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_scribble_interactive(
-        self,
-        image_and_mask: dict[str, np.ndarray],
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        image = image_and_mask['mask']
-        image = HWC3(image)
-        image = resize_image(image, resolution=image_resolution)
-        control_image = PIL.Image.fromarray(image)
-
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_softedge(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name == 'None':
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        elif preprocessor_name in ['HED', 'HED safe']:
-            safe = 'safe' in preprocessor_name
-            self.preprocessor.load('HED')
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-                scribble=safe,
-            )
-        elif preprocessor_name in ['PidiNet', 'PidiNet safe']:
-            safe = 'safe' in preprocessor_name
-            self.preprocessor.load('PidiNet')
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-                safe=safe,
-            )
-        else:
-            raise ValueError
-        self.load_controlnet_weight('softedge')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_openpose(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name == 'None':
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        else:
-            self.preprocessor.load('Openpose')
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-                hand_and_face=True,
-            )
-        self.load_controlnet_weight('Openpose')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_segmentation(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name == 'None':
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        else:
-            self.preprocessor.load(preprocessor_name)
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-            )
-        self.load_controlnet_weight('segmentation')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_depth(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name == 'None':
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        else:
-            self.preprocessor.load(preprocessor_name)
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-            )
-        self.load_controlnet_weight('depth')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_normal(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name == 'None':
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        else:
-            self.preprocessor.load('NormalBae')
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-            )
-        self.load_controlnet_weight('NormalBae')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_lineart(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        preprocess_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name in ['None', 'None (anime)']:
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        elif preprocessor_name in ['Lineart', 'Lineart coarse']:
-            coarse = 'coarse' in preprocessor_name
-            self.preprocessor.load('Lineart')
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-                coarse=coarse,
-            )
-        elif preprocessor_name == 'Lineart (anime)':
-            self.preprocessor.load('LineartAnime')
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-                detect_resolution=preprocess_resolution,
-            )
-        if 'anime' in preprocessor_name:
-            self.load_controlnet_weight('lineart_anime')
-        else:
-            self.load_controlnet_weight('lineart')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_shuffle(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        preprocessor_name: str,
-    ) -> list[PIL.Image.Image]:
-        if preprocessor_name == 'None':
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            control_image = PIL.Image.fromarray(image)
-        else:
-            self.preprocessor.load(preprocessor_name)
-            control_image = self.preprocessor(
-                image=image,
-                image_resolution=image_resolution,
-            )
-        self.load_controlnet_weight('shuffle')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results
-
-    @torch.inference_mode()
-    def process_ip2p(
-        self,
-        image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        image = HWC3(image)
-        image = resize_image(image, resolution=image_resolution)
-        control_image = PIL.Image.fromarray(image)
-        self.load_controlnet_weight('ip2p')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [control_image] + results

spaces/CobaltZvc/Hyper_Bot/README.md

@@ -1,10 +0,0 @@
----
-title: Hyper Bot
-emoji: 🤖
-colorFrom: gray
-colorTo: yellow
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CofAI/chat.b4/g4f/Provider/Providers/Fakeopen.py

@@ -1,54 +0,0 @@
-import os  
-import json
-import requests  
-from typing import Dict, get_type_hints  
-  
-url = 'https://ai.fakeopen.com/v1/'  
-model = [  
-    'gpt-3.5-turbo', 
-    'gpt-3.5-turbo-0613'
-    'gpt-3.5-turbo-16k', 
-    'gpt-3.5-turbo-16k-0613', 
-]  
-
-supports_stream = True  
-needs_auth = False  
-  
-  
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):  
-  
-    headers = {  
-        'Content-Type': 'application/json',  
-        'accept': 'text/event-stream',  
-        'Cache-Control': 'no-cache',  
-        'Proxy-Connection': 'keep-alive',  
-        'Authorization': f"Bearer {os.environ.get('FAKE_OPEN_KEY', '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')}",  
-    }  
-  
-    json_data = {  
-        'messages': messages,  
-        'temperature': 1.0,  
-        'model': model,  
-        'stream': stream,  
-    }  
-  
-    response = requests.post(  
-        'https://ai.fakeopen.com/v1/chat/completions', headers=headers, json=json_data, stream=True  
-    )  
-  
-    for token in response.iter_lines():  
-        decoded = token.decode('utf-8')  
-        if decoded == '[DONE]':  
-            break  
-        if decoded.startswith('data: '):  
-            data_str = decoded.replace('data: ', '')  
-            if data_str != '[DONE]':  
-                data = json.loads(data_str)  
-                if 'choices' in data and 'delta' in data['choices'][0] and 'content' in data['choices'][0]['delta']:  
-                    yield data['choices'][0]['delta']['content']  
-
-
-  
-  
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' +  '(%s)' % ', '.join(  
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])  

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/cpu/dcn_v2_cpu.cpp

@@ -1,229 +0,0 @@
-#include <vector>
-#include "cpu/dcn_v2_im2col_cpu.h"
-#include <iostream>
-
-#include <ATen/ATen.h>
-//#include <ATen/cuda/CUDAContext.h>
-
-#include <TH/TH.h>
-//#include <THC/THCAtomics.cuh>
-//#include <THC/THCDeviceUtils.cuh>
-
-//extern THCState *state;
-
-// author: Charles Shang
-// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu
-
-// modified from the CUDA version for CPU use by Daniel K. Suhendro
-
-// edit by: James Bockman and Matthew Howe
-// modified for torch implementation to remove use of deprecated torch access to Blas
-
-at::Tensor
-dcn_v2_cpu_forward(const at::Tensor &input,
-                    const at::Tensor &weight,
-                    const at::Tensor &bias,
-                    const at::Tensor &offset,
-                    const at::Tensor &mask,
-                    const int kernel_h,
-                    const int kernel_w,
-                    const int stride_h,
-                    const int stride_w,
-                    const int pad_h,
-                    const int pad_w,
-                    const int dilation_h,
-                    const int dilation_w,
-                    const int deformable_group)
-{
-    // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask));
-    /*AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor");
-    AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor");
-    AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor");
-    AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor");
-    AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor");*/
-
-    const int batch = input.size(0);
-    const int channels = input.size(1);
-    const int height = input.size(2);
-    const int width = input.size(3);
-
-    const int channels_out = weight.size(0);
-    const int channels_kernel = weight.size(1);
-    const int kernel_h_ = weight.size(2);
-    const int kernel_w_ = weight.size(3);
-
-    // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h);
-    // printf("Channels: %d %d\n", channels, channels_kernel);
-    // printf("Channels: %d %d\n", channels_out, channels_kernel);
-
-    AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w,
-               "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_);
-
-    AT_ASSERTM(channels == channels_kernel,
-               "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel);
-
-    const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
-    const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-
-    // auto ones = at::ones({height_out, width_out}, input.options());
-    auto ones = at::ones({bias.sizes()[0], height_out, width_out}, input.options());
-    auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options());
-    auto output = at::zeros({batch, channels_out, height_out, width_out}, input.options());
-
-    using scalar_t = float;
-    for (int b = 0; b < batch; b++)
-    {
-        auto input_n = input.select(0, b);
-        auto offset_n = offset.select(0, b);
-        auto mask_n = mask.select(0, b);
-        auto output_n = output.select(0, b);
-        // std::cout << "output_n: " << output_n << "output.select(0,b): " << output.select(0,b) << "\n"; 
-
-        // Do Bias first:
-        // M,N,K are dims of matrix A and B
-        // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm)
-        // (N x 1) (1 x M)
-
-        // torch implementation
-        auto ones_T = at::transpose(ones.contiguous(), 2, 0);
-        ones_T = at::mul(ones_T, bias.contiguous());
-        ones_T = at::transpose(ones_T, 2, 0);
-        output_n = at::add(output_n, ones_T);
-
-        modulated_deformable_im2col_cpu(input_n.data_ptr<scalar_t>(),
-                                         offset_n.data_ptr<scalar_t>(),
-                                         mask_n.data_ptr<scalar_t>(),
-                                         1, channels, height, width,
-                                         height_out, width_out, kernel_h, kernel_w,
-                                         pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
-                                         deformable_group,
-                                         columns.data_ptr<scalar_t>());
-
-        //(k * m)  x  (m * n)
-        // Y = WC
-
-        // torch implementation
-        auto weight_flat = weight.view({channels_out, channels * kernel_h * kernel_w});
-        auto product = at::matmul(weight_flat, columns);
-        output.select(0, b) = at::add(output_n, product.view({channels_out, height_out, width_out}));
-    }
-    return output;
-}
-
-std::vector<at::Tensor> dcn_v2_cpu_backward(const at::Tensor &input,
-                                             const at::Tensor &weight,
-                                             const at::Tensor &bias,
-                                             const at::Tensor &offset,
-                                             const at::Tensor &mask,
-                                             const at::Tensor &grad_output,
-                                             int kernel_h, int kernel_w,
-                                             int stride_h, int stride_w,
-                                             int pad_h, int pad_w,
-                                             int dilation_h, int dilation_w,
-                                             int deformable_group)
-{
-
-    THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous");
-    THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous");
-
-    /*AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor");
-    AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor");
-    AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor");
-    AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor");
-    AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor");*/
-
-    const int batch = input.size(0);
-    const int channels = input.size(1);
-    const int height = input.size(2);
-    const int width = input.size(3);
-
-    const int channels_out = weight.size(0);
-    const int channels_kernel = weight.size(1);
-    const int kernel_h_ = weight.size(2);
-    const int kernel_w_ = weight.size(3);
-
-    AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w,
-               "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_);
-
-    AT_ASSERTM(channels == channels_kernel,
-               "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel);
-
-    const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
-    const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-
-    auto ones = at::ones({height_out, width_out}, input.options());
-    auto columns = at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options());
-    auto output = at::empty({batch, channels_out, height_out, width_out}, input.options());
-
-    auto grad_input = at::zeros_like(input);
-    auto grad_weight = at::zeros_like(weight);
-    auto grad_bias = at::zeros_like(bias);
-    auto grad_offset = at::zeros_like(offset);
-    auto grad_mask = at::zeros_like(mask);
-
-    using scalar_t = float;
-
-    for (int b = 0; b < batch; b++)
-    {
-        auto input_n = input.select(0, b);
-        auto offset_n = offset.select(0, b);
-        auto mask_n = mask.select(0, b);
-        auto grad_output_n = grad_output.select(0, b);
-        auto grad_input_n = grad_input.select(0, b);
-        auto grad_offset_n = grad_offset.select(0, b);
-        auto grad_mask_n = grad_mask.select(0, b);
-
-
-
-        // Torch implementation
-        auto weight_flat = weight.view({channels_out, channels*kernel_h*kernel_w});
-        weight_flat = at::transpose(weight_flat, 1, 0);
-        auto grad_output_n_flat = grad_output_n.view({channels_out, height_out*width_out});
-        columns = at::matmul(weight_flat, grad_output_n_flat);
-
-        // gradient w.r.t. input coordinate data
-        modulated_deformable_col2im_coord_cpu(columns.data_ptr<scalar_t>(),
-                                               input_n.data_ptr<scalar_t>(),
-                                               offset_n.data_ptr<scalar_t>(),
-                                               mask_n.data_ptr<scalar_t>(),
-                                               1, channels, height, width,
-                                               height_out, width_out, kernel_h, kernel_w,
-                                               pad_h, pad_w, stride_h, stride_w,
-                                               dilation_h, dilation_w, deformable_group,
-                                               grad_offset_n.data_ptr<scalar_t>(),
-                                               grad_mask_n.data_ptr<scalar_t>());
-        // gradient w.r.t. input data
-        modulated_deformable_col2im_cpu(columns.data_ptr<scalar_t>(),
-                                         offset_n.data_ptr<scalar_t>(),
-                                         mask_n.data_ptr<scalar_t>(),
-                                         1, channels, height, width,
-                                         height_out, width_out, kernel_h, kernel_w,
-                                         pad_h, pad_w, stride_h, stride_w,
-                                         dilation_h, dilation_w, deformable_group,
-                                         grad_input_n.data_ptr<scalar_t>());
-
-        // gradient w.r.t. weight, dWeight should accumulate across the batch and group
-        modulated_deformable_im2col_cpu(input_n.data_ptr<scalar_t>(),
-                                         offset_n.data_ptr<scalar_t>(),
-                                         mask_n.data_ptr<scalar_t>(),
-                                         1, channels, height, width,
-                                         height_out, width_out, kernel_h, kernel_w,
-                                         pad_h, pad_w, stride_h, stride_w,
-                                         dilation_h, dilation_w, deformable_group,
-                                         columns.data_ptr<scalar_t>());
-
-        // Torch implementation
-        auto product = at::matmul(grad_output_n_flat, at::transpose(columns, 1, 0));
-        grad_weight = at::add(grad_weight, product.view({channels_out, channels, kernel_h, kernel_w}));
-
-
-        // Torch implementation
-        auto ones_flat = ones.view({height_out*width_out});
-        product = at::matmul(grad_output_n_flat, ones_flat);
-        grad_bias = at::add(grad_bias, product);
-    }
-
-    return {
-        grad_input, grad_offset, grad_mask, grad_weight, grad_bias
-    };
-}

spaces/DD0101/Disfluency-base/README.md

@@ -1,12 +0,0 @@
----
-title: Disfluency Base
-emoji: 😻
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/teePen.py

@@ -1,54 +0,0 @@
-"""Pen multiplexing drawing to one or more pens."""
-from fontTools.pens.basePen import AbstractPen
-
-
-__all__ = ["TeePen"]
-
-
-class TeePen(AbstractPen):
-    """Pen multiplexing drawing to one or more pens.
-
-    Use either as TeePen(pen1, pen2, ...) or TeePen(iterableOfPens)."""
-
-    def __init__(self, *pens):
-        if len(pens) == 1:
-            pens = pens[0]
-        self.pens = pens
-
-    def moveTo(self, p0):
-        for pen in self.pens:
-            pen.moveTo(p0)
-
-    def lineTo(self, p1):
-        for pen in self.pens:
-            pen.lineTo(p1)
-
-    def qCurveTo(self, *points):
-        for pen in self.pens:
-            pen.qCurveTo(*points)
-
-    def curveTo(self, *points):
-        for pen in self.pens:
-            pen.curveTo(*points)
-
-    def closePath(self):
-        for pen in self.pens:
-            pen.closePath()
-
-    def endPath(self):
-        for pen in self.pens:
-            pen.endPath()
-
-    def addComponent(self, glyphName, transformation):
-        for pen in self.pens:
-            pen.addComponent(glyphName, transformation)
-
-
-if __name__ == "__main__":
-    from fontTools.pens.basePen import _TestPen
-
-    pen = TeePen(_TestPen(), _TestPen())
-    pen.moveTo((0, 0))
-    pen.lineTo((0, 100))
-    pen.curveTo((50, 75), (60, 50), (50, 25))
-    pen.closePath()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-329f8260.css

@@ -1 +0,0 @@
-.min.svelte-1ybaih5{min-height:var(--size-24)}.hide.svelte-1ybaih5{display:none}div.svelte-1ed2p3z{transition:.15s}.pending.svelte-1ed2p3z{opacity:.2}

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-9d98c4c0.js

@@ -1,2 +0,0 @@
-import{S as v,e as g,s as d,a9 as r,N as q,K as o,as as h,U as f,p as b,ab as w,ac as R,ad as j,z as C,v as S,A as z}from"./index-3370be2a.js";function A(i){let e,_,s;const u=i[6].default,a=r(u,i,i[5],null);return{c(){e=q("div"),a&&a.c(),o(e,"id",i[1]),o(e,"class",_=h(i[2].join(" "))+" svelte-15lo0d8"),f(e,"compact",i[4]==="compact"),f(e,"panel",i[4]==="panel"),f(e,"unequal-height",i[0]===!1),f(e,"stretch",i[0]),f(e,"hide",!i[3])},m(l,t){b(l,e,t),a&&a.m(e,null),s=!0},p(l,[t]){a&&a.p&&(!s||t&32)&&w(a,u,l,l[5],s?j(u,l[5],t,null):R(l[5]),null),(!s||t&2)&&o(e,"id",l[1]),(!s||t&4&&_!==(_=h(l[2].join(" "))+" svelte-15lo0d8"))&&o(e,"class",_),(!s||t&20)&&f(e,"compact",l[4]==="compact"),(!s||t&20)&&f(e,"panel",l[4]==="panel"),(!s||t&5)&&f(e,"unequal-height",l[0]===!1),(!s||t&5)&&f(e,"stretch",l[0]),(!s||t&12)&&f(e,"hide",!l[3])},i(l){s||(C(a,l),s=!0)},o(l){S(a,l),s=!1},d(l){l&&z(e),a&&a.d(l)}}}function K(i,e,_){let{$$slots:s={},$$scope:u}=e,{equal_height:a=!0}=e,{elem_id:l}=e,{elem_classes:t=[]}=e,{visible:m=!0}=e,{variant:c="default"}=e;return i.$$set=n=>{"equal_height"in n&&_(0,a=n.equal_height),"elem_id"in n&&_(1,l=n.elem_id),"elem_classes"in n&&_(2,t=n.elem_classes),"visible"in n&&_(3,m=n.visible),"variant"in n&&_(4,c=n.variant),"$$scope"in n&&_(5,u=n.$$scope)},[a,l,t,m,c,u,s]}class N extends v{constructor(e){super(),g(this,e,K,A,d,{equal_height:0,elem_id:1,elem_classes:2,visible:3,variant:4})}}const k=N,B=["static"];export{k as Component,B as modes};
-//# sourceMappingURL=index-9d98c4c0.js.map

spaces/DataScienceEngineering/2-GradioLiveASR/README.md

@@ -1,13 +0,0 @@
----
-title: 🗣️Live ASR Speech Recognition Gradio🧠💾 
-emoji: 2-Live🗣️
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.5
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DevashishBhake/Question_Generation/README.md

@@ -1,13 +0,0 @@
----
-title: Question Generation
-emoji: 🐨
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Dinoking/Guccio-AI-Designer/netdissect/modelconfig.py

@@ -1,144 +0,0 @@
-'''
-Original from https://github.com/CSAILVision/GANDissect
-Modified by Erik Härkönen, 29.11.2019
-'''
-
-import numbers
-import torch
-from netdissect.autoeval import autoimport_eval
-from netdissect.progress import print_progress
-from netdissect.nethook import InstrumentedModel
-from netdissect.easydict import EasyDict
-
-def create_instrumented_model(args, **kwargs):
-    '''
-    Creates an instrumented model out of a namespace of arguments that
-    correspond to ArgumentParser command-line args:
-      model: a string to evaluate as a constructor for the model.
-      pthfile: (optional) filename of .pth file for the model.
-      layers: a list of layers to instrument, defaulted if not provided.
-      edit: True to instrument the layers for editing.
-      gen: True for a generator model.  One-pixel input assumed.
-      imgsize: For non-generator models, (y, x) dimensions for RGB input.
-      cuda: True to use CUDA.
-  
-    The constructed model will be decorated with the following attributes:
-      input_shape: (usually 4d) tensor shape for single-image input.
-      output_shape: 4d tensor shape for output.
-      feature_shape: map of layer names to 4d tensor shape for featuremaps.
-      retained: map of layernames to tensors, filled after every evaluation.
-      ablation: if editing, map of layernames to [0..1] alpha values to fill.
-      replacement: if editing, map of layernames to values to fill.
-
-    When editing, the feature value x will be replaced by:
-        `x = (replacement * ablation) + (x * (1 - ablation))`
-    '''
-
-    args = EasyDict(vars(args), **kwargs)
-
-    # Construct the network
-    if args.model is None:
-        print_progress('No model specified')
-        return None
-    if isinstance(args.model, torch.nn.Module):
-        model = args.model
-    else:
-        model = autoimport_eval(args.model)
-    # Unwrap any DataParallel-wrapped model
-    if isinstance(model, torch.nn.DataParallel):
-        model = next(model.children())
-
-    # Load its state dict
-    meta = {}
-    if getattr(args, 'pthfile', None) is not None:
-        data = torch.load(args.pthfile)
-        if 'state_dict' in data:
-            meta = {}
-            for key in data:
-                if isinstance(data[key], numbers.Number):
-                    meta[key] = data[key]
-            data = data['state_dict']
-        submodule = getattr(args, 'submodule', None)
-        if submodule is not None and len(submodule):
-            remove_prefix = submodule + '.'
-            data = { k[len(remove_prefix):]: v for k, v in data.items()
-                    if k.startswith(remove_prefix)}
-            if not len(data):
-                print_progress('No submodule %s found in %s' %
-                        (submodule, args.pthfile))
-                return None
-        model.load_state_dict(data, strict=not getattr(args, 'unstrict', False))
-
-    # Decide which layers to instrument.
-    if getattr(args, 'layer', None) is not None:
-        args.layers = [args.layer]
-    if getattr(args, 'layers', None) is None:
-        # Skip wrappers with only one named model
-        container = model
-        prefix = ''
-        while len(list(container.named_children())) == 1:
-            name, container = next(container.named_children())
-            prefix += name + '.'
-        # Default to all nontrivial top-level layers except last.
-        args.layers = [prefix + name
-                for name, module in container.named_children()
-                if type(module).__module__ not in [
-                    # Skip ReLU and other activations.
-                    'torch.nn.modules.activation',
-                    # Skip pooling layers.
-                    'torch.nn.modules.pooling']
-                ][:-1]
-        print_progress('Defaulting to layers: %s' % ' '.join(args.layers))
-
-    # Now wrap the model for instrumentation.
-    model = InstrumentedModel(model)
-    model.meta = meta
-
-    # Instrument the layers.
-    model.retain_layers(args.layers)
-    model.eval()
-    if args.cuda:
-        model.cuda()
-
-    # Annotate input, output, and feature shapes
-    annotate_model_shapes(model,
-            gen=getattr(args, 'gen', False),
-            imgsize=getattr(args, 'imgsize', None),
-            latent_shape=getattr(args, 'latent_shape', None))
-    return model
-
-def annotate_model_shapes(model, gen=False, imgsize=None, latent_shape=None):
-    assert (imgsize is not None) or gen
-
-    # Figure the input shape.
-    if gen:
-        if latent_shape is None:
-            # We can guess a generator's input shape by looking at the model.
-            # Examine first conv in model to determine input feature size.
-            first_layer = [c for c in model.modules()
-                    if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
-                        torch.nn.Linear))][0]
-            # 4d input if convolutional, 2d input if first layer is linear.
-            if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
-                input_shape = (1, first_layer.in_channels, 1, 1)
-            else:
-                input_shape = (1, first_layer.in_features)
-        else:
-            # Specify input shape manually
-            input_shape = latent_shape
-    else:
-        # For a classifier, the input image shape is given as an argument.
-        input_shape = (1, 3) + tuple(imgsize)
-
-    # Run the model once to observe feature shapes.
-    device = next(model.parameters()).device
-    dry_run = torch.zeros(input_shape).to(device)
-    with torch.no_grad():
-        output = model(dry_run)
-
-    # Annotate shapes.
-    model.input_shape = input_shape
-    model.feature_shape = { layer: feature.shape
-            for layer, feature in model.retained_features().items() }
-    model.output_shape = output.shape
-    return model

spaces/Dorado607/ChuanhuChatGPT/readme/README_ja.md

@@ -1,139 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README_origin.md">简体中文</a> | <a title="English" href="README_en.md">English</a> | 日本語
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://github.com/GaiZhenbiao/ChuanhuChatGPT/assets/70903329/aca3a7ec-4f1d-4667-890c-a6f47bf08f63" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>ChatGPT/ChatGLM/LLaMAなどのLLMのための軽量でユーザーフレンドリーなWeb-UI</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        ストリーム出力／会話回数無制限／履歴保存／プリセットプロンプト／ファイルへの質問チャット<br>
-        ウェブ検索／LaTeXレンダリング／表レンダリング／コードハイライト<br>
-        オートダークモード／アダプティブ・ウェブ・インターフェイス／WeChatライク・テーマ<br />
-        マルチパラメーターチューニング／マルチAPI-Key対応／マルチユーザー対応<br>
-        GPT-4対応／LLMのローカルデプロイ可能。
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>動画チュートリアル</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 イントロダクション</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 イントロダクション & チュートリアル</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>オンライントライアル</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>ワンクリックデプロイ</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## サポートされている大規模言語モデル
-
-**APIを通じてアクセス可能な大規模言語モデル**：
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**ローカルに展開された大規模言語モデル**：
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## 使う上でのTips
-
-- ChatGPTをより適切に制御するために、システムプロンプトを使用できます。
-- プロンプトテンプレートを使用するには、プロンプトテンプレートコレクションを選択し、ドロップダウンメニューから特定のプロンプトを選択。回答が不十分な場合は、`🔄再生成`ボタンを使って再試行します。
-- 入力ボックスで改行するには、<kbd>Shift</kbd> + <kbd>Enter</kbd>キーを押してください。
-- 入力履歴を素早く切り替えるには、入力ボックスで <kbd>↑</kbd>と<kbd>↓</kbd>キーを押す。
-- プログラムをサーバーに展開するには、`config.json` 内の `"server_name": "0.0.0.0", "server_port": <ポート番号>`を設定してください。
-- 共有リンクを取得するには、 `config.json` 内の `"share": true` を設定してください。なお、公開リンクでアクセスするためには、プログラムが実行されている必要があることに注意してください。
-- Hugging Face Spacesで使用する場合： より速く、より安全に利用するために、**Duplicate Space**を使用し、自分のスペースでプログラムを実行することをお勧めします。
-
-## クイックスタート
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-次に `config_example.json`をコピーして `config.json`にリネームし、そのファイルにAPI-Keyなどの設定を記入する。
-
-```shell
-python ChuanhuChatbot.py
-```
-
-ブラウザのウィンドウが開き、ChatGPTとチ���ットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるため、依存関係を再度インストールしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">

spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/README.md

@@ -1,58 +0,0 @@
-# ByteTrack-TensorRT in C++
-
-## Installation
-
-Install opencv with ```sudo apt-get install libopencv-dev``` (we don't need a higher version of opencv like v3.3+).
-
-Install eigen-3.3.9 [[google]](https://drive.google.com/file/d/1rqO74CYCNrmRAg8Rra0JP3yZtJ-rfket/view?usp=sharing), [[baidu(code:ueq4)]](https://pan.baidu.com/s/15kEfCxpy-T7tz60msxxExg).
-
-```shell
-unzip eigen-3.3.9.zip
-cd eigen-3.3.9
-mkdir build
-cd build
-cmake ..
-sudo make install
-```
-
-## Prepare serialized engine file
-
-Follow the TensorRT Python demo to convert and save the serialized engine file.
-
-Check the 'model_trt.engine' file, which will be automatically saved at the YOLOX_output dir.
-
-## Build the demo
-
-You should set the TensorRT path and CUDA path in CMakeLists.txt.
-
-For bytetrack_s model, we set the input frame size 1088 x 608. For bytetrack_m, bytetrack_l, bytetrack_x models, we set the input frame size 1440 x 800. You can modify the INPUT_W and INPUT_H in src/bytetrack.cpp
-
-```c++
-static const int INPUT_W = 1088;
-static const int INPUT_H = 608;
-```
-
-You can first build the demo:
-
-```shell
-cd <ByteTrack_HOME>/demo/TensorRT/cpp
-mkdir build
-cd build
-cmake ..
-make
-```
-
-Then you can run the demo with **200 FPS**:
-
-```shell
-./bytetrack ../../../../YOLOX_outputs/yolox_s_mix_det/model_trt.engine -i ../../../../videos/palace.mp4
-```
-
-(If you find the output video lose some frames, you can convert the input video by running:
-
-```shell
-cd <ByteTrack_HOME>
-python3 tools/convert_video.py
-```
-to generate an appropriate input video for TensorRT C++ demo. )
-